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ManthanKulakarni/code_gen_t5_test_push
|
ManthanKulakarni
| 2023-12-13T13:29:45Z | 0 | 0 | null |
[
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Entry not found
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] |
ThomasBontemps/my_awesome_qa_model
|
ThomasBontemps
| 2023-12-14T13:09:33Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-13T13:29:48Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: ThomasBontemps/my_awesome_qa_model
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. -->
# ThomasBontemps/my_awesome_qa_model
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:
- Train Loss: 1.7741
- Validation Loss: 1.7707
- Epoch: 1
## 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.4412 | 2.1351 | 0 |
| 1.7741 | 1.7707 | 1 |
### Framework versions
- Transformers 4.36.0
- TensorFlow 2.10.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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aleie000/robotics_lego
|
aleie000
| 2023-12-13T13:35:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T13:33:59Z |
Entry not found
|
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] |
aaalby/TWICE-deejei
|
aaalby
| 2023-12-13T13:48:08Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T13:34:46Z |
---
license: openrail
---
|
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] |
a1kayda/rubert_tiny2_dictator
|
a1kayda
| 2023-12-13T13:35:30Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-13T13:35:30Z |
---
license: mit
---
|
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] |
tb2pi-persistent/phi-1_5-tb2pi-peft-v1
|
tb2pi-persistent
| 2023-12-13T13:38:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T13:37:54Z |
Entry not found
|
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musiox/kundan
|
musiox
| 2023-12-13T13:39:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T13:38:15Z |
Entry not found
|
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YearSecond/n
|
YearSecond
| 2023-12-13T13:52:11Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-13T13:39:22Z |
Entry not found
|
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] |
FinancialSupport/test_lora
|
FinancialSupport
| 2023-12-13T13:45:57Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"text-generation",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-12-13T13:40:29Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: adapter-transformers
---
|
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] |
Realgon/N_distilbert_agnews_padding60model
|
Realgon
| 2023-12-13T15:13:35Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T13:42:39Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_distilbert_agnews_padding60model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.945
---
<!-- 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. -->
# N_distilbert_agnews_padding60model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6714
- Accuracy: 0.945
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1829 | 1.0 | 7500 | 0.1828 | 0.9445 |
| 0.1406 | 2.0 | 15000 | 0.1995 | 0.9436 |
| 0.1229 | 3.0 | 22500 | 0.2025 | 0.9470 |
| 0.0893 | 4.0 | 30000 | 0.2578 | 0.9464 |
| 0.0547 | 5.0 | 37500 | 0.3250 | 0.9392 |
| 0.0394 | 6.0 | 45000 | 0.3882 | 0.9391 |
| 0.0368 | 7.0 | 52500 | 0.4054 | 0.9383 |
| 0.0221 | 8.0 | 60000 | 0.4602 | 0.9396 |
| 0.0179 | 9.0 | 67500 | 0.4535 | 0.9430 |
| 0.015 | 10.0 | 75000 | 0.4745 | 0.9421 |
| 0.0177 | 11.0 | 82500 | 0.5035 | 0.9439 |
| 0.0147 | 12.0 | 90000 | 0.4848 | 0.9436 |
| 0.0077 | 13.0 | 97500 | 0.5222 | 0.9447 |
| 0.0039 | 14.0 | 105000 | 0.5591 | 0.9432 |
| 0.0042 | 15.0 | 112500 | 0.5863 | 0.9433 |
| 0.0017 | 16.0 | 120000 | 0.6075 | 0.9457 |
| 0.003 | 17.0 | 127500 | 0.6207 | 0.9455 |
| 0.0017 | 18.0 | 135000 | 0.6482 | 0.9454 |
| 0.0004 | 19.0 | 142500 | 0.6646 | 0.9457 |
| 0.0017 | 20.0 | 150000 | 0.6714 | 0.945 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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alhosseini/Llama-2-7b-hf
|
alhosseini
| 2023-12-13T13:56:43Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"llama-2",
"en",
"arxiv:2307.09288",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T13:42:58Z |
---
extra_gated_heading: Access Llama 2 on Hugging Face
extra_gated_description: >-
This is a form to enable access to Llama 2 on Hugging Face after you have been
granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our
license terms and acceptable use policy before submitting this form. Requests
will be processed in 1-2 days.
extra_gated_prompt: "**Your Hugging Face account email address MUST match the email you provide on the Meta website, or your request will not be approved.**"
extra_gated_button_content: Submit
extra_gated_fields:
I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
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] |
mireiaplalis/roberta-base-finetuned-ner-cadec
|
mireiaplalis
| 2023-12-13T14:19:46Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-13T13:43:58Z |
Entry not found
|
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nitsw/mistral_i7
|
nitsw
| 2023-12-13T13:46:38Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2023-12-13T13:45:26Z |
Entry not found
|
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] |
thorirhrafn/llama2_rmh_tiny
|
thorirhrafn
| 2023-12-13T16:06:40Z | 0 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T13:45:54Z |
Entry not found
|
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xuqinyang/chatxu-story
|
xuqinyang
| 2023-12-13T13:50:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T13:47:33Z |
# Markdown编辑器
基本功能应该都能支持吧...
|
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juliocrestrepo/outputs
|
juliocrestrepo
| 2023-12-13T13:48:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T13:48:20Z |
Entry not found
|
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nhantruongcse/mymodel_LORA_base_10k_2e5_3epoch_batch16_T4
|
nhantruongcse
| 2023-12-13T19:09:37Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:VietAI/vit5-base",
"license:mit",
"region:us"
] | null | 2023-12-13T13:48:45Z |
---
license: mit
base_model: VietAI/vit5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mymodel_LORA_base_10k_2e5_3epoch_batch16_T4
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. -->
# mymodel_LORA_base_10k_2e5_3epoch_batch16_T4
This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9509
- Rouge1: 0.5087
- Rouge2: 0.21
- Rougel: 0.3279
- Rougelsum: 0.3278
- Gen Len: 47.1885
## 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.904 | 1.0 | 500 | 2.0384 | 0.4986 | 0.1981 | 0.323 | 0.3231 | 54.0445 |
| 2.167 | 2.0 | 1000 | 1.9662 | 0.5068 | 0.2053 | 0.3269 | 0.3268 | 54.8295 |
| 2.1217 | 3.0 | 1500 | 1.9509 | 0.5087 | 0.21 | 0.3279 | 0.3278 | 47.1885 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.14.1
|
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euneeei/hw-midm-7B-nsmc
|
euneeei
| 2023-12-13T14:24:15Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2023-12-13T13:53:06Z |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
euneeei/hw-midm-7B-nsmc
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
- 한국어로 된 네이버 영화 리뷰 데이터셋입니다.
- ## train dataset : 3000개
- ## test dataset : 1000개
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
learning_rate : 1e-4-> 2e-4
max_steps=500 설정
warmup_steps=100 설정
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
| | precision | recall | f1-score | support|
|----|----|----|-------|------|
negative| 0.87 | 0.95 | 091 | 492
positive | 0.94 | 0.87 | 0.90 | 508
accuracy | | | 0.91 | 1000
macro avg | 0.91 | 0.91 | 0.91 | 1000
weighted avg | 0.91 | 0.91 | 0.91 | 1000
- ### confusion metrics
### [[ 466, 26 ]
### [68, 440]]
[More Information Needed]
### Results
- ## **정확도 0.51 -> 0.91로 높아졌습니다**
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0
|
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stepan9773/rl
|
stepan9773
| 2023-12-13T13:54:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T13:54:18Z |
Entry not found
|
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dfdsd/my-lora-model
|
dfdsd
| 2023-12-13T13:55:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T13:54:52Z |
Entry not found
|
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Ngit/MiniLMv2-L6-H384-goemotions-v1
|
Ngit
| 2023-12-13T13:55:44Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-12-13T13:55:44Z |
Entry not found
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_SystemError0percent_Seed102
|
behzadnet
| 2023-12-13T13:56:21Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-13T13:56:15Z |
---
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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### Model Sources [optional]
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## Uses
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### Direct Use
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### Recommendations
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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
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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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]
[More Information Needed]
#### 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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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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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).
- **Hardware Type:** [More Information Needed]
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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_SystemError0percent_Seed102
|
behzadnet
| 2023-12-13T13:56:30Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-13T13:56:27Z |
---
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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## 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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#### 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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icemansf/linkedinheadshot
|
icemansf
| 2023-12-13T15:48:25Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-13T13:56:30Z |
---
license: mit
---
|
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VorminTox/VorminTox
|
VorminTox
| 2023-12-13T14:01:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T13:57:13Z |
VorminTox: Belarus, капсула, Таблеткі, Кошт, агляды, Інгрэдыенты, Перавагі, Таблеткі, кошт!
