modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
tbrasil/classificador_de_atendimento_2_classes_v1.1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
textattack/xlnet-large-cased-MRPC | null | Entry not found | 15 |
vesteinn/XLMR-ENIS-finetuned-sst2 | [
"negative",
"positive"
] | ---
license: agpl-3.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: XLMR-ENIS-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9277522935779816
---
<!-- 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. -->
# XLMR-ENIS-finetuned-sst2
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3781
- Accuracy: 0.9278
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0675 | 1.0 | 4210 | 0.3781 | 0.9278 |
### Framework versions
- Transformers 4.11.0
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
| 1,579 |
mariagrandury/distilbert-base-uncased-finetuned-sms-spam-detection | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sms_spam
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sms-spam-detection
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sms_spam
type: sms_spam
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9921090387374462
---
<!-- 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-sms-spam-detection
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the sms_spam dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0426
- Accuracy: 0.9921
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0375 | 1.0 | 262 | 0.0549 | 0.9892 |
| 0.0205 | 2.0 | 524 | 0.0426 | 0.9921 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| 1,732 |
ekohrt/qcat | [
"arithmetic",
"average",
"boolean_and",
"boolean_or",
"boolean_retrieval",
"causal_explanation",
"correlation",
"counting",
"datetime_comparison",
"datetime_retrieval",
"definitional",
"mathematical_comparison",
"median",
"mode",
"numeric_comparison",
"numeric_retrieval",
"opinion",
... | ---
license: mit
---
# **Q-Cat**
A pre-trained Distilbert model for classifying question types. For use in QA systems.
Dataset contains ~800 labeled examples. Classifier uses a taxonomy of 27 question types. | 215 |
billfrench/autonlp-cyberlandr-ai-4-614417501 | [
"clear windows",
"close door",
"opaque windows",
"open door"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- billfrench/autonlp-data-cyberlandr-ai-4
co2_eq_emissions: 1.6912535041856878
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 614417501
- CO2 Emissions (in grams): 1.6912535041856878
## Validation Metrics
- Loss: 1.305419921875
- Accuracy: 0.5
- Macro F1: 0.3333333333333333
- Micro F1: 0.5
- Weighted F1: 0.4444444444444444
- Macro Precision: 0.375
- Micro Precision: 0.5
- Weighted Precision: 0.5
- Macro Recall: 0.375
- Micro Recall: 0.5
- Weighted Recall: 0.5
## 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/billfrench/autonlp-cyberlandr-ai-4-614417501
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("billfrench/autonlp-cyberlandr-ai-4-614417501", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("billfrench/autonlp-cyberlandr-ai-4-614417501", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,286 |
antho-data/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: 0.9235
- name: F1
type: f1
value: 0.9237367861627231
---
<!-- 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: 0.2294
- Accuracy: 0.9235
- F1: 0.9237
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8637 | 1.0 | 250 | 0.3319 | 0.9075 | 0.9050 |
| 0.2634 | 2.0 | 500 | 0.2294 | 0.9235 | 0.9237 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
| 1,807 |
farrokhguiahi/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: 0.9275
- name: F1
type: f1
value: 0.9278961513392271
---
<!-- 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: 0.2168
- Accuracy: 0.9275
- F1: 0.9279
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8418 | 1.0 | 250 | 0.3102 | 0.905 | 0.9012 |
| 0.2454 | 2.0 | 500 | 0.2168 | 0.9275 | 0.9279 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,807 |
negfir/distilbert-base-uncased-finetuned-cola | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: negfir/distilbert-base-uncased-finetuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# negfir/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [negfir/uncased_L-12_H-128_A-2](https://huggingface.co/negfir/uncased_L-12_H-128_A-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6077
- Validation Loss: 0.6185
- Train Matthews Correlation: 0.0
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2670, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.6116 | 0.6187 | 0.0 | 0 |
| 0.6070 | 0.6190 | 0.0 | 1 |
| 0.6077 | 0.6185 | 0.0 | 2 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,734 |
cambridgeltl/guardian_news_bert-base-uncased | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | Entry not found | 15 |
clapika2010/movies_predictions | null | Entry not found | 15 |
celine98/canine-c-finetuned-sst2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: canine-c-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8486238532110092
---
<!-- 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. -->
# canine-c-finetuned-sst2
This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6025
- Accuracy: 0.8486
## 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: 4.9121586874695155e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 8
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3415 | 1.0 | 2105 | 0.4196 | 0.8280 |
| 0.2265 | 2.0 | 4210 | 0.4924 | 0.8211 |
| 0.1439 | 3.0 | 6315 | 0.5726 | 0.8337 |
| 0.0974 | 4.0 | 8420 | 0.6025 | 0.8486 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,775 |
dangvantuan/CrossEncoder-camembert-large | [
"LABEL_0"
] | ---
pipeline_tag: sentence-similarity
language: fr
datasets:
- stsb_multi_mt
tags:
- Text
- Sentence Similarity
- Sentence-Embedding
- camembert-base
license: apache-2.0
model-index:
- name: sentence-camembert-base by Van Tuan DANG
results:
- task:
name: Sentence-Embedding
type: Text Similarity
dataset:
name: Text Similarity fr
type: stsb_multi_mt
args: fr
metrics:
- name: Test Pearson correlation coefficient
type: Pearson_correlation_coefficient
value: xx.xx
---
## Model
Cross-Encoder for sentence-similarity
This model was trained using [sentence-transformers](https://www.SBERT.net) Cross-Encoder class.
