modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
Andyrasika/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: 0.9175
- name: F1
type: f1
value: 0.917868093658934
---
<!-- 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: 0.2301
- Accuracy: 0.9175
- F1: 0.9179
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8386 | 1.0 | 250 | 0.3275 | 0.904 | 0.9011 |
| 0.2572 | 2.0 | 500 | 0.2301 | 0.9175 | 0.9179 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,805 |
JXL884/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: 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
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,083 |
carblacac/twitter-sentiment-analysis | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- new_dataset
metrics:
- accuracy
model-index:
- name: sentiment-analysis-twitter
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: new_dataset
type: new_dataset
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7965
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentiment-analysis-twitter
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 new_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4579
- Accuracy: 0.7965
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5315 | 1.0 | 157 | 0.4517 | 0.788 |
| 0.388 | 2.0 | 314 | 0.4416 | 0.8 |
| 0.3307 | 3.0 | 471 | 0.4579 | 0.7965 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,788 |
Seema09/finetuning-sentiment-model-Test | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-Test
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.904
- name: F1
type: f1
value: 0.9047619047619047
---
<!-- 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-Test
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: 0.2703
- Accuracy: 0.904
- F1: 0.9048
## 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 2.3.2
- Tokenizers 0.12.1
| 1,491 |
Alireza1044/MobileBERT_Theseus-rte | [
"entailment",
"not_entailment"
] | Entry not found | 15 |
Alireza1044/MobileBERT_Theseus-qnli | [
"entailment",
"not_entailment"
] | Entry not found | 15 |
MRF18/results | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [MRF18/results](https://huggingface.co/MRF18/results) on the None 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,096 |
CobaltAlchemist/Toxicbot | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | ---
license: gpl-3.0
widget:
- text: "I like you. </s></s> I love you."
---
| 76 |
Neha2608/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: 0.9255
- name: F1
type: f1
value: 0.9256296424769981
---
<!-- 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: 0.2148
- Accuracy: 0.9255
- F1: 0.9256
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8342 | 1.0 | 250 | 0.3147 | 0.9065 | 0.9031 |
| 0.2502 | 2.0 | 500 | 0.2148 | 0.9255 | 0.9256 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,807 |
annahaz/distilbert-base-multilingual-cased-finetuned-misogyny-sexism-multilingual | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-multilingual-cased-finetuned-misogyny-sexism-multilingual
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-multilingual-cased-finetuned-misogyny-sexism-multilingual
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2382
- Accuracy: 0.8435
- F1: 0.7857
- Precision: 0.7689
- Recall: 0.8031
- Mae: 0.1565
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| 0.3663 | 1.0 | 2062 | 0.3696 | 0.8363 | 0.7605 | 0.7967 | 0.7274 | 0.1637 |
| 0.2937 | 2.0 | 4124 | 0.3592 | 0.8504 | 0.7891 | 0.7948 | 0.7834 | 0.1496 |
| 0.2189 | 3.0 | 6186 | 0.4189 | 0.8442 | 0.7855 | 0.7727 | 0.7987 | 0.1558 |
| 0.1418 | 4.0 | 8248 | 0.6393 | 0.8409 | 0.7863 | 0.7558 | 0.8194 | 0.1591 |
| 0.1091 | 5.0 | 10310 | 0.7583 | 0.8284 | 0.7794 | 0.7207 | 0.8486 | 0.1716 |
| 0.0901 | 6.0 | 12372 | 0.8695 | 0.8410 | 0.7836 | 0.7628 | 0.8055 | 0.1590 |
| 0.0562 | 7.0 | 14434 | 1.0722 | 0.8405 | 0.7838 | 0.7600 | 0.8092 | 0.1595 |
| 0.0444 | 8.0 | 16496 | 1.0797 | 0.8433 | 0.7804 | 0.7815 | 0.7794 | 0.1567 |
| 0.0227 | 9.0 | 18558 | 1.1605 | 0.8429 | 0.7823 | 0.7743 | 0.7906 | 0.1571 |
| 0.0131 | 10.0 | 20620 | 1.2382 | 0.8435 | 0.7857 | 0.7689 | 0.8031 | 0.1565 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.9.0+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
| 2,600 |
luztraplet/roberta-large-finetuned-boolq | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- boolq
model-index:
- name: roberta-large-finetuned-boolq
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. -->
# roberta-large-finetuned-boolq
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the boolq dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3791
- eval_accuracy: 0.8459
- eval_runtime: 95.3733
- eval_samples_per_second: 34.286
- eval_steps_per_second: 4.288
- epoch: 2.0
- step: 588
## 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: 8
- eval_batch_size: 8
- seed: 1
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,339 |
Ahmed007/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: 0.937
- name: F1
type: f1
value: 0.9372331942198677
---
<!-- 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: 0.1413
- Accuracy: 0.937
- F1: 0.9372
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7628 | 1.0 | 250 | 0.2489 | 0.9155 | 0.9141 |
| 0.2014 | 2.0 | 500 | 0.1716 | 0.928 | 0.9283 |
| 0.1351 | 3.0 | 750 | 0.1456 | 0.937 | 0.9374 |
| 0.1046 | 4.0 | 1000 | 0.1440 | 0.9355 | 0.9349 |
| 0.0877 | 5.0 | 1250 | 0.1413 | 0.937 | 0.9372 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 2,017 |
MiguelCosta/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: 0.8766666666666667
- name: F1
type: f1
value: 0.8810289389067525
---
<!-- 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: 0.5805
- Accuracy: 0.8767
- F1: 0.8810
## 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: 4
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,521 |
thusken/nb-bert-base-user-needs | [
"need_divert",
"need_educate",
"need_inspire",
"need_perspective",
"need_trend",
"need_update"
] | ---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: nb-bert-base-user-needs
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. -->
# nb-bert-base-user-needs
This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0600
- Accuracy: 0.8479
- F1: 0.8319
- Precision: 0.8315
- Recall: 0.8479
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 98 | 1.1222 | 0.6263 | 0.5185 | 0.5076 | 0.6263 |
| No log | 2.0 | 196 | 1.0066 | 0.7216 | 0.6436 | 0.5899 | 0.7216 |
| No log | 3.0 | 294 | 0.8540 | 0.7577 | 0.7037 | 0.6760 | 0.7577 |
| No log | 4.0 | 392 | 0.8621 | 0.7603 | 0.6998 | 0.6568 | 0.7603 |
| No log | 5.0 | 490 | 0.8062 | 0.7887 | 0.7500 | 0.7449 | 0.7887 |
| 0.91 | 6.0 | 588 | 0.7465 | 0.8041 | 0.7660 | 0.7636 | 0.8041 |
| 0.91 | 7.0 | 686 | 0.6324 | 0.8247 | 0.8163 | 0.8187 | 0.8247 |
| 0.91 | 8.0 | 784 | 0.7333 | 0.7964 | 0.7703 | 0.7740 | 0.7964 |
| 0.91 | 9.0 | 882 | 0.6590 | 0.8325 | 0.8208 | 0.8106 | 0.8325 |
| 0.91 | 10.0 | 980 | 0.9854 | 0.8196 | 0.7890 | 0.7920 | 0.8196 |
| 0.4246 | 11.0 | 1078 | 0.7023 | 0.8247 | 0.8054 | 0.8138 | 0.8247 |
| 0.4246 | 12.0 | 1176 | 0.8995 | 0.8325 | 0.8120 | 0.8068 | 0.8325 |
| 0.4246 | 13.0 | 1274 | 0.8589 | 0.8299 | 0.8145 | 0.8058 | 0.8299 |
| 0.4246 | 14.0 | 1372 | 0.9859 | 0.8376 | 0.8151 | 0.8123 | 0.8376 |
| 0.4246 | 15.0 | 1470 | 0.8452 | 0.8402 | 0.8318 | 0.8341 | 0.8402 |
| 0.1637 | 16.0 | 1568 | 1.1156 | 0.8351 | 0.8157 | 0.8196 | 0.8351 |
| 0.1637 | 17.0 | 1666 | 1.1514 | 0.8325 | 0.8122 | 0.8218 | 0.8325 |
| 0.1637 | 18.0 | 1764 | 1.0092 | 0.8428 | 0.8266 | 0.8320 | 0.8428 |
| 0.1637 | 19.0 | 1862 | 1.0368 | 0.8351 | 0.8229 | 0.8287 | 0.8351 |
| 0.1637 | 20.0 | 1960 | 1.0600 | 0.8479 | 0.8319 | 0.8315 | 0.8479 |
| 0.0391 | 21.0 | 2058 | 1.1046 | 0.8428 | 0.8293 | 0.8269 | 0.8428 |
| 0.0391 | 22.0 | 2156 | 1.1178 | 0.8454 | 0.8262 | 0.8280 | 0.8454 |
| 0.0391 | 23.0 | 2254 | 1.1103 | 0.8428 | 0.8268 | 0.8295 | 0.8428 |
| 0.0391 | 24.0 | 2352 | 1.1179 | 0.8428 | 0.8274 | 0.8313 | 0.8428 |
| 0.0391 | 25.0 | 2450 | 1.1134 | 0.8402 | 0.8233 | 0.8254 | 0.8402 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 3,790 |
hassan4830/xlm-roberta-base-finetuned-urdu | null | ---
language: ur
license: afl-3.0
---
# XLM-RoBERTa-Urdu-Classification
This [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) text classification model trained on Urdu sentiment [data-set](https://huggingface.co/datasets/hassan4830/urdu-binary-classification-data) performs binary sentiment classification on any given Urdu sentence. The model has been fine-tuned for better results in manageable time frames.