Дабаўка ад паразітаў VorminTox спрыяе аптымальнай працы печані і страўніка, асвяжае паветра і знішчае хваробатворныя мікраарганізмы. VorminTox Belarus
VorminTox Купіць зараз!! Націсніце на спасылку ніжэй, каб атрымаць дадатковую інфармацыю і атрымаць зніжку 50% зараз !! спяшайся !!
чытаць далей: https://www.nutritionsee.com/VormBela
➢Назва прадукту — VorminTox
➢Катэгорыя – капсула ад паразітаў
➢Асноўныя перавагі — паляпшаюць здароўе паразітаў і павышаюць імунітэт
➢ Склад — натуральнае арганічнае злучэнне
➢ Пабочныя эфекты - няма
➢Выніковая адзнака: — 4,8
➢ Даступнасць — Інтэрнэт
➢Прапановы і зніжкі; ЗАХАВАЙЦЕ СЁННЯ! КРАМІЦЕ ЗАРАЗ, КАБ СПЕЦЫЯЛЬНАЯ ПРАПАНОВА!!!
Што такое VorminTox?
Гэта БАД, з дапамогай якога можна паспяхова змагацца з шкоднікамі. Гэты прадукт змяшчае інгрэдыенты, якія паляпшаюць працэс детоксікаціі, падтрымліваюць працэс стрававання, зніжаюць таксіны і стымулююць імунную сістэму. З дапамогай гэтага прадукта ваша цела аднаўляецца і аднаўляе энергію. Любы, хто хоча ачысціць свой арганізм ад таксінаў, можа выкарыстоўваць VorminTox.
VorminTox Купіць зараз!! Націсніце на спасылку ніжэй, каб атрымаць дадатковую інфармацыю і атрымаць зніжку 50% зараз !! спяшайся !!
чытаць далей: https://www.nutritionsee.com/VormBela
VorminTox VorminTox Таблеткі VorminTox капсула VorminTox Таблеткі VorminTox Кошт VorminTox агляды VorminTox Інгрэдыенты VorminTox Перавагі VorminTox Пабочныя эфекты VorminTox цана капсулы VorminTox водгукі аб капсулах VorminTox кампазіцыя VorminTox скарга VorminTox Дзе купіць VorminTox Як выкарыстоўваць VorminTox кошт VorminTox працы VorminTox форум VorminTox арыгінальны VorminTox аптэка
https://www.nutritionsee.com/VormBela
https://sites.google.com/view/vormintox-belarus/home
https://healthtoned.blogspot.com/2023/12/vormintox-belarus.html
https://www.dibiz.com/vormintoxbelarus
https://www.weddingwire.com/website/vormintox-and-belarus
https://infogram.com/vormintox-belarus-1hnq4107wr79p23?live
https://softisenilspain.hashnode.dev/vormintox-belarus
https://sway.office.com/huJ3mW0El9Dy9akO
https://vormintoxbelarus.company.site/
https://gamma.app/docs/VorminTox-Belarus-7gxc2qy0sblqj8c?mode=doc
https://healthytalk24x7.wixsite.com/sunshine/post/vormintox-belarus
https://groups.google.com/g/snshine/c/HV2bnYuMlnE
https://medium.com/@vormintoxbelarus/vormintox-belarus-09870e237a70
https://medium.com/@vormintoxbelarus/vormintox-706be065b3db
https://devfolio.co/projects/vormintox-belarus-1490
https://nepal.tribe.so/post/vormintox-belarus-6579b7d611205285e0ff48a6
https://forum.teknofest.az/d/12309-vormintox-belarus
https://replit.com/@vormintoxbelaru
|
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] |
GiladH/roberta-large-ner-english-finetuned-ner
|
GiladH
| 2023-12-13T15:58:38Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-13T13:59:53Z |
Entry not found
|
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srushtibhavsar/FineTuneWithGivenSysMess_Llama2_7b
|
srushtibhavsar
| 2023-12-13T14:00:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:00:17Z |
Entry not found
|
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longduongbao/NLP
|
longduongbao
| 2023-12-13T14:01:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:01:34Z |
Entry not found
|
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] |
hkivancoral/smids_3x_beit_base_sgd_001_fold3
|
hkivancoral
| 2023-12-14T00:03:40Z | 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
| 2023-12-13T14:02:18Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_sgd_001_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: 0.8966666666666666
---
<!-- 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_3x_beit_base_sgd_001_fold3
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: 0.2937
- Accuracy: 0.8967
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8635 | 1.0 | 225 | 0.8411 | 0.6267 |
| 0.6523 | 2.0 | 450 | 0.6173 | 0.7583 |
| 0.5403 | 3.0 | 675 | 0.5212 | 0.8017 |
| 0.4896 | 4.0 | 900 | 0.4692 | 0.8233 |
| 0.4594 | 5.0 | 1125 | 0.4353 | 0.8333 |
| 0.4326 | 6.0 | 1350 | 0.4063 | 0.8467 |
| 0.3692 | 7.0 | 1575 | 0.3897 | 0.855 |
| 0.4088 | 8.0 | 1800 | 0.3756 | 0.8567 |
| 0.4036 | 9.0 | 2025 | 0.3600 | 0.8667 |
| 0.387 | 10.0 | 2250 | 0.3535 | 0.8717 |
| 0.349 | 11.0 | 2475 | 0.3460 | 0.8717 |
| 0.3537 | 12.0 | 2700 | 0.3401 | 0.875 |
| 0.3714 | 13.0 | 2925 | 0.3342 | 0.8783 |
| 0.3497 | 14.0 | 3150 | 0.3327 | 0.8817 |
| 0.2955 | 15.0 | 3375 | 0.3234 | 0.8867 |
| 0.346 | 16.0 | 3600 | 0.3197 | 0.8933 |
| 0.3452 | 17.0 | 3825 | 0.3164 | 0.89 |
| 0.3182 | 18.0 | 4050 | 0.3143 | 0.8867 |
| 0.3047 | 19.0 | 4275 | 0.3110 | 0.8933 |
| 0.3008 | 20.0 | 4500 | 0.3105 | 0.8883 |
| 0.2783 | 21.0 | 4725 | 0.3050 | 0.8917 |
| 0.2751 | 22.0 | 4950 | 0.3037 | 0.8967 |
| 0.2477 | 23.0 | 5175 | 0.3059 | 0.8917 |
| 0.2485 | 24.0 | 5400 | 0.3040 | 0.8917 |
| 0.2841 | 25.0 | 5625 | 0.3099 | 0.8917 |
| 0.2803 | 26.0 | 5850 | 0.3058 | 0.8967 |
| 0.2313 | 27.0 | 6075 | 0.3019 | 0.8933 |
| 0.2302 | 28.0 | 6300 | 0.3005 | 0.895 |
| 0.2775 | 29.0 | 6525 | 0.2994 | 0.895 |
| 0.2039 | 30.0 | 6750 | 0.2961 | 0.9 |
| 0.261 | 31.0 | 6975 | 0.2949 | 0.9 |
| 0.2791 | 32.0 | 7200 | 0.2986 | 0.895 |
| 0.2917 | 33.0 | 7425 | 0.2938 | 0.8983 |
| 0.2364 | 34.0 | 7650 | 0.2966 | 0.895 |
| 0.2087 | 35.0 | 7875 | 0.2917 | 0.9 |
| 0.2544 | 36.0 | 8100 | 0.2944 | 0.8983 |
| 0.2254 | 37.0 | 8325 | 0.2941 | 0.8967 |
| 0.2119 | 38.0 | 8550 | 0.2972 | 0.8933 |
| 0.2445 | 39.0 | 8775 | 0.2905 | 0.9 |
| 0.204 | 40.0 | 9000 | 0.2909 | 0.8967 |
| 0.2353 | 41.0 | 9225 | 0.2968 | 0.895 |
| 0.2574 | 42.0 | 9450 | 0.2926 | 0.9 |
| 0.2197 | 43.0 | 9675 | 0.2953 | 0.8967 |
| 0.2519 | 44.0 | 9900 | 0.2939 | 0.8967 |
| 0.2337 | 45.0 | 10125 | 0.2971 | 0.895 |
| 0.2047 | 46.0 | 10350 | 0.2932 | 0.8967 |
| 0.2633 | 47.0 | 10575 | 0.2935 | 0.8967 |
| 0.2254 | 48.0 | 10800 | 0.2947 | 0.895 |
| 0.2679 | 49.0 | 11025 | 0.2937 | 0.895 |
| 0.2687 | 50.0 | 11250 | 0.2937 | 0.8967 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
zebans/xlm-roberta-base-finetuned-panx-en
|
zebans
| 2023-12-13T14:11:37Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-13T14:03:15Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6837988826815643
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3984
- F1: 0.6838
## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1357 | 1.0 | 50 | 0.5871 | 0.4590 |
| 0.5236 | 2.0 | 100 | 0.4412 | 0.6478 |
| 0.3765 | 3.0 | 150 | 0.3984 | 0.6838 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.1.0+cu118
- Datasets 1.16.1
- Tokenizers 0.15.0
|
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] |