## Training Data
This model was trained on the [STS benchmark dataset](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('dangvantuan/CrossEncoder-camembert-large', max_length=128)
scores = model.predict([('Un avion est en train de décoller.', "Un homme joue d'une grande flûte."), ("Un homme étale du fromage râpé sur une pizza.", "Une personne jette un chat au plafond") ])
```
## Evaluation
The model can be evaluated as follows on the French test data of stsb.
```python
from sentence_transformers.readers import InputExample
from sentence_transformers.cross_encoder.evaluation import CECorrelationEvaluator
from datasets import load_dataset
def convert_dataset(dataset):
dataset_samples=[]
for df in dataset:
score = float(df['similarity_score'])/5.0 # Normalize score to range 0 ... 1
inp_example = InputExample(texts=[df['sentence1'],
df['sentence2']], label=score)
dataset_samples.append(inp_example)
return dataset_samples
# Loading the dataset for evaluation
df_dev = load_dataset("stsb_multi_mt", name="fr", split="dev")
df_test = load_dataset("stsb_multi_mt", name="fr", split="test")
# Convert the dataset for evaluation
# For Dev set:
dev_samples = convert_dataset(df_dev)
val_evaluator = CECorrelationEvaluator.from_input_examples(dev_samples, name='sts-dev')
val_evaluator(model, output_path="./")
# For Test set
test_samples = convert_dataset(df_test)
test_evaluator = CECorrelationEvaluator.from_input_examples(test_samples, name='sts-test')
test_evaluator(models, output_path="./")
```
**Test Result**:
The performance is measured using Pearson and Spearman correlation:
- On dev
| Model | Pearson correlation | Spearman correlation | #params |
| ------------- | ------------- | ------------- |------------- |
| [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 90.11 |90.01 | 336M |
- On test
| Model | Pearson correlation | Spearman correlation |
| ------------- | ------------- | ------------- |
| [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 88.16 | 87.57| | 3,288 |
KeithHorgan/TweetClimateAnalysis | [
"No claim",
"Global warming is not happening - Ice/permafrost/snow cover isn’t melting",
"Global warming is not happening - We’re heading into an ice age/global cooling",
"Global warming is not happening - Weather is cold/snowing",
"Global warming is not happening - Climate hasn’t warmed/changed over the la... | ---
tags: autotrain
language: unk
widget:
- text: "Climate Change is a hoax"
- text: "It is freezing, where is global warming"
datasets:
- KeithHorgan98/autotrain-data-TweetClimateAnalysis
co2_eq_emissions: 133.19491276284793
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 678720226
- CO2 Emissions (in grams): 133.19491276284793
## Validation Metrics
- Loss: 0.4864234924316406
- Accuracy: 0.865424430641822
- Macro F1: 0.7665472174344069
- Micro F1: 0.8654244306418221
- Weighted F1: 0.8586375445115083
- Macro Precision: 0.8281449061702826
- Micro Precision: 0.865424430641822
- Weighted Precision: 0.8619727477790186
- Macro Recall: 0.736576343905098
- Micro Recall: 0.865424430641822
- Weighted Recall: 0.865424430641822
## 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/KeithHorgan98/autotrain-TweetClimateAnalysis-678720226
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("KeithHorgan98/autotrain-TweetClimateAnalysis-678720226", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("KeithHorgan98/autotrain-TweetClimateAnalysis-678720226", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,508 |
princeton-nlp/CoFi-QNLI-s95 | null | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 95% sparsity on dataset QNLI. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model.
| 435 |
abdelrahmanzied/bert-fake-news-classifier | null | ---
license: mit
---
| 24 |
Sam4669/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.923
- name: F1
type: f1
value: 0.9232158277556175
---
<!-- 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: 0.2317
- Accuracy: 0.923
- F1: 0.9232
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8528 | 1.0 | 250 | 0.3332 | 0.897 | 0.8929 |
| 0.26 | 2.0 | 500 | 0.2317 | 0.923 | 0.9232 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,805 |
aprilzoo/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.923
- name: F1
type: f1
value: 0.9232474678171817
---
<!-- 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: 0.2202
- Accuracy: 0.923
- F1: 0.9232
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8244 | 1.0 | 250 | 0.3104 | 0.9025 | 0.8997 |
| 0.2478 | 2.0 | 500 | 0.2202 | 0.923 | 0.9232 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 1,804 |
JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | Entry not found | 15 |
ChrisZeng/bertweet-base-cased-covid19-hateval | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bertweet-base-cased-covid19-hateval
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bertweet-base-cased-covid19-hateval
This model is a fine-tuned version of [vinai/bertweet-covid19-base-cased](https://huggingface.co/vinai/bertweet-covid19-base-cased) on the HatEval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4817
- Accuracy: 0.773
- F1: 0.7722
## 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-06
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|
| 0.6925 | 0.99 | 70 | 0.573 | 0.3643 | 0.6827 |
| 0.6823 | 1.99 | 140 | 0.573 | 0.3643 | 0.6736 |
| 0.6713 | 2.99 | 210 | 0.587 | 0.3993 | 0.6568 |
| 0.6468 | 3.99 | 280 | 0.7 | 0.6708 | 0.6210 |
| 0.6047 | 4.99 | 350 | 0.732 | 0.7286 | 0.5785 |
| 0.5648 | 5.99 | 420 | 0.733 | 0.7319 | 0.5537 |
| 0.536 | 6.99 | 490 | 0.739 | 0.7381 | 0.5406 |
| 0.5175 | 7.99 | 560 | 0.744 | 0.7431 | 0.5308 |
| 0.5018 | 8.99 | 630 | 0.751 | 0.7504 | 0.5235 |
