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Shijia/run1
|
Shijia
| 2023-12-16T00:52:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:sem_eval_2024_task_2",
"base_model:luqh/ClinicalT5-base",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-15T12:03:23Z |
---
base_model: luqh/ClinicalT5-base
tags:
- generated_from_trainer
datasets:
- sem_eval_2024_task_2
metrics:
- rouge
model-index:
- name: run1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: sem_eval_2024_task_2
type: sem_eval_2024_task_2
config: sem_eval_2024_task_2_source
split: validation
args: sem_eval_2024_task_2_source
metrics:
- name: Rouge1
type: rouge
value: 50.0
---
<!-- 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. -->
# run1
This model is a fine-tuned version of [luqh/ClinicalT5-base](https://huggingface.co/luqh/ClinicalT5-base) on the sem_eval_2024_task_2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2336
- Rouge1: 50.0
- Rouge2: 0.0
- Rougel: 50.0
- Rougelsum: 50.0
- Gen Len: 2.22
## 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: 4
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 212 | 0.2358 | 50.5 | 0.0 | 50.5 | 50.5 | 3.93 |
| 1.8021 | 2.0 | 425 | 0.2346 | 50.5 | 0.0 | 50.5 | 50.5 | 3.73 |
| 1.8021 | 3.0 | 637 | 0.2347 | 50.0 | 0.0 | 50.0 | 50.0 | 2.04 |
| 0.2555 | 4.0 | 850 | 0.2342 | 51.0 | 0.0 | 51.0 | 51.0 | 3.46 |
| 0.2555 | 5.0 | 1062 | 0.2333 | 50.5 | 0.0 | 50.5 | 50.5 | 2.33 |
| 0.2518 | 6.0 | 1275 | 0.2327 | 51.0 | 0.0 | 51.0 | 50.5 | 2.52 |
| 0.2518 | 7.0 | 1487 | 0.2351 | 50.0 | 0.0 | 50.0 | 50.0 | 2.0 |
| 0.2516 | 8.0 | 1700 | 0.2354 | 50.0 | 0.0 | 50.0 | 50.0 | 2.0 |
| 0.2516 | 9.0 | 1912 | 0.2329 | 52.0 | 0.0 | 52.0 | 51.5 | 2.42 |
| 0.2516 | 9.98 | 2120 | 0.2336 | 50.0 | 0.0 | 50.0 | 50.0 | 2.22 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_5
|
Jeevesh8
| 2023-12-15T14:57:55Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T12:04:23Z |
Entry not found
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_4
|
Jeevesh8
| 2023-12-15T14:51:52Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T12:04:23Z |
Entry not found
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_3
|
Jeevesh8
| 2023-12-15T15:00:14Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T12:04:23Z |
Entry not found
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_6
|
Jeevesh8
| 2023-12-15T14:58:09Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T12:04:24Z |
Entry not found
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_2
|
Jeevesh8
| 2023-12-15T14:57:28Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T12:04:47Z |
Entry not found
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_1
|
Jeevesh8
| 2023-12-15T14:51:40Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T12:04:47Z |
Entry not found
|
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igorktech/TALANT_mistral_7b-lora
|
igorktech
| 2023-12-15T12:26:29Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2023-12-15T12:04:52Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# Model Card for Model ID
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## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.7.2.dev0
|
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] |
igorktech/TALANT_mistral_7b
|
igorktech
| 2023-12-15T12:26:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T12:05:23Z |
Entry not found
|
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Jbinoggi/test
|
Jbinoggi
| 2023-12-15T12:06:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T12:06:13Z |
Entry not found
|
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Abdou9abdou/my_awesome_model
|
Abdou9abdou
| 2023-12-15T12:07:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T12:07:19Z |
Entry not found
|
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] |
MMoin/llama-2-7b-platypus
|
MMoin
| 2023-12-15T12:28:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T12:08:34Z |
Entry not found
|
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Aditadot23/Speed
|
Aditadot23
| 2023-12-15T12:14:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T12:12:39Z |
Entry not found
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kardosdrur/dfm-sentence-encoder-small-distil_zephyr-checkpoints
|
kardosdrur
| 2023-12-15T12:13:48Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"electra",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-12-15T12:13:45Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# kardosdrur/dfm-sentence-encoder-small-distil_zephyr-checkpoints
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('kardosdrur/dfm-sentence-encoder-small-distil_zephyr-checkpoints')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('kardosdrur/dfm-sentence-encoder-small-distil_zephyr-checkpoints')
model = AutoModel.from_pretrained('kardosdrur/dfm-sentence-encoder-small-distil_zephyr-checkpoints')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=kardosdrur/dfm-sentence-encoder-small-distil_zephyr-checkpoints)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 558 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 200,
"evaluator": "dfm_sentence_trf.evaluation.task_evaluator.TaskListEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 50,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: ElectraModel
(1): Pooling({'word_embedding_dimension': 256, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
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technocrat3128/mistral7b-sharded-finetune-15-dec-23-GGUF
|
technocrat3128
| 2023-12-15T12:59:46Z | 0 | 0 | null |
[
"gguf",
"region:us"
] | null | 2023-12-15T12:15:15Z |
Entry not found
|
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onlyalonery/kaggle-decease
|
onlyalonery
| 2023-12-15T12:17:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T12:17:46Z |
Entry not found
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_7
|
Jeevesh8
| 2023-12-15T14:58:34Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T12:20:39Z |
Entry not found
|
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] |
guipiveti/distilbert-base-uncased-finetuned-cola
|
guipiveti
| 2023-12-15T13:07:49Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T12:21:43Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5427143989115107
---
<!-- 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-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7508
- Matthews Correlation: 0.5427
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5181 | 1.0 | 535 | 0.4509 | 0.4539 |
| 0.3424 | 2.0 | 1070 | 0.4667 | 0.5190 |
| 0.2371 | 3.0 | 1605 | 0.6620 | 0.5026 |
| 0.1695 | 4.0 | 2140 | 0.7508 | 0.5427 |
| 0.1277 | 5.0 | 2675 | 0.8527 | 0.5252 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Gaysa/Kristina-ashm
|
Gaysa
| 2023-12-15T12:28:27Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T12:22:00Z |
---
license: openrail
---
|
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] |
OussamaFajrE/GherkinTest
|
OussamaFajrE
| 2023-12-15T12:24:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T12:24:15Z |
Entry not found
|
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] |
ChiaoJo/esm2_t30_150M_UR50D-finetuned-localization
|
ChiaoJo
| 2023-12-15T12:31:57Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"esm",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T12:24:57Z |
Entry not found
|
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owanr/Sentiment-roberta-base-intra-shuffle-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T05:51:24Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T12:25:14Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: Sentiment-roberta-base-intra-shuffle-model_annots_alpha0.8_whole_1e-05
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. -->
# Sentiment-roberta-base-intra-shuffle-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5348
## 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: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.7008 | 1.0 | 5628 | 4.5657 |
| 4.6092 | 2.0 | 11256 | 4.5348 |
| 4.3849 | 3.0 | 16884 | 4.5348 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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tndklab/wav2vec-jdrt-SpeakerSplitModel-1216
|
tndklab
| 2023-12-15T17:03:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T12:25:57Z |
---
license: apache-2.0
base_model: jonatasgrosman/wav2vec2-large-xlsr-53-japanese
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec-jdrt-SpeakerSplitModel-1216
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. -->
# wav2vec-jdrt-SpeakerSplitModel-1216
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
