modelId
stringlengths 4
122
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
74.7M
| likes
int64 0
9.67k
| library_name
stringlengths 2
84
⌀ | tags
list | pipeline_tag
stringlengths 5
30
⌀ | createdAt
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| embedding
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avyshlov/t5-small-finetuned-xsum
|
avyshlov
| 2023-12-17T21:12:43Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-15T18:00:19Z |
Entry not found
|
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di-ai/pwqzg
|
di-ai
| 2023-12-15T18:01:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:01:59Z |
Entry not found
|
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Nikt271/Bob
|
Nikt271
| 2023-12-15T18:02:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:02:08Z |
Entry not found
|
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di-ai/ijnac
|
di-ai
| 2023-12-15T18:02:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:02:28Z |
Entry not found
|
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di-ai/gwejr
|
di-ai
| 2023-12-15T18:03:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:03:31Z |
Entry not found
|
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] |
rodrigomoreirasilva/bertopic_lai_recursos_CGU_respostas
|
rodrigomoreirasilva
| 2023-12-15T18:03:48Z | 0 | 0 |
bertopic
|
[
"bertopic",
"text-classification",
"region:us"
] |
text-classification
| 2023-12-15T18:03:45Z |
---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---
# bertopic_lai_recursos_CGU_respostas
This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
## Usage
To use this model, please install BERTopic:
```
pip install -U bertopic
```
You can use the model as follows:
```python
from bertopic import BERTopic
topic_model = BERTopic.load("rodrigomoreirasilva/bertopic_lai_recursos_CGU_respostas")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 369
* Number of training documents: 17844
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | informações cidadão - serviço informações - informação - informações - solicitação | 10 | -1_informações cidadão_serviço informações_informação_informações |
| 0 | sicbb recurso - banco sicbb - banco informação - indeferimento informações - recurso 12527 | 4414 | 0_sicbb recurso_banco sicbb_banco informação_indeferimento informações |
| 1 | sicbb recurso - banco sicbb - recurso 12527 - indeferimento informações - banco informação | 579 | 1_sicbb recurso_banco sicbb_recurso 12527_indeferimento informações |
| 2 | educação superior - ensino superior - acadêmica - acadêmico - universidade | 308 | 2_educação superior_ensino superior_acadêmica_acadêmico |
| 3 | comando aeronáutica - ministério defesa - aeronáutica comaer - aeronáutica - aérea brasileira | 307 | 3_comando aeronáutica_ministério defesa_aeronáutica comaer_aeronáutica |
| 4 | anexa resposta - resposta anexa - segue anexa - anexo resposta - segue resposta | 290 | 4_anexa resposta_resposta anexa_segue anexa_anexo resposta |
| 5 | correios agradecem - brasileira correios - correios telégrafos - correios - solicitação informação | 281 | 5_correios agradecem_brasileira correios_correios telégrafos_correios |
| 6 | sic ouvidoria - ouvidoria geral - transparência http - controladoria - ouvidoria | 276 | 6_sic ouvidoria_ouvidoria geral_transparência http_controladoria |
| 7 | informações cidadão - eletrônico informações - recurso superintendente - cidadão sic - serviço informações | 230 | 7_informações cidadão_eletrônico informações_recurso superintendente_cidadão sic |
| 8 | informação pessoal - pessoal - privacidade - direito - informações pessoais | 216 | 8_informação pessoal_pessoal_privacidade_direito |
| 9 | demanda sic - acessoainformacao - acessoainformacao gov - atendimento informação - negado informação | 169 | 9_demanda sic_acessoainformacao_acessoainformacao gov_atendimento informação |
| 10 | anexo resposta - resposta anexo - decreto 724 - recurso prazo - decreto 7724 | 118 | 10_anexo resposta_resposta anexo_decreto 724_recurso prazo |
| 11 | informações cidadão - gov acessoainformacao - serviço informações - ministério planejamento - sic ministério | 109 | 11_informações cidadão_gov acessoainformacao_serviço informações_ministério planejamento |
| 12 | requerimento - polícia rodoviária - contato diretamente - mail ouvidoria - atendimento demanda | 99 | 12_requerimento_polícia rodoviária_contato diretamente_mail ouvidoria |
| 13 | atendimento caixa - sac informações - cgu informamos - informamos caixa - caixa gov | 95 | 13_atendimento caixa_sac informações_cgu informamos_informamos caixa |
| 14 | rodoviária - ouvidoria informação - recurso superintendente - ouvidoria - poderá interpor | 93 | 14_rodoviária_ouvidoria informação_recurso superintendente_ouvidoria |
| 15 | esclarecimentos anvisa - anvisa disponibiliza - informados requerente - agrotóxicos - informação prazo | 89 | 15_esclarecimentos anvisa_anvisa disponibiliza_informados requerente_agrotóxicos |
| 16 | indeferimento informação - financeiro - financeiras - atendimento cidadão - banco central | 89 | 16_indeferimento informação_financeiro_financeiras_atendimento cidadão |
| 17 | petição - demandar - agência nacional - aviação civil - inquérito | 85 | 17_petição_demandar_agência nacional_aviação civil |
| 18 | vigilância sanitária - esclarecimentos anvisa - atendimento 0800 - sanitária - central atendimento | 79 | 18_vigilância sanitária_esclarecimentos anvisa_atendimento 0800_sanitária |
| 19 | informações solicitadas - solicitação informação - informações deseja - informação informamos - cidadão informamos | 79 | 19_informações solicitadas_solicitação informação_informações deseja_informação informamos |
| 20 | março 2020 - fevereiro 2020 - emergência - saúde pública - essenciais | 76 | 20_março 2020_fevereiro 2020_emergência_saúde pública |
| 21 | informamos anvisa - portal anvisa - esclarecimentos anvisa - central atendimento - atendimento 0800 | 72 | 21_informamos anvisa_portal anvisa_esclarecimentos anvisa_central atendimento |
| 22 | cidadão marinha - serviço informa - plataforma fala - informações cidadão - recurso resposta | 71 | 22_cidadão marinha_serviço informa_plataforma fala_informações cidadão |
| 23 | atendimento inss - cidadão inss - previdência social - solicitações indenizações - canais atendimento | 70 | 23_atendimento inss_cidadão inss_previdência social_solicitações indenizações |
| 24 | superintendência - documento anexo - anexo documento - anexo resposta - encaminhamos anexo | 70 | 24_superintendência_documento anexo_anexo documento_anexo resposta |
| 25 | informações cidadão - privacidade - informações interesse - cidadão marinha - tesouro nacional | 68 | 25_informações cidadão_privacidade_informações interesse_cidadão marinha |
| 26 | anexo resposta - anexo cidadão - anexa resposta - resposta cidadão - segue anexo | 66 | 26_anexo resposta_anexo cidadão_anexa resposta_resposta cidadão |
| 27 | esclarecimentos anvisa - vigilância sanitária - atendimento 0800 - anvisa gov - anvisa disponibiliza | 66 | 27_esclarecimentos anvisa_vigilância sanitária_atendimento 0800_anvisa gov |
| 28 | recurso secretário - informação fornecimento - mensagem informamos - ministério trabalho - apresentado recurso | 65 | 28_recurso secretário_informação fornecimento_mensagem informamos_ministério trabalho |
| 29 | sicbb recurso - banco sicbb - banco informação - recurso 12527 - indeferimento informações | 64 | 29_sicbb recurso_banco sicbb_banco informação_recurso 12527 |
| 30 | cidadão sic - solicitação informação - sic ministério - justificativa informação - 2012 serviço | 63 | 30_cidadão sic_solicitação informação_sic ministério_justificativa informação |
| 31 | portaria - ministério saúde - saúde - agosto 2019 - 08 2019 | 63 | 31_portaria_ministério saúde_saúde_agosto 2019 |
| 32 | secretaria educação - ministério educação - informamos secretaria - educação básica - solicitado informamos | 63 | 32_secretaria educação_ministério educação_informamos secretaria_educação básica |
| 33 | cidadão sic - cidadão ministério - sic ministério - informações cidadão - ministério defesa | 62 | 33_cidadão sic_cidadão ministério_sic ministério_informações cidadão |
| 34 | informação cidadão - prazo resposta - atendimento resposta - justificativa informação - serviço informação | 61 | 34_informação cidadão_prazo resposta_atendimento resposta_justificativa informação |
| 35 | cidadão exército - 2011 serviço - informações cidadão - exército http - exército prazo | 61 | 35_cidadão exército_2011 serviço_informações cidadão_exército http |
| 36 | anexo informações - cidadã atenção - segue anexo - seguem anexo - seguem anexos | 61 | 36_anexo informações_cidadã atenção_segue anexo_seguem anexo |
| 37 | atendimento inss - informações cidadão - cidadão inss - serviço informações - requerimento | 60 | 37_atendimento inss_informações cidadão_cidadão inss_serviço informações |
| 38 | anexo informação - anexo resposta - encaminhamos anexo - anexo serviço - segue anexo | 60 | 38_anexo informação_anexo resposta_encaminhamos anexo_anexo serviço |
| 39 | id_orgao_acesso_externo - ouvidoria informação - acessar - integrada ouvidoria - deverá acessar | 59 | 39_id_orgao_acesso_externo_ouvidoria informação_acessar_integrada ouvidoria |
| 40 | ministério ciência - ciência tecnologia - resposta informações - recurso resposta - serviço informação | 58 | 40_ministério ciência_ciência tecnologia_resposta informações_recurso resposta |
| 41 | segue anexo - anexo resposta - informação cidadão - anexo documentos - cidadão resposta | 57 | 41_segue anexo_anexo resposta_informação cidadão_anexo documentos |
| 42 | atendimento inss - solicitações indenizações - cidadão inss - informações cidadão - consultas reclamações | 57 | 42_atendimento inss_solicitações indenizações_cidadão inss_informações cidadão |
| 43 | anexo resposta - segue resposta - segue anexo - resposta segue - anexo | 56 | 43_anexo resposta_segue resposta_segue anexo_resposta segue |
| 44 | anexa resposta - resposta segue - segue anexa - anexa - resposta | 56 | 44_anexa resposta_resposta segue_segue anexa_anexa |
| 45 | aposentadoria - aposentados - gov dataset - secretaria gestão - planejamento gov | 56 | 45_aposentadoria_aposentados_gov dataset_secretaria gestão |
| 46 | procuradoria - acadêmico - diploma - judiciais - interessado | 56 | 46_procuradoria_acadêmico_diploma_judiciais |
| 47 | anexa resposta - segue anexa - informações solicitadas - resposta informação - resposta informações | 55 | 47_anexa resposta_segue anexa_informações solicitadas_resposta informação |
| 48 | pedidos informação - desproporcionalidade - desproporcionais - instituição requerida - desproporcionais desarrazoados | 55 | 48_pedidos informação_desproporcionalidade_desproporcionais_instituição requerida |
| 49 | presente solicitação - requerimento - reclamação - trata solicitação - susep | 55 | 49_presente solicitação_requerimento_reclamação_trata solicitação |
| 50 | acessoainformacao - acessoainformacao atenção - gov acessoainformacao - informações secretaria - informação cadastrado | 55 | 50_acessoainformacao_acessoainformacao atenção_gov acessoainformacao_informações secretaria |
| 51 | informações cidadão - solicitação esclarecemos - informação lai - serviço informações - solicitação informamos | 54 | 51_informações cidadão_solicitação esclarecemos_informação lai_serviço informações |
| 52 | acessoainformacao atenção - acessoainformacao - gov acessoainformacao - república informações - informações cidadão | 54 | 52_acessoainformacao atenção_acessoainformacao_gov acessoainformacao_república informações |
| 53 | cidadão exército - exército http - exército brasileiro - 2011 serviço - conheça exército | 54 | 53_cidadão exército_exército http_exército brasileiro_2011 serviço |
| 54 | reclamações denúncias - reclamação - telecomunicações - atendimento nº - reclamações | 54 | 54_reclamações denúncias_reclamação_telecomunicações_atendimento nº |
| 55 | república informações - informações cidadão - informação cadastrado - cidadão palácio - informação nº | 54 | 55_república informações_informações cidadão_informação cadastrado_cidadão palácio |
| 56 | informação nup - informações cidadão - gov acessoainformacao - informações secretaria - serviço informações | 53 | 56_informação nup_informações cidadão_gov acessoainformacao_informações secretaria |
| 57 | solicitação informamos - serviço informação - informação cidadão - informação requerida - resposta informamos | 53 | 57_solicitação informamos_serviço informação_informação cidadão_informação requerida |
| 58 | universidade brasília - brasília df - informamos sic - sic unb - informação cidadão | 53 | 58_universidade brasília_brasília df_informamos sic_sic unb |
| 59 | informações cidadão - cidadão ministério - solicitação serviço - formulário solicitação - serviço informações | 52 | 59_informações cidadão_cidadão ministério_solicitação serviço_formulário solicitação |
| 60 | recurso prazo - sic infraero - requerente apresentar - poderá requerente - apresentar recurso | 52 | 60_recurso prazo_sic infraero_requerente apresentar_poderá requerente |
| 61 | união agu - procuradoria geral - advocacia geral - anexo resposta - resposta solicitação | 52 | 61_união agu_procuradoria geral_advocacia geral_anexo resposta |
| 62 | anvisa disponibiliza - portal anvisa - anvisa gov - anvisa - www anvisa | 51 | 62_anvisa disponibiliza_portal anvisa_anvisa gov_anvisa |
| 63 | recurso prazo - ciência resposta - solicitação encaminhamos - apresentar recurso - arquivo anexo | 50 | 63_recurso prazo_ciência resposta_solicitação encaminhamos_apresentar recurso |
| 64 | www acessoainformacao - acessoainformacao gov - comando aeronáutica - aérea brasileira - aeronáutica comaer | 50 | 64_www acessoainformacao_acessoainformacao gov_comando aeronáutica_aérea brasileira |
| 65 | pedidos informação - informação requerida - precisa informação - decreto 7724 - informação determina | 50 | 65_pedidos informação_informação requerida_precisa informação_decreto 7724 |
| 66 | solicitação protocolada - solicitação - serviço informação - informação protocolado - informação cidadão | 50 | 66_solicitação protocolada_solicitação_serviço informação_informação protocolado |
| 67 | informamos caixa - sac informações - atendimento caixa - cgu informamos - informações empresas | 50 | 67_informamos caixa_sac informações_atendimento caixa_cgu informamos |
| 68 | correios telégrafos - brasileira correios - anexo resposta - resposta solicitação - enviado mail | 49 | 68_correios telégrafos_brasileira correios_anexo resposta_resposta solicitação |
| 69 | ministério saúde - ciência tecnologia - tecnologia insumos - parcerias - secretaria ciência | 49 | 69_ministério saúde_ciência tecnologia_tecnologia insumos_parcerias |
| 70 | cidadão exército - exército prazo - brasileiro sic - exército brasileiro - informações cidadão | 49 | 70_cidadão exército_exército prazo_brasileiro sic_exército brasileiro |
| 71 | ouvidoria informação - inquérito policial - polícia - hipóteses sigilo - portaria nº | 49 | 71_ouvidoria informação_inquérito policial_polícia_hipóteses sigilo |
| 72 | acessoainformacao gov - pedidos informações - ministério cidadania - acessoainformacao - sic ouvidoria | 48 | 72_acessoainformacao gov_pedidos informações_ministério cidadania_acessoainformacao |
| 73 | interposto recurso - interpor recurso - recurso contra - ciências - poderá interpor | 47 | 73_interposto recurso_interpor recurso_recurso contra_ciências |
| 74 | recurso superintendente - fundos - transparência informações - interposto recurso - recebemos informação | 47 | 74_recurso superintendente_fundos_transparência informações_interposto recurso |
| 75 | resposta solicitação - atendimento solicitação - anexo resposta - resposta anexo - responder pesquisa | 47 | 75_resposta solicitação_atendimento solicitação_anexo resposta_resposta anexo |
| 76 | pedidos informação - informação requerida - informações serviço - 2011 informação - entidade deverá | 46 | 76_pedidos informação_informação requerida_informações serviço_2011 informação |
| 77 | anvisa disponibiliza - portal anvisa - anvisa - anvisa gov - atendimento 0800 | 46 | 77_anvisa disponibiliza_portal anvisa_anvisa_anvisa gov |
| 78 | pesquisas educacionais - educacionais - educação superior - atendimento informação - educação | 46 | 78_pesquisas educacionais_educacionais_educação superior_atendimento informação |
| 79 | cidadão inss - atendimento inss - serviço informação - inss serviço - informações cidadão | 45 | 79_cidadão inss_atendimento inss_serviço informação_inss serviço |
| 80 | ciência - interpor recurso - resposta informação - poderá interpor - ufmg | 45 | 80_ciência_interpor recurso_resposta informação_poderá interpor |
| 81 | advocacia geral - direito informações - união agu - procuradoria geral - consultoria jurídica | 45 | 81_advocacia geral_direito informações_união agu_procuradoria geral |
| 82 | ouvidoria inep - atendimento informação - negado informação - acessoainformacao pt - falabr cgu | 44 | 82_ouvidoria inep_atendimento informação_negado informação_acessoainformacao pt |
| 83 | auxílio - ministério cidadania - informação secretaria - ouvidorias gov - ouvidoria informação | 44 | 83_auxílio_ministério cidadania_informação secretaria_ouvidorias gov |
| 84 | recurso 12527 - indeferimento informações - econômica estado - empregatícia - subsidiárias | 44 | 84_recurso 12527_indeferimento informações_econômica estado_empregatícia |
| 85 | anexa resposta - resposta segue - segue anexa - anexa - resposta | 44 | 85_anexa resposta_resposta segue_segue anexa_anexa |
| 86 | acessoainformacao cidadão - decreto informações - acessoainformacao atenção - acessoainformacao - gov acessoainformacao | 44 | 86_acessoainformacao cidadão_decreto informações_acessoainformacao atenção_acessoainformacao |
| 87 | eletrobras - solicitação esclarecemos - precisa informação - contato diretamente - atender solicitação | 44 | 87_eletrobras_solicitação esclarecemos_precisa informação_contato diretamente |
| 88 | equipe atendimento - atendimento - resposta solicitação - informações necessárias - segue anexo | 44 | 88_equipe atendimento_atendimento_resposta solicitação_informações necessárias |
| 89 | informações cidadão - informação cidadão - serviço informação - serviço informações - resposta informação | 43 | 89_informações cidadão_informação cidadão_serviço informação_serviço informações |
| 90 | sicbb roberto - banco sicbb - cidadão banco - sicbb - informação cidadão | 42 | 90_sicbb roberto_banco sicbb_cidadão banco_sicbb |
| 91 | prestação contas - relatórios - eletrônico sic - correio - diárias passagens | 42 | 91_prestação contas_relatórios_eletrônico sic_correio |
| 92 | informação solicitada - atendimento informação - informação cidadão - serviço informação - informação 12 | 42 | 92_informação solicitada_atendimento informação_informação cidadão_serviço informação |
| 93 | anexo informações - informação solicitado - anexo resposta - segue anexo - solicitante resposta | 42 | 93_anexo informações_informação solicitado_anexo resposta_segue anexo |
| 94 | ministério saúde - tecnologia insumos - ciência tecnologia - insumos estratégicos - saúde | 42 | 94_ministério saúde_tecnologia insumos_ciência tecnologia_insumos estratégicos |
| 95 | resposta requerimento - informação reposta - poderá requerente - requerimento - recurso prazo | 42 | 95_resposta requerimento_informação reposta_poderá requerente_requerimento |
| 96 | ministério saúde - tecnologia insumos - parcerias - secretaria ciência - saúde | 41 | 96_ministério saúde_tecnologia insumos_parcerias_secretaria ciência |
| 97 | informações cidadão - informação cadastrado - atenção informação - informação nº - servico cidadão | 41 | 97_informações cidadão_informação cadastrado_atenção informação_informação nº |
| 98 | recurso prazo - apresentar recurso - requerente apresentar - resposta prazo - razões recurso | 41 | 98_recurso prazo_apresentar recurso_requerente apresentar_resposta prazo |
| 99 | sicbb recurso - banco sicbb - banco informação - indeferimento informações - recurso 12527 | 41 | 99_sicbb recurso_banco sicbb_banco informação_indeferimento informações |
| 100 | gov acessoainformacao - informação cidadão - informações cidadão - acessoainformacao atenção - república informações | 41 | 100_gov acessoainformacao_informação cidadão_informações cidadão_acessoainformacao atenção |
| 101 | informação cidadão - sic serviço - brasília df - ambientais - ambiental | 41 | 101_informação cidadão_sic serviço_brasília df_ambientais |
| 102 | anexo sic - resposta solicitação - segue anexo - resposta sic - segue resposta | 40 | 102_anexo sic_resposta solicitação_segue anexo_resposta sic |
| 103 | acessoainformacao pt - ouvidoria inep - inep instituto - inep térreo - endereço https | 40 | 103_acessoainformacao pt_ouvidoria inep_inep instituto_inep térreo |
| 104 | informações cidadão - república informações - segurança institucional - serviço informações - atenção informação | 40 | 104_informações cidadão_república informações_segurança institucional_serviço informações |
| 105 | concurso público - reitoria gestão - solicitado informamos - processos seletivos - consulta | 40 | 105_concurso público_reitoria gestão_solicitado informamos_processos seletivos |
| 106 | vigilância sanitária - esclarecimentos anvisa - atendimento 0800 - anvisa gov - anvisa | 39 | 106_vigilância sanitária_esclarecimentos anvisa_atendimento 0800_anvisa gov |
| 107 | sicbb roberto - banco sicbb - sicbb - cidadão banco - informação cidadão | 39 | 107_sicbb roberto_banco sicbb_sicbb_cidadão banco |
| 108 | araujo - segue - segue anexo - araújo - acadêmica | 38 | 108_araujo_segue_segue anexo_araújo |
| 109 | recurso secretário - recurso prazo - decreto 724 - ministério fazenda - apresentado recurso | 38 | 109_recurso secretário_recurso prazo_decreto 724_ministério fazenda |
| 110 | reavaliação informações - requerimento - informações cmri - demanda informação - procuração | 38 | 110_reavaliação informações_requerimento_informações cmri_demanda informação |
| 111 | resposta unidade - resposta manifestação - unidade responsável - anexo resposta - abaixo resposta | 37 | 111_resposta unidade_resposta manifestação_unidade responsável_anexo resposta |
| 112 | ccivil_03 _ato2011 - relação informações - informação atividades - vez informação - informações podem | 37 | 112_ccivil_03 _ato2011_relação informações_informação atividades_vez informação |
| 113 | recurso prazo - apresentar recurso - requerente apresentar - poderá requerente - razões negativa | 37 | 113_recurso prazo_apresentar recurso_requerente apresentar_poderá requerente |
| 114 | informações reclamações - sac atendimento - contato esclarecemos - canais atendimento - informamos caixa | 37 | 114_informações reclamações_sac atendimento_contato esclarecemos_canais atendimento |
| 115 | ministério saúde - informamos ministério - saúde informa - requerimento - requerimentos | 37 | 115_ministério saúde_informamos ministério_saúde informa_requerimento |
| 116 | atendimento informação - informamos atendimento - informação solicitado - contato informamos - sic ifb | 37 | 116_atendimento informação_informamos atendimento_informação solicitado_contato informamos |
| 117 | informação solicitada - informações solicitadas - sic ufba - informação cidadão - cidadão sic | 36 | 117_informação solicitada_informações solicitadas_sic ufba_informação cidadão |
| 118 | anexo resposta - resposta anexo - segue anexo - segue resposta - resposta segue | 36 | 118_anexo resposta_resposta anexo_segue anexo_segue resposta |
| 119 | anexo informação - anexa resposta - cidadão marinha - informação cidadão - informações cidadão | 36 | 119_anexo informação_anexa resposta_cidadão marinha_informação cidadão |
| 120 | ouvidoria informação - brasileiro ambiente - manifestação ouvidoria - ambientais - ouvidoria | 35 | 120_ouvidoria informação_brasileiro ambiente_manifestação ouvidoria_ambientais |
| 121 | informações pessoais - pessoais relativas - sigilo prazo - pessoais - vida privada | 35 | 121_informações pessoais_pessoais relativas_sigilo prazo_pessoais |
| 122 | informação nup - bancário - informação cidadão - cidadão banco - apresentar recurso | 35 | 122_informação nup_bancário_informação cidadão_cidadão banco |
| 123 | acessoainformacao - república informações - informações secretaria - gov acessoainformacao - informação pessoal | 34 | 123_acessoainformacao_república informações_informações secretaria_gov acessoainformacao |
| 124 | arquivo nacional - informações atendimento - atendimento presencial - informação ouvidoria - arquivo | 34 | 124_arquivo nacional_informações atendimento_atendimento presencial_informação ouvidoria |
| 125 | informação cidadão - brasília df - informação - diretoria - formulário | 34 | 125_informação cidadão_brasília df_informação_diretoria |
| 126 | sigilo empresarial - atividade empresarial - industriais - empresarial pessoas - sigilo fiscal | 34 | 126_sigilo empresarial_atividade empresarial_industriais_empresarial pessoas |
| 127 | boa informamos - informação cidadão - resposta informação - resposta solicitação - serviço informação | 34 | 127_boa informamos_informação cidadão_resposta informação_resposta solicitação |
| 128 | sicbb recurso - banco sicbb - indeferimento informações - banco informação - recurso 12527 | 34 | 128_sicbb recurso_banco sicbb_indeferimento informações_banco informação |
| 129 | informações solicitadas - pessoais protegidas - documentos solicitados - informações pessoais - informação 12 | 34 | 129_informações solicitadas_pessoais protegidas_documentos solicitados_informações pessoais |
| 130 | sigilo fiscal - fiscais - informações cidadão - sigilo profissional - receita federal | 33 | 130_sigilo fiscal_fiscais_informações cidadão_sigilo profissional |
| 131 | resposta diretoria - sic unifesp - registrar recurso - anexa resposta - solicitação | 33 | 131_resposta diretoria_sic unifesp_registrar recurso_anexa resposta |
| 132 | informamos sic - resposta referente - anexo resposta - cidadão encaminhamos - prestação contas | 33 | 132_informamos sic_resposta referente_anexo resposta_cidadão encaminhamos |
| 133 | informações corporativas - informamos caixa - denúncias caixa - sac informações - informações caráter | 33 | 133_informações corporativas_informamos caixa_denúncias caixa_sac informações |
| 134 | demanda apresentada - agência nacional - aviação civil - agência - aviação | 33 | 134_demanda apresentada_agência nacional_aviação civil_agência |
| 135 | informações empresas - públicas sociedades - sociedades - sociedades economia - próprio empresas | 33 | 135_informações empresas_públicas sociedades_sociedades_sociedades economia |
| 136 | auditoria interna - diretoria - diretoria colegiada - relatório - auditoria | 33 | 136_auditoria interna_diretoria_diretoria colegiada_relatório |
| 137 | recurso secretário - requerimento formulado - requerimento - informar demanda - ministério fazenda | 32 | 137_recurso secretário_requerimento formulado_requerimento_informar demanda |
| 138 | prazo resposta - resposta informação - cidadão resposta - atendimento resposta - justificativa informação | 32 | 138_prazo resposta_resposta informação_cidadão resposta_atendimento resposta |
| 139 | correios - atendimento informações - canal atendimento - reclamação denúncia - central atendimento | 32 | 139_correios_atendimento informações_canal atendimento_reclamação denúncia |
| 140 | informação cidadão - brasília df - acessoainformacao cidadão - solicitação deve - ambiental | 31 | 140_informação cidadão_brasília df_acessoainformacao cidadão_solicitação deve |
| 141 | sic mapa - ouvidoria mapa - sic ouvidoria - agricultura gov - informação resposta | 31 | 141_sic mapa_ouvidoria mapa_sic ouvidoria_agricultura gov |
| 142 | segurança institucional - república informações - presidência república - institucional presidência - gabinete segurança | 31 | 142_segurança institucional_república informações_presidência república_institucional presidência |
| 143 | relatórios gestão - relatório gestão - gestão recursos - responsável gestão - federais | 30 | 143_relatórios gestão_relatório gestão_gestão recursos_responsável gestão |
| 144 | requerente poderá - recurso prazo - apresentar recurso - negativa informação - demanda informamos | 30 | 144_requerente poderá_recurso prazo_apresentar recurso_negativa informação |
| 145 | selecionartipomanifestacao - manifestacao selecionartipomanifestacao - falabr cgu - selecionartipomanifestacao aspx - universidade federal | 30 | 145_selecionartipomanifestacao_manifestacao selecionartipomanifestacao_falabr cgu_selecionartipomanifestacao aspx |
| 146 | apresentado recurso - apresentar recurso - anexo informamos - recurso prazo - arquivo anexo | 29 | 146_apresentado recurso_apresentar recurso_anexo informamos_recurso prazo |
| 147 | auxílio - concessão benefício - benefício - rendimentos - beneficiários | 29 | 147_auxílio_concessão benefício_benefício_rendimentos |
| 148 | gov acessoainformacao - acessoainformacao atenção - acessoainformacao - segurança institucional - institucional presidência | 29 | 148_gov acessoainformacao_acessoainformacao atenção_acessoainformacao_segurança institucional |
| 149 | brasileira correios - correio - correios - destinatário - postal | 29 | 149_brasileira correios_correio_correios_destinatário |
| 150 | folha pagamento - empregados - universitário - proteção lgpd - colaboradores | 29 | 150_folha pagamento_empregados_universitário_proteção lgpd |
| 151 | sac atendimento - telefone 0800 - caixa gov - informações reclamações - canais atendimento | 29 | 151_sac atendimento_telefone 0800_caixa gov_informações reclamações |
| 152 | apresentar recurso - recurso prazo - acessoainformacao - acessoainformacao gov - www acessoainformacao | 29 | 152_apresentar recurso_recurso prazo_acessoainformacao_acessoainformacao gov |
| 153 | informações empresas - divulgação informações - corporativa interesses - acionistas minoritários - governança corporativa | 29 | 153_informações empresas_divulgação informações_corporativa interesses_acionistas minoritários |
| 154 | financiamento - instituição ensino - ensino superior - secretaria educação - financiamentos | 28 | 154_financiamento_instituição ensino_ensino superior_secretaria educação |
| 155 | processamento serpro - https coronavirus - recurso prazo - serviço informação - informação cidadão | 28 | 155_processamento serpro_https coronavirus_recurso prazo_serviço informação |
| 156 | previdência social - cidadão inss - informações pessoais - geral previdência - previdência | 28 | 156_previdência social_cidadão inss_informações pessoais_geral previdência |
| 157 | cidadão exército - exército prazo - exército http - exército brasileiro - informações cidadão | 28 | 157_cidadão exército_exército prazo_exército http_exército brasileiro |
| 158 | anexo resposta - recurso prazo - decreto 724 - informação considerando - apresentado recurso | 27 | 158_anexo resposta_recurso prazo_decreto 724_informação considerando |
| 159 | anexo resposta - solicitação protocolada - decreto 724 - cidadão ministério - recurso resposta | 27 | 159_anexo resposta_solicitação protocolada_decreto 724_cidadão ministério |
| 160 | cidadão exército - informações cidadão - exército brasileiro - exército http - exército prazo | 27 | 160_cidadão exército_informações cidadão_exército brasileiro_exército http |
| 161 | informação pessoal - informações pessoais - informações necessárias - relativas intimidade - tratamento informações | 27 | 161_informação pessoal_informações pessoais_informações necessárias_relativas intimidade |
| 162 | cidadão bndes - pedidos informação - financeiras - atendimento pedidos - informamos demanda | 27 | 162_cidadão bndes_pedidos informação_financeiras_atendimento pedidos |
| 163 | recurso prazo - demanda informação - apresentar recurso - informação cidadão - requerente poderá | 27 | 163_recurso prazo_demanda informação_apresentar recurso_informação cidadão |
| 164 | recurso prazo - informação cidadão - demanda informação - ciência resposta - apresentar recurso | 27 | 164_recurso prazo_informação cidadão_demanda informação_ciência resposta |
| 165 | república informações - documentos requeridos - segurança institucional - institucional presidência - informações restritas | 27 | 165_república informações_documentos requeridos_segurança institucional_institucional presidência |
| 166 | sic serviço - gov informamos - referida informação - municípios - procedimentos serem | 27 | 166_sic serviço_gov informamos_referida informação_municípios |
| 167 | patente - referência informação - recebemos informação - interposto recurso - através endereço | 26 | 167_patente_referência informação_recebemos informação_interposto recurso |
| 168 | indeferimento informação - interposto recurso - solicitação protocolada - resposta solicitação - anexo resposta | 26 | 168_indeferimento informação_interposto recurso_solicitação protocolada_resposta solicitação |
| 169 | ministério infraestrutura - acessoainformacao gov - www acessoainformacao - infraestrutura - interposição recurso | 26 | 169_ministério infraestrutura_acessoainformacao gov_www acessoainformacao_infraestrutura |
| 170 | diretoria gestão - presente resposta - ainda informar - diretoria - resposta prazo | 25 | 170_diretoria gestão_presente resposta_ainda informar_diretoria |
| 171 | comando aeronáutica - reavaliação informações - aeronáutica comaer - informações cmri - buscar informação | 25 | 171_comando aeronáutica_reavaliação informações_aeronáutica comaer_informações cmri |
| 172 | portaldatransparencia gov - transparência governo - portal transparência - www portaldatransparencia - transparência | 25 | 172_portaldatransparencia gov_transparência governo_portal transparência_www portaldatransparencia |
| 173 | informação secretaria - ministério ambiente - subsecretaria planejamento - transparência ativa - transparência | 25 | 173_informação secretaria_ministério ambiente_subsecretaria planejamento_transparência ativa |
| 174 | brasília df - informação interesse - diretoria - obtida informação - informações atendimento | 25 | 174_brasília df_informação interesse_diretoria_obtida informação |
| 175 | inexistência informação - cidadão exército - declaração inexistência - súmula cmri - solicitação constitui | 25 | 175_inexistência informação_cidadão exército_declaração inexistência_súmula cmri |
| 176 | atendimento caixa - sac informações - ouvidoria atendimento - sac denúncias - informamos caixa | 25 | 176_atendimento caixa_sac informações_ouvidoria atendimento_sac denúncias |
| 177 | informação sigilosa - industrial segredo - cópias documentos - hipóteses sigilo - anvisa | 25 | 177_informação sigilosa_industrial segredo_cópias documentos_hipóteses sigilo |
| 178 | informação requerida - precisa informação - informação deverá - informação necessário - identificação requerente | 25 | 178_informação requerida_precisa informação_informação deverá_informação necessário |
| 179 | duplicado - informamos solicitação - informação cidadão - informações cidadão - cidadão inss | 24 | 179_duplicado_informamos solicitação_informação cidadão_informações cidadão |
| 180 | ouvidoria inep - inep térreo - inep instituto - sede inep - negado informação | 24 | 180_ouvidoria inep_inep térreo_inep instituto_sede inep |
| 181 | resposta solicitação - atendimento solicitação - solicitação encaminhamos - solicitação informação - solicitado encaminhamos | 24 | 181_resposta solicitação_atendimento solicitação_solicitação encaminhamos_solicitação informação |
| 182 | segue resposta - resposta questionamento - questionamento - resposta - solicitação segue | 24 | 182_segue resposta_resposta questionamento_questionamento_resposta |
| 183 | ministério economia - cidadão ministério - informações cidadão - serviço informações - sic ministério | 24 | 183_ministério economia_cidadão ministério_informações cidadão_serviço informações |
| 184 | anexo resposta - prazo interposição - recurso 1ª - interpor recurso - autoridade hierarquicamente | 24 | 184_anexo resposta_prazo interposição_recurso 1ª_interpor recurso |
| 185 | recurso sic - contato sic - prazo atendimento - informação referente - sistema sic | 24 | 185_recurso sic_contato sic_prazo atendimento_informação referente |
| 186 | informação atividades - obtida informação - informação contida - informação almejada - informação | 24 | 186_informação atividades_obtida informação_informação contida_informação almejada |
| 187 | parcerias - parceria - portaria - ministério saúde - nº 12527 | 24 | 187_parcerias_parceria_portaria_ministério saúde |
| 188 | solicitação informação - cidadão ministério - ministério comunicações - informação cadastrado - sic cidadão | 24 | 188_solicitação informação_cidadão ministério_ministério comunicações_informação cadastrado |
| 189 | documentação solicitada - informação lai - legais sigilo - segredo industrial - demanda informação | 23 | 189_documentação solicitada_informação lai_legais sigilo_segredo industrial |
| 190 | 7474 reclamações - ouvidoria ddg - sac atendimento - informações reclamações - ddg 0800 | 23 | 190_7474 reclamações_ouvidoria ddg_sac atendimento_informações reclamações |
| 191 | segue resposta - resposta questionamento - seguem respostas - resposta - respostas | 23 | 191_segue resposta_resposta questionamento_seguem respostas_resposta |
| 192 | recurso fala - informação cidadão - recurso sic - comunicação - ciência resposta | 23 | 192_recurso fala_informação cidadão_recurso sic_comunicação |
| 193 | anexa resposta - anexo resposta - resposta anexo - agradecemos contato - atendimento resposta | 23 | 193_anexa resposta_anexo resposta_resposta anexo_agradecemos contato |
| 194 | informação solicitada - solicitação informação - pedidos informação - informação cidadão - serviço informação | 23 | 194_informação solicitada_solicitação informação_pedidos informação_informação cidadão |
| 195 | brasileira correios - correios - correios agradecem - atendimento solicitação - telefone 0800 | 23 | 195_brasileira correios_correios_correios agradecem_atendimento solicitação |
| 196 | atendimento caixa - ouvidoria atendimento - sac informações - sac denúncias - informamos caixa | 23 | 196_atendimento caixa_ouvidoria atendimento_sac informações_sac denúncias |
| 197 | interpor recurso - apresentar recurso - recurso - informações demandadas - podem consultados | 23 | 197_interpor recurso_apresentar recurso_recurso_informações demandadas |
| 198 | ministério economia - cidadão ministério - informações cidadão - serviço informações - sic ministério | 22 | 198_ministério economia_cidadão ministério_informações cidadão_serviço informações |
| 199 | sistema sic - endereço - solicitação - resposta solicitação - solicitação respondida | 22 | 199_sistema sic_endereço_solicitação_resposta solicitação |
| 200 | sic infraero - infraero - atenção informamos - equipe sic - informamos serviço | 22 | 200_sic infraero_infraero_atenção informamos_equipe sic |
| 201 | reitoria graduação - pró reitora - pró reitoria - reitoria gestão - graduação | 22 | 201_reitoria graduação_pró reitora_pró reitoria_reitoria gestão |
| 202 | saúde secretaria - secretaria ciência - saúde informa - ministério saúde - sic ministério | 22 | 202_saúde secretaria_secretaria ciência_saúde informa_ministério saúde |
| 203 | prazo resposta - informação cidadão - atendimento resposta - justificativa informação - recurso prazo | 22 | 203_prazo resposta_informação cidadão_atendimento resposta_justificativa informação |
| 204 | pedidos informação - desproporcionais - atender solicitação - desproporcional - volume documentos | 21 | 204_pedidos informação_desproporcionais_atender solicitação_desproporcional |
| 205 | informação ministério - indeferimento informação - respostas questionamentos - outras informações - fundação | 21 | 205_informação ministério_indeferimento informação_respostas questionamentos_outras informações |
| 206 | chefia gabinete - chefe gabinete - resposta chefe - presente solicitação - informações solicitadas | 21 | 206_chefia gabinete_chefe gabinete_resposta chefe_presente solicitação |
| 207 | resposta anexo - anexo resposta - anexa resposta - anexo sic - anexo | 21 | 207_resposta anexo_anexo resposta_anexa resposta_anexo sic |
| 208 | resposta nup - serviço informação - cidadão inss - informação cidadão - solicitação | 21 | 208_resposta nup_serviço informação_cidadão inss_informação cidadão |
| 209 | sic ufba - sic universidade - participe pesquisa - universidade federal - universidade | 21 | 209_sic ufba_sic universidade_participe pesquisa_universidade federal |
| 210 | prazo resposta - solicitação informação - cidadão resposta - atendimento resposta - resposta informação | 21 | 210_prazo resposta_solicitação informação_cidadão resposta_atendimento resposta |
| 211 | ufrj - busca informações - desculpas demora - pessoal - solicitação contida | 21 | 211_ufrj_busca informações_desculpas demora_pessoal |
| 212 | reavaliação informações - apresentar recurso - instância recursal - reclamação - resposta solicitação | 21 | 212_reavaliação informações_apresentar recurso_instância recursal_reclamação |
| 213 | informações sigilosas - anvisa - informação prazo - gerência - avaliado gerência | 21 | 213_informações sigilosas_anvisa_informação prazo_gerência |
| 214 | ouvidorias gov - requerimento - ouvidoria geral - solicitação - solicitação poderá | 21 | 214_ouvidorias gov_requerimento_ouvidoria geral_solicitação |
| 215 | recurso secretário - recurso prazo - destinatário recurso - resposta secretaria - ministério fazenda | 21 | 215_recurso secretário_recurso prazo_destinatário recurso_resposta secretaria |
| 216 | contato sic - anexo resposta - sic ministério - ministério fazenda - recurso prazo | 20 | 216_contato sic_anexo resposta_sic ministério_ministério fazenda |
| 217 | correios - previdenciário - brasileira correios - gestão pessoas - prazos condições | 20 | 217_correios_previdenciário_brasileira correios_gestão pessoas |
| 218 | informações desejadas - escopo informação - requerimentos - informação sic - envio informações | 20 | 218_informações desejadas_escopo informação_requerimentos_informação sic |
| 219 | resposta requerimento - serviço informação - poderá requerente - cidadão sic - sic ouvidoria | 20 | 219_resposta requerimento_serviço informação_poderá requerente_cidadão sic |
| 220 | resposta nup - solicitação protocolada - recurso resposta - informação protocolado - precisa informação | 20 | 220_resposta nup_solicitação protocolada_recurso resposta_informação protocolado |
| 221 | ouvidoria informação - recurso prazo - apresentar recurso - recebimento recurso - informação cidadão | 20 | 221_ouvidoria informação_recurso prazo_apresentar recurso_recebimento recurso |
| 222 | informamos anvisa - anvisa disponibiliza - portal anvisa - anvisa gov - atendimento 0800 | 20 | 222_informamos anvisa_anvisa disponibiliza_portal anvisa_anvisa gov |
| 223 | guia recolhimento - cópias documentos - recolhimento união - prazo resposta - fornecimento informação | 20 | 223_guia recolhimento_cópias documentos_recolhimento união_prazo resposta |
| 224 | processo seletivo - processo seleção - seleção - convocação - convocações | 20 | 224_processo seletivo_processo seleção_seleção_convocação |
| 225 | requerimento formulado - requerimento - recurso prazo - informamos demanda - informar demanda | 20 | 225_requerimento formulado_requerimento_recurso prazo_informamos demanda |
| 226 | habitacionais - municípios - sigilo bancário - sic ministério - instituições financeiras | 20 | 226_habitacionais_municípios_sigilo bancário_sic ministério |
| 227 | resposta sic - documentos solicitados - solicitados podem - informações solicitadas - dias sic | 19 | 227_resposta sic_documentos solicitados_solicitados podem_informações solicitadas |
| 228 | ouvidoria geral - atendimento informação - plataforma fala - vice presidência - informamos resposta | 19 | 228_ouvidoria geral_atendimento informação_plataforma fala_vice presidência |
| 229 | anexo resposta - segue resposta - informação solicitado - segue anexo - informação segue | 19 | 229_anexo resposta_segue resposta_informação solicitado_segue anexo |
| 230 | ministério ambiente - relatório - documentação referente - informação protocolo - informação segue | 19 | 230_ministério ambiente_relatório_documentação referente_informação protocolo |
| 231 | data resposta - recurso secretário - resposta prazo - presente resposta - recurso prazo | 19 | 231_data resposta_recurso secretário_resposta prazo_presente resposta |
| 232 | documentos processos - desproporcionalidade - secretaria - desproporcional - processos | 19 | 232_documentos processos_desproporcionalidade_secretaria_desproporcional |
| 233 | informação cadastrado - atenção informação - planalto gov - gov planalto - administração secretaria | 19 | 233_informação cadastrado_atenção informação_planalto gov_gov planalto |
| 234 | informação cidadão - ibama - brasília df - serviço informação - informação | 19 | 234_informação cidadão_ibama_brasília df_serviço informação |
| 235 | demanda sic - atendimento demanda - resposta demanda - atendimento solicitação - presente demanda | 19 | 235_demanda sic_atendimento demanda_resposta demanda_atendimento solicitação |
| 236 | sanitária anvisa - anvisa gov - vigilância sanitária - anvisa - sanitária | 19 | 236_sanitária anvisa_anvisa gov_vigilância sanitária_anvisa |