| 0.4874 | 9.99 | 700 | 0.749 | 0.7479 | 0.5145 |
| 0.4749 | 10.99 | 770 | 0.754 | 0.7533 | 0.5104 |
| 0.4666 | 11.99 | 840 | 0.761 | 0.7605 | 0.5052 |
| 0.456 | 12.99 | 910 | 0.761 | 0.7604 | 0.5017 |
| 0.4489 | 13.99 | 980 | 0.764 | 0.7635 | 0.4986 |
| 0.4375 | 14.99 | 1050 | 0.764 | 0.7625 | 0.4932 |
| 0.4319 | 15.99 | 1120 | 0.762 | 0.7608 | 0.4917 |
| 0.427 | 16.99 | 1190 | 0.77 | 0.7693 | 0.4918 |
| 0.4226 | 17.99 | 1260 | 0.772 | 0.7711 | 0.4889 |
| 0.4167 | 18.99 | 1330 | 0.769 | 0.7681 | 0.4874 |
| 0.4127 | 19.99 | 1400 | 0.768 | 0.7673 | 0.4868 |
| 0.4095 | 20.99 | 1470 | 0.774 | 0.7731 | 0.4836 |
| 0.4066 | 21.99 | 1540 | 0.77 | 0.7690 | 0.4829 |
| 0.405 | 22.99 | 1610 | 0.773 | 0.7721 | 0.4822 |
| 0.3993 | 23.99 | 1680 | 0.77 | 0.7692 | 0.4827 |
| 0.3977 | 24.99 | 1750 | 0.4831 | 0.772 | 0.7712 |
| 0.398 | 25.99 | 1820 | 0.4830 | 0.774 | 0.7733 |
| 0.3969 | 26.99 | 1890 | 0.4815 | 0.771 | 0.7701 |
| 0.3945 | 27.99 | 1960 | 0.4818 | 0.772 | 0.7712 |
| 0.3929 | 28.99 | 2030 | 0.4818 | 0.773 | 0.7722 |
| 0.3887 | 29.99 | 2100 | 0.4817 | 0.773 | 0.7722 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
| 3,543 |
btjiong/robbert-twitter-sentiment-tokenized | [
"NEGATIEF",
"NEUTRAAL",
"POSITIEF"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- dutch_social
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: robbert-twitter-sentiment-tokenized
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: dutch_social
type: dutch_social
args: dutch_social
metrics:
- name: Accuracy
type: accuracy
value: 0.814
- name: F1
type: f1
value: 0.8132800039281481
- name: Precision
type: precision
value: 0.8131073640029836
- name: Recall
type: recall
value: 0.814
---
<!-- 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. -->
# robbert-twitter-sentiment-tokenized
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5473
- Accuracy: 0.814
- F1: 0.8133
- Precision: 0.8131
- Recall: 0.814
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6895 | 1.0 | 282 | 0.6307 | 0.7433 | 0.7442 | 0.7500 | 0.7433 |
| 0.4948 | 2.0 | 564 | 0.5189 | 0.8053 | 0.8062 | 0.8081 | 0.8053 |
| 0.2642 | 3.0 | 846 | 0.5473 | 0.814 | 0.8133 | 0.8131 | 0.814 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6
| 2,192 |
SiriusRen/my-awesome-model | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: my-awesome-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-awesome-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
| 1,060 |
Toshifumi/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.927
- name: F1
type: f1
value: 0.9271941874206031
---
<!-- 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: 0.2106
- Accuracy: 0.927
- F1: 0.9272
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8009 | 1.0 | 250 | 0.2968 | 0.912 | 0.9102 |
| 0.24 | 2.0 | 500 | 0.2106 | 0.927 | 0.9272 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,805 |
SiriusRen/my-rubbish-model | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: 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. -->
# 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
| 1,060 |
Intel/albert-base-v2-sst2-int8-static | null | ---
language:
- en
license: apache-2.0
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- PostTrainingStatic
datasets:
- glue
metrics:
- accuracy
model_index:
- name: sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metric:
name: Accuracy
type: accuracy
value: 0.9254587155963303
---
# INT8 albert-base-v2-sst2
### Post-training static quantization
This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [Alireza1044/albert-base-v2-sst2](https://huggingface.co/Alireza1044/albert-base-v2-sst2).
The calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch size 8, so the real sampling size is 304.
The linear modules **albert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.module, albert.encoder.albert_layer_groups.0.albert_layers.0.ffn.module** fall back to fp32 to meet the 1% relative accuracy loss.
### Test result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-accuracy)** |0.9255|0.9232|
| **Model size (MB)** |25|44.6|
### Load with Intel® Neural Compressor:
```python
from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
'Intel/albert-base-v2-sst2-int8-static',
)
```
| 1,498 |
MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4 | null | ---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `w-m-vote-nonstrict-epoch-4`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'w-m-vote-nonstrict-epoch-4'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.996863842010498}, {'label': 'non-racist', 'score': 0.9982976317405701}]
```
For more details, see https://github.com/preyero/neatclass22
| 4,269 |
ShreyaR/finetuned-distil-bert-depression | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-distil-bert-depression
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-distil-bert-depression
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1695
- Accuracy: 0.9445
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0243 | 1.0 | 625 | 0.2303 | 0.9205 |
| 0.0341 | 2.0 | 1250 | 0.1541 | 0.933 |
| 0.0244 | 3.0 | 1875 | 0.1495 | 0.9445 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,495 |
Intel/camembert-base-mrpc-int8-dynamic | [
"0",
"1"
] | ---
language:
- en
license: mit
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- PostTrainingDynamic
datasets:
- glue
metrics:
- f1
model-index:
- name: camembert-base-mrpc-int8-dynamic
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: F1
type: f1
value: 0.8842832469775476
---
# INT8 camembert-base-mrpc
### Post-training dynamic quantization
This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [camembert-base-mrpc](https://huggingface.co/Intel/camembert-base-mrpc).
