modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
yobi/klue-roberta-base-ynat | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | 0 | |
ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-chinese-amazon_zh_20000
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-chinese-amazon_zh_20000
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1518
- Accuracy: 0.5092
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.196 | 1.0 | 1250 | 1.1518 | 0.5092 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
| 1,346 |
ali2066/finetuned_sentence_itr6_2e-05_all_26_02_2022-04_31_13 | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr6_2e-05_all_26_02_2022-04_31_13
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. -->
# finetuned_sentence_itr6_2e-05_all_26_02_2022-04_31_13
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4676
- Accuracy: 0.8299
- F1: 0.8892
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.4087 | 0.8073 | 0.8754 |
| No log | 2.0 | 390 | 0.3952 | 0.8159 | 0.8803 |
| 0.4084 | 3.0 | 585 | 0.4183 | 0.8195 | 0.8831 |
| 0.4084 | 4.0 | 780 | 0.4596 | 0.8280 | 0.8867 |
| 0.4084 | 5.0 | 975 | 0.4919 | 0.8280 | 0.8873 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
| 1,788 |
atlantis/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9195
- name: F1
type: f1
value: 0.9197362586063258
---
<!-- 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: 0.2272
- Accuracy: 0.9195
- F1: 0.9197
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.83 | 1.0 | 250 | 0.3238 | 0.9005 | 0.8983 |
| 0.2503 | 2.0 | 500 | 0.2272 | 0.9195 | 0.9197 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,807 |
Hieu/scam-detection | null | Entry not found | 15 |
bishnu/finetuning-sentiment-model-3000-samples | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.86
- name: F1
type: f1
value: 0.8556701030927835
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5523
- Accuracy: 0.86
- F1: 0.8557
## 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: 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: 4
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
| 1,504 |
artemis13fowl/bert-base-uncased-imdb | null | ## bert-base-uncased finetuned on IMDB dataset
Evaluation set was created by taking 1000 samples from test set
```
DatasetDict({
train: Dataset({
features: ['text', 'label'],
num_rows: 25000
})
dev: Dataset({
features: ['text', 'label'],
num_rows: 1000
})
test: Dataset({
features: ['text', 'label'],
num_rows: 24000
})
})
```
## Parameters
```
max_sequence_length = 128
batch_size = 32
eval_steps = 100
learning_rate=2e-05
num_train_epochs=5
early_stopping_patience = 10
```
## Training Run
```
[2700/3910 1:11:43 < 32:09, 0.63 it/s, Epoch 3/5]
Step Training Loss Validation Loss Accuracy Precision Recall F1 Runtime Samples Per Second
100 No log 0.371974 0.845000 0.798942 0.917004 0.853911 15.256900 65.544000
200 No log 0.349631 0.850000 0.873913 0.813765 0.842767 15.288600 65.408000
300 No log 0.359376 0.845000 0.869281 0.807692 0.837356 15.303900 65.343000
400 No log 0.307613 0.870000 0.851351 0.892713 0.871542 15.358400 65.111000
500 0.364500 0.309362 0.856000 0.807018 0.931174 0.864662 15.326100 65.248000
600 0.364500 0.302709 0.867000 0.881607 0.844130 0.862461 15.324400 65.255000
700 0.364500 0.300102 0.871000 0.894168 0.838057 0.865204 15.474900 64.621000
800 0.364500 0.383784 0.866000 0.833333 0.910931 0.870406 15.380100 65.019000
900 0.364500 0.309934 0.874000 0.881743 0.860324 0.870902 15.358900 65.109000
1000 0.254600 0.332236 0.872000 0.894397 0.840081 0.866388 15.442700 64.756000
1100 0.254600 0.330807 0.871000 0.877847 0.858300 0.867963 15.410900 64.889000
1200 0.254600 0.352724 0.872000 0.925581 0.805668 0.861472 15.272800 65.476000
1300 0.254600 0.278529 0.881000 0.891441 0.864372 0.877698 15.408200 64.900000
1400 0.254600 0.291371 0.878000 0.854962 0.906883 0.880157 15.427400 64.820000
1500 0.208400 0.324827 0.869000 0.904232 0.821862 0.861082 15.338600 65.195000
1600 0.208400 0.377024 0.884000 0.898734 0.862348 0.880165 15.414500 64.874000
1700 0.208400 0.375274 0.885000 0.881288 0.886640 0.883956 15.367200 65.073000
1800 0.208400 0.378904 0.880000 0.877016 0.880567 0.878788 15.363900 65.088000
1900 0.208400 0.410517 0.874000 0.866534 0.880567 0.873494 15.324700 65.254000
2000 0.130800 0.404030 0.876000 0.888655 0.856275 0.872165 15.414200 64.875000
2100 0.130800 0.390763 0.883000 0.882353 0.880567 0.881459 15.341500 65.183000
2200 0.130800 0.417967 0.880000 0.875502 0.882591 0.879032 15.351300 65.141000
2300 0.130800 0.390974 0.883000 0.898520 0.860324 0.879007 15.396100 64.952000
2400 0.130800 0.479739 0.874000 0.856589 0.894737 0.875248 15.460500 64.681000
2500 0.098400 0.473215 0.875000 0.883576 0.860324 0.871795 15.392200 64.968000
2600 0.098400 0.532294 0.872000 0.889362 0.846154 0.867220 15.364100 65.087000
2700 0.098400 0.536664 0.881000 0.880325 0.878543 0.879433 15.351100 65.142000
TrainOutput(global_step=2700, training_loss=0.2004435383832013, metrics={'train_runtime': 4304.5331, 'train_samples_per_second': 0.908, 'total_flos': 7258763970957312, 'epoch': 3.45})
```
## Classification Report
```
precision recall f1-score support
0 0.90 0.87 0.89 11994
1 0.87 0.90 0.89 12006
accuracy 0.89 24000
macro avg 0.89 0.89 0.89 24000
weighted avg 0.89 0.89 0.89 24000
```
| 3,601 |
Ramu/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.926
- name: F1
type: f1
value: 0.9262005126757141
---
<!-- 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: 0.2167
- Accuracy: 0.926
- F1: 0.9262
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8112 | 1.0 | 250 | 0.3147 | 0.903 | 0.8992 |
| 0.2454 | 2.0 | 500 | 0.2167 | 0.926 | 0.9262 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.8.1+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,804 |
kapilchauhan/efl-finetuned-cola | null | ---
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: efl-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.6097804486545971
---
<!-- 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. -->
# efl-finetuned-cola
This model is a fine-tuned version of [nghuyong/ernie-2.0-en](https://huggingface.co/nghuyong/ernie-2.0-en) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4688
- Matthews Correlation: 0.6098
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 1.0 | 134 | 0.4795 | 0.5403 |
| No log | 2.0 | 268 | 0.4061 | 0.6082 |
| No log | 3.0 | 402 | 0.4688 | 0.6098 |
| 0.2693 | 4.0 | 536 | 0.5332 | 0.6050 |
| 0.2693 | 5.0 | 670 | 0.6316 | 0.6098 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
| 1,936 |
nikolamilosevic/distil_bert_uncased-finetuned-relations | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
model-index:
- name: distil_bert_uncased-finetuned-relations
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. -->
# distil_bert_uncased-finetuned-relations
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: 0.4191
- Accuracy: 0.8866
- Prec: 0.8771
- Recall: 0.8866
- F1: 0.8808
## 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: 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Prec | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|
| 1.1823 | 1.0 | 232 | 0.5940 | 0.8413 | 0.8273 | 0.8413 | 0.8224 |
| 0.4591 | 2.0 | 464 | 0.4600 | 0.8607 | 0.8539 | 0.8607 | 0.8555 |
| 0.3106 | 3.0 | 696 | 0.4160 | 0.8812 | 0.8763 | 0.8812 | 0.8785 |
| 0.246 | 4.0 | 928 | 0.4113 | 0.8834 | 0.8766 | 0.8834 | 0.8796 |
| 0.2013 | 5.0 | 1160 | 0.4191 | 0.8866 | 0.8771 | 0.8866 | 0.8808 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.13.0.dev20220614
- Datasets 2.2.2
- Tokenizers 0.11.6
| 1,884 |
RaghuramKol/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.927
- name: F1
type: f1
value: 0.9271888946173477
---
<!-- 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: 0.2218
- Accuracy: 0.927
- F1: 0.9272
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8487 | 1.0 | 250 | 0.3274 | 0.906 | 0.9030 |
| 0.2595 | 2.0 | 500 | 0.2218 | 0.927 | 0.9272 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,805 |
cb2-kai/finetuning-sentiment-model-3000-samples | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.86
- name: F1
type: f1
value: 0.8679245283018867