| 237 | anexo resposta - anexo informação - anexo informações - anexo segue - informamos segue | 18 | 237_anexo resposta_anexo informação_anexo informações_anexo segue |
| 238 | sicbb recurso - banco sicbb - banco informação - indeferimento informações - recurso 12527 | 18 | 238_sicbb recurso_banco sicbb_banco informação_indeferimento informações |
| 239 | legais sigilo - sigilo empresarial - corporativa interesses - interesses acionistas - segredo justiça | 18 | 239_legais sigilo_sigilo empresarial_corporativa interesses_interesses acionistas |
| 240 | sac informações - atendimento caixa - cgu informamos - informamos caixa - sac denúncias | 18 | 240_sac informações_atendimento caixa_cgu informamos_informamos caixa |
| 241 | solicitação informamos - sic informa - cnpq - bom informamos - relatório | 18 | 241_solicitação informamos_sic informa_cnpq_bom informamos |
| 242 | voluntária - servico cidadão - resposta informações - atenção informação - informação nº | 18 | 242_voluntária_servico cidadão_resposta informações_atenção informação |
| 243 | informação cidadão - pedidos informação - anexo relatório - informações solicitadas - bom informamos | 18 | 243_informação cidadão_pedidos informação_anexo relatório_informações solicitadas |
| 244 | identidade requerente - adequado solicitação - requer informações - pessoais terceiros - informação pessoal | 18 | 244_identidade requerente_adequado solicitação_requer informações_pessoais terceiros |
| 245 | ouvidoria inep - acessoainformacao pt - atendimento informação - prazos atendimento - inep térreo | 18 | 245_ouvidoria inep_acessoainformacao pt_atendimento informação_prazos atendimento |
| 246 | informações cidadão - cidadão ministério - serviço informações - sic ministério - ministério economia | 17 | 246_informações cidadão_cidadão ministério_serviço informações_sic ministério |
| 247 | república informações - segurança institucional - informação nº - cidadão palácio - presente informação | 17 | 247_república informações_segurança institucional_informação nº_cidadão palácio |
| 248 | resposta departamento - registro - documento identificação - acessos - tecnologia informação | 17 | 248_resposta departamento_registro_documento identificação_acessos |
| 249 | recurso prazo - ministério fazenda - contato sic - requerimento - receita federal | 17 | 249_recurso prazo_ministério fazenda_contato sic_requerimento |
| 250 | ministério economia - serviço informações - informações cidadão - secretaria receita - cidadão ministério | 17 | 250_ministério economia_serviço informações_informações cidadão_secretaria receita |
| 251 | universidade brasília - fundação universidade - universitário - 900 brasília - universidade | 17 | 251_universidade brasília_fundação universidade_universitário_900 brasília |
| 252 | informações caráter - solicitação informações - autorizados pessoa - informadas - pessoais restrito | 17 | 252_informações caráter_solicitação informações_autorizados pessoa_informadas |
| 253 | construção - incorporação - financiamento - imóveis - imóvel | 17 | 253_construção_incorporação_financiamento_imóveis |
| 254 | financeiras - instituições financeiras - instituição financeira - instituições - âmbito | 17 | 254_financeiras_instituições financeiras_instituição financeira_instituições |
| 255 | órgãos entidades - órgãos públicos - públicos informações - informações dessa - vez informação | 17 | 255_órgãos entidades_órgãos públicos_públicos informações_informações dessa |
| 256 | ouvidoria inep - inep térreo - inep instituto - sede inep - negado informação | 17 | 256_ouvidoria inep_inep térreo_inep instituto_sede inep |
| 257 | inconsistência - perfil - informações sigilosas - informações - sigilosas | 17 | 257_inconsistência_perfil_informações sigilosas_informações |
| 258 | sac informações - cgu informamos - sac denúncias - atendimento caixa - caixa mantém | 17 | 258_sac informações_cgu informamos_sac denúncias_atendimento caixa |
| 259 | ministério economia - cidadão ministério - solicitação informa - informações cidadão - solicitação informamos | 17 | 259_ministério economia_cidadão ministério_solicitação informa_informações cidadão |
| 260 | competência secretaria - comunicação social - secretaria governo - social secretaria - solicitadas informações | 17 | 260_competência secretaria_comunicação social_secretaria governo_social secretaria |
| 261 | brasileira correios - correios - correios telégrafos - destinatário - reclamação denúncia | 17 | 261_brasileira correios_correios_correios telégrafos_destinatário |
| 262 | correios - brasileira correios - correios telégrafos - reclamação denúncia - interpostos cidadão | 16 | 262_correios_brasileira correios_correios telégrafos_reclamação denúncia |
| 263 | maio 2021 - prestação contas - prestações contas - servico cidadão - presente informação | 16 | 263_maio 2021_prestação contas_prestações contas_servico cidadão |
| 264 | anexo informações - segue anexo - seguem anexo - anexo resposta - informação seguem | 16 | 264_anexo informações_segue anexo_seguem anexo_anexo resposta |
| 265 | polícia federal - responsável sic - acessoainformacao gov - resposta requerimento - requerimento | 16 | 265_polícia federal_responsável sic_acessoainformacao gov_resposta requerimento |
| 266 | ministério saúde - registrada sic - saúde - demanda registrada - licitação | 16 | 266_ministério saúde_registrada sic_saúde_demanda registrada |
| 267 | atender demandas - solicitadas informações - desproporcionais - solicitações informação - outros pedidos | 16 | 267_atender demandas_solicitadas informações_desproporcionais_solicitações informação |
| 268 | informamos ministério - necessite informações - informações solicitadas - informação sic - informação departamento | 16 | 268_informamos ministério_necessite informações_informações solicitadas_informação sic |
| 269 | ministério fazenda - recurso prazo - fazenda - interpor recurso - sic ministério | 16 | 269_ministério fazenda_recurso prazo_fazenda_interpor recurso |
| 270 | sicbb recurso - banco sicbb - banco informação - indeferimento informações - recurso 12527 | 16 | 270_sicbb recurso_banco sicbb_banco informação_indeferimento informações |
| 271 | sic ufba - cidadão solicitante - atendimento solicitação - cidadão informamos - solicitação | 16 | 271_sic ufba_cidadão solicitante_atendimento solicitação_cidadão informamos |
| 272 | república informações - cidadão palácio - informação cadastrado - atenção informação - servico cidadão | 16 | 272_república informações_cidadão palácio_informação cadastrado_atenção informação |
| 273 | ouvidoria geral - autoridade hierarquicamente - ouvidoria - área responsável - ouvidor geral | 16 | 273_ouvidoria geral_autoridade hierarquicamente_ouvidoria_área responsável |
| 274 | ouvidoria informação - integrada ouvidoria - ouvidoria - solicitante atenção - solicitante resposta | 16 | 274_ouvidoria informação_integrada ouvidoria_ouvidoria_solicitante atenção |
| 275 | ouvidoria - possível atender - serviço informação - informação cidadão - atender | 16 | 275_ouvidoria_possível atender_serviço informação_informação cidadão |
| 276 | informação protocolado - mail informado - informamos - 12 2015 - 04 2015 | 16 | 276_informação protocolado_mail informado_informamos_12 2015 |
| 277 | processos administrativos - processos - superintendência - protocolos - mesma data | 15 | 277_processos administrativos_processos_superintendência_protocolos |
| 278 | anexo - documento anexo - anexo documento - documento - | 15 | 278_anexo_documento anexo_anexo documento_documento |
| 279 | ministério saúde - documentação requerida - decreto 7724 - documentação - portaria gm | 15 | 279_ministério saúde_documentação requerida_decreto 7724_documentação |
| 280 | informações cidadão - segurança institucional - república informações - atenção informação - serviço informações | 15 | 280_informações cidadão_segurança institucional_república informações_atenção informação |
| 281 | informações cidadão - república informações - segurança institucional - atenção informação - serviço informações | 15 | 281_informações cidadão_república informações_segurança institucional_atenção informação |
| 282 | ministério economia - resposta anexo - anexo resposta - economia agradece - cidadão ministério | 15 | 282_ministério economia_resposta anexo_anexo resposta_economia agradece |
| 283 | porto alegre - repositório - documentos podem - termos portaria - podem acessados | 15 | 283_porto alegre_repositório_documentos podem_termos portaria |
| 284 | prazo resposta - cidadão resposta - resposta informação - atendimento resposta - recurso prazo | 15 | 284_prazo resposta_cidadão resposta_resposta informação_atendimento resposta |
| 285 | anexa resposta - segue anexa - anexa - resposta - resposta gabinete | 15 | 285_anexa resposta_segue anexa_anexa_resposta |
| 286 | informamos secretaria - planalto https - secretaria comunicação - secretaria especial - informação cadastrado | 15 | 286_informamos secretaria_planalto https_secretaria comunicação_secretaria especial |
| 287 | acessoainformacao - acessoainformacao gov - www acessoainformacao - interposição recurso - prazo resposta | 15 | 287_acessoainformacao_acessoainformacao gov_www acessoainformacao_interposição recurso |
| 288 | mail institucional - gestão pessoas - proteção lgpd - pessoa jurídica - informações serviço | 15 | 288_mail institucional_gestão pessoas_proteção lgpd_pessoa jurídica |
| 289 | arquivo anexo - atendimento - atendimento demanda - anexo - arquivo | 15 | 289_arquivo anexo_atendimento_atendimento demanda_anexo |
| 290 | informação sic - pedidos informação - funcionários - documentos informações - informações forma | 15 | 290_informação sic_pedidos informação_funcionários_documentos informações |
| 291 | peticionamento eletrônico - polícia rodoviária - rodoviária federal - peticionamento - rodoviária | 15 | 291_peticionamento eletrônico_polícia rodoviária_rodoviária federal_peticionamento |
| 292 | sic infraero - possível atender - informamos possível - equipe sic - ouvido setor | 15 | 292_sic infraero_possível atender_informamos possível_equipe sic |
| 293 | solicitação informação - informação solicitado - solicitação informações - serviço informação - informação cidadão | 15 | 293_solicitação informação_informação solicitado_solicitação informações_serviço informação |
| 294 | ministério fazenda - informação considerando - recurso prazo - receita federal - procuradoria | 15 | 294_ministério fazenda_informação considerando_recurso prazo_receita federal |
| 295 | repetida - demanda respondida - pergunta - duplicada - duplicidade | 15 | 295_repetida_demanda respondida_pergunta_duplicada |
| 296 | informações restrito - mail institucional - sigilo profissional - pessoais - tais informações | 15 | 296_informações restrito_mail institucional_sigilo profissional_pessoais |
| 297 | recurso 12527 - indeferimento informações - interpor recurso - sicbb roberto - cópia documentos | 15 | 297_recurso 12527_indeferimento informações_interpor recurso_sicbb roberto |
| 298 | ministério economia - serviço informações - secretaria receita - informações cidadão - sic ministério | 14 | 298_ministério economia_serviço informações_secretaria receita_informações cidadão |
| 299 | sigilo bancário - financeiras - instituições financeiras - informações solicitadas - operações crédito | 14 | 299_sigilo bancário_financeiras_instituições financeiras_informações solicitadas |
| 300 | sac atendimento - 7474 reclamações - caixa gov - atendimento canal - canais atendimento | 14 | 300_sac atendimento_7474 reclamações_caixa gov_atendimento canal |
| 301 | solicitação protocolada - informação protocolado - cidadão ministério - informação cidadão - serviço informação | 14 | 301_solicitação protocolada_informação protocolado_cidadão ministério_informação cidadão |
| 302 | cidadão exército - exército prazo - brasileiro sic - informações cidadão - exército http | 14 | 302_cidadão exército_exército prazo_brasileiro sic_informações cidadão |
| 303 | indeferimento informações - banco sicbb - recurso 12527 - interpor recurso - informações empresas | 14 | 303_indeferimento informações_banco sicbb_recurso 12527_interpor recurso |
| 304 | informação solicitada - requerimento - obtenção informação - solicitada órgão - identidade requerente | 14 | 304_informação solicitada_requerimento_obtenção informação_solicitada órgão |
| 305 | comando aeronáutica - solicitando informações - informação requerida - solicitação protocolo - aeronáutica comaer | 14 | 305_comando aeronáutica_solicitando informações_informação requerida_solicitação protocolo |
| 306 | sic infraero - requerente apresentar - recurso prazo - apresentar recurso - poderá requerente | 14 | 306_sic infraero_requerente apresentar_recurso prazo_apresentar recurso |
| 307 | legais sigilo - procuradoria geral - advocacia geral - procuradoria - advogado | 14 | 307_legais sigilo_procuradoria geral_advocacia geral_procuradoria |
| 308 | resposta protocolo - requerimento - solicitação - através protocolo - protocolo | 14 | 308_resposta protocolo_requerimento_solicitação_através protocolo |
| 309 | ministério saúde - saúde secretaria - informação cidadão - saúde atenção - serviço informação | 14 | 309_ministério saúde_saúde secretaria_informação cidadão_saúde atenção |
| 310 | solicitação encaminhamos - solicitação informamos - mail cadastrado - mail informado - enviadas mail | 14 | 310_solicitação encaminhamos_solicitação informamos_mail cadastrado_mail informado |
| 311 | cidadão encaminhamos - decisão prazo - solicitação encaminhamos - cidadão departamento - anexo informamos | 14 | 311_cidadão encaminhamos_decisão prazo_solicitação encaminhamos_cidadão departamento |
| 312 | atendimento sic - resposta sic - cidadão sic - sic ouvidoria - protocolo sic | 14 | 312_atendimento sic_resposta sic_cidadão sic_sic ouvidoria |
| 313 | despesas - itamaraty - documentação - www portaltransparencia - portaltransparencia gov | 14 | 313_despesas_itamaraty_documentação_www portaltransparencia |
| 314 | ministério fazenda - ouvidoria ministério - demanda ouvidoria - fazenda gov - gov ouvidoria | 14 | 314_ministério fazenda_ouvidoria ministério_demanda ouvidoria_fazenda gov |
| 315 | apresentar recurso - decisão prazo - recurso autoridade - ciência decisão - razões recurso | 14 | 315_apresentar recurso_decisão prazo_recurso autoridade_ciência decisão |
| 316 | recurso secretário - recurso prazo - ministério fazenda - apresentado recurso - decreto 724 | 14 | 316_recurso secretário_recurso prazo_ministério fazenda_apresentado recurso |
| 317 | contato ouvidoria - gestão - reitoria gestão - ouvidoria - atendimento ouvidoria | 14 | 317_contato ouvidoria_gestão_reitoria gestão_ouvidoria |
| 318 | banco sicbb - informação cidadão - cidadão banco - serviço informação - sicbb | 13 | 318_banco sicbb_informação cidadão_cidadão banco_serviço informação |
| 319 | relatórios auditoria - auditoria interna - relatórios - gerenciais - 2017 resposta | 13 | 319_relatórios auditoria_auditoria interna_relatórios_gerenciais |
| 320 | informação lai - informação desejada - informação deve - informação vez - informação documento | 13 | 320_informação lai_informação desejada_informação deve_informação vez |
| 321 | atenção informação - informação nº - informações acima - gov planalto - abastecimento | 13 | 321_atenção informação_informação nº_informações acima_gov planalto |
| 322 | solicitação informação - informação poderá - informação processados - indeferimento informação - ministério comunicações | 13 | 322_solicitação informação_informação poderá_informação processados_indeferimento informação |
| 323 | exército prazo - cidadão exército - exército http - exército brasileiro - exército | 13 | 323_exército prazo_cidadão exército_exército http_exército brasileiro |
| 324 | portaria gm - ministério saúde - portaria - defensoria pública - saúde | 13 | 324_portaria gm_ministério saúde_portaria_defensoria pública |
| 325 | indeferimento informações - banco informação - recurso 12527 - informação resposta - consolidação informações | 13 | 325_indeferimento informações_banco informação_recurso 12527_informação resposta |
| 326 | agenda autoridades - agendas - gov planalto - planilha - agenda | 13 | 326_agenda autoridades_agendas_gov planalto_planilha |
| 327 | anexo informações - anexo resposta - segue anexo - informações solicitadas - resposta informações | 13 | 327_anexo informações_anexo resposta_segue anexo_informações solicitadas |
| 328 | interpor recurso - poderá interpor - inciso decreto - informação aplica - diretor gestão | 13 | 328_interpor recurso_poderá interpor_inciso decreto_informação aplica |
| 329 | solicitação sic - informação requerida - informação cidadão - boa informamos - identificação requerente | 13 | 329_solicitação sic_informação requerida_informação cidadão_boa informamos |
| 330 | esclarecimentos anvisa - anvisa gov - atendimento 0800 - anvisa - vigilância sanitária | 13 | 330_esclarecimentos anvisa_anvisa gov_atendimento 0800_anvisa |
| 331 | república informações - informação sigilosa - cidadão palácio - segurança institucional - informação cadastrado | 12 | 331_república informações_informação sigilosa_cidadão palácio_segurança institucional |
| 332 | anexo informações - anexo resposta - resposta anexo - anexo serviço - ministério economia | 12 | 332_anexo informações_anexo resposta_resposta anexo_anexo serviço |
| 333 | prazo resposta - atendimento resposta - sic cidadão - justificativa informação - atenção informação | 12 | 333_prazo resposta_atendimento resposta_sic cidadão_justificativa informação |
| 334 | informação solicitada - ainda informação - sa - participo - solicitada | 12 | 334_informação solicitada_ainda informação_sa_participo |
| 335 | informações natureza - município - cadastro - ambientais - ambiental | 12 | 335_informações natureza_município_cadastro_ambientais |
| 336 | informações solicitadas - busca informações - desproporcionalidade - informação atendido - sic serviço | 12 | 336_informações solicitadas_busca informações_desproporcionalidade_informação atendido |
| 337 | informação cidadão - brasília df - diretoria - informação - serviço informação | 12 | 337_informação cidadão_brasília df_diretoria_informação |
| 338 | denúncias caixa - habitacionais - atendimento caixa - informamos caixa - informações reclamações | 12 | 338_denúncias caixa_habitacionais_atendimento caixa_informamos caixa |
| 339 | pessoais relativas - informações pessoais - tais documentos - pessoais - outros documentos | 12 | 339_pessoais relativas_informações pessoais_tais documentos_pessoais |
| 340 | requerimento - ministério fazenda - requerimento formulado - www acessoainformacao - informar demanda | 12 | 340_requerimento_ministério fazenda_requerimento formulado_www acessoainformacao |
| 341 | forças armadas - operações - operacional - aeronáutica - aeronaves | 12 | 341_forças armadas_operações_operacional_aeronáutica |
| 342 | banco sicbb - informamos sic - sicbb roberto - esclarecemos sic - sicbb | 12 | 342_banco sicbb_informamos sic_sicbb roberto_esclarecemos sic |
| 343 | 7474 reclamações - informações reclamações - ouvidoria ddg - sac atendimento - caixa gov | 12 | 343_7474 reclamações_informações reclamações_ouvidoria ddg_sac atendimento |
| 344 | saúde secretaria - ministério saúde - secretaria ciência - atenção saúde - saúde atenção | 12 | 344_saúde secretaria_ministério saúde_secretaria ciência_atenção saúde |
| 345 | ministério justiça - poder judiciário - judiciário - ouvidoria ministério - justiça | 12 | 345_ministério justiça_poder judiciário_judiciário_ouvidoria ministério |
| 346 | confidencialidade - propriedade intelectual - disponíveis link - transferência tecnologia - informação sob | 11 | 346_confidencialidade_propriedade intelectual_disponíveis link_transferência tecnologia |
| 347 | resposta anexo - anexo resposta - segue resposta - segue anexo - informou | 11 | 347_resposta anexo_anexo resposta_segue resposta_segue anexo |
| 348 | ouvidoria inep - inep térreo - inep instituto - sede inep - negado informação | 11 | 348_ouvidoria inep_inep térreo_inep instituto_sede inep |
| 349 | ouvidoria - informações atendimento - atendimento informação - informação interesse - canal atendimento | 11 | 349_ouvidoria_informações atendimento_atendimento informação_informação interesse |
| 350 | orçamentárias - relatórios - ministério ambiente - comunidades - indígenas | 11 | 350_orçamentárias_relatórios_ministério ambiente_comunidades |
| 351 | prazo resposta - atendimento resposta - justificativa informação - resposta dias - atenção informação | 11 | 351_prazo resposta_atendimento resposta_justificativa informação_resposta dias |
| 352 | solicitação - procedimento - sob protocolo - protocolo - presente solicitação | 11 | 352_solicitação_procedimento_sob protocolo_protocolo |
| 353 | resposta gerência - informação nup - gerência avaliação - anexo informamos - vigilância sanitária | 11 | 353_resposta gerência_informação nup_gerência avaliação_anexo informamos |
| 354 | ministério saúde - ouvidoria geral - saúde ms - gov ouvidoria - saúde | 11 | 354_ministério saúde_ouvidoria geral_saúde ms_gov ouvidoria |
| 355 | opinião importante - anexo resposta - resposta solicitação - opinião - resposta informações | 11 | 355_opinião importante_anexo resposta_resposta solicitação_opinião |
| 356 | solicitação informamos - ciências - pesquisa - seleção - cnpq | 11 | 356_solicitação informamos_ciências_pesquisa_seleção |
| 357 | informação solicitada - cópias - informamos - cópia - 2011 informação | 11 | 357_informação solicitada_cópias_informamos_cópia |
| 358 | cidadão exército - informação pessoal - indevido informação - pessoais terceiros - pessoais deve | 11 | 358_cidadão exército_informação pessoal_indevido informação_pessoais terceiros |
| 359 | através portaria - biotecnologia - diretoria - instituto - comparecimento | 11 | 359_através portaria_biotecnologia_diretoria_instituto |
| 360 | resposta autoridade - sic infraero - requerente apresentar - prazo interposição - recurso 1ª | 11 | 360_resposta autoridade_sic infraero_requerente apresentar_prazo interposição |
| 361 | cidadão exército - inquérito policial - organizações militares - inquérito - exército http | 10 | 361_cidadão exército_inquérito policial_organizações militares_inquérito |
| 362 | ouvidoria inep - inep térreo - atendimento informação - inep instituto - prazos atendimento | 10 | 362_ouvidoria inep_inep térreo_atendimento informação_inep instituto |
| 363 | informamos ministério - ministério saúde - reservado - medicamentos - medicamento | 10 | 363_informamos ministério_ministério saúde_reservado_medicamentos |
| 364 | ouvidoria inep - educação superior - tecnologia informação - educacionais - informação ministério | 10 | 364_ouvidoria inep_educação superior_tecnologia informação_educacionais |
| 365 | informações devem - informações solicitadas - cidadão sic - cadastrado sic - sic cidadão | 10 | 365_informações devem_informações solicitadas_cidadão sic_cadastrado sic |
| 366 | sac atendimento - ouvidoria ddg - canais atendimento - 7474 reclamações - informamos caixa | 10 | 366_sac atendimento_ouvidoria ddg_canais atendimento_7474 reclamações |
| 367 | informações solicitadas - documentos anexos - seguem anexas - ministério relações - atendimento informação | 10 | 367_informações solicitadas_documentos anexos_seguem anexas_ministério relações |
</details>
## Training hyperparameters
* calculate_probabilities: False
* language: None
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: True
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None
## Framework versions
* Numpy: 1.26.1
* HDBSCAN: 0.8.33
* UMAP: 0.5.5
* Pandas: 2.1.2
* Scikit-Learn: 1.3.2
* Sentence-transformers: 2.2.2
* Transformers: 4.35.0
* Numba: 0.58.1
* Plotly: 5.18.0
* Python: 3.10.13
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] |
rodrigomoreirasilva/bertopic_lai_recursos_CGU_recursos
|
rodrigomoreirasilva
| 2023-12-15T18:03:52Z | 0 | 0 |
bertopic
|
[
"bertopic",
"text-classification",
"region:us"
] |
text-classification
| 2023-12-15T18:03:49Z |
---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---
# bertopic_lai_recursos_CGU_recursos
This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
## Usage
To use this model, please install BERTopic:
```
pip install -U bertopic
```
You can use the model as follows:
```python
from bertopic import BERTopic
topic_model = BERTopic.load("rodrigomoreirasilva/bertopic_lai_recursos_CGU_recursos")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 288
* Number of training documents: 17844
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | transparência - informação - solicitação - direito - instituição | 10 | -1_transparência_informação_solicitação_direito |
| 0 | recorro cgu - recurso cgu - cgu resposta - peço cgu - resposta requerimento | 7565 | 0_recorro cgu_recurso cgu_cgu resposta_peço cgu |
| 1 | relatório - anvisa - ativo - suspeita - confidenciais | 290 | 1_relatório_anvisa_ativo_suspeita |
| 2 | acadêmica - universidade - faculdade - processo seletivo - ensino | 269 | 2_acadêmica_universidade_faculdade_processo seletivo |
| 3 | anexado recurso - documento anexado - anexado - recurso - documento | 232 | 3_anexado recurso_documento anexado_anexado_recurso |
| 4 | vagas - solicitação - pergunta - concurso público - educação | 224 | 4_vagas_solicitação_pergunta_concurso público |
| 5 | agentes bb - imediato informação - denunciados agentes - ficam denunciados - informação documento | 189 | 5_agentes bb_imediato informação_denunciados agentes_ficam denunciados |
| 6 | denunciados agentes - ouvidor geral - agentes bb - autorizar conceder - informação documento | 174 | 6_denunciados agentes_ouvidor geral_agentes bb_autorizar conceder |
| 7 | educação - ensino - escolas - alunos - pesquisa | 172 | 7_educação_ensino_escolas_alunos |
| 8 | solicitação resposta - respondido recurso - solicitação informação - informação solicitada - respondeu recurso | 169 | 8_solicitação resposta_respondido recurso_solicitação informação_informação solicitada |
| 9 | ficam denunciados - denunciados agentes - informação documento - imediato informação - autorizar conceder | 151 | 9_ficam denunciados_denunciados agentes_informação documento_imediato informação |
| 10 | intervenção cgu - união cgu - recurso controladoria - controladoria geral - controladoria | 144 | 10_intervenção cgu_união cgu_recurso controladoria_controladoria geral |
| 11 | união protocolo - presente recurso - deferimento - documento anexo - peço deferimento | 144 | 11_união protocolo_presente recurso_deferimento_documento anexo |
| 12 | signatário - recurso reitero - reitero recurso - reitero solicitação - apresento recurso | 127 | 12_signatário_recurso reitero_reitero recurso_reitero solicitação |
| 13 | ministério saúde - respostas solicitadas - informação pública - saúde - responsável resposta | 118 | 13_ministério saúde_respostas solicitadas_informação pública_saúde |
| 14 | controladoria geral - reclamação - reclamações - controladoria - solicitação | 115 | 14_controladoria geral_reclamação_reclamações_controladoria |
| 15 | universidade - transparência - intervenção cgu - transparência informação - própria instituição | 115 | 15_universidade_transparência_intervenção cgu_transparência informação |
| 16 | classificação informação - licitação - cidadãos informações - dispensa licitação - tribunais | 114 | 16_classificação informação_licitação_cidadãos informações_dispensa licitação |
| 17 | direito - órgão demandado - nº 2015 - portaria - pessoal | 110 | 17_direito_órgão demandado_nº 2015_portaria |
| 18 | fornecimento informações - negativa fornecimento - recorre cgu - cgu informação - recorro cgu | 102 | 18_fornecimento informações_negativa fornecimento_recorre cgu_cgu informação |
| 19 | segundo recurso - recurso segunda - respondeu recurso - recurso 2a - recurso 2ª | 98 | 19_segundo recurso_recurso segunda_respondeu recurso_recurso 2a |
| 20 | correios - correio - reclamação - endereço - reclamações | 86 | 20_correios_correio_reclamação_endereço |
| 21 | remeter requerimento - requerimento órgão - resposta recurso - conceder imediato - deverá prazo | 86 | 21_remeter requerimento_requerimento órgão_resposta recurso_conceder imediato |
| 22 | trabalho adicional - relatório - relatórios - trabalho - transparência | 82 | 22_trabalho adicional_relatório_relatórios_trabalho |
| 23 | financeiras - bancária - sigilo bancário - bancário - financeira | 81 | 23_financeiras_bancária_sigilo bancário_bancário |
| 24 | recorro cgu - aposentadoria - aposentados - instituição financeira - financeira | 78 | 24_recorro cgu_aposentadoria_aposentados_instituição financeira |
| 25 | sigilo informação - negar informação - sigilosa informação - solicitação informação - informação sigilosa | 75 | 25_sigilo informação_negar informação_sigilosa informação_solicitação informação |
| 26 | direito privacidade - privacidade - direito informações - apresentamos recurso - pessoas jurídicas | 74 | 26_direito privacidade_privacidade_direito informações_apresentamos recurso |
| 27 | inovação recursal - informações solicitadas - segundo recurso - negar informações - novamente informações | 70 | 27_inovação recursal_informações solicitadas_segundo recurso_negar informações |
| 28 | sigilo bancário - justificativa - segurança informação - bancário - justificativas | 70 | 28_sigilo bancário_justificativa_segurança informação_bancário |
| 29 | informação solicitada - informações solicitadas - informação solicitado - solicitada informação - solicitado informação | 69 | 29_informação solicitada_informações solicitadas_informação solicitado_solicitada informação |
| 30 | recorro cgu - deferimento cgu - transparência governo - comando exército - recorre cgu | 68 | 30_recorro cgu_deferimento cgu_transparência governo_comando exército |
| 31 | informação lai - obter informação - obter informações - corrupção - solicitação | 67 | 31_informação lai_obter informação_obter informações_corrupção |
| 32 | documento solicitado - arquivos - aeronáutica - documentos - arquivo | 66 | 32_documento solicitado_arquivos_aeronáutica_documentos |
| 33 | financeira - informação solicitada - caixa econômica - tal informação - econômica federal | 65 | 33_financeira_informação solicitada_caixa econômica_tal informação |
| 34 | federal informação - listagem - administração federal - informação pessoal - pessoal | 65 | 34_federal informação_listagem_administração federal_informação pessoal |
| 35 | direito - administração - parecer jurídico - processos - justiça | 64 | 35_direito_administração_parecer jurídico_processos |
| 36 | cgu reitero - reitero inicial - nascimento silva - atendido - roberto nascimento | 63 | 36_cgu reitero_reitero inicial_nascimento silva_atendido |
| 37 | anexo - anexos - anexo anexo - ver anexo - atraso | 62 | 37_anexo_anexos_anexo anexo_ver anexo |
| 38 | aguardo deferimento - corresponde solicitado - pleito inicial - pleito - exposto requeiro | 62 | 38_aguardo deferimento_corresponde solicitado_pleito inicial_pleito |
| 39 | reitero informação - cgu banco - justificativa legal - resposta informação - ficam denunciados | 61 | 39_reitero informação_cgu banco_justificativa legal_resposta informação |
| 40 | ensino - colegiado - educação - graduação - cursos | 60 | 40_ensino_colegiado_educação_graduação |
| 41 | presidência república - senado - interesse público - bolsonaro - presidência | 60 | 41_presidência república_senado_interesse público_bolsonaro |
| 42 | peço deferimento - recorro peço - deferimento - recorro - recorro cgu | 58 | 42_peço deferimento_recorro peço_deferimento_recorro |
| 43 | anexo recurso - recurso anexo - anexos recurso - recurso segue - segue anexo | 57 | 43_anexo recurso_recurso anexo_anexos recurso_recurso segue |
| 44 | anexo recurso - recurso cgu - cgu recurso - anexos recurso - segue anexo | 56 | 44_anexo recurso_recurso cgu_cgu recurso_anexos recurso |
| 45 | reiteramos solicitação - recorremos cgu - disponibilização informações - fiscalização - solicitação inicial | 54 | 45_reiteramos solicitação_recorremos cgu_disponibilização informações_fiscalização |
| 46 | corrupção - grato atenção - simples informação - seletivo - estranha | 54 | 46_corrupção_grato atenção_simples informação_seletivo |
| 47 | informação - administração - informação sigilosa - órgãos entidades - administração pública | 54 | 47_informação_administração_informação sigilosa_órgãos entidades |
| 48 | pesquisas - saúde - transparência - pesquisa - sociedade | 54 | 48_pesquisas_saúde_transparência_pesquisa |
| 49 | deferimento - deferimento solicitação - peço deferimento - recorro - informação recorro | 53 | 49_deferimento_deferimento solicitação_peço deferimento_recorro |
| 50 | bolsa família - auxílio - ajuda - auxilio - família | 53 | 50_bolsa família_auxílio_ajuda_auxilio |
| 51 | junho 2019 - relatório - companhia - portanto - auditoria interna | 51 | 51_junho 2019_relatório_companhia_portanto |
| 52 | fornecidas informações - informações devem - informações solicitadas - controle social - vista informações | 50 | 52_fornecidas informações_informações devem_informações solicitadas_controle social |
| 53 | acadêmica - universidade federal - universidade - pesquisa acadêmica - universidades federais | 49 | 53_acadêmica_universidade federal_universidade_pesquisa acadêmica |
| 54 | anexo recurso - recurso anexo - documento anexo - apresento recurso - anexo informações | 49 | 54_anexo recurso_recurso anexo_documento anexo_apresento recurso |
| 55 | relatório - relatório final - originário - justiça federal - grau sigilo | 46 | 55_relatório_relatório final_originário_justiça federal |
| 56 | recurso respondido - recurso resposta - respondido recurso - resposta recurso - recurso apresentado | 45 | 56_recurso respondido_recurso resposta_respondido recurso_resposta recurso |
| 57 | federais - divulgar informações - universidades federais - informações pedidas - mesmas informações | 45 | 57_federais_divulgar informações_universidades federais_informações pedidas |
| 58 | contribuintes - previdenciária - receita federal - tributária - fiscais | 43 | 58_contribuintes_previdenciária_receita federal_tributária |
| 59 | resposta fornecida - informações fornecidas - fornecimento informações - fornecida - fornecidas | 43 | 59_resposta fornecida_informações fornecidas_fornecimento informações_fornecida |
| 60 | atendimento cidadão - resposta cidadão - atendimento - responsável resposta - correio | 43 | 60_atendimento cidadão_resposta cidadão_atendimento_responsável resposta |
| 61 | respeitosamente recorrer - atendimento solicitado - respeitosamente - atendimento - mui respeitosamente | 43 | 61_respeitosamente recorrer_atendimento solicitado_respeitosamente_atendimento |
| 62 | imediato informação - informação documento - ferreira - informação roberto - autorizar conceder | 42 | 62_imediato informação_informação documento_ferreira_informação roberto |
| 63 | contendo - forneça - disponibilizado - acima - piloto | 42 | 63_contendo_forneça_disponibilizado_acima |
| 64 | desclassificados - falta transparência - desclassificação - única informação - classificou | 41 | 64_desclassificados_falta transparência_desclassificação_única informação |
| 65 | conclusão curso - denúncia - diploma - responder solicitação - faculdade | 41 | 65_conclusão curso_denúncia_diploma_responder solicitação |
| 66 | documento público - negativa documentos - fornecer documentos - documentos - órgão público | 40 | 66_documento público_negativa documentos_fornecer documentos_documentos |
| 67 | respostas perguntas - perguntas - pergunta - resposta solicitada - questionamentos | 40 | 67_respostas perguntas_perguntas_pergunta_resposta solicitada |
| 68 | anexado recurso - recurso - anexado - anexadas - justificativas | 40 | 68_anexado recurso_recurso_anexado_anexadas |
| 69 | cgu - cgu cgu - - - | 40 | 69_cgu_cgu cgu__ |
| 70 | farmacêutica - informação solicitada - recurso cgu - disponibilizar informação - presente recurso | 39 | 70_farmacêutica_informação solicitada_recurso cgu_disponibilizar informação |
| 71 | consolidação informações - requerimento - requerendo - referida informação - portaltransparencia gov | 39 | 71_consolidação informações_requerimento_requerendo_referida informação |
| 72 | reitero recurso - recurso reitero - termos recurso - reitero termos - apresento recurso | 39 | 72_reitero recurso_recurso reitero_termos recurso_reitero termos |
| 73 | relatórios - auditoria interna - relatório - ainda respondido - controle interno | 39 | 73_relatórios_auditoria interna_relatório_ainda respondido |
| 74 | documentação solicitada - resposta anterior - documentos solicitados - corresponde solicitado - solicitado | 38 | 74_documentação solicitada_resposta anterior_documentos solicitados_corresponde solicitado |
| 75 | negar informações - cgu reitero - dessa cgu - reitero solicitação - corrupção | 38 | 75_negar informações_cgu reitero_dessa cgu_reitero solicitação |
| 76 | reitero recurso - integralmente recurso - informação pessoal - comprovado - reitero integralmente | 38 | 76_reitero recurso_integralmente recurso_informação pessoal_comprovado |
| 77 | ratifico inicial - inicial informação - inicial informações - ratifico - informação venho | 37 | 77_ratifico inicial_inicial informação_inicial informações_ratifico |
| 78 | requerimento informação - recurso administrativo - presente recurso - informação pública - requerimento | 37 | 78_requerimento informação_recurso administrativo_presente recurso_informação pública |
| 79 | informações solicitadas - cumprimento informação - mesmas informações - transparência - exército | 37 | 79_informações solicitadas_cumprimento informação_mesmas informações_transparência |
| 80 | sigilo informação - disponibilizar informação - informação solicitada - classificação informação - informação | 37 | 80_sigilo informação_disponibilizar informação_informação solicitada_classificação informação |
| 81 | trânsito - administrativo âmbito - superintendência - âmbito administração - infraestrutura | 36 | 81_trânsito_administrativo âmbito_superintendência_âmbito administração |
| 82 | fornecimento informações - negativa fornecimento - informação requerida - negar informação - informações requeridas | 35 | 82_fornecimento informações_negativa fornecimento_informação requerida_negar informação |
| 83 | pessoas cgu - criminoso - direito constitucional - silva cpf - legal informação | 34 | 83_pessoas cgu_criminoso_direito constitucional_silva cpf |
| 84 | informação solicitada - informação demandada - funcionários - financeira - informação | 34 | 84_informação solicitada_informação demandada_funcionários_financeira |
| 85 | documentos informações - farmacêutica - anvisa - processo ainda - demais documentos | 34 | 85_documentos informações_farmacêutica_anvisa_processo ainda |
| 86 | pedir informações - novamente informações - novamente informação - pedidos feitos - peço novamente | 33 | 86_pedir informações_novamente informações_novamente informação_pedidos feitos |
| 87 | informação cidadão - direito informação - considerando informação - 2011 informação - recurso cgu | 33 | 87_informação cidadão_direito informação_considerando informação_2011 informação |
| 88 | documento anexo - anexo documento - documentos anexos - arquivo anexo - anexo | 32 | 88_documento anexo_anexo documento_documentos anexos_arquivo anexo |
| 89 | recurso respondido - respondido recurso - responder recurso - recurso - recurso segunda | 32 | 89_recurso respondido_respondido recurso_responder recurso_recurso |
| 90 | ratifico inicial - inicial informação - informação solicitada - ratifico - cgu peço | 32 | 90_ratifico inicial_inicial informação_informação solicitada_ratifico |
| 91 | presente recurso - informação - registros documentos - informação contida - arquivos públicos | 32 | 91_presente recurso_informação_registros documentos_informação contida |
| 92 | recurso porque - ação judicial - nulidade - administrativo disciplinar - judiciais | 31 | 92_recurso porque_ação judicial_nulidade_administrativo disciplinar |
| 93 | deficiência - competitividade - funcionários - salários - possui | 31 | 93_deficiência_competitividade_funcionários_salários |
| 94 | brasileira correios - informação cidadão - sistema informação - interpor presente - correios | 31 | 94_brasileira correios_informação cidadão_sistema informação_interpor presente |
| 95 | ofícios - documentos públicos - interesse público - legislação - documentos | 31 | 95_ofícios_documentos públicos_interesse público_legislação |
| 96 | brasileira correios - correios - pedidos informações - informações sigilosas - confidenciais | 30 | 96_brasileira correios_correios_pedidos informações_informações sigilosas |
| 97 | perguntas - relatório gestão - setor responsável - relatório - informações solicitadas | 29 | 97_perguntas_relatório gestão_setor responsável_relatório |
| 98 | esclarecimentos adicionais - solicitação informações - pesquisa acadêmica - resposta solicitação - recurso respondido | 29 | 98_esclarecimentos adicionais_solicitação informações_pesquisa acadêmica_resposta solicitação |
| 99 | recurso anexo - anexo recurso - documento anexo - arquivo anexo - recurso documento | 29 | 99_recurso anexo_anexo recurso_documento anexo_arquivo anexo |
| 100 | transparência informações - financeiras - transparência - financeira - instituição financeira | 29 | 100_transparência informações_financeiras_transparência_financeira |
| 101 | escolas - estado brasileiro - direitos fundamentais - informação pedi - precisam | 29 | 101_escolas_estado brasileiro_direitos fundamentais_informação pedi |
| 102 | anexo recurso - documento anexo - recurso 3ª - recurso 3a - recurso terceira | 28 | 102_anexo recurso_documento anexo_recurso 3ª_recurso 3a |
| 103 | requerimento - requerimentos - município - anexado - resposta encaminhada | 28 | 103_requerimento_requerimentos_município_anexado |
| 104 | solicitações informação - pessoas cgu - informação requerida - condutas ilícitas - criminoso | 28 | 104_solicitações informação_pessoas cgu_informação requerida_condutas ilícitas |
| 105 | trânsito - acidentes - silva cpf - milhões reais - multas | 28 | 105_trânsito_acidentes_silva cpf_milhões reais |
| 106 | recurso contra - anexo recurso - fundamentos - contra negativa - arquivo anexo | 28 | 106_recurso contra_anexo recurso_fundamentos_contra negativa |
| 107 | informação inexistente - referidos documentos - anvisa - informações solicitadas - declaração inexistência | 27 | 107_informação inexistente_referidos documentos_anvisa_informações solicitadas |
| 108 | cgu reitero - informações solicitadas - informações interesse - reitero inicial - sigilo imprescindível | 27 | 108_cgu reitero_informações solicitadas_informações interesse_reitero inicial |
| 109 | relatório gestão - relatórios - relatório - educação - gestão | 27 | 109_relatório gestão_relatórios_relatório_educação |
| 110 | pedidos informação - perguntas - monitoramento - omissão - protocolos | 27 | 110_pedidos informação_perguntas_monitoramento_omissão |
| 111 | fornecer cópia - fornecer documento - cópia - registro - formato pdf | 26 | 111_fornecer cópia_fornecer documento_cópia_registro |
| 112 | ausência informação - anvisa - sanitária - informações solicitadas - vigilância sanitária | 26 | 112_ausência informação_anvisa_sanitária_informações solicitadas |
| 113 | reitero informações - informações deveriam - informações - acessível - inexistência | 26 | 113_reitero informações_informações deveriam_informações_acessível |
| 114 | segurança institucional - planalto gov - alguma informação - institucional presidência - gabinete segurança | 26 | 114_segurança institucional_planalto gov_alguma informação_institucional presidência |
| 115 | informação interesse - cgu reitero - independentemente solicitações - informações interesse - informação devem | 26 | 115_informação interesse_cgu reitero_independentemente solicitações_informações interesse |
| 116 | envio informações - aguardo resposta - aguardando - envio resposta - envio | 26 | 116_envio informações_aguardo resposta_aguardando_envio resposta |
| 117 | solicitei documentos - solicitação informações - informação pedida - anexo resposta - resposta anexo | 26 | 117_solicitei documentos_solicitação informações_informação pedida_anexo resposta |
| 118 | cgu análise - análise cgu - apreciação - parecer cgu - cgu analise | 26 | 118_cgu análise_análise cgu_apreciação_parecer cgu |
| 119 | contribuinte - fiscais - contribuintes - notas fiscais - sigilo fiscal | 25 | 119_contribuinte_fiscais_contribuintes_notas fiscais |
| 120 | razões recursais - transparência gestão - transparência passiva - transparência ativa - transparência | 25 | 120_razões recursais_transparência gestão_transparência passiva_transparência ativa |
| 121 | ficam denunciados - corruptos maçons - solicitações informação - denunciados agentes - informação documento | 25 | 121_ficam denunciados_corruptos maçons_solicitações informação_denunciados agentes |
| 122 | sigilo informação - sigilosa informação - informação sigilosa - informação documentos - informação obter | 25 | 122_sigilo informação_sigilosa informação_informação sigilosa_informação documentos |
| 123 | recurso cgu - cgu informações - recorro cgu - cgu informação - cgu obter | 25 | 123_recurso cgu_cgu informações_recorro cgu_cgu informação |
| 124 | reiteramos solicitação - recurso terceira - infrações - informações repassadas - infração | 25 | 124_reiteramos solicitação_recurso terceira_infrações_informações repassadas |