The linear module **roberta.encoder.layer.6.attention.self.query** falls back to fp32 to meet the 1% relative accuracy loss.
### Test result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.8843|0.8928|
| **Model size (MB)** |180|422|
### Load with Intel® Neural Compressor:
```python
from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
'Intel/camembert-base-mrpc-int8-dynamic',
)
```
| 1,220 |
PrasunMishra/finetuning-sentiment-model-3000-samples | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: finetuning-sentiment-model-3000-samples
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. -->
# 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.
## 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 1.9.1
- Datasets 2.1.0
- Tokenizers 0.11.6
| 1,095 |
praptishadmaan/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: 0.93192
- name: F1
type: f1
value: 0.9323583180987203
---
<!-- 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: 0.2345
- Accuracy: 0.9319
- F1: 0.9324
## 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 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,510 |
dmjimenezbravo/electricidad-small-discriminator-finetuned-usElectionTweets1Jul11Nov-spanish | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: electricidad-small-discriminator-finetuned-usElectionTweets1Jul11Nov-spanish
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. -->
# electricidad-small-discriminator-finetuned-usElectionTweets1Jul11Nov-spanish
This model is a fine-tuned version of [mrm8488/electricidad-small-discriminator](https://huggingface.co/mrm8488/electricidad-small-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3327
- Accuracy: 0.7642
- F1: 0.7642
## 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: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.88 | 1.0 | 1222 | 0.7491 | 0.6943 | 0.6943 |
| 0.7292 | 2.0 | 2444 | 0.6253 | 0.7544 | 0.7544 |
| 0.6346 | 3.0 | 3666 | 0.5292 | 0.7971 | 0.7971 |
| 0.565 | 4.0 | 4888 | 0.4831 | 0.8168 | 0.8168 |
| 0.4898 | 5.0 | 6110 | 0.4086 | 0.8532 | 0.8532 |
| 0.4375 | 6.0 | 7332 | 0.3411 | 0.8831 | 0.8831 |
| 0.3968 | 7.0 | 8554 | 0.2735 | 0.9100 | 0.9100 |
| 0.3321 | 8.0 | 9776 | 0.2343 | 0.9253 | 0.9253 |
| 0.3045 | 9.0 | 10998 | 0.1855 | 0.9450 | 0.9450 |
| 0.2837 | 10.0 | 12220 | 0.1539 | 0.9591 | 0.9591 |
| 0.2411 | 11.0 | 13442 | 0.1309 | 0.9650 | 0.9650 |
| 0.2203 | 12.0 | 14664 | 0.1100 | 0.9716 | 0.9716 |
| 0.1953 | 13.0 | 15886 | 0.1067 | 0.9760 | 0.9760 |
| 0.1836 | 14.0 | 17108 | 0.0755 | 0.9813 | 0.9813 |
| 0.1611 | 15.0 | 18330 | 0.0731 | 0.9829 | 0.9829 |
| 0.1479 | 16.0 | 19552 | 0.0746 | 0.9839 | 0.9839 |
| 0.138 | 17.0 | 20774 | 0.0516 | 0.9895 | 0.9895 |
| 0.129 | 18.0 | 21996 | 0.0481 | 0.9903 | 0.9903 |
| 0.1182 | 19.0 | 23218 | 0.0401 | 0.9926 | 0.9926 |
| 0.1065 | 20.0 | 24440 | 0.0488 | 0.9895 | 0.9895 |
| 0.096 | 21.0 | 25662 | 0.0333 | 0.9928 | 0.9928 |
| 0.0889 | 22.0 | 26884 | 0.0222 | 0.9951 | 0.9951 |
| 0.0743 | 23.0 | 28106 | 0.0236 | 0.9951 | 0.9951 |
| 0.0821 | 24.0 | 29328 | 0.0322 | 0.9931 | 0.9931 |
| 0.0866 | 25.0 | 30550 | 0.0135 | 0.9974 | 0.9974 |
| 0.0616 | 26.0 | 31772 | 0.0100 | 0.9980 | 0.9980 |
| 0.0641 | 27.0 | 32994 | 0.0112 | 0.9977 | 0.9977 |
| 0.0603 | 28.0 | 34216 | 0.0071 | 0.9987 | 0.9987 |
| 0.0491 | 29.0 | 35438 | 0.0088 | 0.9982 | 0.9982 |
| 0.0563 | 30.0 | 36660 | 0.0071 | 0.9982 | 0.9982 |
| 0.0467 | 31.0 | 37882 | 0.0045 | 0.9990 | 0.9990 |
| 0.0545 | 32.0 | 39104 | 0.0057 | 0.9987 | 0.9987 |
| 0.0519 | 33.0 | 40326 | 0.0048 | 0.9992 | 0.9992 |
| 0.0524 | 34.0 | 41548 | 0.0030 | 0.9995 | 0.9995 |
| 0.044 | 35.0 | 42770 | 0.0046 | 0.9990 | 0.9990 |
| 0.0442 | 36.0 | 43992 | 0.0029 | 0.9995 | 0.9995 |
| 0.0352 | 37.0 | 45214 | 0.0035 | 0.9995 | 0.9995 |