- Wer: 0.0911
- Cer: 0.0308
## 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: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.745 | 1.0 | 240 | 1.4392 | 0.9999 | 0.7806 |
| 0.6041 | 2.0 | 480 | 0.4724 | 0.7218 | 0.4437 |
| 0.2543 | 3.0 | 720 | 0.1068 | 0.1049 | 0.0368 |
| 0.1511 | 4.0 | 960 | 0.0303 | 0.0559 | 0.0227 |
| 0.099 | 5.0 | 1200 | 0.0129 | 0.0636 | 0.0237 |
| 0.0632 | 6.0 | 1440 | 0.0106 | 0.0508 | 0.0217 |
| 0.053 | 7.0 | 1680 | 0.0057 | 0.1089 | 0.0428 |
| 0.0567 | 8.0 | 1920 | 0.0045 | 0.1047 | 0.0402 |
| 0.0342 | 9.0 | 2160 | 0.0033 | 0.0818 | 0.0293 |
| 0.044 | 10.0 | 2400 | 0.0032 | 0.0900 | 0.0328 |
| 0.0387 | 11.0 | 2640 | 0.0031 | 0.0967 | 0.0359 |
| 0.0304 | 12.0 | 2880 | 0.0027 | 0.0817 | 0.0320 |
| 0.0341 | 13.0 | 3120 | 0.0022 | 0.0855 | 0.0312 |
| 0.0334 | 14.0 | 3360 | 0.0019 | 0.0860 | 0.0314 |
| 0.0382 | 15.0 | 3600 | 0.0014 | 0.0530 | 0.0192 |
| 0.0275 | 16.0 | 3840 | 0.0017 | 0.0597 | 0.0200 |
| 0.0162 | 17.0 | 4080 | 0.0014 | 0.0881 | 0.0335 |
| 0.0323 | 18.0 | 4320 | 0.0011 | 0.0899 | 0.0336 |
| 0.0247 | 19.0 | 4560 | 0.0009 | 0.0800 | 0.0280 |
| 0.0201 | 20.0 | 4800 | 0.0008 | 0.0647 | 0.0224 |
| 0.0097 | 21.0 | 5040 | 0.0008 | 0.0942 | 0.0331 |
| 0.0167 | 22.0 | 5280 | 0.0006 | 0.1054 | 0.0380 |
| 0.0242 | 23.0 | 5520 | 0.0006 | 0.1187 | 0.0421 |
| 0.0169 | 24.0 | 5760 | 0.0005 | 0.0956 | 0.0340 |
| 0.027 | 25.0 | 6000 | 0.0006 | 0.1026 | 0.0369 |
| 0.0244 | 26.0 | 6240 | 0.0006 | 0.1137 | 0.0390 |
| 0.0176 | 27.0 | 6480 | 0.0007 | 0.1080 | 0.0367 |
| 0.0182 | 28.0 | 6720 | 0.0007 | 0.0897 | 0.0304 |
| 0.0182 | 29.0 | 6960 | 0.0006 | 0.1053 | 0.0351 |
| 0.009 | 30.0 | 7200 | 0.0004 | 0.0747 | 0.0255 |
| 0.0153 | 31.0 | 7440 | 0.0005 | 0.0821 | 0.0283 |
| 0.0156 | 32.0 | 7680 | 0.0003 | 0.0905 | 0.0310 |
| 0.0086 | 33.0 | 7920 | 0.0003 | 0.0819 | 0.0279 |
| 0.0109 | 34.0 | 8160 | 0.0002 | 0.0838 | 0.0288 |
| 0.0178 | 35.0 | 8400 | 0.0002 | 0.0739 | 0.0252 |
| 0.0133 | 36.0 | 8640 | 0.0002 | 0.0687 | 0.0231 |
| 0.0136 | 37.0 | 8880 | 0.0002 | 0.0792 | 0.0267 |
| 0.0175 | 38.0 | 9120 | 0.0002 | 0.0862 | 0.0292 |
| 0.0157 | 39.0 | 9360 | 0.0002 | 0.0877 | 0.0297 |
| 0.0143 | 40.0 | 9600 | 0.0002 | 0.0911 | 0.0308 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
|
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teamichelle13/Ai-Model-Test
|
teamichelle13
| 2023-12-15T18:49:18Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-12-15T12:26:22Z |
---
{}
---
# Model Card for Model ID
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|
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] |
ragnartrem/lucas
|
ragnartrem
| 2023-12-15T12:26:52Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T12:26:52Z |
---
license: openrail
---
|
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MahtaFetrat/parsig_tokenizer
|
MahtaFetrat
| 2023-12-17T19:43:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T12:29:35Z |
Entry not found
|
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] |
mccoole/llama-2-7b-miniguanaco
|
mccoole
| 2023-12-15T12:42:39Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T12:31:49Z |
Entry not found
|
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] |
owanr/SBIC-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T05:24:20Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T12:32:42Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05
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. -->
# SBIC-roberta-base-intra-frequency-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5669
## 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: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.5166 | 1.0 | 12516 | 2.5671 |
| 2.6689 | 2.0 | 25032 | 2.5669 |
| 2.3197 | 3.0 | 37548 | 2.5669 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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hikmatfarhat/wgan-gp
|
hikmatfarhat
| 2023-12-15T12:34:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T12:33:54Z |
Entry not found
|
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Yannenou/myceline
|
Yannenou
| 2023-12-15T12:34:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T12:34:32Z |
Entry not found
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_8
|
Jeevesh8
| 2023-12-15T14:55:36Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T12:36:45Z |
Entry not found
|
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] |
behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_humanMix_Seed102
|
behzadnet
| 2023-12-15T12:40:19Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-15T12:40:12Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
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## Model Details
### Model Description
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## Uses
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## How to Get Started with the Model
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## Training Details
### Training Data
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_humanMix_Seed102
|
behzadnet
| 2023-12-15T12:40:28Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-15T12:40:26Z |
---
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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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.
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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
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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#### Metrics
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### Results
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#### Summary
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<!-- 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
|
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khushal261261/CHATBOT
|
khushal261261
| 2023-12-15T12:41:06Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T12:41:06Z |
---
license: openrail
---
|
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thrunlab/mistral7b_boolq
|
thrunlab
| 2023-12-15T12:41:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T12:41:38Z |
Entry not found
|
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] |
hkivancoral/smids_5x_beit_base_sgd_001_fold1
|
hkivancoral
| 2023-12-15T14:05:51Z | 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
| 2023-12-15T12:46:11Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_sgd_001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8914858096828047
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_beit_base_sgd_001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2771
- Accuracy: 0.8915
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7737 | 1.0 | 376 | 1.0623 | 0.3957 |
| 0.6499 | 2.0 | 752 | 0.5904 | 0.7679 |
| 0.5039 | 3.0 | 1128 | 0.4948 | 0.8114 |
| 0.4425 | 4.0 | 1504 | 0.4355 | 0.8314 |
| 0.4117 | 5.0 | 1880 | 0.4034 | 0.8381 |
| 0.3506 | 6.0 | 2256 | 0.3735 | 0.8497 |
| 0.3184 | 7.0 | 2632 | 0.3579 | 0.8614 |
| 0.3583 | 8.0 | 3008 | 0.3422 | 0.8664 |
| 0.3344 | 9.0 | 3384 | 0.3340 | 0.8765 |
| 0.3048 | 10.0 | 3760 | 0.3243 | 0.8731 |
| 0.3275 | 11.0 | 4136 | 0.3128 | 0.8815 |
| 0.2663 | 12.0 | 4512 | 0.3111 | 0.8765 |
| 0.2691 | 13.0 | 4888 | 0.3046 | 0.8815 |
| 0.2761 | 14.0 | 5264 | 0.3135 | 0.8715 |
| 0.3203 | 15.0 | 5640 | 0.2998 | 0.8831 |
| 0.2245 | 16.0 | 6016 | 0.2963 | 0.8881 |
| 0.2282 | 17.0 | 6392 | 0.2992 | 0.8881 |
| 0.3086 | 18.0 | 6768 | 0.2841 | 0.8881 |
| 0.2882 | 19.0 | 7144 | 0.2985 | 0.8831 |
| 0.2358 | 20.0 | 7520 | 0.2906 | 0.8898 |
| 0.25 | 21.0 | 7896 | 0.2925 | 0.8848 |
| 0.2381 | 22.0 | 8272 | 0.2832 | 0.8915 |
| 0.2558 | 23.0 | 8648 | 0.2829 | 0.8898 |
| 0.2316 | 24.0 | 9024 | 0.2855 | 0.8865 |
| 0.2594 | 25.0 | 9400 | 0.2808 | 0.8915 |
| 0.2312 | 26.0 | 9776 | 0.2815 | 0.8915 |
| 0.1664 | 27.0 | 10152 | 0.2829 | 0.8865 |
| 0.2051 | 28.0 | 10528 | 0.2875 | 0.8815 |
| 0.229 | 29.0 | 10904 | 0.2816 | 0.8881 |
| 0.1761 | 30.0 | 11280 | 0.2816 | 0.8915 |
| 0.2039 | 31.0 | 11656 | 0.2802 | 0.8965 |
| 0.2453 | 32.0 | 12032 | 0.2762 | 0.8965 |
| 0.186 | 33.0 | 12408 | 0.2762 | 0.8965 |
| 0.1739 | 34.0 | 12784 | 0.2763 | 0.8948 |
| 0.1942 | 35.0 | 13160 | 0.2781 | 0.8898 |
| 0.2172 | 36.0 | 13536 | 0.2768 | 0.8898 |
| 0.1982 | 37.0 | 13912 | 0.2760 | 0.8965 |
| 0.2031 | 38.0 | 14288 | 0.2780 | 0.8881 |
| 0.2045 | 39.0 | 14664 | 0.2746 | 0.8948 |
| 0.1936 | 40.0 | 15040 | 0.2754 | 0.8998 |
| 0.2051 | 41.0 | 15416 | 0.2792 | 0.8948 |
| 0.2059 | 42.0 | 15792 | 0.2787 | 0.8932 |
| 0.2037 | 43.0 | 16168 | 0.2780 | 0.8932 |