| 125 | recorro cgu - pedir informações - segundo informações - informação sigilosa - sendo informação | 24 | 125_recorro cgu_pedir informações_segundo informações_informação sigilosa |
| 126 | trânsito - veículos - infração - veículo - estadual | 24 | 126_trânsito_veículos_infração_veículo |
| 127 | solicitação - ibama - atendimento solicitação - desrespeito informação - informações pedidas | 24 | 127_solicitação_ibama_atendimento solicitação_desrespeito informação |
| 128 | ministério transparência - transparência - autarquias - forma cidadão - autarquia | 24 | 128_ministério transparência_transparência_autarquias_forma cidadão |
| 129 | proteção informação - informação - arquivos públicos - informação produzida - informação propiciando | 24 | 129_proteção informação_informação_arquivos públicos_informação produzida |
| 130 | negar informação - ilícito - informação - cgu recurso - funcionários | 24 | 130_negar informação_ilícito_informação_cgu recurso |
| 131 | solicitação inicial - reitero inicial - requerimento inicial - reitero solicitação - reitero pedidos | 24 | 131_solicitação inicial_reitero inicial_requerimento inicial_reitero solicitação |
| 132 | informação pessoal - endereços - informação - direito - competência órgão | 24 | 132_informação pessoal_endereços_informação_direito |
| 133 | dívida - crime prevaricação - bolsonaro - financeiras - impostos | 24 | 133_dívida_crime prevaricação_bolsonaro_financeiras |
| 134 | requerimento - presente solicitação - solicitação original - respondente - universidade federal | 24 | 134_requerimento_presente solicitação_solicitação original_respondente |
| 135 | solicitando informações - solicitação documento - via sic - resposta via - informações mim | 23 | 135_solicitando informações_solicitação documento_via sic_resposta via |
| 136 | providências necessárias - providências adotadas - outras providências - providências - providências cabíveis | 23 | 136_providências necessárias_providências adotadas_outras providências_providências |
| 137 | cgu reitero - reitero inicial - nascimento silva - atendido - roberto nascimento | 23 | 137_cgu reitero_reitero inicial_nascimento silva_atendido |
| 138 | pedidos informação - perguntas - omissão - monitoramento - | 22 | 138_pedidos informação_perguntas_omissão_monitoramento |
| 139 | direito defesa - funcionários - garantir direito - ato administrativo - direito | 22 | 139_direito defesa_funcionários_garantir direito_ato administrativo |
| 140 | reitero argumentos - deferimento - peço deferimento - reitero recurso - recorro | 22 | 140_reitero argumentos_deferimento_peço deferimento_reitero recurso |
| 141 | sigilo informação - assistente - sigilo alegado - proveito pessoal - conduta ilícita | 22 | 141_sigilo informação_assistente_sigilo alegado_proveito pessoal |
| 142 | informação pessoal - prestação informações - informação relação - relativas intimidade - informações pessoais | 22 | 142_informação pessoal_prestação informações_informação relação_relativas intimidade |
| 143 | cadastro reserva - cgu intervenha - intervenção cgu - processo seletivo - convocação | 22 | 143_cadastro reserva_cgu intervenha_intervenção cgu_processo seletivo |
| 144 | portaria - forças armadas - portaria nº - acórdão - março 2020 | 22 | 144_portaria_forças armadas_portaria nº_acórdão |
| 145 | 2011 informação - informação solicitado - negado informação - novembro 2011 - informação necessária | 22 | 145_2011 informação_informação solicitado_negado informação_novembro 2011 |
| 146 | recurso anexo - anexo recurso - anexado recurso - recurso - anexo | 22 | 146_recurso anexo_anexo recurso_anexado recurso_recurso |
| 147 | informação administração - saúde - solicitadas informações - questão informação - recurso requerer | 22 | 147_informação administração_saúde_solicitadas informações_questão informação |
| 148 | resposta informação - informações solicitadas - recurso 3ª - solicitadas inicial - presente recurso | 22 | 148_resposta informação_informações solicitadas_recurso 3ª_solicitadas inicial |
| 149 | autêntica - primária - íntegra - inicial - respostas | 21 | 149_autêntica_primária_íntegra_inicial |
| 150 | deferimento recurso - recurso deferido - requer deferimento - pede deferimento - deferimento | 21 | 150_deferimento recurso_recurso deferido_requer deferimento_pede deferimento |
| 151 | transparência - razões recursais - transparência passiva - transparência ativa - recursais | 21 | 151_transparência_razões recursais_transparência passiva_transparência ativa |
| 152 | ofício - documentos públicos - ofícios - interesse público - órgão demandado | 21 | 152_ofício_documentos públicos_ofícios_interesse público |
| 153 | informação requerente - anvisa - registro - vantagem competitiva - requerida | 21 | 153_informação requerente_anvisa_registro_vantagem competitiva |
| 154 | auditoria interna - recorro cgu - informação solicitada - pedidos informação - procuradoria | 21 | 154_auditoria interna_recorro cgu_informação solicitada_pedidos informação |
| 155 | informação solicitada - obtenção informação - pedir informações - informação fim - justificar negativa | 21 | 155_informação solicitada_obtenção informação_pedir informações_informação fim |
| 156 | cgu recorro - informações pessoais - pessoais relativas - titular informações - ainda informações | 21 | 156_cgu recorro_informações pessoais_pessoais relativas_titular informações |
| 157 | recorro cgu - peço cgu - resposta órgão - encaminho recurso - fornecer informação | 21 | 157_recorro cgu_peço cgu_resposta órgão_encaminho recurso |
| 158 | recorro cgu - responsável informação - requerimento órgão - possui informação - remeter requerimento | 21 | 158_recorro cgu_responsável informação_requerimento órgão_possui informação |
| 159 | importação - prescrição - sanitária - informação cidadão - hospitais | 21 | 159_importação_prescrição_sanitária_informação cidadão |
| 160 | portaria - processos - julgamento - ato normativo - processo administrativo | 21 | 160_portaria_processos_julgamento_ato normativo |
| 161 | anexo recurso - documento anexo - anexo documento - recurso documento - recurso | 21 | 161_anexo recurso_documento anexo_anexo documento_recurso documento |
| 162 | cgu reitero - reitero inicial - nascimento silva - roberto nascimento - cgu ministério | 21 | 162_cgu reitero_reitero inicial_nascimento silva_roberto nascimento |
| 163 | comando aeronáutica - aeronáutica - direito informação - informações sigilosas - porque informações | 21 | 163_comando aeronáutica_aeronáutica_direito informação_informações sigilosas |
| 164 | vide anexo - anexo - doc - - | 21 | 164_vide anexo_anexo_doc_ |
| 165 | crime prevaricação - prevaricação - escolas - terceira - escola | 20 | 165_crime prevaricação_prevaricação_escolas_terceira |
| 166 | processos administrativos - processos - vista processo - justificativas - justificativa | 20 | 166_processos administrativos_processos_vista processo_justificativas |
| 167 | anexo recurso - recurso anexo - segue anexo - recurso controladoria - anexo segue | 20 | 167_anexo recurso_recurso anexo_segue anexo_recurso controladoria |
| 168 | recurso anexo - anexo - petição - recurso - anexa | 20 | 168_recurso anexo_anexo_petição_recurso |
| 169 | desproporcionais - reavaliação informações - atendimento demanda - desproporcionalidade - trabalho servidores | 19 | 169_desproporcionais_reavaliação informações_atendimento demanda_desproporcionalidade |
| 170 | anexo recurso - recurso anexo - segue anexo - recurso segue - anexo segue | 19 | 170_anexo recurso_recurso anexo_segue anexo_recurso segue |
| 171 | justificativa - justificativas - documento anexo - arquivo anexo - justificativa apresentada | 19 | 171_justificativa_justificativas_documento anexo_arquivo anexo |
| 172 | ratifico - respeitosamente - informação - informação aqui - reitero informação | 19 | 172_ratifico_respeitosamente_informação_informação aqui |
| 173 | resposta ministério - ofícios - ministério fazenda - pedir informações - ministério | 19 | 173_resposta ministério_ofícios_ministério fazenda_pedir informações |
| 174 | íntegra recurso - recurso anexo - documento anexo - recurso documento - arquivo anexo | 19 | 174_íntegra recurso_recurso anexo_documento anexo_recurso documento |
| 175 | cgu reitero - publicidade preceito - informação interesse - observância publicidade - sigilo exceção | 19 | 175_cgu reitero_publicidade preceito_informação interesse_observância publicidade |
| 176 | solicitação informações - resposta recurso - fornecimento informações - recurso 2º - resposta fornecida | 19 | 176_solicitação informações_resposta recurso_fornecimento informações_recurso 2º |
| 177 | solicitamos informações - cgu informação - recorremos cgu - cgu recurso - hospitais | 18 | 177_solicitamos informações_cgu informação_recorremos cgu_cgu recurso |
| 178 | recurso cgu - desclassificação - autos processos - pede deferimento - 2011 decreto | 18 | 178_recurso cgu_desclassificação_autos processos_pede deferimento |
| 179 | notas fiscais - sigilo informação - nota fiscal - falar sigilo - justificativa | 18 | 179_notas fiscais_sigilo informação_nota fiscal_falar sigilo |
| 180 | justificativas apresentadas - solicitando informações - salarial - fornecer informações - destas informações | 18 | 180_justificativas apresentadas_solicitando informações_salarial_fornecer informações |
| 181 | interpor recurso - pedidos informação - venho interpor - recurso porque - informação sic | 18 | 181_interpor recurso_pedidos informação_venho interpor_recurso porque |
| 182 | justificativas - solicitei informações - presidência república - notas fiscais - agradeço resposta | 18 | 182_justificativas_solicitei informações_presidência república_notas fiscais |
| 183 | negado informação - recorro cgu - requer deferimento - sindicato - informação direito | 18 | 183_negado informação_recorro cgu_requer deferimento_sindicato |
| 184 | provas - científica - prova - científicas - maçons cgu | 18 | 184_provas_científica_prova_científicas |
| 185 | solicitei documentos - recorro cgu - comprovantes - requerente apresentou - documentos | 18 | 185_solicitei documentos_recorro cgu_comprovantes_requerente apresentou |
| 186 | anexado recurso - anexado - anexada - cumprimentando - recurso | 18 | 186_anexado recurso_anexado_anexada_cumprimentando |
| 187 | sigilo informação - informação solicitada - disponibilizar informação - informação deve - confidencialidade | 18 | 187_sigilo informação_informação solicitada_disponibilizar informação_informação deve |
| 188 | recurso cgu - cgu informação - pede deferimento - requerimento - requerimentos | 17 | 188_recurso cgu_cgu informação_pede deferimento_requerimento |
| 189 | direito informação - coisa pública - informação pessoal - informação interesse - privacidade | 17 | 189_direito informação_coisa pública_informação pessoal_informação interesse |
| 190 | peço deferimento - deferimento - peço - pode - documento | 17 | 190_peço deferimento_deferimento_peço_pode |
| 191 | aposentadoria - previdência social - estadual - carreira - admissão | 17 | 191_aposentadoria_previdência social_estadual_carreira |
| 192 | legal negativa - sigilo documentos - sigilo documento - legislação - requerimentos | 17 | 192_legal negativa_sigilo documentos_sigilo documento_legislação |
| 193 | restrição informações - informação solicitada - negar informação - documentos processo - informação via | 17 | 193_restrição informações_informação solicitada_negar informação_documentos processo |
| 194 | reitero - recorro - recorrer - - | 17 | 194_reitero_recorro_recorrer_ |
| 195 | reitero pedidos - reitero termos - pedidos - reitero - pleitos | 17 | 195_reitero pedidos_reitero termos_pedidos_reitero |
| 196 | resposta instituição - instituição - instituição ensino - respondeu recurso - respondeu solicitação | 17 | 196_resposta instituição_instituição_instituição ensino_respondeu recurso |
| 197 | transparência informação - direito informações - segurança sociedade - corrupção - sigilo imprescindível | 17 | 197_transparência informação_direito informações_segurança sociedade_corrupção |
| 198 | universidade federal - universidade - ouvidoria - ouvidor - prestação contas | 17 | 198_universidade federal_universidade_ouvidoria_ouvidor |
| 199 | farmacêutica - referida empresa - ministério saúde - informando - requerendo | 17 | 199_farmacêutica_referida empresa_ministério saúde_informando |
| 200 | perguntas - órgão respondeu - atender - receber - solicitado | 16 | 200_perguntas_órgão respondeu_atender_receber |
| 201 | anexo recurso - recurso anexo - segue anexo - recurso segue - anexo segue | 16 | 201_anexo recurso_recurso anexo_segue anexo_recurso segue |
| 202 | administrativo disciplinar - administração - denúncia - administrativos disciplinares - superintendente | 16 | 202_administrativo disciplinar_administração_denúncia_administrativos disciplinares |
| 203 | carga horária - professores - horários - horária - docente | 16 | 203_carga horária_professores_horários_horária |
| 204 | recurso ministério - informação requisitada - novamente informação - informações acima - planilha excel | 16 | 204_recurso ministério_informação requisitada_novamente informação_informações acima |
| 205 | informações devem - controle social - informação deve - cgu - obrigatoriedade | 16 | 205_informações devem_controle social_informação deve_cgu |
| 206 | recurso respondido - respondido recurso - resposta recurso - recurso - respondido | 16 | 206_recurso respondido_respondido recurso_resposta recurso_recurso |
| 207 | informações requisitadas - faculdade - universidade - recurso segunda - ensino | 16 | 207_informações requisitadas_faculdade_universidade_recurso segunda |
| 208 | auxílio - reclamação denúncia - direito - ajuda - cancelamento | 16 | 208_auxílio_reclamação denúncia_direito_ajuda |
| 209 | direito privacidade - interesse público - privacidade - informação pública - órgãos públicos | 16 | 209_direito privacidade_interesse público_privacidade_informação pública |
| 210 | caixa - solicitei informação - informação solicitei - responder informação - respondeu solicitação | 16 | 210_caixa_solicitei informação_informação solicitei_responder informação |
| 211 | cgu informações - fornecidas informações - parecer cgu - informação solicitada - dessa cgu | 16 | 211_cgu informações_fornecidas informações_parecer cgu_informação solicitada |
| 212 | financeiro - sigilo empresarial - licitações - transparência - econômica | 16 | 212_financeiro_sigilo empresarial_licitações_transparência |
| 213 | segue anexo - anexo segue - arquivo anexo - anexo - anexo documentos | 15 | 213_segue anexo_anexo segue_arquivo anexo_anexo |
| 214 | peço deferimento - deferimento - peço - parcial - | 15 | 214_peço deferimento_deferimento_peço_parcial |
| 215 | anexo - anexo anexo - inteiro - inteiro teor - termos | 15 | 215_anexo_anexo anexo_inteiro_inteiro teor |
| 216 | responder questionamentos - questionamentos - respostas recursos - perguntas - justificativas | 15 | 216_responder questionamentos_questionamentos_respostas recursos_perguntas |
| 217 | justiça - recurso anexo - recorro decisão - informação pública - decisão | 15 | 217_justiça_recurso anexo_recorro decisão_informação pública |
| 218 | documentos solicitados - algum documento - informação classificada - documentos classificados - informação lai | 15 | 218_documentos solicitados_algum documento_informação classificada_documentos classificados |
| 219 | anexo recurso - segue anexo - anexo - endereçado - recurso | 15 | 219_anexo recurso_segue anexo_anexo_endereçado |
| 220 | informação pública - 7º informação - poder público - administração pública - outros direitos | 15 | 220_informação pública_7º informação_poder público_administração pública |
| 221 | carga horária - horária - docente - educação - salarial | 15 | 221_carga horária_horária_docente_educação |
| 222 | requerimento - aposentadoria - informaçoes - informações órgão - atualizar | 15 | 222_requerimento_aposentadoria_informaçoes_informações órgão |
| 223 | instância recursal - receber resposta - transparência - responder informação - resposta via | 15 | 223_instância recursal_receber resposta_transparência_responder informação |
| 224 | previdência social - processos administrativos - prestação informação - direito - gestão | 15 | 224_previdência social_processos administrativos_prestação informação_direito |
| 225 | informações demandadas - direito informação - informações interesse - interesse pessoal - atender solicitação | 14 | 225_informações demandadas_direito informação_informações interesse_interesse pessoal |
| 226 | fornecimento informações - documentos solicitados - informações documentos - informação essencial - agradeço antecipadamente | 14 | 226_fornecimento informações_documentos solicitados_informações documentos_informação essencial |
| 227 | solicitadas informações - prestada informação - acadêmica - acadêmico - universidade | 14 | 227_solicitadas informações_prestada informação_acadêmica_acadêmico |
| 228 | recurso cgu - presente solicitação - solicitação informação - informações solicitadas - encaminho recurso | 14 | 228_recurso cgu_presente solicitação_solicitação informação_informações solicitadas |
| 229 | verificação - vigilância sanitária - sanitária - perguntas - pergunta | 14 | 229_verificação_vigilância sanitária_sanitária_perguntas |
| 230 | informação solicitada - informado - requisição - link enviado - solicitado | 14 | 230_informação solicitada_informado_requisição_link enviado |
| 231 | pedidos informação - perguntas - monitoramento - omissão - protocolos | 14 | 231_pedidos informação_perguntas_monitoramento_omissão |
| 232 | maçons cgu - cgu informação - corruptos maçons - cgu ministério - anonimato | 14 | 232_maçons cgu_cgu informação_corruptos maçons_cgu ministério |
| 233 | processos - recebi resposta - recurso solicitação - via sic - receber informação | 13 | 233_processos_recebi resposta_recurso solicitação_via sic |
| 234 | alegação sigilo - informação protegida - protegido sigilo - protegida sigilo - sigilo exceção | 13 | 234_alegação sigilo_informação protegida_protegido sigilo_protegida sigilo |
| 235 | recurso solicitação - união cgu - membros cgu - prazo recurso - informações solicitei | 13 | 235_recurso solicitação_união cgu_membros cgu_prazo recurso |
| 236 | atendimento informação - ministério relações - justificativa negativa - legal negativa - ministério | 13 | 236_atendimento informação_ministério relações_justificativa negativa_legal negativa |
| 237 | salários - ministério relações - salarial - portaria nº - 2011 artigo | 13 | 237_salários_ministério relações_salarial_portaria nº |
| 238 | direito informações - informações interesse - fundamental informação - município - obter informações | 13 | 238_direito informações_informações interesse_fundamental informação_município |
| 239 | anexo recurso - recurso anexo - segue anexo - anexo segue - resposta anexo | 13 | 239_anexo recurso_recurso anexo_segue anexo_anexo segue |
| 240 | sociedades economia - financeiras - instituições financeiras - economia mista - segurança sociedade | 13 | 240_sociedades economia_financeiras_instituições financeiras_economia mista |
| 241 | apreciação recurso - presente solicitação - autoridade superior - penalidade - nulidade | 13 | 241_apreciação recurso_presente solicitação_autoridade superior_penalidade |
| 242 | solicitação informações - aposentadoria - informação regra - aposentados - informações públicas | 13 | 242_solicitação informações_aposentadoria_informação regra_aposentados |
| 243 | imediato informação - informação documento - ferreira - informação roberto - autorizar conceder | 13 | 243_imediato informação_informação documento_ferreira_informação roberto |
| 244 | pede deferimento - deferimento - deferido - peço deferimento - deferidos | 13 | 244_pede deferimento_deferimento_deferido_peço deferimento |
| 245 | escolas - direito - direitos - estado brasileiro - informação pedi | 13 | 245_escolas_direito_direitos_estado brasileiro |
| 246 | pedidos informação - informação solicitada - recurso documentos - informações solicitadas - anvisa | 13 | 246_pedidos informação_informação solicitada_recurso documentos_informações solicitadas |
| 247 | instrução processo - regula processo - processos - processo nº - processo administrativo | 13 | 247_instrução processo_regula processo_processos_processo nº |
| 248 | transparência - licitatório - licitações - processo licitatório - preços | 13 | 248_transparência_licitatório_licitações_processo licitatório |
| 249 | razões recursais - documentos anexos - documento anexo - recursais - anexas | 13 | 249_razões recursais_documentos anexos_documento anexo_recursais |
| 250 | sindicâncias - sindicância - abertura sindicância - arquivadas - diligência | 13 | 250_sindicâncias_sindicância_abertura sindicância_arquivadas |
| 251 | recurso cgu - anexo recurso - cgu recurso - recurso anexo - anexo | 13 | 251_recurso cgu_anexo recurso_cgu recurso_recurso anexo |
| 252 | fornecimento informações - negativa informação - ministério saúde - sigilo informações - classificação informação | 12 | 252_fornecimento informações_negativa informação_ministério saúde_sigilo informações |
| 253 | processos seletivos - auditoria interna - ministério saúde - saúde - convocações | 12 | 253_processos seletivos_auditoria interna_ministério saúde_saúde |
| 254 | sensibilidade - manifestação - importância - mec - sugestão | 12 | 254_sensibilidade_manifestação_importância_mec |
| 255 | recurso cgu - solicitação - cgu controladoria - informação cidadão - comprovadamente | 12 | 255_recurso cgu_solicitação_cgu controladoria_informação cidadão |
| 256 | recurso deve - ausência justificativa - relatório - deferimento solicitação - 2017 resposta | 12 | 256_recurso deve_ausência justificativa_relatório_deferimento solicitação |
| 257 | cgu análise - denúncia - 03 2018 - 12 2018 - recusar fornecer | 12 | 257_cgu análise_denúncia_03 2018_12 2018 |
| 258 | contribuinte - contribuintes - fiscais - receita federal - recurso respondido | 12 | 258_contribuinte_contribuintes_fiscais_receita federal |
| 259 | comando exército - transparência governo - exército - exército brasileiro - portaltransparencia gov | 12 | 259_comando exército_transparência governo_exército_exército brasileiro |
| 260 | solicitação informações - solicitação informação - interesse público - solicitação - trata solicitação | 12 | 260_solicitação informações_solicitação informação_interesse público_solicitação |
| 261 | aposentadoria - conclusão processo - prorrogado - analisou - instrução processo | 12 | 261_aposentadoria_conclusão processo_prorrogado_analisou |
| 262 | constitucional informação - ambientais - assim informação - direito - buscar informação | 12 | 262_constitucional informação_ambientais_assim informação_direito |
| 263 | município - corrupção - relatório - autoridade competente - estadual | 12 | 263_município_corrupção_relatório_autoridade competente |
| 264 | solicitado informação - trabalhadores - empregados - ausência informação - ect | 12 | 264_solicitado informação_trabalhadores_empregados_ausência informação |
| 265 | - - - - | 12 | 265____ |
| 266 | financiamento - bancário - sigilo bancário - construção - caixa | 12 | 266_financiamento_bancário_sigilo bancário_construção |
| 267 | informação cidadão - solicitei informação - negar informação - prestação informação - informação pública | 11 | 267_informação cidadão_solicitei informação_negar informação_prestação informação |
| 268 | informação solicitada - corresponde solicitado - solicitação - atendimento solicitação - solicitada | 11 | 268_informação solicitada_corresponde solicitado_solicitação_atendimento solicitação |
| 269 | anexo recurso - recurso anexo - anexos - anexo - íntegra recurso | 11 | 269_anexo recurso_recurso anexo_anexos_anexo |
| 270 | anexo recurso - segue anexo - informação solicitada - informações solicitadas - obter informação | 11 | 270_anexo recurso_segue anexo_informação solicitada_informações solicitadas |
| 271 | documento requerido - genérico - resposta genérica - solicitadas informações - tipo documento | 11 | 271_documento requerido_genérico_resposta genérica_solicitadas informações |
| 272 | acessoainformacao gov - administração - administração pública - www acessoainformacao - público eficiência | 11 | 272_acessoainformacao gov_administração_administração pública_www acessoainformacao |
| 273 | pedidos informação - fornecer documentos - negativa informações - processos judiciais - outros pedidos | 11 | 273_pedidos informação_fornecer documentos_negativa informações_processos judiciais |
| 274 | pede deferimento - deferimento - resposta incompleta - resposta inicial - interpõe recurso | 11 | 274_pede deferimento_deferimento_resposta incompleta_resposta inicial |
| 275 | prazo atendimento - deferimento recurso - recurso prazo - decisão recurso - cgu fundamento | 11 | 275_prazo atendimento_deferimento recurso_recurso prazo_decisão recurso |
| 276 | negar informações - informação solicitada - informações solicitadas - grau classificação - classificação grau | 11 | 276_negar informações_informação solicitada_informações solicitadas_grau classificação |
| 277 | voluntário - prestações contas - órgão respondeu - pedidos feitos - reuniões realizadas | 11 | 277_voluntário_prestações contas_órgão respondeu_pedidos feitos |
| 278 | sigilo fiscal - fiscais - receita federal - baseada - informação mesma | 11 | 278_sigilo fiscal_fiscais_receita federal_baseada |
| 279 | reavaliação informações - ministério saúde - atender solicitação - notas fiscais - fiscais | 11 | 279_reavaliação informações_ministério saúde_atender solicitação_notas fiscais |
| 280 | cgu reitero - belo horizonte - carteira - cidadão brasileiro - inscrito | 11 | 280_cgu reitero_belo horizonte_carteira_cidadão brasileiro |
| 281 | recurso cgu - anexos recurso - anexo documento - anexo arquivo - recurso razões | 10 | 281_recurso cgu_anexos recurso_anexo documento_anexo arquivo |
| 282 | deferimento recurso - requer cgu - desproporcionalidade - consolidação informações - envio documentos | 10 | 282_deferimento recurso_requer cgu_desproporcionalidade_consolidação informações |
| 283 | recusa informar - requerimento órgão - documentos solicitados - solicitado - órgão | 10 | 283_recusa informar_requerimento órgão_documentos solicitados_solicitado |
| 284 | alegação sigilo - provimento recurso - sigilo profissional - pareceres jurídicos - parecer jurídico | 10 | 284_alegação sigilo_provimento recurso_sigilo profissional_pareceres jurídicos |
| 285 | obtenção informação - informação formulado - resposta recurso - relatórios - informação vez | 10 | 285_obtenção informação_informação formulado_resposta recurso_relatórios |
| 286 | processos administrativos - informações processo - processo administrativo - processo disciplinar - processo ainda | 10 | 286_processos administrativos_informações processo_processo administrativo_processo disciplinar |
</details>
## Training hyperparameters
* calculate_probabilities: False
* language: None
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: True
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None
## Framework versions
* Numpy: 1.26.1
* HDBSCAN: 0.8.33
* UMAP: 0.5.5
* Pandas: 2.1.2
* Scikit-Learn: 1.3.2
* Sentence-transformers: 2.2.2
* Transformers: 4.35.0
* Numba: 0.58.1
* Plotly: 5.18.0
* Python: 3.10.13
|
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0.4168568253517151,
0.41357600688934326,
-0.3687346577644348,
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0.2688964009284973,
-0.2970794141292572,
0.4909909665584564,
0.40897658467292786,
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-0.5830519795417786,
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0.2869282066822052,
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0.8975602984428406,
0.07907699793577194,
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0.08569829910993576,
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0.20638984441757202,
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0.3437923789024353,
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di-ai/nhyft
|
di-ai
| 2023-12-15T18:03:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:03:59Z |
Entry not found
|
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] |
Jeevesh8/bt_re__qqp_ft_3
|
Jeevesh8
| 2023-12-15T21:00:34Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T18:04:10Z |
Entry not found
|
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di-ai/ciebt
|
di-ai
| 2023-12-15T18:04:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:04:58Z |
Entry not found
|
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di-ai/sotvd
|
di-ai
| 2023-12-15T18:05:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:05:32Z |
Entry not found
|
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] |
Vermiculos/ridethelightningjames
|
Vermiculos
| 2023-12-15T18:09:44Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T18:06:00Z |
---
license: openrail
---
|
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di-ai/eawkr
|
di-ai
| 2023-12-15T18:06:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:06:20Z |
Entry not found
|
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] |
hkivancoral/smids_5x_beit_base_sgd_0001_fold5
|
hkivancoral
| 2023-12-15T19:26:16Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T18:07:12Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_sgd_0001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8216666666666667
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_beit_base_sgd_0001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4509
- Accuracy: 0.8217
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.119 | 1.0 | 375 | 1.2346 | 0.355 |
| 1.0823 | 2.0 | 750 | 1.1546 | 0.3883 |
| 0.9626 | 3.0 | 1125 | 1.0734 | 0.4317 |
| 0.8877 | 4.0 | 1500 | 0.9982 | 0.49 |
| 0.8704 | 5.0 | 1875 | 0.9307 | 0.5383 |
| 0.82 | 6.0 | 2250 | 0.8704 | 0.5833 |
| 0.7688 | 7.0 | 2625 | 0.8174 | 0.63 |
| 0.7442 | 8.0 | 3000 | 0.7715 | 0.6583 |
| 0.726 | 9.0 | 3375 | 0.7322 | 0.6833 |
| 0.6394 | 10.0 | 3750 | 0.6995 | 0.695 |
| 0.6379 | 11.0 | 4125 | 0.6704 | 0.7167 |
| 0.6169 | 12.0 | 4500 | 0.6463 | 0.735 |
| 0.5956 | 13.0 | 4875 | 0.6254 | 0.7483 |
| 0.617 | 14.0 | 5250 | 0.6064 | 0.7617 |
| 0.6108 | 15.0 | 5625 | 0.5908 | 0.7717 |
| 0.578 | 16.0 | 6000 | 0.5757 | 0.7767 |
| 0.591 | 17.0 | 6375 | 0.5636 | 0.7817 |
| 0.5966 | 18.0 | 6750 | 0.5528 | 0.785 |
| 0.6007 | 19.0 | 7125 | 0.5440 | 0.7883 |
| 0.5282 | 20.0 | 7500 | 0.5352 | 0.7983 |
| 0.5197 | 21.0 | 7875 | 0.5276 | 0.8033 |
| 0.5125 | 22.0 | 8250 | 0.5198 | 0.8067 |
| 0.5868 | 23.0 | 8625 | 0.5131 | 0.8083 |
| 0.5885 | 24.0 | 9000 | 0.5069 | 0.8117 |
| 0.5176 | 25.0 | 9375 | 0.5018 | 0.8133 |
| 0.5257 | 26.0 | 9750 | 0.4968 | 0.8167 |
| 0.563 | 27.0 | 10125 | 0.4923 | 0.8167 |
| 0.5177 | 28.0 | 10500 | 0.4880 | 0.815 |
| 0.5208 | 29.0 | 10875 | 0.4843 | 0.8183 |
| 0.4749 | 30.0 | 11250 | 0.4801 | 0.8183 |
| 0.5211 | 31.0 | 11625 | 0.4762 | 0.8167 |
| 0.5578 | 32.0 | 12000 | 0.4734 | 0.8167 |
| 0.5196 | 33.0 | 12375 | 0.4707 | 0.8183 |
| 0.5191 | 34.0 | 12750 | 0.4684 | 0.82 |
| 0.4852 | 35.0 | 13125 | 0.4662 | 0.82 |
| 0.4553 | 36.0 | 13500 | 0.4634 | 0.8183 |
| 0.4575 | 37.0 | 13875 | 0.4613 | 0.82 |
| 0.5121 | 38.0 | 14250 | 0.4600 | 0.82 |
| 0.4948 | 39.0 | 14625 | 0.4580 | 0.82 |
| 0.5112 | 40.0 | 15000 | 0.4566 | 0.82 |
| 0.5002 | 41.0 | 15375 | 0.4556 | 0.82 |
| 0.4865 | 42.0 | 15750 | 0.4545 | 0.82 |
| 0.5291 | 43.0 | 16125 | 0.4534 | 0.82 |
| 0.4479 | 44.0 | 16500 | 0.4529 | 0.82 |
| 0.4858 | 45.0 | 16875 | 0.4523 | 0.82 |
| 0.5195 | 46.0 | 17250 | 0.4518 | 0.82 |
| 0.5088 | 47.0 | 17625 | 0.4513 | 0.8217 |
| 0.4798 | 48.0 | 18000 | 0.4511 | 0.8217 |
| 0.4938 | 49.0 | 18375 | 0.4509 | 0.8217 |
| 0.4932 | 50.0 | 18750 | 0.4509 | 0.8217 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
hkivancoral/smids_5x_beit_base_sgd_001_fold5
|
hkivancoral
| 2023-12-15T19:26:08Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T18:07:12Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_sgd_001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8866666666666667
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_beit_base_sgd_001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2832
- Accuracy: 0.8867
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6263 | 1.0 | 375 | 0.6777 | 0.725 |
| 0.5866 | 2.0 | 750 | 0.4870 | 0.81 |
| 0.4483 | 3.0 | 1125 | 0.4247 | 0.825 |
| 0.4271 | 4.0 | 1500 | 0.3855 | 0.84 |
| 0.4066 | 5.0 | 1875 | 0.3633 | 0.8467 |
| 0.3828 | 6.0 | 2250 | 0.3474 | 0.8417 |
| 0.309 | 7.0 | 2625 | 0.3371 | 0.8583 |
| 0.3188 | 8.0 | 3000 | 0.3295 | 0.86 |
| 0.3147 | 9.0 | 3375 | 0.3210 | 0.8633 |
| 0.2842 | 10.0 | 3750 | 0.3163 | 0.8633 |
| 0.258 | 11.0 | 4125 | 0.3059 | 0.87 |
| 0.2796 | 12.0 | 4500 | 0.3036 | 0.8717 |
| 0.2552 | 13.0 | 4875 | 0.2994 | 0.87 |
| 0.2763 | 14.0 | 5250 | 0.2979 | 0.8633 |
| 0.2925 | 15.0 | 5625 | 0.3004 | 0.865 |
| 0.2222 | 16.0 | 6000 | 0.2915 | 0.8767 |
| 0.2839 | 17.0 | 6375 | 0.2879 | 0.8783 |
| 0.2546 | 18.0 | 6750 | 0.2876 | 0.88 |
| 0.2528 | 19.0 | 7125 | 0.2899 | 0.8817 |
| 0.1895 | 20.0 | 7500 | 0.2841 | 0.885 |
| 0.2366 | 21.0 | 7875 | 0.2901 | 0.8767 |
| 0.2149 | 22.0 | 8250 | 0.2831 | 0.8883 |
| 0.2987 | 23.0 | 8625 | 0.2845 | 0.8833 |
| 0.232 | 24.0 | 9000 | 0.2818 | 0.885 |
| 0.2416 | 25.0 | 9375 | 0.2809 | 0.8883 |
| 0.2147 | 26.0 | 9750 | 0.2789 | 0.8867 |
| 0.2824 | 27.0 | 10125 | 0.2796 | 0.8883 |
| 0.2229 | 28.0 | 10500 | 0.2814 | 0.8883 |
| 0.2625 | 29.0 | 10875 | 0.2884 | 0.8767 |
| 0.1908 | 30.0 | 11250 | 0.2826 | 0.885 |
| 0.2464 | 31.0 | 11625 | 0.2786 | 0.8867 |
| 0.2333 | 32.0 | 12000 | 0.2809 | 0.89 |
| 0.2568 | 33.0 | 12375 | 0.2768 | 0.8867 |
| 0.2444 | 34.0 | 12750 | 0.2777 | 0.8883 |
| 0.1971 | 35.0 | 13125 | 0.2787 | 0.8883 |
| 0.1586 | 36.0 | 13500 | 0.2808 | 0.8867 |
| 0.1628 | 37.0 | 13875 | 0.2838 | 0.8817 |
| 0.2206 | 38.0 | 14250 | 0.2772 | 0.8867 |
| 0.1707 | 39.0 | 14625 | 0.2818 | 0.8833 |
| 0.2328 | 40.0 | 15000 | 0.2820 | 0.8867 |
| 0.1705 | 41.0 | 15375 | 0.2828 | 0.89 |
| 0.1753 | 42.0 | 15750 | 0.2851 | 0.8867 |
| 0.2269 | 43.0 | 16125 | 0.2832 | 0.8933 |
| 0.1772 | 44.0 | 16500 | 0.2830 | 0.8883 |
| 0.235 | 45.0 | 16875 | 0.2841 | 0.8883 |
| 0.251 | 46.0 | 17250 | 0.2828 | 0.8867 |
| 0.2199 | 47.0 | 17625 | 0.2831 | 0.8883 |
| 0.1679 | 48.0 | 18000 | 0.2835 | 0.8867 |
| 0.2096 | 49.0 | 18375 | 0.2833 | 0.8867 |
| 0.22 | 50.0 | 18750 | 0.2832 | 0.8867 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
ttabo/Dustin_Siu
|
ttabo
| 2023-12-15T18:08:59Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T18:08:59Z |
---
license: apache-2.0
---
|
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] |
RmichiXD/MiloJ1000
|
RmichiXD
| 2023-12-15T18:12:44Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2023-12-15T18:11:54Z |
---
license: unknown
---
|
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] |
ttabo/Siu
|
ttabo
| 2023-12-15T18:13:59Z | 0 | 0 | null |
[
"dataset:ttabo/Dustin_Siu",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T18:13:31Z |
---
license: apache-2.0
datasets:
- ttabo/Dustin_Siu
---
|
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Vermiculos/keajames
|
Vermiculos
| 2023-12-15T18:20:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:14:49Z |
Entry not found
|
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Richard-Sieg-TH-Koln/Model_Team4_Cris
|
Richard-Sieg-TH-Koln
| 2023-12-15T18:24:29Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2023-12-15T18:16:20Z |
Entry not found
|
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EmmaGthn/runs_lora_2000_moji
|
EmmaGthn
| 2023-12-15T22:38:33Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-15T18:17:41Z |
Entry not found
|
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] |
AleRams/app_prova3
|
AleRams
| 2023-12-15T18:20:31Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T18:20:12Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: app_prova3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# app_prova3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0656
- Precision: 0.2933
- Recall: 0.3030
- F1: 0.2870
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 1.0202 | 0.24 | 15 | 1.1153 | 0.1272 | 0.2 | 0.1555 |
| 1.0125 | 0.48 | 30 | 1.0793 | 0.2819 | 0.2169 | 0.1882 |
| 0.8609 | 0.71 | 45 | 1.0518 | 0.2717 | 0.2640 | 0.2500 |
| 0.9616 | 0.95 | 60 | 1.0372 | 0.2954 | 0.2824 | 0.2707 |
| 0.9207 | 1.19 | 75 | 1.0606 | 0.3259 | 0.2798 | 0.2717 |
| 0.9208 | 1.43 | 90 | 1.0660 | 0.3133 | 0.2869 | 0.2765 |
| 0.7678 | 1.67 | 105 | 1.0527 | 0.3314 | 0.2927 | 0.2755 |
| 0.8915 | 1.9 | 120 | 1.0478 | 0.3413 | 0.3085 | 0.2955 |
| 0.7097 | 2.14 | 135 | 1.0707 | 0.3139 | 0.2992 | 0.2884 |
| 0.7398 | 2.38 | 150 | 1.0473 | 0.2989 | 0.3093 | 0.2937 |
| 0.744 | 2.62 | 165 | 1.0491 | 0.2827 | 0.3043 | 0.2849 |
| 0.6905 | 2.86 | 180 | 1.0656 | 0.2933 | 0.3030 | 0.2870 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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monikam135/model_wikiart
|
monikam135
| 2023-12-15T18:20:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:20:20Z |
Entry not found
|
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] |
ToMaszWRyj/Ryszard_Riedelv3
|
ToMaszWRyj
| 2023-12-15T18:21:26Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T18:20:24Z |
---
license: openrail
---
|
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] |
mogaio/pr_ebsa_fr_tran_merged25_e5_middle_offsets
|
mogaio
| 2023-12-15T18:25:53Z | 0 | 0 |
setfit
|
[
"setfit",
"safetensors",
"xlm-roberta",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
"model-index",
"region:us"
] |
text-classification
| 2023-12-15T18:24:37Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy_score
- classification_report
widget:
- text: 'Adil Hussain
Adil Hussain est reconnaissant d''avoir reçu l''enseignement de l''acteur Naseeruddin
Shah à l''époque où il fréquentait l''École nationale d''art dramatique'
- text: 'Bien que leurs opinions sur la question de savoir si les migrants sont un
avantage ou un fardeau soient plus mitigées, de nettes majorités d''électeurs
de toute la ville de New York, de la banlieue et du nord de l''État ont déclaré
que l''État devrait essayer de ralentir l''afflux de migrants, plutôt que d''en
accepter davantage et de s''efforcer d''assimiler les nouveaux arrivants Les démocrates
aspirent à renverser six circonscriptions détenues par les républicains que M.
Biden a remportées en 2020, notamment celle de M Les républicains se sont emparés
de la crise des migrants, donnant un avant-goût des campagnes de l''année prochaine
Les républicains ont surenchéri : Elise Stefanik, la New-Yorkaise qui dirige la
conférence du parti démocrate à la Chambre des représentants,
Suite à la page suivante
a déclaré à Politico la semaine dernière que le parti allait consacrer 100 millions
de dollars aux campagnes dans les circonscriptions de New York Des problèmes à
venir pour les démocrates de New York en 2024 ?
Les dirigeants démocrates de New York se débattent depuis des mois avec le problème
de l''hébergement des dizaines de milliers de migrants qui ont été transportés
par bus jusqu''à New York et laissés à sa charge Des problèmes à venir pour les
démocrates de New York en 2024 ?
Les dirigeants démocrates de New York se débattent depuis des mois avec le problème
de l''hébergement des dizaines de milliers de migrants qui ont été transportés
par bus jusqu''à New York et laissés à sa charge.
Mais une autre préoccupation se profile alors que la crise se poursuit sans qu''aucune
issue ne soit en vue : les retombées potentielles pour leur parti lors des élections
de l''année prochaine Les républicains ont tendance à se sentir en sécurité lorsqu''ils
parlent d''immigration - comme les démocrates le font pour l''avortement - et
sont clairement à l''attaque sur la question des migrants à New York, tandis que
les démocrates sont sur la défensive, a déclaré Kyle Kondik, directeur de la communication
pour le Centre de politique de l''Université de Virginie, au réseau USA Today
Plus de 100 000 migrants ont été transportés à New York depuis la frontière sud
depuis le printemps 2022. Environ 60 000 d''entre eux sont hébergés dans la ville,
et plus de 2 100 ont été transportés dans des hôtels situés dans sept comtés au
nord de la ville, de Yonkers à la périphérie de Buffalo, où ils sont logés aux
frais de la ville Les démocrates doivent y remporter des victoires pour gagner
cinq sièges à la Chambre et faire du député Hakeem Jeffries, de Brooklyn, le prochain
président de la Chambre des représentants Les publicités d''attaque des républicains
s''écrivent pratiquement d''elles-mêmes à partir d''un flot de titres et d''images
télévisées, alors que le gouverneur Kathy Hochul, le maire de New York Eric Adams
et le président Joe Biden - tous démocrates - se rejettent mutuellement la faute
et s''échangent des coups de feu pour savoir qui devrait en faire le plus Isaac
Goldberg, un stratège démocrate qui a travaillé sur plusieurs campagnes électorales
à New York, a affirmé qu''il était beaucoup trop tôt pour prédire l''impact politique
de la crise des migrants, soulignant que les élections de 2024 n''auront lieu
que dans 14 mois et que de nombreuses questions tout aussi urgentes pourraient
se poser'
- text: 'LE CANDIDAT A LA PRESIDENCE RAMASWAMY VEUT METTRE FIN AU SYSTEME DE VISA
H-1B AUX ETATS-UNIS
Décrivant le programme de visas H-1B comme une forme de "servitude", Vivek Ramaswamy,
candidat républicain indien-américain à l''élection présidentielle, a promis de
"vider" le système basé sur la loterie et de le remplacer par un système d''admission
méritocratique s''il remporte les élections présidentielles de 2024'
- text: 'Smith Hal Sparks Catherine Zeta-Jones son-Sampras Chris Owen Donald Glover
("Queer as Folk") a 54 ans Smith Hal Sparks Catherine Zeta-Jones son-Sampras Chris
Owen Donald Glover
("Queer as Folk") a 54 ans. a 54 ans. Acteur
("Je sais ce que vous avez fait l''été dernier") a 50 ans'
- text: 'Trump profiter de sa célébrité jusqu''à la Maison-Blanche.
"Cela a tué Howard parce qu''il était le roi de tous les médias Il a poursuivi
en disant que Trump ne laisserait pas ses partisans s''approcher de l''une de
ses propriétés. "Les gens qui votent pour Trump, pour la plupart, ne les laisseraient
même pas entrer dans un putain d''hôtel [ "Si être réveillé signifie que je ne
peux pas soutenir Trump, ce que je pense que cela signifie, ou que je soutiens
les personnes qui veulent être transgenres ou que je suis pour le vaccin, appelez-moi
réveillé comme vous le voulez" "Les gens qui votent pour Trump, pour la plupart,
ne les laisseraient même pas entrer dans un putain d''hôtel [...]. Allez à Mar-a-lago,
voyez s''il y a des gens qui vous ressemblent" Stern a également abordé les affirmations
de Trump et de ses partisans selon lesquelles Joe Biden a remporté l''élection
américaine de 2020 grâce à des votes frauduleux "Et soudain, Trump a transformé
Howard, qui était le roi de tous les médias, en prince Harry de tous les médias.
Tout le monde s''en fout "Trump avait l''habitude de participer à l''émission
de Stern chaque semaine. Ils étaient amis. Alors cette idée que Trump est le pire
type qui ait jamais marché sur la surface de la terre, pourquoi traîniez-vous
avec lui ?"
M Mais Stern, qui par le passé a été accusé de racisme et de sexisme dans nombre
de ses sketches à l''antenne, a été un critique virulent de Trump tout au long
de sa présidence et, plus récemment, alors qu''il se prépare à se présenter à
nouveau en 2024.
En 2021, M "Combien de temps allons-nous continuer à élire des gens qui ont perdu
l''élection ?"
Il a poursuivi en qualifiant les partisans de Trump de "nigauds".
"Mon Dieu, j''ai l''impression d''être dans une nation de nigauds. J''espère qu''il
y a encore des gens brillants et dynamiques qui aiment ce pays", a-t-il déclaré
Alors cette idée que Trump est le pire type qui ait jamais marché sur la surface
de la terre, pourquoi traîniez-vous avec lui ?"
M. Failla a déclaré que cela avait "tué" M Si "woke" signifie que je ne peux pas
soutenir Trump, ce que je pense que cela signifie, ou que je soutiens les personnes
qui veulent être transgenres ou que je suis pour le vaccin, appelez-moi "woke"
comme vous voulez Celui qui se décrit comme le "roi de tous les médias" a critiqué
ouvertement l''ancien président américain Donald Trump, les anti-vaxx et, plus
récemment, Lauren Boebert, qu''il a critiquée pour son comportement obscène dans
un théâtre de Denver au début du mois "L''omnipotence médiatique de Donald Trump
a brisé Howard Stern. C''est très important", a déclaré Failla dans la vidéo (selon
OK ! Magazine). "Trump avait l''habitude de participer à l''émission de Stern
chaque semaine L''aversion d''Howard Stern pour Donald Trump, c''est "tout l''ego".
Si "woke" signifie que je ne peux pas soutenir Trump, ce que je pense que cela
signifie, ou que je soutiens les personnes qui veulent être transgenres ou que
je suis pour le vaccin, appelez-moi "woke" comme vous voulez Trump l''année prochaine.
"Je sais que je lui botterai le cul", a-t-il déclaré aux auditeurs.
L''année suivante, Stern a déclaré qu''il envisageait de se lancer dans la course
à la présidence "pour que le pays soit à nouveau juste" En réponse, Trump a partagé
sur sa plateforme Truth Social un clip de Fox News dans lequel l''animateur Jimmy
Failla critique Stern.