hkivancoral/smids_3x_beit_base_rms_0001_fold3
|
hkivancoral
| 2023-12-13T14:52:53Z | 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
| 2023-12-13T14:04:30Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_rms_0001_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: 0.855
---
<!-- 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_3x_beit_base_rms_0001_fold3
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: 1.6713
- Accuracy: 0.855
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7637 | 1.0 | 225 | 0.7530 | 0.7217 |
| 0.5551 | 2.0 | 450 | 0.6161 | 0.7583 |
| 0.4831 | 3.0 | 675 | 0.4948 | 0.7833 |
| 0.3281 | 4.0 | 900 | 0.5414 | 0.8033 |
| 0.3506 | 5.0 | 1125 | 0.4226 | 0.815 |
| 0.3328 | 6.0 | 1350 | 0.4220 | 0.83 |
| 0.2581 | 7.0 | 1575 | 0.5786 | 0.7883 |
| 0.1949 | 8.0 | 1800 | 0.5329 | 0.8133 |
| 0.2071 | 9.0 | 2025 | 0.4652 | 0.8417 |
| 0.1906 | 10.0 | 2250 | 0.5303 | 0.82 |
| 0.1705 | 11.0 | 2475 | 0.6288 | 0.8283 |
| 0.153 | 12.0 | 2700 | 0.5236 | 0.8383 |
| 0.0688 | 13.0 | 2925 | 0.7459 | 0.8133 |
| 0.0899 | 14.0 | 3150 | 0.8275 | 0.8117 |
| 0.0904 | 15.0 | 3375 | 0.7966 | 0.8467 |
| 0.0822 | 16.0 | 3600 | 0.8714 | 0.835 |
| 0.112 | 17.0 | 3825 | 0.8906 | 0.8433 |
| 0.0635 | 18.0 | 4050 | 0.8797 | 0.835 |
| 0.0639 | 19.0 | 4275 | 0.8962 | 0.85 |
| 0.0335 | 20.0 | 4500 | 1.1500 | 0.815 |
| 0.0798 | 21.0 | 4725 | 0.9654 | 0.82 |
| 0.0508 | 22.0 | 4950 | 1.1138 | 0.8467 |
| 0.0254 | 23.0 | 5175 | 0.9088 | 0.8367 |
| 0.0398 | 24.0 | 5400 | 1.1000 | 0.83 |
| 0.083 | 25.0 | 5625 | 0.9695 | 0.8367 |
| 0.0354 | 26.0 | 5850 | 1.1614 | 0.8317 |
| 0.018 | 27.0 | 6075 | 1.1091 | 0.8533 |
| 0.0554 | 28.0 | 6300 | 1.0860 | 0.8417 |
| 0.0435 | 29.0 | 6525 | 1.0082 | 0.8533 |
| 0.0521 | 30.0 | 6750 | 1.0251 | 0.8333 |
| 0.0523 | 31.0 | 6975 | 1.1028 | 0.8267 |
| 0.0058 | 32.0 | 7200 | 1.2099 | 0.8433 |
| 0.0089 | 33.0 | 7425 | 1.4585 | 0.8383 |
| 0.0197 | 34.0 | 7650 | 1.2388 | 0.8483 |
| 0.0029 | 35.0 | 7875 | 1.3364 | 0.83 |
| 0.0016 | 36.0 | 8100 | 1.4458 | 0.8417 |
| 0.0092 | 37.0 | 8325 | 1.4004 | 0.835 |
| 0.0003 | 38.0 | 8550 | 1.4317 | 0.8417 |
| 0.0011 | 39.0 | 8775 | 1.2820 | 0.8417 |
| 0.0089 | 40.0 | 9000 | 1.5154 | 0.8417 |
| 0.0236 | 41.0 | 9225 | 1.3755 | 0.8467 |
| 0.0009 | 42.0 | 9450 | 1.6899 | 0.8517 |
| 0.0128 | 43.0 | 9675 | 1.5784 | 0.845 |
| 0.0006 | 44.0 | 9900 | 1.6022 | 0.8517 |
| 0.0002 | 45.0 | 10125 | 1.4557 | 0.8467 |
| 0.0206 | 46.0 | 10350 | 1.5017 | 0.855 |
| 0.006 | 47.0 | 10575 | 1.5387 | 0.855 |
| 0.0004 | 48.0 | 10800 | 1.6762 | 0.855 |
| 0.001 | 49.0 | 11025 | 1.7088 | 0.855 |
| 0.0003 | 50.0 | 11250 | 1.6713 | 0.855 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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dconcu/my_awesome_model
|
dconcu
| 2023-12-13T14:04:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:04:55Z |
Entry not found
|
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] |
LekhaR/Llama-2-7b-chat-hf
|
LekhaR
| 2023-12-13T14:05:29Z | 0 | 0 | null |
[
"license:llama2",
"region:us"
] | null | 2023-12-13T14:05:29Z |
---
license: llama2
---
|
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deneesk/urpm-13-inpaint
|
deneesk
| 2023-12-13T14:24:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:05:36Z |
Entry not found
|
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dconcu/prova_NLP
|
dconcu
| 2023-12-13T14:05:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:05:36Z |
Entry not found
|
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Jerome1-3/t5-small-finetuned-xsum
|
Jerome1-3
| 2023-12-13T14:05:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:05:38Z |
Invalid username or password.
|
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prisha123/hi
|
prisha123
| 2023-12-13T14:07:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:07:32Z |
Entry not found
|
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leofn3/modelo_racismo_13_dez
|
leofn3
| 2023-12-13T14:41:18Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T14:08:24Z |
---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: modelo_racismo_13_dez
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. -->
# modelo_racismo_13_dez
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0996
- F1: 0.9861
- Accuracy: 0.9844
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.6264 | 1.0 | 643 | 0.6426 | 0.8423 | 0.8179 |
| 0.7407 | 2.0 | 1286 | 0.4516 | 0.9121 | 0.9019 |
| 0.5953 | 3.0 | 1929 | 0.2944 | 0.9492 | 0.9416 |
| 0.3275 | 4.0 | 2572 | 0.3128 | 0.9522 | 0.9447 |
| 0.3185 | 5.0 | 3215 | 0.1861 | 0.9732 | 0.9696 |
| 0.1702 | 6.0 | 3858 | 0.1225 | 0.9818 | 0.9798 |
| 0.1411 | 7.0 | 4501 | 0.1027 | 0.9854 | 0.9837 |
| 0.0895 | 8.0 | 5144 | 0.0996 | 0.9861 | 0.9844 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Anughanu/wav2vec2-large-xls-r-300m-punjabi-colab
|
Anughanu
| 2023-12-13T14:54:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:09:49Z |
Entry not found
|
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] |
lauratomokiyo/whisper-small-commonvoice-english-indacc2_lmtcmu
|
lauratomokiyo
| 2023-12-13T20:30:59Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-13T14:10:27Z |
Entry not found
|
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abraxasu/GPT2_large_summarization_peft
|
abraxasu
| 2023-12-13T14:12:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:12:56Z |
Entry not found
|
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alinerodrigues/wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-01
|
alinerodrigues
| 2023-12-13T19:57:49Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-13T14:14:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-01
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. -->
# wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-01
This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1531
- Wer: 0.0951
- Cer: 0.0280
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 27.9637 | 1.0 | 67 | 7.7844 | 0.9680 | 0.9846 |
| 10.0192 | 2.0 | 134 | 6.8622 | 0.9613 | 0.9448 |
| 4.9566 | 3.0 | 201 | 6.6997 | 0.9617 | 0.9567 |
| 4.9566 | 4.0 | 268 | 6.8259 | 0.9662 | 0.9720 |
| 4.4483 | 5.0 | 335 | 6.8626 | 0.9680 | 0.9780 |
| 4.2073 | 6.0 | 402 | 5.6960 | 0.9732 | 0.9821 |
| 4.2073 | 7.0 | 469 | 6.6086 | 0.9728 | 0.9818 |
| 4.077 | 8.0 | 536 | 6.2581 | 0.9714 | 0.9821 |
| 3.7594 | 9.0 | 603 | 6.0266 | 0.9718 | 0.9827 |
| 3.7594 | 10.0 | 670 | 5.7485 | 0.9756 | 0.9795 |
| 3.7788 | 11.0 | 737 | 5.0587 | 0.9714 | 0.9816 |
| 3.6071 | 12.0 | 804 | 4.7627 | 0.9714 | 0.9827 |
| 3.6071 | 13.0 | 871 | 3.5214 | 0.9725 | 0.9820 |
| 3.3363 | 14.0 | 938 | 2.9877 | 0.9753 | 0.9798 |