| 0.2183 | 44.0 | 16544 | 0.2796 | 0.8898 |
| 0.1934 | 45.0 | 16920 | 0.2779 | 0.8965 |
| 0.2385 | 46.0 | 17296 | 0.2770 | 0.8915 |
| 0.1872 | 47.0 | 17672 | 0.2768 | 0.8948 |
| 0.1967 | 48.0 | 18048 | 0.2773 | 0.8898 |
| 0.1829 | 49.0 | 18424 | 0.2770 | 0.8932 |
| 0.1506 | 50.0 | 18800 | 0.2771 | 0.8915 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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hkivancoral/smids_5x_beit_base_sgd_0001_fold1
|
hkivancoral
| 2023-12-15T14:06:18Z | 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
| 2023-12-15T12:47:37Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_sgd_0001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7946577629382304
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_beit_base_sgd_0001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5159
- Accuracy: 0.7947
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1623 | 1.0 | 376 | 1.2650 | 0.3406 |
| 1.0872 | 2.0 | 752 | 1.1797 | 0.3656 |
| 0.9959 | 3.0 | 1128 | 1.0972 | 0.4040 |
| 0.9117 | 4.0 | 1504 | 1.0182 | 0.4641 |
| 0.8307 | 5.0 | 1880 | 0.9466 | 0.5359 |
| 0.8065 | 6.0 | 2256 | 0.8845 | 0.5943 |
| 0.7607 | 7.0 | 2632 | 0.8316 | 0.6277 |
| 0.7796 | 8.0 | 3008 | 0.7902 | 0.6477 |
| 0.7103 | 9.0 | 3384 | 0.7560 | 0.6611 |
| 0.6802 | 10.0 | 3760 | 0.7267 | 0.6745 |
| 0.6695 | 11.0 | 4136 | 0.7035 | 0.6945 |
| 0.6571 | 12.0 | 4512 | 0.6839 | 0.6995 |
| 0.6174 | 13.0 | 4888 | 0.6660 | 0.7045 |
| 0.6036 | 14.0 | 5264 | 0.6517 | 0.7145 |
| 0.6026 | 15.0 | 5640 | 0.6384 | 0.7212 |
| 0.5646 | 16.0 | 6016 | 0.6262 | 0.7312 |
| 0.5262 | 17.0 | 6392 | 0.6162 | 0.7396 |
| 0.5941 | 18.0 | 6768 | 0.6069 | 0.7412 |
| 0.5547 | 19.0 | 7144 | 0.5985 | 0.7513 |
| 0.5277 | 20.0 | 7520 | 0.5904 | 0.7529 |
| 0.5447 | 21.0 | 7896 | 0.5840 | 0.7563 |
| 0.4895 | 22.0 | 8272 | 0.5780 | 0.7613 |
| 0.5534 | 23.0 | 8648 | 0.5722 | 0.7613 |
| 0.5434 | 24.0 | 9024 | 0.5667 | 0.7629 |
| 0.5157 | 25.0 | 9400 | 0.5616 | 0.7696 |
| 0.521 | 26.0 | 9776 | 0.5574 | 0.7763 |
| 0.5165 | 27.0 | 10152 | 0.5535 | 0.7780 |
| 0.4725 | 28.0 | 10528 | 0.5496 | 0.7796 |
| 0.5119 | 29.0 | 10904 | 0.5463 | 0.7780 |
| 0.5112 | 30.0 | 11280 | 0.5429 | 0.7746 |
| 0.5018 | 31.0 | 11656 | 0.5394 | 0.7796 |
| 0.5237 | 32.0 | 12032 | 0.5367 | 0.7846 |
| 0.4645 | 33.0 | 12408 | 0.5340 | 0.7863 |
| 0.4409 | 34.0 | 12784 | 0.5315 | 0.7896 |
| 0.4837 | 35.0 | 13160 | 0.5294 | 0.7880 |
| 0.4766 | 36.0 | 13536 | 0.5273 | 0.7863 |
| 0.4405 | 37.0 | 13912 | 0.5255 | 0.7880 |
| 0.4999 | 38.0 | 14288 | 0.5239 | 0.7930 |
| 0.4612 | 39.0 | 14664 | 0.5226 | 0.7913 |
| 0.4901 | 40.0 | 15040 | 0.5213 | 0.7913 |
| 0.5002 | 41.0 | 15416 | 0.5202 | 0.7930 |
| 0.4394 | 42.0 | 15792 | 0.5193 | 0.7930 |
| 0.4541 | 43.0 | 16168 | 0.5184 | 0.7913 |
| 0.463 | 44.0 | 16544 | 0.5178 | 0.7947 |
| 0.4704 | 45.0 | 16920 | 0.5171 | 0.7930 |
| 0.5003 | 46.0 | 17296 | 0.5167 | 0.7913 |
| 0.4732 | 47.0 | 17672 | 0.5163 | 0.7947 |
| 0.4967 | 48.0 | 18048 | 0.5161 | 0.7947 |
| 0.455 | 49.0 | 18424 | 0.5159 | 0.7947 |
| 0.4609 | 50.0 | 18800 | 0.5159 | 0.7947 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
abhinav-32/distilbert-base-uncased-finetuned-emotion
|
abhinav-32
| 2023-12-15T13:07:01Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T12:47:38Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.925142929457776
---
<!-- 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-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2137
- Accuracy: 0.925
- F1: 0.9251
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8205 | 1.0 | 250 | 0.3140 | 0.9105 | 0.9107 |
| 0.2428 | 2.0 | 500 | 0.2137 | 0.925 | 0.9251 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Lin25/wav2vec2-large-mms-1b-england-1215-finetuning
|
Lin25
| 2023-12-15T14:39:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T12:48:14Z |
Entry not found
|
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] |
hkivancoral/smids_5x_deit_tiny_sgd_0001_fold1
|
hkivancoral
| 2023-12-15T13:43:57Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T12:49:41Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_sgd_0001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7762938230383973
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_tiny_sgd_0001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5289
- Accuracy: 0.7763
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.163 | 1.0 | 376 | 1.1394 | 0.3756 |
| 1.1206 | 2.0 | 752 | 1.0830 | 0.4240 |
| 1.0283 | 3.0 | 1128 | 1.0398 | 0.4624 |
| 1.0304 | 4.0 | 1504 | 1.0025 | 0.4925 |
| 0.9327 | 5.0 | 1880 | 0.9675 | 0.5159 |
| 0.9025 | 6.0 | 2256 | 0.9343 | 0.5342 |
| 0.813 | 7.0 | 2632 | 0.9040 | 0.5492 |
| 0.8373 | 8.0 | 3008 | 0.8750 | 0.5793 |
| 0.8156 | 9.0 | 3384 | 0.8442 | 0.6093 |
| 0.7741 | 10.0 | 3760 | 0.8156 | 0.6344 |
| 0.6787 | 11.0 | 4136 | 0.7888 | 0.6477 |
| 0.6914 | 12.0 | 4512 | 0.7651 | 0.6561 |
| 0.6673 | 13.0 | 4888 | 0.7424 | 0.6761 |
| 0.6471 | 14.0 | 5264 | 0.7219 | 0.6895 |
| 0.5827 | 15.0 | 5640 | 0.7043 | 0.6978 |
| 0.5679 | 16.0 | 6016 | 0.6891 | 0.7062 |
| 0.5387 | 17.0 | 6392 | 0.6730 | 0.7095 |
| 0.5827 | 18.0 | 6768 | 0.6602 | 0.7145 |
| 0.5764 | 19.0 | 7144 | 0.6481 | 0.7179 |
| 0.5667 | 20.0 | 7520 | 0.6375 | 0.7312 |
| 0.5598 | 21.0 | 7896 | 0.6271 | 0.7329 |
| 0.4963 | 22.0 | 8272 | 0.6181 | 0.7346 |
| 0.5399 | 23.0 | 8648 | 0.6097 | 0.7396 |
| 0.6005 | 24.0 | 9024 | 0.6025 | 0.7429 |
| 0.5535 | 25.0 | 9400 | 0.5952 | 0.7479 |
| 0.5292 | 26.0 | 9776 | 0.5886 | 0.7513 |
| 0.4834 | 27.0 | 10152 | 0.5826 | 0.7529 |
| 0.4735 | 28.0 | 10528 | 0.5772 | 0.7546 |
| 0.5034 | 29.0 | 10904 | 0.5722 | 0.7563 |
| 0.4846 | 30.0 | 11280 | 0.5675 | 0.7579 |
| 0.5398 | 31.0 | 11656 | 0.5634 | 0.7596 |
| 0.5623 | 32.0 | 12032 | 0.5594 | 0.7646 |
| 0.5122 | 33.0 | 12408 | 0.5557 | 0.7646 |
| 0.483 | 34.0 | 12784 | 0.5523 | 0.7663 |
| 0.4676 | 35.0 | 13160 | 0.5492 | 0.7663 |
| 0.4655 | 36.0 | 13536 | 0.5464 | 0.7679 |
| 0.4231 | 37.0 | 13912 | 0.5438 | 0.7663 |
| 0.5103 | 38.0 | 14288 | 0.5415 | 0.7663 |
| 0.4626 | 39.0 | 14664 | 0.5394 | 0.7679 |
| 0.4372 | 40.0 | 15040 | 0.5375 | 0.7679 |
| 0.496 | 41.0 | 15416 | 0.5357 | 0.7713 |
| 0.4002 | 42.0 | 15792 | 0.5343 | 0.7746 |
| 0.4506 | 43.0 | 16168 | 0.5329 | 0.7746 |
| 0.472 | 44.0 | 16544 | 0.5318 | 0.7746 |
| 0.467 | 45.0 | 16920 | 0.5309 | 0.7763 |
| 0.4861 | 46.0 | 17296 | 0.5301 | 0.7746 |
| 0.4576 | 47.0 | 17672 | 0.5296 | 0.7746 |
| 0.4597 | 48.0 | 18048 | 0.5292 | 0.7763 |
| 0.4465 | 49.0 | 18424 | 0.5290 | 0.7763 |
| 0.3972 | 50.0 | 18800 | 0.5289 | 0.7763 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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FrameByFrame/neural-chat-7b-v3-1-function-calling-adapters
|
FrameByFrame
| 2023-12-15T12:50:39Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Intel/neural-chat-7b-v3-1",
"region:us"
] | null | 2023-12-15T12:50:17Z |
---
library_name: peft
base_model: Intel/neural-chat-7b-v3-1
---
# Model Card for Model ID
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
[More Information Needed]
#### Software
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## Citation [optional]
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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## Model Card Contact
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## 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: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
|
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gloryspace/Taesanboynextdoor
|
gloryspace
| 2023-12-15T12:54:40Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T12:50:32Z |
---
license: openrail
---
|
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gkallergis/bbbp_0_hp_opt
|
gkallergis
| 2023-12-15T16:11:17Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-12-15T12:52:27Z |
---
license: cc-by-nc-nd-4.0
---
|
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dondosss/segformer-b5-finetuned-ade-640-640-scene-parse-4500-lora
|
dondosss
| 2023-12-15T12:53:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T12:53:01Z |
Entry not found
|
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] |
owanr/ghc-roberta-base-inter-frequency-human_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T05:55:08Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T12:53:04Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: ghc-roberta-base-inter-frequency-human_annots_alpha0.8_whole_1e-05
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. -->
# ghc-roberta-base-inter-frequency-human_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7443
## 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: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.9334 | 1.0 | 11020 | 0.7323 |
| 0.4705 | 2.0 | 22040 | 0.7443 |