"L''omnipotence médiatique de Donald Trump a brisé Howard Stern "Je vais faire
la chose très simple qui remettra le pays sur le droit chemin : un vote, une personne",
a expliqué Stern, affirmant que Trump a en fait perdu l''élection de 2016 contre
Hillary Clinton qui a remporté le vote populaire - mais pas le collège électoral'
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy_score
value: 0.923784494086728
name: Accuracy_Score
- type: classification_report
value:
'0':
precision: 0.9251101321585903
recall: 0.8898305084745762
f1-score: 0.9071274298056154
support: 236
'1':
precision: 0.9081967213114754
recall: 0.920265780730897
f1-score: 0.9141914191419142
support: 301
'2':
precision: 0.9432314410480349
recall: 0.9642857142857143
f1-score: 0.9536423841059601
support: 224
accuracy: 0.923784494086728
macro avg:
precision: 0.9255127648393668
recall: 0.9247940011637291
f1-score: 0.9249870776844965
support: 761
weighted avg:
precision: 0.9237543325873079
recall: 0.923784494086728
f1-score: 0.9236131204146865
support: 761
name: Classification_Report
---
# SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| pos | <ul><li>"Les PHL lèvent 1,26 milliard de dollars grâce aux obligations en dollars de détail\nLE GOUVERNEMENT PHILIPPIN a levé 1,26 milliard de dollars lors de la première émission d'obligations de détail en dollars (RDB) sous l'administration Marcos, a déclaré le ministère des Finances (DoF)"</li><li>"Atom Egoyan revient à Salomé, l'opéra qu'il a monté en 1996, avec Seven Veils\nAtom Egoyan n'a pas été surpris lorsque la Canadian Opera Company lui a demandé de remonter Salomé pour la saison 2022-23 Atom Egoyan revient à Salomé, l'opéra qu'il a monté en 1996, avec Seven Veils\nAtom Egoyan n'a pas été surpris lorsque la Canadian Opera Company lui a demandé de remonter Salomé pour la saison 2022-23. Avec ses éléments de film et de vidéo, son interprétation psychologique et sombre de l'opéra de Richard Strauss avait un solide palmarès de reprises - depuis sa création en 1996, elle avait été présentée deux fois de plus à la COC et avait été reprise par plusieurs autres compagnies"</li><li>'Paul Simon présente un documentaire sur sa carrière\nAprès un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public Paul Simon présente un documentaire sur sa carrière\nAprès un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public.\nTORONTO >> Après un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public "Il n\'y a pas de raison que vous soyez épuisés", a dit Simon à la foule après la première du documentaire d\'Alex Gibney "In Restless Dreams : The Music of Paul Simon" d\'Alex Gibney, dimanche au Festival international du film de Toronto.\nSimon, âgé de 81 ans, n\'avait pas regardé le film avant la première, et il ne l\'a pas regardé non plus dimanche TORONTO >> Après un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public.\n"Il n\'y a pas de raison que vous soyez épuisés", a dit Simon à la foule après la première du documentaire d\'Alex Gibney "In Restless Dreams : The Music of Paul Simon" d\'Alex Gibney, dimanche au Festival international du film de Toronto'</li></ul> |
| neg | <ul><li>'Le groupe Al-Mostaqilla de l\'université du Koweït a appelé les étudiants à organiser un sit-in à l\'université du Koweït lundi pour protester contre la décision de mettre fin aux classes mixtes La décision a été prise la semaine dernière par le nouveau ministre de l\'éducation, Adel Al-Mane, et le directeur par intérim de l\'université du Koweït, Fayez Al-Dhafiri, et mise en œuvre mercredi, trois jours seulement avant le début de la nouvelle année universitaire à la faculté de droit L\'association a également demandé au gouvernement de "cesser ses interventions politiques et médiatiques injustifiées" dans les affaires de l\'université du Koweït.\nL\'association a appelé le directeur par intérim de l\'université du Koweït à ne pas céder aux pressions politiques et médiatiques et à s\'efforcer de protéger l\'indépendance de l\'université Dhafiri a déclaré que la décision avait été prise en application de la loi de 1996 qui interdisait l\'enseignement mixte à l\'université du Koweït, malgré une décision de la Cour constitutionnelle de 2015 autorisant l\'enseignement mixte lorsqu\'il était nécessaire et dans des cas exceptionnels Parallèlement, l\'association des professeurs de l\'université du Koweït a publié samedi une déclaration demandant aux députés et au gouvernement de "cesser d\'interférer dans les affaires de l\'université du Koweït" et de maintenir l\'indépendance de l\'université "L\'université du Koweït était, est et sera toujours le porte-drapeau de la connaissance et des valeurs, à l\'abri de toute influence extérieure Le député Abdulwahab Al-Essa a reproché à l\'administration de l\'université du Koweït d\'avoir succombé à la pression politique au détriment de l\'intérêt public, ajoutant que l\'université du Koweït avait appliqué correctement une décision de la cour constitutionnelle autorisant les classes mixtes chaque fois que cela était nécessaire'</li><li>"L'immigration étant l'un des défis les plus difficiles à relever pour le président Joe Biden et apparaissant comme un enjeu majeur des élections de l'année prochaine, l'administration délocalise essentiellement la question en s'appuyant sur les pays d'Amérique centrale et d'Amérique du Sud pour empêcher les migrants de se diriger vers le nord"</li><li>'Lors d\'une réunion d\'information mardi, le porte-parole de l\'armée, le lieutenant-colonel Richard Hecht, a suggéré que les Palestiniens tentent de quitter la bande de Gaza par le poste-frontière de Rafah, en Égypte.\nLa perspective d\'un exode des habitants de Gaza vers le territoire égyptien a alarmé les autorités égyptiennes La question qui se pose est de savoir si Israël lancera une offensive terrestre dans la bande de Gaza, une bande de terre de 25 miles de long coincée entre Israël, l\'Égypte et la mer Méditerranée, où vivent 2,3 millions de personnes et qui est gouvernée par le Hamas depuis 2007 Israël pilonne la bande de Gaza ; les habitants se précipitent pour se mettre à l\'abri\nJERUSALEM - Les avions de combat israéliens ont bombardé la bande de Gaza quartier par quartier mardi, réduisant les bâtiments en ruines et poussant les habitants à se précipiter pour se mettre à l\'abri dans ce minuscule territoire isolé, alors qu\'Israël promet des représailles pour l\'attaque surprise du Hamas du week-end qui "se répercuteront Les autorités égyptiennes discutent avec Israël et les États-Unis afin de mettre en place des corridors humanitaires dans la bande de Gaza pour acheminer l\'aide, a déclaré un responsable égyptien. Des négociations sont en cours avec les Israéliens pour que la zone autour du point de passage de Rafah entre l\'Égypte et Gaza soit déclarée "zone d\'interdiction de feu", a déclaré le responsable, sous couvert d\'anonymat car il n\'était pas autorisé à parler aux médias'</li></ul> |
| obj | <ul><li>"L'économie pèse sur les Américains Ils sont plus nombreux à faire confiance à Trump qu'à Biden pour alléger leur fardeau\nWASHINGTON - Linda Muñoz a peur de l'économie Trump, le candidat républicain à la primaire de 2024, pour améliorer l'économie, avec une marge de 47 % à 36 %. L'écart est de 46 %-26 % en faveur de M. Trump parmi les électeurs indépendants Presque tous les républicains interrogés ont exprimé leur pessimisme à l'égard de l'économie, selon le sondage : 96 % d'entre eux estiment que la situation se dégrade au lieu de s'améliorer Le logement. L'essence. Tous ces éléments poussent les gens à s'endetter de plus en plus, disent-ils.\nSelon le sondage, près de 70 % des Américains estiment que la situation économique se dégrade, tandis que 22 % seulement estiment qu'elle s'améliore L'économie pèse sur les Américains Ils sont plus nombreux à faire confiance à Trump qu'à Biden pour alléger leur fardeau\nWASHINGTON - Linda Muñoz a peur de l'économie. Elle a puisé dans son épargne d'urgence cette année. Et elle ne croit pas que le président Joe Biden ressente sa douleur L'épicerie. Le logement. L'essence. Tous ces éléments poussent les gens à s'endetter de plus en plus, disent-ils.\nSelon le sondage, près de 70 % des Américains estiment que la situation économique se dégrade, tandis que 22 % seulement estiment qu'elle s'améliore"</li><li>'Le Pentagone va interroger d\'autres militaires sur l\'attentat suicide de l\'aéroport de Kaboul en 2021\nLe commandement central du Pentagone a ordonné l\'audition d\'une vingtaine de militaires supplémentaires qui se trouvaient à l\'aéroport de Kaboul lorsque des kamikazes ont attaqué pendant le retrait chaotique des forces américaines d\'Afghanistan, alors que les critiques persistent sur le fait que l\'attaque meurtrière aurait pu être stoppée Certaines familles des personnes tuées ou blessées se sont plaintes que le Pentagone n\'avait pas fait preuve de suffisamment de transparence au sujet de l\'attentat à la bombe qui a tué 170 Afghans\net 13 militaires américains.\nL\'enquête du commandement central américain a conclu en novembre 2021 qu\'étant donné la détérioration de la sécurité à la porte de l\'Abbaye de l\'aéroport alors que les Afghans cherchaient de plus en plus à fuir, "l\'attaque n\'aurait pas pu être évitée au niveau tactique sans dégrader la mission visant à maximiser le nombre d\'évacués" Le Pentagone a déclaré que l\'examen de l\'attentat suicide n\'avait révélé aucune identification préalable d\'un attaquant possible ni aucune demande d\'"escalade des règles d\'engagement existantes" régissant l\'utilisation de la force par les troupes américaines'</li><li>'Les retombées de la guerre se répercutent sur les lieux de travail aux États-Unis.\nNEW YORK - Les retombées de la guerre entre Israël et le Hamas se sont répandues sur les lieux de travail partout dans le monde, les dirigeants de grandes entreprises exprimant leur point de vue tandis que les travailleurs se plaignent de ne pas être entendus "À quoi me sert mon travail si je compromets ma propre morale et mon éthique ?\nL\'un des conflits les plus importants s\'est produit chez Starbucks après que Starbucks Workers United, un syndicat représentant 9 000 travailleurs dans plus de 360 magasins aux États-Unis, a tweeté "Solidarité avec la Palestine" deux jours après l\'attaque du Hamas. Le tweet a été supprimé au bout de 40 minutes, mais l\'entreprise a déclaré qu\'il avait donné lieu à plus de 1 000 plaintes, à des actes de vandalisme et à des affrontements dans ses magasins NEW YORK - Les retombées de la guerre entre Israël et le Hamas se sont répandues sur les lieux de travail partout dans le monde, les dirigeants de grandes entreprises exprimant leur point de vue tandis que les travailleurs se plaignent de ne pas être entendus'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy_Score | Classification_Report |
|:--------|:---------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **all** | 0.9238 | {'0': {'precision': 0.9251101321585903, 'recall': 0.8898305084745762, 'f1-score': 0.9071274298056154, 'support': 236}, '1': {'precision': 0.9081967213114754, 'recall': 0.920265780730897, 'f1-score': 0.9141914191419142, 'support': 301}, '2': {'precision': 0.9432314410480349, 'recall': 0.9642857142857143, 'f1-score': 0.9536423841059601, 'support': 224}, 'accuracy': 0.923784494086728, 'macro avg': {'precision': 0.9255127648393668, 'recall': 0.9247940011637291, 'f1-score': 0.9249870776844965, 'support': 761}, 'weighted avg': {'precision': 0.9237543325873079, 'recall': 0.923784494086728, 'f1-score': 0.9236131204146865, 'support': 761}} |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mogaio/pr_ebsa_fr_tran_merged25_e5_middle_offsets")
# Run inference
preds = model("Adil Hussain
Adil Hussain est reconnaissant d'avoir reçu l'enseignement de l'acteur Naseeruddin Shah à l'époque où il fréquentait l'École nationale d'art dramatique")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:-----|
| Word count | 9 | 247.2638 | 2089 |
| Label | Training Sample Count |
|:------|:----------------------|
| neg | 913 |
| obj | 1216 |
| pos | 911 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (5, 5)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 1
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0013 | 1 | 0.3703 | - |
| 0.0658 | 50 | 0.3145 | - |
| 0.1316 | 100 | 0.1839 | - |
| 0.1974 | 150 | 0.2558 | - |
| 0.2632 | 200 | 0.2683 | - |
| 0.3289 | 250 | 0.1572 | - |
| 0.3947 | 300 | 0.1953 | - |
| 0.4605 | 350 | 0.171 | - |
| 0.5263 | 400 | 0.2326 | - |
| 0.5921 | 450 | 0.1762 | - |
| 0.6579 | 500 | 0.2818 | - |
| 0.7237 | 550 | 0.2733 | - |
| 0.7895 | 600 | 0.195 | - |
| 0.8553 | 650 | 0.2104 | - |
| 0.9211 | 700 | 0.2124 | - |
| 0.9868 | 750 | 0.0818 | - |
| 1.0526 | 800 | 0.1046 | - |
| 1.1184 | 850 | 0.1633 | - |
| 1.1842 | 900 | 0.3207 | - |
| 1.25 | 950 | 0.2703 | - |
| 1.3158 | 1000 | 0.1934 | - |
| 1.3816 | 1050 | 0.2547 | - |
| 1.4474 | 1100 | 0.0933 | - |
| 1.5132 | 1150 | 0.2102 | - |
| 1.5789 | 1200 | 0.0699 | - |
| 1.6447 | 1250 | 0.1778 | - |
| 1.7105 | 1300 | 0.1796 | - |
| 1.7763 | 1350 | 0.0221 | - |
| 1.8421 | 1400 | 0.2154 | - |
| 1.9079 | 1450 | 0.1683 | - |
| 1.9737 | 1500 | 0.3096 | - |
| 2.0395 | 1550 | 0.201 | - |
| 2.1053 | 1600 | 0.1954 | - |
| 2.1711 | 1650 | 0.2301 | - |
| 2.2368 | 1700 | 0.1141 | - |
| 2.3026 | 1750 | 0.1949 | - |
| 2.3684 | 1800 | 0.164 | - |
| 2.4342 | 1850 | 0.2307 | - |
| 2.5 | 1900 | 0.1912 | - |
| 2.5658 | 1950 | 0.2349 | - |
| 2.6316 | 2000 | 0.0922 | - |
| 2.6974 | 2050 | 0.0702 | - |
| 2.7632 | 2100 | 0.1089 | - |
| 2.8289 | 2150 | 0.1711 | - |
| 2.8947 | 2200 | 0.1432 | - |
| 2.9605 | 2250 | 0.2739 | - |
| 3.0263 | 2300 | 0.1889 | - |
| 3.0921 | 2350 | 0.1036 | - |
| 3.1579 | 2400 | 0.1372 | - |
| 3.2237 | 2450 | 0.028 | - |
| 3.2895 | 2500 | 0.1739 | - |
| 3.3553 | 2550 | 0.142 | - |
| 3.4211 | 2600 | 0.0838 | - |
| 3.4868 | 2650 | 0.0657 | - |
| 3.5526 | 2700 | 0.0054 | - |
| 3.6184 | 2750 | 0.0426 | - |
| 3.6842 | 2800 | 0.1974 | - |
| 3.75 | 2850 | 0.0279 | - |
| 3.8158 | 2900 | 0.1326 | - |
| 3.8816 | 2950 | 0.1614 | - |
| 3.9474 | 3000 | 0.1251 | - |
| 4.0132 | 3050 | 0.1174 | - |
| 4.0789 | 3100 | 0.1948 | - |
| 4.1447 | 3150 | 0.0555 | - |
| 4.2105 | 3200 | 0.0064 | - |
| 4.2763 | 3250 | 0.064 | - |
| 4.3421 | 3300 | 0.0013 | - |
| 4.4079 | 3350 | 0.135 | - |
| 4.4737 | 3400 | 0.0574 | - |
| 4.5395 | 3450 | 0.174 | - |
| 4.6053 | 3500 | 0.2199 | - |
| 4.6711 | 3550 | 0.387 | - |
| 4.7368 | 3600 | 0.114 | - |
| 4.8026 | 3650 | 0.0853 | - |
| 4.8684 | 3700 | 0.0325 | - |
| 4.9342 | 3750 | 0.019 | - |
| 5.0 | 3800 | 0.0572 | - |
| 0.0013 | 1 | 0.1435 | - |
| 0.0658 | 50 | 0.0969 | - |
| 0.1316 | 100 | 0.1085 | - |
| 0.1974 | 150 | 0.0271 | - |
| 0.2632 | 200 | 0.0138 | - |
| 0.3289 | 250 | 0.058 | - |
| 0.3947 | 300 | 0.1205 | - |
| 0.4605 | 350 | 0.0788 | - |
| 0.5263 | 400 | 0.1449 | - |
| 0.5921 | 450 | 0.0383 | - |
| 0.6579 | 500 | 0.0338 | - |
| 0.7237 | 550 | 0.1253 | - |
| 0.7895 | 600 | 0.069 | - |
| 0.8553 | 650 | 0.104 | - |
| 0.9211 | 700 | 0.0462 | - |
| 0.9868 | 750 | 0.1975 | - |
| 1.0526 | 800 | 0.0241 | - |
| 1.1184 | 850 | 0.0426 | - |
| 1.1842 | 900 | 0.0519 | - |
| 1.25 | 950 | 0.0815 | - |
| 1.3158 | 1000 | 0.1839 | - |
| 1.3816 | 1050 | 0.0198 | - |
| 1.4474 | 1100 | 0.0128 | - |
| 1.5132 | 1150 | 0.1645 | - |
| 1.5789 | 1200 | 0.0019 | - |
| 1.6447 | 1250 | 0.0557 | - |
| 1.7105 | 1300 | 0.0098 | - |
| 1.7763 | 1350 | 0.001 | - |
| 1.8421 | 1400 | 0.1557 | - |
| 1.9079 | 1450 | 0.1286 | - |
| 1.9737 | 1500 | 0.094 | - |
| 2.0395 | 1550 | 0.0059 | - |
| 2.1053 | 1600 | 0.0227 | - |
| 2.1711 | 1650 | 0.0899 | - |
| 2.2368 | 1700 | 0.0053 | - |
| 2.3026 | 1750 | 0.0021 | - |
| 2.3684 | 1800 | 0.0114 | - |
| 2.4342 | 1850 | 0.1163 | - |
| 2.5 | 1900 | 0.0959 | - |
| 2.5658 | 1950 | 0.0252 | - |
| 2.6316 | 2000 | 0.0921 | - |
| 2.6974 | 2050 | 0.1159 | - |
| 2.7632 | 2100 | 0.0026 | - |
| 2.8289 | 2150 | 0.1211 | - |
| 2.8947 | 2200 | 0.1843 | - |
| 2.9605 | 2250 | 0.0014 | - |
| 3.0263 | 2300 | 0.0085 | - |
| 3.0921 | 2350 | 0.0839 | - |
| 3.1579 | 2400 | 0.2372 | - |
| 3.2237 | 2450 | 0.0213 | - |
| 3.2895 | 2500 | 0.0155 | - |
| 3.3553 | 2550 | 0.1128 | - |
| 3.4211 | 2600 | 0.0945 | - |
| 3.4868 | 2650 | 0.0917 | - |
| 3.5526 | 2700 | 0.0011 | - |
| 3.6184 | 2750 | 0.0024 | - |
| 3.6842 | 2800 | 0.0044 | - |
| 3.75 | 2850 | 0.121 | - |
| 3.8158 | 2900 | 0.0056 | - |
| 3.8816 | 2950 | 0.003 | - |
| 3.9474 | 3000 | 0.0899 | - |
| 4.0132 | 3050 | 0.0157 | - |
| 4.0789 | 3100 | 0.1188 | - |
| 4.1447 | 3150 | 0.001 | - |
| 4.2105 | 3200 | 0.0222 | - |
| 4.2763 | 3250 | 0.1209 | - |
| 4.3421 | 3300 | 0.1085 | - |
| 4.4079 | 3350 | 0.0054 | - |
| 4.4737 | 3400 | 0.0009 | - |
| 4.5395 | 3450 | 0.0015 | - |
| 4.6053 | 3500 | 0.003 | - |
| 4.6711 | 3550 | 0.0009 | - |
| 4.7368 | 3600 | 0.0003 | - |
| 4.8026 | 3650 | 0.0009 | - |
| 4.8684 | 3700 | 0.03 | - |
| 4.9342 | 3750 | 0.1206 | - |
| 5.0 | 3800 | 0.0003 | - |
| 0.0013 | 1 | 0.2045 | - |
| 0.0658 | 50 | 0.0078 | - |
| 0.1316 | 100 | 0.0087 | - |
| 0.1974 | 150 | 0.0386 | - |
| 0.2632 | 200 | 0.1015 | - |
| 0.3289 | 250 | 0.0022 | - |
| 0.3947 | 300 | 0.0291 | - |
| 0.4605 | 350 | 0.0013 | - |
| 0.5263 | 400 | 0.0022 | - |
| 0.5921 | 450 | 0.1324 | - |
| 0.6579 | 500 | 0.113 | - |
| 0.7237 | 550 | 0.0011 | - |
| 0.7895 | 600 | 0.1723 | - |
| 0.8553 | 650 | 0.0049 | - |
| 0.9211 | 700 | 0.206 | - |
| 0.9868 | 750 | 0.1683 | - |
| 1.0526 | 800 | 0.0954 | - |
| 1.1184 | 850 | 0.018 | - |
| 1.1842 | 900 | 0.1854 | - |
| 1.25 | 950 | 0.0342 | - |
| 1.3158 | 1000 | 0.0015 | - |
| 1.3816 | 1050 | 0.0062 | - |
| 1.4474 | 1100 | 0.1187 | - |
| 1.5132 | 1150 | 0.0048 | - |
| 1.5789 | 1200 | 0.0011 | - |
| 1.6447 | 1250 | 0.002 | - |
| 1.7105 | 1300 | 0.092 | - |
| 1.7763 | 1350 | 0.1245 | - |
| 1.8421 | 1400 | 0.0009 | - |
| 1.9079 | 1450 | 0.1185 | - |
| 1.9737 | 1500 | 0.0017 | - |
| 2.0395 | 1550 | 0.008 | - |
| 2.1053 | 1600 | 0.0049 | - |
| 2.1711 | 1650 | 0.0083 | - |
| 2.2368 | 1700 | 0.0026 | - |
| 2.3026 | 1750 | 0.0081 | - |
| 2.3684 | 1800 | 0.0036 | - |
| 2.4342 | 1850 | 0.0016 | - |
| 2.5 | 1900 | 0.0017 | - |
| 2.5658 | 1950 | 0.0014 | - |
| 2.6316 | 2000 | 0.0017 | - |
| 2.6974 | 2050 | 0.002 | - |
| 2.7632 | 2100 | 0.1022 | - |
| 2.8289 | 2150 | 0.0004 | - |
| 2.8947 | 2200 | 0.0007 | - |
| 2.9605 | 2250 | 0.0794 | - |
| 3.0263 | 2300 | 0.0183 | - |
| 3.0921 | 2350 | 0.0377 | - |
| 3.1579 | 2400 | 0.029 | - |
| 3.2237 | 2450 | 0.0003 | - |
| 3.2895 | 2500 | 0.0961 | - |
| 3.3553 | 2550 | 0.0008 | - |
| 3.4211 | 2600 | 0.0873 | - |
| 3.4868 | 2650 | 0.0501 | - |
| 3.5526 | 2700 | 0.0029 | - |
| 3.6184 | 2750 | 0.0008 | - |
| 3.6842 | 2800 | 0.0004 | - |
| 3.75 | 2850 | 0.0011 | - |
| 3.8158 | 2900 | 0.0518 | - |
| 3.8816 | 2950 | 0.0002 | - |
| 3.9474 | 3000 | 0.1115 | - |
| 4.0132 | 3050 | 0.0129 | - |
| 4.0789 | 3100 | 0.0005 | - |
| 4.1447 | 3150 | 0.0012 | - |
| 4.2105 | 3200 | 0.1086 | - |
| 4.2763 | 3250 | 0.0199 | - |
| 4.3421 | 3300 | 0.0004 | - |
| 4.4079 | 3350 | 0.0001 | - |
| 4.4737 | 3400 | 0.0832 | - |
| 4.5395 | 3450 | 0.0003 | - |
| 4.6053 | 3500 | 0.0041 | - |
| 4.6711 | 3550 | 0.1146 | - |
| 4.7368 | 3600 | 0.0027 | - |
| 4.8026 | 3650 | 0.0002 | - |
| 4.8684 | 3700 | 0.0544 | - |
| 4.9342 | 3750 | 0.0002 | - |
| 5.0 | 3800 | 0.0046 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.15.0
- Tokenizers: 0.15.0
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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|
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CharlosHyens/bv2
|
CharlosHyens
| 2023-12-15T19:02:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:25:15Z |
Entry not found
|
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] |
mogaio/pr_ebsa_fr_tran_merged25_e5_end_offsets
|
mogaio
| 2023-12-15T18:27:02Z | 0 | 0 |
setfit
|
[
"setfit",
"safetensors",
"xlm-roberta",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
"model-index",
"region:us"
] |
text-classification
| 2023-12-15T18:25:53Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy_score
- classification_report
widget:
- text: 'Adil Hussain
Adil Hussain est reconnaissant d''avoir reçu l''enseignement de l''acteur Naseeruddin
Shah à l''époque où il fréquentait l''École nationale d''art dramatique'
- text: 'Bien que leurs opinions sur la question de savoir si les migrants sont un
avantage ou un fardeau soient plus mitigées, de nettes majorités d''électeurs
de toute la ville de New York, de la banlieue et du nord de l''État ont déclaré
que l''État devrait essayer de ralentir l''afflux de migrants, plutôt que d''en
accepter davantage et de s''efforcer d''assimiler les nouveaux arrivants Les démocrates
aspirent à renverser six circonscriptions détenues par les républicains que M.
Biden a remportées en 2020, notamment celle de M Les républicains se sont emparés
de la crise des migrants, donnant un avant-goût des campagnes de l''année prochaine
Les républicains ont surenchéri : Elise Stefanik, la New-Yorkaise qui dirige la
conférence du parti démocrate à la Chambre des représentants,
Suite à la page suivante
a déclaré à Politico la semaine dernière que le parti allait consacrer 100 millions
de dollars aux campagnes dans les circonscriptions de New York Des problèmes à
venir pour les démocrates de New York en 2024 ?
Les dirigeants démocrates de New York se débattent depuis des mois avec le problème
de l''hébergement des dizaines de milliers de migrants qui ont été transportés
par bus jusqu''à New York et laissés à sa charge Des problèmes à venir pour les
démocrates de New York en 2024 ?
Les dirigeants démocrates de New York se débattent depuis des mois avec le problème
de l''hébergement des dizaines de milliers de migrants qui ont été transportés
par bus jusqu''à New York et laissés à sa charge.
Mais une autre préoccupation se profile alors que la crise se poursuit sans qu''aucune
issue ne soit en vue : les retombées potentielles pour leur parti lors des élections
de l''année prochaine Les républicains ont tendance à se sentir en sécurité lorsqu''ils
parlent d''immigration - comme les démocrates le font pour l''avortement - et
sont clairement à l''attaque sur la question des migrants à New York, tandis que
les démocrates sont sur la défensive, a déclaré Kyle Kondik, directeur de la communication
pour le Centre de politique de l''Université de Virginie, au réseau USA Today
Plus de 100 000 migrants ont été transportés à New York depuis la frontière sud
depuis le printemps 2022. Environ 60 000 d''entre eux sont hébergés dans la ville,
et plus de 2 100 ont été transportés dans des hôtels situés dans sept comtés au
nord de la ville, de Yonkers à la périphérie de Buffalo, où ils sont logés aux
frais de la ville Les démocrates doivent y remporter des victoires pour gagner
cinq sièges à la Chambre et faire du député Hakeem Jeffries, de Brooklyn, le prochain
président de la Chambre des représentants Les publicités d''attaque des républicains
s''écrivent pratiquement d''elles-mêmes à partir d''un flot de titres et d''images
télévisées, alors que le gouverneur Kathy Hochul, le maire de New York Eric Adams
et le président Joe Biden - tous démocrates - se rejettent mutuellement la faute
et s''échangent des coups de feu pour savoir qui devrait en faire le plus Isaac
Goldberg, un stratège démocrate qui a travaillé sur plusieurs campagnes électorales
à New York, a affirmé qu''il était beaucoup trop tôt pour prédire l''impact politique
de la crise des migrants, soulignant que les élections de 2024 n''auront lieu
que dans 14 mois et que de nombreuses questions tout aussi urgentes pourraient
se poser'
- text: 'LE CANDIDAT A LA PRESIDENCE RAMASWAMY VEUT METTRE FIN AU SYSTEME DE VISA
H-1B AUX ETATS-UNIS
Décrivant le programme de visas H-1B comme une forme de "servitude", Vivek Ramaswamy,
candidat républicain indien-américain à l''élection présidentielle, a promis de
"vider" le système basé sur la loterie et de le remplacer par un système d''admission
méritocratique s''il remporte les élections présidentielles de 2024'
- text: 'Smith Hal Sparks Catherine Zeta-Jones son-Sampras Chris Owen Donald Glover
("Queer as Folk") a 54 ans Smith Hal Sparks Catherine Zeta-Jones son-Sampras Chris
Owen Donald Glover
("Queer as Folk") a 54 ans. a 54 ans. Acteur
("Je sais ce que vous avez fait l''été dernier") a 50 ans'
- text: 'Trump profiter de sa célébrité jusqu''à la Maison-Blanche.
"Cela a tué Howard parce qu''il était le roi de tous les médias Il a poursuivi
en disant que Trump ne laisserait pas ses partisans s''approcher de l''une de
ses propriétés. "Les gens qui votent pour Trump, pour la plupart, ne les laisseraient
même pas entrer dans un putain d''hôtel [ "Si être réveillé signifie que je ne
peux pas soutenir Trump, ce que je pense que cela signifie, ou que je soutiens
les personnes qui veulent être transgenres ou que je suis pour le vaccin, appelez-moi
réveillé comme vous le voulez" "Les gens qui votent pour Trump, pour la plupart,
ne les laisseraient même pas entrer dans un putain d''hôtel [...]. Allez à Mar-a-lago,
voyez s''il y a des gens qui vous ressemblent" Stern a également abordé les affirmations
de Trump et de ses partisans selon lesquelles Joe Biden a remporté l''élection
américaine de 2020 grâce à des votes frauduleux "Et soudain, Trump a transformé
Howard, qui était le roi de tous les médias, en prince Harry de tous les médias.
Tout le monde s''en fout "Trump avait l''habitude de participer à l''émission
de Stern chaque semaine. Ils étaient amis. Alors cette idée que Trump est le pire
type qui ait jamais marché sur la surface de la terre, pourquoi traîniez-vous
avec lui ?"
M Mais Stern, qui par le passé a été accusé de racisme et de sexisme dans nombre
de ses sketches à l''antenne, a été un critique virulent de Trump tout au long
de sa présidence et, plus récemment, alors qu''il se prépare à se présenter à
nouveau en 2024.
En 2021, M "Combien de temps allons-nous continuer à élire des gens qui ont perdu
l''élection ?"
Il a poursuivi en qualifiant les partisans de Trump de "nigauds".
"Mon Dieu, j''ai l''impression d''être dans une nation de nigauds. J''espère qu''il
y a encore des gens brillants et dynamiques qui aiment ce pays", a-t-il déclaré
Alors cette idée que Trump est le pire type qui ait jamais marché sur la surface
de la terre, pourquoi traîniez-vous avec lui ?"
M. Failla a déclaré que cela avait "tué" M Si "woke" signifie que je ne peux pas
soutenir Trump, ce que je pense que cela signifie, ou que je soutiens les personnes
qui veulent être transgenres ou que je suis pour le vaccin, appelez-moi "woke"
comme vous voulez Celui qui se décrit comme le "roi de tous les médias" a critiqué
ouvertement l''ancien président américain Donald Trump, les anti-vaxx et, plus
récemment, Lauren Boebert, qu''il a critiquée pour son comportement obscène dans
un théâtre de Denver au début du mois "L''omnipotence médiatique de Donald Trump
a brisé Howard Stern. C''est très important", a déclaré Failla dans la vidéo (selon
OK ! Magazine). "Trump avait l''habitude de participer à l''émission de Stern
chaque semaine L''aversion d''Howard Stern pour Donald Trump, c''est "tout l''ego".
Si "woke" signifie que je ne peux pas soutenir Trump, ce que je pense que cela
signifie, ou que je soutiens les personnes qui veulent être transgenres ou que
je suis pour le vaccin, appelez-moi "woke" comme vous voulez Trump l''année prochaine.
"Je sais que je lui botterai le cul", a-t-il déclaré aux auditeurs.
L''année suivante, Stern a déclaré qu''il envisageait de se lancer dans la course
à la présidence "pour que le pays soit à nouveau juste" En réponse, Trump a partagé
sur sa plateforme Truth Social un clip de Fox News dans lequel l''animateur Jimmy
Failla critique Stern.
"L''omnipotence médiatique de Donald Trump a brisé Howard Stern "Je vais faire
la chose très simple qui remettra le pays sur le droit chemin : un vote, une personne",
a expliqué Stern, affirmant que Trump a en fait perdu l''élection de 2016 contre
Hillary Clinton qui a remporté le vote populaire - mais pas le collège électoral'
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy_score
value: 0.923784494086728
name: Accuracy_Score
- type: classification_report
value:
'0':
precision: 0.9251101321585903
recall: 0.8898305084745762
f1-score: 0.9071274298056154
support: 236
'1':
precision: 0.9081967213114754
recall: 0.920265780730897
f1-score: 0.9141914191419142
support: 301
'2':
precision: 0.9432314410480349
recall: 0.9642857142857143
f1-score: 0.9536423841059601
support: 224
accuracy: 0.923784494086728
macro avg:
precision: 0.9255127648393668
recall: 0.9247940011637291
f1-score: 0.9249870776844965
support: 761
weighted avg:
precision: 0.9237543325873079
recall: 0.923784494086728
f1-score: 0.9236131204146865
support: 761
name: Classification_Report
---
# SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| pos | <ul><li>"Les PHL lèvent 1,26 milliard de dollars grâce aux obligations en dollars de détail\nLE GOUVERNEMENT PHILIPPIN a levé 1,26 milliard de dollars lors de la première émission d'obligations de détail en dollars (RDB) sous l'administration Marcos, a déclaré le ministère des Finances (DoF)"</li><li>"Atom Egoyan revient à Salomé, l'opéra qu'il a monté en 1996, avec Seven Veils\nAtom Egoyan n'a pas été surpris lorsque la Canadian Opera Company lui a demandé de remonter Salomé pour la saison 2022-23 Atom Egoyan revient à Salomé, l'opéra qu'il a monté en 1996, avec Seven Veils\nAtom Egoyan n'a pas été surpris lorsque la Canadian Opera Company lui a demandé de remonter Salomé pour la saison 2022-23. Avec ses éléments de film et de vidéo, son interprétation psychologique et sombre de l'opéra de Richard Strauss avait un solide palmarès de reprises - depuis sa création en 1996, elle avait été présentée deux fois de plus à la COC et avait été reprise par plusieurs autres compagnies"</li><li>'Paul Simon présente un documentaire sur sa carrière\nAprès un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public Paul Simon présente un documentaire sur sa carrière\nAprès un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public.\nTORONTO >> Après un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public "Il n\'y a pas de raison que vous soyez épuisés", a dit Simon à la foule après la première du documentaire d\'Alex Gibney "In Restless Dreams : The Music of Paul Simon" d\'Alex Gibney, dimanche au Festival international du film de Toronto.\nSimon, âgé de 81 ans, n\'avait pas regardé le film avant la première, et il ne l\'a pas regardé non plus dimanche TORONTO >> Après un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public.\n"Il n\'y a pas de raison que vous soyez épuisés", a dit Simon à la foule après la première du documentaire d\'Alex Gibney "In Restless Dreams : The Music of Paul Simon" d\'Alex Gibney, dimanche au Festival international du film de Toronto'</li></ul> |
| neg | <ul><li>'Le groupe Al-Mostaqilla de l\'université du Koweït a appelé les étudiants à organiser un sit-in à l\'université du Koweït lundi pour protester contre la décision de mettre fin aux classes mixtes La décision a été prise la semaine dernière par le nouveau ministre de l\'éducation, Adel Al-Mane, et le directeur par intérim de l\'université du Koweït, Fayez Al-Dhafiri, et mise en œuvre mercredi, trois jours seulement avant le début de la nouvelle année universitaire à la faculté de droit L\'association a également demandé au gouvernement de "cesser ses interventions politiques et médiatiques injustifiées" dans les affaires de l\'université du Koweït.\nL\'association a appelé le directeur par intérim de l\'université du Koweït à ne pas céder aux pressions politiques et médiatiques et à s\'efforcer de protéger l\'indépendance de l\'université Dhafiri a déclaré que la décision avait été prise en application de la loi de 1996 qui interdisait l\'enseignement mixte à l\'université du Koweït, malgré une décision de la Cour constitutionnelle de 2015 autorisant l\'enseignement mixte lorsqu\'il était nécessaire et dans des cas exceptionnels Parallèlement, l\'association des professeurs de l\'université du Koweït a publié samedi une déclaration demandant aux députés et au gouvernement de "cesser d\'interférer dans les affaires de l\'université du Koweït" et de maintenir l\'indépendance de l\'université "L\'université du Koweït était, est et sera toujours le porte-drapeau de la connaissance et des valeurs, à l\'abri de toute influence extérieure Le député Abdulwahab Al-Essa a reproché à l\'administration de l\'université du Koweït d\'avoir succombé à la pression politique au détriment de l\'intérêt public, ajoutant que l\'université du Koweït avait appliqué correctement une décision de la cour constitutionnelle autorisant les classes mixtes chaque fois que cela était nécessaire'</li><li>"L'immigration étant l'un des défis les plus difficiles à relever pour le président Joe Biden et apparaissant comme un enjeu majeur des élections de l'année prochaine, l'administration délocalise essentiellement la question en s'appuyant sur les pays d'Amérique centrale et d'Amérique du Sud pour empêcher les migrants de se diriger vers le nord"</li><li>'Lors d\'une réunion d\'information mardi, le porte-parole de l\'armée, le lieutenant-colonel Richard Hecht, a suggéré que les Palestiniens tentent de quitter la bande de Gaza par le poste-frontière de Rafah, en Égypte.\nLa perspective d\'un exode des habitants de Gaza vers le territoire égyptien a alarmé les autorités égyptiennes La question qui se pose est de savoir si Israël lancera une offensive terrestre dans la bande de Gaza, une bande de terre de 25 miles de long coincée entre Israël, l\'Égypte et la mer Méditerranée, où vivent 2,3 millions de personnes et qui est gouvernée par le Hamas depuis 2007 Israël pilonne la bande de Gaza ; les habitants se précipitent pour se mettre à l\'abri\nJERUSALEM - Les avions de combat israéliens ont bombardé la bande de Gaza quartier par quartier mardi, réduisant les bâtiments en ruines et poussant les habitants à se précipiter pour se mettre à l\'abri dans ce minuscule territoire isolé, alors qu\'Israël promet des représailles pour l\'attaque surprise du Hamas du week-end qui "se répercuteront Les autorités égyptiennes discutent avec Israël et les États-Unis afin de mettre en place des corridors humanitaires dans la bande de Gaza pour acheminer l\'aide, a déclaré un responsable égyptien. Des négociations sont en cours avec les Israéliens pour que la zone autour du point de passage de Rafah entre l\'Égypte et Gaza soit déclarée "zone d\'interdiction de feu", a déclaré le responsable, sous couvert d\'anonymat car il n\'était pas autorisé à parler aux médias'</li></ul> |
| obj | <ul><li>"L'économie pèse sur les Américains Ils sont plus nombreux à faire confiance à Trump qu'à Biden pour alléger leur fardeau\nWASHINGTON - Linda Muñoz a peur de l'économie Trump, le candidat républicain à la primaire de 2024, pour améliorer l'économie, avec une marge de 47 % à 36 %. L'écart est de 46 %-26 % en faveur de M. Trump parmi les électeurs indépendants Presque tous les républicains interrogés ont exprimé leur pessimisme à l'égard de l'économie, selon le sondage : 96 % d'entre eux estiment que la situation se dégrade au lieu de s'améliorer Le logement. L'essence. Tous ces éléments poussent les gens à s'endetter de plus en plus, disent-ils.\nSelon le sondage, près de 70 % des Américains estiment que la situation économique se dégrade, tandis que 22 % seulement estiment qu'elle s'améliore L'économie pèse sur les Américains Ils sont plus nombreux à faire confiance à Trump qu'à Biden pour alléger leur fardeau\nWASHINGTON - Linda Muñoz a peur de l'économie. Elle a puisé dans son épargne d'urgence cette année. Et elle ne croit pas que le président Joe Biden ressente sa douleur L'épicerie. Le logement. L'essence. Tous ces éléments poussent les gens à s'endetter de plus en plus, disent-ils.\nSelon le sondage, près de 70 % des Américains estiment que la situation économique se dégrade, tandis que 22 % seulement estiment qu'elle s'améliore"</li><li>'Le Pentagone va interroger d\'autres militaires sur l\'attentat suicide de l\'aéroport de Kaboul en 2021\nLe commandement central du Pentagone a ordonné l\'audition d\'une vingtaine de militaires supplémentaires qui se trouvaient à l\'aéroport de Kaboul lorsque des kamikazes ont attaqué pendant le retrait chaotique des forces américaines d\'Afghanistan, alors que les critiques persistent sur le fait que l\'attaque meurtrière aurait pu être stoppée Certaines familles des personnes tuées ou blessées se sont plaintes que le Pentagone n\'avait pas fait preuve de suffisamment de transparence au sujet de l\'attentat à la bombe qui a tué 170 Afghans\net 13 militaires américains.\nL\'enquête du commandement central américain a conclu en novembre 2021 qu\'étant donné la détérioration de la sécurité à la porte de l\'Abbaye de l\'aéroport alors que les Afghans cherchaient de plus en plus à fuir, "l\'attaque n\'aurait pas pu être évitée au niveau tactique sans dégrader la mission visant à maximiser le nombre d\'évacués" Le Pentagone a déclaré que l\'examen de l\'attentat suicide n\'avait révélé aucune identification préalable d\'un attaquant possible ni aucune demande d\'"escalade des règles d\'engagement existantes" régissant l\'utilisation de la force par les troupes américaines'</li><li>'Les retombées de la guerre se répercutent sur les lieux de travail aux États-Unis.\nNEW YORK - Les retombées de la guerre entre Israël et le Hamas se sont répandues sur les lieux de travail partout dans le monde, les dirigeants de grandes entreprises exprimant leur point de vue tandis que les travailleurs se plaignent de ne pas être entendus "À quoi me sert mon travail si je compromets ma propre morale et mon éthique ?\nL\'un des conflits les plus importants s\'est produit chez Starbucks après que Starbucks Workers United, un syndicat représentant 9 000 travailleurs dans plus de 360 magasins aux États-Unis, a tweeté "Solidarité avec la Palestine" deux jours après l\'attaque du Hamas. Le tweet a été supprimé au bout de 40 minutes, mais l\'entreprise a déclaré qu\'il avait donné lieu à plus de 1 000 plaintes, à des actes de vandalisme et à des affrontements dans ses magasins NEW YORK - Les retombées de la guerre entre Israël et le Hamas se sont répandues sur les lieux de travail partout dans le monde, les dirigeants de grandes entreprises exprimant leur point de vue tandis que les travailleurs se plaignent de ne pas être entendus'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy_Score | Classification_Report |
|:--------|:---------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **all** | 0.9238 | {'0': {'precision': 0.9251101321585903, 'recall': 0.8898305084745762, 'f1-score': 0.9071274298056154, 'support': 236}, '1': {'precision': 0.9081967213114754, 'recall': 0.920265780730897, 'f1-score': 0.9141914191419142, 'support': 301}, '2': {'precision': 0.9432314410480349, 'recall': 0.9642857142857143, 'f1-score': 0.9536423841059601, 'support': 224}, 'accuracy': 0.923784494086728, 'macro avg': {'precision': 0.9255127648393668, 'recall': 0.9247940011637291, 'f1-score': 0.9249870776844965, 'support': 761}, 'weighted avg': {'precision': 0.9237543325873079, 'recall': 0.923784494086728, 'f1-score': 0.9236131204146865, 'support': 761}} |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mogaio/pr_ebsa_fr_tran_merged25_e5_end_offsets")
# Run inference
preds = model("Adil Hussain
Adil Hussain est reconnaissant d'avoir reçu l'enseignement de l'acteur Naseeruddin Shah à l'époque où il fréquentait l'École nationale d'art dramatique")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:-----|
| Word count | 9 | 247.2638 | 2089 |
| Label | Training Sample Count |
|:------|:----------------------|
| neg | 913 |
| obj | 1216 |
| pos | 911 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (5, 5)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 1
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0013 | 1 | 0.3703 | - |
| 0.0658 | 50 | 0.3145 | - |
| 0.1316 | 100 | 0.1839 | - |
| 0.1974 | 150 | 0.2558 | - |
| 0.2632 | 200 | 0.2683 | - |
| 0.3289 | 250 | 0.1572 | - |
| 0.3947 | 300 | 0.1953 | - |
| 0.4605 | 350 | 0.171 | - |
| 0.5263 | 400 | 0.2326 | - |
| 0.5921 | 450 | 0.1762 | - |
| 0.6579 | 500 | 0.2818 | - |
| 0.7237 | 550 | 0.2733 | - |
| 0.7895 | 600 | 0.195 | - |
| 0.8553 | 650 | 0.2104 | - |
| 0.9211 | 700 | 0.2124 | - |
| 0.9868 | 750 | 0.0818 | - |
| 1.0526 | 800 | 0.1046 | - |
| 1.1184 | 850 | 0.1633 | - |
| 1.1842 | 900 | 0.3207 | - |
| 1.25 | 950 | 0.2703 | - |
| 1.3158 | 1000 | 0.1934 | - |
| 1.3816 | 1050 | 0.2547 | - |
| 1.4474 | 1100 | 0.0933 | - |
| 1.5132 | 1150 | 0.2102 | - |
| 1.5789 | 1200 | 0.0699 | - |
| 1.6447 | 1250 | 0.1778 | - |
| 1.7105 | 1300 | 0.1796 | - |
| 1.7763 | 1350 | 0.0221 | - |
| 1.8421 | 1400 | 0.2154 | - |
| 1.9079 | 1450 | 0.1683 | - |
| 1.9737 | 1500 | 0.3096 | - |
| 2.0395 | 1550 | 0.201 | - |
| 2.1053 | 1600 | 0.1954 | - |
| 2.1711 | 1650 | 0.2301 | - |
| 2.2368 | 1700 | 0.1141 | - |
| 2.3026 | 1750 | 0.1949 | - |
| 2.3684 | 1800 | 0.164 | - |
| 2.4342 | 1850 | 0.2307 | - |
| 2.5 | 1900 | 0.1912 | - |
| 2.5658 | 1950 | 0.2349 | - |
| 2.6316 | 2000 | 0.0922 | - |
| 2.6974 | 2050 | 0.0702 | - |
| 2.7632 | 2100 | 0.1089 | - |
| 2.8289 | 2150 | 0.1711 | - |
| 2.8947 | 2200 | 0.1432 | - |
| 2.9605 | 2250 | 0.2739 | - |
| 3.0263 | 2300 | 0.1889 | - |
| 3.0921 | 2350 | 0.1036 | - |
| 3.1579 | 2400 | 0.1372 | - |
| 3.2237 | 2450 | 0.028 | - |
| 3.2895 | 2500 | 0.1739 | - |
| 3.3553 | 2550 | 0.142 | - |
| 3.4211 | 2600 | 0.0838 | - |
| 3.4868 | 2650 | 0.0657 | - |
| 3.5526 | 2700 | 0.0054 | - |
| 3.6184 | 2750 | 0.0426 | - |
| 3.6842 | 2800 | 0.1974 | - |
| 3.75 | 2850 | 0.0279 | - |
| 3.8158 | 2900 | 0.1326 | - |
| 3.8816 | 2950 | 0.1614 | - |
| 3.9474 | 3000 | 0.1251 | - |
| 4.0132 | 3050 | 0.1174 | - |
| 4.0789 | 3100 | 0.1948 | - |
| 4.1447 | 3150 | 0.0555 | - |
| 4.2105 | 3200 | 0.0064 | - |
| 4.2763 | 3250 | 0.064 | - |
| 4.3421 | 3300 | 0.0013 | - |
| 4.4079 | 3350 | 0.135 | - |
| 4.4737 | 3400 | 0.0574 | - |
| 4.5395 | 3450 | 0.174 | - |
| 4.6053 | 3500 | 0.2199 | - |
| 4.6711 | 3550 | 0.387 | - |
| 4.7368 | 3600 | 0.114 | - |
| 4.8026 | 3650 | 0.0853 | - |
| 4.8684 | 3700 | 0.0325 | - |
| 4.9342 | 3750 | 0.019 | - |
| 5.0 | 3800 | 0.0572 | - |
| 0.0013 | 1 | 0.1435 | - |
| 0.0658 | 50 | 0.0969 | - |
| 0.1316 | 100 | 0.1085 | - |
| 0.1974 | 150 | 0.0271 | - |
| 0.2632 | 200 | 0.0138 | - |
| 0.3289 | 250 | 0.058 | - |
| 0.3947 | 300 | 0.1205 | - |
| 0.4605 | 350 | 0.0788 | - |
| 0.5263 | 400 | 0.1449 | - |
| 0.5921 | 450 | 0.0383 | - |
| 0.6579 | 500 | 0.0338 | - |
| 0.7237 | 550 | 0.1253 | - |
| 0.7895 | 600 | 0.069 | - |
| 0.8553 | 650 | 0.104 | - |
| 0.9211 | 700 | 0.0462 | - |
| 0.9868 | 750 | 0.1975 | - |
| 1.0526 | 800 | 0.0241 | - |
| 1.1184 | 850 | 0.0426 | - |
| 1.1842 | 900 | 0.0519 | - |
| 1.25 | 950 | 0.0815 | - |
| 1.3158 | 1000 | 0.1839 | - |
| 1.3816 | 1050 | 0.0198 | - |
| 1.4474 | 1100 | 0.0128 | - |
| 1.5132 | 1150 | 0.1645 | - |
| 1.5789 | 1200 | 0.0019 | - |
| 1.6447 | 1250 | 0.0557 | - |
| 1.7105 | 1300 | 0.0098 | - |
| 1.7763 | 1350 | 0.001 | - |
| 1.8421 | 1400 | 0.1557 | - |
| 1.9079 | 1450 | 0.1286 | - |
| 1.9737 | 1500 | 0.094 | - |
| 2.0395 | 1550 | 0.0059 | - |
| 2.1053 | 1600 | 0.0227 | - |
| 2.1711 | 1650 | 0.0899 | - |
| 2.2368 | 1700 | 0.0053 | - |
| 2.3026 | 1750 | 0.0021 | - |
| 2.3684 | 1800 | 0.0114 | - |
| 2.4342 | 1850 | 0.1163 | - |
| 2.5 | 1900 | 0.0959 | - |
| 2.5658 | 1950 | 0.0252 | - |
| 2.6316 | 2000 | 0.0921 | - |
| 2.6974 | 2050 | 0.1159 | - |
| 2.7632 | 2100 | 0.0026 | - |
| 2.8289 | 2150 | 0.1211 | - |
| 2.8947 | 2200 | 0.1843 | - |
| 2.9605 | 2250 | 0.0014 | - |
| 3.0263 | 2300 | 0.0085 | - |
| 3.0921 | 2350 | 0.0839 | - |
| 3.1579 | 2400 | 0.2372 | - |
| 3.2237 | 2450 | 0.0213 | - |
| 3.2895 | 2500 | 0.0155 | - |
| 3.3553 | 2550 | 0.1128 | - |
| 3.4211 | 2600 | 0.0945 | - |
| 3.4868 | 2650 | 0.0917 | - |
| 3.5526 | 2700 | 0.0011 | - |
| 3.6184 | 2750 | 0.0024 | - |
| 3.6842 | 2800 | 0.0044 | - |
| 3.75 | 2850 | 0.121 | - |
| 3.8158 | 2900 | 0.0056 | - |
| 3.8816 | 2950 | 0.003 | - |
| 3.9474 | 3000 | 0.0899 | - |
| 4.0132 | 3050 | 0.0157 | - |
| 4.0789 | 3100 | 0.1188 | - |
| 4.1447 | 3150 | 0.001 | - |
| 4.2105 | 3200 | 0.0222 | - |
| 4.2763 | 3250 | 0.1209 | - |
| 4.3421 | 3300 | 0.1085 | - |
| 4.4079 | 3350 | 0.0054 | - |
| 4.4737 | 3400 | 0.0009 | - |
| 4.5395 | 3450 | 0.0015 | - |
| 4.6053 | 3500 | 0.003 | - |
| 4.6711 | 3550 | 0.0009 | - |
| 4.7368 | 3600 | 0.0003 | - |
| 4.8026 | 3650 | 0.0009 | - |
| 4.8684 | 3700 | 0.03 | - |
| 4.9342 | 3750 | 0.1206 | - |
| 5.0 | 3800 | 0.0003 | - |
| 0.0013 | 1 | 0.2045 | - |
| 0.0658 | 50 | 0.0078 | - |
| 0.1316 | 100 | 0.0087 | - |
| 0.1974 | 150 | 0.0386 | - |
| 0.2632 | 200 | 0.1015 | - |
| 0.3289 | 250 | 0.0022 | - |
| 0.3947 | 300 | 0.0291 | - |
| 0.4605 | 350 | 0.0013 | - |
| 0.5263 | 400 | 0.0022 | - |
| 0.5921 | 450 | 0.1324 | - |
| 0.6579 | 500 | 0.113 | - |
| 0.7237 | 550 | 0.0011 | - |
| 0.7895 | 600 | 0.1723 | - |
| 0.8553 | 650 | 0.0049 | - |
| 0.9211 | 700 | 0.206 | - |
| 0.9868 | 750 | 0.1683 | - |
| 1.0526 | 800 | 0.0954 | - |
| 1.1184 | 850 | 0.018 | - |
| 1.1842 | 900 | 0.1854 | - |
| 1.25 | 950 | 0.0342 | - |
| 1.3158 | 1000 | 0.0015 | - |
| 1.3816 | 1050 | 0.0062 | - |
| 1.4474 | 1100 | 0.1187 | - |
| 1.5132 | 1150 | 0.0048 | - |
| 1.5789 | 1200 | 0.0011 | - |
| 1.6447 | 1250 | 0.002 | - |
| 1.7105 | 1300 | 0.092 | - |
| 1.7763 | 1350 | 0.1245 | - |
| 1.8421 | 1400 | 0.0009 | - |
| 1.9079 | 1450 | 0.1185 | - |
| 1.9737 | 1500 | 0.0017 | - |
| 2.0395 | 1550 | 0.008 | - |
| 2.1053 | 1600 | 0.0049 | - |
| 2.1711 | 1650 | 0.0083 | - |
| 2.2368 | 1700 | 0.0026 | - |
| 2.3026 | 1750 | 0.0081 | - |
| 2.3684 | 1800 | 0.0036 | - |
| 2.4342 | 1850 | 0.0016 | - |
| 2.5 | 1900 | 0.0017 | - |
| 2.5658 | 1950 | 0.0014 | - |
| 2.6316 | 2000 | 0.0017 | - |
| 2.6974 | 2050 | 0.002 | - |
| 2.7632 | 2100 | 0.1022 | - |
| 2.8289 | 2150 | 0.0004 | - |
| 2.8947 | 2200 | 0.0007 | - |
| 2.9605 | 2250 | 0.0794 | - |
| 3.0263 | 2300 | 0.0183 | - |
| 3.0921 | 2350 | 0.0377 | - |
| 3.1579 | 2400 | 0.029 | - |
| 3.2237 | 2450 | 0.0003 | - |
| 3.2895 | 2500 | 0.0961 | - |
| 3.3553 | 2550 | 0.0008 | - |
| 3.4211 | 2600 | 0.0873 | - |
| 3.4868 | 2650 | 0.0501 | - |
| 3.5526 | 2700 | 0.0029 | - |
| 3.6184 | 2750 | 0.0008 | - |
| 3.6842 | 2800 | 0.0004 | - |
| 3.75 | 2850 | 0.0011 | - |
| 3.8158 | 2900 | 0.0518 | - |
| 3.8816 | 2950 | 0.0002 | - |
| 3.9474 | 3000 | 0.1115 | - |
| 4.0132 | 3050 | 0.0129 | - |
| 4.0789 | 3100 | 0.0005 | - |
| 4.1447 | 3150 | 0.0012 | - |
| 4.2105 | 3200 | 0.1086 | - |
| 4.2763 | 3250 | 0.0199 | - |
| 4.3421 | 3300 | 0.0004 | - |
| 4.4079 | 3350 | 0.0001 | - |
| 4.4737 | 3400 | 0.0832 | - |
| 4.5395 | 3450 | 0.0003 | - |
| 4.6053 | 3500 | 0.0041 | - |
| 4.6711 | 3550 | 0.1146 | - |
| 4.7368 | 3600 | 0.0027 | - |
| 4.8026 | 3650 | 0.0002 | - |
| 4.8684 | 3700 | 0.0544 | - |
| 4.9342 | 3750 | 0.0002 | - |
| 5.0 | 3800 | 0.0046 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.15.0
- Tokenizers: 0.15.0
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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|
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] |
mogaio/pr_ebsa_fr_tran_merged25_e5_beginning_offsets
|
mogaio
| 2023-12-15T18:28:05Z | 0 | 0 |
setfit
|
[
"setfit",
"safetensors",
"xlm-roberta",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
"model-index",
"region:us"
] |
text-classification
| 2023-12-15T18:27:03Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy_score
- classification_report
widget:
- text: 'Adil Hussain
Adil Hussain est reconnaissant d''avoir reçu l''enseignement de l''acteur Naseeruddin
Shah à l''époque où il fréquentait l''École nationale d''art dramatique'
- text: 'Bien que leurs opinions sur la question de savoir si les migrants sont un
avantage ou un fardeau soient plus mitigées, de nettes majorités d''électeurs
de toute la ville de New York, de la banlieue et du nord de l''État ont déclaré
que l''État devrait essayer de ralentir l''afflux de migrants, plutôt que d''en
accepter davantage et de s''efforcer d''assimiler les nouveaux arrivants Les démocrates
aspirent à renverser six circonscriptions détenues par les républicains que M.