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3568
- Accuracy: 0.86
- F1: 0.8679
## 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: 2
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,505 |
antonio-artur/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.926
- name: F1
type: f1
value: 0.9260113300845928
---
<!-- 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: 0.2280
- Accuracy: 0.926
- F1: 0.9260
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8646 | 1.0 | 250 | 0.3326 | 0.9045 | 0.9009 |
| 0.2663 | 2.0 | 500 | 0.2280 | 0.926 | 0.9260 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,804 |
dapang/distilbert-base-uncased-finetuned-moral-action | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-moral-action
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-finetuned-moral-action
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4632
- Accuracy: 0.7912
- F1: 0.7912
## 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: 9.716387809233253e-05
- train_batch_size: 2000
- eval_batch_size: 2000
- 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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 10 | 0.5406 | 0.742 | 0.7399 |
| No log | 2.0 | 20 | 0.4810 | 0.7628 | 0.7616 |
| No log | 3.0 | 30 | 0.4649 | 0.786 | 0.7856 |
| No log | 4.0 | 40 | 0.4600 | 0.7916 | 0.7916 |
| No log | 5.0 | 50 | 0.4632 | 0.7912 | 0.7912 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1
- Datasets 2.0.0
- Tokenizers 0.11.0
| 1,746 |
Shadman-Rohan/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9245
- name: F1
type: f1
value: 0.9247907524762314
---
<!-- 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: 0.2083
- Accuracy: 0.9245
- F1: 0.9248
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7794 | 1.0 | 250 | 0.2870 | 0.9115 | 0.9099 |
| 0.2311 | 2.0 | 500 | 0.2083 | 0.9245 | 0.9248 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,806 |
palakagl/distilbert_MultiClass_TextClassification | [
"alarm_query",
"alarm_remove",
"alarm_set",
"audio_volume_down",
"audio_volume_mute",
"audio_volume_up",
"calendar_query",
"calendar_remove",
"calendar_set",
"cooking_recipe",
"datetime_convert",
"datetime_query",
"email_addcontact",
"email_query",
"email_querycontact",
"email_sendemai... | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- palakagl/autotrain-data-PersonalAssitant
co2_eq_emissions: 2.258363491829382
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 717221781
- CO2 Emissions (in grams): 2.258363491829382
## Validation Metrics
- Loss: 0.38660314679145813
- Accuracy: 0.9042081949058693
- Macro F1: 0.9079200295131094
- Micro F1: 0.9042081949058692
- Weighted F1: 0.9052766730963512
- Macro Precision: 0.9116101664087508
- Micro Precision: 0.9042081949058693
- Weighted Precision: 0.9097680514456175
- Macro Recall: 0.9080246002936301
- Micro Recall: 0.9042081949058693
- Weighted Recall: 0.9042081949058693
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/palakagl/autotrain-PersonalAssitant-717221781
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221781", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221781", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,419 |
Xenova/sponsorblock-classifier-v2 | [
"INTERACTION",
"NONE",
"SELFPROMO",
"SPONSOR"
] | ---
tags:
- text-classification
- generic
library_name: generic
widget:
- text: 'This video is sponsored by squarespace'
example_title: Sponsor
- text: 'Check out the merch at linustechtips.com'
example_title: Unpaid/self promotion
- text: "Don't forget to like, comment and subscribe"
example_title: Interaction reminder
- text: 'pqh4LfPeCYs,824.695,826.267,826.133,829.876,835.933,927.581'
example_title: Extract text from video
---
| 443 |
dfsj/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.922
- name: F1
type: f1
value: 0.9222074564200887
---
<!-- 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: 0.2170
- Accuracy: 0.922
- F1: 0.9222
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8116 | 1.0 | 250 | 0.3076 | 0.9035 | 0.9013 |
| 0.2426 | 2.0 | 500 | 0.2170 | 0.922 | 0.9222 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.0.0
- Tokenizers 0.12.1
| 1,804 |
Intel/camembert-base-mrpc | [
"equivalent",
"not_equivalent"
] | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: camembert-base-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8504901960784313
- name: F1
type: f1
value: 0.8927943760984183
---
<!-- 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. -->
# camembert-base-mrpc
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4286
- Accuracy: 0.8505
- F1: 0.8928
- Combined Score: 0.8716
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.1.0
- Tokenizers 0.11.6
| 1,501 |
Intel/electra-small-discriminator-mrpc | [
"equivalent",
"not_equivalent"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: electra-small-discriminator-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8529411764705882
- name: F1
type: f1
value: 0.8983050847457628
---
<!-- 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. -->
# electra-small-discriminator-mrpc
This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3909
- Accuracy: 0.8529
- F1: 0.8983
- Combined Score: 0.8756
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.1.0
- Tokenizers 0.11.6
| 1,574 |
UT/MULTIBRT | null | Entry not found | 15 |
charlieoneill/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.938
- name: F1
type: f1
value: 0.9383526007023721
---
<!-- 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: 0.1622
- Accuracy: 0.938
- F1: 0.9384
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0917 | 1.0 | 250 | 0.1935 | 0.9305 | 0.9306 |
| 0.0719 | 2.0 | 500 | 0.1622 | 0.938 | 0.9384 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.1
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,797 |
Parsa/Chemical_explosion_classification | null | For testing it yourself, the easiest way is using the colab link below.
Github repo: https://github.com/mephisto121/Chemical_explosion_classification
[](https://colab.research.google.com/drive/1GQmh1g2bRdqgQCnM6b_iY-eAQCRfhMJP?usp=sharing) | 313 |
ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERT_FINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False
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_FINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7321
- Precision: 0.9795
- Recall: 0.7277
- F1: 0.835
- Accuracy: 0.7208
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 130 | 0.3755 | 0.8521 | 0.9910 | 0.9163 | 0.8529 |
| No log | 2.0 | 260 | 0.3352 | 0.8875 | 0.9638 | 0.9241 | 0.8713 |
| No log | 3.0 | 390 | 0.3370 | 0.8918 | 0.9321 | 0.9115 | 0.8529 |
| 0.4338 | 4.0 | 520 | 0.3415 | 0.8957 | 0.9321 | 0.9135 | 0.8566 |
| 0.4338 | 5.0 | 650 | 0.3416 | 0.8918 | 0.9321 | 0.9115 | 0.8529 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
| 2,044 |
tomhosking/bert-base-uncased-debiased-nli | [
"ENTAILMENT",
"NEUTRAL",
"CONTRADICTION"
] | ---
license: apache-2.0
widget:
- text: "[CLS] Rover is a dog. [SEP] Rover is a cat. [SEP]"
---
`bert-base-uncased`, fine tuned on the debiased NLI dataset from "Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets", Wu et al., 2022.
Tuned using the code at https://github.com/jimmycode/gen-debiased-nli
| 342 |
pile-of-law/distilbert-base-uncased-finetuned-eoir_privacy | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eoir_privacy
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-eoir_privacy
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: eoir_privacy
type: eoir_privacy
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.9052835051546392
- name: F1
type: f1
value: 0.8088426527958388
---
<!-- 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-eoir_privacy
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the eoir_privacy dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3681
- Accuracy: 0.9053
- F1: 0.8088
## Model description
Model predicts whether to mask names as pseudonyms in any text. Input format should be a paragraph with names masked. It will then output whether to use a pseudonym because the EOIR courts would not allow such private/sensitive information to become public unmasked.