| 0.692 | 3.0 | 33060 | 0.7443 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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FrameByFrame/neural-chat-7b-v3-1-function-calling
|
FrameByFrame
| 2023-12-15T12:53:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T12:53:46Z |
Entry not found
|
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] |
karahansahin/bert-base-turkish-128k-uncased-type-classification-v0
|
karahansahin
| 2023-12-15T13:15:41Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T12:53:48Z |
Entry not found
|
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gkallergis/bbbp_5_hp_opt
|
gkallergis
| 2023-12-15T13:06:04Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-12-15T12:53:49Z |
---
license: cc-by-nc-nd-4.0
---
|
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gkallergis/bbbp_10_hp_opt
|
gkallergis
| 2023-12-15T13:06:22Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-12-15T12:54:53Z |
---
license: cc-by-nc-nd-4.0
---
|
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] |
EINFOCHIP/Avaitor
|
EINFOCHIP
| 2023-12-15T12:55:58Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T12:55:58Z |
---
license: apache-2.0
---
|
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] |
InquisitorNova/InquisitorNovaCycloneStudy
|
InquisitorNova
| 2023-12-15T20:07:25Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2023-12-15T12:56:14Z |
Entry not found
|
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] |
Mithilss/whisper-large-v3-chinese
|
Mithilss
| 2023-12-16T03:58:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-15T12:57:15Z |
Entry not found
|
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megha-shroff/zephyr-support-chatbot-formated
|
megha-shroff
| 2023-12-15T13:13:18Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-15T12:57:53Z |
Entry not found
|
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qualis2006/distilbert-base-uncased-lora-text-classification
|
qualis2006
| 2023-12-16T05:37:03Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T12:58:11Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-lora-text-classification
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. -->
# distilbert-base-uncased-lora-text-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0087
- Accuracy: {'accuracy': 0.886}
## 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: 4
- eval_batch_size: 4
- 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 | 250 | 0.3263 | {'accuracy': 0.882} |
| 0.4298 | 2.0 | 500 | 0.4513 | {'accuracy': 0.871} |
| 0.4298 | 3.0 | 750 | 0.6971 | {'accuracy': 0.864} |
| 0.2176 | 4.0 | 1000 | 0.6914 | {'accuracy': 0.877} |
| 0.2176 | 5.0 | 1250 | 0.7609 | {'accuracy': 0.889} |
| 0.095 | 6.0 | 1500 | 0.8447 | {'accuracy': 0.894} |
| 0.095 | 7.0 | 1750 | 0.9361 | {'accuracy': 0.888} |
| 0.024 | 8.0 | 2000 | 0.9976 | {'accuracy': 0.893} |
| 0.024 | 9.0 | 2250 | 1.0071 | {'accuracy': 0.885} |
| 0.0097 | 10.0 | 2500 | 1.0087 | {'accuracy': 0.886} |
### Framework versions
- Transformers 4.34.1
- Pytorch 1.13.0+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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] |
Jeevesh8/bt__qqp_ft_only_data_shuff_9
|
Jeevesh8
| 2023-12-15T14:57:54Z | 0 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T13:00:14Z |
Entry not found
|
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] |
owanr/Sentiment-roberta-base-inter-frequency-human_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T06:11:19Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T13:00:48Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: Sentiment-roberta-base-inter-frequency-human_annots_alpha0.8_whole_1e-05
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. -->
# Sentiment-roberta-base-inter-frequency-human_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0170
## 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: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3384 | 1.0 | 5628 | 3.1628 |
| 2.7496 | 2.0 | 11256 | 3.0170 |
| 2.7169 | 3.0 | 16884 | 3.0170 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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dondosss/segformer-b0-finetuned-ade-512-512-scene-parse-4500-lora
|
dondosss
| 2023-12-15T13:01:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T13:01:31Z |
Entry not found
|
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] |
h4iku/codet5-small-t5apr-c
|
h4iku
| 2023-12-15T16:40:14Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-15T13:04:17Z |
Entry not found
|
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] |
GiuseppeECardinale/CHIARAferragni
|
GiuseppeECardinale
| 2023-12-15T14:50:23Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T13:04:55Z |
---
license: openrail
---
|
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Nubletz/msi-nat-mini-pretrain
|
Nubletz
| 2023-12-15T13:06:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T13:06:15Z |
Entry not found
|
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] |
stvhuang/rcr-run-v57whqm7-61968-master-0_20231212T103333-ep08
|
stvhuang
| 2023-12-15T13:09:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-15T13:07:42Z |
Entry not found
|
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gkallergis/bbbp_15_hp_opt
|
gkallergis
| 2023-12-15T13:23:48Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-12-15T13:09:54Z |
---
license: cc-by-nc-nd-4.0
---
|
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gkallergis/bbbp_20_hp_opt
|
gkallergis
| 2023-12-15T13:23:45Z | 0 | 0 | null |
[
"license:cc-by-nd-4.0",
"region:us"
] | null | 2023-12-15T13:10:15Z |
---
license: cc-by-nd-4.0
---
|
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stvhuang/rcr-run-g6knz4zq-v2-61961-master-0_20231212T101450-ep12
|
stvhuang
| 2023-12-15T13:16:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-15T13:12:54Z |
Entry not found
|
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dondosss/segformer-b0-finetuned-ade-512-512-scene-parse-450-lora
|
dondosss
| 2023-12-15T13:27:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T13:13:46Z |
Entry not found
|
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peemsurat/BaoYaoNaYong
|
peemsurat
| 2023-12-15T13:18:36Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T13:16:36Z |
---
license: openrail
---
|
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] |
PaulDerG/Marcoroni_RP-7B
|
PaulDerG
| 2023-12-15T13:29:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T13:17:19Z |
Entry not found
|
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] |
aisuko/ft-vilt-b32-mlm
|
aisuko
| 2023-12-15T13:50:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vilt",
"visual-question-answering",
"generated_from_trainer",
"dataset:vqa",
"base_model:dandelin/vilt-b32-mlm",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
visual-question-answering
| 2023-12-15T13:18:11Z |
---
license: apache-2.0
base_model: dandelin/vilt-b32-mlm
tags:
- generated_from_trainer
datasets:
- vqa
model-index:
- name: ft-vilt-b32-mlm
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. -->
# ft-vilt-b32-mlm
This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) on the vqa dataset.
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Jonitab/jonitabmalaysia
|
Jonitab
| 2023-12-15T13:20:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T13:19:20Z |
Jonitab: Jonitab Malaysia, kapsul, harga, ulasan, Pil, kos, tablet, asal, farmasi!
Jonitab campuran kuat bahan-bahan semula jadi melegakan kesakitan pada sendi yang sakit di seluruh badan. Ia berjanji untuk membantu orang ramai meningkatkan kesihatan sendi mereka dan mengurangkan kesakitan yang datang dengan semakin tua, Jonitab Malaysia
<a href="https://www.boxdrug.com/JonitMala">Jonitab Beli sekarang!! Klik Pautan Di Bawah untuk maklumat lanjut dan dapatkan diskaun 50% sekarang!! cepatlah!!</a>
Laman web rasmi: <a href="https://www.boxdrug.com/JonitMala">www.Jonitab.com</a>
<p><a href="https://www.boxdrug.com/JonitMala"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBOks2_fsgfnVvcX35yfIx0_6KFcRWJyvxBWFDNhfWnpuwFCQN8T6Nxxm6oLVrrm-y5becWDzNrWgC71Zd_F5vTUAYmLZzzaj2JLXpsyr9zLk-Q8V787W1Ji0SyWYbHzgLE1lsG8OJr5VtuXgKg1FWp9pv6DsazPxjivLYPNdYFWTkyFinIY_4HL-gcR4/w620-h316/Jonitab%20Malaysia.png" alt="enter image description here"> </a></p>
➢Nama Produk — Jonitab
➢Kategori –Kesihatan Sendi
➢Faedah Utama — Memberi anda lebih keselesaan sendi & menjadikan hidup anda lebih bahagia dan lebih aktif.