## Model description
XLM-RoBERTa is a scaled cross-lingual sentence encoder. It is trained on 2.5T of data across 100 languages data filtered from Common Crawl. XLM-R achieves state-of-the-arts results on multiple cross-lingual benchmarks.
The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov.
It is based on Facebook’s RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data.
### How to use
You can import this model directly from the transformers library:
```python
>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("hassan4830/xlm-roberta-base-finetuned-urdu")
>>> model = AutoModelForSequenceClassification.from_pretrained("hassan4830/xlm-roberta-base-finetuned-urdu")
```
Here is how to use this model to get the label of a given text:
```python
>>> from transformers import TextClassificationPipeline
>>> text = "وہ ایک برا شخص ہے"
>>> pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, device = 0)
>>> pipe(text)
```
| 1,759 |
SIMAS-UN/blaming_migrants | null | Entry not found | 15 |
EthanChen0418/few-shot-model-five-classes | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
Maelstrom77/bert-base-uncased-MRPC | null | Entry not found | 15 |
Maelstrom77/bert-base-uncased-QQP | null | Entry not found | 15 |
Maelstrom77/bert-base-uncased-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ```
for i in range(len(predictions)):
if predictions[i] == 0:
predictions[i] = 2
elif predictions[i] == 1:
predictions[i] = 0
elif predictions[i] == 2:
predictions[i] = 1
``` | 192 |
PubChimps/dl-bert | null | Entry not found | 15 |
aXhyra/presentation_hate_42 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: presentation_hate_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
type: f1
value: 0.7692074096568478
---
<!-- 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_hate_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: 0.8711
- F1: 0.7692
## 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: 5.436235805743952e-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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5207 | 1.0 | 282 | 0.4815 | 0.7513 |
| 0.3047 | 2.0 | 564 | 0.5557 | 0.7510 |
| 0.2335 | 3.0 | 846 | 0.6627 | 0.7585 |
| 0.0056 | 4.0 | 1128 | 0.8711 | 0.7692 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,767 |
aloxatel/W2L | null | Entry not found | 15 |
anirudh21/albert-xxlarge-v2-finetuned-wnli | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: albert-xxlarge-v2-finetuned-wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5070422535211268
---
<!-- 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. -->
# albert-xxlarge-v2-finetuned-wnli
This model is a fine-tuned version of [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6970
- Accuracy: 0.5070
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 13 | 0.8066 | 0.4366 |
| No log | 2.0 | 26 | 0.6970 | 0.5070 |
| No log | 3.0 | 39 | 0.7977 | 0.4507 |
| No log | 4.0 | 52 | 0.7906 | 0.4930 |
| No log | 5.0 | 65 | 0.8459 | 0.4366 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
| 1,844 |
beomi/beep-klue-roberta-base-hate | [
"hate",
"none",
"offensive"
] | Entry not found | 15 |
beomi/korean-hatespeech-classifier | [
"None",
"Hate",
"Offensive"
] | Entry not found | 15 |
crystina-z/monoELECTRA_LCE_nneg31 | null | Entry not found | 15 |
emrecan/distilbert-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 |
ewriji/heil-A.412C-classification | null | Entry not found | 15 |
gchhablani/bert-base-cased-finetuned-rte | [
"entailment",
"not_entailment"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-cased-finetuned-rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE RTE
type: glue
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.6714801444043321
---
<!-- 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-finetuned-rte
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7260
- Accuracy: 0.6715
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name rte \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-rte \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6915 | 1.0 | 156 | 0.6491 | 0.6606 |
| 0.55 | 2.0 | 312 | 0.6737 | 0.6570 |
| 0.3955 | 3.0 | 468 | 0.7260 | 0.6715 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
| 2,636 |
google/tapas-medium-finetuned-tabfact | null | ---
language: en
tags:
- tapas
- sequence-classification
license: apache-2.0
datasets:
- tab_fact
---
# TAPAS medium model fine-tuned on Tabular Fact Checking (TabFact)
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_medium_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned on [TabFact](https://github.com/wenhuchen/Table-Fact-Checking). It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table).
The other (non-default) version which can be used is the one with absolute position embeddings:
- `no_reset`, which corresponds to `tapas_tabfact_inter_masklm_medium`
Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
the Hugging Face team and contributors.
## Model description
TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion.
This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it
can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in
the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words.
This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other,
or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional
representation of a table and associated text.
- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating
a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence
is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements.
This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used
to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed
or refuted by the contents of a table. Fine-tuning is done by adding a classification head on top of the pre-trained model, and then
jointly train this randomly initialized classification head with the base model on TabFact.
## Intended uses & limitations
You can use this model for classifying whether a sentence is supported or refuted by the contents of a table.
For code examples, we refer to the documentation of TAPAS on the HuggingFace website.
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence [SEP] Flattened table [SEP]
```
### Fine-tuning
The model was fine-tuned on 32 Cloud TPU v3 cores for 80,000 steps with maximum sequence length 512 and batch size of 512.
In this setup, fine-tuning takes around 14 hours. The optimizer used is Adam with a learning rate of 2e-5, and a warmup
ratio of 0.05. See the [paper](https://arxiv.org/abs/2010.00571) for more details (appendix A2).