Biden a remportées en 2020, notamment celle de M Les républicains se sont emparés
de la crise des migrants, donnant un avant-goût des campagnes de l''année prochaine
Les républicains ont surenchéri : Elise Stefanik, la New-Yorkaise qui dirige la
conférence du parti démocrate à la Chambre des représentants,
Suite à la page suivante
a déclaré à Politico la semaine dernière que le parti allait consacrer 100 millions
de dollars aux campagnes dans les circonscriptions de New York Des problèmes à
venir pour les démocrates de New York en 2024 ?
Les dirigeants démocrates de New York se débattent depuis des mois avec le problème
de l''hébergement des dizaines de milliers de migrants qui ont été transportés
par bus jusqu''à New York et laissés à sa charge Des problèmes à venir pour les
démocrates de New York en 2024 ?
Les dirigeants démocrates de New York se débattent depuis des mois avec le problème
de l''hébergement des dizaines de milliers de migrants qui ont été transportés
par bus jusqu''à New York et laissés à sa charge.
Mais une autre préoccupation se profile alors que la crise se poursuit sans qu''aucune
issue ne soit en vue : les retombées potentielles pour leur parti lors des élections
de l''année prochaine Les républicains ont tendance à se sentir en sécurité lorsqu''ils
parlent d''immigration - comme les démocrates le font pour l''avortement - et
sont clairement à l''attaque sur la question des migrants à New York, tandis que
les démocrates sont sur la défensive, a déclaré Kyle Kondik, directeur de la communication
pour le Centre de politique de l''Université de Virginie, au réseau USA Today
Plus de 100 000 migrants ont été transportés à New York depuis la frontière sud
depuis le printemps 2022. Environ 60 000 d''entre eux sont hébergés dans la ville,
et plus de 2 100 ont été transportés dans des hôtels situés dans sept comtés au
nord de la ville, de Yonkers à la périphérie de Buffalo, où ils sont logés aux
frais de la ville Les démocrates doivent y remporter des victoires pour gagner
cinq sièges à la Chambre et faire du député Hakeem Jeffries, de Brooklyn, le prochain
président de la Chambre des représentants Les publicités d''attaque des républicains
s''écrivent pratiquement d''elles-mêmes à partir d''un flot de titres et d''images
télévisées, alors que le gouverneur Kathy Hochul, le maire de New York Eric Adams
et le président Joe Biden - tous démocrates - se rejettent mutuellement la faute
et s''échangent des coups de feu pour savoir qui devrait en faire le plus Isaac
Goldberg, un stratège démocrate qui a travaillé sur plusieurs campagnes électorales
à New York, a affirmé qu''il était beaucoup trop tôt pour prédire l''impact politique
de la crise des migrants, soulignant que les élections de 2024 n''auront lieu
que dans 14 mois et que de nombreuses questions tout aussi urgentes pourraient
se poser'
- text: 'LE CANDIDAT A LA PRESIDENCE RAMASWAMY VEUT METTRE FIN AU SYSTEME DE VISA
H-1B AUX ETATS-UNIS
Décrivant le programme de visas H-1B comme une forme de "servitude", Vivek Ramaswamy,
candidat républicain indien-américain à l''élection présidentielle, a promis de
"vider" le système basé sur la loterie et de le remplacer par un système d''admission
méritocratique s''il remporte les élections présidentielles de 2024'
- text: 'Smith Hal Sparks Catherine Zeta-Jones son-Sampras Chris Owen Donald Glover
("Queer as Folk") a 54 ans Smith Hal Sparks Catherine Zeta-Jones son-Sampras Chris
Owen Donald Glover
("Queer as Folk") a 54 ans. a 54 ans. Acteur
("Je sais ce que vous avez fait l''été dernier") a 50 ans'
- text: 'Trump profiter de sa célébrité jusqu''à la Maison-Blanche.
"Cela a tué Howard parce qu''il était le roi de tous les médias Il a poursuivi
en disant que Trump ne laisserait pas ses partisans s''approcher de l''une de
ses propriétés. "Les gens qui votent pour Trump, pour la plupart, ne les laisseraient
même pas entrer dans un putain d''hôtel [ "Si être réveillé signifie que je ne
peux pas soutenir Trump, ce que je pense que cela signifie, ou que je soutiens
les personnes qui veulent être transgenres ou que je suis pour le vaccin, appelez-moi
réveillé comme vous le voulez" "Les gens qui votent pour Trump, pour la plupart,
ne les laisseraient même pas entrer dans un putain d''hôtel [...]. Allez à Mar-a-lago,
voyez s''il y a des gens qui vous ressemblent" Stern a également abordé les affirmations
de Trump et de ses partisans selon lesquelles Joe Biden a remporté l''élection
américaine de 2020 grâce à des votes frauduleux "Et soudain, Trump a transformé
Howard, qui était le roi de tous les médias, en prince Harry de tous les médias.
Tout le monde s''en fout "Trump avait l''habitude de participer à l''émission
de Stern chaque semaine. Ils étaient amis. Alors cette idée que Trump est le pire
type qui ait jamais marché sur la surface de la terre, pourquoi traîniez-vous
avec lui ?"
M Mais Stern, qui par le passé a été accusé de racisme et de sexisme dans nombre
de ses sketches à l''antenne, a été un critique virulent de Trump tout au long
de sa présidence et, plus récemment, alors qu''il se prépare à se présenter à
nouveau en 2024.
En 2021, M "Combien de temps allons-nous continuer à élire des gens qui ont perdu
l''élection ?"
Il a poursuivi en qualifiant les partisans de Trump de "nigauds".
"Mon Dieu, j''ai l''impression d''être dans une nation de nigauds. J''espère qu''il
y a encore des gens brillants et dynamiques qui aiment ce pays", a-t-il déclaré
Alors cette idée que Trump est le pire type qui ait jamais marché sur la surface
de la terre, pourquoi traîniez-vous avec lui ?"
M. Failla a déclaré que cela avait "tué" M Si "woke" signifie que je ne peux pas
soutenir Trump, ce que je pense que cela signifie, ou que je soutiens les personnes
qui veulent être transgenres ou que je suis pour le vaccin, appelez-moi "woke"
comme vous voulez Celui qui se décrit comme le "roi de tous les médias" a critiqué
ouvertement l''ancien président américain Donald Trump, les anti-vaxx et, plus
récemment, Lauren Boebert, qu''il a critiquée pour son comportement obscène dans
un théâtre de Denver au début du mois "L''omnipotence médiatique de Donald Trump
a brisé Howard Stern. C''est très important", a déclaré Failla dans la vidéo (selon
OK ! Magazine). "Trump avait l''habitude de participer à l''émission de Stern
chaque semaine L''aversion d''Howard Stern pour Donald Trump, c''est "tout l''ego".
Si "woke" signifie que je ne peux pas soutenir Trump, ce que je pense que cela
signifie, ou que je soutiens les personnes qui veulent être transgenres ou que
je suis pour le vaccin, appelez-moi "woke" comme vous voulez Trump l''année prochaine.
"Je sais que je lui botterai le cul", a-t-il déclaré aux auditeurs.
L''année suivante, Stern a déclaré qu''il envisageait de se lancer dans la course
à la présidence "pour que le pays soit à nouveau juste" En réponse, Trump a partagé
sur sa plateforme Truth Social un clip de Fox News dans lequel l''animateur Jimmy
Failla critique Stern.
"L''omnipotence médiatique de Donald Trump a brisé Howard Stern "Je vais faire
la chose très simple qui remettra le pays sur le droit chemin : un vote, une personne",
a expliqué Stern, affirmant que Trump a en fait perdu l''élection de 2016 contre
Hillary Clinton qui a remporté le vote populaire - mais pas le collège électoral'
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy_score
value: 0.923784494086728
name: Accuracy_Score
- type: classification_report
value:
'0':
precision: 0.9251101321585903
recall: 0.8898305084745762
f1-score: 0.9071274298056154
support: 236
'1':
precision: 0.9081967213114754
recall: 0.920265780730897
f1-score: 0.9141914191419142
support: 301
'2':
precision: 0.9432314410480349
recall: 0.9642857142857143
f1-score: 0.9536423841059601
support: 224
accuracy: 0.923784494086728
macro avg:
precision: 0.9255127648393668
recall: 0.9247940011637291
f1-score: 0.9249870776844965
support: 761
weighted avg:
precision: 0.9237543325873079
recall: 0.923784494086728
f1-score: 0.9236131204146865
support: 761
name: Classification_Report
---
# SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| pos | <ul><li>"Les PHL lèvent 1,26 milliard de dollars grâce aux obligations en dollars de détail\nLE GOUVERNEMENT PHILIPPIN a levé 1,26 milliard de dollars lors de la première émission d'obligations de détail en dollars (RDB) sous l'administration Marcos, a déclaré le ministère des Finances (DoF)"</li><li>"Atom Egoyan revient à Salomé, l'opéra qu'il a monté en 1996, avec Seven Veils\nAtom Egoyan n'a pas été surpris lorsque la Canadian Opera Company lui a demandé de remonter Salomé pour la saison 2022-23 Atom Egoyan revient à Salomé, l'opéra qu'il a monté en 1996, avec Seven Veils\nAtom Egoyan n'a pas été surpris lorsque la Canadian Opera Company lui a demandé de remonter Salomé pour la saison 2022-23. Avec ses éléments de film et de vidéo, son interprétation psychologique et sombre de l'opéra de Richard Strauss avait un solide palmarès de reprises - depuis sa création en 1996, elle avait été présentée deux fois de plus à la COC et avait été reprise par plusieurs autres compagnies"</li><li>'Paul Simon présente un documentaire sur sa carrière\nAprès un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public Paul Simon présente un documentaire sur sa carrière\nAprès un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public.\nTORONTO >> Après un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public "Il n\'y a pas de raison que vous soyez épuisés", a dit Simon à la foule après la première du documentaire d\'Alex Gibney "In Restless Dreams : The Music of Paul Simon" d\'Alex Gibney, dimanche au Festival international du film de Toronto.\nSimon, âgé de 81 ans, n\'avait pas regardé le film avant la première, et il ne l\'a pas regardé non plus dimanche TORONTO >> Après un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public.\n"Il n\'y a pas de raison que vous soyez épuisés", a dit Simon à la foule après la première du documentaire d\'Alex Gibney "In Restless Dreams : The Music of Paul Simon" d\'Alex Gibney, dimanche au Festival international du film de Toronto'</li></ul> |
| neg | <ul><li>'Le groupe Al-Mostaqilla de l\'université du Koweït a appelé les étudiants à organiser un sit-in à l\'université du Koweït lundi pour protester contre la décision de mettre fin aux classes mixtes La décision a été prise la semaine dernière par le nouveau ministre de l\'éducation, Adel Al-Mane, et le directeur par intérim de l\'université du Koweït, Fayez Al-Dhafiri, et mise en œuvre mercredi, trois jours seulement avant le début de la nouvelle année universitaire à la faculté de droit L\'association a également demandé au gouvernement de "cesser ses interventions politiques et médiatiques injustifiées" dans les affaires de l\'université du Koweït.\nL\'association a appelé le directeur par intérim de l\'université du Koweït à ne pas céder aux pressions politiques et médiatiques et à s\'efforcer de protéger l\'indépendance de l\'université Dhafiri a déclaré que la décision avait été prise en application de la loi de 1996 qui interdisait l\'enseignement mixte à l\'université du Koweït, malgré une décision de la Cour constitutionnelle de 2015 autorisant l\'enseignement mixte lorsqu\'il était nécessaire et dans des cas exceptionnels Parallèlement, l\'association des professeurs de l\'université du Koweït a publié samedi une déclaration demandant aux députés et au gouvernement de "cesser d\'interférer dans les affaires de l\'université du Koweït" et de maintenir l\'indépendance de l\'université "L\'université du Koweït était, est et sera toujours le porte-drapeau de la connaissance et des valeurs, à l\'abri de toute influence extérieure Le député Abdulwahab Al-Essa a reproché à l\'administration de l\'université du Koweït d\'avoir succombé à la pression politique au détriment de l\'intérêt public, ajoutant que l\'université du Koweït avait appliqué correctement une décision de la cour constitutionnelle autorisant les classes mixtes chaque fois que cela était nécessaire'</li><li>"L'immigration étant l'un des défis les plus difficiles à relever pour le président Joe Biden et apparaissant comme un enjeu majeur des élections de l'année prochaine, l'administration délocalise essentiellement la question en s'appuyant sur les pays d'Amérique centrale et d'Amérique du Sud pour empêcher les migrants de se diriger vers le nord"</li><li>'Lors d\'une réunion d\'information mardi, le porte-parole de l\'armée, le lieutenant-colonel Richard Hecht, a suggéré que les Palestiniens tentent de quitter la bande de Gaza par le poste-frontière de Rafah, en Égypte.\nLa perspective d\'un exode des habitants de Gaza vers le territoire égyptien a alarmé les autorités égyptiennes La question qui se pose est de savoir si Israël lancera une offensive terrestre dans la bande de Gaza, une bande de terre de 25 miles de long coincée entre Israël, l\'Égypte et la mer Méditerranée, où vivent 2,3 millions de personnes et qui est gouvernée par le Hamas depuis 2007 Israël pilonne la bande de Gaza ; les habitants se précipitent pour se mettre à l\'abri\nJERUSALEM - Les avions de combat israéliens ont bombardé la bande de Gaza quartier par quartier mardi, réduisant les bâtiments en ruines et poussant les habitants à se précipiter pour se mettre à l\'abri dans ce minuscule territoire isolé, alors qu\'Israël promet des représailles pour l\'attaque surprise du Hamas du week-end qui "se répercuteront Les autorités égyptiennes discutent avec Israël et les États-Unis afin de mettre en place des corridors humanitaires dans la bande de Gaza pour acheminer l\'aide, a déclaré un responsable égyptien. Des négociations sont en cours avec les Israéliens pour que la zone autour du point de passage de Rafah entre l\'Égypte et Gaza soit déclarée "zone d\'interdiction de feu", a déclaré le responsable, sous couvert d\'anonymat car il n\'était pas autorisé à parler aux médias'</li></ul> |
| obj | <ul><li>"L'économie pèse sur les Américains Ils sont plus nombreux à faire confiance à Trump qu'à Biden pour alléger leur fardeau\nWASHINGTON - Linda Muñoz a peur de l'économie Trump, le candidat républicain à la primaire de 2024, pour améliorer l'économie, avec une marge de 47 % à 36 %. L'écart est de 46 %-26 % en faveur de M. Trump parmi les électeurs indépendants Presque tous les républicains interrogés ont exprimé leur pessimisme à l'égard de l'économie, selon le sondage : 96 % d'entre eux estiment que la situation se dégrade au lieu de s'améliorer Le logement. L'essence. Tous ces éléments poussent les gens à s'endetter de plus en plus, disent-ils.\nSelon le sondage, près de 70 % des Américains estiment que la situation économique se dégrade, tandis que 22 % seulement estiment qu'elle s'améliore L'économie pèse sur les Américains Ils sont plus nombreux à faire confiance à Trump qu'à Biden pour alléger leur fardeau\nWASHINGTON - Linda Muñoz a peur de l'économie. Elle a puisé dans son épargne d'urgence cette année. Et elle ne croit pas que le président Joe Biden ressente sa douleur L'épicerie. Le logement. L'essence. Tous ces éléments poussent les gens à s'endetter de plus en plus, disent-ils.\nSelon le sondage, près de 70 % des Américains estiment que la situation économique se dégrade, tandis que 22 % seulement estiment qu'elle s'améliore"</li><li>'Le Pentagone va interroger d\'autres militaires sur l\'attentat suicide de l\'aéroport de Kaboul en 2021\nLe commandement central du Pentagone a ordonné l\'audition d\'une vingtaine de militaires supplémentaires qui se trouvaient à l\'aéroport de Kaboul lorsque des kamikazes ont attaqué pendant le retrait chaotique des forces américaines d\'Afghanistan, alors que les critiques persistent sur le fait que l\'attaque meurtrière aurait pu être stoppée Certaines familles des personnes tuées ou blessées se sont plaintes que le Pentagone n\'avait pas fait preuve de suffisamment de transparence au sujet de l\'attentat à la bombe qui a tué 170 Afghans\net 13 militaires américains.\nL\'enquête du commandement central américain a conclu en novembre 2021 qu\'étant donné la détérioration de la sécurité à la porte de l\'Abbaye de l\'aéroport alors que les Afghans cherchaient de plus en plus à fuir, "l\'attaque n\'aurait pas pu être évitée au niveau tactique sans dégrader la mission visant à maximiser le nombre d\'évacués" Le Pentagone a déclaré que l\'examen de l\'attentat suicide n\'avait révélé aucune identification préalable d\'un attaquant possible ni aucune demande d\'"escalade des règles d\'engagement existantes" régissant l\'utilisation de la force par les troupes américaines'</li><li>'Les retombées de la guerre se répercutent sur les lieux de travail aux États-Unis.\nNEW YORK - Les retombées de la guerre entre Israël et le Hamas se sont répandues sur les lieux de travail partout dans le monde, les dirigeants de grandes entreprises exprimant leur point de vue tandis que les travailleurs se plaignent de ne pas être entendus "À quoi me sert mon travail si je compromets ma propre morale et mon éthique ?\nL\'un des conflits les plus importants s\'est produit chez Starbucks après que Starbucks Workers United, un syndicat représentant 9 000 travailleurs dans plus de 360 magasins aux États-Unis, a tweeté "Solidarité avec la Palestine" deux jours après l\'attaque du Hamas. Le tweet a été supprimé au bout de 40 minutes, mais l\'entreprise a déclaré qu\'il avait donné lieu à plus de 1 000 plaintes, à des actes de vandalisme et à des affrontements dans ses magasins NEW YORK - Les retombées de la guerre entre Israël et le Hamas se sont répandues sur les lieux de travail partout dans le monde, les dirigeants de grandes entreprises exprimant leur point de vue tandis que les travailleurs se plaignent de ne pas être entendus'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy_Score | Classification_Report |
|:--------|:---------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **all** | 0.9238 | {'0': {'precision': 0.9251101321585903, 'recall': 0.8898305084745762, 'f1-score': 0.9071274298056154, 'support': 236}, '1': {'precision': 0.9081967213114754, 'recall': 0.920265780730897, 'f1-score': 0.9141914191419142, 'support': 301}, '2': {'precision': 0.9432314410480349, 'recall': 0.9642857142857143, 'f1-score': 0.9536423841059601, 'support': 224}, 'accuracy': 0.923784494086728, 'macro avg': {'precision': 0.9255127648393668, 'recall': 0.9247940011637291, 'f1-score': 0.9249870776844965, 'support': 761}, 'weighted avg': {'precision': 0.9237543325873079, 'recall': 0.923784494086728, 'f1-score': 0.9236131204146865, 'support': 761}} |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mogaio/pr_ebsa_fr_tran_merged25_e5_beginning_offsets")
# Run inference
preds = model("Adil Hussain
Adil Hussain est reconnaissant d'avoir reçu l'enseignement de l'acteur Naseeruddin Shah à l'époque où il fréquentait l'École nationale d'art dramatique")
```
<!--
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-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:-----|
| Word count | 9 | 247.2638 | 2089 |
| Label | Training Sample Count |
|:------|:----------------------|
| neg | 913 |
| obj | 1216 |
| pos | 911 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (5, 5)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 1
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0013 | 1 | 0.3703 | - |
| 0.0658 | 50 | 0.3145 | - |
| 0.1316 | 100 | 0.1839 | - |
| 0.1974 | 150 | 0.2558 | - |
| 0.2632 | 200 | 0.2683 | - |
| 0.3289 | 250 | 0.1572 | - |
| 0.3947 | 300 | 0.1953 | - |
| 0.4605 | 350 | 0.171 | - |
| 0.5263 | 400 | 0.2326 | - |
| 0.5921 | 450 | 0.1762 | - |
| 0.6579 | 500 | 0.2818 | - |
| 0.7237 | 550 | 0.2733 | - |
| 0.7895 | 600 | 0.195 | - |
| 0.8553 | 650 | 0.2104 | - |
| 0.9211 | 700 | 0.2124 | - |
| 0.9868 | 750 | 0.0818 | - |
| 1.0526 | 800 | 0.1046 | - |
| 1.1184 | 850 | 0.1633 | - |
| 1.1842 | 900 | 0.3207 | - |
| 1.25 | 950 | 0.2703 | - |
| 1.3158 | 1000 | 0.1934 | - |
| 1.3816 | 1050 | 0.2547 | - |
| 1.4474 | 1100 | 0.0933 | - |
| 1.5132 | 1150 | 0.2102 | - |
| 1.5789 | 1200 | 0.0699 | - |
| 1.6447 | 1250 | 0.1778 | - |
| 1.7105 | 1300 | 0.1796 | - |
| 1.7763 | 1350 | 0.0221 | - |
| 1.8421 | 1400 | 0.2154 | - |
| 1.9079 | 1450 | 0.1683 | - |
| 1.9737 | 1500 | 0.3096 | - |
| 2.0395 | 1550 | 0.201 | - |
| 2.1053 | 1600 | 0.1954 | - |
| 2.1711 | 1650 | 0.2301 | - |
| 2.2368 | 1700 | 0.1141 | - |
| 2.3026 | 1750 | 0.1949 | - |
| 2.3684 | 1800 | 0.164 | - |
| 2.4342 | 1850 | 0.2307 | - |
| 2.5 | 1900 | 0.1912 | - |
| 2.5658 | 1950 | 0.2349 | - |
| 2.6316 | 2000 | 0.0922 | - |
| 2.6974 | 2050 | 0.0702 | - |
| 2.7632 | 2100 | 0.1089 | - |
| 2.8289 | 2150 | 0.1711 | - |
| 2.8947 | 2200 | 0.1432 | - |
| 2.9605 | 2250 | 0.2739 | - |
| 3.0263 | 2300 | 0.1889 | - |
| 3.0921 | 2350 | 0.1036 | - |
| 3.1579 | 2400 | 0.1372 | - |
| 3.2237 | 2450 | 0.028 | - |
| 3.2895 | 2500 | 0.1739 | - |
| 3.3553 | 2550 | 0.142 | - |
| 3.4211 | 2600 | 0.0838 | - |
| 3.4868 | 2650 | 0.0657 | - |
| 3.5526 | 2700 | 0.0054 | - |
| 3.6184 | 2750 | 0.0426 | - |
| 3.6842 | 2800 | 0.1974 | - |
| 3.75 | 2850 | 0.0279 | - |
| 3.8158 | 2900 | 0.1326 | - |
| 3.8816 | 2950 | 0.1614 | - |
| 3.9474 | 3000 | 0.1251 | - |
| 4.0132 | 3050 | 0.1174 | - |
| 4.0789 | 3100 | 0.1948 | - |
| 4.1447 | 3150 | 0.0555 | - |
| 4.2105 | 3200 | 0.0064 | - |
| 4.2763 | 3250 | 0.064 | - |
| 4.3421 | 3300 | 0.0013 | - |
| 4.4079 | 3350 | 0.135 | - |
| 4.4737 | 3400 | 0.0574 | - |
| 4.5395 | 3450 | 0.174 | - |
| 4.6053 | 3500 | 0.2199 | - |
| 4.6711 | 3550 | 0.387 | - |
| 4.7368 | 3600 | 0.114 | - |
| 4.8026 | 3650 | 0.0853 | - |
| 4.8684 | 3700 | 0.0325 | - |
| 4.9342 | 3750 | 0.019 | - |
| 5.0 | 3800 | 0.0572 | - |
| 0.0013 | 1 | 0.1435 | - |
| 0.0658 | 50 | 0.0969 | - |
| 0.1316 | 100 | 0.1085 | - |
| 0.1974 | 150 | 0.0271 | - |
| 0.2632 | 200 | 0.0138 | - |
| 0.3289 | 250 | 0.058 | - |
| 0.3947 | 300 | 0.1205 | - |
| 0.4605 | 350 | 0.0788 | - |
| 0.5263 | 400 | 0.1449 | - |
| 0.5921 | 450 | 0.0383 | - |
| 0.6579 | 500 | 0.0338 | - |
| 0.7237 | 550 | 0.1253 | - |
| 0.7895 | 600 | 0.069 | - |
| 0.8553 | 650 | 0.104 | - |
| 0.9211 | 700 | 0.0462 | - |
| 0.9868 | 750 | 0.1975 | - |
| 1.0526 | 800 | 0.0241 | - |
| 1.1184 | 850 | 0.0426 | - |
| 1.1842 | 900 | 0.0519 | - |
| 1.25 | 950 | 0.0815 | - |
| 1.3158 | 1000 | 0.1839 | - |
| 1.3816 | 1050 | 0.0198 | - |
| 1.4474 | 1100 | 0.0128 | - |
| 1.5132 | 1150 | 0.1645 | - |
| 1.5789 | 1200 | 0.0019 | - |
| 1.6447 | 1250 | 0.0557 | - |
| 1.7105 | 1300 | 0.0098 | - |
| 1.7763 | 1350 | 0.001 | - |
| 1.8421 | 1400 | 0.1557 | - |
| 1.9079 | 1450 | 0.1286 | - |
| 1.9737 | 1500 | 0.094 | - |
| 2.0395 | 1550 | 0.0059 | - |
| 2.1053 | 1600 | 0.0227 | - |
| 2.1711 | 1650 | 0.0899 | - |
| 2.2368 | 1700 | 0.0053 | - |
| 2.3026 | 1750 | 0.0021 | - |
| 2.3684 | 1800 | 0.0114 | - |
| 2.4342 | 1850 | 0.1163 | - |
| 2.5 | 1900 | 0.0959 | - |
| 2.5658 | 1950 | 0.0252 | - |
| 2.6316 | 2000 | 0.0921 | - |
| 2.6974 | 2050 | 0.1159 | - |
| 2.7632 | 2100 | 0.0026 | - |
| 2.8289 | 2150 | 0.1211 | - |
| 2.8947 | 2200 | 0.1843 | - |
| 2.9605 | 2250 | 0.0014 | - |
| 3.0263 | 2300 | 0.0085 | - |
| 3.0921 | 2350 | 0.0839 | - |
| 3.1579 | 2400 | 0.2372 | - |
| 3.2237 | 2450 | 0.0213 | - |
| 3.2895 | 2500 | 0.0155 | - |
| 3.3553 | 2550 | 0.1128 | - |
| 3.4211 | 2600 | 0.0945 | - |
| 3.4868 | 2650 | 0.0917 | - |
| 3.5526 | 2700 | 0.0011 | - |
| 3.6184 | 2750 | 0.0024 | - |
| 3.6842 | 2800 | 0.0044 | - |
| 3.75 | 2850 | 0.121 | - |
| 3.8158 | 2900 | 0.0056 | - |
| 3.8816 | 2950 | 0.003 | - |
| 3.9474 | 3000 | 0.0899 | - |
| 4.0132 | 3050 | 0.0157 | - |
| 4.0789 | 3100 | 0.1188 | - |
| 4.1447 | 3150 | 0.001 | - |
| 4.2105 | 3200 | 0.0222 | - |
| 4.2763 | 3250 | 0.1209 | - |
| 4.3421 | 3300 | 0.1085 | - |
| 4.4079 | 3350 | 0.0054 | - |
| 4.4737 | 3400 | 0.0009 | - |
| 4.5395 | 3450 | 0.0015 | - |
| 4.6053 | 3500 | 0.003 | - |
| 4.6711 | 3550 | 0.0009 | - |
| 4.7368 | 3600 | 0.0003 | - |
| 4.8026 | 3650 | 0.0009 | - |
| 4.8684 | 3700 | 0.03 | - |
| 4.9342 | 3750 | 0.1206 | - |
| 5.0 | 3800 | 0.0003 | - |
| 0.0013 | 1 | 0.2045 | - |
| 0.0658 | 50 | 0.0078 | - |
| 0.1316 | 100 | 0.0087 | - |
| 0.1974 | 150 | 0.0386 | - |
| 0.2632 | 200 | 0.1015 | - |
| 0.3289 | 250 | 0.0022 | - |
| 0.3947 | 300 | 0.0291 | - |
| 0.4605 | 350 | 0.0013 | - |
| 0.5263 | 400 | 0.0022 | - |
| 0.5921 | 450 | 0.1324 | - |
| 0.6579 | 500 | 0.113 | - |
| 0.7237 | 550 | 0.0011 | - |
| 0.7895 | 600 | 0.1723 | - |
| 0.8553 | 650 | 0.0049 | - |
| 0.9211 | 700 | 0.206 | - |
| 0.9868 | 750 | 0.1683 | - |
| 1.0526 | 800 | 0.0954 | - |
| 1.1184 | 850 | 0.018 | - |
| 1.1842 | 900 | 0.1854 | - |
| 1.25 | 950 | 0.0342 | - |
| 1.3158 | 1000 | 0.0015 | - |
| 1.3816 | 1050 | 0.0062 | - |
| 1.4474 | 1100 | 0.1187 | - |
| 1.5132 | 1150 | 0.0048 | - |
| 1.5789 | 1200 | 0.0011 | - |
| 1.6447 | 1250 | 0.002 | - |
| 1.7105 | 1300 | 0.092 | - |
| 1.7763 | 1350 | 0.1245 | - |
| 1.8421 | 1400 | 0.0009 | - |
| 1.9079 | 1450 | 0.1185 | - |
| 1.9737 | 1500 | 0.0017 | - |
| 2.0395 | 1550 | 0.008 | - |
| 2.1053 | 1600 | 0.0049 | - |
| 2.1711 | 1650 | 0.0083 | - |
| 2.2368 | 1700 | 0.0026 | - |
| 2.3026 | 1750 | 0.0081 | - |
| 2.3684 | 1800 | 0.0036 | - |
| 2.4342 | 1850 | 0.0016 | - |
| 2.5 | 1900 | 0.0017 | - |
| 2.5658 | 1950 | 0.0014 | - |
| 2.6316 | 2000 | 0.0017 | - |
| 2.6974 | 2050 | 0.002 | - |
| 2.7632 | 2100 | 0.1022 | - |
| 2.8289 | 2150 | 0.0004 | - |
| 2.8947 | 2200 | 0.0007 | - |
| 2.9605 | 2250 | 0.0794 | - |
| 3.0263 | 2300 | 0.0183 | - |
| 3.0921 | 2350 | 0.0377 | - |
| 3.1579 | 2400 | 0.029 | - |
| 3.2237 | 2450 | 0.0003 | - |
| 3.2895 | 2500 | 0.0961 | - |
| 3.3553 | 2550 | 0.0008 | - |
| 3.4211 | 2600 | 0.0873 | - |
| 3.4868 | 2650 | 0.0501 | - |
| 3.5526 | 2700 | 0.0029 | - |
| 3.6184 | 2750 | 0.0008 | - |
| 3.6842 | 2800 | 0.0004 | - |
| 3.75 | 2850 | 0.0011 | - |
| 3.8158 | 2900 | 0.0518 | - |
| 3.8816 | 2950 | 0.0002 | - |
| 3.9474 | 3000 | 0.1115 | - |
| 4.0132 | 3050 | 0.0129 | - |
| 4.0789 | 3100 | 0.0005 | - |
| 4.1447 | 3150 | 0.0012 | - |
| 4.2105 | 3200 | 0.1086 | - |
| 4.2763 | 3250 | 0.0199 | - |
| 4.3421 | 3300 | 0.0004 | - |
| 4.4079 | 3350 | 0.0001 | - |
| 4.4737 | 3400 | 0.0832 | - |
| 4.5395 | 3450 | 0.0003 | - |
| 4.6053 | 3500 | 0.0041 | - |
| 4.6711 | 3550 | 0.1146 | - |
| 4.7368 | 3600 | 0.0027 | - |
| 4.8026 | 3650 | 0.0002 | - |
| 4.8684 | 3700 | 0.0544 | - |
| 4.9342 | 3750 | 0.0002 | - |
| 5.0 | 3800 | 0.0046 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.15.0
- Tokenizers: 0.15.0
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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|
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Jackstarrealyt/MashleEdit
|
Jackstarrealyt
| 2023-12-15T18:35:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:27:41Z |
Entry not found
|
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arunlokanatha/zephyr-7b-sft-full
|
arunlokanatha
| 2023-12-15T18:28:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:28:36Z |
Entry not found
|
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] |
hkivancoral/smids_5x_deit_tiny_sgd_00001_fold1
|
hkivancoral
| 2023-12-15T19:24:02Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T18:28:54Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_sgd_00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.4540901502504174
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_tiny_sgd_00001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0635
- Accuracy: 0.4541
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.346 | 1.0 | 376 | 1.2991 | 0.3489 |
| 1.3817 | 2.0 | 752 | 1.2686 | 0.3589 |
| 1.3103 | 3.0 | 1128 | 1.2425 | 0.3656 |
| 1.3556 | 4.0 | 1504 | 1.2205 | 0.3656 |
| 1.2443 | 5.0 | 1880 | 1.2020 | 0.3723 |
| 1.1947 | 6.0 | 2256 | 1.1865 | 0.3806 |
| 1.184 | 7.0 | 2632 | 1.1737 | 0.3940 |
| 1.2121 | 8.0 | 3008 | 1.1630 | 0.3873 |
| 1.1793 | 9.0 | 3384 | 1.1540 | 0.3773 |
| 1.1564 | 10.0 | 3760 | 1.1464 | 0.3740 |
| 1.148 | 11.0 | 4136 | 1.1397 | 0.3756 |
| 1.1774 | 12.0 | 4512 | 1.1340 | 0.3756 |
| 1.1493 | 13.0 | 4888 | 1.1288 | 0.3790 |
| 1.1491 | 14.0 | 5264 | 1.1241 | 0.3790 |
| 1.1465 | 15.0 | 5640 | 1.1198 | 0.3856 |
| 1.1089 | 16.0 | 6016 | 1.1159 | 0.3990 |
| 1.1015 | 17.0 | 6392 | 1.1122 | 0.4057 |
| 1.1166 | 18.0 | 6768 | 1.1086 | 0.4073 |
| 1.1502 | 19.0 | 7144 | 1.1053 | 0.4124 |
| 1.124 | 20.0 | 7520 | 1.1022 | 0.4174 |
| 1.1102 | 21.0 | 7896 | 1.0992 | 0.4207 |
| 1.0904 | 22.0 | 8272 | 1.0964 | 0.4190 |
| 1.0897 | 23.0 | 8648 | 1.0937 | 0.4207 |
| 1.1449 | 24.0 | 9024 | 1.0912 | 0.4190 |
| 1.0609 | 25.0 | 9400 | 1.0888 | 0.4157 |
| 1.0747 | 26.0 | 9776 | 1.0865 | 0.4207 |
| 1.0631 | 27.0 | 10152 | 1.0844 | 0.4240 |
| 1.0872 | 28.0 | 10528 | 1.0823 | 0.4274 |
| 1.0811 | 29.0 | 10904 | 1.0804 | 0.4290 |
| 1.1082 | 30.0 | 11280 | 1.0786 | 0.4307 |
| 1.0863 | 31.0 | 11656 | 1.0769 | 0.4324 |
| 1.103 | 32.0 | 12032 | 1.0753 | 0.4290 |
| 1.0918 | 33.0 | 12408 | 1.0738 | 0.4324 |
| 1.06 | 34.0 | 12784 | 1.0725 | 0.4391 |
| 1.0723 | 35.0 | 13160 | 1.0712 | 0.4424 |
| 1.0366 | 36.0 | 13536 | 1.0701 | 0.4457 |
| 1.0655 | 37.0 | 13912 | 1.0690 | 0.4474 |
| 1.0787 | 38.0 | 14288 | 1.0681 | 0.4457 |
| 1.0751 | 39.0 | 14664 | 1.0672 | 0.4474 |
| 1.0508 | 40.0 | 15040 | 1.0665 | 0.4541 |
| 1.0565 | 41.0 | 15416 | 1.0658 | 0.4541 |
| 1.0404 | 42.0 | 15792 | 1.0652 | 0.4541 |
| 1.0767 | 43.0 | 16168 | 1.0648 | 0.4541 |
| 1.076 | 44.0 | 16544 | 1.0644 | 0.4541 |
| 1.0183 | 45.0 | 16920 | 1.0640 | 0.4541 |
| 1.0393 | 46.0 | 17296 | 1.0638 | 0.4541 |
| 1.065 | 47.0 | 17672 | 1.0636 | 0.4541 |
| 1.0432 | 48.0 | 18048 | 1.0635 | 0.4541 |
| 1.0432 | 49.0 | 18424 | 1.0635 | 0.4541 |
| 1.0255 | 50.0 | 18800 | 1.0635 | 0.4541 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
gloryspace/taesanrap
|
gloryspace
| 2023-12-15T18:34:30Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T18:33:02Z |
---
license: openrail
---
|
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] |
maoweiwei/whisper-hakka-small-2
|
maoweiwei
| 2023-12-16T17:50:35Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T18:33:32Z |
Entry not found
|
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argodevops/learning-model
|
argodevops
| 2023-12-15T18:33:51Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T18:33:51Z |
---
license: apache-2.0
---
|
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EmmaGthn/runs_prefix_2000_moji
|
EmmaGthn
| 2023-12-15T21:42:20Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-15T18:35:36Z |
Entry not found
|
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Xenova/llama2-chat-tokenizer
|
Xenova
| 2023-12-15T21:39:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:36:13Z |
Entry not found
|
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lliillyy/M87-deblur-MAD-ksize-23-finetune-lr_5e-05-steps_15
|
lliillyy
| 2023-12-15T18:38:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:38:36Z |
Entry not found
|
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Omidranjbar202030/Omidranjbar202030
|
Omidranjbar202030
| 2023-12-15T18:45:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:45:55Z |
Entry not found
|
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herisan/Mistral-7B-finetune
|
herisan
| 2023-12-15T18:46:44Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2023-12-15T18:46:26Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# Model Card for Model ID
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## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Model Card Contact
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### Framework versions
- PEFT 0.7.2.dev0
|
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JeeDay/t5-small-billsum
|
JeeDay
| 2023-12-15T18:46:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T18:46:52Z |
Entry not found
|
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] |
El-Palmera/VOSdetailedTrans
|
El-Palmera
| 2023-12-15T18:59:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T18:53:50Z |
Entry not found
|
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-0.12546733021736145,
-0.13699735701084137,
0.7621601819992065,
1.1796802282333374,
-0.7432090640068054,
0.07975759357213974,
-1.0386395454406738,
0.6594986319541931,
-0.24194595217704773,
-0.3457581400871277,
-0.4864424169063568,
0.3832801282405853,
0.35236993432044983,
0.4404810070991516,
0.6148124933242798,
0.1408468782901764,
0.8338430523872375,
0.31260547041893005,
-0.17026935517787933,
0.26989811658859253,
-0.45591965317726135,
-0.02893328294157982,
-0.05796278640627861,
0.31015974283218384,
-1.026215672492981
] |
mogaio/pr_ebsa_fr_tran_merged25_e1_middle_offsets
|
mogaio
| 2023-12-15T19:01:07Z | 0 | 0 |
setfit
|
[
"setfit",
"safetensors",
"xlm-roberta",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
"model-index",
"region:us"
] |
text-classification
| 2023-12-15T18:59:40Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy_score
- classification_report
widget:
- text: 'Adil Hussain
Adil Hussain est reconnaissant d''avoir reçu l''enseignement de l''acteur Naseeruddin
Shah à l''époque où il fréquentait l''École nationale d''art dramatique'
- text: 'Bien que leurs opinions sur la question de savoir si les migrants sont un
avantage ou un fardeau soient plus mitigées, de nettes majorités d''électeurs
de toute la ville de New York, de la banlieue et du nord de l''État ont déclaré
que l''État devrait essayer de ralentir l''afflux de migrants, plutôt que d''en
accepter davantage et de s''efforcer d''assimiler les nouveaux arrivants Les démocrates
aspirent à renverser six circonscriptions détenues par les républicains que M.
Biden a remportées en 2020, notamment celle de M Les républicains se sont emparés
de la crise des migrants, donnant un avant-goût des campagnes de l''année prochaine
Les républicains ont surenchéri : Elise Stefanik, la New-Yorkaise qui dirige la
conférence du parti démocrate à la Chambre des représentants,
Suite à la page suivante
a déclaré à Politico la semaine dernière que le parti allait consacrer 100 millions
de dollars aux campagnes dans les circonscriptions de New York Des problèmes à
venir pour les démocrates de New York en 2024 ?
Les dirigeants démocrates de New York se débattent depuis des mois avec le problème
de l''hébergement des dizaines de milliers de migrants qui ont été transportés
par bus jusqu''à New York et laissés à sa charge Des problèmes à venir pour les
démocrates de New York en 2024 ?
Les dirigeants démocrates de New York se débattent depuis des mois avec le problème
de l''hébergement des dizaines de milliers de migrants qui ont été transportés
par bus jusqu''à New York et laissés à sa charge.
Mais une autre préoccupation se profile alors que la crise se poursuit sans qu''aucune
issue ne soit en vue : les retombées potentielles pour leur parti lors des élections
de l''année prochaine Les républicains ont tendance à se sentir en sécurité lorsqu''ils
parlent d''immigration - comme les démocrates le font pour l''avortement - et
sont clairement à l''attaque sur la question des migrants à New York, tandis que
les démocrates sont sur la défensive, a déclaré Kyle Kondik, directeur de la communication
pour le Centre de politique de l''Université de Virginie, au réseau USA Today
Plus de 100 000 migrants ont été transportés à New York depuis la frontière sud
depuis le printemps 2022. Environ 60 000 d''entre eux sont hébergés dans la ville,
et plus de 2 100 ont été transportés dans des hôtels situés dans sept comtés au
nord de la ville, de Yonkers à la périphérie de Buffalo, où ils sont logés aux
frais de la ville Les démocrates doivent y remporter des victoires pour gagner
cinq sièges à la Chambre et faire du député Hakeem Jeffries, de Brooklyn, le prochain
président de la Chambre des représentants Les publicités d''attaque des républicains
s''écrivent pratiquement d''elles-mêmes à partir d''un flot de titres et d''images
télévisées, alors que le gouverneur Kathy Hochul, le maire de New York Eric Adams
et le président Joe Biden - tous démocrates - se rejettent mutuellement la faute
et s''échangent des coups de feu pour savoir qui devrait en faire le plus Isaac
Goldberg, un stratège démocrate qui a travaillé sur plusieurs campagnes électorales
à New York, a affirmé qu''il était beaucoup trop tôt pour prédire l''impact politique
de la crise des migrants, soulignant que les élections de 2024 n''auront lieu
que dans 14 mois et que de nombreuses questions tout aussi urgentes pourraient
se poser'
- text: 'LE CANDIDAT A LA PRESIDENCE RAMASWAMY VEUT METTRE FIN AU SYSTEME DE VISA
H-1B AUX ETATS-UNIS
Décrivant le programme de visas H-1B comme une forme de "servitude", Vivek Ramaswamy,
candidat républicain indien-américain à l''élection présidentielle, a promis de
"vider" le système basé sur la loterie et de le remplacer par un système d''admission
méritocratique s''il remporte les élections présidentielles de 2024'
- text: 'Smith Hal Sparks Catherine Zeta-Jones son-Sampras Chris Owen Donald Glover
("Queer as Folk") a 54 ans Smith Hal Sparks Catherine Zeta-Jones son-Sampras Chris
Owen Donald Glover
("Queer as Folk") a 54 ans. a 54 ans. Acteur
("Je sais ce que vous avez fait l''été dernier") a 50 ans'
- text: 'Trump profiter de sa célébrité jusqu''à la Maison-Blanche.
"Cela a tué Howard parce qu''il était le roi de tous les médias Il a poursuivi
en disant que Trump ne laisserait pas ses partisans s''approcher de l''une de
ses propriétés. "Les gens qui votent pour Trump, pour la plupart, ne les laisseraient
même pas entrer dans un putain d''hôtel [ "Si être réveillé signifie que je ne
peux pas soutenir Trump, ce que je pense que cela signifie, ou que je soutiens
les personnes qui veulent être transgenres ou que je suis pour le vaccin, appelez-moi
réveillé comme vous le voulez" "Les gens qui votent pour Trump, pour la plupart,
ne les laisseraient même pas entrer dans un putain d''hôtel [...]. Allez à Mar-a-lago,
voyez s''il y a des gens qui vous ressemblent" Stern a également abordé les affirmations
de Trump et de ses partisans selon lesquelles Joe Biden a remporté l''élection
américaine de 2020 grâce à des votes frauduleux "Et soudain, Trump a transformé
Howard, qui était le roi de tous les médias, en prince Harry de tous les médias.