| 3.1039 | 15.0 | 1005 | 2.9151 | 0.9805 | 0.9840 |
| 3.1039 | 16.0 | 1072 | 2.8843 | 0.9847 | 0.9909 |
| 2.9064 | 17.0 | 1139 | 2.8720 | 0.9857 | 0.9846 |
| 2.8703 | 18.0 | 1206 | 2.7859 | 0.9972 | 0.9364 |
| 2.8703 | 19.0 | 1273 | 2.5571 | 1.0 | 0.8941 |
| 2.6946 | 20.0 | 1340 | 1.6485 | 0.9944 | 0.4890 |
| 1.8247 | 21.0 | 1407 | 0.7422 | 0.7133 | 0.1540 |
| 1.8247 | 22.0 | 1474 | 0.4440 | 0.3640 | 0.0807 |
| 0.8703 | 23.0 | 1541 | 0.3507 | 0.2090 | 0.0564 |
| 0.5944 | 24.0 | 1608 | 0.3083 | 0.1881 | 0.0500 |
| 0.5944 | 25.0 | 1675 | 0.2676 | 0.1721 | 0.0456 |
| 0.4905 | 26.0 | 1742 | 0.2580 | 0.1536 | 0.0428 |
| 0.4306 | 27.0 | 1809 | 0.2281 | 0.1428 | 0.0391 |
| 0.4306 | 28.0 | 1876 | 0.2203 | 0.1299 | 0.0364 |
| 0.356 | 29.0 | 1943 | 0.2066 | 0.1250 | 0.0352 |
| 0.3299 | 30.0 | 2010 | 0.2104 | 0.1177 | 0.0335 |
| 0.3299 | 31.0 | 2077 | 0.2006 | 0.1181 | 0.0330 |
| 0.3279 | 32.0 | 2144 | 0.1907 | 0.1181 | 0.0335 |
| 0.293 | 33.0 | 2211 | 0.1819 | 0.1135 | 0.0322 |
| 0.293 | 34.0 | 2278 | 0.1876 | 0.1097 | 0.0320 |
| 0.2869 | 35.0 | 2345 | 0.1862 | 0.1083 | 0.0311 |
| 0.2771 | 36.0 | 2412 | 0.1806 | 0.1160 | 0.0323 |
| 0.2771 | 37.0 | 2479 | 0.1757 | 0.1087 | 0.0311 |
| 0.2668 | 38.0 | 2546 | 0.1799 | 0.1052 | 0.0308 |
| 0.2394 | 39.0 | 2613 | 0.1840 | 0.1059 | 0.0313 |
| 0.2394 | 40.0 | 2680 | 0.1826 | 0.1031 | 0.0303 |
| 0.2269 | 41.0 | 2747 | 0.1759 | 0.1059 | 0.0312 |
| 0.2153 | 42.0 | 2814 | 0.1822 | 0.1024 | 0.0308 |
| 0.2153 | 43.0 | 2881 | 0.1667 | 0.1021 | 0.0305 |
| 0.238 | 44.0 | 2948 | 0.1757 | 0.1021 | 0.0308 |
| 0.2251 | 45.0 | 3015 | 0.1704 | 0.1031 | 0.0306 |
| 0.2251 | 46.0 | 3082 | 0.1809 | 0.1000 | 0.0299 |
| 0.2196 | 47.0 | 3149 | 0.1676 | 0.1000 | 0.0307 |
| 0.1932 | 48.0 | 3216 | 0.1658 | 0.1007 | 0.0300 |
| 0.1932 | 49.0 | 3283 | 0.1652 | 0.1000 | 0.0299 |
| 0.199 | 50.0 | 3350 | 0.1720 | 0.0993 | 0.0303 |
| 0.199 | 51.0 | 3417 | 0.1619 | 0.1021 | 0.0305 |
| 0.199 | 52.0 | 3484 | 0.1642 | 0.0986 | 0.0295 |
| 0.1747 | 53.0 | 3551 | 0.1655 | 0.1000 | 0.0299 |
| 0.1785 | 54.0 | 3618 | 0.1697 | 0.0965 | 0.0292 |
| 0.1785 | 55.0 | 3685 | 0.1605 | 0.0993 | 0.0294 |
| 0.1734 | 56.0 | 3752 | 0.1693 | 0.0979 | 0.0293 |
| 0.1813 | 57.0 | 3819 | 0.1658 | 0.0968 | 0.0292 |
| 0.1813 | 58.0 | 3886 | 0.1638 | 0.0996 | 0.0298 |
| 0.1718 | 59.0 | 3953 | 0.1705 | 0.0961 | 0.0290 |
| 0.1646 | 60.0 | 4020 | 0.1678 | 0.0965 | 0.0286 |
| 0.1646 | 61.0 | 4087 | 0.1647 | 0.0989 | 0.0288 |
| 0.1706 | 62.0 | 4154 | 0.1598 | 0.1010 | 0.0291 |
| 0.1559 | 63.0 | 4221 | 0.1555 | 0.0982 | 0.0288 |
| 0.1559 | 64.0 | 4288 | 0.1622 | 0.0965 | 0.0285 |
| 0.171 | 65.0 | 4355 | 0.1678 | 0.0940 | 0.0288 |
| 0.1655 | 66.0 | 4422 | 0.1643 | 0.0913 | 0.0281 |
| 0.1655 | 67.0 | 4489 | 0.1618 | 0.0947 | 0.0281 |
| 0.1628 | 68.0 | 4556 | 0.1587 | 0.0947 | 0.0283 |
| 0.149 | 69.0 | 4623 | 0.1614 | 0.0954 | 0.0281 |
| 0.149 | 70.0 | 4690 | 0.1636 | 0.0951 | 0.0280 |
| 0.1531 | 71.0 | 4757 | 0.1584 | 0.0989 | 0.0284 |
| 0.1677 | 72.0 | 4824 | 0.1638 | 0.0958 | 0.0284 |
| 0.1677 | 73.0 | 4891 | 0.1608 | 0.0947 | 0.0277 |
| 0.153 | 74.0 | 4958 | 0.1579 | 0.0951 | 0.0276 |
| 0.1464 | 75.0 | 5025 | 0.1633 | 0.0961 | 0.0280 |
| 0.1464 | 76.0 | 5092 | 0.1561 | 0.0958 | 0.0277 |
| 0.1533 | 77.0 | 5159 | 0.1554 | 0.0944 | 0.0279 |
| 0.1487 | 78.0 | 5226 | 0.1617 | 0.0930 | 0.0279 |
| 0.1487 | 79.0 | 5293 | 0.1574 | 0.0927 | 0.0274 |
| 0.1492 | 80.0 | 5360 | 0.1531 | 0.0951 | 0.0280 |
| 0.151 | 81.0 | 5427 | 0.1632 | 0.0954 | 0.0279 |
| 0.151 | 82.0 | 5494 | 0.1613 | 0.0972 | 0.0283 |
| 0.151 | 83.0 | 5561 | 0.1581 | 0.0968 | 0.0281 |
| 0.1398 | 84.0 | 5628 | 0.1569 | 0.0965 | 0.0278 |
| 0.1398 | 85.0 | 5695 | 0.1586 | 0.0968 | 0.0283 |
| 0.1344 | 86.0 | 5762 | 0.1595 | 0.0951 | 0.0280 |
| 0.1367 | 87.0 | 5829 | 0.1594 | 0.0937 | 0.0277 |
| 0.1367 | 88.0 | 5896 | 0.1583 | 0.0954 | 0.0282 |
| 0.1543 | 89.0 | 5963 | 0.1604 | 0.0968 | 0.0281 |
| 0.134 | 90.0 | 6030 | 0.1607 | 0.0923 | 0.0274 |
| 0.134 | 91.0 | 6097 | 0.1578 | 0.0944 | 0.0278 |
| 0.1498 | 92.0 | 6164 | 0.1595 | 0.0951 | 0.0280 |
| 0.133 | 93.0 | 6231 | 0.1557 | 0.0968 | 0.0282 |
| 0.133 | 94.0 | 6298 | 0.1598 | 0.0951 | 0.0277 |
| 0.1487 | 95.0 | 6365 | 0.1576 | 0.0940 | 0.0278 |
| 0.1343 | 96.0 | 6432 | 0.1558 | 0.0944 | 0.0280 |
| 0.1343 | 97.0 | 6499 | 0.1561 | 0.0951 | 0.0280 |
| 0.132 | 98.0 | 6566 | 0.1570 | 0.0933 | 0.0276 |
| 0.1416 | 99.0 | 6633 | 0.1574 | 0.0930 | 0.0274 |
| 0.13 | 100.0 | 6700 | 0.1574 | 0.0930 | 0.0275 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.13.3
|
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bio-datasets/Mistral-7B-Instruct-v0.1-twilight-shadow-190-epoch-0
|
bio-datasets
| 2023-12-13T14:43:18Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-13T14:15:36Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
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] |
umit/whisper-tiny-kmr
|
umit
| 2023-12-13T15:43:47Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-13T14:15:55Z |
Entry not found
|
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] |
Dimon-ton/food_classifier
|
Dimon-ton
| 2023-12-13T14:47:21Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T14:16:29Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: Dimon-ton/food_classifier
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. -->
# Dimon-ton/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3617
- Validation Loss: 0.3222
- Train Accuracy: 0.926
- Epoch: 4
## 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': 3e-05, 'decay_steps': 20000, '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': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7251 | 1.6243 | 0.821 | 0 |
| 1.2000 | 0.8153 | 0.904 | 1 |
| 0.6925 | 0.5191 | 0.907 | 2 |
| 0.4992 | 0.3969 | 0.916 | 3 |
| 0.3617 | 0.3222 | 0.926 | 4 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
tanooki426/MarkusVaughn
|
tanooki426
| 2023-12-13T14:20:50Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T14:17:42Z |
---
license: openrail
---
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] |
bfish15/Fine_Tune_LLaMA_Identification
|
bfish15
| 2023-12-13T14:18:10Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T14:18:10Z |
---
license: apache-2.0
---
|
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] |
Vital65/kijin
|
Vital65
| 2023-12-13T14:22:52Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T14:21:25Z |
---
license: openrail
---
|
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BrianHsu/my_awesome_qa_model
|
BrianHsu
| 2023-12-13T16:35:26Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-13T14:24:48Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_model