| 0.0348 | 38.0 | 46436 | 0.0029 | 0.9995 | 0.9995 |
| 0.0295 | 39.0 | 47658 | 0.0023 | 0.9995 | 0.9995 |
| 0.0289 | 40.0 | 48880 | 0.0035 | 0.9995 | 0.9995 |
| 0.0292 | 41.0 | 50102 | 0.0023 | 0.9995 | 0.9995 |
| 0.0259 | 42.0 | 51324 | 0.0027 | 0.9995 | 0.9995 |
| 0.0217 | 43.0 | 52546 | 0.0031 | 0.9995 | 0.9995 |
| 0.0278 | 44.0 | 53768 | 0.0018 | 0.9995 | 0.9995 |
| 0.0254 | 45.0 | 54990 | 0.0023 | 0.9995 | 0.9995 |
| 0.0164 | 46.0 | 56212 | 0.0016 | 0.9997 | 0.9997 |
| 0.0277 | 47.0 | 57434 | 0.0027 | 0.9997 | 0.9997 |
| 0.0158 | 48.0 | 58656 | 0.0029 | 0.9997 | 0.9997 |
| 0.0178 | 49.0 | 59878 | 0.0023 | 0.9997 | 0.9997 |
| 0.022 | 50.0 | 61100 | 0.0019 | 0.9997 | 0.9997 |
| 0.0167 | 51.0 | 62322 | 0.0018 | 0.9997 | 0.9997 |
| 0.0159 | 52.0 | 63544 | 0.0017 | 0.9997 | 0.9997 |
| 0.0105 | 53.0 | 64766 | 0.0016 | 0.9997 | 0.9997 |
| 0.0111 | 54.0 | 65988 | 0.0015 | 0.9997 | 0.9997 |
| 0.0139 | 55.0 | 67210 | 0.0021 | 0.9997 | 0.9997 |
| 0.0152 | 56.0 | 68432 | 0.0026 | 0.9997 | 0.9997 |
| 0.0191 | 57.0 | 69654 | 0.0022 | 0.9997 | 0.9997 |
| 0.0075 | 58.0 | 70876 | 0.0017 | 0.9997 | 0.9997 |
| 0.0141 | 59.0 | 72098 | 0.0016 | 0.9997 | 0.9997 |
| 0.0086 | 60.0 | 73320 | 0.0014 | 0.9997 | 0.9997 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 5,769 |
jsoutherland/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
metric:
name: F1
type: f1
value: 0.9327347950817506
model-index:
- name: jsoutherland/distilbert-base-uncased-finetuned-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.925
verified: true
- name: Precision Macro
type: precision
value: 0.8954208010579672
verified: true
- name: Precision Micro
type: precision
value: 0.925
verified: true
- name: Precision Weighted
type: precision
value: 0.9256567173431012
verified: true
- name: Recall Macro
type: recall
value: 0.8711059962680445
verified: true
- name: Recall Micro
type: recall
value: 0.925
verified: true
- name: Recall Weighted
type: recall
value: 0.925
verified: true
- name: F1 Macro
type: f1
value: 0.8794773714607985
verified: true
- name: F1 Micro
type: f1
value: 0.925
verified: true
- name: F1 Weighted
type: f1
value: 0.9244781949774824
verified: true
- name: loss
type: loss
value: 0.17752596735954285
verified: true
---
<!-- 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: 0.1649
- Accuracy: 0.9325
- F1: 0.9327
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.2838 | 0.9065 | 0.9036 |
| No log | 2.0 | 500 | 0.1795 | 0.9255 | 0.9255 |
| No log | 3.0 | 750 | 0.1649 | 0.9325 | 0.9327 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 2.1.0
- Tokenizers 0.10.3
| 3,073 |
cynthiachan/procedure_classification_distilbert | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_100",
"LABEL_101",
"LABEL_102",
"LABEL_103",
"LABEL_104",
"LABEL_105",
"LABEL_106",
"LABEL_107",
"LABEL_108",
"LABEL_109",
"LABEL_11",
"LABEL_110",
"LABEL_111",
"LABEL_112",
"LABEL_113",
"LABEL_114",
"LABEL_115",
"LABEL_116",
"LABEL_... | Entry not found | 15 |
nbroad/longformer-base-health-fact | [
"false",
"mixture",
"true",
"unproven"
] | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- health_fact
model-index:
- name: longformer-base-health-fact2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: health_fact
type: health_fact
split: test
metrics:
- name: F1
type: f1
value: 0.6732897445517078
- name: Accuracy
type: accuracy
value: 0.797242497972425
- name: False Accuracy
type: accuracy
value: 0.8092783505154639
- name: Mixture Accuracy
type: accuracy
value: 0.5323383084577115
- name: True Accuracy
type: accuracy
value: 0.9081803005008348
- name: Unproven Accuracy
type: accuracy
value: 0.4
---
# longformer-base-health-fact2
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the health_fact dataset.