Tout le monde s''en fout "Trump avait l''habitude de participer à l''émission
de Stern chaque semaine. Ils étaient amis. Alors cette idée que Trump est le pire
type qui ait jamais marché sur la surface de la terre, pourquoi traîniez-vous
avec lui ?"
M Mais Stern, qui par le passé a été accusé de racisme et de sexisme dans nombre
de ses sketches à l''antenne, a été un critique virulent de Trump tout au long
de sa présidence et, plus récemment, alors qu''il se prépare à se présenter à
nouveau en 2024.
En 2021, M "Combien de temps allons-nous continuer à élire des gens qui ont perdu
l''élection ?"
Il a poursuivi en qualifiant les partisans de Trump de "nigauds".
"Mon Dieu, j''ai l''impression d''être dans une nation de nigauds. J''espère qu''il
y a encore des gens brillants et dynamiques qui aiment ce pays", a-t-il déclaré
Alors cette idée que Trump est le pire type qui ait jamais marché sur la surface
de la terre, pourquoi traîniez-vous avec lui ?"
M. Failla a déclaré que cela avait "tué" M Si "woke" signifie que je ne peux pas
soutenir Trump, ce que je pense que cela signifie, ou que je soutiens les personnes
qui veulent être transgenres ou que je suis pour le vaccin, appelez-moi "woke"
comme vous voulez Celui qui se décrit comme le "roi de tous les médias" a critiqué
ouvertement l''ancien président américain Donald Trump, les anti-vaxx et, plus
récemment, Lauren Boebert, qu''il a critiquée pour son comportement obscène dans
un théâtre de Denver au début du mois "L''omnipotence médiatique de Donald Trump
a brisé Howard Stern. C''est très important", a déclaré Failla dans la vidéo (selon
OK ! Magazine). "Trump avait l''habitude de participer à l''émission de Stern
chaque semaine L''aversion d''Howard Stern pour Donald Trump, c''est "tout l''ego".
Si "woke" signifie que je ne peux pas soutenir Trump, ce que je pense que cela
signifie, ou que je soutiens les personnes qui veulent être transgenres ou que
je suis pour le vaccin, appelez-moi "woke" comme vous voulez Trump l''année prochaine.
"Je sais que je lui botterai le cul", a-t-il déclaré aux auditeurs.
L''année suivante, Stern a déclaré qu''il envisageait de se lancer dans la course
à la présidence "pour que le pays soit à nouveau juste" En réponse, Trump a partagé
sur sa plateforme Truth Social un clip de Fox News dans lequel l''animateur Jimmy
Failla critique Stern.
"L''omnipotence médiatique de Donald Trump a brisé Howard Stern "Je vais faire
la chose très simple qui remettra le pays sur le droit chemin : un vote, une personne",
a expliqué Stern, affirmant que Trump a en fait perdu l''élection de 2016 contre
Hillary Clinton qui a remporté le vote populaire - mais pas le collège électoral'
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy_score
value: 0.9434954007884363
name: Accuracy_Score
- type: classification_report
value:
'0':
precision: 0.9361702127659575
recall: 0.9322033898305084
f1-score: 0.9341825902335456
support: 236
'1':
precision: 0.9333333333333333
recall: 0.9302325581395349
f1-score: 0.9317803660565723
support: 301
'2':
precision: 0.9646017699115044
recall: 0.9732142857142857
f1-score: 0.9688888888888889
support: 224
accuracy: 0.9434954007884363
macro avg:
precision: 0.9447017720035985
recall: 0.945216744561443
f1-score: 0.9449506150596689
support: 761
weighted avg:
precision: 0.9434169513880108
recall: 0.9434954007884363
f1-score: 0.9434482162802315
support: 761
name: Classification_Report
---
# SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| pos | <ul><li>"Les PHL lèvent 1,26 milliard de dollars grâce aux obligations en dollars de détail\nLE GOUVERNEMENT PHILIPPIN a levé 1,26 milliard de dollars lors de la première émission d'obligations de détail en dollars (RDB) sous l'administration Marcos, a déclaré le ministère des Finances (DoF)"</li><li>"Atom Egoyan revient à Salomé, l'opéra qu'il a monté en 1996, avec Seven Veils\nAtom Egoyan n'a pas été surpris lorsque la Canadian Opera Company lui a demandé de remonter Salomé pour la saison 2022-23 Atom Egoyan revient à Salomé, l'opéra qu'il a monté en 1996, avec Seven Veils\nAtom Egoyan n'a pas été surpris lorsque la Canadian Opera Company lui a demandé de remonter Salomé pour la saison 2022-23. Avec ses éléments de film et de vidéo, son interprétation psychologique et sombre de l'opéra de Richard Strauss avait un solide palmarès de reprises - depuis sa création en 1996, elle avait été présentée deux fois de plus à la COC et avait été reprise par plusieurs autres compagnies"</li><li>'Paul Simon présente un documentaire sur sa carrière\nAprès un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public Paul Simon présente un documentaire sur sa carrière\nAprès un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public.\nTORONTO >> Après un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public "Il n\'y a pas de raison que vous soyez épuisés", a dit Simon à la foule après la première du documentaire d\'Alex Gibney "In Restless Dreams : The Music of Paul Simon" d\'Alex Gibney, dimanche au Festival international du film de Toronto.\nSimon, âgé de 81 ans, n\'avait pas regardé le film avant la première, et il ne l\'a pas regardé non plus dimanche TORONTO >> Après un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public.\n"Il n\'y a pas de raison que vous soyez épuisés", a dit Simon à la foule après la première du documentaire d\'Alex Gibney "In Restless Dreams : The Music of Paul Simon" d\'Alex Gibney, dimanche au Festival international du film de Toronto'</li></ul> |
| neg | <ul><li>'Le groupe Al-Mostaqilla de l\'université du Koweït a appelé les étudiants à organiser un sit-in à l\'université du Koweït lundi pour protester contre la décision de mettre fin aux classes mixtes La décision a été prise la semaine dernière par le nouveau ministre de l\'éducation, Adel Al-Mane, et le directeur par intérim de l\'université du Koweït, Fayez Al-Dhafiri, et mise en œuvre mercredi, trois jours seulement avant le début de la nouvelle année universitaire à la faculté de droit L\'association a également demandé au gouvernement de "cesser ses interventions politiques et médiatiques injustifiées" dans les affaires de l\'université du Koweït.\nL\'association a appelé le directeur par intérim de l\'université du Koweït à ne pas céder aux pressions politiques et médiatiques et à s\'efforcer de protéger l\'indépendance de l\'université Dhafiri a déclaré que la décision avait été prise en application de la loi de 1996 qui interdisait l\'enseignement mixte à l\'université du Koweït, malgré une décision de la Cour constitutionnelle de 2015 autorisant l\'enseignement mixte lorsqu\'il était nécessaire et dans des cas exceptionnels Parallèlement, l\'association des professeurs de l\'université du Koweït a publié samedi une déclaration demandant aux députés et au gouvernement de "cesser d\'interférer dans les affaires de l\'université du Koweït" et de maintenir l\'indépendance de l\'université "L\'université du Koweït était, est et sera toujours le porte-drapeau de la connaissance et des valeurs, à l\'abri de toute influence extérieure Le député Abdulwahab Al-Essa a reproché à l\'administration de l\'université du Koweït d\'avoir succombé à la pression politique au détriment de l\'intérêt public, ajoutant que l\'université du Koweït avait appliqué correctement une décision de la cour constitutionnelle autorisant les classes mixtes chaque fois que cela était nécessaire'</li><li>"L'immigration étant l'un des défis les plus difficiles à relever pour le président Joe Biden et apparaissant comme un enjeu majeur des élections de l'année prochaine, l'administration délocalise essentiellement la question en s'appuyant sur les pays d'Amérique centrale et d'Amérique du Sud pour empêcher les migrants de se diriger vers le nord"</li><li>'Lors d\'une réunion d\'information mardi, le porte-parole de l\'armée, le lieutenant-colonel Richard Hecht, a suggéré que les Palestiniens tentent de quitter la bande de Gaza par le poste-frontière de Rafah, en Égypte.\nLa perspective d\'un exode des habitants de Gaza vers le territoire égyptien a alarmé les autorités égyptiennes La question qui se pose est de savoir si Israël lancera une offensive terrestre dans la bande de Gaza, une bande de terre de 25 miles de long coincée entre Israël, l\'Égypte et la mer Méditerranée, où vivent 2,3 millions de personnes et qui est gouvernée par le Hamas depuis 2007 Israël pilonne la bande de Gaza ; les habitants se précipitent pour se mettre à l\'abri\nJERUSALEM - Les avions de combat israéliens ont bombardé la bande de Gaza quartier par quartier mardi, réduisant les bâtiments en ruines et poussant les habitants à se précipiter pour se mettre à l\'abri dans ce minuscule territoire isolé, alors qu\'Israël promet des représailles pour l\'attaque surprise du Hamas du week-end qui "se répercuteront Les autorités égyptiennes discutent avec Israël et les États-Unis afin de mettre en place des corridors humanitaires dans la bande de Gaza pour acheminer l\'aide, a déclaré un responsable égyptien. Des négociations sont en cours avec les Israéliens pour que la zone autour du point de passage de Rafah entre l\'Égypte et Gaza soit déclarée "zone d\'interdiction de feu", a déclaré le responsable, sous couvert d\'anonymat car il n\'était pas autorisé à parler aux médias'</li></ul> |
| obj | <ul><li>"L'économie pèse sur les Américains Ils sont plus nombreux à faire confiance à Trump qu'à Biden pour alléger leur fardeau\nWASHINGTON - Linda Muñoz a peur de l'économie Trump, le candidat républicain à la primaire de 2024, pour améliorer l'économie, avec une marge de 47 % à 36 %. L'écart est de 46 %-26 % en faveur de M. Trump parmi les électeurs indépendants Presque tous les républicains interrogés ont exprimé leur pessimisme à l'égard de l'économie, selon le sondage : 96 % d'entre eux estiment que la situation se dégrade au lieu de s'améliorer Le logement. L'essence. Tous ces éléments poussent les gens à s'endetter de plus en plus, disent-ils.\nSelon le sondage, près de 70 % des Américains estiment que la situation économique se dégrade, tandis que 22 % seulement estiment qu'elle s'améliore L'économie pèse sur les Américains Ils sont plus nombreux à faire confiance à Trump qu'à Biden pour alléger leur fardeau\nWASHINGTON - Linda Muñoz a peur de l'économie. Elle a puisé dans son épargne d'urgence cette année. Et elle ne croit pas que le président Joe Biden ressente sa douleur L'épicerie. Le logement. L'essence. Tous ces éléments poussent les gens à s'endetter de plus en plus, disent-ils.\nSelon le sondage, près de 70 % des Américains estiment que la situation économique se dégrade, tandis que 22 % seulement estiment qu'elle s'améliore"</li><li>'Le Pentagone va interroger d\'autres militaires sur l\'attentat suicide de l\'aéroport de Kaboul en 2021\nLe commandement central du Pentagone a ordonné l\'audition d\'une vingtaine de militaires supplémentaires qui se trouvaient à l\'aéroport de Kaboul lorsque des kamikazes ont attaqué pendant le retrait chaotique des forces américaines d\'Afghanistan, alors que les critiques persistent sur le fait que l\'attaque meurtrière aurait pu être stoppée Certaines familles des personnes tuées ou blessées se sont plaintes que le Pentagone n\'avait pas fait preuve de suffisamment de transparence au sujet de l\'attentat à la bombe qui a tué 170 Afghans\net 13 militaires américains.\nL\'enquête du commandement central américain a conclu en novembre 2021 qu\'étant donné la détérioration de la sécurité à la porte de l\'Abbaye de l\'aéroport alors que les Afghans cherchaient de plus en plus à fuir, "l\'attaque n\'aurait pas pu être évitée au niveau tactique sans dégrader la mission visant à maximiser le nombre d\'évacués" Le Pentagone a déclaré que l\'examen de l\'attentat suicide n\'avait révélé aucune identification préalable d\'un attaquant possible ni aucune demande d\'"escalade des règles d\'engagement existantes" régissant l\'utilisation de la force par les troupes américaines'</li><li>'Les retombées de la guerre se répercutent sur les lieux de travail aux États-Unis.\nNEW YORK - Les retombées de la guerre entre Israël et le Hamas se sont répandues sur les lieux de travail partout dans le monde, les dirigeants de grandes entreprises exprimant leur point de vue tandis que les travailleurs se plaignent de ne pas être entendus "À quoi me sert mon travail si je compromets ma propre morale et mon éthique ?\nL\'un des conflits les plus importants s\'est produit chez Starbucks après que Starbucks Workers United, un syndicat représentant 9 000 travailleurs dans plus de 360 magasins aux États-Unis, a tweeté "Solidarité avec la Palestine" deux jours après l\'attaque du Hamas. Le tweet a été supprimé au bout de 40 minutes, mais l\'entreprise a déclaré qu\'il avait donné lieu à plus de 1 000 plaintes, à des actes de vandalisme et à des affrontements dans ses magasins NEW YORK - Les retombées de la guerre entre Israël et le Hamas se sont répandues sur les lieux de travail partout dans le monde, les dirigeants de grandes entreprises exprimant leur point de vue tandis que les travailleurs se plaignent de ne pas être entendus'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy_Score | Classification_Report |
|:--------|:---------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **all** | 0.9435 | {'0': {'precision': 0.9361702127659575, 'recall': 0.9322033898305084, 'f1-score': 0.9341825902335456, 'support': 236}, '1': {'precision': 0.9333333333333333, 'recall': 0.9302325581395349, 'f1-score': 0.9317803660565723, 'support': 301}, '2': {'precision': 0.9646017699115044, 'recall': 0.9732142857142857, 'f1-score': 0.9688888888888889, 'support': 224}, 'accuracy': 0.9434954007884363, 'macro avg': {'precision': 0.9447017720035985, 'recall': 0.945216744561443, 'f1-score': 0.9449506150596689, 'support': 761}, 'weighted avg': {'precision': 0.9434169513880108, 'recall': 0.9434954007884363, 'f1-score': 0.9434482162802315, 'support': 761}} |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mogaio/pr_ebsa_fr_tran_merged25_e1_middle_offsets")
# Run inference
preds = model("Adil Hussain
Adil Hussain est reconnaissant d'avoir reçu l'enseignement de l'acteur Naseeruddin Shah à l'époque où il fréquentait l'École nationale d'art dramatique")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:-----|
| Word count | 9 | 247.2638 | 2089 |
| Label | Training Sample Count |
|:------|:----------------------|
| neg | 913 |
| obj | 1216 |
| pos | 911 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 1
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0013 | 1 | 0.3703 | - |
| 0.0658 | 50 | 0.3145 | - |
| 0.1316 | 100 | 0.1839 | - |
| 0.1974 | 150 | 0.2558 | - |
| 0.2632 | 200 | 0.2683 | - |
| 0.3289 | 250 | 0.1572 | - |
| 0.3947 | 300 | 0.1953 | - |
| 0.4605 | 350 | 0.171 | - |
| 0.5263 | 400 | 0.2326 | - |
| 0.5921 | 450 | 0.1762 | - |
| 0.6579 | 500 | 0.2818 | - |
| 0.7237 | 550 | 0.2733 | - |
| 0.7895 | 600 | 0.195 | - |
| 0.8553 | 650 | 0.2104 | - |
| 0.9211 | 700 | 0.2124 | - |
| 0.9868 | 750 | 0.0818 | - |
| 1.0526 | 800 | 0.1046 | - |
| 1.1184 | 850 | 0.1633 | - |
| 1.1842 | 900 | 0.3207 | - |
| 1.25 | 950 | 0.2703 | - |
| 1.3158 | 1000 | 0.1934 | - |
| 1.3816 | 1050 | 0.2547 | - |
| 1.4474 | 1100 | 0.0933 | - |
| 1.5132 | 1150 | 0.2102 | - |
| 1.5789 | 1200 | 0.0699 | - |
| 1.6447 | 1250 | 0.1778 | - |
| 1.7105 | 1300 | 0.1796 | - |
| 1.7763 | 1350 | 0.0221 | - |
| 1.8421 | 1400 | 0.2154 | - |
| 1.9079 | 1450 | 0.1683 | - |
| 1.9737 | 1500 | 0.3096 | - |
| 2.0395 | 1550 | 0.201 | - |
| 2.1053 | 1600 | 0.1954 | - |
| 2.1711 | 1650 | 0.2301 | - |
| 2.2368 | 1700 | 0.1141 | - |
| 2.3026 | 1750 | 0.1949 | - |
| 2.3684 | 1800 | 0.164 | - |
| 2.4342 | 1850 | 0.2307 | - |
| 2.5 | 1900 | 0.1912 | - |
| 2.5658 | 1950 | 0.2349 | - |
| 2.6316 | 2000 | 0.0922 | - |
| 2.6974 | 2050 | 0.0702 | - |
| 2.7632 | 2100 | 0.1089 | - |
| 2.8289 | 2150 | 0.1711 | - |
| 2.8947 | 2200 | 0.1432 | - |
| 2.9605 | 2250 | 0.2739 | - |
| 3.0263 | 2300 | 0.1889 | - |
| 3.0921 | 2350 | 0.1036 | - |
| 3.1579 | 2400 | 0.1372 | - |
| 3.2237 | 2450 | 0.028 | - |
| 3.2895 | 2500 | 0.1739 | - |
| 3.3553 | 2550 | 0.142 | - |
| 3.4211 | 2600 | 0.0838 | - |
| 3.4868 | 2650 | 0.0657 | - |
| 3.5526 | 2700 | 0.0054 | - |
| 3.6184 | 2750 | 0.0426 | - |
| 3.6842 | 2800 | 0.1974 | - |
| 3.75 | 2850 | 0.0279 | - |
| 3.8158 | 2900 | 0.1326 | - |
| 3.8816 | 2950 | 0.1614 | - |
| 3.9474 | 3000 | 0.1251 | - |
| 4.0132 | 3050 | 0.1174 | - |
| 4.0789 | 3100 | 0.1948 | - |
| 4.1447 | 3150 | 0.0555 | - |
| 4.2105 | 3200 | 0.0064 | - |
| 4.2763 | 3250 | 0.064 | - |
| 4.3421 | 3300 | 0.0013 | - |
| 4.4079 | 3350 | 0.135 | - |
| 4.4737 | 3400 | 0.0574 | - |
| 4.5395 | 3450 | 0.174 | - |
| 4.6053 | 3500 | 0.2199 | - |
| 4.6711 | 3550 | 0.387 | - |
| 4.7368 | 3600 | 0.114 | - |
| 4.8026 | 3650 | 0.0853 | - |
| 4.8684 | 3700 | 0.0325 | - |
| 4.9342 | 3750 | 0.019 | - |
| 5.0 | 3800 | 0.0572 | - |
| 0.0013 | 1 | 0.1435 | - |
| 0.0658 | 50 | 0.0969 | - |
| 0.1316 | 100 | 0.1085 | - |
| 0.1974 | 150 | 0.0271 | - |
| 0.2632 | 200 | 0.0138 | - |
| 0.3289 | 250 | 0.058 | - |
| 0.3947 | 300 | 0.1205 | - |
| 0.4605 | 350 | 0.0788 | - |
| 0.5263 | 400 | 0.1449 | - |
| 0.5921 | 450 | 0.0383 | - |
| 0.6579 | 500 | 0.0338 | - |
| 0.7237 | 550 | 0.1253 | - |
| 0.7895 | 600 | 0.069 | - |
| 0.8553 | 650 | 0.104 | - |
| 0.9211 | 700 | 0.0462 | - |
| 0.9868 | 750 | 0.1975 | - |
| 1.0526 | 800 | 0.0241 | - |
| 1.1184 | 850 | 0.0426 | - |
| 1.1842 | 900 | 0.0519 | - |
| 1.25 | 950 | 0.0815 | - |
| 1.3158 | 1000 | 0.1839 | - |
| 1.3816 | 1050 | 0.0198 | - |
| 1.4474 | 1100 | 0.0128 | - |
| 1.5132 | 1150 | 0.1645 | - |
| 1.5789 | 1200 | 0.0019 | - |
| 1.6447 | 1250 | 0.0557 | - |
| 1.7105 | 1300 | 0.0098 | - |
| 1.7763 | 1350 | 0.001 | - |
| 1.8421 | 1400 | 0.1557 | - |
| 1.9079 | 1450 | 0.1286 | - |
| 1.9737 | 1500 | 0.094 | - |
| 2.0395 | 1550 | 0.0059 | - |
| 2.1053 | 1600 | 0.0227 | - |
| 2.1711 | 1650 | 0.0899 | - |
| 2.2368 | 1700 | 0.0053 | - |
| 2.3026 | 1750 | 0.0021 | - |
| 2.3684 | 1800 | 0.0114 | - |
| 2.4342 | 1850 | 0.1163 | - |
| 2.5 | 1900 | 0.0959 | - |
| 2.5658 | 1950 | 0.0252 | - |
| 2.6316 | 2000 | 0.0921 | - |
| 2.6974 | 2050 | 0.1159 | - |
| 2.7632 | 2100 | 0.0026 | - |
| 2.8289 | 2150 | 0.1211 | - |
| 2.8947 | 2200 | 0.1843 | - |
| 2.9605 | 2250 | 0.0014 | - |
| 3.0263 | 2300 | 0.0085 | - |
| 3.0921 | 2350 | 0.0839 | - |
| 3.1579 | 2400 | 0.2372 | - |
| 3.2237 | 2450 | 0.0213 | - |
| 3.2895 | 2500 | 0.0155 | - |
| 3.3553 | 2550 | 0.1128 | - |
| 3.4211 | 2600 | 0.0945 | - |
| 3.4868 | 2650 | 0.0917 | - |
| 3.5526 | 2700 | 0.0011 | - |
| 3.6184 | 2750 | 0.0024 | - |
| 3.6842 | 2800 | 0.0044 | - |
| 3.75 | 2850 | 0.121 | - |
| 3.8158 | 2900 | 0.0056 | - |
| 3.8816 | 2950 | 0.003 | - |
| 3.9474 | 3000 | 0.0899 | - |
| 4.0132 | 3050 | 0.0157 | - |
| 4.0789 | 3100 | 0.1188 | - |
| 4.1447 | 3150 | 0.001 | - |
| 4.2105 | 3200 | 0.0222 | - |
| 4.2763 | 3250 | 0.1209 | - |
| 4.3421 | 3300 | 0.1085 | - |
| 4.4079 | 3350 | 0.0054 | - |
| 4.4737 | 3400 | 0.0009 | - |
| 4.5395 | 3450 | 0.0015 | - |
| 4.6053 | 3500 | 0.003 | - |
| 4.6711 | 3550 | 0.0009 | - |
| 4.7368 | 3600 | 0.0003 | - |
| 4.8026 | 3650 | 0.0009 | - |
| 4.8684 | 3700 | 0.03 | - |
| 4.9342 | 3750 | 0.1206 | - |
| 5.0 | 3800 | 0.0003 | - |
| 0.0013 | 1 | 0.2045 | - |
| 0.0658 | 50 | 0.0078 | - |
| 0.1316 | 100 | 0.0087 | - |
| 0.1974 | 150 | 0.0386 | - |
| 0.2632 | 200 | 0.1015 | - |
| 0.3289 | 250 | 0.0022 | - |
| 0.3947 | 300 | 0.0291 | - |
| 0.4605 | 350 | 0.0013 | - |
| 0.5263 | 400 | 0.0022 | - |
| 0.5921 | 450 | 0.1324 | - |
| 0.6579 | 500 | 0.113 | - |
| 0.7237 | 550 | 0.0011 | - |
| 0.7895 | 600 | 0.1723 | - |
| 0.8553 | 650 | 0.0049 | - |
| 0.9211 | 700 | 0.206 | - |
| 0.9868 | 750 | 0.1683 | - |
| 1.0526 | 800 | 0.0954 | - |
| 1.1184 | 850 | 0.018 | - |
| 1.1842 | 900 | 0.1854 | - |
| 1.25 | 950 | 0.0342 | - |
| 1.3158 | 1000 | 0.0015 | - |
| 1.3816 | 1050 | 0.0062 | - |
| 1.4474 | 1100 | 0.1187 | - |
| 1.5132 | 1150 | 0.0048 | - |
| 1.5789 | 1200 | 0.0011 | - |
| 1.6447 | 1250 | 0.002 | - |
| 1.7105 | 1300 | 0.092 | - |
| 1.7763 | 1350 | 0.1245 | - |
| 1.8421 | 1400 | 0.0009 | - |
| 1.9079 | 1450 | 0.1185 | - |
| 1.9737 | 1500 | 0.0017 | - |
| 2.0395 | 1550 | 0.008 | - |
| 2.1053 | 1600 | 0.0049 | - |
| 2.1711 | 1650 | 0.0083 | - |
| 2.2368 | 1700 | 0.0026 | - |
| 2.3026 | 1750 | 0.0081 | - |
| 2.3684 | 1800 | 0.0036 | - |
| 2.4342 | 1850 | 0.0016 | - |
| 2.5 | 1900 | 0.0017 | - |
| 2.5658 | 1950 | 0.0014 | - |
| 2.6316 | 2000 | 0.0017 | - |
| 2.6974 | 2050 | 0.002 | - |
| 2.7632 | 2100 | 0.1022 | - |
| 2.8289 | 2150 | 0.0004 | - |
| 2.8947 | 2200 | 0.0007 | - |
| 2.9605 | 2250 | 0.0794 | - |
| 3.0263 | 2300 | 0.0183 | - |
| 3.0921 | 2350 | 0.0377 | - |
| 3.1579 | 2400 | 0.029 | - |
| 3.2237 | 2450 | 0.0003 | - |
| 3.2895 | 2500 | 0.0961 | - |
| 3.3553 | 2550 | 0.0008 | - |
| 3.4211 | 2600 | 0.0873 | - |
| 3.4868 | 2650 | 0.0501 | - |
| 3.5526 | 2700 | 0.0029 | - |
| 3.6184 | 2750 | 0.0008 | - |
| 3.6842 | 2800 | 0.0004 | - |
| 3.75 | 2850 | 0.0011 | - |
| 3.8158 | 2900 | 0.0518 | - |
| 3.8816 | 2950 | 0.0002 | - |
| 3.9474 | 3000 | 0.1115 | - |
| 4.0132 | 3050 | 0.0129 | - |
| 4.0789 | 3100 | 0.0005 | - |
| 4.1447 | 3150 | 0.0012 | - |
| 4.2105 | 3200 | 0.1086 | - |
| 4.2763 | 3250 | 0.0199 | - |
| 4.3421 | 3300 | 0.0004 | - |
| 4.4079 | 3350 | 0.0001 | - |
| 4.4737 | 3400 | 0.0832 | - |
| 4.5395 | 3450 | 0.0003 | - |
| 4.6053 | 3500 | 0.0041 | - |
| 4.6711 | 3550 | 0.1146 | - |
| 4.7368 | 3600 | 0.0027 | - |
| 4.8026 | 3650 | 0.0002 | - |
| 4.8684 | 3700 | 0.0544 | - |
| 4.9342 | 3750 | 0.0002 | - |
| 5.0 | 3800 | 0.0046 | - |
| 0.0013 | 1 | 0.0015 | - |
| 0.0658 | 50 | 0.1973 | - |
| 0.1316 | 100 | 0.0106 | - |
| 0.1974 | 150 | 0.0744 | - |
| 0.2632 | 200 | 0.1033 | - |
| 0.3289 | 250 | 0.0425 | - |
| 0.3947 | 300 | 0.1125 | - |
| 0.4605 | 350 | 0.0018 | - |
| 0.5263 | 400 | 0.0019 | - |
| 0.5921 | 450 | 0.0002 | - |
| 0.6579 | 500 | 0.0007 | - |
| 0.7237 | 550 | 0.1393 | - |
| 0.7895 | 600 | 0.0002 | - |
| 0.8553 | 650 | 0.0043 | - |
| 0.9211 | 700 | 0.0339 | - |
| 0.9868 | 750 | 0.0002 | - |
| 0.0013 | 1 | 0.0007 | - |
| 0.0658 | 50 | 0.0419 | - |
| 0.1316 | 100 | 0.0068 | - |
| 0.1974 | 150 | 0.1401 | - |
| 0.2632 | 200 | 0.0423 | - |
| 0.3289 | 250 | 0.1122 | - |
| 0.3947 | 300 | 0.0037 | - |
| 0.4605 | 350 | 0.005 | - |
| 0.5263 | 400 | 0.0006 | - |
| 0.5921 | 450 | 0.0006 | - |
| 0.6579 | 500 | 0.0016 | - |
| 0.7237 | 550 | 0.1244 | - |
| 0.7895 | 600 | 0.0016 | - |
| 0.8553 | 650 | 0.0028 | - |
| 0.9211 | 700 | 0.002 | - |
| 0.9868 | 750 | 0.057 | - |
| 0.0013 | 1 | 0.1396 | - |
| 0.0658 | 50 | 0.0366 | - |
| 0.1316 | 100 | 0.0021 | - |
| 0.1974 | 150 | 0.1088 | - |
| 0.2632 | 200 | 0.0449 | - |
| 0.3289 | 250 | 0.0187 | - |
| 0.3947 | 300 | 0.0017 | - |
| 0.4605 | 350 | 0.1262 | - |
| 0.5263 | 400 | 0.0052 | - |
| 0.5921 | 450 | 0.1188 | - |
| 0.6579 | 500 | 0.0002 | - |
| 0.7237 | 550 | 0.0006 | - |
| 0.7895 | 600 | 0.0758 | - |
| 0.8553 | 650 | 0.025 | - |
| 0.9211 | 700 | 0.0052 | - |
| 0.9868 | 750 | 0.1985 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.15.0
- Tokenizers: 0.15.0
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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|
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] |
mogaio/pr_ebsa_fr_tran_merged25_e1_end_offsets
|
mogaio
| 2023-12-15T19:02:21Z | 0 | 0 |
setfit
|
[
"setfit",
"safetensors",
"xlm-roberta",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
"model-index",
"region:us"
] |
text-classification
| 2023-12-15T19:01:07Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy_score
- classification_report
widget:
- text: 'Adil Hussain
Adil Hussain est reconnaissant d''avoir reçu l''enseignement de l''acteur Naseeruddin
Shah à l''époque où il fréquentait l''École nationale d''art dramatique'
- text: 'Bien que leurs opinions sur la question de savoir si les migrants sont un
avantage ou un fardeau soient plus mitigées, de nettes majorités d''électeurs
de toute la ville de New York, de la banlieue et du nord de l''État ont déclaré
que l''État devrait essayer de ralentir l''afflux de migrants, plutôt que d''en
accepter davantage et de s''efforcer d''assimiler les nouveaux arrivants Les démocrates
aspirent à renverser six circonscriptions détenues par les républicains que M.
Biden a remportées en 2020, notamment celle de M Les républicains se sont emparés
de la crise des migrants, donnant un avant-goût des campagnes de l''année prochaine
Les républicains ont surenchéri : Elise Stefanik, la New-Yorkaise qui dirige la
conférence du parti démocrate à la Chambre des représentants,
Suite à la page suivante
a déclaré à Politico la semaine dernière que le parti allait consacrer 100 millions
de dollars aux campagnes dans les circonscriptions de New York Des problèmes à
venir pour les démocrates de New York en 2024 ?
Les dirigeants démocrates de New York se débattent depuis des mois avec le problème
de l''hébergement des dizaines de milliers de migrants qui ont été transportés
par bus jusqu''à New York et laissés à sa charge Des problèmes à venir pour les
démocrates de New York en 2024 ?
Les dirigeants démocrates de New York se débattent depuis des mois avec le problème
de l''hébergement des dizaines de milliers de migrants qui ont été transportés
par bus jusqu''à New York et laissés à sa charge.
Mais une autre préoccupation se profile alors que la crise se poursuit sans qu''aucune
issue ne soit en vue : les retombées potentielles pour leur parti lors des élections
de l''année prochaine Les républicains ont tendance à se sentir en sécurité lorsqu''ils
parlent d''immigration - comme les démocrates le font pour l''avortement - et
sont clairement à l''attaque sur la question des migrants à New York, tandis que
les démocrates sont sur la défensive, a déclaré Kyle Kondik, directeur de la communication
pour le Centre de politique de l''Université de Virginie, au réseau USA Today
Plus de 100 000 migrants ont été transportés à New York depuis la frontière sud
depuis le printemps 2022. Environ 60 000 d''entre eux sont hébergés dans la ville,
et plus de 2 100 ont été transportés dans des hôtels situés dans sept comtés au
nord de la ville, de Yonkers à la périphérie de Buffalo, où ils sont logés aux
frais de la ville Les démocrates doivent y remporter des victoires pour gagner
cinq sièges à la Chambre et faire du député Hakeem Jeffries, de Brooklyn, le prochain
président de la Chambre des représentants Les publicités d''attaque des républicains
s''écrivent pratiquement d''elles-mêmes à partir d''un flot de titres et d''images
télévisées, alors que le gouverneur Kathy Hochul, le maire de New York Eric Adams
et le président Joe Biden - tous démocrates - se rejettent mutuellement la faute
et s''échangent des coups de feu pour savoir qui devrait en faire le plus Isaac
Goldberg, un stratège démocrate qui a travaillé sur plusieurs campagnes électorales
à New York, a affirmé qu''il était beaucoup trop tôt pour prédire l''impact politique
de la crise des migrants, soulignant que les élections de 2024 n''auront lieu
que dans 14 mois et que de nombreuses questions tout aussi urgentes pourraient
se poser'
- text: 'LE CANDIDAT A LA PRESIDENCE RAMASWAMY VEUT METTRE FIN AU SYSTEME DE VISA
H-1B AUX ETATS-UNIS
Décrivant le programme de visas H-1B comme une forme de "servitude", Vivek Ramaswamy,
candidat républicain indien-américain à l''élection présidentielle, a promis de
"vider" le système basé sur la loterie et de le remplacer par un système d''admission
méritocratique s''il remporte les élections présidentielles de 2024'
- text: 'Smith Hal Sparks Catherine Zeta-Jones son-Sampras Chris Owen Donald Glover
("Queer as Folk") a 54 ans Smith Hal Sparks Catherine Zeta-Jones son-Sampras Chris
Owen Donald Glover
("Queer as Folk") a 54 ans. a 54 ans. Acteur
("Je sais ce que vous avez fait l''été dernier") a 50 ans'
- text: 'Trump profiter de sa célébrité jusqu''à la Maison-Blanche.
"Cela a tué Howard parce qu''il était le roi de tous les médias Il a poursuivi
en disant que Trump ne laisserait pas ses partisans s''approcher de l''une de
ses propriétés. "Les gens qui votent pour Trump, pour la plupart, ne les laisseraient
même pas entrer dans un putain d''hôtel [ "Si être réveillé signifie que je ne
peux pas soutenir Trump, ce que je pense que cela signifie, ou que je soutiens
les personnes qui veulent être transgenres ou que je suis pour le vaccin, appelez-moi
réveillé comme vous le voulez" "Les gens qui votent pour Trump, pour la plupart,
ne les laisseraient même pas entrer dans un putain d''hôtel [...]. Allez à Mar-a-lago,
voyez s''il y a des gens qui vous ressemblent" Stern a également abordé les affirmations
de Trump et de ses partisans selon lesquelles Joe Biden a remporté l''élection
américaine de 2020 grâce à des votes frauduleux "Et soudain, Trump a transformé
Howard, qui était le roi de tous les médias, en prince Harry de tous les médias.
Tout le monde s''en fout "Trump avait l''habitude de participer à l''émission
de Stern chaque semaine. Ils étaient amis. Alors cette idée que Trump est le pire
type qui ait jamais marché sur la surface de la terre, pourquoi traîniez-vous
avec lui ?"
M Mais Stern, qui par le passé a été accusé de racisme et de sexisme dans nombre
de ses sketches à l''antenne, a été un critique virulent de Trump tout au long
de sa présidence et, plus récemment, alors qu''il se prépare à se présenter à
nouveau en 2024.
En 2021, M "Combien de temps allons-nous continuer à élire des gens qui ont perdu
l''élection ?"
Il a poursuivi en qualifiant les partisans de Trump de "nigauds".
"Mon Dieu, j''ai l''impression d''être dans une nation de nigauds. J''espère qu''il
y a encore des gens brillants et dynamiques qui aiment ce pays", a-t-il déclaré
Alors cette idée que Trump est le pire type qui ait jamais marché sur la surface
de la terre, pourquoi traîniez-vous avec lui ?"
M. Failla a déclaré que cela avait "tué" M Si "woke" signifie que je ne peux pas
soutenir Trump, ce que je pense que cela signifie, ou que je soutiens les personnes
qui veulent être transgenres ou que je suis pour le vaccin, appelez-moi "woke"
comme vous voulez Celui qui se décrit comme le "roi de tous les médias" a critiqué
ouvertement l''ancien président américain Donald Trump, les anti-vaxx et, plus
récemment, Lauren Boebert, qu''il a critiquée pour son comportement obscène dans
un théâtre de Denver au début du mois "L''omnipotence médiatique de Donald Trump
a brisé Howard Stern. C''est très important", a déclaré Failla dans la vidéo (selon
OK ! Magazine). "Trump avait l''habitude de participer à l''émission de Stern
chaque semaine L''aversion d''Howard Stern pour Donald Trump, c''est "tout l''ego".
Si "woke" signifie que je ne peux pas soutenir Trump, ce que je pense que cela
signifie, ou que je soutiens les personnes qui veulent être transgenres ou que
je suis pour le vaccin, appelez-moi "woke" comme vous voulez Trump l''année prochaine.
"Je sais que je lui botterai le cul", a-t-il déclaré aux auditeurs.
L''année suivante, Stern a déclaré qu''il envisageait de se lancer dans la course
à la présidence "pour que le pays soit à nouveau juste" En réponse, Trump a partagé
sur sa plateforme Truth Social un clip de Fox News dans lequel l''animateur Jimmy
Failla critique Stern.