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. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7465
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.6438 |
| 2.8238 | 2.0 | 500 | 1.8808 |
| 2.8238 | 3.0 | 750 | 1.7465 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
madhaviit/llama-2-7b-madhav-t1
|
madhaviit
| 2023-12-13T14:38:22Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T14:26:31Z |
Entry not found
|
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] |
salmon54561/Mephy_the_skunkgirl
|
salmon54561
| 2023-12-14T11:22:25Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2023-12-13T14:28:05Z |
サイバーエージェントさんが出しているオープンソースの言語モデルcalm2-7bをLoRAファインチューニングしました。
男勝りな口調で話すスカンク娘、メフィーです。R-18なのでご注意ください。※オナラや臭液など、特殊なフェチ要素があります。text-generation-webuiでの使用を想定しています。
text-generation-webuiのlorasフォルダにフォルダごと入れてください。
WebUIの使い方をよくわかってないので、画像のように設定していただいたら上手く動作すると思います。
注意点として、ParametersタブのGenerationのmax_new_tokensの値を256程度に、Custom stopping stringsに"\n"を入力してください。
|
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isp-uv-es/DTACS
|
isp-uv-es
| 2023-12-13T14:45:20Z | 0 | 0 | null |
[
"remote sensing",
"sentinel2",
"onboard",
"image-segmentation",
"license:cc-by-nc-4.0",
"region:us"
] |
image-segmentation
| 2023-12-13T14:28:28Z |
---
license: cc-by-nc-4.0
pipeline_tag: image-segmentation
tags:
- remote sensing
- sentinel2
- onboard
---
# DTACS main trained models
Nano- and microsatellites have expanded the acquisition of satellite images with a higher spatial, temporal and
spectral resolution. However, as remote sensing data rises, its downlink and power capability are depleted. Tailored
onboard algorithms offer a unique solution to decrease the transmitted imagery by processing and selecting specific
information from raw data resulting in huge savings of computational resources. However, those models often require
accurate, calibrated and consistent surface reflectance (SR) estimations as inputs. This paper presents DTACSNet an
onboard atmospheric correction processor based on lightweight Convolutional Neural Networks. DTACSNet provides results
four times faster than the operational Sentinel-2 Level-2A processor under identical CPU settings and nine times faster
when a GPU accelerator is provided. Our validation results on independent datasets show that DTACSNet can efficiently
replicate Sen2Cor SR estimates, reporting a competitive accuracy with absolute errors below 2\% in reflectance units.
Additionally, the DTACSNet scene classification module (SC) based on lightweight neural networks obtains the highest
F2-score (0.92), followed by Fmask (0.7) and Sen2Cor (0.65) for cloud and cloud shadow masking.
<center>
<img src="https://cdn-uploads.huggingface.co/production/uploads/6402474cfa1acad600659e92/eUiCVb7xFo4beyJAUG2Ej.png" width="80%">
</center>
This repository contains the trained models of the publication:
```
TODO
```
We include the trained models:
* **DTACS S2 bands** SR model with all the S2 bands
* **DTACS phi-sat II bands** SR model with RGBN S2 bands
Examples here: https://github.com/spaceml-org/DTACSNet/tree/main
|
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dodiaz2111/distilbert-base-uncased-distilled-clinc
|
dodiaz2111
| 2023-12-13T14:43:05Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T14:28:33Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9496774193548387
---
<!-- 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. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3448
- Accuracy: 0.9497
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 2.4732 | 0.7284 |
| 2.9195 | 2.0 | 636 | 1.2381 | 0.8626 |
| 2.9195 | 3.0 | 954 | 0.6700 | 0.9171 |
| 1.0799 | 4.0 | 1272 | 0.4672 | 0.9390 |
| 0.4447 | 5.0 | 1590 | 0.3966 | 0.9426 |
| 0.4447 | 6.0 | 1908 | 0.3701 | 0.9458 |
| 0.2865 | 7.0 | 2226 | 0.3595 | 0.9452 |
| 0.24 | 8.0 | 2544 | 0.3475 | 0.9490 |
| 0.24 | 9.0 | 2862 | 0.3453 | 0.9494 |
| 0.2199 | 10.0 | 3180 | 0.3448 | 0.9497 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.13.3
|
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] |
ManthanKulakarni/code_gen_t5_testpush
|
ManthanKulakarni
| 2023-12-13T15:00:30Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-13T14:31:11Z |
Entry not found
|
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] |
amphion/hifigan-speech_bigdata
|
amphion
| 2023-12-13T15:22:10Z | 0 | 0 | null |
[
"arxiv:2311.14957",
"license:mit",
"region:us"
] | null | 2023-12-13T14:31:33Z |
---
license: mit
---
# Amphion Vocoder Pretrained Models
We provide a [HiFi-GAN](https://github.com/open-mmlab/Amphion/tree/main/egs/vocoder/gan/tfr_enhanced_hifigan) pretrained checkpoint for speech, which is trained on 685 hours of speech data.
## Quick Start
To utilize these pretrained vocoders, just run the following commands:
### Step1: Download the checkpoint
```bash
git lfs install
git clone https://huggingface.co/amphion/vocoder
```
### Step2: Clone the Amphion's Source Code of GitHub
```bash
git clone https://github.com/open-mmlab/Amphion.git
```
### Step3: Specify the checkpoint's path
Use the soft link to specify the downloaded checkpoint in the first step:
```bash
cd Amphion
mkdir ckpts/vocoder
cd ckpts/vocoder
ln -s ../vocoder/hifigan_speech ckpts/vocoder/hifigan_speech
```
### Step4: Inference
For analysis synthesis on the processed dataset, raw waveform, or predicted mel spectrograms, you can follow the inference part of [this recipe](https://github.com/open-mmlab/Amphion/blob/main/egs/vocoder/gan/tfr_enhanced_hifigan/README.md).
```bash
sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 3 \
--infer_mode [Your chosen inference mode] \
--infer_datasets [Datasets you want to inference, needed when infer_from_dataset] \
--infer_feature_dir [Your path to your predicted acoustic features, needed when infer_from_feature] \
--infer_audio_dir [Your path to your audio files, needed when infer_form_audio] \
--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \
```
## Citaions
```bibtex
@misc{gu2023cqt,
title={Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fidelity Vocoder},
author={Yicheng Gu and Xueyao Zhang and Liumeng Xue and Zhizheng Wu},
year={2023},
eprint={2311.14957},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
```
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] |
Tashin/zachet
|
Tashin
| 2023-12-13T19:30:08Z | 0 | 0 |
keras
|
[
"keras",
"ru",
"dataset:mnist",
"region:us"
] | null | 2023-12-13T14:33:57Z |
---
datasets:
- mnist
language:
- ru
metrics:
- accuracy
library_name: keras
---
1. Описание задачи которую выполняет НС.