➢ Komposisi — Sebatian Organik Semulajadi
➢ Kesan Sampingan—NA
➢Penilaian Akhir: — 4.8
➢ Ketersediaan — Dalam Talian
➢Tawaran & Diskaun; JIMAT HARI INI! BELI SEKARANG UNTUK MEMBELI TAWARAN ISTIMEWA!!!
Apakah Jonitab?
Menurut syarikat itu, semua masalah sendi yang datang dengan semakin tua adalah disebabkan oleh kekurangan zarah dalam cecair sinovial. Kerana anda tidak mempunyai cukup, cecair di antara sendi anda menjadi lebih nipis dan kering. Ini bermakna tulang rawan di antara tulang anda kurang pelincir, kering, dan diberi makan. Kerana ini, tulang rawan rosak dan sel-sel mati, yang menyebabkan bengkak sendi. Joint membantu anda memastikan sendi anda sihat dengan memberikan tulang rawan anda oksigen dan makanan yang diperlukan.
<a href="https://www.boxdrug.com/JonitMala">Jonitab Beli sekarang!! Klik Pautan Di Bawah untuk maklumat lanjut dan dapatkan diskaun 50% sekarang!! cepatlah!!</a>
Laman web rasmi: <a href="https://www.boxdrug.com/JonitMala">www.Jonitab.com</a>
<p><a href="https://www.boxdrug.com/JonitMala"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBOks2_fsgfnVvcX35yfIx0_6KFcRWJyvxBWFDNhfWnpuwFCQN8T6Nxxm6oLVrrm-y5becWDzNrWgC71Zd_F5vTUAYmLZzzaj2JLXpsyr9zLk-Q8V787W1Ji0SyWYbHzgLE1lsG8OJr5VtuXgKg1FWp9pv6DsazPxjivLYPNdYFWTkyFinIY_4HL-gcR4/w620-h316/Jonitab%20Malaysia.png" alt="enter image description here"> </a></p>
Jonitab Jonitab Pil Jonitab kapsul Jonitab tablet Jonitab harga Jonitab ulasan Jonitab bahan-bahan Jonitab Faedah Jonitab Kesan sampingan Jonitab harga kapsul Jonitab ulasan kapsul Jonitab gubahan Jonitab aduan Jonitab Di manakah boleh dibeli Jonitab Bagaimana nak guna Jonitab kos Jonitab berfungsi Jonitab forum Jonitab asal Jonitab farmasi
Bagaimanakah Jonitab berfungsi?
Matlamat Jonitab adalah untuk mendapatkan punca sakit sendi.Jonitab Ulasan bertujuan untuk melegakan kekakuan, sakit, dan bengkak yang disebabkan oleh sakit sendi dengan mendapatkan punca. Ubat untuk sakit sendi merawat simptom. Dengan mengambil Jonitab setiap hari, anda boleh membantu badan anda dan mendapatkan kelegaan sakit yang kuat dari sendi anda. ini bertujuan untuk menjadi cara yang disokong secara saintifik untuk memastikan sendi anda kuat, fleksibel dan muda seiring dengan usia anda.
Di Mana Nak Beli Jonitab?
Pelanggan hanya boleh mendapatkan Jonitab. Bergantung pada berapa banyak ubat yang dikehendaki oleh seseorang sekali gus, pencipta telah membuat tiga pakej berbeza.
<a href="https://www.boxdrug.com/JonitMala">Jonitab Beli sekarang!! Klik Pautan Di Bawah untuk maklumat lanjut dan dapatkan diskaun 50% sekarang!! cepatlah!!</a>
Laman web rasmi: <a href="https://www.boxdrug.com/JonitMala">www.Jonitab.com</a>
<p><a href="https://www.boxdrug.com/JonitMala"> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBOks2_fsgfnVvcX35yfIx0_6KFcRWJyvxBWFDNhfWnpuwFCQN8T6Nxxm6oLVrrm-y5becWDzNrWgC71Zd_F5vTUAYmLZzzaj2JLXpsyr9zLk-Q8V787W1Ji0SyWYbHzgLE1lsG8OJr5VtuXgKg1FWp9pv6DsazPxjivLYPNdYFWTkyFinIY_4HL-gcR4/w620-h316/Jonitab%20Malaysia.png" alt="enter image description here"> </a></p>
Kata Akhir:
Jonitab untuk sakit sendi dibuat untuk merawat punca sebenar sakit sendi, iaitu masalah dengan cecair sinovial anda. Menggunakan campuran bahan semula jadi untuk membantu cecair sinovial, Jonitab dikatakan boleh menyasarkan dan melegakan sakit sendi dengan cepat, menjadikan anda lebih mudah alih dan bebas kesakitan pada sebarang umur.
Jonitab Jonitab Pil Jonitab kapsul Jonitab tablet Jonitab harga Jonitab ulasan Jonitab bahan-bahan Jonitab Faedah Jonitab Kesan sampingan Jonitab harga kapsul Jonitab ulasan kapsul Jonitab gubahan Jonitab aduan Jonitab Di manakah boleh dibeli Jonitab Bagaimana nak guna Jonitab kos Jonitab berfungsi Jonitab forum Jonitab asal Jonitab farmasi
baca lebih lanjut: https://www.boxdrug.com/JonitMala
https://www.facebook.com/Jonitab.Malaysia
https://sites.google.com/view/jonitab/home
https://healthtoned.blogspot.com/2023/12/jonitab-malaysia.html
https://www.weddingwire.com/website/jonitab-and-malaysia
https://infogram.com/jonitab-malaysia-1hnp27mwqy5mn2g?live
https://softisenilspain.hashnode.dev/jonitab-malaysia
https://jonitabmalaysia.company.site/
https://sway.office.com/DWCQSEBItdpozR9t
https://gamma.app/docs/Jonitab-Malaysia-09wxunt58io06lz?mode=doc
https://healthytalk24x7.wixsite.com/sunshine/post/jonitab-malaysia
https://groups.google.com/g/snshine/c/f7LS1Q4FokA
https://www.sunflower-cissp.com/glossary/cissp/7312/jonitab-malaysia
https://nepal.tribe.so/post/jonitab-jonitab-malaysia-kapsul-harga-ulasan-pil-kos-tablet-asal-farmasi-657c50a02fd45252b920dabb
https://community.thebatraanumerology.com/
https://community.thebatraanumerology.com/post/jonitab-jonitab-malaysia-kapsul-harga-ulasan-pil-kos-tablet-asal-farmasi-657c512bd274ea3a5078ce7f
https://enkling.com/read-blog/9044
https://medium.com/@jonitabmalaysia/jonitab-malaysia-1713502b101e
https://medium.com/@jonitabmalaysia/jonitab-jonitab-malaysia-kapsul-harga-ulasan-pil-kos-tablet-asal-farmasi-a0e195c7b072
https://soundcloud.com/jonitabmalaysia/jonitab-malaysia?si=f9ef05f54f8547c4b7a71eb34af19907&utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing
https://www.deviantart.com/jonitabmalaysia/art/Jonitab-Malaysia-1002172303
|
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shanjay/mgc-ds-l
|
shanjay
| 2023-12-15T13:27:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T13:19:42Z |
Entry not found
|
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CasanovaSan/Secret
|
CasanovaSan
| 2023-12-15T13:28:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T13:21:01Z |
Entry not found
|
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aliakbazare/vit-base-patch16-224-finetuned-flower
|
aliakbazare
| 2023-12-15T14:13:34Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T13:24:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-base-patch16-224-finetuned-flower
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. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.1.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
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] |
Kooten/DaringMaid-13B-8bpw-exl2
|
Kooten
| 2023-12-15T18:16:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T13:26:12Z |
---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-generation
---
# This is a 8BPW EXL2 quant of DaringMaid-13B
My goal was to make a Noromaid that's smarter and better at following instructions.
After trying a bunch of different recipes I think this one turned out pretty good
- I used [sequelbox/DynamicFactor](https://huggingface.co/sequelbox/DynamicFactor) as a base to as its supposed "improve overall knowledge, precise communication, conceptual understanding, and technical skill" over the base llama2.
- [NeverSleep/Noromaid](https://huggingface.co/NeverSleep/Noromaid-13b-v0.1.1) of course.
- [Undi95/Utopia](https://huggingface.co/Undi95/Utopia-13B) has been recommended again recently and its still really good so in the mixer it goes
- I liked [tavtav/Rose](https://huggingface.co/tavtav/Rose-20B) so i threw in a bit of [CalderaAI/Thorns](https://huggingface.co/CalderaAI/13B-Thorns-l2)
- There was recently a model that tried to pass itself off as [Gryphe/MythoMax](https://huggingface.co/Gryphe/MythoMax-L2-13b), i made a merge with that model before it was revealed to be MythoMax and it turned out pretty good so i used it.