### BibTeX entry and citation info
```bibtex
@misc{herzig2020tapas,
title={TAPAS: Weakly Supervised Table Parsing via Pre-training},
author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos},
year={2020},
eprint={2004.02349},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
```bibtex
@misc{eisenschlos2020understanding,
title={Understanding tables with intermediate pre-training},
author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller},
year={2020},
eprint={2010.00571},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@inproceedings{2019TabFactA,
title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
booktitle = {International Conference on Learning Representations (ICLR)},
address = {Addis Ababa, Ethiopia},
month = {April},
year = {2020}
}
``` | 4,873 |
hectorcotelo/autonlp-spanish_songs-202661 | [
"average",
"bad",
"good",
"hit",
"worst"
] | ---
tags: autonlp
language: es
widget:
- text: "Y si me tomo una cerveza
Vuelves a mi cabeza
Y empiezo a recordarte
Es que me gusta cómo besas
Con tu delicadeza
Puede ser que
Tú y yo, somos el uno para el otro
Que no dejo de pensarte
Quise olvidarte y tomé un poco
Y resultó extrañarte, yeah"
datasets:
- hectorcotelo/autonlp-data-spanish_songs
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 202661
## Validation Metrics
- Loss: 1.5369086265563965
- Accuracy: 0.30762817840766987
- Macro F1: 0.28034259092597485
- Micro F1: 0.30762817840766987
- Weighted F1: 0.28072818168048186
- Macro Precision: 0.3113843896292072
- Micro Precision: 0.30762817840766987
- Weighted Precision: 0.3128459166476807
- Macro Recall: 0.3071652685939504
- Micro Recall: 0.30762817840766987
- Weighted Recall: 0.30762817840766987
## 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/hectorcotelo/autonlp-spanish_songs-202661
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("hectorcotelo/autonlp-spanish_songs-202661", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("hectorcotelo/autonlp-spanish_songs-202661", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,544 |
jcblaise/electra-tagalog-small-uncased-discriminator-newsphnli | null | Entry not found | 15 |
joelito/bert-base-uncased-sem_eval_2010_task_8 | [
"Cause-Effect(e1,e2)",
"Cause-Effect(e2,e1)",
"Component-Whole(e1,e2)",
"Component-Whole(e2,e1)",
"Content-Container(e1,e2)",
"Content-Container(e2,e1)",
"Entity-Destination(e1,e2)",
"Entity-Destination(e2,e1)",
"Entity-Origin(e1,e2)",
"Entity-Origin(e2,e1)",
"Instrument-Agency(e1,e2)",
"Instr... | # bert-base-uncased-sem_eval_2010_task_8
Task: sem_eval_2010_task_8
Base Model: bert-base-uncased
Trained for 3 epochs
Batch-size: 6
Seed: 42
Test F1-Score: 0.8 | 166 |
jxuhf/roberta-base-finetuned-cola | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model_index:
- name: roberta-base-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metric:
name: Matthews Correlation
type: matthews_correlation
value: 0.557882735147727
---
<!-- 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. -->
# roberta-base-finetuned-cola
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4716
- Matthews Correlation: 0.5579
## Model description
More information needed
## Intended uses & limitations
```python
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("jxuhf/roberta-base-finetuned-cola")
```
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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4981 | 1.0 | 535 | 0.5162 | 0.5081 |
| 0.314 | 2.0 | 1070 | 0.4716 | 0.5579 |
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| 1,892 |
luiz826/roberta-to-music-genre | [
"Alternative",
"Country",
"Eletronic Music",
"Gospel and Worship Songs",
"Hip-Hop",
"Jazz/Blues",
"Pop",
"R&B/Soul",
"Reggae",
"Rock"
] | This model was made for a project in the NLP group of the Technology and Artificial Intelligence League (TAIL).
We try to predict a music genre from the lyrics. | 161 |
mrm8488/distilroberta-finetuned-banking77 | [
"activate_my_card",
"age_limit",
"apple_pay_or_google_pay",
"atm_support",
"automatic_top_up",
"balance_not_updated_after_bank_transfer",
"balance_not_updated_after_cheque_or_cash_deposit",
"beneficiary_not_allowed",
"cancel_transfer",
"card_about_to_expire",
"card_acceptance",
"card_arrival",... | ---
language: en
tags:
- banking
- intent
- multiclass
datasets:
- banking77
widget:
- text: "How long until my transfer goes through?"
---
# distilroberta-base fine-tuned on banking77 dataset for intent classification
Test set accuray: 0.896
## How to use
```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
ckpt = 'mrm8488/distilroberta-finetuned-banking77'
tokenizer = AutoTokenizer.from_pretrained(ckpt)
model = AutoModelForSequenceClassification.from_pretrained(ckpt)
classifier = pipeline('text-classification', tokenizer=tokenizer, model=model)
classifier('What is the base of the exchange rates?')
# Output: [{'label': 'exchange_rate', 'score': 0.8509947657585144}]
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain | 915 |
murathankurfali/bert-large-uncased-pdtb2-explicit-four-way | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | Entry not found | 15 |
sismetanin/xlm_roberta_base-ru-sentiment-rureviews | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language:
- ru
tags:
- sentiment analysis
- Russian
---
## XLM-RoBERTa-Base-ru-sentiment-RuReviews
XLM-RoBERTa-Base-ru-sentiment-RuReviews is a [XLM-RoBERTa-Base](https://huggingface.co/xlm-roberta-base) model fine-tuned on [RuReviews dataset](https://github.com/sismetanin/rureviews) of Russian-language reviews from the ”Women’s Clothes and Accessories” product category on the primary e-commerce site in Russia.
<table>
<thead>
<tr>
<th rowspan="4">Model</th>
<th rowspan="4">Score<br></th>
<th rowspan="4">Rank</th>
<th colspan="12">Dataset</th>
</tr>
<tr>
<td colspan="6">SentiRuEval-2016<br></td>
<td colspan="2" rowspan="2">RuSentiment</td>
<td rowspan="2">KRND</td>
<td rowspan="2">LINIS Crowd</td>
<td rowspan="2">RuTweetCorp</td>
<td rowspan="2">RuReviews</td>
</tr>
<tr>
<td colspan="3">TC</td>
<td colspan="3">Banks</td>
</tr>
<tr>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>wighted</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
</tr>
</thead>
<tbody>
<tr>
<td>SOTA</td>
<td>n/s</td>
<td></td>
<td>76.71</td>
<td>66.40</td>
<td>70.68</td>
<td>67.51</td>
<td>69.53</td>
<td>74.06</td>
<td>78.50</td>
<td>n/s</td>
<td>73.63</td>
<td>60.51</td>
<td>83.68</td>
<td>77.44</td>
</tr>
<tr>
<td>XLM-RoBERTa-Large</td>
<td>76.37</td>
<td>1</td>
<td>82.26</td>
<td>76.36</td>
<td>79.42</td>
<td>76.35</td>
<td>76.08</td>
<td>80.89</td>
<td>78.31</td>
<td>75.27</td>
<td>75.17</td>
<td>60.03</td>
<td>88.91</td>
<td>78.81</td>
</tr>
<tr>
<td>SBERT-Large</td>
<td>75.43</td>
<td>2</td>
<td>78.40</td>
<td>71.36</td>
<td>75.14</td>
<td>72.39</td>
<td>71.87</td>
<td>77.72</td>
<td>78.58</td>
<td>75.85</td>
<td>74.20</td>
<td>60.64</td>
<td>88.66</td>
<td>77.41</td>
</tr>
<tr>
<td>MBARTRuSumGazeta</td>
<td>74.70</td>
<td>3</td>
<td>76.06</td>
<td>68.95</td>
<td>73.04</td>
<td>72.34</td>
<td>71.93</td>
<td>77.83</td>
<td>76.71</td>
<td>73.56</td>
<td>74.18</td>
<td>60.54</td>
<td>87.22</td>
<td>77.51</td>
</tr>
<tr>
<td>Conversational RuBERT</td>
<td>74.44</td>
<td>4</td>
<td>76.69</td>
<td>69.09</td>
<td>73.11</td>