Вариант 6. Используя датасет mnist был построен автоэнкодер, принимающий на вход изображение цифры и
создающий её же изображение на выходе.
2. Изображение послойной архитектуры НС на которой указаны размеры слоя, функция
активации.
3. Общее количество обучаемых параметров НС.
Оно составляет 131457, можно увидеть в коде.
4. Используемый алгоритмы оптимизации и функция ошибки.
Алгоритм оптимизации - adam, функция ошибки - mse (mean_squared_error).
5. Размеры тренировочного, валидационного и тестового датасетов.
Тренировочный равен 48 000.
Тестовый равен 10 000.
Валидационный равен 12 000 (то есть 20% от изначального 60 000 тренировочного датасета)
6. Результаты обучения модели: loss и accuracy на всех трёх датасетах.
Для тестового датасета loss: 0.0339 и accuracy: 0.0097
Результат обучения:
|
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LarryAIDraw/rumi_bluearchive
|
LarryAIDraw
| 2023-12-13T14:42:22Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-13T14:36:15Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/227310/rumi-blue-archive
|
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EleutherAI/pythia-14m-seed2
|
EleutherAI
| 2023-12-13T14:36:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:36:23Z |
Entry not found
|
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] |
LarryAIDraw/CHAR-IzayoiMiku
|
LarryAIDraw
| 2023-12-13T14:42:35Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-13T14:36:38Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/228682/miku-izayoi-3-outfits-or-date-a-live
|
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Manelzc/ViT_Proyect
|
Manelzc
| 2023-12-14T16:33:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:36:51Z |
Entry not found
|
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] |
LarryAIDraw/Acheron-08
|
LarryAIDraw
| 2023-12-13T14:42:49Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-13T14:37:00Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/231009/acheron-honkai-star-rail-lora
|
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amphion/bigvgan-singing_bigdata
|
amphion
| 2023-12-13T14:57:11Z | 0 | 0 | null |
[
"arxiv:2311.14957",
"license:mit",
"region:us"
] | null | 2023-12-13T14:37:02Z |
---
license: mit
---
# Amphion Vocoder Pretrained Models
We provide the a [BigVGAN](https://github.com/open-mmlab/Amphion/tree/main/egs/vocoder/gan) pretrained checkpoint for singing voice, which is trained on over 120 hours of singing voice data.
## Quick Start
To utilize these pretrained vocoders, just run the following commands:
### Step1: Download the checkpoint
```bash
git lfs install
git clone https://huggingface.co/amphion/vocoder
```
### Step2: Clone the Amphion's Source Code of GitHub
```bash
git clone https://github.com/open-mmlab/Amphion.git
```
### Step3: Specify the checkpoint's path
Use the soft link to specify the downloaded checkpoint in the first step:
```bash
cd Amphion
mkdir ckpts/vocoder
cd ckpts/vocoder
ln -s ../vocoder/bigvgan_singing ckpts/vocoder/bigvgan_singing
```
### Step4: Inference
For analysis synthesis on the processed dataset, raw waveform or predicted mel spectrograms, you can follow the inference part of [this recipe](https://github.com/open-mmlab/Amphion/blob/main/egs/vocoder/gan/tfr_enhanced_hifigan/README.md).
```bash
sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 3 \
--infer_mode [Your chosen inference mode] \
--infer_datasets [Datasets you want to inference, needed when infer_from_dataset] \
--infer_feature_dir [Your path to your predicted acoustic features, needed when infer_from_feature] \
--infer_audio_dir [Your path to your audio files, needed when infer_form_audio] \
--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \
```
## Citaions
```bibtex
@misc{gu2023cqt,
title={Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fidelity Vocoder},
author={Yicheng Gu and Xueyao Zhang and Liumeng Xue and Zhizheng Wu},
year={2023},
eprint={2311.14957},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
```
|
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] |
paulovsantanas/llm-deberta-v3-swag
|
paulovsantanas
| 2023-12-13T14:39:23Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"multiple-choice",
"generated_from_trainer",
"en",
"dataset:swag",
"base_model:microsoft/deberta-v3-base",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-12-13T14:37:13Z |
---
language:
- en
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: llm-deberta-v3-swag
results:
- task:
name: Multiple Choice
type: multiple-choice
dataset:
name: SWAG
type: swag
config: regular
split: validation
args: regular
metrics:
- name: Accuracy
type: accuracy
value: 0.8679895997047424
---
<!-- 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. -->
# llm-deberta-v3-swag
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the SWAG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7839
- Accuracy: 0.8680
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
LarryAIDraw/Genshin_Rosaria
|
LarryAIDraw
| 2023-12-13T14:42:59Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-13T14:37:25Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/231262/rosaria-genshin-impact
|
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LarryAIDraw/itsuki_nakano_final_final
|
LarryAIDraw
| 2023-12-13T14:43:10Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-13T14:37:55Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/231294/nakano-itsuki
|
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] |
Defetya/Falcon-JAX-super-exp
|
Defetya
| 2023-12-13T14:38:17Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T14:38:17Z |
---
license: apache-2.0
---
|
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] |
ecc-andrewb/sdv2_players
|
ecc-andrewb
| 2023-12-13T15:14:23Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-12-13T14:38:27Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: football player, white background
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - ecc-andrewb/sdv2_players
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on football player, white background using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
|
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] |
LarryAIDraw/lappland
|
LarryAIDraw
| 2023-12-13T14:43:19Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-13T14:38:37Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/224838/lappland-arknights-or-goofy-ai
|
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] |
LarryAIDraw/Roxanne
|
LarryAIDraw
| 2023-12-13T14:43:29Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-13T14:39:16Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/230904/roxanne-or-slave-harem-in-the-labyrinth-of-the-other-world
|
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shabarish-balaji/midjourney-falcon-7b
|
shabarish-balaji
| 2023-12-13T14:39:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:39:47Z |
Entry not found
|
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] |
aaa12963337/msi-mini2
|
aaa12963337
| 2023-12-13T15:28:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"nat",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:shi-labs/nat-mini-in1k-224",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T14:41:01Z |
---
license: mit
base_model: shi-labs/nat-mini-in1k-224
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: msi-mini2
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. -->
# msi-mini2
This model is a fine-tuned version of [shi-labs/nat-mini-in1k-224](https://huggingface.co/shi-labs/nat-mini-in1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.0132
- eval_accuracy: 0.6103
- eval_runtime: 97.1589
- eval_samples_per_second: 294.528
- eval_steps_per_second: 18.413
- epoch: 2.0
- step: 4031
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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hkivancoral/smids_3x_beit_base_sgd_00001_fold1
|
hkivancoral
| 2023-12-13T14:41:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:41:12Z |
Entry not found
|
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] |
mantagen/dreambooth_mg_portraits
|
mantagen
| 2023-12-13T14:46:42Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-12-13T14:41:15Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of a mantagen person
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - mantagen/dreambooth_mg_portraits
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of a mantagen person using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
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] |
tanooki426/ValerieBlaylock
|
tanooki426