It achieves the following results on the VALIDATION set:
- Loss: 0.5858
- Micro F1: 0.8122
- Macro F1: 0.6830
- False F1: 0.7941
- Mixture F1: 0.5015
- True F1: 0.9234
- Unproven F1: 0.5128
The following are the results on the TEST set:
- Macro F1: 0.6732897445517078
- Accuracy: 0.797242497972425
- False Accuracy: 0.8092783505154639
- Mixture Accuracy: 0.5323383084577115
- True Accuracy: 0.9081803005008348
- Unproven Accuracy: 0.4
## Model description
The health fact dataset is for building fact-checking models related to health. Here is how you can use this model:
```python
import torch
from transformers import pipeline
claim = "A mother revealed to her child in a letter after her death that she had just one eye because she had donated the other to him."
text = "In April 2005, we spotted a tearjerker on the Internet about a mother who gave up one of her eyes to a son who had lost one of his at an early age. By February 2007 the item was circulating in e-mail in the following shortened version: My mom only had one eye. I hated her… She was such an embarrassment. She cooked for students and teachers to support the family. There was this one day during elementary school where my mom came to say hello to me. I was so embarrassed. How could she do this to me? I ignored her, threw her a hateful look and ran out. The next day at school one of my classmates said, “EEEE, your mom only has one eye!” I wanted to bury myself. I also wanted my mom to just disappear. I confronted her that day and said, “If you’re only gonna make me a laughing stock, why don’t you just die?” My mom did not respond… I didn’t even stop to think for a second about what I had said, because I was full of anger. I was oblivious to her feelings. I wanted out of that house, and have nothing to do with her. So I studied real hard, got a chance to go abroad to study. Then, I got married. I bought a house of my own. I had kids of my own. I was happy with my life, my kids and the comforts. Then one day, my Mother came to visit me. She hadn’t seen me in years and she didn’t even meet her grandchildren. When she stood by the door, my children laughed at her, and I yelled at her for coming over uninvited. I screamed at her, “How dare you come to my house and scare my children! GET OUT OF HERE! NOW!! !” And to this, my mother quietly answered, “Oh, I’m so sorry. I may have gotten the wrong address,” and she disappeared out of sight. One day, a letter regarding a school reunion came to my house. So I lied to my wife that I was going on a business trip. After the reunion, I went to the old shack just out of curiosity. My neighbors said that she died. I did not shed a single tear. They handed me a letter that she had wanted me to have. My dearest son, I think of you all the time. I’m sorry that I came to your house and scared your children. I was so glad when I heard you were coming for the reunion. But I may not be able to even get out of bed to see you. I’m sorry that I was a constant embarrassment to you when you were growing up. You see……..when you were very little, you got into an accident, and lost your eye. As a mother, I couldn’t stand watching you having to grow up with one eye. So I gave you mine. I was so proud of my son who was seeing a whole new world for me, in my place, with that eye. With all my love to you, Your mother. In its earlier incarnation, the story identified by implication its location as Korea through statements made by both the mother and the son (the son’s “I left my mother and came to Seoul” and the mother’s “I won’t visit Seoul anymore”). It also supplied a reason for the son’s behavior when his mother arrived unexpectedly to visit him (“My little girl ran away, scared of my mom’s eye” and “I screamed at her, ‘How dare you come to my house and scare my daughter!'”). A further twist was provided in the original: rather than gaining the news of his mother’s death from neighbors (who hand him her letter), the son instead discovered the woman who bore him lying dead on the floor of what used to be his childhood home, her missive to him clutched in her lifeless hand: Give your parents roses while they are alive, not deadMY mom only had one eye. I hated her … she was such an embarrassment. My mom ran a small shop at a flea market. She collected little weeds and such to sell … anything for the money we needed she was such an embarrassment. There was this one day during elementary school … It was field day, and my mom came. I was so embarrassed. How could she do this to me? I threw her a hateful look and ran out. The next day at school … “your mom only has one eye?!? !” … And they taunted me. I wished that my mom would just disappear from this world so I said to my mom, “mom … Why don’t you have the other eye?! If you’re only going to make me a laughingstock, why don’t you just die?!! !” my mom did not respond … I guess I felt a little bad, but at the same time, it felt good to think that I had said what I’d wanted to say all this time… maybe it was because my mom hadn’t punished me, but I didn’t think that I had hurt her feelings very badly. That night… I woke up, and went to the kitchen to get a glass of water. My mom was crying there, so quietly, as if she was afraid that she might wake me. I took a look at her, and then turned away. Because of the thing I had said to her earlier, there was something pinching at me in the corner of my heart. Even so, I hated my mother who was crying out of her one eye. So I told myself that I would grow up and become successful. Because I hated my one-eyed mom and our desperate poverty… then I studied real hard. I left my mother and came to Seoul and studied, and got accepted in the Seoul University with all the confidence I had. Then, I got married. I bought a house of my own. Then I had kids, too… now I’m living happily as a successful man. I like it here because it’s a place that doesn’t remind me of my mom. This happiness was getting bigger and bigger, when… what?! Who’s this…it was my mother… still with her