## Intended uses & limitations
This is a minimal privacy standard and will likely not work on out-of-distribution data.
## Training and evaluation data
We train on the EOIR Privacy dataset and evaluate further using sensitivity analyses.
## 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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 395 | 0.3053 | 0.8789 | 0.7432 |
| 0.3562 | 2.0 | 790 | 0.2857 | 0.8976 | 0.7883 |
| 0.2217 | 3.0 | 1185 | 0.3358 | 0.8905 | 0.7550 |
| 0.1509 | 4.0 | 1580 | 0.3505 | 0.9040 | 0.8077 |
| 0.1509 | 5.0 | 1975 | 0.3681 | 0.9053 | 0.8088 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
### Citation
```
@misc{hendersonkrass2022pileoflaw,
url = {https://arxiv.org/abs/2207.00220},
author = {Henderson*, Peter and Krass*, Mark S. and Zheng, Lucia and Guha, Neel and Manning, Christopher D. and Jurafsky, Dan and Ho, Daniel E.},
title = {Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset},
publisher = {arXiv},
year = {2022}
}
```
| 2,831 |
allermat/distilbert-base-uncased-finetuned-emotion | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.923
- name: F1
type: f1
value: 0.9233300539962602
---
<!-- 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: 0.2244
- Accuracy: 0.923
- F1: 0.9233
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8412 | 1.0 | 250 | 0.3186 | 0.904 | 0.9022 |
| 0.2501 | 2.0 | 500 | 0.2244 | 0.923 | 0.9233 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,798 |
Paleontolog/bert_sentence_classifier | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-48 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-73 | null | Entry not found | 15 |
Leizhang/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
model-index:
- name: distilbert-base-uncased-finetuned-emotion
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-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.12.1
| 1,081 |
CEBaB/roberta-base.CEBaB.absa.exclusive.seed_42 | [
"0",
"1",
"2"
] | Entry not found | 15 |
priyamm/autotrain-KeywordExtraction-882328335 | [
"neg",
"pos"
] | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- priyamm/autotrain-data-KeywordExtraction
co2_eq_emissions: 0.21373468108000182
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 882328335
- CO2 Emissions (in grams): 0.21373468108000182
## Validation Metrics
- Loss: 0.2641160488128662
- Accuracy: 0.9128
- Precision: 0.9444444444444444
- Recall: 0.8772
- AUC: 0.9709556000000001
- F1: 0.9095810866860223
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/priyamm/autotrain-KeywordExtraction-882328335
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("priyamm/autotrain-KeywordExtraction-882328335", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("priyamm/autotrain-KeywordExtraction-882328335", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,184 |
DuboiJ/finetuning-sentiment-model-3000-samples | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8633333333333333
- name: F1
type: f1
value: 0.8637873754152824
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3211
- Accuracy: 0.8633
- F1: 0.8638
## 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: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,521 |
arcAman07/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.924
- name: F1
type: f1
value: 0.9240598378254522
---
<!-- 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: 0.2222
- Accuracy: 0.924
- F1: 0.9241
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8294 | 1.0 | 250 | 0.3209 | 0.9025 | 0.9001 |
| 0.2536 | 2.0 | 500 | 0.2222 | 0.924 | 0.9241 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,804 |
Ritvik19/autotrain-sentiment_polarity-918130222 | [
"0.0",
"1.0"
] | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Ritvik19/autotrain-data-sentiment_polarity
co2_eq_emissions: 4.280488237750762
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 918130222
- CO2 Emissions (in grams): 4.280488237750762
## Validation Metrics
- Loss: 0.13608604669570923
- Accuracy: 0.9504804036293305
- Precision: 0.9792047060317863
- Recall: 0.9647185343057701
- AUC: 0.9791895292939061
- F1: 0.9719076444852428
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Ritvik19/autotrain-sentiment_polarity-918130222
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Ritvik19/autotrain-sentiment_polarity-918130222", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Ritvik19/autotrain-sentiment_polarity-918130222", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,213 |
CH0KUN/autotrain-TNC_Domain_WangchanBERTa-921730254 | [
"Applied Science",
"Arts",
"Belief & Thought",
"Commerce & Finance",
"History",
"Imaginative",
"Natural & Pure Science",
"Social Science "
] | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- CH0KUN/autotrain-data-TNC_Domain_WangchanBERTa
co2_eq_emissions: 25.144394918865913
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 921730254
- CO2 Emissions (in grams): 25.144394918865913
## Validation Metrics
- Loss: 0.7080970406532288
- Accuracy: 0.7775925925925926
- Macro F1: 0.7758012615987406
- Micro F1: 0.7775925925925925
- Weighted F1: 0.7758012615987406
- Macro Precision: 0.7833307663368776
- Micro Precision: 0.7775925925925926
- Weighted Precision: 0.7833307663368777
- Macro Recall: 0.7775925925925926
- Micro Recall: 0.7775925925925926
- Weighted Recall: 0.7775925925925926
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/CH0KUN/autotrain-TNC_Domain_WangchanBERTa-921730254
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("CH0KUN/autotrain-TNC_Domain_WangchanBERTa-921730254", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("CH0KUN/autotrain-TNC_Domain_WangchanBERTa-921730254", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,445 |
Jherb/finetuning-sentiment-model-3000-samples | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.8666666666666667
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3063
- Accuracy: 0.8667
- F1: 0.8667
## 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: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,521 |
Marvin67/distil_covid | [
"Bio-weapon",
"COVID-19 cases and deaths statistics",
"Chinese government (Chinese communist party - CCP)",
"Other",
"Wet market and eating habits",
"Wuhan virus lab"
] | ---
license: other
---
| 23 |
Jeevesh8/std_pnt_04_feather_berts-13 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/std_pnt_04_feather_berts-11 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Alireza1044/mobilebert_cola | [
"acceptable",
"unacceptable"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5277813760438573
---
<!-- 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. -->
# cola
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6337
- Matthews Correlation: 0.5278
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,443 |
mosesju/distilbert-base-uncased-finetuned-news | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-news
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9388157894736842
- name: F1
type: f1
value: 0.9388275184627893
---
<!-- 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-news
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2117
- Accuracy: 0.9388
- F1: 0.9388
## Model description
This model is intended to categorize news headlines into one of four categories; World, Sports, Science & Technology, or Business
## Intended uses & limitations
The model is limited by the training data it used. If you use the model with a news story that falls outside of the four intended categories, it produces quite confused results.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2949 | 1.0 | 3750 | 0.2501 | 0.9262 | 0.9261 |