<td>69.44</td>
<td>68.68</td>
<td>75.56</td>
<td>77.31</td>
<td>74.40</td>
<td>73.10</td>
<td>59.95</td>
<td>87.86</td>
<td>77.78</td>
</tr>
<tr>
<td>LaBSE</td>
<td>74.11</td>
<td>5</td>
<td>77.00</td>
<td>69.19</td>
<td>73.55</td>
<td>70.34</td>
<td>69.83</td>
<td>76.38</td>
<td>74.94</td>
<td>70.84</td>
<td>73.20</td>
<td>59.52</td>
<td>87.89</td>
<td>78.47</td>
</tr>
<tr>
<td>XLM-RoBERTa-Base</td>
<td>73.60</td>
<td>6</td>
<td>76.35</td>
<td>69.37</td>
<td>73.42</td>
<td>68.45</td>
<td>67.45</td>
<td>74.05</td>
<td>74.26</td>
<td>70.44</td>
<td>71.40</td>
<td>60.19</td>
<td>87.90</td>
<td>78.28</td>
</tr>
<tr>
<td>RuBERT</td>
<td>73.45</td>
<td>7</td>
<td>74.03</td>
<td>66.14</td>
<td>70.75</td>
<td>66.46</td>
<td>66.40</td>
<td>73.37</td>
<td>75.49</td>
<td>71.86</td>
<td>72.15</td>
<td>60.55</td>
<td>86.99</td>
<td>77.41</td>
</tr>
<tr>
<td>MBART-50-Large-Many-to-Many</td>
<td>73.15</td>
<td>8</td>
<td>75.38</td>
<td>67.81</td>
<td>72.26</td>
<td>67.13</td>
<td>66.97</td>
<td>73.85</td>
<td>74.78</td>
<td>70.98</td>
<td>71.98</td>
<td>59.20</td>
<td>87.05</td>
<td>77.24</td>
</tr>
<tr>
<td>SlavicBERT</td>
<td>71.96</td>
<td>9</td>
<td>71.45</td>
<td>63.03</td>
<td>68.44</td>
<td>64.32</td>
<td>63.99</td>
<td>71.31</td>
<td>72.13</td>
<td>67.57</td>
<td>72.54</td>
<td>58.70</td>
<td>86.43</td>
<td>77.16</td>
</tr>
<tr>
<td>EnRuDR-BERT</td>
<td>71.51</td>
<td>10</td>
<td>72.56</td>
<td>64.74</td>
<td>69.07</td>
<td>61.44</td>
<td>60.21</td>
<td>68.34</td>
<td>74.19</td>
<td>69.94</td>
<td>69.33</td>
<td>56.55</td>
<td>87.12</td>
<td>77.95</td>
</tr>
<tr>
<td>RuDR-BERT</td>
<td>71.14</td>
<td>11</td>
<td>72.79</td>
<td>64.23</td>
<td>68.36</td>
<td>61.86</td>
<td>60.92</td>
<td>68.48</td>
<td>74.65</td>
<td>70.63</td>
<td>68.74</td>
<td>54.45</td>
<td>87.04</td>
<td>77.91</td>
</tr>
<tr>
<td>MBART-50-Large</td>
<td>69.46</td>
<td>12</td>
<td>70.91</td>
<td>62.67</td>
<td>67.24</td>
<td>61.12</td>
<td>60.25</td>
<td>68.41</td>
<td>72.88</td>
<td>68.63</td>
<td>70.52</td>
<td>46.39</td>
<td>86.48</td>
<td>77.52</td>
</tr>
</tbody>
</table>
The table shows per-task scores and a macro-average of those scores to determine a models’s position on the leaderboard. For datasets with multiple evaluation metrics (e.g., macro F1 and weighted F1 for RuSentiment), we use an unweighted average of the metrics as the score for the task when computing the overall macro-average. The same strategy for comparing models’ results was applied in the GLUE benchmark.
## Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{Smetanin2021Deep,
author = {Sergey Smetanin and Mikhail Komarov},
title = {Deep transfer learning baselines for sentiment analysis in Russian},
journal = {Information Processing & Management},
volume = {58},
number = {3},
pages = {102484},
year = {2021},
issn = {0306-4573},
doi = {0.1016/j.ipm.2020.102484}
}
```
Dataset:
```
@INPROCEEDINGS{Smetanin2019Sentiment,
author={Sergey Smetanin and Michail Komarov},
booktitle={2019 IEEE 21st Conference on Business Informatics (CBI)},
title={Sentiment Analysis of Product Reviews in Russian using Convolutional Neural Networks},
year={2019},
volume={01},
pages={482-486},
doi={10.1109/CBI.2019.00062},
ISSN={2378-1963},
month={July}
}
``` | 6,358 |
symanto/mpnet-base-snli-mnli | [
"ENTAILMENT",
"NEUTRAL",
"CONTRADICTION"
] | ---
language:
- en
datasets:
- SNLI
- MNLI
tags:
- zero-shot-classification
---
A cross-attention NLI model trained for zero-shot and few-shot text classification.
The base model is [mpnet-base](https://huggingface.co/microsoft/mpnet-base), trained with the code from [here](https://github.com/facebookresearch/anli);
on [SNLI](https://nlp.stanford.edu/projects/snli/) and [MNLI](https://cims.nyu.edu/~sbowman/multinli/).
Usage:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
import numpy as np
model = AutoModelForSequenceClassification.from_pretrained("symanto/mpnet-base-snli-mnli")
tokenizer = AutoTokenizer.from_pretrained("symanto/mpnet-base-snli-mnli")
input_pairs = [("I like this pizza.", "The sentence is positive."), ("I like this pizza.", "The sentence is negative.")]
inputs = tokenizer(["</s></s>".join(input_pair) for input_pair in input_pairs], return_tensors="pt")
logits = model(**inputs).logits
probs = torch.softmax(logits, dim=1).tolist()
print("probs", probs)
np.testing.assert_almost_equal(probs, [[0.86, 0.14, 0.00], [0.16, 0.15, 0.69]], decimal=2)
```
| 1,139 |
tuhailong/cross-encoder-bert-base | [
"LABEL_0"
] | ---
language: zh
tags:
- sbert
datasets:
- dialogue
---
# Data
train data is similarity sentence data from E-commerce dialogue, about 20w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder
### Usage
```python
>>> from sentence_transformers.cross_encoder import CrossEncoder
>>> model = CrossEncoder('tuhailong/cross-encoder')
>>> scores = model.predict([["今天天气不错", "今天心情不错"]])
>>> print(scores)
``` | 479 |
inovex/multi2convai-quality-fr-bert | [
"neo.magnetklammern",
"neo.start",
"neo.back",
"neo.gearbox",
"neo.motor.brushcollar",
"neo.motor.worm",
"neo.magnet",
"neo.magnetisierung",
"neo.motor",
"neo.verschaubung",
"neo.zusammenfuehrung",
"neo.zahnradgross",
"neo.zahnradklein",
"neo.yes",
"neo.no",
"neo.einpressen",
"neo.mo... | ---
tags:
- text-classification
widget:
- text: "Lancer le programme"
license: mit
language: fr
---
# Multi2ConvAI-Quality: finetuned Bert for French
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Quality (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: French (fr)
- model type: finetuned Bert
## How to run
Requires:
- Huggingface transformers
### Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-quality-fr-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-quality-fr-bert")
````
## Further information on Multi2ConvAI:
- https://multi2conv.ai
- https://github.com/inovex/multi2convai
- mailto: info@multi2conv.ai | 965 |
Jackett/subject_classifier | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | Label association
{'Biology': 0, 'Physics': 1, 'Chemistry': 2, 'Maths': 3}
| 75 |
edubz/anne_bradstreet | null | ---
license: mit
---
This model was trained on a new dataset composed of available poems by Anne Bradstreet hosted by [Public Domain Poetry.](https://www.public-domain-poetry.com/anne-bradstreet) Specifically I downloaded all 40 poems and fine-tuned a bert-base-uncased text classification model on Amazon SageMaker. For the negative class, I actually generated GPT-2 samples of length 70. That is to say, for each line of Bradstreet I generated a generic GPT-2 reposes. I considered these responses my negative class.
In the classifier, I had a total of 6947 positive lines written by Anne Bradstreet, and 5219 lines generated by GPT-2 in response, totally a dataset of 12,166 labeled lines. I used only the GPT-2 responses in the training set, keeping the actual Bradstreet lines in the positive samples alone.
I split the train and test set in 80/20, leaving a total of 9732 labeled samples in training, and 2435 samples in test.
These I trained on SageMaker, using the Hugging Face deep learning container. I also used SageMaker Training Compiler, which achieved 64 samples per batch on an ml.p3.2xlarge. After 42 minutes of training, on only 5 epochs, I achieved a train loss of 0.0714. Test loss is forthcoming.