"L''omnipotence médiatique de Donald Trump a brisé Howard Stern "Je vais faire
la chose très simple qui remettra le pays sur le droit chemin : un vote, une personne",
a expliqué Stern, affirmant que Trump a en fait perdu l''élection de 2016 contre
Hillary Clinton qui a remporté le vote populaire - mais pas le collège électoral'
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy_score
value: 0.9434954007884363
name: Accuracy_Score
- type: classification_report
value:
'0':
precision: 0.9361702127659575
recall: 0.9322033898305084
f1-score: 0.9341825902335456
support: 236
'1':
precision: 0.9333333333333333
recall: 0.9302325581395349
f1-score: 0.9317803660565723
support: 301
'2':
precision: 0.9646017699115044
recall: 0.9732142857142857
f1-score: 0.9688888888888889
support: 224
accuracy: 0.9434954007884363
macro avg:
precision: 0.9447017720035985
recall: 0.945216744561443
f1-score: 0.9449506150596689
support: 761
weighted avg:
precision: 0.9434169513880108
recall: 0.9434954007884363
f1-score: 0.9434482162802315
support: 761
name: Classification_Report
---
# SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| pos | <ul><li>"Les PHL lèvent 1,26 milliard de dollars grâce aux obligations en dollars de détail\nLE GOUVERNEMENT PHILIPPIN a levé 1,26 milliard de dollars lors de la première émission d'obligations de détail en dollars (RDB) sous l'administration Marcos, a déclaré le ministère des Finances (DoF)"</li><li>"Atom Egoyan revient à Salomé, l'opéra qu'il a monté en 1996, avec Seven Veils\nAtom Egoyan n'a pas été surpris lorsque la Canadian Opera Company lui a demandé de remonter Salomé pour la saison 2022-23 Atom Egoyan revient à Salomé, l'opéra qu'il a monté en 1996, avec Seven Veils\nAtom Egoyan n'a pas été surpris lorsque la Canadian Opera Company lui a demandé de remonter Salomé pour la saison 2022-23. Avec ses éléments de film et de vidéo, son interprétation psychologique et sombre de l'opéra de Richard Strauss avait un solide palmarès de reprises - depuis sa création en 1996, elle avait été présentée deux fois de plus à la COC et avait été reprise par plusieurs autres compagnies"</li><li>'Paul Simon présente un documentaire sur sa carrière\nAprès un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public Paul Simon présente un documentaire sur sa carrière\nAprès un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public.\nTORONTO >> Après un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public "Il n\'y a pas de raison que vous soyez épuisés", a dit Simon à la foule après la première du documentaire d\'Alex Gibney "In Restless Dreams : The Music of Paul Simon" d\'Alex Gibney, dimanche au Festival international du film de Toronto.\nSimon, âgé de 81 ans, n\'avait pas regardé le film avant la première, et il ne l\'a pas regardé non plus dimanche TORONTO >> Après un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public.\n"Il n\'y a pas de raison que vous soyez épuisés", a dit Simon à la foule après la première du documentaire d\'Alex Gibney "In Restless Dreams : The Music of Paul Simon" d\'Alex Gibney, dimanche au Festival international du film de Toronto'</li></ul> |
| neg | <ul><li>'Le groupe Al-Mostaqilla de l\'université du Koweït a appelé les étudiants à organiser un sit-in à l\'université du Koweït lundi pour protester contre la décision de mettre fin aux classes mixtes La décision a été prise la semaine dernière par le nouveau ministre de l\'éducation, Adel Al-Mane, et le directeur par intérim de l\'université du Koweït, Fayez Al-Dhafiri, et mise en œuvre mercredi, trois jours seulement avant le début de la nouvelle année universitaire à la faculté de droit L\'association a également demandé au gouvernement de "cesser ses interventions politiques et médiatiques injustifiées" dans les affaires de l\'université du Koweït.\nL\'association a appelé le directeur par intérim de l\'université du Koweït à ne pas céder aux pressions politiques et médiatiques et à s\'efforcer de protéger l\'indépendance de l\'université Dhafiri a déclaré que la décision avait été prise en application de la loi de 1996 qui interdisait l\'enseignement mixte à l\'université du Koweït, malgré une décision de la Cour constitutionnelle de 2015 autorisant l\'enseignement mixte lorsqu\'il était nécessaire et dans des cas exceptionnels Parallèlement, l\'association des professeurs de l\'université du Koweït a publié samedi une déclaration demandant aux députés et au gouvernement de "cesser d\'interférer dans les affaires de l\'université du Koweït" et de maintenir l\'indépendance de l\'université "L\'université du Koweït était, est et sera toujours le porte-drapeau de la connaissance et des valeurs, à l\'abri de toute influence extérieure Le député Abdulwahab Al-Essa a reproché à l\'administration de l\'université du Koweït d\'avoir succombé à la pression politique au détriment de l\'intérêt public, ajoutant que l\'université du Koweït avait appliqué correctement une décision de la cour constitutionnelle autorisant les classes mixtes chaque fois que cela était nécessaire'</li><li>"L'immigration étant l'un des défis les plus difficiles à relever pour le président Joe Biden et apparaissant comme un enjeu majeur des élections de l'année prochaine, l'administration délocalise essentiellement la question en s'appuyant sur les pays d'Amérique centrale et d'Amérique du Sud pour empêcher les migrants de se diriger vers le nord"</li><li>'Lors d\'une réunion d\'information mardi, le porte-parole de l\'armée, le lieutenant-colonel Richard Hecht, a suggéré que les Palestiniens tentent de quitter la bande de Gaza par le poste-frontière de Rafah, en Égypte.\nLa perspective d\'un exode des habitants de Gaza vers le territoire égyptien a alarmé les autorités égyptiennes La question qui se pose est de savoir si Israël lancera une offensive terrestre dans la bande de Gaza, une bande de terre de 25 miles de long coincée entre Israël, l\'Égypte et la mer Méditerranée, où vivent 2,3 millions de personnes et qui est gouvernée par le Hamas depuis 2007 Israël pilonne la bande de Gaza ; les habitants se précipitent pour se mettre à l\'abri\nJERUSALEM - Les avions de combat israéliens ont bombardé la bande de Gaza quartier par quartier mardi, réduisant les bâtiments en ruines et poussant les habitants à se précipiter pour se mettre à l\'abri dans ce minuscule territoire isolé, alors qu\'Israël promet des représailles pour l\'attaque surprise du Hamas du week-end qui "se répercuteront Les autorités égyptiennes discutent avec Israël et les États-Unis afin de mettre en place des corridors humanitaires dans la bande de Gaza pour acheminer l\'aide, a déclaré un responsable égyptien. Des négociations sont en cours avec les Israéliens pour que la zone autour du point de passage de Rafah entre l\'Égypte et Gaza soit déclarée "zone d\'interdiction de feu", a déclaré le responsable, sous couvert d\'anonymat car il n\'était pas autorisé à parler aux médias'</li></ul> |
| obj | <ul><li>"L'économie pèse sur les Américains Ils sont plus nombreux à faire confiance à Trump qu'à Biden pour alléger leur fardeau\nWASHINGTON - Linda Muñoz a peur de l'économie Trump, le candidat républicain à la primaire de 2024, pour améliorer l'économie, avec une marge de 47 % à 36 %. L'écart est de 46 %-26 % en faveur de M. Trump parmi les électeurs indépendants Presque tous les républicains interrogés ont exprimé leur pessimisme à l'égard de l'économie, selon le sondage : 96 % d'entre eux estiment que la situation se dégrade au lieu de s'améliorer Le logement. L'essence. Tous ces éléments poussent les gens à s'endetter de plus en plus, disent-ils.\nSelon le sondage, près de 70 % des Américains estiment que la situation économique se dégrade, tandis que 22 % seulement estiment qu'elle s'améliore L'économie pèse sur les Américains Ils sont plus nombreux à faire confiance à Trump qu'à Biden pour alléger leur fardeau\nWASHINGTON - Linda Muñoz a peur de l'économie. Elle a puisé dans son épargne d'urgence cette année. Et elle ne croit pas que le président Joe Biden ressente sa douleur L'épicerie. Le logement. L'essence. Tous ces éléments poussent les gens à s'endetter de plus en plus, disent-ils.\nSelon le sondage, près de 70 % des Américains estiment que la situation économique se dégrade, tandis que 22 % seulement estiment qu'elle s'améliore"</li><li>'Le Pentagone va interroger d\'autres militaires sur l\'attentat suicide de l\'aéroport de Kaboul en 2021\nLe commandement central du Pentagone a ordonné l\'audition d\'une vingtaine de militaires supplémentaires qui se trouvaient à l\'aéroport de Kaboul lorsque des kamikazes ont attaqué pendant le retrait chaotique des forces américaines d\'Afghanistan, alors que les critiques persistent sur le fait que l\'attaque meurtrière aurait pu être stoppée Certaines familles des personnes tuées ou blessées se sont plaintes que le Pentagone n\'avait pas fait preuve de suffisamment de transparence au sujet de l\'attentat à la bombe qui a tué 170 Afghans\net 13 militaires américains.\nL\'enquête du commandement central américain a conclu en novembre 2021 qu\'étant donné la détérioration de la sécurité à la porte de l\'Abbaye de l\'aéroport alors que les Afghans cherchaient de plus en plus à fuir, "l\'attaque n\'aurait pas pu être évitée au niveau tactique sans dégrader la mission visant à maximiser le nombre d\'évacués" Le Pentagone a déclaré que l\'examen de l\'attentat suicide n\'avait révélé aucune identification préalable d\'un attaquant possible ni aucune demande d\'"escalade des règles d\'engagement existantes" régissant l\'utilisation de la force par les troupes américaines'</li><li>'Les retombées de la guerre se répercutent sur les lieux de travail aux États-Unis.\nNEW YORK - Les retombées de la guerre entre Israël et le Hamas se sont répandues sur les lieux de travail partout dans le monde, les dirigeants de grandes entreprises exprimant leur point de vue tandis que les travailleurs se plaignent de ne pas être entendus "À quoi me sert mon travail si je compromets ma propre morale et mon éthique ?\nL\'un des conflits les plus importants s\'est produit chez Starbucks après que Starbucks Workers United, un syndicat représentant 9 000 travailleurs dans plus de 360 magasins aux États-Unis, a tweeté "Solidarité avec la Palestine" deux jours après l\'attaque du Hamas. Le tweet a été supprimé au bout de 40 minutes, mais l\'entreprise a déclaré qu\'il avait donné lieu à plus de 1 000 plaintes, à des actes de vandalisme et à des affrontements dans ses magasins NEW YORK - Les retombées de la guerre entre Israël et le Hamas se sont répandues sur les lieux de travail partout dans le monde, les dirigeants de grandes entreprises exprimant leur point de vue tandis que les travailleurs se plaignent de ne pas être entendus'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy_Score | Classification_Report |
|:--------|:---------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **all** | 0.9435 | {'0': {'precision': 0.9361702127659575, 'recall': 0.9322033898305084, 'f1-score': 0.9341825902335456, 'support': 236}, '1': {'precision': 0.9333333333333333, 'recall': 0.9302325581395349, 'f1-score': 0.9317803660565723, 'support': 301}, '2': {'precision': 0.9646017699115044, 'recall': 0.9732142857142857, 'f1-score': 0.9688888888888889, 'support': 224}, 'accuracy': 0.9434954007884363, 'macro avg': {'precision': 0.9447017720035985, 'recall': 0.945216744561443, 'f1-score': 0.9449506150596689, 'support': 761}, 'weighted avg': {'precision': 0.9434169513880108, 'recall': 0.9434954007884363, 'f1-score': 0.9434482162802315, 'support': 761}} |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mogaio/pr_ebsa_fr_tran_merged25_e1_end_offsets")
# Run inference
preds = model("Adil Hussain
Adil Hussain est reconnaissant d'avoir reçu l'enseignement de l'acteur Naseeruddin Shah à l'époque où il fréquentait l'École nationale d'art dramatique")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:-----|
| Word count | 9 | 247.2638 | 2089 |
| Label | Training Sample Count |
|:------|:----------------------|
| neg | 913 |
| obj | 1216 |
| pos | 911 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 1
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0013 | 1 | 0.3703 | - |
| 0.0658 | 50 | 0.3145 | - |
| 0.1316 | 100 | 0.1839 | - |
| 0.1974 | 150 | 0.2558 | - |
| 0.2632 | 200 | 0.2683 | - |
| 0.3289 | 250 | 0.1572 | - |
| 0.3947 | 300 | 0.1953 | - |
| 0.4605 | 350 | 0.171 | - |
| 0.5263 | 400 | 0.2326 | - |
| 0.5921 | 450 | 0.1762 | - |
| 0.6579 | 500 | 0.2818 | - |
| 0.7237 | 550 | 0.2733 | - |
| 0.7895 | 600 | 0.195 | - |
| 0.8553 | 650 | 0.2104 | - |
| 0.9211 | 700 | 0.2124 | - |
| 0.9868 | 750 | 0.0818 | - |
| 1.0526 | 800 | 0.1046 | - |
| 1.1184 | 850 | 0.1633 | - |
| 1.1842 | 900 | 0.3207 | - |
| 1.25 | 950 | 0.2703 | - |
| 1.3158 | 1000 | 0.1934 | - |
| 1.3816 | 1050 | 0.2547 | - |
| 1.4474 | 1100 | 0.0933 | - |
| 1.5132 | 1150 | 0.2102 | - |
| 1.5789 | 1200 | 0.0699 | - |
| 1.6447 | 1250 | 0.1778 | - |
| 1.7105 | 1300 | 0.1796 | - |
| 1.7763 | 1350 | 0.0221 | - |
| 1.8421 | 1400 | 0.2154 | - |
| 1.9079 | 1450 | 0.1683 | - |
| 1.9737 | 1500 | 0.3096 | - |
| 2.0395 | 1550 | 0.201 | - |
| 2.1053 | 1600 | 0.1954 | - |
| 2.1711 | 1650 | 0.2301 | - |
| 2.2368 | 1700 | 0.1141 | - |
| 2.3026 | 1750 | 0.1949 | - |
| 2.3684 | 1800 | 0.164 | - |
| 2.4342 | 1850 | 0.2307 | - |
| 2.5 | 1900 | 0.1912 | - |
| 2.5658 | 1950 | 0.2349 | - |
| 2.6316 | 2000 | 0.0922 | - |
| 2.6974 | 2050 | 0.0702 | - |
| 2.7632 | 2100 | 0.1089 | - |
| 2.8289 | 2150 | 0.1711 | - |
| 2.8947 | 2200 | 0.1432 | - |
| 2.9605 | 2250 | 0.2739 | - |
| 3.0263 | 2300 | 0.1889 | - |
| 3.0921 | 2350 | 0.1036 | - |
| 3.1579 | 2400 | 0.1372 | - |
| 3.2237 | 2450 | 0.028 | - |
| 3.2895 | 2500 | 0.1739 | - |
| 3.3553 | 2550 | 0.142 | - |
| 3.4211 | 2600 | 0.0838 | - |
| 3.4868 | 2650 | 0.0657 | - |
| 3.5526 | 2700 | 0.0054 | - |
| 3.6184 | 2750 | 0.0426 | - |
| 3.6842 | 2800 | 0.1974 | - |
| 3.75 | 2850 | 0.0279 | - |
| 3.8158 | 2900 | 0.1326 | - |
| 3.8816 | 2950 | 0.1614 | - |
| 3.9474 | 3000 | 0.1251 | - |
| 4.0132 | 3050 | 0.1174 | - |
| 4.0789 | 3100 | 0.1948 | - |
| 4.1447 | 3150 | 0.0555 | - |
| 4.2105 | 3200 | 0.0064 | - |
| 4.2763 | 3250 | 0.064 | - |
| 4.3421 | 3300 | 0.0013 | - |
| 4.4079 | 3350 | 0.135 | - |
| 4.4737 | 3400 | 0.0574 | - |
| 4.5395 | 3450 | 0.174 | - |
| 4.6053 | 3500 | 0.2199 | - |
| 4.6711 | 3550 | 0.387 | - |
| 4.7368 | 3600 | 0.114 | - |
| 4.8026 | 3650 | 0.0853 | - |
| 4.8684 | 3700 | 0.0325 | - |
| 4.9342 | 3750 | 0.019 | - |
| 5.0 | 3800 | 0.0572 | - |
| 0.0013 | 1 | 0.1435 | - |
| 0.0658 | 50 | 0.0969 | - |
| 0.1316 | 100 | 0.1085 | - |
| 0.1974 | 150 | 0.0271 | - |
| 0.2632 | 200 | 0.0138 | - |
| 0.3289 | 250 | 0.058 | - |
| 0.3947 | 300 | 0.1205 | - |
| 0.4605 | 350 | 0.0788 | - |
| 0.5263 | 400 | 0.1449 | - |
| 0.5921 | 450 | 0.0383 | - |
| 0.6579 | 500 | 0.0338 | - |
| 0.7237 | 550 | 0.1253 | - |
| 0.7895 | 600 | 0.069 | - |
| 0.8553 | 650 | 0.104 | - |
| 0.9211 | 700 | 0.0462 | - |
| 0.9868 | 750 | 0.1975 | - |
| 1.0526 | 800 | 0.0241 | - |
| 1.1184 | 850 | 0.0426 | - |
| 1.1842 | 900 | 0.0519 | - |
| 1.25 | 950 | 0.0815 | - |
| 1.3158 | 1000 | 0.1839 | - |
| 1.3816 | 1050 | 0.0198 | - |
| 1.4474 | 1100 | 0.0128 | - |
| 1.5132 | 1150 | 0.1645 | - |
| 1.5789 | 1200 | 0.0019 | - |
| 1.6447 | 1250 | 0.0557 | - |
| 1.7105 | 1300 | 0.0098 | - |
| 1.7763 | 1350 | 0.001 | - |
| 1.8421 | 1400 | 0.1557 | - |
| 1.9079 | 1450 | 0.1286 | - |
| 1.9737 | 1500 | 0.094 | - |
| 2.0395 | 1550 | 0.0059 | - |
| 2.1053 | 1600 | 0.0227 | - |
| 2.1711 | 1650 | 0.0899 | - |
| 2.2368 | 1700 | 0.0053 | - |
| 2.3026 | 1750 | 0.0021 | - |
| 2.3684 | 1800 | 0.0114 | - |
| 2.4342 | 1850 | 0.1163 | - |
| 2.5 | 1900 | 0.0959 | - |
| 2.5658 | 1950 | 0.0252 | - |
| 2.6316 | 2000 | 0.0921 | - |
| 2.6974 | 2050 | 0.1159 | - |
| 2.7632 | 2100 | 0.0026 | - |
| 2.8289 | 2150 | 0.1211 | - |
| 2.8947 | 2200 | 0.1843 | - |
| 2.9605 | 2250 | 0.0014 | - |
| 3.0263 | 2300 | 0.0085 | - |
| 3.0921 | 2350 | 0.0839 | - |
| 3.1579 | 2400 | 0.2372 | - |
| 3.2237 | 2450 | 0.0213 | - |
| 3.2895 | 2500 | 0.0155 | - |
| 3.3553 | 2550 | 0.1128 | - |
| 3.4211 | 2600 | 0.0945 | - |
| 3.4868 | 2650 | 0.0917 | - |
| 3.5526 | 2700 | 0.0011 | - |
| 3.6184 | 2750 | 0.0024 | - |
| 3.6842 | 2800 | 0.0044 | - |
| 3.75 | 2850 | 0.121 | - |
| 3.8158 | 2900 | 0.0056 | - |
| 3.8816 | 2950 | 0.003 | - |
| 3.9474 | 3000 | 0.0899 | - |
| 4.0132 | 3050 | 0.0157 | - |
| 4.0789 | 3100 | 0.1188 | - |
| 4.1447 | 3150 | 0.001 | - |
| 4.2105 | 3200 | 0.0222 | - |
| 4.2763 | 3250 | 0.1209 | - |
| 4.3421 | 3300 | 0.1085 | - |
| 4.4079 | 3350 | 0.0054 | - |
| 4.4737 | 3400 | 0.0009 | - |
| 4.5395 | 3450 | 0.0015 | - |
| 4.6053 | 3500 | 0.003 | - |
| 4.6711 | 3550 | 0.0009 | - |
| 4.7368 | 3600 | 0.0003 | - |
| 4.8026 | 3650 | 0.0009 | - |
| 4.8684 | 3700 | 0.03 | - |
| 4.9342 | 3750 | 0.1206 | - |
| 5.0 | 3800 | 0.0003 | - |
| 0.0013 | 1 | 0.2045 | - |
| 0.0658 | 50 | 0.0078 | - |
| 0.1316 | 100 | 0.0087 | - |
| 0.1974 | 150 | 0.0386 | - |
| 0.2632 | 200 | 0.1015 | - |
| 0.3289 | 250 | 0.0022 | - |
| 0.3947 | 300 | 0.0291 | - |
| 0.4605 | 350 | 0.0013 | - |
| 0.5263 | 400 | 0.0022 | - |
| 0.5921 | 450 | 0.1324 | - |
| 0.6579 | 500 | 0.113 | - |
| 0.7237 | 550 | 0.0011 | - |
| 0.7895 | 600 | 0.1723 | - |
| 0.8553 | 650 | 0.0049 | - |
| 0.9211 | 700 | 0.206 | - |
| 0.9868 | 750 | 0.1683 | - |
| 1.0526 | 800 | 0.0954 | - |
| 1.1184 | 850 | 0.018 | - |
| 1.1842 | 900 | 0.1854 | - |
| 1.25 | 950 | 0.0342 | - |
| 1.3158 | 1000 | 0.0015 | - |
| 1.3816 | 1050 | 0.0062 | - |
| 1.4474 | 1100 | 0.1187 | - |
| 1.5132 | 1150 | 0.0048 | - |
| 1.5789 | 1200 | 0.0011 | - |
| 1.6447 | 1250 | 0.002 | - |
| 1.7105 | 1300 | 0.092 | - |
| 1.7763 | 1350 | 0.1245 | - |
| 1.8421 | 1400 | 0.0009 | - |
| 1.9079 | 1450 | 0.1185 | - |
| 1.9737 | 1500 | 0.0017 | - |
| 2.0395 | 1550 | 0.008 | - |
| 2.1053 | 1600 | 0.0049 | - |
| 2.1711 | 1650 | 0.0083 | - |
| 2.2368 | 1700 | 0.0026 | - |
| 2.3026 | 1750 | 0.0081 | - |
| 2.3684 | 1800 | 0.0036 | - |
| 2.4342 | 1850 | 0.0016 | - |
| 2.5 | 1900 | 0.0017 | - |
| 2.5658 | 1950 | 0.0014 | - |
| 2.6316 | 2000 | 0.0017 | - |
| 2.6974 | 2050 | 0.002 | - |
| 2.7632 | 2100 | 0.1022 | - |
| 2.8289 | 2150 | 0.0004 | - |
| 2.8947 | 2200 | 0.0007 | - |
| 2.9605 | 2250 | 0.0794 | - |
| 3.0263 | 2300 | 0.0183 | - |
| 3.0921 | 2350 | 0.0377 | - |
| 3.1579 | 2400 | 0.029 | - |
| 3.2237 | 2450 | 0.0003 | - |
| 3.2895 | 2500 | 0.0961 | - |
| 3.3553 | 2550 | 0.0008 | - |
| 3.4211 | 2600 | 0.0873 | - |
| 3.4868 | 2650 | 0.0501 | - |
| 3.5526 | 2700 | 0.0029 | - |
| 3.6184 | 2750 | 0.0008 | - |
| 3.6842 | 2800 | 0.0004 | - |
| 3.75 | 2850 | 0.0011 | - |
| 3.8158 | 2900 | 0.0518 | - |
| 3.8816 | 2950 | 0.0002 | - |
| 3.9474 | 3000 | 0.1115 | - |
| 4.0132 | 3050 | 0.0129 | - |
| 4.0789 | 3100 | 0.0005 | - |
| 4.1447 | 3150 | 0.0012 | - |
| 4.2105 | 3200 | 0.1086 | - |
| 4.2763 | 3250 | 0.0199 | - |
| 4.3421 | 3300 | 0.0004 | - |
| 4.4079 | 3350 | 0.0001 | - |
| 4.4737 | 3400 | 0.0832 | - |
| 4.5395 | 3450 | 0.0003 | - |
| 4.6053 | 3500 | 0.0041 | - |
| 4.6711 | 3550 | 0.1146 | - |
| 4.7368 | 3600 | 0.0027 | - |
| 4.8026 | 3650 | 0.0002 | - |
| 4.8684 | 3700 | 0.0544 | - |
| 4.9342 | 3750 | 0.0002 | - |
| 5.0 | 3800 | 0.0046 | - |
| 0.0013 | 1 | 0.0015 | - |
| 0.0658 | 50 | 0.1973 | - |
| 0.1316 | 100 | 0.0106 | - |
| 0.1974 | 150 | 0.0744 | - |
| 0.2632 | 200 | 0.1033 | - |
| 0.3289 | 250 | 0.0425 | - |
| 0.3947 | 300 | 0.1125 | - |
| 0.4605 | 350 | 0.0018 | - |
| 0.5263 | 400 | 0.0019 | - |
| 0.5921 | 450 | 0.0002 | - |
| 0.6579 | 500 | 0.0007 | - |
| 0.7237 | 550 | 0.1393 | - |
| 0.7895 | 600 | 0.0002 | - |
| 0.8553 | 650 | 0.0043 | - |
| 0.9211 | 700 | 0.0339 | - |
| 0.9868 | 750 | 0.0002 | - |
| 0.0013 | 1 | 0.0007 | - |
| 0.0658 | 50 | 0.0419 | - |
| 0.1316 | 100 | 0.0068 | - |
| 0.1974 | 150 | 0.1401 | - |
| 0.2632 | 200 | 0.0423 | - |
| 0.3289 | 250 | 0.1122 | - |
| 0.3947 | 300 | 0.0037 | - |
| 0.4605 | 350 | 0.005 | - |
| 0.5263 | 400 | 0.0006 | - |
| 0.5921 | 450 | 0.0006 | - |
| 0.6579 | 500 | 0.0016 | - |
| 0.7237 | 550 | 0.1244 | - |
| 0.7895 | 600 | 0.0016 | - |
| 0.8553 | 650 | 0.0028 | - |
| 0.9211 | 700 | 0.002 | - |
| 0.9868 | 750 | 0.057 | - |
| 0.0013 | 1 | 0.1396 | - |
| 0.0658 | 50 | 0.0366 | - |
| 0.1316 | 100 | 0.0021 | - |
| 0.1974 | 150 | 0.1088 | - |
| 0.2632 | 200 | 0.0449 | - |
| 0.3289 | 250 | 0.0187 | - |
| 0.3947 | 300 | 0.0017 | - |
| 0.4605 | 350 | 0.1262 | - |
| 0.5263 | 400 | 0.0052 | - |
| 0.5921 | 450 | 0.1188 | - |
| 0.6579 | 500 | 0.0002 | - |
| 0.7237 | 550 | 0.0006 | - |
| 0.7895 | 600 | 0.0758 | - |
| 0.8553 | 650 | 0.025 | - |
| 0.9211 | 700 | 0.0052 | - |
| 0.9868 | 750 | 0.1985 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.15.0
- Tokenizers: 0.15.0
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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|
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] |
igorktech/TALANT_rugpt3medium-lora
|
igorktech
| 2023-12-15T19:16:29Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:IlyaGusev/rugpt3medium_sum_gazeta",
"region:us"
] | null | 2023-12-15T19:02:11Z |
---
library_name: peft
base_model: IlyaGusev/rugpt3medium_sum_gazeta
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.2.dev0
|
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] |
mogaio/pr_ebsa_fr_tran_merged25_e1_beginning_offsets
|
mogaio
| 2023-12-15T19:03:37Z | 0 | 0 |
setfit
|
[
"setfit",
"safetensors",
"xlm-roberta",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
"model-index",
"region:us"
] |
text-classification
| 2023-12-15T19:02:22Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy_score
- classification_report
widget:
- text: 'Adil Hussain
Adil Hussain est reconnaissant d''avoir reçu l''enseignement de l''acteur Naseeruddin
Shah à l''époque où il fréquentait l''École nationale d''art dramatique'
- text: 'Bien que leurs opinions sur la question de savoir si les migrants sont un
avantage ou un fardeau soient plus mitigées, de nettes majorités d''électeurs
de toute la ville de New York, de la banlieue et du nord de l''État ont déclaré
que l''État devrait essayer de ralentir l''afflux de migrants, plutôt que d''en
accepter davantage et de s''efforcer d''assimiler les nouveaux arrivants Les démocrates
aspirent à renverser six circonscriptions détenues par les républicains que M.
Biden a remportées en 2020, notamment celle de M Les républicains se sont emparés
de la crise des migrants, donnant un avant-goût des campagnes de l''année prochaine
Les républicains ont surenchéri : Elise Stefanik, la New-Yorkaise qui dirige la
conférence du parti démocrate à la Chambre des représentants,
Suite à la page suivante
a déclaré à Politico la semaine dernière que le parti allait consacrer 100 millions
de dollars aux campagnes dans les circonscriptions de New York Des problèmes à
venir pour les démocrates de New York en 2024 ?
Les dirigeants démocrates de New York se débattent depuis des mois avec le problème
de l''hébergement des dizaines de milliers de migrants qui ont été transportés
par bus jusqu''à New York et laissés à sa charge Des problèmes à venir pour les
démocrates de New York en 2024 ?
Les dirigeants démocrates de New York se débattent depuis des mois avec le problème
de l''hébergement des dizaines de milliers de migrants qui ont été transportés
par bus jusqu''à New York et laissés à sa charge.
Mais une autre préoccupation se profile alors que la crise se poursuit sans qu''aucune
issue ne soit en vue : les retombées potentielles pour leur parti lors des élections
de l''année prochaine Les républicains ont tendance à se sentir en sécurité lorsqu''ils
parlent d''immigration - comme les démocrates le font pour l''avortement - et
sont clairement à l''attaque sur la question des migrants à New York, tandis que
les démocrates sont sur la défensive, a déclaré Kyle Kondik, directeur de la communication
pour le Centre de politique de l''Université de Virginie, au réseau USA Today
Plus de 100 000 migrants ont été transportés à New York depuis la frontière sud
depuis le printemps 2022. Environ 60 000 d''entre eux sont hébergés dans la ville,
et plus de 2 100 ont été transportés dans des hôtels situés dans sept comtés au
nord de la ville, de Yonkers à la périphérie de Buffalo, où ils sont logés aux
frais de la ville Les démocrates doivent y remporter des victoires pour gagner
cinq sièges à la Chambre et faire du député Hakeem Jeffries, de Brooklyn, le prochain
président de la Chambre des représentants Les publicités d''attaque des républicains
s''écrivent pratiquement d''elles-mêmes à partir d''un flot de titres et d''images
télévisées, alors que le gouverneur Kathy Hochul, le maire de New York Eric Adams
et le président Joe Biden - tous démocrates - se rejettent mutuellement la faute
et s''échangent des coups de feu pour savoir qui devrait en faire le plus Isaac
Goldberg, un stratège démocrate qui a travaillé sur plusieurs campagnes électorales
à New York, a affirmé qu''il était beaucoup trop tôt pour prédire l''impact politique
de la crise des migrants, soulignant que les élections de 2024 n''auront lieu
que dans 14 mois et que de nombreuses questions tout aussi urgentes pourraient
se poser'
- text: 'LE CANDIDAT A LA PRESIDENCE RAMASWAMY VEUT METTRE FIN AU SYSTEME DE VISA
H-1B AUX ETATS-UNIS
Décrivant le programme de visas H-1B comme une forme de "servitude", Vivek Ramaswamy,
candidat républicain indien-américain à l''élection présidentielle, a promis de
"vider" le système basé sur la loterie et de le remplacer par un système d''admission
méritocratique s''il remporte les élections présidentielles de 2024'
- text: 'Smith Hal Sparks Catherine Zeta-Jones son-Sampras Chris Owen Donald Glover
("Queer as Folk") a 54 ans Smith Hal Sparks Catherine Zeta-Jones son-Sampras Chris
Owen Donald Glover
("Queer as Folk") a 54 ans. a 54 ans. Acteur
("Je sais ce que vous avez fait l''été dernier") a 50 ans'
- text: 'Trump profiter de sa célébrité jusqu''à la Maison-Blanche.
"Cela a tué Howard parce qu''il était le roi de tous les médias Il a poursuivi
en disant que Trump ne laisserait pas ses partisans s''approcher de l''une de
ses propriétés. "Les gens qui votent pour Trump, pour la plupart, ne les laisseraient
même pas entrer dans un putain d''hôtel [ "Si être réveillé signifie que je ne
peux pas soutenir Trump, ce que je pense que cela signifie, ou que je soutiens
les personnes qui veulent être transgenres ou que je suis pour le vaccin, appelez-moi
réveillé comme vous le voulez" "Les gens qui votent pour Trump, pour la plupart,
ne les laisseraient même pas entrer dans un putain d''hôtel [...]. Allez à Mar-a-lago,
voyez s''il y a des gens qui vous ressemblent" Stern a également abordé les affirmations
de Trump et de ses partisans selon lesquelles Joe Biden a remporté l''élection
américaine de 2020 grâce à des votes frauduleux "Et soudain, Trump a transformé
Howard, qui était le roi de tous les médias, en prince Harry de tous les médias.
Tout le monde s''en fout "Trump avait l''habitude de participer à l''émission
de Stern chaque semaine. Ils étaient amis. Alors cette idée que Trump est le pire
type qui ait jamais marché sur la surface de la terre, pourquoi traîniez-vous
avec lui ?"
M Mais Stern, qui par le passé a été accusé de racisme et de sexisme dans nombre
de ses sketches à l''antenne, a été un critique virulent de Trump tout au long
de sa présidence et, plus récemment, alors qu''il se prépare à se présenter à
nouveau en 2024.
En 2021, M "Combien de temps allons-nous continuer à élire des gens qui ont perdu
l''élection ?"
Il a poursuivi en qualifiant les partisans de Trump de "nigauds".
"Mon Dieu, j''ai l''impression d''être dans une nation de nigauds. J''espère qu''il
y a encore des gens brillants et dynamiques qui aiment ce pays", a-t-il déclaré
Alors cette idée que Trump est le pire type qui ait jamais marché sur la surface
de la terre, pourquoi traîniez-vous avec lui ?"
M. Failla a déclaré que cela avait "tué" M Si "woke" signifie que je ne peux pas
soutenir Trump, ce que je pense que cela signifie, ou que je soutiens les personnes
qui veulent être transgenres ou que je suis pour le vaccin, appelez-moi "woke"
comme vous voulez Celui qui se décrit comme le "roi de tous les médias" a critiqué
ouvertement l''ancien président américain Donald Trump, les anti-vaxx et, plus
récemment, Lauren Boebert, qu''il a critiquée pour son comportement obscène dans
un théâtre de Denver au début du mois "L''omnipotence médiatique de Donald Trump
a brisé Howard Stern. C''est très important", a déclaré Failla dans la vidéo (selon
OK ! Magazine). "Trump avait l''habitude de participer à l''émission de Stern
chaque semaine L''aversion d''Howard Stern pour Donald Trump, c''est "tout l''ego".
Si "woke" signifie que je ne peux pas soutenir Trump, ce que je pense que cela
signifie, ou que je soutiens les personnes qui veulent être transgenres ou que
je suis pour le vaccin, appelez-moi "woke" comme vous voulez Trump l''année prochaine.
"Je sais que je lui botterai le cul", a-t-il déclaré aux auditeurs.
L''année suivante, Stern a déclaré qu''il envisageait de se lancer dans la course
à la présidence "pour que le pays soit à nouveau juste" En réponse, Trump a partagé
sur sa plateforme Truth Social un clip de Fox News dans lequel l''animateur Jimmy
Failla critique Stern.
"L''omnipotence médiatique de Donald Trump a brisé Howard Stern "Je vais faire
la chose très simple qui remettra le pays sur le droit chemin : un vote, une personne",
a expliqué Stern, affirmant que Trump a en fait perdu l''élection de 2016 contre
Hillary Clinton qui a remporté le vote populaire - mais pas le collège électoral'
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy_score
value: 0.9434954007884363
name: Accuracy_Score
- type: classification_report
value:
'0':
precision: 0.9361702127659575
recall: 0.9322033898305084
f1-score: 0.9341825902335456
support: 236
'1':
precision: 0.9333333333333333
recall: 0.9302325581395349
f1-score: 0.9317803660565723
support: 301
'2':
precision: 0.9646017699115044
recall: 0.9732142857142857
f1-score: 0.9688888888888889
support: 224
accuracy: 0.9434954007884363
macro avg:
precision: 0.9447017720035985
recall: 0.945216744561443
f1-score: 0.9449506150596689
support: 761
weighted avg:
precision: 0.9434169513880108
recall: 0.9434954007884363
f1-score: 0.9434482162802315
support: 761
name: Classification_Report
---
# SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| pos | <ul><li>"Les PHL lèvent 1,26 milliard de dollars grâce aux obligations en dollars de détail\nLE GOUVERNEMENT PHILIPPIN a levé 1,26 milliard de dollars lors de la première émission d'obligations de détail en dollars (RDB) sous l'administration Marcos, a déclaré le ministère des Finances (DoF)"</li><li>"Atom Egoyan revient à Salomé, l'opéra qu'il a monté en 1996, avec Seven Veils\nAtom Egoyan n'a pas été surpris lorsque la Canadian Opera Company lui a demandé de remonter Salomé pour la saison 2022-23 Atom Egoyan revient à Salomé, l'opéra qu'il a monté en 1996, avec Seven Veils\nAtom Egoyan n'a pas été surpris lorsque la Canadian Opera Company lui a demandé de remonter Salomé pour la saison 2022-23. Avec ses éléments de film et de vidéo, son interprétation psychologique et sombre de l'opéra de Richard Strauss avait un solide palmarès de reprises - depuis sa création en 1996, elle avait été présentée deux fois de plus à la COC et avait été reprise par plusieurs autres compagnies"</li><li>'Paul Simon présente un documentaire sur sa carrière\nAprès un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public Paul Simon présente un documentaire sur sa carrière\nAprès un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public.\nTORONTO >> Après un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public "Il n\'y a pas de raison que vous soyez épuisés", a dit Simon à la foule après la première du documentaire d\'Alex Gibney "In Restless Dreams : The Music of Paul Simon" d\'Alex Gibney, dimanche au Festival international du film de Toronto.\nSimon, âgé de 81 ans, n\'avait pas regardé le film avant la première, et il ne l\'a pas regardé non plus dimanche TORONTO >> Après un documentaire de trois heures et demie sur sa vie, Paul Simon n\'avait que de la sympathie pour le public.\n"Il n\'y a pas de raison que vous soyez épuisés", a dit Simon à la foule après la première du documentaire d\'Alex Gibney "In Restless Dreams : The Music of Paul Simon" d\'Alex Gibney, dimanche au Festival international du film de Toronto'</li></ul> |
| neg | <ul><li>'Le groupe Al-Mostaqilla de l\'université du Koweït a appelé les étudiants à organiser un sit-in à l\'université du Koweït lundi pour protester contre la décision de mettre fin aux classes mixtes La décision a été prise la semaine dernière par le nouveau ministre de l\'éducation, Adel Al-Mane, et le directeur par intérim de l\'université du Koweït, Fayez Al-Dhafiri, et mise en œuvre mercredi, trois jours seulement avant le début de la nouvelle année universitaire à la faculté de droit L\'association a également demandé au gouvernement de "cesser ses interventions politiques et médiatiques injustifiées" dans les affaires de l\'université du Koweït.\nL\'association a appelé le directeur par intérim de l\'université du Koweït à ne pas céder aux pressions politiques et médiatiques et à s\'efforcer de protéger l\'indépendance de l\'université Dhafiri a déclaré que la décision avait été prise en application de la loi de 1996 qui interdisait l\'enseignement mixte à l\'université du Koweït, malgré une décision de la Cour constitutionnelle de 2015 autorisant l\'enseignement mixte lorsqu\'il était nécessaire et dans des cas exceptionnels Parallèlement, l\'association des professeurs de l\'université du Koweït a publié samedi une déclaration demandant aux députés et au gouvernement de "cesser d\'interférer dans les affaires de l\'université du Koweït" et de maintenir l\'indépendance de l\'université "L\'université du Koweït était, est et sera toujours le porte-drapeau de la connaissance et des valeurs, à l\'abri de toute influence extérieure Le député Abdulwahab Al-Essa a reproché à l\'administration de l\'université du Koweït d\'avoir succombé à la pression politique au détriment de l\'intérêt public, ajoutant que l\'université du Koweït avait appliqué correctement une décision de la cour constitutionnelle autorisant les classes mixtes chaque fois que cela était nécessaire'</li><li>"L'immigration étant l'un des défis les plus difficiles à relever pour le président Joe Biden et apparaissant comme un enjeu majeur des élections de l'année prochaine, l'administration délocalise essentiellement la question en s'appuyant sur les pays d'Amérique centrale et d'Amérique du Sud pour empêcher les migrants de se diriger vers le nord"</li><li>'Lors d\'une réunion d\'information mardi, le porte-parole de l\'armée, le lieutenant-colonel Richard Hecht, a suggéré que les Palestiniens tentent de quitter la bande de Gaza par le poste-frontière de Rafah, en Égypte.\nLa perspective d\'un exode des habitants de Gaza vers le territoire égyptien a alarmé les autorités égyptiennes La question qui se pose est de savoir si Israël lancera une offensive terrestre dans la bande de Gaza, une bande de terre de 25 miles de long coincée entre Israël, l\'Égypte et la mer Méditerranée, où vivent 2,3 millions de personnes et qui est gouvernée par le Hamas depuis 2007 Israël pilonne la bande de Gaza ; les habitants se précipitent pour se mettre à l\'abri\nJERUSALEM - Les avions de combat israéliens ont bombardé la bande de Gaza quartier par quartier mardi, réduisant les bâtiments en ruines et poussant les habitants à se précipiter pour se mettre à l\'abri dans ce minuscule territoire isolé, alors qu\'Israël promet des représailles pour l\'attaque surprise du Hamas du week-end qui "se répercuteront Les autorités égyptiennes discutent avec Israël et les États-Unis afin de mettre en place des corridors humanitaires dans la bande de Gaza pour acheminer l\'aide, a déclaré un responsable égyptien. Des négociations sont en cours avec les Israéliens pour que la zone autour du point de passage de Rafah entre l\'Égypte et Gaza soit déclarée "zone d\'interdiction de feu", a déclaré le responsable, sous couvert d\'anonymat car il n\'était pas autorisé à parler aux médias'</li></ul> |
| obj | <ul><li>"L'économie pèse sur les Américains Ils sont plus nombreux à faire confiance à Trump qu'à Biden pour alléger leur fardeau\nWASHINGTON - Linda Muñoz a peur de l'économie Trump, le candidat républicain à la primaire de 2024, pour améliorer l'économie, avec une marge de 47 % à 36 %. L'écart est de 46 %-26 % en faveur de M. Trump parmi les électeurs indépendants Presque tous les républicains interrogés ont exprimé leur pessimisme à l'égard de l'économie, selon le sondage : 96 % d'entre eux estiment que la situation se dégrade au lieu de s'améliorer Le logement. L'essence. Tous ces éléments poussent les gens à s'endetter de plus en plus, disent-ils.\nSelon le sondage, près de 70 % des Américains estiment que la situation économique se dégrade, tandis que 22 % seulement estiment qu'elle s'améliore L'économie pèse sur les Américains Ils sont plus nombreux à faire confiance à Trump qu'à Biden pour alléger leur fardeau\nWASHINGTON - Linda Muñoz a peur de l'économie. Elle a puisé dans son épargne d'urgence cette année. Et elle ne croit pas que le président Joe Biden ressente sa douleur L'épicerie. Le logement. L'essence. Tous ces éléments poussent les gens à s'endetter de plus en plus, disent-ils.\nSelon le sondage, près de 70 % des Américains estiment que la situation économique se dégrade, tandis que 22 % seulement estiment qu'elle s'améliore"</li><li>'Le Pentagone va interroger d\'autres militaires sur l\'attentat suicide de l\'aéroport de Kaboul en 2021\nLe commandement central du Pentagone a ordonné l\'audition d\'une vingtaine de militaires supplémentaires qui se trouvaient à l\'aéroport de Kaboul lorsque des kamikazes ont attaqué pendant le retrait chaotique des forces américaines d\'Afghanistan, alors que les critiques persistent sur le fait que l\'attaque meurtrière aurait pu être stoppée Certaines familles des personnes tuées ou blessées se sont plaintes que le Pentagone n\'avait pas fait preuve de suffisamment de transparence au sujet de l\'attentat à la bombe qui a tué 170 Afghans\net 13 militaires américains.\nL\'enquête du commandement central américain a conclu en novembre 2021 qu\'étant donné la détérioration de la sécurité à la porte de l\'Abbaye de l\'aéroport alors que les Afghans cherchaient de plus en plus à fuir, "l\'attaque n\'aurait pas pu être évitée au niveau tactique sans dégrader la mission visant à maximiser le nombre d\'évacués" Le Pentagone a déclaré que l\'examen de l\'attentat suicide n\'avait révélé aucune identification préalable d\'un attaquant possible ni aucune demande d\'"escalade des règles d\'engagement existantes" régissant l\'utilisation de la force par les troupes américaines'</li><li>'Les retombées de la guerre se répercutent sur les lieux de travail aux États-Unis.\nNEW YORK - Les retombées de la guerre entre Israël et le Hamas se sont répandues sur les lieux de travail partout dans le monde, les dirigeants de grandes entreprises exprimant leur point de vue tandis que les travailleurs se plaignent de ne pas être entendus "À quoi me sert mon travail si je compromets ma propre morale et mon éthique ?\nL\'un des conflits les plus importants s\'est produit chez Starbucks après que Starbucks Workers United, un syndicat représentant 9 000 travailleurs dans plus de 360 magasins aux États-Unis, a tweeté "Solidarité avec la Palestine" deux jours après l\'attaque du Hamas. Le tweet a été supprimé au bout de 40 minutes, mais l\'entreprise a déclaré qu\'il avait donné lieu à plus de 1 000 plaintes, à des actes de vandalisme et à des affrontements dans ses magasins NEW YORK - Les retombées de la guerre entre Israël et le Hamas se sont répandues sur les lieux de travail partout dans le monde, les dirigeants de grandes entreprises exprimant leur point de vue tandis que les travailleurs se plaignent de ne pas être entendus'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy_Score | Classification_Report |
|:--------|:---------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **all** | 0.9435 | {'0': {'precision': 0.9361702127659575, 'recall': 0.9322033898305084, 'f1-score': 0.9341825902335456, 'support': 236}, '1': {'precision': 0.9333333333333333, 'recall': 0.9302325581395349, 'f1-score': 0.9317803660565723, 'support': 301}, '2': {'precision': 0.9646017699115044, 'recall': 0.9732142857142857, 'f1-score': 0.9688888888888889, 'support': 224}, 'accuracy': 0.9434954007884363, 'macro avg': {'precision': 0.9447017720035985, 'recall': 0.945216744561443, 'f1-score': 0.9449506150596689, 'support': 761}, 'weighted avg': {'precision': 0.9434169513880108, 'recall': 0.9434954007884363, 'f1-score': 0.9434482162802315, 'support': 761}} |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mogaio/pr_ebsa_fr_tran_merged25_e1_beginning_offsets")
# Run inference
preds = model("Adil Hussain
Adil Hussain est reconnaissant d'avoir reçu l'enseignement de l'acteur Naseeruddin Shah à l'époque où il fréquentait l'École nationale d'art dramatique")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:-----|
| Word count | 9 | 247.2638 | 2089 |
| Label | Training Sample Count |
|:------|:----------------------|
| neg | 913 |
| obj | 1216 |
| pos | 911 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 1
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0013 | 1 | 0.3703 | - |
| 0.0658 | 50 | 0.3145 | - |
| 0.1316 | 100 | 0.1839 | - |
| 0.1974 | 150 | 0.2558 | - |
| 0.2632 | 200 | 0.2683 | - |
| 0.3289 | 250 | 0.1572 | - |
| 0.3947 | 300 | 0.1953 | - |
| 0.4605 | 350 | 0.171 | - |
| 0.5263 | 400 | 0.2326 | - |
| 0.5921 | 450 | 0.1762 | - |
| 0.6579 | 500 | 0.2818 | - |
| 0.7237 | 550 | 0.2733 | - |
| 0.7895 | 600 | 0.195 | - |
| 0.8553 | 650 | 0.2104 | - |
| 0.9211 | 700 | 0.2124 | - |
| 0.9868 | 750 | 0.0818 | - |
| 1.0526 | 800 | 0.1046 | - |
| 1.1184 | 850 | 0.1633 | - |
| 1.1842 | 900 | 0.3207 | - |
| 1.25 | 950 | 0.2703 | - |
| 1.3158 | 1000 | 0.1934 | - |
| 1.3816 | 1050 | 0.2547 | - |
| 1.4474 | 1100 | 0.0933 | - |
| 1.5132 | 1150 | 0.2102 | - |
| 1.5789 | 1200 | 0.0699 | - |
| 1.6447 | 1250 | 0.1778 | - |
| 1.7105 | 1300 | 0.1796 | - |
| 1.7763 | 1350 | 0.0221 | - |
| 1.8421 | 1400 | 0.2154 | - |
| 1.9079 | 1450 | 0.1683 | - |
| 1.9737 | 1500 | 0.3096 | - |
| 2.0395 | 1550 | 0.201 | - |
| 2.1053 | 1600 | 0.1954 | - |
| 2.1711 | 1650 | 0.2301 | - |
| 2.2368 | 1700 | 0.1141 | - |
| 2.3026 | 1750 | 0.1949 | - |
| 2.3684 | 1800 | 0.164 | - |
| 2.4342 | 1850 | 0.2307 | - |
| 2.5 | 1900 | 0.1912 | - |
| 2.5658 | 1950 | 0.2349 | - |
| 2.6316 | 2000 | 0.0922 | - |
| 2.6974 | 2050 | 0.0702 | - |
| 2.7632 | 2100 | 0.1089 | - |
| 2.8289 | 2150 | 0.1711 | - |
| 2.8947 | 2200 | 0.1432 | - |
| 2.9605 | 2250 | 0.2739 | - |
| 3.0263 | 2300 | 0.1889 | - |
| 3.0921 | 2350 | 0.1036 | - |
| 3.1579 | 2400 | 0.1372 | - |
| 3.2237 | 2450 | 0.028 | - |
| 3.2895 | 2500 | 0.1739 | - |
| 3.3553 | 2550 | 0.142 | - |
| 3.4211 | 2600 | 0.0838 | - |
| 3.4868 | 2650 | 0.0657 | - |
| 3.5526 | 2700 | 0.0054 | - |
| 3.6184 | 2750 | 0.0426 | - |
| 3.6842 | 2800 | 0.1974 | - |
| 3.75 | 2850 | 0.0279 | - |
| 3.8158 | 2900 | 0.1326 | - |
| 3.8816 | 2950 | 0.1614 | - |
| 3.9474 | 3000 | 0.1251 | - |
| 4.0132 | 3050 | 0.1174 | - |
| 4.0789 | 3100 | 0.1948 | - |
| 4.1447 | 3150 | 0.0555 | - |
| 4.2105 | 3200 | 0.0064 | - |
| 4.2763 | 3250 | 0.064 | - |
| 4.3421 | 3300 | 0.0013 | - |
| 4.4079 | 3350 | 0.135 | - |
| 4.4737 | 3400 | 0.0574 | - |
| 4.5395 | 3450 | 0.174 | - |
| 4.6053 | 3500 | 0.2199 | - |
| 4.6711 | 3550 | 0.387 | - |
| 4.7368 | 3600 | 0.114 | - |
| 4.8026 | 3650 | 0.0853 | - |