| 0.1569 | 2.0 | 7500 | 0.2117 | 0.9388 | 0.9388 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 2,073 |
corgito/finetuning-sentiment-model-3000-samples | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.87
- name: F1
type: f1
value: 0.8712871287128714
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3105
- Accuracy: 0.87
- F1: 0.8713
## 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: 2
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
| 1,505 |
amissier/distilbert-amazon-shoe-reviews | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-amazon-shoe-reviews
results:
- task:
type: text-classification
name: Text Classification
dataset:
type: amazon_us_reviews
name: Amazon US reviews
split: Shoes
metrics:
- type: accuracy
value: 0.48
name: Accuracy
---
<!-- 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-amazon-shoe-reviews
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: 1.3445
- Accuracy: 0.48
- F1: [0. 0. 0. 0. 0.64864865]
- Precision: [0. 0. 0. 0. 0.48]
- Recall: [0. 0. 0. 0. 1.]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------------------------------------------------:|:--------------------------:|:----------------:|
| No log | 1.0 | 15 | 1.3445 | 0.48 | [0. 0. 0. 0. 0.64864865] | [0. 0. 0. 0. 0.48] | [0. 0. 0. 0. 1.] |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1
| 2,069 |
Alireza1044/MobileBERT_Theseus-mnli | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
Sayan01/tiny-bert-qnli-distilled | [
"entailment",
"not_entailment"
] | Entry not found | 15 |
zunicd/finetuning-sentiment-model-3000-samples | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8733333333333333
- name: F1
type: f1
value: 0.8741721854304636
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3349
- Accuracy: 0.8733
- F1: 0.8742
## 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: 2
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,521 |
dminiotas05/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
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-finetuned-emotion
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: 1.1027
- Accuracy: 0.5447
- F1: 0.4832
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.1848 | 1.0 | 188 | 1.1199 | 0.538 | 0.4607 |
| 1.0459 | 2.0 | 376 | 1.1027 | 0.5447 | 0.4832 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,504 |
shubhamitra/distilbert-base-uncased-finetuned-toxic-classification | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-toxic-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-finetuned-toxic-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown 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: 64
- eval_batch_size: 64
- seed: 123
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log | 1.0 | 498 | 0.0419 | 0.7754 | 0.8736 | 0.9235 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
| 1,479 |
upsalite/xlm-roberta-base-finetuned-emotion-2-labels | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-finetuned-emotion-2-labels
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. -->
# xlm-roberta-base-finetuned-emotion-2-labels
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1200
- Accuracy: 0.835
- F1: 0.8335
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6973 | 1.0 | 25 | 0.6917 | 0.5 | 0.3333 |
| 0.6626 | 2.0 | 50 | 0.5690 | 0.745 | 0.7431 |
| 0.5392 | 3.0 | 75 | 0.4598 | 0.76 | 0.7591 |
| 0.4253 | 4.0 | 100 | 0.4313 | 0.8 | 0.7993 |
| 0.2973 | 5.0 | 125 | 0.5872 | 0.795 | 0.7906 |
| 0.2327 | 6.0 | 150 | 0.4951 | 0.805 | 0.8049 |
| 0.173 | 7.0 | 175 | 0.6095 | 0.815 | 0.8142 |
| 0.1159 | 8.0 | 200 | 0.6523 | 0.825 | 0.8246 |
| 0.0791 | 9.0 | 225 | 0.6651 | 0.825 | 0.8243 |
| 0.0557 | 10.0 | 250 | 0.8242 | 0.83 | 0.8286 |
| 0.0643 | 11.0 | 275 | 0.6710 | 0.825 | 0.8243 |
| 0.0507 | 12.0 | 300 | 0.7729 | 0.83 | 0.8294 |
| 0.0239 | 13.0 | 325 | 0.8618 | 0.83 | 0.8283 |
| 0.0107 | 14.0 | 350 | 0.9683 | 0.835 | 0.8335 |
| 0.0233 | 15.0 | 375 | 1.0850 | 0.825 | 0.8227 |
| 0.0134 | 16.0 | 400 | 0.9801 | 0.835 | 0.8343 |
| 0.0122 | 17.0 | 425 | 1.0427 | 0.845 | 0.8439 |
| 0.0046 | 18.0 | 450 | 1.0867 | 0.84 | 0.8387 |
| 0.0038 | 19.0 | 475 | 1.0950 | 0.83 | 0.8289 |
| 0.002 | 20.0 | 500 | 1.1200 | 0.835 | 0.8335 |
### Framework versions
- Transformers 4.19.0
- Pytorch 1.12.0+cu113
- Datasets 1.16.1
- Tokenizers 0.12.1
| 2,764 |
michauhl/distilbert-base-uncased-finetuned-emotion | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9405
- name: F1
type: f1
value: 0.9404976918144629
---
<!-- 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: 0.1891
- Accuracy: 0.9405
- F1: 0.9405
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1344 | 1.0 | 1000 | 0.1760 | 0.933 | 0.9331 |
| 0.0823 | 2.0 | 2000 | 0.1891 | 0.9405 | 0.9405 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0.post202
- Datasets 2.3.2
- Tokenizers 0.11.0
| 1,808 |
jhonparra18/bert-base-cased-fine-tuning-cvs-hf-studio-name | [
"Agile Delivery",
"Business Hacking",
"Cloud Ops",
"Data and AI",
"Design",
"Digital Marketing",
"Digital eXperience Platforms",
"Enterprise Apps",
"Gaming",
"Generic",
"Process Optimization",
"Product Acceleration",
"Quality Engineering",
"Salesforce",
"Scalable Platforms",
"Staff Gen... | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-base-cased-fine-tuning-cvs-hf-studio-name
results: []
widget:
- text: "Egresado de la carrera Ingeniería en Computación Conocimientos de lenguajes HTML, CSS, Javascript y MySQL. Experiencia trabajando en ámbitos de redes de pequeña y mediana escala. Inglés Hablado nivel básico, escrito nivel intermedio.HTML, CSS y JavaScript. Realidad aumentada. Lenguaje R. HTML5, JavaScript y Nodejs"
---
<!-- 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. -->
# bert-base-cased-fine-tuning-cvs-hf-studio-name
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2601
- Accuracy: 0.6500
- F1: 0.6500
- Precision: 0.6500
- Recall: 0.6500
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.4407 | 0.24 | 500 | 1.5664 | 0.5528 | 0.5528 | 0.5528 | 0.5528 |
| 1.3055 | 0.49 | 1000 | 1.4891 | 0.5745 | 0.5745 | 0.5745 | 0.5745 |
| 1.373 | 0.73 | 1500 | 1.3634 | 0.6180 | 0.6180 | 0.6180 | 0.6180 |
| 1.3621 | 0.98 | 2000 | 1.3768 | 0.6139 | 0.6139 | 0.6139 | 0.6139 |
| 1.1677 | 1.22 | 2500 | 1.3330 | 0.6395 | 0.6395 | 0.6395 | 0.6395 |
| 1.0826 | 1.47 | 3000 | 1.4003 | 0.6146 | 0.6146 | 0.6146 | 0.6146 |
| 1.0968 | 1.71 | 3500 | 1.2601 | 0.6500 | 0.6500 | 0.6500 | 0.6500 |
| 1.0896 | 1.96 | 4000 | 1.2826 | 0.6564 | 0.6564 | 0.6564 | 0.6564 |
| 0.8572 | 2.2 | 4500 | 1.3254 | 0.6569 | 0.6569 | 0.6569 | 0.6569 |
| 0.822 | 2.44 | 5000 | 1.3024 | 0.6571 | 0.6571 | 0.6571 | 0.6571 |
| 0.8022 | 2.69 | 5500 | 1.2971 | 0.6608 | 0.6608 | 0.6608 | 0.6608 |
| 0.834 | 2.93 | 6000 | 1.2900 | 0.6630 | 0.6630 | 0.6630 | 0.6630 |
### Framework versions
- Transformers 4.19.0
- Pytorch 1.8.2+cu111
- Datasets 1.6.2
- Tokenizers 0.12.1
| 2,894 |
jhonparra18/bert-base-cased-cv-studio_name-medium | [
"Agile Delivery",
"Business Hacking",
"Cloud Ops",
"Data and AI",
"Design",
"Digital Marketing",
"Digital eXperience Platforms",
"Enterprise Apps",
"Gaming",
"Generic",
"Process Optimization",
"Product Acceleration",
"Quality Engineering",
"Salesforce",
"Scalable Platforms",
"Staff Gen... | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-cased-cv-studio_name-medium
results: []
widget:
- text: "Egresado de la carrera Ingeniería en Computación Conocimientos de lenguajes HTML, CSS, Javascript y MySQL. Experiencia trabajando en ámbitos de redes de pequeña y mediana escala. Inglés Hablado nivel básico, escrito nivel intermedio.HTML, CSS y JavaScript. Realidad aumentada. Lenguaje R. HTML5, JavaScript y Nodejs"
- text: "mi nombre es Ivan Ducales Marquez, hago de subpresidente en la republica de Colombia. tengo experiencia en seguir órdenes de mis patrocinadores y repartir los recursos del país a empresarios corruptos"
---
<!-- 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. -->
# bert-base-cased-cv-studio_name-medium
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3310
- F1 Micro: 0.6388
- F1 Macro: 0.5001
## Model description
Predicts a studio name based on a CV text
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | Precision Micro | Recall Micro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|:------------:|
| 1.4139 | 0.98 | 1000 | 1.3831 | 0.6039 | 0.6039 | 0.4188 | 0.6039 | 0.6039 |
| 1.1561 | 1.96 | 2000 | 1.2386 | 0.6554 | 0.6554 | 0.4743 | 0.6554 | 0.6554 |
| 0.9183 | 2.93 | 3000 | 1.2201 | 0.6576 | 0.6576 | 0.5011 | 0.6576 | 0.6576 |
| 0.677 | 3.91 | 4000 | 1.3478 | 0.6442 | 0.6442 | 0.5206 | 0.6442 | 0.6442 |
| 0.4857 | 4.89 | 5000 | 1.4765 | 0.6393 | 0.6393 | 0.5215 | 0.6393 | 0.6393 |
| 0.3318 | 5.87 | 6000 | 1.6924 | 0.6442 | 0.6442 | 0.5024 | 0.6442 | 0.6442 |
| 0.2273 | 6.84 | 7000 | 1.8645 | 0.6444 | 0.6444 | 0.5060 | 0.6444 | 0.6444 |
| 0.1396 | 7.82 | 8000 | 2.1143 | 0.6381 | 0.6381 | 0.5181 | 0.6381 | 0.6381 |
| 0.0841 | 8.8 | 9000 | 2.2699 | 0.6359 | 0.6359 | 0.5065 | 0.6359 | 0.6359 |
| 0.0598 | 9.78 | 10000 | 2.3310 | 0.6388 | 0.6388 | 0.5001 | 0.6388 | 0.6388 |
### Framework versions
- Transformers 4.19.0
- Pytorch 1.8.2+cu111
- Datasets 1.6.2
- Tokenizers 0.12.1
| 3,079 |
acho0057/sentiment_analysis_custom | [
"Negative",
"Neutral",
"Positive"
] | 0 | |
2umm3r/distilbert-base-uncased-finetuned-cola | null | ---
license: apache-2.0
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
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5155709926752544
---
<!-- 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: 0.7816
- Matthews Correlation: 0.5156
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5291 | 1.0 | 535 | 0.5027 | 0.4092 |
| 0.3492 | 2.0 | 1070 | 0.5136 | 0.4939 |
| 0.2416 | 3.0 | 1605 | 0.6390 | 0.5056 |
| 0.1794 | 4.0 | 2140 | 0.7816 | 0.5156 |
| 0.1302 | 5.0 | 2675 | 0.8836 | 0.5156 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
| 1,999 |
Alireza1044/albert-base-v2-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model_index:
- name: mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metric:
name: Accuracy
type: accuracy
value: 0.8500813669650122
---
<!-- 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. -->
# mnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5383
- Accuracy: 0.8501
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| 1,371 |
CleveGreen/JobClassifier | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_100",
"LABEL_101",
"LABEL_102",
"LABEL_103",
"LABEL_104",
"LABEL_105",
"LABEL_106",
"LABEL_107",
"LABEL_108",
"LABEL_109",
"LABEL_11",
"LABEL_110",
"LABEL_111",
"LABEL_112",
"LABEL_113",
"LABEL_114",
"LABEL_115",
"LABEL_116",
"LABEL_... | Entry not found | 15 |
DeadBeast/emoBERTTamil | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tamilmixsentiment
metrics:
- accuracy
model_index:
- name: emoBERTTamil
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tamilmixsentiment
type: tamilmixsentiment
args: default
metric:
name: Accuracy
type: accuracy
value: 0.671
---
<!-- 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. -->
# emoBERTTamil
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tamilmixsentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9666
- Accuracy: 0.671
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1128 | 1.0 | 250 | 1.0290 | 0.672 |
| 1.0226 | 2.0 | 500 | 1.0172 | 0.686 |
| 0.9137 | 3.0 | 750 | 0.9666 | 0.671 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
| 1,556 |
DeadBeast/mbert-base-cased-finetuned-bengali-fakenews | null | ---
language: bengali
license: apache-2.0
datasets:
- BanFakeNews
---
# **mBERT-base-cased-finetuned-bengali-fakenews**
This model is a fine-tune checkpoint of mBERT-base-cased over **[Bengali-fake-news Dataset](https://www.kaggle.com/cryptexcode/banfakenews)** for Text classification. This model reaches an accuracy of 96.3 with an f1-score of 79.1 on the dev set.
### **How to use?**
**Task**: binary-classification
- LABEL_1: Authentic (*Authentic means news is authentic*)
- LABEL_0: Fake (*Fake means news is fake*)
```
from transformers import pipeline
print(pipeline("sentiment-analysis",model="DeadBeast/mbert-base-cased-finetuned-bengali-fakenews",tokenizer="DeadBeast/mbert-base-cased-finetuned-bengali-fakenews")("অভিনেতা আফজাল শরীফকে ২০ লাখ টাকার অনুদান অসুস্থ অভিনেতা আফজাল শরীফকে চিকিৎসার জন্য ২০ লাখ টাকা অনুদান দিয়েছেন প্রধানমন্ত্রী শেখ হাসিনা।"))
``` | 874 |
EnsarEmirali/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
- name: F1
type: f1
value: 0.9268984054036417
---
<!-- 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: 0.2131
- Accuracy: 0.9265
- F1: 0.9269
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8031 | 1.0 | 250 | 0.2973 | 0.9125 | 0.9110 |
| 0.2418 | 2.0 | 500 | 0.2131 | 0.9265 | 0.9269 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,801 |
Fengkai/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9385
- name: F1
type: f1
value: 0.9383492808338979
---
<!-- 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: 0.1495
- Accuracy: 0.9385
- F1: 0.9383
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1739 | 1.0 | 250 | 0.1827 | 0.931 | 0.9302 |
| 0.1176 | 2.0 | 500 | 0.1567 | 0.9325 | 0.9326 |
| 0.0994 | 3.0 | 750 | 0.1555 | 0.9385 | 0.9389 |
| 0.08 | 4.0 | 1000 | 0.1496 | 0.9445 | 0.9443 |
| 0.0654 | 5.0 | 1250 | 0.1495 | 0.9385 | 0.9383 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
| 2,025 |
Giannipinelli/xlm-roberta-base-finetuned-marc-en | [
"good",
"great",
"ok",
"poor",
"terrible"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
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. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9161
- Mae: 0.4634
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1217 | 1.0 | 235 | 0.9396 | 0.4878 |
| 0.9574 | 2.0 | 470 | 0.9161 | 0.4634 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,430 |
Hate-speech-CNERG/deoffxlmr-mono-kannada | [
"Not_offensive",
"Not_in_intended_language",
"Off_target_other",
"Off_target_group",
"Profanity",
"Off_target_ind"
] | ---
language: kn
license: apache-2.0
---
This model is used to detect **Offensive Content** in **Kannada Code-Mixed language**. The mono in the name refers to the monolingual setting, where the model is trained using only Kannada(pure and code-mixed) data. The weights are initialized from pretrained XLM-Roberta-Base and pretrained using Masked Language Modelling on the target dataset before fine-tuning using Cross-Entropy Loss.
This model is the best of multiple trained for **EACL 2021 Shared Task on Offensive Language Identification in Dravidian Languages**. Genetic-Algorithm based ensembled test predictions got the second-highest weighted F1 score at the leaderboard (Weighted F1 score on hold out test set: This model - 0.73, Ensemble - 0.74)
### For more details about our paper
Debjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. "[Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection](https://www.aclweb.org/anthology/2021.dravidianlangtech-1.38/)".