In my own tests, the model seems to be always very confident. That is to say, it routinely gives a confidence score of at least 99.8%. All predictions should be single-lines only, as this is how the model was fine-tuned. Multiple lines in a prediction request will always result in a Label0 response, ie not written by Anne Bradstreet, even if pulled directly from her works.
In short, the model seems to know the difference between generic GPT-2 text responding to a Bradstreet prompt, vs the output of a model fine-tuned on Bradstreet text and generating based on Bradstreet responses.
This was developed exclusively for use at an upcoming workshop. | 1,897 |
Roshan777/finetuning-sentiment-model-300-samples | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-300-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.6833333333333333
- name: F1
type: f1
value: 0.6153846153846154
---
<!-- 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-300-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: 0.6567
- Accuracy: 0.6833
- F1: 0.6154
## 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
| 1,519 |
hackathon-pln-es/readability-es-sentences | [
"complex",
"simple"
] | ---
language: es
license: cc-by-4.0
tags:
- spanish
- roberta
- bertin
pipeline_tag: text-classification
widget:
- text: La ciencia nos enseña, en efecto, a someter nuestra razón a la verdad y a conocer y juzgar las cosas como son, es decir, como ellas mismas eligen ser y no como quisiéramos que fueran.
---
# Readability ES Sentences for two classes
Model based on the Roberta architecture finetuned on [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for readability assessment of Spanish texts.
## Description and performance
This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs binary classification among the following classes:
- Simple.
- Complex.
It achieves a F1 macro average score of 0.8923, measured on the validation set.
## Model variants
- `readability-es-sentences` (this model). Two classes, sentence-based dataset.
- [`readability-es-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-paragraphs). Two classes, paragraph-based dataset.
- [`readability-es-3class-sentences`](https://huggingface.co/hackathon-pln-es/readability-es-3class-sentences). Three classes, sentence-based dataset.
- [`readability-es-3class-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-3class-paragraphs). Three classes, paragraph-based dataset.
## Datasets
- [`readability-es-hackathon-pln-public`](https://huggingface.co/datasets/hackathon-pln-es/readability-es-hackathon-pln-public), composed of:
* coh-metrix-esp corpus.
* Various text resources scraped from websites.
- Other non-public datasets: newsela-es, simplext.
## Training details
Please, refer to [this training run](https://wandb.ai/readability-es/readability-es/runs/3rgvwps0/overview) for full details on hyperparameters and training regime.
## Biases and Limitations
- Due to the scarcity of data and the lack of a reliable gold test set, performance metrics are reported on the validation set.
- One of the datasets involved is the Spanish version of newsela, which is frequently used as a reference. However, it was created by translating previous datasets, and therefore it may contain somewhat unnatural phrases.
- Some of the datasets used cannot be publicly disseminated, making it more difficult to assess the existence of biases or mistakes.
- Language might be biased towards the Spanish dialect spoken in Spain. Other regional variants might be sub-represented.
- No effort has been performed to alleviate the shortcomings and biases described in the [original implementation of BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish#bias-examples-spanish).
## Authors
- [Laura Vásquez-Rodríguez](https://lmvasque.github.io/)
- [Pedro Cuenca](https://twitter.com/pcuenq)
- [Sergio Morales](https://www.fireblend.com/)
- [Fernando Alva-Manchego](https://feralvam.github.io/)
| 2,922 |
nqcccccc/phobert-vlsp-absa-qab | null | Entry not found | 15 |
course5i/SEAD-L-6_H-384_A-12-sst2 | [
"0",
"1"
] | ---
language:
- en
license: apache-2.0
tags:
- SEAD
datasets:
- glue
- sst2
---
## Paper
## [SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63)
Aurthors: *Moyan Mei*, *Rohit Sroch*
## Abstract
With the widespread use of pre-trained language models (PLM), there has been increased research on how to make them applicable, especially in limited-resource or low latency high throughput scenarios. One of the dominant approaches is knowledge distillation (KD), where a smaller model is trained by receiving guidance from a large PLM. While there are many successful designs for learning knowledge from teachers, it remains unclear how students can learn better. Inspired by real university teaching processes, in this work we further explore knowledge distillation and propose a very simple yet effective framework, SEAD, to further improve task-specific generalization by utilizing multiple teachers. Our experiments show that SEAD leads to better performance compared to other popular KD methods [[1](https://arxiv.org/abs/1910.01108)] [[2](https://arxiv.org/abs/1909.10351)] [[3](https://arxiv.org/abs/2002.10957)] and achieves comparable or superior performance to its teacher model such as BERT [[4](https://arxiv.org/abs/1810.04805)] on total 13 tasks for the GLUE [[5](https://arxiv.org/abs/1804.07461)] and SuperGLUE [[6](https://arxiv.org/abs/1905.00537)] benchmarks.
*Moyan Mei and Rohit Sroch. 2022. [SEAD: Simple ensemble and knowledge distillation framework for natural language understanding](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63).
Lattice, THE MACHINE LEARNING JOURNAL by Association of Data Scientists, 3(1).*
## SEAD-L-6_H-384_A-12-sst2
This is a student model distilled from [**BERT base**](https://huggingface.co/bert-base-uncased) as teacher by using SEAD framework on **sst2** task. For weights initialization, we used [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased)
## All SEAD Checkpoints
Other Community Checkpoints: [here](https://huggingface.co/models?search=SEAD)
## Intended uses & limitations
More information needed
### Training hyperparameters
Please take a look at the `training_args.bin` file
```python
$ import torch
$ hyperparameters = torch.load(os.path.join('training_args.bin'))
```
### Evaluation results
| eval_accuracy | eval_runtime | eval_samples_per_second | eval_steps_per_second | eval_loss | eval_samples |
|:-------------:|:------------:|:-----------------------:|:---------------------:|:---------:|:------------:|
| 0.9312 | 1.5334 | 568.684 | 18.261 | 0.2929 | 872 |
### Framework versions
- Transformers >=4.8.0
- Pytorch >=1.6.0
- TensorFlow >=2.5.0
- Flax >=0.3.5
- Datasets >=1.10.2
- Tokenizers >=0.11.6
If you use these models, please cite the following paper:
```
@article{article,
author={Mei, Moyan and Sroch, Rohit},
title={SEAD: Simple Ensemble and Knowledge Distillation Framework for Natural Language Understanding},
volume={3},
number={1},
journal={Lattice, The Machine Learning Journal by Association of Data Scientists},
day={26},
year={2022},
month={Feb},
url = {www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63}
}
```
| 3,519 |
luckydog/bert-base-chinese-finetuned-ChnSenti | null | Entry not found | 15 |
SiriusRen/OH_my-rubbish-model | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: OH_my-rubbish-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# OH_my-rubbish-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0
- Datasets 2.0.1.dev0
- Tokenizers 0.11.6
| 1,066 |
Hate-speech-CNERG/tamil-codemixed-abusive-MuRIL | null | ---
language: ta-en