| 2023-12-13T14:46:15Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T14:43:03Z |
---
license: openrail
---
|
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HandSemLin/my_awesome_model
|
HandSemLin
| 2023-12-13T14:43:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:43:41Z |
Entry not found
|
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paulovsantanas/llm-mdeberta-v3-swag
|
paulovsantanas
| 2023-12-13T14:45:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:45:04Z |
---
language:
- en
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: llm-mdeberta-v3-swag
results:
- task:
name: Multiple Choice
type: multiple-choice
dataset:
name: SWAG
type: swag
config: regular
split: validation
args: regular
metrics:
- name: Accuracy
type: accuracy
value: 0.777816653251648
---
<!-- 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. -->
# llm-mdeberta-v3-swag
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the SWAG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8202
- Accuracy: 0.7778
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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TheBloke/blossom-v3_1-yi-34b-GPTQ
|
TheBloke
| 2023-12-13T17:03:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"zh",
"en",
"dataset:Azure99/blossom-chat-v1",
"dataset:Azure99/blossom-math-v2",
"dataset:Azure99/blossom-wizard-v1",
"dataset:Azure99/blossom-orca-v1",
"base_model:Azure99/blossom-v3_1-yi-34b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-13T14:45:34Z |
---
base_model: Azure99/blossom-v3_1-yi-34b
datasets:
- Azure99/blossom-chat-v1
- Azure99/blossom-math-v2
- Azure99/blossom-wizard-v1
- Azure99/blossom-orca-v1
inference: false
language:
- zh
- en
license: apache-2.0
model_creator: Azure99
model_name: Blossom V3_1 Yi 34B
model_type: yi
prompt_template: "A chat between a human and an artificial intelligence bot. The bot\
\ gives helpful, detailed, and polite answers to the human's questions.\n|Human|:\
\ {prompt}\n|Bot|: \n"
quantized_by: TheBloke
---
<!-- 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 -->
# Blossom V3_1 Yi 34B - GPTQ
- Model creator: [Azure99](https://huggingface.co/Azure99)
- Original model: [Blossom V3_1 Yi 34B](https://huggingface.co/Azure99/blossom-v3_1-yi-34b)
<!-- description start -->
# Description
This repo contains GPTQ model files for [Azure99's Blossom V3_1 Yi 34B](https://huggingface.co/Azure99/blossom-v3_1-yi-34b).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/blossom-v3_1-yi-34b-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/blossom-v3_1-yi-34b-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/blossom-v3_1-yi-34b-GGUF)
* [Azure99's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Azure99/blossom-v3_1-yi-34b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Blossom-System
```
A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: {prompt}
|Bot|:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/blossom-v3_1-yi-34b-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 18.60 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/blossom-v3_1-yi-34b-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 19.25 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/blossom-v3_1-yi-34b-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 21.21 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/blossom-v3_1-yi-34b-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 15.03 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/blossom-v3_1-yi-34b-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 35.34 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/blossom-v3_1-yi-34b-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 16.90 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/blossom-v3_1-yi-34b-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 36.11 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/blossom-v3_1-yi-34b-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/blossom-v3_1-yi-34b-GPTQ:gptq-4bit-128g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `blossom-v3_1-yi-34b-GPTQ`:
```shell
mkdir blossom-v3_1-yi-34b-GPTQ
huggingface-cli download TheBloke/blossom-v3_1-yi-34b-GPTQ --local-dir blossom-v3_1-yi-34b-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir blossom-v3_1-yi-34b-GPTQ
huggingface-cli download TheBloke/blossom-v3_1-yi-34b-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir blossom-v3_1-yi-34b-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
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
mkdir blossom-v3_1-yi-34b-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/blossom-v3_1-yi-34b-GPTQ --local-dir blossom-v3_1-yi-34b-GPTQ --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>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/blossom-v3_1-yi-34b-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/blossom-v3_1-yi-34b-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/blossom-v3_1-yi-34b-GPTQ:gptq-4bit-128g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `blossom-v3_1-yi-34b-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/blossom-v3_1-yi-34b-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: {prompt}
|Bot|:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/blossom-v3_1-yi-34b-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: {prompt}
|Bot|:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility 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: Azure99's Blossom V3_1 Yi 34B
# **BLOSSOM-v3.1-yi-34b**
Try blossom online demo! [Blossom Chat[🚀](https://blossom-chat.com/)](https://huggingface.co/spaces/Azure99/BlossomChat)
### Introduction
Blossom is a conversational large language model, fine-tuned on the Blossom Orca/Wizard/Chat/Math mixed dataset based on the Yi-34B pre-trained model. Blossom possesses robust general capabilities and context comprehension. Additionally, the high-quality Chinese and English datasets used for training have been made open source.
Training was conducted in two stages. The first stage used 100K Wizard, 100K Orca single-turn instruction datasets, training for 1 epoch; the second stage used a 2K Blossom math reasoning dataset, 50K Blossom chat multi-turn dialogue dataset, and 1% randomly sampled data from the first stage, training for 3 epochs.
### Inference
Inference is performed in the form of dialogue continuation.
Single-turn dialogue
```
A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: hello
|Bot|:
```
Multi-turn dialogue
```
A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: hello
|Bot|: Hello! How can I assist you today?</s>
|Human|: Generate a random number using python
|Bot|:
```
Note: At the end of the Bot's output in the historical conversation, append a `</s>`.
|
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] |
Vinnyyw/Anahisolos
|
Vinnyyw
| 2023-12-13T14:47:25Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T14:46:26Z |
---
license: openrail
---
|
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imi2/Llama-2-70b-longlora-32k-adapter-ggml
|
imi2
| 2023-12-13T14:47:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T14:46:53Z |
Entry not found
|
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] |
tushar27/Env-Claims
|
tushar27
| 2023-12-13T15:06:52Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T14:49:10Z |
---
license: mit
---
|
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] |
hkivancoral/smids_3x_beit_base_sgd_001_fold4
|
hkivancoral
| 2023-12-14T00:52:25Z | 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
| 2023-12-13T14:50:54Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_sgd_001_fold4
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: 0.8483333333333334
---
<!-- 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_3x_beit_base_sgd_001_fold4
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: 0.3939
- Accuracy: 0.8483
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8245 | 1.0 | 225 | 0.8340 | 0.6383 |
| 0.6387 | 2.0 | 450 | 0.6132 | 0.7483 |
| 0.532 | 3.0 | 675 | 0.5292 | 0.7867 |
| 0.4946 | 4.0 | 900 | 0.4935 | 0.8017 |
| 0.5105 | 5.0 | 1125 | 0.4602 | 0.8217 |
| 0.3964 | 6.0 | 1350 | 0.4420 | 0.8217 |
| 0.4068 | 7.0 | 1575 | 0.4284 | 0.83 |
| 0.4501 | 8.0 | 1800 | 0.4257 | 0.8217 |
| 0.3713 | 9.0 | 2025 | 0.4132 | 0.835 |
| 0.3427 | 10.0 | 2250 | 0.4081 | 0.8383 |
| 0.4054 | 11.0 | 2475 | 0.4089 | 0.8367 |
| 0.3818 | 12.0 | 2700 | 0.4017 | 0.84 |
| 0.3036 | 13.0 | 2925 | 0.4061 | 0.8317 |
| 0.2784 | 14.0 | 3150 | 0.3991 | 0.84 |
| 0.2822 | 15.0 | 3375 | 0.3953 | 0.8383 |
| 0.3106 | 16.0 | 3600 | 0.3913 | 0.8383 |
| 0.2716 | 17.0 | 3825 | 0.3985 | 0.8367 |
| 0.3166 | 18.0 | 4050 | 0.3943 | 0.8417 |