***Please cite our paper in any published work that uses any of these resources.***
~~~
@inproceedings{saha-etal-2021-hate,
title = "Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection",
author = "Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh",
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.dravidianlangtech-1.38",
pages = "270--276",
abstract = "Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {``}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.",
}
~~~ | 2,514 |
M47Labs/english_news_classification_headlines | [
"arts, culture, entertainment and media",
"conflict, war and peace",
"crime, law and justice",
"disaster, accident and emergency incident",
"economy, business and finance",
"education",
"enviroment",
"health",
"labour",
"lifestyle and leisure",
"politics",
"religion and belief",
"science and... | Entry not found | 15 |
Manishl7/xlm-roberta-large-language-detection | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | Language Detection Model for Nepali, English, Hindi and Spanish
Model fine tuned on xlm-roberta-large | 101 |
MhF/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9215
- name: F1
type: f1
value: 0.9217985126397109
---
<!-- 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: 0.2232
- Accuracy: 0.9215
- F1: 0.9218
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8098 | 1.0 | 250 | 0.3138 | 0.9025 | 0.9001 |
| 0.2429 | 2.0 | 500 | 0.2232 | 0.9215 | 0.9218 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| 1,807 |
MohammadABH/bertweet-finetuned-rbam | [
"attack",
"neutral",
"support"
] | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bertweet-finetuned-rbam
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. -->
# bertweet-finetuned-rbam
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3971
- F1: 0.6620
## 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7138 | 1.0 | 1632 | 0.7529 | 0.6814 |
| 0.5692 | 2.0 | 3264 | 0.8473 | 0.6803 |
| 0.4126 | 3.0 | 4896 | 1.0029 | 0.6617 |
| 0.2854 | 4.0 | 6528 | 1.2167 | 0.6635 |
| 0.2007 | 5.0 | 8160 | 1.3971 | 0.6620 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| 1,545 |
MoritzLaurer/MiniLM-L6-mnli | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- en
tags:
- text-classification
- zero-shot-classification
metrics:
- accuracy
widget:
- text: "I liked the movie. [SEP] The movie was good."
---
# MiniLM-L6-mnli
## Model description
This model was trained on the [MultiNLI](https://huggingface.co/datasets/multi_nli) dataset.
The base model is MiniLM-L6 from Microsoft, which is very fast, but a bit less accurate than other models.
## Intended uses & limitations
#### How to use the model
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "MoritzLaurer/MiniLM-L6-mnli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
premise = "I liked the movie"
hypothesis = "The movie was good."
input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu"
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "neutral", "contradiction"]
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
print(prediction)
```
### Training data
[MultiNLI](https://huggingface.co/datasets/multi_nli).
### Training procedure
MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters.
```
training_args = TrainingArguments(
num_train_epochs=5, # total number of training epochs
learning_rate=2e-05,
per_device_train_batch_size=32, # batch size per device during training
per_device_eval_batch_size=32, # batch size for evaluation
warmup_ratio=0.1, # number of warmup steps for learning rate scheduler
weight_decay=0.06, # strength of weight decay
fp16=True # mixed precision training
)
```
### Eval results
The model was evaluated using the (matched) test set from MultiNLI. Accuracy: 0.814
## Limitations and bias
Please consult the original MiniLM paper and literature on different NLI datasets for potential biases.
### BibTeX entry and citation info
If you want to cite this model, please cite the original MiniLM paper, the respective NLI datasets and include a link to this model on the Hugging Face hub. | 2,310 |
ReynaQuita/twitter_disaster_distilbert | null | Entry not found | 15 |
SetFit/deberta-v3-large__sst2__train-16-8 | [
"negative",
"positive"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-16-8
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. -->
# deberta-v3-large__sst2__train-16-8
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6915
- Accuracy: 0.6579
## 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: 4
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7129 | 1.0 | 7 | 0.7309 | 0.2857 |
| 0.6549 | 2.0 | 14 | 0.7316 | 0.4286 |
| 0.621 | 3.0 | 21 | 0.7131 | 0.5714 |
| 0.3472 | 4.0 | 28 | 0.5703 | 0.4286 |
| 0.2041 | 5.0 | 35 | 0.6675 | 0.5714 |
| 0.031 | 6.0 | 42 | 1.6750 | 0.5714 |
| 0.0141 | 7.0 | 49 | 1.8743 | 0.5714 |
| 0.0055 | 8.0 | 56 | 1.1778 | 0.5714 |
| 0.0024 | 9.0 | 63 | 1.0699 | 0.5714 |
| 0.0019 | 10.0 | 70 | 1.0933 | 0.5714 |
| 0.0012 | 11.0 | 77 | 1.1218 | 0.7143 |
| 0.0007 | 12.0 | 84 | 1.1468 | 0.7143 |
| 0.0006 | 13.0 | 91 | 1.1584 | 0.7143 |
| 0.0006 | 14.0 | 98 | 1.3092 | 0.7143 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,216 |
TransQuest/monotransquest-hter-de_en-pharmaceutical | [
"LABEL_0"
] | ---
language: de-en
tags:
- Quality Estimation
- monotransquest
- hter
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
import torch
from transquest.algo.sentence_level.monotransquest.run_model import MonoTransQuestModel
model = MonoTransQuestModel("xlmroberta", "TransQuest/monotransquest-hter-de_en-pharmaceutical", num_labels=1, use_cuda=torch.cuda.is_available())
predictions, raw_outputs = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]])
print(predictions)
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
| 5,415 |
aXhyra/presentation_emotion_42 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: presentation_emotion_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: F1
type: f1
value: 0.732897530282475
---
<!-- 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. -->
# presentation_emotion_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0989
- F1: 0.7329
## 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: 5.18796906442746e-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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3703 | 1.0 | 408 | 0.6624 | 0.7029 |
| 0.2122 | 2.0 | 816 | 0.6684 | 0.7258 |
| 0.9452 | 3.0 | 1224 | 1.0001 | 0.7041 |
| 0.0023 | 4.0 | 1632 | 1.0989 | 0.7329 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,772 |
annafavaro/bert-base-uncased-finetuned-addresso | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-addresso
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. -->
# bert-base-uncased-finetuned-addresso
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown 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: 2e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.12.5
- Pytorch 1.8.1
- Datasets 1.15.1
- Tokenizers 0.10.3
| 1,037 |
anthonymirand/haha_2019_adaptation_task | [
"LABEL_0"
] | Entry not found | 15 |
benjaminbeilharz/bert-base-uncased-empatheticdialogues-sentiment-classifier | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_20",
"LABEL_21",
"LABEL_22",
"LABEL_23",
"LABEL_24",
"LABEL_25",
"LABEL_26",
"LABEL_27",
"LABEL_28",
"LABEL_29",... | ---
dataset: empathetic_dialogues
---
| 38 |
beomi/beep-KcELECTRA-base-bias | [
"gender",
"none",
"others"
] | Entry not found | 15 |
chgk13/tiny_russian_toxic_bert | [
"neutral",
"toxic"
] | Entry not found | 15 |
cscottp27/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.923
- name: F1
type: f1
value: 0.9232542847906783
---
<!-- 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: 0.2175
- Accuracy: 0.923
- F1: 0.9233
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8352 | 1.0 | 250 | 0.3079 | 0.91 | 0.9086 |
| 0.247 | 2.0 | 500 | 0.2175 | 0.923 | 0.9233 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,805 |
dhtocks/tunib-electra-stereotype-classifier | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | ### TUNiB-Electra Stereotype Detector
Finetuned TUNiB-Electra base with K-StereoSet.
Original Code: https://github.com/newfull5/Stereotype-Detector | 149 |
diwank/silicone-deberta-pair | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | ---
license: mit
---
# diwank/silicone-deberta-pair
`deberta-base`-based dialog acts classifier. Trained on the `balanced` variant of the [silicone-merged](https://huggingface.co/datasets/diwank/silicone-merged) dataset: a simplified merged dialog act data from datasets in the [silicone](https://huggingface.co/datasets/silicone) collection.