license: afl-3.0
---
This model is used to detect **abusive speech** in **Code-Mixed Tamil**. It is finetuned on MuRIL model using Code-Mixed Tamil abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive)
LABEL_0 :-> Normal
LABEL_1 :-> Abusive
### For more details about our paper
Mithun Das, Somnath Banerjee and Animesh Mukherjee. "[Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://arxiv.org/abs/2204.12543)". Accepted at ACM HT 2022.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{das2022data,
title={Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages},
author={Das, Mithun and Banerjee, Somnath and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2204.12543},
year={2022}
}
~~~ | 982 |
Preetiha/clause_classification | [
"Adjustments",
"Agreements",
"Amendments",
"Anti-Corruption Laws",
"Applicable Laws",
"Approvals",
"Arbitration",
"Assignments",
"Assigns",
"Authority",
"Authorizations",
"Base Salary",
"Benefits",
"Binding Effects",
"Books",
"Brokers",
"Capitalization",
"Change In Control",
"Clo... | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Preetiha/autotrain-data-clause-classification
co2_eq_emissions: 44.494127975699804
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 812025458
- CO2 Emissions (in grams): 44.494127975699804
## Validation Metrics
- Loss: 0.5240132808685303
- Accuracy: 0.8673
- Macro F1: 0.7979496833221609
- Micro F1: 0.8673
- Weighted F1: 0.8616433030199793
- Macro Precision: 0.8263528446923423
- Micro Precision: 0.8673
- Weighted Precision: 0.8702574307362431
- Macro Recall: 0.7953048612545152
- Micro Recall: 0.8673
- Weighted Recall: 0.8673
## 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/Preetiha/autotrain-clause-classification-812025458
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Preetiha/autotrain-clause-classification-812025458", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Preetiha/autotrain-clause-classification-812025458", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,380 |
jerryKakooza/language-detection-fine-tuned-on-xlm-roberta-base | [
"English",
"Luganda"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- common_language
metrics:
- accuracy
model-index:
- name: language-detection-fine-tuned-on-xlm-roberta-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: common_language
type: common_language
args: full
metrics:
- name: Accuracy
type: accuracy
value: 0.9760187824920342
---
<!-- 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. -->
# language-detection-fine-tuned-on-xlm-roberta-base
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1642
- Accuracy: 0.9760
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0725 | 1.0 | 22194 | 0.1642 | 0.9760 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,737 |
VictorZhu/Anchor-Classification-DMV | null | Entry not found | 15 |
anuj55/distilbert-base-uncased-finetuned-polifact | null | Entry not found | 15 |
CEBaB/roberta-base.CEBaB.absa.exclusive.seed_99 | [
"0",
"1",
"2"
] | Entry not found | 15 |
ericklerouge123/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: 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
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,089 |
connectivity/bert_ft_qqp-1 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-7 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-12 | null | Entry not found | 15 |
aakorolyova/outcome_significance_relation | null | <h1>Model description</h1>
This is a fine-tuned BioBERT model for extracting the relation between clinical trial outcome and its significance level. The task is framed as sentence classification:
- you first need to extract the entities - outcomes and significance levels. For outcomes, you could use the model https://huggingface.co/aakorolyova/reported_outcome_extraction. For significance levels, we have previously used a rule-based approach that worked well; we plan to make the code available in https://github.com/aakorolyova/DeSpin-2.0 soon.
- then, for each pair of outcome and significance level, you mask the entity texts as @OUTCOME$ and @SIGNIFICANCE$
- you run the prediction on the sentence with the masked outcome-significance level pair to get the label (0 if the entities are unrelated, 1 if they are related).
For example, the sentence "Intubation conditions (succinylcholine 8.3 ± 0.8; rocuronium 8.2 ± 0.9; P = 0.7) and failed first intubation attempts (succinylcholine 32/200; rocuronium 36/201; P = 1.0) did not differ between the groups." contains several outcomes ("Intubation conditions", "failed first intubation attempts") and significance levels ("P = 0.7", "P = 1.0"). Masked sentence for each pair and the expected label are as follows:
```
@OUTCOME$ (succinylcholine 8.3 ± 0.8; rocuronium 8.2 ± 0.9; @SIGNIFICANCE$) and failed first intubation attempts (succinylcholine 32/200; rocuronium 36/201; P = 1.0) did not differ between the groups. 1
@OUTCOME$ (succinylcholine 8.3 ± 0.8; rocuronium 8.2 ± 0.9; P = 0.7) and failed first intubation attempts (succinylcholine 32/200; rocuronium 36/201; @SIGNIFICANCE$) did not differ between the groups. 0
Intubation conditions (succinylcholine 8.3 ± 0.8; rocuronium 8.2 ± 0.9; P = 0.7) and @OUTCOME$ (succinylcholine 32/200; rocuronium 36/201; @SIGNIFICANCE$) did not differ between the groups. 1
Intubation conditions (succinylcholine 8.3 ± 0.8; rocuronium 8.2 ± 0.9; @SIGNIFICANCE$) and @OUTCOME$ (succinylcholine 32/200; rocuronium 36/201; P = 1.0) did not differ between the groups. 0
```
This is the second version of the model; the original model development was reported in:
Anna Koroleva, Patrick Paroubek. Extracting relations between outcome and significance level in Randomized Controlled Trials (RCTs) publications. Proceedings of ACL BioNLP workshop, 2019 https://aclanthology.org/W19-5038/
The original work was conducted within the scope of the Assisted authoring for avoiding inadequate claims in scientific reporting PhD project of the Methods for Research on Research (MiRoR, http://miror-ejd.eu/) program.
Model creator: Anna Koroleva
<h1>Intended uses & limitations</h1>
The model was originally intended to be used as a part of spin (unjustified presentation of trial results) detection pipeline in articles reporting Randomised controlled trials (see Anna Koroleva, Sanjay Kamath, Patrick MM Bossuyt, Patrick Paroubek. DeSpin: a prototype system for detecting spin in biomedical publications. Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing. https://aclanthology.org/2020.bionlp-1.5/). It can also be used separately, for predicting outcome - significance level relation.
The main limitation is that the model was trained on a fairly small sample of data annotated by a single annotator. Annotating more data or involvig more annotators was not possible within the PhD project.
<h1>How to use</h1>
The model should be used with the BioBERT tokeniser. A sample code for getting model predictions is below:
```
import numpy as np
from transformers import AutoModelForTokenClassification
from transformers import AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('dmis-lab/biobert-v1.1')
model = AutoModelForSequenceClassification.from_pretrained("aakorolyova/outcome_significance_relation")
text1 = "@OUTCOME$ (succinylcholine 8.3 ± 0.8; rocuronium 8.2 ± 0.9; @SIGNIFICANCE$) and failed first intubation attempts (succinylcholine 32/200; rocuronium 36/201; P = 1.0) did not differ between the groups."
text2 = "@OUTCOME$ (succinylcholine 8.3 ± 0.8; rocuronium 8.2 ± 0.9; P = 0.7) and failed first intubation attempts (succinylcholine 32/200; rocuronium 36/201; @SIGNIFICANCE$) did not differ between the groups."