one eye. It felt as if the whole sky was falling apart on me. My little girl ran away, scared of my mom’s eye. And I asked her, “who are you? !” “I don’t know you!! !” as if trying to make that real. I screamed at her, “How dare you come to my house and scare my daughter!” “GET OUT OF HERE! NOW!! !” and to this, my mother quietly answered, “oh, I’m so sorry. I may have gotten the wrong address,” and she disappeared out of sight. Thank goodness… she doesn’t recognize me… I was quite relieved. I told myself that I wasn’t going to care, or think about this for the rest of my life. Then a wave of relief came upon me… One day, a letter regarding a school reunion came to my house. So, lying to my wife that I was going on a business trip, I went. After the reunion, I went down to the old shack, that I used to call a house… just out of curiosity there, I found my mother fallen on the cold ground. But I did not shed a single tear. She had a piece of paper in her hand…. it was a letter to me. My son… I think my life has been long enough now… And… I won’t visit Seoul anymore… but would it be too much to ask if I wanted you to come visit me once in a while? I miss you so much… and I was so glad when I heard you were coming for the reunion. But I decided not to go to the school. …for you… and I’m sorry that I only have one eye, and I was an embarrassment for you. You see, when you were very little, you got into an accident, and lost your eye. as a mom, I couldn’t stand watching you having to grow up with only one eye… so I gave you mine… I was so proud of my son that was seeing a whole new world for me, in my place, with that eye. I was never upset at you for anything you did… the couple times that you were angry with me, I thought to myself, ‘it’s because he loves me…’ my son. Oh, my son… I don’t want you to cry for me, because of my death. My son, I love you my son, I love you so much. With all modern medical technology, transplantation of the eyeball is still impossible. The optic nerve isn’t an ordinary nerve, but instead an inset running from the brain. Modern medicine isn’t able to “connect” an eyeball back to brain after an optic nerve has been severed, let alone transplant the eye from a different person. (The only exception is the cornea, the transparent part in front of the eye: corneas are transplanted to replace injured and opaque ones.) We won’t try to comment on whether any surgeon would accept an eye from a living donor for transplant into another — we’ll leave that to others who are far more knowledgeable about medical ethics and transplant procedures. But we will note that the plot device of a mother’s dramatic sacrifice for the sake of her child’s being revealed in a written communication delivered after her demise appears in another legend about maternal love: the 2008 tale about a woman who left a touching message on her cell phone even as life ebbed from her as she used her body to shield the tot during an earthquake. Giving up one’s own life for a loved one is central to a 2005 urban legend about a boy on a motorcycle who has his girlfriend hug him one last time and put on his helmet just before the crash that kills him and spares her. Returning to the “notes from the dead” theme is the 1995 story about a son who discovers only through a posthumous letter from his mother what their occasional dinner “dates” had meant to her. Another legend we’re familiar with features a meme used in the one-eyed mother story (the coming to light of the enduring love of the person who died for the completely unworthy person she’d lavished it on), but that one involves a terminally ill woman and her cheating husband. In it, an about-to-be-spurned wife begs the adulterous hoon she’d married to stick around for another 30 days and to carry her over the threshold of their home once every day of that month as her way of keeping him around long enough for her to kick the bucket and thus spare their son the knowledge that his parents were on the verge of divorce."
label = "false"
device = 0 if torch.cuda.is_available() else -1
pl = pipeline("text-classification", model="nbroad/longformer-base-health-fact", device=device)
input_text = claim+pl.tokenizer.sep_token+text
print(len(pl.tokenizer(input_text).input_ids))
# 2361 (which is why longformer is useful)
pl(input_text)
# [{'label': 'false', 'score': 0.8015491962432861}]
```
## 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: 32
- seed: 18
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Micro F1 | Macro F1 | False F1 | Mixture F1 | True F1 | Unproven F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:----------:|:-------:|:-----------:|
| 0.555 | 1.0 | 613 | 0.5243 | 0.7842 | 0.5535 | 0.7698 | 0.4170 | 0.8938 | 0.1333 |
| 0.4282 | 2.0 | 1226 | 0.5008 | 0.8031 | 0.6393 | 0.7829 | 0.4605 | 0.9199 | 0.3939 |
| 0.2897 | 3.0 | 1839 | 0.5858 | 0.8122 | 0.6830 | 0.7941 | 0.5015 | 0.9234 | 0.5128 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.1.1.dev0
- Tokenizers 0.12.1
| 12,597 |
alla1101/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.924
- name: F1
type: f1
value: 0.9240869504197766
---
<!-- 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: 0.2236
- Accuracy: 0.924
- F1: 0.9241
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3293 | 0.901 | 0.8979 |
| No log | 2.0 | 500 | 0.2236 | 0.924 | 0.9241 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 1,804 |
Truefilter/bertweet_lg_text_quality | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
peter2000/distilbert-base-uncased-finetuned-osdg | [
"sdg_1",
"sdg_10",
"sdg_11",
"sdg_12",
"sdg_13",
"sdg_14",
"sdg_15",
"sdg_2",
"sdg_3",
"sdg_4",
"sdg_5",
"sdg_6",
"sdg_7",
"sdg_8",
"sdg_9"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-osdg
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-osdg
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8193
- F1 Score: 0.7962
- Accuracy: 0.8434
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.3769 | 1.0 | 1017 | 0.8258 | 0.7729 | 0.8257 |