The .yml config files for mergekit with the exact merges can be found in the ["Recipe"](https://huggingface.co/Kooten/DaringMaid-13B/tree/main/Recipe) folder in the [fp16 repo](https://huggingface.co/Kooten/DaringMaid-13B)
# Quants
EXL2: [8bpw](https://huggingface.co/Kooten/DaringMaid-13B-8bpw-exl2), [5bpw](https://huggingface.co/Kooten/DaringMaid-13B-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/DaringMaid-13B-4bpw-exl2)
[GGUF](https://huggingface.co/Kooten/DaringMaid-13B-GGUF):
[Q3_K_M](https://huggingface.co/Kooten/DaringMaid-13B-GGUF/blob/main/DaringMaid-13B-Q3_K_M.gguf) - [Q4_K_M](https://huggingface.co/Kooten/DaringMaid-13B-GGUF/blob/main/DaringMaid-13B-Q4_K_M.gguf) - [Q5_K_M](https://huggingface.co/Kooten/DaringMaid-13B-GGUF/blob/main/DaringMaid-13B-Q5_K_M.gguf)
## Prompt template:
I have been using Undi/Ikaris SillyTavern presets for Noromaid: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
### Alpaca:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not include descriptions of non-visual qualities such as personality, movements, scents, mental traits, or anything which could not be seen in a still photograph. Do not write in full sentences. Prefix your description with the phrase 'full body portrait,'
### Instruction:
{prompt}
### Response:
```
### Contact
Kooten on discord.
|
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] |
karahansahin/ellipsis-tr-bert-base-turkish-128k-uncased-type-classification-v0
|
karahansahin
| 2023-12-15T17:02:13Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T13:28:22Z |
Entry not found
|
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dondosss/segformer-b0-finetuned-ade-512-512-scene-parse-1800-lora
|
dondosss
| 2023-12-15T13:38:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T13:30:08Z |
Entry not found
|
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THUDM/CogAgent
|
THUDM
| 2023-12-16T12:29:38Z | 0 | 1 | null |
[
"en",
"arxiv:2312.08914",
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T13:30:17Z |
---
license: apache-2.0
language:
- en
---
# CogAgent
### Reminder: This is the repository for CogAgent of [SAT (SwissArmyTransformer)](https://github.com/THUDM/SwissArmyTransformer/) version.
### Please refer to [https://huggingface.co/THUDM/cogagent-chat-hf](https://huggingface.co/THUDM/cogagent-chat-hf) for CogAgent of Huggingface version.
## Introduction
**CogAgent** is an open-source visual language model improved based on **CogVLM**. CogAgent-18B has 11 billion visual and 7 billion language parameters.
📖 Paper: https://arxiv.org/abs/2312.08914
🚀 GitHub: For more information, please refer to [Our GitHub](https://github.com/THUDM/CogVLM/)
CogAgent demonstrates **strong performance** in image understanding and GUI agent:
1. CogAgent-18B **achieves state-of-the-art generalist performance on 9 cross-modal benchmarks**, including: VQAv2, MM-Vet, POPE, ST-VQA, OK-VQA, TextVQA, ChartQA, InfoVQA, DocVQA.
2. CogAgent-18B significantly **surpasses existing models on GUI operation datasets**, including AITW and Mind2Web.
In addition to all the **features** already present in **CogVLM** (visual multi-round dialogue, visual grounding), **CogAgent**:
1. Supports higher resolution visual input and dialogue question-answering. It supports ultra-high-resolution image inputs of **1120x1120**.
2. Possesses the capabilities of a visual Agent, being able to return a plan, next action, and specific operations with coordinates for any given task on any GUI screenshot.
3. Enhanced GUI-related question-answering capabilities, allowing it to handle questions about any GUI screenshot, such as web pages, PC apps, mobile applications, etc.
4. Enhanced capabilities in OCR-related tasks through improved pre-training and fine-tuning.
<div align="center">
<img src="https://raw.githubusercontent.com/THUDM/CogVLM/master/assets/cogagent_function.jpg" alt="img" style="zoom: 50%;" />
</div>
## Quick Start
Please refer to the instructions located at [our GitHub - section cli-SAT](https://github.com/THUDM/CogVLM?tab=readme-ov-file#situation-21-cli-sat-version) for inference and fine-tuning of the SAT version of the model.
You only need to use a command for easy inference.
```bash
python cli_demo_sat.py --from_pretrained cogagent-chat --version chat --bf16 --stream_chat
```
## License
The code in this repository is open source under the [Apache-2.0 license](./LICENSE), while the use of CogAgent and CogVLM model weights must comply with the [Model License](./MODEL_LICENSE).
## Citation & Acknowledgements
If you find our work helpful, please consider citing the following papers
```
@misc{hong2023cogagent,
title={CogAgent: A Visual Language Model for GUI Agents},
author={Wenyi Hong and Weihan Wang and Qingsong Lv and Jiazheng Xu and Wenmeng Yu and Junhui Ji and Yan Wang and Zihan Wang and Yuxiao Dong and Ming Ding and Jie Tang},
year={2023},
eprint={2312.08914},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
In the instruction fine-tuning phase of the CogVLM, there are some English image-text data from the [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4), [LLAVA](https://github.com/haotian-liu/LLaVA), [LRV-Instruction](https://github.com/FuxiaoLiu/LRV-Instruction), [LLaVAR](https://github.com/SALT-NLP/LLaVAR) and [Shikra](https://github.com/shikras/shikra) projects, as well as many classic cross-modal work datasets. We sincerely thank them for their contributions.
|
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] |
gkallergis/bbbp_25_hp_opt
|
gkallergis
| 2023-12-15T13:45:27Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-12-15T13:32:26Z |
---
license: cc-by-nc-nd-4.0
---
|
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] |
gkallergis/bbbp_40_hp_opt
|
gkallergis
| 2023-12-15T13:45:27Z | 0 | 0 | null |
[
"license:cc-by-nd-4.0",
"region:us"
] | null | 2023-12-15T13:33:08Z |
---
license: cc-by-nd-4.0
---
|
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AbhishekkV19/distilbert-base-uncased-10k-vul-2k-nv
|
AbhishekkV19
| 2023-12-15T13:34:05Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T13:33:54Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-10k-vul-2k-nv
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. -->
# distilbert-base-uncased-10k-vul-2k-nv
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5493
- Accuracy: 0.9004
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0701 | 1.0 | 1313 | 0.5987 | 0.9025 |
| 0.0463 | 2.0 | 2626 | 0.6532 | 0.8771 |
| 0.0344 | 3.0 | 3939 | 0.5493 | 0.9004 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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owanr/SBIC-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T05:50:33Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T13:34:33Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05
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. -->
# SBIC-roberta-base-inter-data-frequency-human_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3398
## 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: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0614 | 1.0 | 12516 | 1.3432 |
| 0.9202 | 2.0 | 25032 | 1.3398 |
| 0.9171 | 3.0 | 37548 | 1.3398 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
GraydientPlatformAPI/beautyfool-xl
|
GraydientPlatformAPI
| 2023-12-15T13:57:19Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"license:openrail",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2023-12-15T13:35:02Z |
---
license: openrail
---
|
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] |
genericgod/GerMerge-em-leo-mistral-v0.2-SLERP-GGUF
|
genericgod
| 2023-12-15T14:17:32Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"pytorch",
"german",
"deutsch",
"mistral",
"leolm",
"merge",
"text-generation",
"de",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-12-15T13:35:07Z |
---
inference: false
language:
- de
library_name: transformers
license: apache-2.0
model_creator: genericgod
model_name: GerMerge
model_type: mistral
pipeline_tag: text-generation
tags:
- pytorch
- german
- deutsch
- mistral
- leolm
- merge
---
[f16 here](https://huggingface.co/genericgod/GerMerge-em-leo-mistral-v0.2-SLERP)
I made this merge to add better German capabilities to the new Mistral-7B-Instruct-v0.2 model
**Disclaimer:** I am a complete noob. I dont know what I am doing, just playing around with the parameters.
<!-- description start -->
## Description
This repo contains GGUF files of the following two models merged with SLERP.
<!-- description end -->
<!-- description start -->
## Models used
- mistralai/Mistral-7B-Instruct-v0.2 (base model)
- jphme/em_german_leo_mistral
Thanks to all people involved into making these Models!
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Mistral
```
[INST] <instruction> [/INST]
```
# Examples
```
Prompt: [INST]Ich habe heute 4 Äpfel. Gestern aß ich 3 Äpfel. Wie viele Äpfel habe ich heute?[/INST]
Output: Heute hast du 4 Äpfel. Die Äpfel, die du gestern gegessen hast, ändern nichts an der Anzahl der Äpfel, die du heute hast. Daher hast du immer noch 4 Äpfel.