tokenized_input1 = tokenizer(text1, padding="max_length", truncation=True, return_tensors='pt')
output1 = model(**tokenized_input1)['logits']
output1 = np.argmax(output1.detach().numpy(), axis=1)
print(output1)
tokenized_input2 = tokenizer(text2, padding="max_length", truncation=True, return_tensors='pt')
output2 = model(**tokenized_input2)['logits']
output2 = np.argmax(output2.detach().numpy(), axis=1)
print(output2)
```
Some more useful functions can be found in or Github repository: https://github.com/aakorolyova/DeSpin-2.0
<h1>Training data</h1>
Training data can be found in https://github.com/aakorolyova/DeSpin-2.0/tree/main/data/Outcome_significance_relation
<h1>Training procedure</h1>
The model was fine-tuned using Huggingface Trainer API. Training scripts can be found in https://github.com/aakorolyova/DeSpin-2.0
<h1>Evaluation</h1>
Precision: 94.96%
Recall: 96.35%
F1: 95.65%
| 5,216 |
abdulmatinomotoso/emotion_detection_finetuned_distilbert | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
StanKrewinkel/finetuning-sentiment-model-3000-samples | null | Entry not found | 15 |
Cole/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: 0.936
- name: F1
type: f1
value: 0.936288073257115
---
<!-- 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: 0.1558
- Accuracy: 0.936
- F1: 0.9363
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1701 | 1.0 | 250 | 0.1817 | 0.932 | 0.9311 |
| 0.1109 | 2.0 | 500 | 0.1558 | 0.936 | 0.9363 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,803 |
OneFly/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: 0.928
- name: F1
type: f1
value: 0.9279829352545553
---
<!-- 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: 0.2108
- Accuracy: 0.928
- F1: 0.9280
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8434 | 1.0 | 250 | 0.3075 | 0.9085 | 0.9058 |
| 0.2472 | 2.0 | 500 | 0.2108 | 0.928 | 0.9280 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,804 |
Jeevesh8/lecun_feather_berts-28 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-17 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/std_pnt_04_feather_berts-58 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Rajesh222/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: 0.9265425929085783
---
<!-- 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: 0.2133
- Accuracy: 0.9265
- F1: 0.9265
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8085 | 1.0 | 250 | 0.3033 | 0.9065 | 0.9037 |
| 0.2458 | 2.0 | 500 | 0.2133 | 0.9265 | 0.9265 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.3.0
- Tokenizers 0.11.6
| 1,800 |
Hardeep/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | ---
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: 0.9225
- name: F1
type: f1
value: 0.9222308123735177
---
<!-- 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: 0.2118
- Accuracy: 0.9225
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7992 | 1.0 | 250 | 0.3046 | 0.9085 | 0.9063 |
| 0.2352 | 2.0 | 500 | 0.2118 | 0.9225 | 0.9222 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,800 |
Alireza1044/MobileBERT_Theseus-mrpc | [
"equivalent",
"not_equivalent"
] | Entry not found | 15 |
Alireza1044/MobileBERT_Theseus-cola | [
"acceptable",
"unacceptable"
] | Entry not found | 15 |
Alireza1044/MobileBERT_Theseus-qqp | [
"duplicate",
"not_duplicate"
] | Entry not found | 15 |
davidcechak/DNADeberta_fine_ | null | Entry not found | 15 |
anita-clmnt/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
domenicrosati/scibert-finetuned-DAGPap22 | null | ---
tags:
- text-classification
- generated_from_trainer
model-index:
- name: scibert-finetuned-DAGPap22
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. -->
# scibert-finetuned-DAGPap22
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_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: 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,114 |
deepesh0x/autotrain-finetunedmodel1-1034535555 | [
"negative",
"positive"
] | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- deepesh0x/autotrain-data-finetunedmodel1
co2_eq_emissions: 29.194903746653306
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1034535555
- CO2 Emissions (in grams): 29.194903746653306
## Validation Metrics
- Loss: 0.16423887014389038
- Accuracy: 0.9402375649591685
- Precision: 0.94876254180602
- Recall: 0.9438381687516636
- AUC: 0.9843968335444757
- F1: 0.9462939488958569
## 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/deepesh0x/autotrain-finetunedmodel1-1034535555
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("deepesh0x/autotrain-finetunedmodel1-1034535555", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("deepesh0x/autotrain-finetunedmodel1-1034535555", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,209 |
austinmw/distilbert-base-uncased-finetuned-tweets-sentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-tweets-sentiment
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: Accuracy
type: accuracy
value: 0.7295
- name: F1
type: f1
value: 0.7303196028048928
---
<!-- 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-tweets-sentiment
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: 0.8192
- Accuracy: 0.7295
- F1: 0.7303
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7126 | 1.0 | 713 | 0.6578 | 0.7185 | 0.7181 |
| 0.5514 | 2.0 | 1426 | 0.6249 | 0.7005 | 0.7046 |
| 0.4406 | 3.0 | 2139 | 0.7053 | 0.731 | 0.7296 |
| 0.3511 | 4.0 | 2852 | 0.7580 | 0.718 | 0.7180 |
| 0.2809 | 5.0 | 3565 | 0.8192 | 0.7295 | 0.7303 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
| 2,047 |
heoji/koelectra_senti_1 | [
"0",
"1",
"2"
] | Entry not found | 15 |
Team-PIXEL/pixel-base-finetuned-stsb | [
"LABEL_0"
] | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
model-index:
- name: pixel-base-finetuned-stsb
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. -->
# pixel-base-finetuned-stsb
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/pixel-base) on the GLUE STSB 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: 64
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 15000
- mixed_precision_training: Apex, opt level O1
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.12.1
| 1,126 |
pnr-svc/ConvBert-Sentiment-Analysis-Turkish | [
"negatif",
"normal",
"pozitif"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pnr-svc/Turkish-Multiclass-Dataset
metrics:
- accuracy
model-index:
- name: multi-class-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: pnr-svc/Turkish-Multiclass-Dataset
type: pnr-svc/Turkish-Multiclass-Dataset
args: TurkishMulticlassDataset
metrics:
- name: Accuracy
type: accuracy
value: 0.859
- task:
type: text-classification
name: Text Classification
dataset:
name: pnr-svc/Turkish-Multiclass-Dataset
type: pnr-svc/Turkish-Multiclass-Dataset
config: TurkishMulticlassDataset
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.859
verified: true
- name: loss
type: loss
value: 0.4957726299762726
verified: true
---
# multi-class-classification
This model is a fine-tuned version of [dbmdz/convbert-base-turkish-cased](https://huggingface.co/dbmdz/convbert-base-turkish-cased) on the pnr-svc/Turkish-Multiclass-Dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.495773
- Accuracy: 0.859
## 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-5
- train_batch_size: 16
- eval_batch_size: 16
- num_epochs:6
### Training results
| Training Loss | Epoch | Validation Loss | Accuracy |
|:-------------:|:-----:|:---------------:|:--------:|
| 0.495773 | 6.0 | 0.4957 | 0.859 |
| 1,746 |
Adi2K/Priv-Consent | [
"CON",
"NOT"
] | ---
language: eng
widget:
- text: "You can control cookies and tracking tools. To learn how to manage how we - and our vendors - use cookies and other tracking tools, please click here."
datasets:
- Adi2K/autonlp-data-Priv-Consent
---
# Model
- Problem type: Binary Classification
- Model ID: 12592372
## Validation Metrics
- Loss: 0.23033875226974487
- Accuracy: 0.9138655462184874
- Precision: 0.9087136929460581
- Recall: 0.9201680672268907
- AUC: 0.9690346726926065
- F1: 0.9144050104384133
## 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/Adi2K/autonlp-Priv-Consent-12592372
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Adi2K/autonlp-Priv-Consent-12592372", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Adi2K/autonlp-Priv-Consent-12592372", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,172 |
Alireza1044/albert-base-v2-wnli | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model_index:
- name: wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
args: wnli
metric:
name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- 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. -->
# wnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6898
- Accuracy: 0.5634
## 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: 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 |
CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | emilyalsentzer/Bio_ClinicalBERT with additional training through the finetuning pipeline described in "Extracting Seizure Frequency From Epilepsy Clinic Notes: A Machine Reading Approach To Natural Language Processing."
Citation: Kevin Xie, Ryan S Gallagher, Erin C Conrad, Chadric O Garrick, Steven N Baldassano, John M Bernabei, Peter D Galer, Nina J Ghosn, Adam S Greenblatt, Tara Jennings, Alana Kornspun, Catherine V Kulick-Soper, Jal M Panchal, Akash R Pattnaik, Brittany H Scheid, Danmeng Wei, Micah Weitzman, Ramya Muthukrishnan, Joongwon Kim, Brian Litt, Colin A Ellis, Dan Roth, Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing, Journal of the American Medical Informatics Association, 2022;, ocac018, https://doi.org/10.1093/jamia/ocac018
Bio_ClinicalBERT_for_seizureFreedom_classification classifies patients has having seizures or being seizure free using the HPI and/or Interval History paragraphs from a medical note. | 1,003 |
Crives/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: 0.9215538311282218
---
<!-- 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: 0.9215
- F1: 0.9216
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7814 | 1.0 | 250 | 0.3105 | 0.907 | 0.9046 |
| 0.2401 | 2.0 | 500 | 0.2175 | 0.9215 | 0.9216 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,807 |
EasthShin/Emotion-Classification-bert-base | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
Hate-speech-CNERG/deoffxlmr-mono-malyalam | [
"Not_offensive",
"Not_in_intended_language",
"Off_target_group",
"Profanity",
"Off_target_ind"
] | ---
language: ml
license: apache-2.0
---
This model is used to detect **Offensive Content** in **Malayalam Code-Mixed language**. The mono in the name refers to the monolingual setting, where the model is trained using only Malayalam(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 highest weighted F1 score at the leaderboard (Weighted F1 score on hold out test set: This model - 0.97, Ensemble - 0.97)
### 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.",
}
~~~ | 2,511 |
JonatanGk/roberta-base-ca-finetuned-cyberbullying-catalan | [
"Not_bullying",
"Bullying"
] | ---
language: ca
tags:
- "catalan"
metrics:
- accuracy
widget:
- text: "Ets més petita que un barrufet!!"