Takes two sentences as inputs (one previous and one current utterance of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. **Outputs one of 11 labels**:
```python
(0, 'acknowledge')
(1, 'answer')
(2, 'backchannel')
(3, 'reply_yes')
(4, 'exclaim')
(5, 'say')
(6, 'reply_no')
(7, 'hold')
(8, 'ask')
(9, 'intent')
(10, 'ask_yes_no')
```
## Example:
```python
from simpletransformers.classification import (
ClassificationModel, ClassificationArgs
)
model = ClassificationModel("deberta", "diwank/silicone-deberta-pair")
convert_to_label = lambda n: [
['acknowledge',
'answer',
'backchannel',
'reply_yes',
'exclaim',
'say',
'reply_no',
'hold',
'ask',
'intent',
'ask_yes_no'
][i] for i in n
]
predictions, raw_outputs = model.predict([["Say what is the meaning of life?", "I dont know"]])
convert_to_label(predictions) # answer
```
## Report from W&B
https://wandb.ai/diwank/da-silicone-combined/reports/silicone-deberta-pair--VmlldzoxNTczNjE5?accessToken=yj1jz4c365z0y5b3olgzye7qgsl7qv9lxvqhmfhtb6300hql6veqa5xiq1skn8ys | 1,552 |
dmiller1/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.926
- name: F1
type: f1
value: 0.9261144741040841
---
<!-- 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: 0.2161
- Accuracy: 0.926
- F1: 0.9261
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8436 | 1.0 | 250 | 0.3175 | 0.9105 | 0.9081 |
| 0.2492 | 2.0 | 500 | 0.2161 | 0.926 | 0.9261 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.7.1
- Datasets 1.17.0
- Tokenizers 0.10.3
| 1,798 |
emrecan/bert-base-turkish-cased-snli_tr | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: apache-2.0
datasets:
- nli_tr
widget:
- text: "Dolar yükselmeye devam ediyor."
candidate_labels: "ekonomi, siyaset, spor"
- text: "Senaryo çok saçmaydı, beğendim diyemem."
candidate_labels: "olumlu, olumsuz"
---
| 332 |
justinqbui/bertweet-covid-vaccine-tweets-finetuned | [
"false",
"misleading",
"true"
] | ---
tags:
model-index:
- name: bertweet-covid--vaccine-tweets-finetuned
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. -->
# bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets
This model is a fine-tuned version of [justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets](https://huggingface.co/justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets) which was finetuned by using [this google fact check](https://huggingface.co/datasets/justinqbui/covid_fact_checked_google_api) ~3k dataset size and webscraped data from [polifact covid info](https://huggingface.co/datasets/justinqbui/covid_fact_checked_polifact) ~ 1200 dataset size and ~1200 tweets pulled from the CDC with tweets containing the words covid or vaccine.
It achieves the following results on the evaluation set (20% from the dataset randomly shuffled and selected to serve as a test set):
- Validation Loss: 0.267367
- Accuracy: 91.1370%
To use the model, use the inference API.
Alternatively, to run locally
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("justinqbui/bertweet-covid-vaccine-tweets-finetuned")
model = AutoModelForSequenceClassification.from_pretrained("justinqbui/bertweet-covid-vaccine-tweets-finetuned")
```
## Model description
This model is a fine-tuned version of pretrained version [justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets](https://huggingface.co/justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets). Click on [this](https://huggingface.co/justinqbui/bertweet-covid19-base-uncased-pretraining-covid-vaccine-tweets) to see how the pre-training was done.
This model was fine-tuned with a dataset of ~5500. A web scraper was used to scrape polifact and a script was used to pull from the google fact check API. Because ~80% of both these datasets were either false or misleading, I pulled about ~1200 tweets from the CDC related to covid and labelled them as true. ~30% of this dataset is considered true and the rest false or misleading. Please see the published datasets above for more detailed information.
The tokenizer requires the emoji library to be installed.
```
!pip install nltk emoji
```
## Intended uses & limitations
The intended use of this model is to detect if the contents of a covid tweet is potentially false or misleading. This model is not an end all be all. It has many limitations. For example, if someone makes a post containing an image, but has attached a satirical image, this model would not be able to distinguish this. If a user links a website, the tokenizer allocates a special token for links, meaning the contents of the linked website is completely lost. If someone tweets a reply, this model can't look at the parent tweets, and will lack context.
This model's dataset relies on the crowd-sourcing annotations being accurate. This data is only accurate of up until early December 2021. For example, it probably wouldn't do very ell with tweets regarded the new omicron variant.
Example true inputs:
```
Covid vaccines are safe and effective. -> 97% true
Vaccinations are safe and help prevent covid. -> 97% true
```
Example false inputs:
```
Covid vaccines will kill you. -> 97% false
covid vaccines make you infertile. -> 97% false
```
## Training and evaluation data
This model was finetuned by using [this google fact check](https://huggingface.co/datasets/justinqbui/covid_fact_checked_google_api) ~3k dataset size and webscraped data from [polifact covid info](https://huggingface.co/datasets/justinqbui/covid_fact_checked_polifact) ~ 1200 dataset size and ~1200 tweets pulled from the CDC with tweets containing the words covid or vaccine.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-5
- train_batch_size: 128
- eval_batch_size: 128
- 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 | Validation Loss | Accuracy |
|:-------------:|:-----:|:---------------:|:--------:|
| 0.435500 | 1.0 | 0.401900 | 0.906893 |
| 0.309700 | 2.0 | 0.265500 | 0.907789 |
| 0.266200 | 3.0 | 0.216500 | 0.911370 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 4,609 |
l3cube-pune/hate-multi-roberta-hasoc-hindi | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
language: hi
tags:
- roberta
license: cc-by-4.0
datasets:
- HASOC 2021
widget:
- text: "I like you. </s></s> I love you."
---
## hate-roberta-hasoc-hindi
hate-roberta-hasoc-hindi is a multi-class hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Offensive, 2 -> Hate, 3 -> Profane.
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2110.12200)
```
@article{velankar2021hate,
title={Hate and Offensive Speech Detection in Hindi and Marathi},
author={Velankar, Abhishek and Patil, Hrushikesh and Gore, Amol and Salunke, Shubham and Joshi, Raviraj},
journal={arXiv preprint arXiv:2110.12200},
year={2021}
}
``` | 742 |
lewtun/bert-base-uncased-finetuned-boolq | null | Entry not found | 15 |
mrm8488/RuPERTa-base-finetuned-pawsx-es | null | ---
language: es
datasets:
- xtreme
tags:
- nli
widget:
- text: "En 2009 se mudó a Filadelfia y en la actualidad vive en Nueva York. Se mudó nuevamente a Filadelfia en 2009 y ahora vive en la ciudad de Nueva York."
---
# RuPERTa-base fine-tuned on PAWS-X-es for Paraphrase Identification (NLI)
| 295 |
mrm8488/camembert-base-finetuned-movie-review-sentiment-analysis | null | Entry not found | 15 |
pertschuk/albert-base-squad-classifier-ms | null | Entry not found | 15 |
pertschuk/albert-base-squad-classifier | null | Entry not found | 15 |
severo/autonlp-sentiment_detection-1781580 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- severo/autonlp-data-sentiment_detection-3c8bcd36
---
# Model Trained Using AutoNLP
_debug - I want to update this model_
- Problem type: Binary Classification
- Model ID: 1781580
## Validation Metrics
- Loss: 0.16026505827903748
- Accuracy: 0.9426
- Precision: 0.9305057745917961
- Recall: 0.95406288280931
- AUC: 0.9861051024994563
- F1: 0.9421370967741935
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/severo/autonlp-sentiment_detection-1781580
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("severo/autonlp-sentiment_detection-1781580", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("severo/autonlp-sentiment_detection-1781580", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,134 |
textattack/xlnet-large-cased-CoLA | null | Entry not found | 15 |
unideeplearning/polibert_sa | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language: it
tags:
- sentiment
- Italian
license: mit
widget:
- text: Giuseppe Rossi è un ottimo politico
---
# 🤗 + polibert_SA - POLItic BERT based Sentiment Analysis
## Model description
This model performs sentiment analysis on Italian political twitter sentences. It was trained starting from an instance of "bert-base-italian-uncased-xxl" and fine-tuned on an Italian dataset of tweets. You can try it out at https://www.unideeplearning.com/twitter_sa/ (in italian!)