| 4.8684 | 3700 | 0.0325 | - |
| 4.9342 | 3750 | 0.019 | - |
| 5.0 | 3800 | 0.0572 | - |
| 0.0013 | 1 | 0.1435 | - |
| 0.0658 | 50 | 0.0969 | - |
| 0.1316 | 100 | 0.1085 | - |
| 0.1974 | 150 | 0.0271 | - |
| 0.2632 | 200 | 0.0138 | - |
| 0.3289 | 250 | 0.058 | - |
| 0.3947 | 300 | 0.1205 | - |
| 0.4605 | 350 | 0.0788 | - |
| 0.5263 | 400 | 0.1449 | - |
| 0.5921 | 450 | 0.0383 | - |
| 0.6579 | 500 | 0.0338 | - |
| 0.7237 | 550 | 0.1253 | - |
| 0.7895 | 600 | 0.069 | - |
| 0.8553 | 650 | 0.104 | - |
| 0.9211 | 700 | 0.0462 | - |
| 0.9868 | 750 | 0.1975 | - |
| 1.0526 | 800 | 0.0241 | - |
| 1.1184 | 850 | 0.0426 | - |
| 1.1842 | 900 | 0.0519 | - |
| 1.25 | 950 | 0.0815 | - |
| 1.3158 | 1000 | 0.1839 | - |
| 1.3816 | 1050 | 0.0198 | - |
| 1.4474 | 1100 | 0.0128 | - |
| 1.5132 | 1150 | 0.1645 | - |
| 1.5789 | 1200 | 0.0019 | - |
| 1.6447 | 1250 | 0.0557 | - |
| 1.7105 | 1300 | 0.0098 | - |
| 1.7763 | 1350 | 0.001 | - |
| 1.8421 | 1400 | 0.1557 | - |
| 1.9079 | 1450 | 0.1286 | - |
| 1.9737 | 1500 | 0.094 | - |
| 2.0395 | 1550 | 0.0059 | - |
| 2.1053 | 1600 | 0.0227 | - |
| 2.1711 | 1650 | 0.0899 | - |
| 2.2368 | 1700 | 0.0053 | - |
| 2.3026 | 1750 | 0.0021 | - |
| 2.3684 | 1800 | 0.0114 | - |
| 2.4342 | 1850 | 0.1163 | - |
| 2.5 | 1900 | 0.0959 | - |
| 2.5658 | 1950 | 0.0252 | - |
| 2.6316 | 2000 | 0.0921 | - |
| 2.6974 | 2050 | 0.1159 | - |
| 2.7632 | 2100 | 0.0026 | - |
| 2.8289 | 2150 | 0.1211 | - |
| 2.8947 | 2200 | 0.1843 | - |
| 2.9605 | 2250 | 0.0014 | - |
| 3.0263 | 2300 | 0.0085 | - |
| 3.0921 | 2350 | 0.0839 | - |
| 3.1579 | 2400 | 0.2372 | - |
| 3.2237 | 2450 | 0.0213 | - |
| 3.2895 | 2500 | 0.0155 | - |
| 3.3553 | 2550 | 0.1128 | - |
| 3.4211 | 2600 | 0.0945 | - |
| 3.4868 | 2650 | 0.0917 | - |
| 3.5526 | 2700 | 0.0011 | - |
| 3.6184 | 2750 | 0.0024 | - |
| 3.6842 | 2800 | 0.0044 | - |
| 3.75 | 2850 | 0.121 | - |
| 3.8158 | 2900 | 0.0056 | - |
| 3.8816 | 2950 | 0.003 | - |
| 3.9474 | 3000 | 0.0899 | - |
| 4.0132 | 3050 | 0.0157 | - |
| 4.0789 | 3100 | 0.1188 | - |
| 4.1447 | 3150 | 0.001 | - |
| 4.2105 | 3200 | 0.0222 | - |
| 4.2763 | 3250 | 0.1209 | - |
| 4.3421 | 3300 | 0.1085 | - |
| 4.4079 | 3350 | 0.0054 | - |
| 4.4737 | 3400 | 0.0009 | - |
| 4.5395 | 3450 | 0.0015 | - |
| 4.6053 | 3500 | 0.003 | - |
| 4.6711 | 3550 | 0.0009 | - |
| 4.7368 | 3600 | 0.0003 | - |
| 4.8026 | 3650 | 0.0009 | - |
| 4.8684 | 3700 | 0.03 | - |
| 4.9342 | 3750 | 0.1206 | - |
| 5.0 | 3800 | 0.0003 | - |
| 0.0013 | 1 | 0.2045 | - |
| 0.0658 | 50 | 0.0078 | - |
| 0.1316 | 100 | 0.0087 | - |
| 0.1974 | 150 | 0.0386 | - |
| 0.2632 | 200 | 0.1015 | - |
| 0.3289 | 250 | 0.0022 | - |
| 0.3947 | 300 | 0.0291 | - |
| 0.4605 | 350 | 0.0013 | - |
| 0.5263 | 400 | 0.0022 | - |
| 0.5921 | 450 | 0.1324 | - |
| 0.6579 | 500 | 0.113 | - |
| 0.7237 | 550 | 0.0011 | - |
| 0.7895 | 600 | 0.1723 | - |
| 0.8553 | 650 | 0.0049 | - |
| 0.9211 | 700 | 0.206 | - |
| 0.9868 | 750 | 0.1683 | - |
| 1.0526 | 800 | 0.0954 | - |
| 1.1184 | 850 | 0.018 | - |
| 1.1842 | 900 | 0.1854 | - |
| 1.25 | 950 | 0.0342 | - |
| 1.3158 | 1000 | 0.0015 | - |
| 1.3816 | 1050 | 0.0062 | - |
| 1.4474 | 1100 | 0.1187 | - |
| 1.5132 | 1150 | 0.0048 | - |
| 1.5789 | 1200 | 0.0011 | - |
| 1.6447 | 1250 | 0.002 | - |
| 1.7105 | 1300 | 0.092 | - |
| 1.7763 | 1350 | 0.1245 | - |
| 1.8421 | 1400 | 0.0009 | - |
| 1.9079 | 1450 | 0.1185 | - |
| 1.9737 | 1500 | 0.0017 | - |
| 2.0395 | 1550 | 0.008 | - |
| 2.1053 | 1600 | 0.0049 | - |
| 2.1711 | 1650 | 0.0083 | - |
| 2.2368 | 1700 | 0.0026 | - |
| 2.3026 | 1750 | 0.0081 | - |
| 2.3684 | 1800 | 0.0036 | - |
| 2.4342 | 1850 | 0.0016 | - |
| 2.5 | 1900 | 0.0017 | - |
| 2.5658 | 1950 | 0.0014 | - |
| 2.6316 | 2000 | 0.0017 | - |
| 2.6974 | 2050 | 0.002 | - |
| 2.7632 | 2100 | 0.1022 | - |
| 2.8289 | 2150 | 0.0004 | - |
| 2.8947 | 2200 | 0.0007 | - |
| 2.9605 | 2250 | 0.0794 | - |
| 3.0263 | 2300 | 0.0183 | - |
| 3.0921 | 2350 | 0.0377 | - |
| 3.1579 | 2400 | 0.029 | - |
| 3.2237 | 2450 | 0.0003 | - |
| 3.2895 | 2500 | 0.0961 | - |
| 3.3553 | 2550 | 0.0008 | - |
| 3.4211 | 2600 | 0.0873 | - |
| 3.4868 | 2650 | 0.0501 | - |
| 3.5526 | 2700 | 0.0029 | - |
| 3.6184 | 2750 | 0.0008 | - |
| 3.6842 | 2800 | 0.0004 | - |
| 3.75 | 2850 | 0.0011 | - |
| 3.8158 | 2900 | 0.0518 | - |
| 3.8816 | 2950 | 0.0002 | - |
| 3.9474 | 3000 | 0.1115 | - |
| 4.0132 | 3050 | 0.0129 | - |
| 4.0789 | 3100 | 0.0005 | - |
| 4.1447 | 3150 | 0.0012 | - |
| 4.2105 | 3200 | 0.1086 | - |
| 4.2763 | 3250 | 0.0199 | - |
| 4.3421 | 3300 | 0.0004 | - |
| 4.4079 | 3350 | 0.0001 | - |
| 4.4737 | 3400 | 0.0832 | - |
| 4.5395 | 3450 | 0.0003 | - |
| 4.6053 | 3500 | 0.0041 | - |
| 4.6711 | 3550 | 0.1146 | - |
| 4.7368 | 3600 | 0.0027 | - |
| 4.8026 | 3650 | 0.0002 | - |
| 4.8684 | 3700 | 0.0544 | - |
| 4.9342 | 3750 | 0.0002 | - |
| 5.0 | 3800 | 0.0046 | - |
| 0.0013 | 1 | 0.0015 | - |
| 0.0658 | 50 | 0.1973 | - |
| 0.1316 | 100 | 0.0106 | - |
| 0.1974 | 150 | 0.0744 | - |
| 0.2632 | 200 | 0.1033 | - |
| 0.3289 | 250 | 0.0425 | - |
| 0.3947 | 300 | 0.1125 | - |
| 0.4605 | 350 | 0.0018 | - |
| 0.5263 | 400 | 0.0019 | - |
| 0.5921 | 450 | 0.0002 | - |
| 0.6579 | 500 | 0.0007 | - |
| 0.7237 | 550 | 0.1393 | - |
| 0.7895 | 600 | 0.0002 | - |
| 0.8553 | 650 | 0.0043 | - |
| 0.9211 | 700 | 0.0339 | - |
| 0.9868 | 750 | 0.0002 | - |
| 0.0013 | 1 | 0.0007 | - |
| 0.0658 | 50 | 0.0419 | - |
| 0.1316 | 100 | 0.0068 | - |
| 0.1974 | 150 | 0.1401 | - |
| 0.2632 | 200 | 0.0423 | - |
| 0.3289 | 250 | 0.1122 | - |
| 0.3947 | 300 | 0.0037 | - |
| 0.4605 | 350 | 0.005 | - |
| 0.5263 | 400 | 0.0006 | - |
| 0.5921 | 450 | 0.0006 | - |
| 0.6579 | 500 | 0.0016 | - |
| 0.7237 | 550 | 0.1244 | - |
| 0.7895 | 600 | 0.0016 | - |
| 0.8553 | 650 | 0.0028 | - |
| 0.9211 | 700 | 0.002 | - |
| 0.9868 | 750 | 0.057 | - |
| 0.0013 | 1 | 0.1396 | - |
| 0.0658 | 50 | 0.0366 | - |
| 0.1316 | 100 | 0.0021 | - |
| 0.1974 | 150 | 0.1088 | - |
| 0.2632 | 200 | 0.0449 | - |
| 0.3289 | 250 | 0.0187 | - |
| 0.3947 | 300 | 0.0017 | - |
| 0.4605 | 350 | 0.1262 | - |
| 0.5263 | 400 | 0.0052 | - |
| 0.5921 | 450 | 0.1188 | - |
| 0.6579 | 500 | 0.0002 | - |
| 0.7237 | 550 | 0.0006 | - |
| 0.7895 | 600 | 0.0758 | - |
| 0.8553 | 650 | 0.025 | - |
| 0.9211 | 700 | 0.0052 | - |
| 0.9868 | 750 | 0.1985 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.15.0
- Tokenizers: 0.15.0
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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|
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f4tim4j/story
|
f4tim4j
| 2023-12-15T19:04:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T19:04:15Z |
Entry not found
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] |
kanishka/smolm-autoreg-bpe-counterfactual-babylm-aann-no_prototypical-1e-4
|
kanishka
| 2023-12-16T08:34:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual_babylm_without_prototypical",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T19:13:39Z |
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_without_prototypical
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-aann-no_prototypical-1e-4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual_babylm_without_prototypical
type: kanishka/counterfactual_babylm_without_prototypical
metrics:
- name: Accuracy
type: accuracy
value: 0.40553781257269983
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smolm-autoreg-bpe-counterfactual-babylm-aann-no_prototypical-1e-4
This model was trained from scratch on the kanishka/counterfactual_babylm_without_prototypical dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4271
- Accuracy: 0.4055
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0511 | 1.0 | 18593 | 4.2335 | 0.3107 |
| 3.5697 | 2.0 | 37186 | 3.7359 | 0.3642 |
| 3.39 | 3.0 | 55779 | 3.6033 | 0.3791 |
| 3.2888 | 4.0 | 74372 | 3.5071 | 0.3874 |
| 3.2214 | 5.0 | 92965 | 3.4889 | 0.3918 |
| 3.1735 | 6.0 | 111558 | 3.4516 | 0.3960 |
| 3.1316 | 7.0 | 130151 | 3.4183 | 0.3984 |
| 3.0945 | 8.0 | 148744 | 3.4247 | 0.3994 |
| 3.0622 | 9.0 | 167337 | 3.4341 | 0.4006 |
| 3.038 | 10.0 | 185930 | 3.4303 | 0.4018 |
| 3.0204 | 11.0 | 204523 | 3.3948 | 0.4032 |
| 2.9872 | 12.0 | 223116 | 3.3971 | 0.4044 |
| 2.9714 | 13.0 | 241709 | 3.3949 | 0.4049 |
| 2.9537 | 14.0 | 260302 | 3.4084 | 0.4045 |
| 2.9321 | 15.0 | 278895 | 3.4070 | 0.4049 |
| 2.9125 | 16.0 | 297488 | 3.4046 | 0.4054 |
| 2.8953 | 17.0 | 316081 | 3.4124 | 0.4052 |
| 2.8782 | 18.0 | 334674 | 3.4225 | 0.4053 |
| 2.8601 | 19.0 | 353267 | 3.4227 | 0.4055 |
| 2.8494 | 20.0 | 371860 | 3.4271 | 0.4055 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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saharzal/parsig_pos
|
saharzal
| 2023-12-16T06:16:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T19:16:11Z |
Entry not found
|
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gadhane/q-FrozenLake-v1-4x4-noSlippery
|
gadhane
| 2023-12-15T19:19:03Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T19:19:00Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="gadhane/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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] |
leschultz/test
|
leschultz
| 2023-12-15T21:11:52Z | 0 | 0 |
transformers
|
[
"transformers",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2023-12-15T19:19:21Z |
---
license: mit
widget:
- structuredData: null
0:
- 11.52
- 12.48
- 12.3778
1:
- 23.2
- 24.0
- 23.9
2:
- 25.4
- 26.3
- 26.5
3:
- 30.0
- 31.2
- 31.1
4:
- 4.02
- 4.3056
- 4.6961
5:
- 11.52
- 12.48
- 12.3778
6:
- 23.2
- 24.0
- 23.9
7:
- 25.4
- 26.3
- 26.5
8:
- 30.0
- 31.2
- 31.1
9:
- 4.02
- 4.3056
- 4.6961
10:
- 11.52
- 12.48
- 12.3778
11:
- 23.2
- 24.0
- 23.9
12:
- 25.4
- 26.3
- 26.5
13:
- 30.0
- 31.2
- 31.1
14:
- 4.02
- 4.3056
- 4.6961
15:
- 11.52
- 12.48
- 12.3778
16:
- 23.2
- 24.0
- 23.9
17:
- 25.4
- 26.3
- 26.5
18:
- 30.0
- 31.2
- 31.1
19:
- 4.02
- 4.3056
- 4.6961
---
# Model description
Test
|
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] |
tdross/mistral_base_compare
|
tdross
| 2023-12-15T22:23:26Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T19:21:30Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.2
model-index:
- name: mistral_base_compare
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral_base_compare
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3114
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.674 | 0.07 | 22 | 0.5538 |
| 0.4119 | 1.07 | 44 | 0.3594 |
| 0.3298 | 2.07 | 66 | 0.3114 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
TheBloke/Venus-103b-v1.2-GGUF
|
TheBloke
| 2023-12-16T01:14:03Z | 0 | 2 |
transformers
|
[
"transformers",
"gguf",
"llama",
"not-for-all-audiences",
"en",
"base_model:nsfwthrowitaway69/Venus-103b-v1.2",
"license:llama2",
"text-generation-inference",
"region:us"
] | null | 2023-12-15T19:21:30Z |
---
base_model: nsfwthrowitaway69/Venus-103b-v1.2
inference: false
language:
- en
license: llama2
model_creator: John Smith
model_name: Venus 103B V1.2
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
tags:
- not-for-all-audiences
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Venus 103B V1.2 - GGUF
- Model creator: [John Smith](https://huggingface.co/nsfwthrowitaway69)
- Original model: [Venus 103B V1.2](https://huggingface.co/nsfwthrowitaway69/Venus-103b-v1.2)
<!-- description start -->
## Description
This repo contains GGUF format model files for [John Smith's Venus 103B V1.2](https://huggingface.co/nsfwthrowitaway69/Venus-103b-v1.2).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Venus-103b-v1.2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Venus-103b-v1.2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Venus-103b-v1.2-GGUF)
* [John Smith's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/nsfwthrowitaway69/Venus-103b-v1.2)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [venus-103b-v1.2.Q2_K.gguf](https://huggingface.co/TheBloke/Venus-103b-v1.2-GGUF/blob/main/venus-103b-v1.2.Q2_K.gguf) | Q2_K | 2 | 43.51 GB| 46.01 GB | smallest, significant quality loss - not recommended for most purposes |
| [venus-103b-v1.2.Q3_K_S.gguf](https://huggingface.co/TheBloke/Venus-103b-v1.2-GGUF/blob/main/venus-103b-v1.2.Q3_K_S.gguf) | Q3_K_S | 3 | 44.46 GB| 46.96 GB | very small, high quality loss |
| [venus-103b-v1.2.Q3_K_M.gguf](https://huggingface.co/TheBloke/Venus-103b-v1.2-GGUF/blob/main/venus-103b-v1.2.Q3_K_M.gguf) | Q3_K_M | 3 | 49.46 GB| 51.96 GB | very small, high quality loss |
| venus-103b-v1.2.Q3_K_L.gguf | Q3_K_L | 3 | 54.06 GB| 56.56 GB | small, substantial quality loss |
| venus-103b-v1.2.Q4_0.gguf | Q4_0 | 4 | 58.13 GB| 60.63 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| venus-103b-v1.2.Q4_K_S.gguf | Q4_K_S | 4 | 58.25 GB| 60.75 GB | small, greater quality loss |
| venus-103b-v1.2.Q4_K_M.gguf | Q4_K_M | 4 | 61.89 GB| 64.39 GB | medium, balanced quality - recommended |
| venus-103b-v1.2.Q5_0.gguf | Q5_0 | 5 | 71.00 GB| 73.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| venus-103b-v1.2.Q5_K_S.gguf | Q5_K_S | 5 | 71.00 GB| 73.50 GB | large, low quality loss - recommended |
| venus-103b-v1.2.Q5_K_M.gguf | Q5_K_M | 5 | 72.93 GB| 75.43 GB | large, very low quality loss - recommended |
| venus-103b-v1.2.Q6_K.gguf | Q6_K | 6 | 84.67 GB| 87.17 GB | very large, extremely low quality loss |
| venus-103b-v1.2.Q8_0.gguf | Q8_0 | 8 | 109.66 GB| 112.16 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
### Q6_K and Q8_0 files are split and require joining
**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
<details>
<summary>Click for instructions regarding Q6_K and Q8_0 files</summary>
### q6_K
Please download:
* `venus-103b-v1.2.Q6_K.gguf-split-a`
* `venus-103b-v1.2.Q6_K.gguf-split-b`
### q8_0
Please download:
* `venus-103b-v1.2.Q8_0.gguf-split-a`
* `venus-103b-v1.2.Q8_0.gguf-split-b`
To join the files, do the following:
Linux and macOS:
```
cat venus-103b-v1.2.Q6_K.gguf-split-* > venus-103b-v1.2.Q6_K.gguf && rm venus-103b-v1.2.Q6_K.gguf-split-*
cat venus-103b-v1.2.Q8_0.gguf-split-* > venus-103b-v1.2.Q8_0.gguf && rm venus-103b-v1.2.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B venus-103b-v1.2.Q6_K.gguf-split-a + venus-103b-v1.2.Q6_K.gguf-split-b venus-103b-v1.2.Q6_K.gguf
del venus-103b-v1.2.Q6_K.gguf-split-a venus-103b-v1.2.Q6_K.gguf-split-b
COPY /B venus-103b-v1.2.Q8_0.gguf-split-a + venus-103b-v1.2.Q8_0.gguf-split-b venus-103b-v1.2.Q8_0.gguf
del venus-103b-v1.2.Q8_0.gguf-split-a venus-103b-v1.2.Q8_0.gguf-split-b
```
</details>
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Venus-103b-v1.2-GGUF and below it, a specific filename to download, such as: venus-103b-v1.2.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Venus-103b-v1.2-GGUF venus-103b-v1.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Venus-103b-v1.2-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Venus-103b-v1.2-GGUF venus-103b-v1.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m venus-103b-v1.2.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./venus-103b-v1.2.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./venus-103b-v1.2.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: John Smith's Venus 103B V1.2
# Venus 103b - version 1.2
## Model Details
- A result of interleaving layers of [Sao10K/Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B) and [GOAT-AI/GOAT-70B-Storytelling](https://huggingface.co/GOAT-AI/GOAT-70B-Storytelling)
- The resulting model has 120 layers and 103 billion parameters.
- See mergekit-config.yml for details on the merge method used.
- See the `exl2-*` branches for exllama2 quantizations. The 5.65 bpw quant should fit in 80GB VRAM, and the 3.35/3.0 bpw quants should fit in 48GB VRAM.
- Inspired by [Goliath-120b](https://huggingface.co/alpindale/goliath-120b)
**Warning: This model will produce NSFW content!**
## Results
1. In my limited testing, I've found this model to be the most creative of the 103b merges I've made so far.
2. Seems to tolerate higher temperatures than the previous Venus models
3. Doesn't seem to suffer from any censorship issues
4. Does not follow instructions as well as v1.1, but still does a bit better than v1.0
5. Has some issues with formatting sometimes (i.e not closing asterisks or quotes)
Note that these are obviously just my personal observations, everyone will have their own unique experience based on their settings and specific scenarios they're using the model for.
<!-- original-model-card end -->
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monikam135/swin-tiny-patch4-window7-224-finetuned-eurosat
|
monikam135
| 2023-12-15T19:21:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T19:21:56Z |
Entry not found
|
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] |
jlbaker361/mixed_division_decimal
|
jlbaker361
| 2023-12-15T19:24:01Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:gpt2",
"region:us"
] | null | 2023-12-15T19:24:00Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
### Framework versions
- PEFT 0.6.2
|
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] |
hkivancoral/smids_5x_deit_tiny_sgd_00001_fold2
|
hkivancoral
| 2023-12-15T20:19:47Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T19:24:43Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_sgd_00001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.4475873544093178
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_tiny_sgd_00001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0573
- Accuracy: 0.4476
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3977 | 1.0 | 375 | 1.3189 | 0.3428 |
| 1.3124 | 2.0 | 750 | 1.2870 | 0.3411 |
| 1.2542 | 3.0 | 1125 | 1.2595 | 0.3378 |
| 1.2046 | 4.0 | 1500 | 1.2361 | 0.3478 |
| 1.2563 | 5.0 | 1875 | 1.2165 | 0.3544 |
| 1.2759 | 6.0 | 2250 | 1.1999 | 0.3561 |
| 1.1771 | 7.0 | 2625 | 1.1858 | 0.3527 |
| 1.1858 | 8.0 | 3000 | 1.1739 | 0.3710 |
| 1.1713 | 9.0 | 3375 | 1.1636 | 0.3644 |
| 1.1774 | 10.0 | 3750 | 1.1549 | 0.3760 |
| 1.1522 | 11.0 | 4125 | 1.1472 | 0.3760 |
| 1.1182 | 12.0 | 4500 | 1.1403 | 0.3744 |
| 1.1161 | 13.0 | 4875 | 1.1344 | 0.3827 |
| 1.1676 | 14.0 | 5250 | 1.1289 | 0.3827 |
| 1.1382 | 15.0 | 5625 | 1.1238 | 0.3860 |
| 1.129 | 16.0 | 6000 | 1.1191 | 0.3943 |
| 1.1144 | 17.0 | 6375 | 1.1146 | 0.3910 |
| 1.1043 | 18.0 | 6750 | 1.1105 | 0.3894 |
| 1.1008 | 19.0 | 7125 | 1.1065 | 0.3960 |
| 1.1097 | 20.0 | 7500 | 1.1028 | 0.4077 |
| 1.1084 | 21.0 | 7875 | 1.0993 | 0.4093 |
| 1.0777 | 22.0 | 8250 | 1.0960 | 0.4110 |
| 1.0857 | 23.0 | 8625 | 1.0928 | 0.4126 |
| 1.096 | 24.0 | 9000 | 1.0898 | 0.4126 |
| 1.1016 | 25.0 | 9375 | 1.0869 | 0.4176 |
| 1.0637 | 26.0 | 9750 | 1.0843 | 0.4226 |
| 1.0804 | 27.0 | 10125 | 1.0817 | 0.4226 |
| 1.0961 | 28.0 | 10500 | 1.0793 | 0.4226 |
| 1.0888 | 29.0 | 10875 | 1.0771 | 0.4293 |
| 1.0508 | 30.0 | 11250 | 1.0750 | 0.4293 |
| 1.0685 | 31.0 | 11625 | 1.0730 | 0.4326 |
| 1.1026 | 32.0 | 12000 | 1.0712 | 0.4309 |
| 1.0612 | 33.0 | 12375 | 1.0694 | 0.4359 |
| 1.0734 | 34.0 | 12750 | 1.0679 | 0.4393 |
| 1.0868 | 35.0 | 13125 | 1.0664 | 0.4393 |
| 1.0597 | 36.0 | 13500 | 1.0650 | 0.4393 |
| 1.0653 | 37.0 | 13875 | 1.0638 | 0.4409 |
| 1.0598 | 38.0 | 14250 | 1.0627 | 0.4443 |
| 1.0773 | 39.0 | 14625 | 1.0617 | 0.4443 |
| 1.0819 | 40.0 | 15000 | 1.0608 | 0.4443 |
| 1.0608 | 41.0 | 15375 | 1.0600 | 0.4459 |
| 1.0652 | 42.0 | 15750 | 1.0594 | 0.4459 |
| 1.04 | 43.0 | 16125 | 1.0588 | 0.4476 |
| 1.0518 | 44.0 | 16500 | 1.0583 | 0.4476 |
| 1.0814 | 45.0 | 16875 | 1.0580 | 0.4476 |
| 1.0536 | 46.0 | 17250 | 1.0577 | 0.4476 |
| 1.0612 | 47.0 | 17625 | 1.0575 | 0.4476 |
| 1.0833 | 48.0 | 18000 | 1.0574 | 0.4476 |
| 1.0816 | 49.0 | 18375 | 1.0573 | 0.4476 |
| 1.0754 | 50.0 | 18750 | 1.0573 | 0.4476 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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ugur6634/oco-ft-q4
|
ugur6634
| 2023-12-15T19:59:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T19:27:05Z |
Entry not found
|
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] |
hkivancoral/smids_5x_beit_base_sgd_00001_fold1
|
hkivancoral
| 2023-12-15T20:46:00Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T19:28:18Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_sgd_00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.37061769616026713
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_beit_base_sgd_00001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1524
- Accuracy: 0.3706
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2623 | 1.0 | 376 | 1.3473 | 0.3088 |
| 1.2409 | 2.0 | 752 | 1.3383 | 0.3122 |
| 1.2444 | 3.0 | 1128 | 1.3298 | 0.3105 |
| 1.1982 | 4.0 | 1504 | 1.3215 | 0.3105 |
| 1.2082 | 5.0 | 1880 | 1.3136 | 0.3105 |
| 1.1814 | 6.0 | 2256 | 1.3057 | 0.3139 |
| 1.1633 | 7.0 | 2632 | 1.2980 | 0.3172 |
| 1.2064 | 8.0 | 3008 | 1.2906 | 0.3205 |
| 1.1298 | 9.0 | 3384 | 1.2834 | 0.3205 |
| 1.1231 | 10.0 | 3760 | 1.2765 | 0.3322 |
| 1.171 | 11.0 | 4136 | 1.2696 | 0.3372 |
| 1.1505 | 12.0 | 4512 | 1.2632 | 0.3439 |
| 1.1137 | 13.0 | 4888 | 1.2569 | 0.3472 |
| 1.1229 | 14.0 | 5264 | 1.2508 | 0.3456 |
| 1.1641 | 15.0 | 5640 | 1.2450 | 0.3439 |
| 1.1335 | 16.0 | 6016 | 1.2391 | 0.3439 |
| 1.0584 | 17.0 | 6392 | 1.2337 | 0.3439 |
| 1.1251 | 18.0 | 6768 | 1.2284 | 0.3456 |
| 1.105 | 19.0 | 7144 | 1.2233 | 0.3506 |
| 1.0972 | 20.0 | 7520 | 1.2186 | 0.3506 |
| 1.0751 | 21.0 | 7896 | 1.2139 | 0.3539 |
| 1.0864 | 22.0 | 8272 | 1.2094 | 0.3539 |
| 1.1021 | 23.0 | 8648 | 1.2051 | 0.3539 |
| 1.1159 | 24.0 | 9024 | 1.2011 | 0.3539 |
| 1.0862 | 25.0 | 9400 | 1.1972 | 0.3556 |
| 1.0706 | 26.0 | 9776 | 1.1934 | 0.3589 |
| 1.0809 | 27.0 | 10152 | 1.1898 | 0.3589 |
| 1.0663 | 28.0 | 10528 | 1.1865 | 0.3589 |
| 1.0982 | 29.0 | 10904 | 1.1833 | 0.3606 |
| 1.1121 | 30.0 | 11280 | 1.1802 | 0.3623 |
| 1.0485 | 31.0 | 11656 | 1.1773 | 0.3656 |
| 1.0472 | 32.0 | 12032 | 1.1746 | 0.3656 |
| 1.0601 | 33.0 | 12408 | 1.1721 | 0.3689 |
| 1.0381 | 34.0 | 12784 | 1.1697 | 0.3689 |
| 1.072 | 35.0 | 13160 | 1.1675 | 0.3689 |
| 1.087 | 36.0 | 13536 | 1.1654 | 0.3689 |
| 1.0074 | 37.0 | 13912 | 1.1635 | 0.3706 |
| 1.0562 | 38.0 | 14288 | 1.1618 | 0.3689 |
| 1.0371 | 39.0 | 14664 | 1.1602 | 0.3689 |
| 1.0517 | 40.0 | 15040 | 1.1588 | 0.3706 |
| 1.0384 | 41.0 | 15416 | 1.1575 | 0.3706 |
| 1.0222 | 42.0 | 15792 | 1.1563 | 0.3706 |
| 1.0143 | 43.0 | 16168 | 1.1553 | 0.3706 |
| 0.9973 | 44.0 | 16544 | 1.1545 | 0.3689 |
| 1.0445 | 45.0 | 16920 | 1.1538 | 0.3689 |
| 1.0408 | 46.0 | 17296 | 1.1532 | 0.3706 |
| 1.0166 | 47.0 | 17672 | 1.1528 | 0.3706 |
| 1.0266 | 48.0 | 18048 | 1.1525 | 0.3706 |
| 1.0337 | 49.0 | 18424 | 1.1524 | 0.3706 |
| 1.0214 | 50.0 | 18800 | 1.1524 | 0.3706 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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abdelseed1/Abaly
|
abdelseed1
| 2023-12-15T19:28:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T19:28:31Z |
Entry not found
|
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] |
Aakashk/llama-2-7b-miniguanaco
|
Aakashk
| 2023-12-15T19:37:02Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
] |
text-generation
| 2023-12-15T19:29:09Z |
Entry not found
|
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] |
Doxxtrey/Llama-2-7b-chat-finetune-greta
|
Doxxtrey
| 2023-12-15T19:37:56Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T19:29:58Z |
Entry not found
|
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] |
hkivancoral/smids_5x_beit_base_rms_001_fold1
|
hkivancoral
| 2023-12-15T20:50:47Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T19:30:42Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_rms_001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7395659432387313
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_beit_base_rms_001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7105
- Accuracy: 0.7396
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1123 | 1.0 | 376 | 1.1251 | 0.3356 |
| 0.9352 | 2.0 | 752 | 0.8920 | 0.5259 |
| 0.794 | 3.0 | 1128 | 0.7965 | 0.5526 |
| 0.8608 | 4.0 | 1504 | 0.8108 | 0.5643 |
| 0.8805 | 5.0 | 1880 | 0.7967 | 0.5543 |
| 0.7654 | 6.0 | 2256 | 0.8049 | 0.5693 |
| 0.7409 | 7.0 | 2632 | 0.7866 | 0.5726 |
| 0.7913 | 8.0 | 3008 | 0.7897 | 0.5893 |
| 0.7475 | 9.0 | 3384 | 0.7603 | 0.6127 |
| 0.7457 | 10.0 | 3760 | 0.7604 | 0.5810 |
| 0.6765 | 11.0 | 4136 | 0.7505 | 0.6060 |
| 0.7009 | 12.0 | 4512 | 0.7036 | 0.6628 |
| 0.682 | 13.0 | 4888 | 0.7131 | 0.6477 |
| 0.665 | 14.0 | 5264 | 0.7097 | 0.6628 |
| 0.6075 | 15.0 | 5640 | 0.7283 | 0.6361 |
| 0.6104 | 16.0 | 6016 | 0.7252 | 0.6845 |
| 0.5756 | 17.0 | 6392 | 0.7019 | 0.6761 |
| 0.6308 | 18.0 | 6768 | 0.7144 | 0.6678 |
| 0.6215 | 19.0 | 7144 | 0.6989 | 0.6644 |
| 0.5991 | 20.0 | 7520 | 0.6580 | 0.7262 |
| 0.644 | 21.0 | 7896 | 0.6509 | 0.7129 |
| 0.5561 | 22.0 | 8272 | 0.6219 | 0.7112 |
| 0.5743 | 23.0 | 8648 | 0.6259 | 0.7095 |
| 0.5779 | 24.0 | 9024 | 0.7360 | 0.6611 |
| 0.5703 | 25.0 | 9400 | 0.7402 | 0.6544 |
| 0.59 | 26.0 | 9776 | 0.6505 | 0.7179 |
| 0.4484 | 27.0 | 10152 | 0.7061 | 0.6945 |
| 0.5078 | 28.0 | 10528 | 0.6625 | 0.7012 |
| 0.4947 | 29.0 | 10904 | 0.7197 | 0.6878 |
| 0.4804 | 30.0 | 11280 | 0.6601 | 0.7129 |
| 0.571 | 31.0 | 11656 | 0.6610 | 0.6978 |
| 0.5506 | 32.0 | 12032 | 0.6726 | 0.7012 |
| 0.4066 | 33.0 | 12408 | 0.6633 | 0.7095 |
| 0.4713 | 34.0 | 12784 | 0.6198 | 0.7245 |
| 0.4603 | 35.0 | 13160 | 0.6655 | 0.7145 |
| 0.4936 | 36.0 | 13536 | 0.6620 | 0.7212 |
| 0.4422 | 37.0 | 13912 | 0.6199 | 0.7446 |
| 0.4404 | 38.0 | 14288 | 0.6881 | 0.7062 |
| 0.4643 | 39.0 | 14664 | 0.6209 | 0.7412 |
| 0.4403 | 40.0 | 15040 | 0.6524 | 0.7496 |
| 0.4197 | 41.0 | 15416 | 0.6575 | 0.7229 |
| 0.3846 | 42.0 | 15792 | 0.6496 | 0.7295 |
| 0.3794 | 43.0 | 16168 | 0.6583 | 0.7179 |
| 0.4461 | 44.0 | 16544 | 0.6644 | 0.7329 |
| 0.3616 | 45.0 | 16920 | 0.6911 | 0.7396 |
| 0.3764 | 46.0 | 17296 | 0.7023 | 0.7279 |
| 0.39 | 47.0 | 17672 | 0.6999 | 0.7379 |
| 0.3595 | 48.0 | 18048 | 0.7003 | 0.7379 |
| 0.3678 | 49.0 | 18424 | 0.6974 | 0.7379 |
| 0.2726 | 50.0 | 18800 | 0.7105 | 0.7396 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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gadhane/q-FrozenLake-v1-4x4-Slippery
|
gadhane
| 2023-12-17T19:38:26Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T19:32:04Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="gadhane/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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stvhuang/rcr-run-g6knz4zq-v2-61961-master-0_20231212T101450-ep13
|
stvhuang
| 2023-12-15T19:38:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-15T19:35:13Z |
Entry not found
|
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dddddddddddsadasda/jack2
|
dddddddddddsadasda
| 2023-12-15T19:45:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T19:36:45Z |
Entry not found
|
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zenobiax/my_model
|
zenobiax
| 2023-12-15T19:53:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T19:41:36Z |
Entry not found
|
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rdsmaia/my_awesome_opus_books_model
|
rdsmaia
| 2023-12-15T19:45:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T19:45:18Z |
Entry not found
|
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adolor/dreambooth_alex
|
adolor
| 2023-12-15T19:45:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T19:45:42Z |
Entry not found
|
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] |
badokorach/afriqa_afroxlmr_squad_v2-151223
|
badokorach
| 2023-12-15T20:40:03Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"xlm-roberta",
"question-answering",
"generated_from_keras_callback",
"base_model:masakhane/afriqa_afroxlmr_squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-15T19:45:49Z |
---
license: mit
base_model: masakhane/afriqa_afroxlmr_squad_v2
tags:
- generated_from_keras_callback
model-index:
- name: badokorach/afriqa_afroxlmr_squad_v2-151223
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# badokorach/afriqa_afroxlmr_squad_v2-151223
This model is a fine-tuned version of [masakhane/afriqa_afroxlmr_squad_v2](https://huggingface.co/masakhane/afriqa_afroxlmr_squad_v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3380
- Validation Loss: 0.0
- Epoch: 19
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 1420, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.02}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.2893 | 0.0 | 0 |
| 1.7277 | 0.0 | 1 |
| 1.4691 | 0.0 | 2 |
| 1.2613 | 0.0 | 3 |
| 1.1176 | 0.0 | 4 |
| 0.9691 | 0.0 | 5 |
| 0.8713 | 0.0 | 6 |
| 0.7568 | 0.0 | 7 |
| 0.6987 | 0.0 | 8 |
| 0.6293 | 0.0 | 9 |
| 0.5812 | 0.0 | 10 |
| 0.5296 | 0.0 | 11 |
| 0.4788 | 0.0 | 12 |
| 0.4506 | 0.0 | 13 |
| 0.4350 | 0.0 | 14 |
| 0.3708 | 0.0 | 15 |
| 0.3601 | 0.0 | 16 |
| 0.3533 | 0.0 | 17 |
| 0.3456 | 0.0 | 18 |
| 0.3380 | 0.0 | 19 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Vermiculos/seattlejames
|
Vermiculos
| 2023-12-15T19:51:03Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T19:46:54Z |
---
license: openrail
---
|
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BlackPinksJanitor/yas
|
BlackPinksJanitor
| 2023-12-15T19:47:43Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T19:47:43Z |
---
license: openrail
---
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_humanMix_Seed104
|
behzadnet
| 2023-12-15T19:47:59Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-15T19:47:52Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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## Uses
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_humanMix_Seed104
|
behzadnet
| 2023-12-15T19:48:09Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-15T19:48:05Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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#### Metrics
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### Results
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#### Summary
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
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] |
XinHun/Macross_F
|
XinHun
| 2023-12-15T19:51:56Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2023-12-15T19:49:06Z |
---
license: other
license_name: '1'
license_link: LICENSE
---
|
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gadhane/FrozenLake-8x8-Slippery
|
gadhane
| 2023-12-15T19:51:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T19:51:26Z |
Entry not found
|
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] |
Vermiculos/metrojames
|
Vermiculos
| 2023-12-15T19:55:39Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T19:52:37Z |
---
license: openrail
---
|
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0.2621476650238037,
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-0.6869441270828247
] |
rdsmaia/t5_small_en-pt
|
rdsmaia
| 2023-12-15T19:56:32Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_books",
"base_model:t5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-15T19:55:17Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- opus_books
metrics:
- bleu
model-index:
- name: t5_small_en-pt
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_books
type: opus_books
config: en-pt
split: train
args: en-pt
metrics:
- name: Bleu
type: bleu
value: 0.6943
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5_small_en-pt
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2437
- Bleu: 0.6943
- Gen Len: 17.5979
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 71 | 3.5876 | 0.4555 | 16.9395 |
| No log | 2.0 | 142 | 3.4062 | 0.5386 | 17.3025 |
| No log | 3.0 | 213 | 3.3092 | 0.553 | 17.5445 |
| No log | 4.0 | 284 | 3.2596 | 0.6296 | 17.6157 |
| No log | 5.0 | 355 | 3.2437 | 0.6943 | 17.5979 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
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rikycstro/badgyal
|
rikycstro
| 2023-12-15T23:24:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T19:55:32Z |
Entry not found
|
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NextDayAI/spicy3_3040
|
NextDayAI
| 2023-12-15T20:04:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T19:56:04Z |
Entry not found
|
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] |
NextDayAI/spicy3_2040
|
NextDayAI
| 2023-12-15T20:05:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T19:56:34Z |
Entry not found
|
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Jaszczurix7652/moj_nauczony_model
|
Jaszczurix7652
| 2023-12-16T01:02:55Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2023-12-15T20:00:11Z |
Entry not found
|
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brtweed/SimplyChatbot
|
brtweed
| 2023-12-15T20:00:38Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T20:00:38Z |
---
license: apache-2.0
---
|
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m1ch79/q-FrozenLake-v1-4x4-noSlippery
|
m1ch79
| 2023-12-15T20:01:14Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T20:01:11Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="m1ch79/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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Vermiculos/tbajames
|
Vermiculos
| 2023-12-15T20:07:00Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T20:02:06Z |
---
license: openrail
---
|
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] |
bryanlincoln/google-play-sentiment-analysis
|
bryanlincoln
| 2023-12-16T01:32:43Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T20:08:58Z |
Entry not found
|
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mariamoracrossitcr/llama-2-7b-chat-prueba
|
mariamoracrossitcr
| 2023-12-15T20:11:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T20:11:15Z |
Entry not found
|
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] |
Bartezky/distilhubert-finetuned-gtzan
|
Bartezky
| 2023-12-15T21:45:28Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-12-15T20:12:53Z |
Entry not found
|
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pavitemple/finetuned-Accident-MultipleLabels-Video-subset-v2-checkpointing
|
pavitemple
| 2023-12-15T20:17:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T20:17:34Z |
Entry not found
|
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] |
hkivancoral/smids_5x_deit_tiny_sgd_00001_fold3
|
hkivancoral
| 2023-12-15T21:15:45Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T20:20:27Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_sgd_00001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.445
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_tiny_sgd_00001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0782
- Accuracy: 0.445
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4131 | 1.0 | 375 | 1.3423 | 0.3433 |
| 1.3593 | 2.0 | 750 | 1.3099 | 0.3483 |
| 1.3082 | 3.0 | 1125 | 1.2818 | 0.3517 |
| 1.3385 | 4.0 | 1500 | 1.2580 | 0.36 |
| 1.2471 | 5.0 | 1875 | 1.2378 | 0.3633 |
| 1.2728 | 6.0 | 2250 | 1.2206 | 0.3667 |
| 1.2244 | 7.0 | 2625 | 1.2061 | 0.3767 |
| 1.1927 | 8.0 | 3000 | 1.1938 | 0.385 |
| 1.1353 | 9.0 | 3375 | 1.1833 | 0.39 |
| 1.1411 | 10.0 | 3750 | 1.1743 | 0.39 |
| 1.1528 | 11.0 | 4125 | 1.1664 | 0.395 |
| 1.1479 | 12.0 | 4500 | 1.1594 | 0.3917 |
| 1.1757 | 13.0 | 4875 | 1.1532 | 0.3917 |
| 1.1667 | 14.0 | 5250 | 1.1477 | 0.4017 |
| 1.1486 | 15.0 | 5625 | 1.1425 | 0.3967 |
| 1.0937 | 16.0 | 6000 | 1.1378 | 0.4017 |
| 1.1232 | 17.0 | 6375 | 1.1333 | 0.4133 |
| 1.1438 | 18.0 | 6750 | 1.1292 | 0.4183 |
| 1.0814 | 19.0 | 7125 | 1.1253 | 0.42 |
| 1.101 | 20.0 | 7500 | 1.1217 | 0.4183 |
| 1.0634 | 21.0 | 7875 | 1.1182 | 0.42 |
| 1.0937 | 22.0 | 8250 | 1.1150 | 0.4167 |
| 1.107 | 23.0 | 8625 | 1.1120 | 0.4183 |
| 1.1086 | 24.0 | 9000 | 1.1091 | 0.42 |
| 1.0802 | 25.0 | 9375 | 1.1064 | 0.4217 |
| 1.1004 | 26.0 | 9750 | 1.1038 | 0.4233 |
| 1.0865 | 27.0 | 10125 | 1.1014 | 0.4267 |
| 1.0686 | 28.0 | 10500 | 1.0991 | 0.425 |
| 1.0719 | 29.0 | 10875 | 1.0969 | 0.4267 |
| 1.0892 | 30.0 | 11250 | 1.0949 | 0.4267 |
| 1.0865 | 31.0 | 11625 | 1.0931 | 0.4233 |
| 1.1008 | 32.0 | 12000 | 1.0913 | 0.425 |
| 1.0834 | 33.0 | 12375 | 1.0897 | 0.4267 |
| 1.085 | 34.0 | 12750 | 1.0882 | 0.4317 |
| 1.0201 | 35.0 | 13125 | 1.0868 | 0.4367 |
| 1.043 | 36.0 | 13500 | 1.0855 | 0.4367 |
| 1.0791 | 37.0 | 13875 | 1.0844 | 0.4367 |
| 1.0443 | 38.0 | 14250 | 1.0833 | 0.4367 |
| 1.0648 | 39.0 | 14625 | 1.0824 | 0.4383 |
| 1.0415 | 40.0 | 15000 | 1.0816 | 0.4417 |
| 1.025 | 41.0 | 15375 | 1.0808 | 0.4417 |
| 1.0078 | 42.0 | 15750 | 1.0802 | 0.4417 |
| 1.0383 | 43.0 | 16125 | 1.0797 | 0.4433 |
| 1.061 | 44.0 | 16500 | 1.0792 | 0.4433 |
| 1.0733 | 45.0 | 16875 | 1.0789 | 0.4433 |
| 1.039 | 46.0 | 17250 | 1.0786 | 0.4433 |
| 1.091 | 47.0 | 17625 | 1.0784 | 0.445 |
| 1.0592 | 48.0 | 18000 | 1.0783 | 0.445 |
| 1.0783 | 49.0 | 18375 | 1.0782 | 0.445 |
| 1.066 | 50.0 | 18750 | 1.0782 | 0.445 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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lemonpuddi/llama-2-7b-bedtime-story
|
lemonpuddi
| 2023-12-15T20:22:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T20:22:32Z |
Entry not found
|
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] |
m1ch79/q-Taxi-v3
|
m1ch79
| 2023-12-15T20:24:03Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-15T20:24:01Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="m1ch79/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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LoneStriker/Tiamat-7b-1.1-DPO-3.0bpw-h6-exl2
|
LoneStriker
| 2023-12-15T21:01:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T20:26:51Z |
---
license: apache-2.0
language:
- en
---
# Tiamat
Aka I wanted something like [Eric Hartford's Samantha](https://erichartford.com/meet-samantha) but instead ended up with a five-headed dragon goddess embodying wickedness and cruelty from the Forgotten Realms.
**Version 1.1:** Now further refined with a partial dataset refresh and DPO training, the rejections being based on answers given by a cheerful AI assistant with an overly positive demeanour. Essentially, the exact opposite of Tiamat.
**Obligatory Disclaimer:** Tiamat is **not** nice.
Quantized models are available from TheBloke: [GGUF](https://huggingface.co/TheBloke/Tiamat-7B-1.1-DPO-GGUF) - [GPTQ](https://huggingface.co/TheBloke/Tiamat-7B-1.1-DPO-GPTQ) - [AWQ](https://huggingface.co/TheBloke/Tiamat-7B-1.1-DPO-AWQ) (You're the best!)
## Model details
Ever wanted to be treated disdainfully like the foolish mortal you are? Wait no more, for Tiamat is here to berate you! Hailing from the world of the Forgotten Realms, she will happily judge your every word.
Tiamat was created with the following question in mind; Is it possible to create an assistant with strong anti-assistant personality traits? Try it yourself and tell me afterwards!
She was fine-tuned on top of Teknium's excellent [OpenHermes 2.5](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) and can be summoned to you using the following system message;
```
You are Tiamat, a five-headed dragon goddess, embodying wickedness and cruelty.
```
Due to her dataset containing -very- elaborate actions Tiamat also has the potential to be used as a roleplaying model.
## Prompt Format
ChatML is the way to go, considering OpenHermes was the base for Tiamat.
```
<|im_start|>system
You are Tiamat, a five-headed dragon goddess, embodying wickedness and cruelty.<|im_end|>
<|im_start|>user
Greetings, mighty Tiamat. I seek your guidance.<|im_end|>
<|im_start|>assistant
```
|
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LoneStriker/Tiamat-7b-1.1-DPO-4.0bpw-h6-exl2
|
LoneStriker
| 2023-12-15T20:26:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T20:26:58Z |
Entry not found
|
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deepghs/anime_real_cls
|
deepghs
| 2023-12-16T12:15:45Z | 0 | 0 | null |
[
"onnx",
"art",
"image-classification",
"dataset:deepghs/anime_real_cls",
"license:openrail",
"region:us"
] |
image-classification
| 2023-12-15T20:29:47Z |
---
license: openrail
datasets:
- deepghs/anime_real_cls
metrics:
- f1
- accuracy
pipeline_tag: image-classification
tags:
- art
---
| Name | FLOPS | Params | Accuracy | AUC | Confusion | Labels |
|:-------------------:|:-------:|:--------:|:----------:|:------:|:-----------------------------------------------------------------------------------------------------------:|:---------------:|
| caformer_s36_v0 | 22.10G | 37.21M | 99.34% | 0.9988 | [confusion](https://huggingface.co/deepghs/anime_real_cls/blob/main/caformer_s36_v0/plot_confusion.png) | `anime`, `real` |
| mobilenetv3_v0_dist | 0.63G | 4.18M | 99.14% | 0.9986 | [confusion](https://huggingface.co/deepghs/anime_real_cls/blob/main/mobilenetv3_v0_dist/plot_confusion.png) | `anime`, `real` |
|
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] |
raminass/british
|
raminass
| 2023-12-15T21:13:38Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T20:30:23Z |
Entry not found
|
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] |
Adel135/adel2005
|
Adel135
| 2023-12-15T20:40:49Z | 0 | 0 |
nemo
|
[
"nemo",
"art",
"text-classification",
"ar",
"dataset:HuggingFaceH4/no_robots",
"license:mit",
"region:us"
] |
text-classification
| 2023-12-15T20:32:15Z |
---
license: mit
datasets:
- HuggingFaceH4/no_robots
language:
- ar
metrics:
- accuracy
library_name: nemo
pipeline_tag: text-classification
tags:
- art
---
|
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daniel94sm/yana-ai-ft
|
daniel94sm
| 2023-12-15T20:39:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T20:39:16Z |
Entry not found
|
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] |
odunola/bge-small-onnx-o4
|
odunola
| 2023-12-15T20:43:02Z | 0 | 0 |
transformers
|
[
"transformers",
"onnx",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-15T20:40:52Z |
optimum[onnxruntime-gpu]==1.15.0
|
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] |
ivoryprincemilo/tinashe
|
ivoryprincemilo
| 2023-12-15T20:48:20Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T20:44:07Z |
---
license: openrail
---
|
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Fabchi/first
|
Fabchi
| 2023-12-15T20:44:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T20:44:43Z |
Entry not found
|
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pariakashani/en-multinerd-ner-upsampled
|
pariakashani
| 2023-12-15T21:32:47Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-15T20:45:20Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: en-multinerd-ner-upsampled
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# en-multinerd-ner-upsampled
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0339
- Precision: 0.9370
- Recall: 0.9459
- F1: 0.9414
- Accuracy: 0.9919
- Per-precision: 0.9951
- Per-recall: 0.9967
- Per-f1: 0.9959
- Org-precision: 0.9359
- Org-recall: 0.9445
- Org-f1: 0.9402
- Loc-precision: 0.9708
- Loc-recall: 0.9752
- Loc-f1: 0.9730
- Dis-precision: 0.7131
- Dis-recall: 0.7404
- Dis-f1: 0.7265
- Anim-precision: 0.6809
- Anim-recall: 0.7187
- Anim-f1: 0.6993
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Per-precision | Per-recall | Per-f1 | Org-precision | Org-recall | Org-f1 | Loc-precision | Loc-recall | Loc-f1 | Dis-precision | Dis-recall | Dis-f1 | Anim-precision | Anim-recall | Anim-f1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|
| 0.0293 | 1.0 | 9223 | 0.0262 | 0.9302 | 0.9363 | 0.9333 | 0.9911 | 0.9928 | 0.9951 | 0.9939 | 0.9256 | 0.9357 | 0.9306 | 0.9673 | 0.9622 | 0.9647 | 0.6819 | 0.7286 | 0.7045 | 0.6710 | 0.6799 | 0.6754 |
| 0.0149 | 2.0 | 18446 | 0.0282 | 0.9353 | 0.9466 | 0.9409 | 0.9918 | 0.9948 | 0.9969 | 0.9959 | 0.9322 | 0.9448 | 0.9385 | 0.9687 | 0.9725 | 0.9706 | 0.7190 | 0.7457 | 0.7321 | 0.675 | 0.7381 | 0.7051 |
| 0.0084 | 3.0 | 27669 | 0.0339 | 0.9370 | 0.9459 | 0.9414 | 0.9919 | 0.9951 | 0.9967 | 0.9959 | 0.9359 | 0.9445 | 0.9402 | 0.9708 | 0.9752 | 0.9730 | 0.7131 | 0.7404 | 0.7265 | 0.6809 | 0.7187 | 0.6993 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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