- text: "Ets tan lletja que et donaven de menjar per sota la porta."
---
# roberta-base-ca-finetuned-cyberbullying-catalan
This model is a fine-tuned version of [BSC-TeMU/roberta-base-ca](https://huggingface.co/BSC-TeMU/roberta-base-ca) on the dataset generated scrapping all social networks (Twitter, Youtube ...) to detect cyberbullying on Catalan.
It achieves the following results on the evaluation set:
- Loss: 0.1508
- Accuracy: 0.9665
## Training and evaluation data
I use the concatenation from multiple datasets generated scrapping social networks (Twitter,Youtube,Discord...) to fine-tune this model. The total number of sentence pairs is above 410k sentences. Trained similar method at [roberta-base-bne-finetuned-cyberbullying-spanish](https://huggingface.co/JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish)
## Training procedure
<details>
### 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: 4
</details>
### Model in action 🚀
Fast usage with **pipelines**:
```python
from transformers import pipeline
model_path = "JonatanGk/roberta-base-ca-finetuned-ciberbullying-catalan"
bullying_analysis = pipeline("text-classification", model=model_path, tokenizer=model_path)
bullying_analysis(
"Des que et vaig veure m'en vaig enamorar de tu."
)
# Output:
[{'label': 'Not_bullying', 'score': 0.9996786117553711}]
bullying_analysis(
"Ets tan lletja que et donaven de menjar per sota la porta."
)
# Output:
[{'label': 'Bullying', 'score': 0.9927878975868225}]
```
[](https://colab.research.google.com/github/JonatanGk/Shared-Colab/blob/master/Cyberbullying_detection_(CATALAN).ipynb)
### Framework versions
- Transformers 4.10.3
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
## Citation
```bibtex
@inproceedings{armengol-estape-etal-2021-multilingual,
title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
author = "Armengol-Estap{\'e}, Jordi and
Carrino, Casimiro Pio and
Rodriguez-Penagos, Carlos and
de Gibert Bonet, Ona and
Armentano-Oller, Carme and
Gonzalez-Agirre, Aitor and
Melero, Maite and
Villegas, Marta",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.437",
doi = "10.18653/v1/2021.findings-acl.437",
pages = "4933--4946",
}
```
> Special thx to [Manuel Romero/@mrm8488](https://huggingface.co/mrm8488) as my mentor & R.C.
> Created by [Jonatan Luna](https://JonatanGk.github.io) | [LinkedIn](https://www.linkedin.com/in/JonatanGk/)
| 3,193 |
Jungwoo/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: 0.541356878970505
---
<!-- 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: 0.7470
- Matthews Correlation: 0.5414
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5237 | 1.0 | 535 | 0.5327 | 0.4248 |
| 0.347 | 2.0 | 1070 | 0.5105 | 0.5239 |
| 0.2344 | 3.0 | 1605 | 0.6639 | 0.5224 |
| 0.1672 | 4.0 | 2140 | 0.7470 | 0.5414 |
| 0.1228 | 5.0 | 2675 | 0.8352 | 0.5377 |
### Framework versions
- Transformers 4.12.2
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
| 1,998 |
Maelstrom77/roberta-large-mrpc | null | Entry not found | 15 |
Maelstrom77/roberta-large-snli | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
MickyMike/0-GPT2SP-jirasoftware | [
"LABEL_0"
] | Entry not found | 15 |
Monsia/camembert-fr-covid-tweet-classification | [
"statistiques",
"divers",
"mesures",
"opinions",
"symptomes"
] | ---
language:
- fr
tags:
- classification
license: apache-2.0
metrics:
- accuracy
widget:
- text: "tchai on est morts. on va se faire vacciner et ils vont contrôler comme les marionnettes avec des fils. d'après les 'ont dit'..."
---
# camembert-fr-covid-tweet-classification
This model is a fine-tune checkpoint of [Yanzhu/bertweetfr-base](https://huggingface.co/Yanzhu/bertweetfr-base), fine-tuned on SST-2.
This model reaches an accuracy of 66.00% on the dev set.
In this dataset, given a tweet, the goal was to infer the underlying topic of the tweet by choosing from four topics classes:
- chiffres : this means, the tweet talk about statistics of covid.
- mesures : this means, the tweet talk about measures take by government of covid
- opinions : this means, the tweet talk about opinion of people like fake new.
- symptomes : this means, the tweet talk about symptoms or variant of covid.
- divers : or other
# Pipelining the Model
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("Monsia/camembert-fr-covid-tweet-classification")
model = AutoModelForSequenceClassification.from_pretrained("Monsia/camembert-fr-covid-tweet-classification")
nlp_topic_classif = transformers.pipeline('topics-classification', model = model, tokenizer = tokenizer)
nlp_topic_classif("tchai on est morts. on va se faire vacciner et ils vont contrôler comme les marionnettes avec des fils. d'après les '' ont dit ''...")
# Output: [{'label': 'opinions', 'score': 0.831]
```
| 1,562 |
Osiris/emotion_classifier | null | ### Introduction:
This model belongs to text-classification. You can determine the emotion behind a sentence.
### Label Explaination:
LABEL_0: Positive (have positive emotion)
LABEL_1: Negative (have negative emotion)
### Usage:
```python
>>> from transformers import pipeline
>>> ec = pipeline('text-classification', model='Osiris/emotion_classifier')
>>> ec("Hello, I'm a good model.")
```
### Accuracy:
We reach 83.82% for validation dataset, and 84.42% for test dataset. | 476 |
SetFit/distilbert-base-uncased__TREC-QC__all-train | [
"a group or organization of persons",
"abbreviation",
"an individual",
"animals",
"cities",
"colors",
"countries",
"currency names",
"dates",
"definition of something",
"description of a person",
"description of something",
"diseases and medicine",
"elements and substances",
"equivalent ... | Entry not found | 15 |
Yaia/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: 0.9255
- name: F1
type: f1
value: 0.9257196896784097
---
<!-- 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: 0.2086
- Accuracy: 0.9255
- F1: 0.9257
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8249 | 1.0 | 250 | 0.3042 | 0.9085 | 0.9068 |
| 0.2437 | 2.0 | 500 | 0.2086 | 0.9255 | 0.9257 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
| 1,801 |
aXhyra/demo_sentiment_42 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: demo_sentiment_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
type: f1
value: 0.7113620044371958
---
<!-- 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. -->
# demo_sentiment_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: 0.6332
- F1: 0.7114
## 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: 8.62486660723695e-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 0
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7592 | 1.0 | 713 | 0.6509 | 0.6834 |
| 0.6389 | 2.0 | 1426 | 0.6318 | 0.7011 |
| 0.5647 | 3.0 | 2139 | 0.6320 | 0.7041 |
| 0.5391 | 4.0 | 2852 | 0.6332 | 0.7114 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,764 |
abdelkader/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: 0.9215604730468001
---
<!-- 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: 0.2162
- Accuracy: 0.9215
- F1: 0.9216
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8007 | 1.0 | 250 | 0.3082 | 0.907 | 0.9045 |
| 0.2438 | 2.0 | 500 | 0.2162 | 0.9215 | 0.9216 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
| 1,807 |
boronbrown48/1_model_topic_classification_v2 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | Entry not found | 15 |
boronbrown48/1_topic_classification | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | Entry not found | 15 |
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