| 0.334 | 19.0 | 4275 | 0.3982 | 0.8333 |
| 0.2592 | 20.0 | 4500 | 0.3982 | 0.8383 |
| 0.2836 | 21.0 | 4725 | 0.3926 | 0.8367 |
| 0.2688 | 22.0 | 4950 | 0.3918 | 0.8417 |
| 0.2602 | 23.0 | 5175 | 0.3951 | 0.8417 |
| 0.2941 | 24.0 | 5400 | 0.3932 | 0.8417 |
| 0.254 | 25.0 | 5625 | 0.3963 | 0.8433 |
| 0.2248 | 26.0 | 5850 | 0.3967 | 0.8417 |
| 0.2349 | 27.0 | 6075 | 0.3902 | 0.8417 |
| 0.2318 | 28.0 | 6300 | 0.3960 | 0.8417 |
| 0.2339 | 29.0 | 6525 | 0.3900 | 0.8467 |
| 0.2256 | 30.0 | 6750 | 0.3940 | 0.8483 |
| 0.2306 | 31.0 | 6975 | 0.3948 | 0.84 |
| 0.1769 | 32.0 | 7200 | 0.3920 | 0.8433 |
| 0.2714 | 33.0 | 7425 | 0.3958 | 0.8483 |
| 0.2441 | 34.0 | 7650 | 0.3973 | 0.845 |
| 0.2336 | 35.0 | 7875 | 0.3946 | 0.8483 |
| 0.2411 | 36.0 | 8100 | 0.3957 | 0.8517 |
| 0.2513 | 37.0 | 8325 | 0.3968 | 0.845 |
| 0.2269 | 38.0 | 8550 | 0.3976 | 0.8467 |
| 0.2515 | 39.0 | 8775 | 0.3973 | 0.8517 |
| 0.2727 | 40.0 | 9000 | 0.3940 | 0.8467 |
| 0.2023 | 41.0 | 9225 | 0.3933 | 0.845 |
| 0.2359 | 42.0 | 9450 | 0.3953 | 0.85 |
| 0.2348 | 43.0 | 9675 | 0.3957 | 0.8483 |
| 0.2703 | 44.0 | 9900 | 0.3944 | 0.8517 |
| 0.2898 | 45.0 | 10125 | 0.3951 | 0.8483 |
| 0.2247 | 46.0 | 10350 | 0.3937 | 0.85 |
| 0.2326 | 47.0 | 10575 | 0.3934 | 0.85 |
| 0.2372 | 48.0 | 10800 | 0.3941 | 0.8483 |
| 0.2457 | 49.0 | 11025 | 0.3940 | 0.8483 |
| 0.2302 | 50.0 | 11250 | 0.3939 | 0.8483 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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IshimaIshimsky/ifrit
|
IshimaIshimsky
| 2023-12-13T14:53:22Z | 0 | 0 | null |
[
"ru",
"license:unknown",
"region:us"
] | null | 2023-12-13T14:51:31Z |
---
license: unknown
language:
- ru
---
Ифрит/Ifrit rmvpe 250epoches
|
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] |
bbillapati/distilhubert-finetuned-gtzan
|
bbillapati
| 2023-12-15T11:39:02Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-12-13T14:52:49Z |
Entry not found
|
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hkivancoral/smids_3x_beit_base_rms_0001_fold4
|
hkivancoral
| 2023-12-13T15:42:13Z | 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
| 2023-12-13T14:53:44Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_rms_0001_fold4
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: 0.81
---
<!-- 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_3x_beit_base_rms_0001_fold4
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: 1.8372
- Accuracy: 0.81
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.787 | 1.0 | 225 | 0.7726 | 0.5917 |
| 0.7085 | 2.0 | 450 | 0.7059 | 0.6467 |
| 0.6145 | 3.0 | 675 | 0.6591 | 0.6983 |
| 0.6005 | 4.0 | 900 | 0.5422 | 0.7817 |
| 0.572 | 5.0 | 1125 | 0.5970 | 0.7567 |
| 0.4372 | 6.0 | 1350 | 0.5610 | 0.785 |
| 0.3918 | 7.0 | 1575 | 0.5957 | 0.7917 |
| 0.4058 | 8.0 | 1800 | 0.5296 | 0.7933 |
| 0.3971 | 9.0 | 2025 | 0.6041 | 0.7833 |
| 0.3274 | 10.0 | 2250 | 0.5347 | 0.8 |
| 0.2417 | 11.0 | 2475 | 0.6768 | 0.785 |
| 0.1989 | 12.0 | 2700 | 0.6501 | 0.8133 |
| 0.2222 | 13.0 | 2925 | 0.6337 | 0.7933 |
| 0.1654 | 14.0 | 3150 | 0.7865 | 0.7867 |
| 0.1241 | 15.0 | 3375 | 0.7840 | 0.8033 |
| 0.1208 | 16.0 | 3600 | 0.9856 | 0.795 |
| 0.0877 | 17.0 | 3825 | 1.0442 | 0.7767 |
| 0.1165 | 18.0 | 4050 | 0.9465 | 0.8117 |
| 0.1328 | 19.0 | 4275 | 0.8299 | 0.81 |
| 0.0427 | 20.0 | 4500 | 1.1880 | 0.7917 |
| 0.0826 | 21.0 | 4725 | 1.0665 | 0.8083 |
| 0.0679 | 22.0 | 4950 | 1.2201 | 0.7917 |
| 0.1018 | 23.0 | 5175 | 1.1824 | 0.8 |
| 0.0255 | 24.0 | 5400 | 1.2359 | 0.8117 |
| 0.0956 | 25.0 | 5625 | 1.2156 | 0.805 |
| 0.0725 | 26.0 | 5850 | 1.3671 | 0.81 |
| 0.0849 | 27.0 | 6075 | 1.3399 | 0.7917 |
| 0.068 | 28.0 | 6300 | 1.3279 | 0.8117 |
| 0.0512 | 29.0 | 6525 | 1.1460 | 0.82 |
| 0.0439 | 30.0 | 6750 | 1.4730 | 0.8017 |
| 0.0414 | 31.0 | 6975 | 1.2224 | 0.8067 |
| 0.0174 | 32.0 | 7200 | 1.6967 | 0.7983 |
| 0.0407 | 33.0 | 7425 | 1.5401 | 0.7983 |
| 0.0316 | 34.0 | 7650 | 1.2844 | 0.8017 |
| 0.0008 | 35.0 | 7875 | 1.7477 | 0.805 |
| 0.0104 | 36.0 | 8100 | 1.5173 | 0.8167 |
| 0.0005 | 37.0 | 8325 | 1.6340 | 0.7967 |
| 0.0286 | 38.0 | 8550 | 1.4323 | 0.7983 |
| 0.0292 | 39.0 | 8775 | 1.4953 | 0.805 |
| 0.0108 | 40.0 | 9000 | 1.6930 | 0.8183 |
| 0.022 | 41.0 | 9225 | 1.7083 | 0.8033 |
| 0.0101 | 42.0 | 9450 | 1.8030 | 0.8083 |
| 0.0122 | 43.0 | 9675 | 1.8925 | 0.8133 |
| 0.0071 | 44.0 | 9900 | 1.7250 | 0.815 |
| 0.0004 | 45.0 | 10125 | 1.7937 | 0.8017 |
| 0.0008 | 46.0 | 10350 | 1.9056 | 0.8067 |
| 0.0003 | 47.0 | 10575 | 1.8311 | 0.8083 |
| 0.0001 | 48.0 | 10800 | 1.9401 | 0.8033 |
| 0.0001 | 49.0 | 11025 | 1.8499 | 0.8083 |
| 0.0 | 50.0 | 11250 | 1.8372 | 0.81 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
danlindb/Reinforce-Pixelcopter-PLE-v0
|
danlindb
| 2023-12-13T14:56:59Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-13T14:56:10Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 56.80 +/- 43.03
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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EmmaGthn/results_lora_40_3000
|
EmmaGthn
| 2023-12-13T17:10:22Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-13T14:58:57Z |
Entry not found
|
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] |
Ogpoggi/donut_receipt
|
Ogpoggi
| 2023-12-13T15:00:14Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-13T14:59:15Z |
Entry not found
|
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] |
ChayanM/llama-2-7b-chat-mimic-QA-v1
|
ChayanM
| 2023-12-13T15:07:09Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T14:59:58Z |
Entry not found
|
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vvwvvw/zhaiyao_zuoye
|
vvwvvw
| 2023-12-13T15:00:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T15:00:26Z |
Entry not found
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Aqua134/Info
|
Aqua134
| 2023-12-13T15:01:00Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T15:01:00Z |
---
license: apache-2.0
---
|
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] |
stvhuang/rcr-run-v57whqm7-61968-master-0_20231212T103333-ep03
|
stvhuang
| 2023-12-13T15:02:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-13T15:01:20Z |
Entry not found
|
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simoHamlili/chatbot413
|
simoHamlili
| 2023-12-13T15:05:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T15:05:30Z |
Entry not found
|
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mwezizar/my_awesome_model
|
mwezizar
| 2023-12-13T15:06:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T15:06:05Z |
Entry not found
|
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] |
tanooki426/JohnTravolta
|
tanooki426
| 2023-12-13T15:11:33Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T15:07:55Z |
---
license: openrail
---
|
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] |
manojpawar/test
|
manojpawar
| 2023-12-13T15:08:09Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T15:08:09Z |
---
license: apache-2.0
---
|
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] |
Abo3Adel/AraQA
|
Abo3Adel
| 2023-12-13T15:16:54Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T15:08:49Z |
Entry not found
|
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] |
EE21/Fine-tuned-BART-ToS-Summarization
|
EE21
| 2023-12-13T15:10:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-13T15:09:23Z |
---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4618
## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7612 | 1.0 | 405 | 0.7033 |
| 0.4783 | 2.0 | 810 | 0.5197 |
| 0.2821 | 3.0 | 1215 | 0.4618 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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sandorscog/test_trainer
|
sandorscog
| 2023-12-13T15:09:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T15:09:26Z |
Entry not found
|
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] |
kienprb/lao_viet_translation
|
kienprb
| 2023-12-15T01:24:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T15:11:03Z |
Muốn dịch đúng như báo cáo.
Đầu tiên ta phải chạy file cleandata.py với câu lệnh:
python cleandata.py --input_path path_data --out_path save_path
Dữ liệu sẽ được clean data và lưu lại và đường dẫn save path
Vd: forder input data có dạng:
input_data:
test.lo
test.vi
muốn lưu vào forder output_data sẽ chạy:
python cleandata.py --input_path 'input_data/test' --out_path 'output_data/test'
dữ liệu mới sẽ được lưu vào hai file:
output_data/test.lo
output_data/test.vi
|
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situty/academia
|
situty
| 2023-12-13T15:11:11Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T15:11:11Z |
---
license: apache-2.0
---
|
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