```
```
Prompt: [INST]Welche Möglichkeiten gibt es im Alltag möglichst umweltbewusst zu Leben? Nenne mir 5 Punkte.[/INST]
Output: 1. Reduziere den Energieverbrauch: Du kannst umweltbewusst leben, indem du Energie effizienter nutzt und versuchst, deinen CO2-Fußabdruck zu minimieren. Dazu gehört zum Beispiel die Nutzung von LED-Lampen, das Ausschalten von Geräten bei Nichtgebrauch, die Regulierung der Heizungstemperatur und die Wäsche bei niedrigen Temperaturen zu waschen.
2. Nutze nachhaltige Verkehrsmittel: Umweltfreundlich ist die Nutzung von öffentlichen Verkehrsmitteln, dem Fahrrad oder zu Fuß zu gehen oder mit dem E-Auto zu fahren. Wenn das nicht möglich ist, kannst du CO2-neutral mit dem Zug oder dem Bus fahren.
3. Kaufe saisonale und regionale Produkte: Umweltbewusstes Einkaufen bedeutet, saisonale und regionale Produkte zu kaufen, um die Transportwege zu verkürzen und die Unterstützung lokaler Landwirte und Hersteller zu fördern. Zudem solltest du so wenig Verpackungsmaterialien wie möglich verwenden und Plastiktüten vermeiden.
4. Vermeide Einwegprodukte: Versuche Einwegprodukte zu vermeiden und stattdessen auf Mehrwegprodukte umzusteigen. Nutze wiederverwendbare Taschen, Flaschen und Behälter und kaufe Lebensmittel in großen Mengen, um Verpackungsmüll zu reduzieren.
5. Trenne und recyceln richtig: Richtiges Trennen und Recyceln von Müll ist ein wichtiger Beitrag zum Umweltschutz. Informiere dich über die richtigen Mülltrennsysteme in deiner Gemeinde und trenne deinen Abfall entsprechend. Nutze außerdem Produkte mit möglichst wenig Verpackungsmaterial und kaufe Recyclingprodukte, um die Wiederverwertung von Rohstoffen zu fördern.
```
# Mergekit config
```
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: jphme/em_german_leo_mistral
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
t:
- filter: self_attn
value: [0, 0.1, 0.6, 0.8, 1]
- filter: mlp
value: [0, 0.1, 0.3, 0.6, 0.9]
- value: 0.8
dtype: float16
```
|
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] |
gugucasfer/dlinkotoro
|
gugucasfer
| 2023-12-15T13:35:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T13:35:32Z |
Entry not found
|
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] |
gugucasfer/dlinkotoz
|
gugucasfer
| 2023-12-15T18:32:43Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T13:36:36Z |
---
license: openrail
---
|
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owanr/Sentiment-roberta-base-inter-frequency-model_annots_alpha0.8_whole_1e-05
|
owanr
| 2023-12-16T06:32:05Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-15T13:37:00Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: Sentiment-roberta-base-inter-frequency-model_annots_alpha0.8_whole_1e-05
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. -->
# Sentiment-roberta-base-inter-frequency-model_annots_alpha0.8_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8677
## 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: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.8204 | 1.0 | 5628 | 4.8305 |
| 4.7826 | 2.0 | 11256 | 4.8677 |
| 4.7094 | 3.0 | 16884 | 4.8677 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
yukiarimo/yuna-ai
|
yukiarimo
| 2023-12-15T13:38:39Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-15T13:38:39Z |
---
license: mit
---
|
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diana9m/whisper_lora_dk
|
diana9m
| 2023-12-15T13:38:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T13:38:50Z |
Entry not found
|
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LarryAIDraw/UnzenV2
|
LarryAIDraw
| 2023-12-15T13:41:45Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-15T13:39:43Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/143157/unzen-azur-lane
|
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LarryAIDraw/Unzen_azur_
|
LarryAIDraw
| 2023-12-15T13:41:55Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-15T13:40:15Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/148887/unzen-azur-lane
|
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] |
Opps/maiju
|
Opps
| 2023-12-15T13:40:38Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T13:40:38Z |
---
license: apache-2.0
---
|
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dondosss/mit-b0-scene-parse-1800-lora
|
dondosss
| 2023-12-15T14:36:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T13:41:28Z |
Entry not found
|
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] |
hkivancoral/smids_5x_deit_tiny_sgd_0001_fold2
|
hkivancoral
| 2023-12-15T14:39:02Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-15T13:44:39Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_sgd_0001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.800332778702163
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_tiny_sgd_0001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4980
- Accuracy: 0.8003
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1772 | 1.0 | 375 | 1.1487 | 0.3760 |
| 1.0767 | 2.0 | 750 | 1.0817 | 0.4276 |
| 0.9966 | 3.0 | 1125 | 1.0309 | 0.4742 |
| 0.9479 | 4.0 | 1500 | 0.9881 | 0.5158 |
| 0.9443 | 5.0 | 1875 | 0.9501 | 0.5374 |
| 0.9172 | 6.0 | 2250 | 0.9138 | 0.5574 |
| 0.8419 | 7.0 | 2625 | 0.8792 | 0.5857 |
| 0.8745 | 8.0 | 3000 | 0.8452 | 0.6023 |
| 0.8037 | 9.0 | 3375 | 0.8135 | 0.6273 |
| 0.7725 | 10.0 | 3750 | 0.7849 | 0.6556 |
| 0.73 | 11.0 | 4125 | 0.7582 | 0.6705 |
| 0.7472 | 12.0 | 4500 | 0.7325 | 0.6822 |
| 0.6618 | 13.0 | 4875 | 0.7082 | 0.7105 |
| 0.6872 | 14.0 | 5250 | 0.6883 | 0.7255 |
| 0.6348 | 15.0 | 5625 | 0.6702 | 0.7304 |
| 0.5716 | 16.0 | 6000 | 0.6521 | 0.7388 |
| 0.6306 | 17.0 | 6375 | 0.6373 | 0.7454 |
| 0.604 | 18.0 | 6750 | 0.6231 | 0.7537 |
| 0.5325 | 19.0 | 7125 | 0.6109 | 0.7604 |
| 0.602 | 20.0 | 7500 | 0.6001 | 0.7604 |
| 0.5143 | 21.0 | 7875 | 0.5907 | 0.7604 |
| 0.5394 | 22.0 | 8250 | 0.5814 | 0.7671 |
| 0.5319 | 23.0 | 8625 | 0.5730 | 0.7671 |
| 0.5182 | 24.0 | 9000 | 0.5659 | 0.7704 |
| 0.5353 | 25.0 | 9375 | 0.5592 | 0.7720 |
| 0.5323 | 26.0 | 9750 | 0.5527 | 0.7787 |
| 0.5286 | 27.0 | 10125 | 0.5472 | 0.7787 |
| 0.4649 | 28.0 | 10500 | 0.5420 | 0.7820 |
| 0.5396 | 29.0 | 10875 | 0.5373 | 0.7837 |
| 0.4863 | 30.0 | 11250 | 0.5330 | 0.7837 |
| 0.4735 | 31.0 | 11625 | 0.5292 | 0.7837 |
| 0.5595 | 32.0 | 12000 | 0.5255 | 0.7887 |
| 0.4938 | 33.0 | 12375 | 0.5219 | 0.7920 |
| 0.468 | 34.0 | 12750 | 0.5190 | 0.7970 |
| 0.4879 | 35.0 | 13125 | 0.5162 | 0.7970 |
| 0.4592 | 36.0 | 13500 | 0.5136 | 0.7970 |
| 0.4625 | 37.0 | 13875 | 0.5113 | 0.7953 |
| 0.4476 | 38.0 | 14250 | 0.5091 | 0.7970 |
| 0.4681 | 39.0 | 14625 | 0.5073 | 0.8003 |
| 0.4644 | 40.0 | 15000 | 0.5056 | 0.8003 |
| 0.4624 | 41.0 | 15375 | 0.5040 | 0.8003 |
| 0.4244 | 42.0 | 15750 | 0.5026 | 0.8003 |
| 0.457 | 43.0 | 16125 | 0.5015 | 0.8003 |
| 0.4802 | 44.0 | 16500 | 0.5005 | 0.8003 |
| 0.4285 | 45.0 | 16875 | 0.4996 | 0.8003 |
| 0.4665 | 46.0 | 17250 | 0.4990 | 0.8003 |
| 0.4627 | 47.0 | 17625 | 0.4985 | 0.8003 |
| 0.4554 | 48.0 | 18000 | 0.4982 | 0.8003 |
| 0.4888 | 49.0 | 18375 | 0.4980 | 0.8003 |
| 0.4643 | 50.0 | 18750 | 0.4980 | 0.8003 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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GilbertClaus/Morgpie
|
GilbertClaus
| 2023-12-15T17:40:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T13:45:29Z |
Entry not found
|
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platesfork/rvc2
|
platesfork
| 2023-12-17T14:06:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T13:48:46Z |
Entry not found
|
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] |
HinaBl/HanaMacchia
|
HinaBl
| 2023-12-15T13:49:58Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-15T13:49:19Z |
---
license: openrail
---
|
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] |
AbhishekkV19/distilbert-base-uncased-10k-vul-1k-nv
|
AbhishekkV19
| 2023-12-15T13:49:50Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-15T13:49:37Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-10k-vul-1k-nv
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. -->
# distilbert-base-uncased-10k-vul-1k-nv
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0958
- Accuracy: 0.9852
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.071 | 1.0 | 1188 | 0.0499 | 0.9873 |
| 0.0349 | 2.0 | 2376 | 0.0851 | 0.9873 |
| 0.0113 | 3.0 | 3564 | 0.0958 | 0.9852 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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playgroundshw/angle_curved_m
|
playgroundshw
| 2023-12-15T13:53:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-15T13:53:28Z |
Entry not found
|
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] |
Arekku21/vivit-b-16x2-kinetics400-finetuned-MSL_40_classes_8
|
Arekku21
| 2023-12-15T15:09:53Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vivit",
"video-classification",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-12-15T13:55:28Z |
Entry not found
|
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Chikanamakalaka/jfoyboy_LoRA
|
Chikanamakalaka
| 2023-12-15T13:57:25Z | 0 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-15T13:57:23Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of jfoyboy
license: openrail++
---
# SDXL LoRA DreamBooth - Chikanamakalaka/jfoyboy_LoRA
<Gallery />
## Model description
These are Chikanamakalaka/jfoyboy_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of jfoyboy to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Chikanamakalaka/jfoyboy_LoRA/tree/main) them in the Files & versions tab.