#### Hands-on
```python
import torch
from torch import nn
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("unideeplearning/polibert_sa")
model = AutoModelForSequenceClassification.from_pretrained("unideeplearning/polibert_sa")
text = "Giuseppe Rossi è un pessimo politico"
input_ids = tokenizer.encode(text, add_special_tokens=True, return_tensors= 'pt')
logits, = model(input_ids)
logits = logits.squeeze(0)
prob = nn.functional.softmax(logits, dim=0)
# 0 Negative, 1 Neutral, 2 Positive
print(prob.argmax().tolist())
```
#### Hyperparameters
- Optimizer: **AdamW** with learning rate of **2e-5**, epsilon of **1e-8**
- Max epochs: **2**
- Batch size: **16**
## Acknowledgments
Thanks to the support from:
the [Hugging Face](https://huggingface.co/), https://www.unioneprofessionisti.com
https://www.unideeplearning.com/
| 1,407 |
warwickai/fin-perceiver | [
"negative",
"neutral",
"positive"
] | ---
language: "en"
license: apache-2.0
tags:
- financial-sentiment-analysis
- sentiment-analysis
- language-perceiver
datasets:
- financial_phrasebank
widget:
- text: "INDEX100 fell sharply today."
- text: "ImaginaryJetCo bookings hit by Omicron variant as losses total £1bn."
- text: "Q1 ImaginaryGame's earnings beat expectations."
- text: "Should we buy IMAGINARYSTOCK today?"
metrics:
- recall
- f1
- accuracy
- precision
model-index:
- name: fin-perceiver
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: financial_phrasebank
args: sentences_50agree
metrics:
- name: Accuracy
type: accuracy
value: 0.8624
- name: F1
type: f1
value: 0.8416
args: macro
- name: Precision
type: precision
value: 0.8438
args: macro
- name: Recall
type: recall
value: 0.8415
args: macro
---
# FINPerceiver
FINPerceiver is a fine-tuned Perceiver IO language model for financial sentiment analysis.
More details on the training process of this model are available on the [GitHub repository](https://github.com/warwickai/fin-perceiver).
Weights & Biases was used to track experiments.
We achieved the following results with 10-fold cross validation.
```
eval/accuracy 0.8624 (stdev 0.01922)
eval/f1 0.8416 (stdev 0.03738)
eval/loss 0.4314 (stdev 0.05295)
eval/precision 0.8438 (stdev 0.02938)
eval/recall 0.8415 (stdev 0.04458)
```
The hyperparameters used are as follows.
```
per_device_train_batch_size 16
per_device_eval_batch_size 16
num_train_epochs 4
learning_rate 2e-5
```
## Datasets
This model was trained on the Financial PhraseBank (>= 50% agreement)
| 1,781 |
yoshitomo-matsubara/bert-large-uncased-wnli | null | ---
language: en
tags:
- bert
- wnli
- glue
- torchdistill
license: apache-2.0
datasets:
- wnli
metrics:
- accuracy
---
`bert-large-uncased` fine-tuned on WNLI dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/wnli/ce/bert_large_uncased.yaml).
I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **80.2**.
| 828 |
microsoft/tapex-base-finetuned-tabfact | [
"Entailed",
"Refused"
] | ---
language: en
tags:
- tapex
datasets:
- tab_fact
license: mit
---
# TAPEX (base-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraining).
## Model description
TAPEX (**Ta**ble **P**re-training via **Ex**ecution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with *table reasoning* skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries.
TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder.
This model is the `tapex-base` model fine-tuned on the [Tabfact](https://huggingface.co/datasets/tab_fact) dataset.
## Intended Uses
You can use the model for table fact verficiation.
### How to Use
Here is how to use this model in transformers:
```python
from transformers import TapexTokenizer, BartForSequenceClassification
import pandas as pd
tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-base-finetuned-tabfact")
model = BartForSequenceClassification.from_pretrained("microsoft/tapex-base-finetuned-tabfact")
data = {
"year": [1896, 1900, 1904, 2004, 2008, 2012],
"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
}
table = pd.DataFrame.from_dict(data)
# tapex accepts uncased input since it is pre-trained on the uncased corpus
query = "beijing hosts the olympic games in 2012"
encoding = tokenizer(table=table, query=query, return_tensors="pt")
outputs = model(**encoding)
output_id = int(outputs.logits[0].argmax(dim=0))
print(model.config.id2label[output_id])
# Refused
```
### How to Eval
Please find the eval script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex).
### BibTeX entry and citation info
```bibtex
@inproceedings{
liu2022tapex,
title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor},
author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=O50443AsCP}
}
``` | 2,546 |
cnu/distilbert-base-uncased-finetuned-cola | null | ---
license: apache-2.0
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
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5474713423103301
---
<!-- 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: 0.8651
- Matthews Correlation: 0.5475
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5233 | 1.0 | 535 | 0.5353 | 0.4004 |
| 0.3497 | 2.0 | 1070 | 0.5165 | 0.5076 |
| 0.2386 | 3.0 | 1605 | 0.6661 | 0.5161 |
| 0.1745 | 4.0 | 2140 | 0.7730 | 0.5406 |
| 0.1268 | 5.0 | 2675 | 0.8651 | 0.5475 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.6
| 1,994 |
nickmuchi/sec-bert-finetuned-finance-classification | [
"bearish",
"bullish",
"neutral"
] | ---
license: cc-by-sa-4.0
tags:
- financial-sentiment-analysis
- sentiment-analysis
- sentence_50agree
- generated_from_trainer
- financial
- stocks
- sentiment
datasets:
- financial_phrasebank
- Kaggle Self label
- nickmuchi/financial-classification
metrics:
- accuracy
- f1
- precision
- recall
widget:
- text: "The USD rallied by 10% last night"
example_title: "Bullish Sentiment"
- text: "Covid-19 cases have been increasing over the past few months impacting earnings for global firms"
example_title: "Bearish Sentiment"
- text: "the USD has been trending lower"
example_title: "Mildly Bearish Sentiment"
model-index:
- name: sec-bert-finetuned-finance-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. -->
# sec-bert-finetuned-finance-classification
This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/nlpaueb/sec-bert-base) on the sentence_50Agree [financial-phrasebank + Kaggle Dataset](https://huggingface.co/datasets/nickmuchi/financial-classification), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). The Kaggle dataset includes Covid-19 sentiment data and can be found here: [sentiment-classification-selflabel-dataset](https://www.kaggle.com/percyzheng/sentiment-classification-selflabel-dataset).
It achieves the following results on the evaluation set:
- Loss: 0.5277
- Accuracy: 0.8755
- F1: 0.8744
- Precision: 0.8754
- Recall: 0.8755
## 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6005 | 0.99 | 71 | 0.3702 | 0.8478 | 0.8465 | 0.8491 | 0.8478 |
| 0.3226 | 1.97 | 142 | 0.3172 | 0.8834 | 0.8822 | 0.8861 | 0.8834 |
| 0.2299 | 2.96 | 213 | 0.3313 | 0.8814 | 0.8805 | 0.8821 | 0.8814 |
| 0.1277 | 3.94 | 284 | 0.3925 | 0.8775 | 0.8771 | 0.8770 | 0.8775 |
| 0.0764 | 4.93 | 355 | 0.4517 | 0.8715 | 0.8704 | 0.8717 | 0.8715 |
| 0.0533 | 5.92 | 426 | 0.4851 | 0.8735 | 0.8728 | 0.8731 | 0.8735 |
| 0.0363 | 6.9 | 497 | 0.5107 | 0.8755 | 0.8743 | 0.8757 | 0.8755 |
| 0.0248 | 7.89 | 568 | 0.5277 | 0.8755 | 0.8744 | 0.8754 | 0.8755 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
| 3,159 |
cloudblack/bert-base-finetuned-sts | [
"LABEL_0"
] | Entry not found | 15 |
clapika2010/flights_finetuned | null | Entry not found | 15 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.