|
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viktor-shevchuk/t5-small-finetuned-laws_articles
|
viktor-shevchuk
| 2023-12-15T17:13:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-15T13:57:29Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetuned-laws_articles
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. -->
# t5-small-finetuned-laws_articles
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4459
- Rouge1: 28.5123
- Rouge2: 10.7847
- Rougel: 23.1779
- Rougelsum: 23.1054
- Gen Len: 18.7143
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 87 | 3.0316 | 27.6181 | 11.4154 | 23.0601 | 23.0895 | 18.6494 |
| No log | 2.0 | 174 | 2.8530 | 28.2521 | 11.5239 | 23.2405 | 23.2335 | 18.5325 |
| No log | 3.0 | 261 | 2.7724 | 28.1619 | 10.4465 | 22.4885 | 22.5004 | 18.4545 |
| No log | 4.0 | 348 | 2.7206 | 27.6965 | 10.4247 | 22.5084 | 22.504 | 18.5714 |
| No log | 5.0 | 435 | 2.6820 | 28.0781 | 10.4734 | 23.0002 | 22.9096 | 18.5455 |
| 3.0366 | 6.0 | 522 | 2.6535 | 28.2097 | 10.5925 | 23.4952 | 23.474 | 18.4675 |
| 3.0366 | 7.0 | 609 | 2.6330 | 27.9031 | 10.4349 | 23.1427 | 23.1499 | 18.5325 |
| 3.0366 | 8.0 | 696 | 2.6191 | 27.7764 | 10.1563 | 22.7929 | 22.6971 | 18.4286 |
| 3.0366 | 9.0 | 783 | 2.5998 | 27.2375 | 9.7613 | 22.2731 | 22.2206 | 18.2727 |
| 3.0366 | 10.0 | 870 | 2.5866 | 27.2295 | 9.6843 | 22.5222 | 22.3697 | 18.3377 |
| 3.0366 | 11.0 | 957 | 2.5723 | 26.7174 | 9.3882 | 22.2336 | 22.0906 | 18.3377 |
| 2.7071 | 12.0 | 1044 | 2.5651 | 27.4541 | 10.5671 | 22.7291 | 22.6063 | 18.2468 |
| 2.7071 | 13.0 | 1131 | 2.5539 | 27.1483 | 9.9641 | 22.252 | 22.0612 | 18.2857 |
| 2.7071 | 14.0 | 1218 | 2.5451 | 27.4547 | 10.3266 | 22.6335 | 22.4141 | 18.3247 |
| 2.7071 | 15.0 | 1305 | 2.5366 | 27.3057 | 10.0068 | 22.5939 | 22.4321 | 18.3377 |
| 2.7071 | 16.0 | 1392 | 2.5291 | 27.0093 | 9.8964 | 22.1854 | 22.0594 | 18.4416 |
| 2.7071 | 17.0 | 1479 | 2.5210 | 27.4076 | 9.8442 | 22.4911 | 22.3641 | 18.3117 |
| 2.5937 | 18.0 | 1566 | 2.5130 | 27.8067 | 10.1345 | 22.5757 | 22.4506 | 18.3896 |
| 2.5937 | 19.0 | 1653 | 2.5086 | 28.5205 | 10.8396 | 22.902 | 22.7628 | 18.4416 |
| 2.5937 | 20.0 | 1740 | 2.5043 | 28.1191 | 10.3793 | 22.6607 | 22.5025 | 18.5584 |
| 2.5937 | 21.0 | 1827 | 2.4971 | 28.0615 | 10.3535 | 22.7174 | 22.5795 | 18.6104 |
| 2.5937 | 22.0 | 1914 | 2.4935 | 28.0575 | 10.2809 | 22.9132 | 22.7487 | 18.5974 |
| 2.5208 | 23.0 | 2001 | 2.4886 | 27.8841 | 10.3683 | 22.66 | 22.5543 | 18.7532 |
| 2.5208 | 24.0 | 2088 | 2.4851 | 27.5211 | 9.9966 | 22.5687 | 22.4658 | 18.6753 |
| 2.5208 | 25.0 | 2175 | 2.4823 | 27.7727 | 10.2515 | 22.7401 | 22.5633 | 18.5974 |
| 2.5208 | 26.0 | 2262 | 2.4785 | 28.042 | 10.4358 | 22.9115 | 22.7964 | 18.7273 |
| 2.5208 | 27.0 | 2349 | 2.4731 | 27.6472 | 10.2882 | 22.6181 | 22.4979 | 18.6364 |
| 2.5208 | 28.0 | 2436 | 2.4714 | 27.8088 | 10.4361 | 22.885 | 22.7203 | 18.6623 |
| 2.4649 | 29.0 | 2523 | 2.4694 | 27.73 | 10.5222 | 22.7567 | 22.6331 | 18.7013 |
| 2.4649 | 30.0 | 2610 | 2.4660 | 27.6279 | 10.377 | 22.4668 | 22.4061 | 18.7013 |
| 2.4649 | 31.0 | 2697 | 2.4649 | 27.3504 | 10.1574 | 22.224 | 22.1656 | 18.7013 |
| 2.4649 | 32.0 | 2784 | 2.4639 | 27.3448 | 9.9756 | 22.1962 | 22.1349 | 18.7013 |
| 2.4649 | 33.0 | 2871 | 2.4609 | 27.3083 | 10.0656 | 22.2803 | 22.2228 | 18.7143 |
| 2.4649 | 34.0 | 2958 | 2.4593 | 27.51 | 10.0941 | 22.3699 | 22.3382 | 18.6623 |
| 2.4181 | 35.0 | 3045 | 2.4562 | 27.5434 | 10.1665 | 22.5002 | 22.4415 | 18.7143 |
| 2.4181 | 36.0 | 3132 | 2.4545 | 27.5664 | 10.0953 | 22.5101 | 22.4741 | 18.6364 |
| 2.4181 | 37.0 | 3219 | 2.4530 | 27.5874 | 10.333 | 22.5099 | 22.4576 | 18.7013 |
| 2.4181 | 38.0 | 3306 | 2.4522 | 27.3705 | 10.1022 | 22.4695 | 22.3849 | 18.6623 |
| 2.4181 | 39.0 | 3393 | 2.4512 | 27.8864 | 10.2695 | 22.834 | 22.759 | 18.6753 |
| 2.4181 | 40.0 | 3480 | 2.4504 | 27.6797 | 10.2472 | 22.5788 | 22.5353 | 18.7013 |
| 2.4038 | 41.0 | 3567 | 2.4495 | 27.6797 | 10.2472 | 22.5788 | 22.5353 | 18.7013 |
| 2.4038 | 42.0 | 3654 | 2.4492 | 27.1595 | 9.6186 | 22.1003 | 22.0487 | 18.6883 |
| 2.4038 | 43.0 | 3741 | 2.4490 | 27.8341 | 10.2826 | 22.8117 | 22.7244 | 18.7013 |
| 2.4038 | 44.0 | 3828 | 2.4479 | 28.0005 | 10.4067 | 22.8833 | 22.8177 | 18.7013 |
| 2.4038 | 45.0 | 3915 | 2.4475 | 28.0811 | 10.4117 | 22.8975 | 22.8276 | 18.7013 |
| 2.3785 | 46.0 | 4002 | 2.4471 | 28.0811 | 10.4117 | 22.8975 | 22.8276 | 18.7013 |
| 2.3785 | 47.0 | 4089 | 2.4466 | 28.4435 | 10.793 | 23.0757 | 23.0082 | 18.7013 |
| 2.3785 | 48.0 | 4176 | 2.4463 | 28.5123 | 10.7847 | 23.1779 | 23.1054 | 18.7143 |
| 2.3785 | 49.0 | 4263 | 2.4460 | 28.2551 | 10.653 | 22.9815 | 22.8896 | 18.7143 |
| 2.3785 | 50.0 | 4350 | 2.4459 | 28.5123 | 10.7847 | 23.1779 | 23.1054 | 18.7143 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.7
- Tokenizers 0.14.1
|
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