| 0.2759 | 2.0 | 2034 | 0.8364 | 0.7773 | 0.8262 |
| 0.1412 | 3.0 | 3051 | 1.0203 | 0.7833 | 0.8379 |
| 0.1423 | 4.0 | 4068 | 1.1603 | 0.7683 | 0.8224 |
| 0.0939 | 5.0 | 5085 | 1.3029 | 0.7843 | 0.8329 |
| 0.0757 | 6.0 | 6102 | 1.3562 | 0.7931 | 0.8379 |
| 0.0801 | 7.0 | 7119 | 1.2925 | 0.7840 | 0.8395 |
| 0.0311 | 8.0 | 8136 | 1.4632 | 0.7750 | 0.8318 |
| 0.0263 | 9.0 | 9153 | 1.5760 | 0.7843 | 0.8312 |
| 0.0196 | 10.0 | 10170 | 1.5689 | 0.7890 | 0.8417 |
| 0.0313 | 11.0 | 11187 | 1.6034 | 0.7909 | 0.8417 |
| 0.0007 | 12.0 | 12204 | 1.6725 | 0.7889 | 0.8406 |
| 0.0081 | 13.0 | 13221 | 1.6463 | 0.7911 | 0.8395 |
| 0.0061 | 14.0 | 14238 | 1.7730 | 0.7861 | 0.8345 |
| 0.003 | 15.0 | 15255 | 1.8001 | 0.7847 | 0.8379 |
| 0.0002 | 16.0 | 16272 | 1.7328 | 0.7912 | 0.8434 |
| 0.0 | 17.0 | 17289 | 1.7914 | 0.8011 | 0.8489 |
| 0.0009 | 18.0 | 18306 | 1.7772 | 0.7958 | 0.8456 |
| 0.0 | 19.0 | 19323 | 1.8028 | 0.7958 | 0.8434 |
| 0.0 | 20.0 | 20340 | 1.8193 | 0.7962 | 0.8434 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 2,842 |
Jeevesh8/6ep_bert_ft_cola-76 | null | Entry not found | 15 |
Jeevesh8/6ep_bert_ft_cola-78 | null | Entry not found | 15 |
miyagawaorj/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: 0.9425
- name: F1
type: f1
value: 0.9422011075095515
---
<!-- 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: 0.2285
- Accuracy: 0.9425
- F1: 0.9422
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.4656 | 1.0 | 8000 | 0.2912 | 0.9365 | 0.9362 |
| 0.2046 | 2.0 | 16000 | 0.2285 | 0.9425 | 0.9422 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,803 |
elvaklose/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: 0.8786885245901639
---
<!-- 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: 0.2896
- Accuracy: 0.8767
- F1: 0.8787
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
| 1,521 |
nqcccccc/phobert-social-media-text-classify | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7"
] | Entry not found | 15 |
connectivity/bert_ft_qqp-4 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-98 | null | Entry not found | 15 |
wonscha/my-awesome-model | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- yelp_review_full
metrics:
- accuracy
model-index:
- name: my-awesome-model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: yelp_review_full
type: yelp_review_full
args: yelp_review_full
metrics:
- name: Accuracy
type: accuracy
value: 0.559
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-awesome-model
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5680
- Accuracy: 0.559
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 125 | 1.1345 | 0.523 |
| No log | 2.0 | 250 | 1.5381 | 0.539 |
| No log | 3.0 | 375 | 1.5680 | 0.559 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.10.3
| 1,729 |
sbenel/emotion-distilbert | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
language: en
tags:
- text-classification
- pytorch
- emotion
metrics:
- accuracy, F1 score
dataset:
- emotion
---
## Training Parameters
```
learning rate: 2e-5
epochs: 40
weight decay: 0.01
batch size: 16
```
## Metrics
```
acuraccy: 0.93
macro-F1 (macro avg): 0.88
best epoch: 15
```
## Dataset:
[Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion).
| 409 |
GioReg/bertMULTINEGsentiment | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bertMULTINEGsentiment
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. -->
# bertMULTINEGsentiment
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,063 |
M47Labs/spanish_news_classification_headlines_untrained | [
"ciencia_tecnologia",
"clickbait",
"cultura",
"deportes",
"economia",
"educacion",
"medio_ambiente",
"opinion",
"politica",
"sociedad"
] | ---
widget:
- text: "El dólar se dispara tras la reunión de la Fed"
---
# Spanish News Classification Headlines
SNCH: this model was developed by [M47Labs](https://www.m47labs.com/es/) the goal is text classification, the base model use was [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased), however this model has not been fine-tuned on any dataset. The objective is to show the performance of this model when is used with the objective of inference without training at all.
## Dataset validation Sample
Dataset size : 1000
Columns: idTask,task content 1,idTag,tag.
|task content|tag|
|------|------|
|Alcalá de Guadaíra celebra la IV Semana de la Diversidad Sexual con acciones de sensibilización|sociedad|
|El Archipiélago Chinijo Graciplus se impone en el Trofeo Centro Comercial Rubicón|deportes|
|Un total de 39 personas padecen ELA actualmente en la provincia|sociedad|
|Eurocopa 2021 : Italia vence a Gales y pasa a octavos con su candidatura reforzada|deportes|
|Resolución de 10 de junio de 2021, del Ayuntamiento de Tarazona de La Mancha (Albacete), referente a la convocatoria para proveer una plaza.|sociedad|
|El primer ministro sueco pierde una moción de censura|politica|
|El dólar se dispara tras la reunión de la Fed|economia|
## Labels:
* ciencia_tecnologia
* clickbait
* cultura
* deportes
* economia
* educacion
* medio_ambiente
* opinion
* politica
* sociedad
## Example of Use
### Pipeline
```{python}
import torch
from transformers import AutoTokenizer, BertForSequenceClassification,TextClassificationPipeline
review_text = 'los vehiculos que esten esperando pasajaeros deberan estar apagados para reducir emisiones'
path = "M47Labs/spanish_news_classification_headlines_untrained"
tokenizer = AutoTokenizer.from_pretrained(path)
model = BertForSequenceClassification.from_pretrained(path)
nlp = TextClassificationPipeline(task = "text-classification",
model = model,
tokenizer = tokenizer)
print(nlp(review_text))
```
```[{'label': 'medio_ambiente', 'score': 0.2834321384291023}]```
### Pytorch
```{python}
import torch
from transformers import AutoTokenizer, BertForSequenceClassification,TextClassificationPipeline
from numpy import np
model_name = 'M47Labs/spanish_news_classification_headlines_untrained'
MAX_LEN = 32
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
texto = "las emisiones estan bajando, debido a las medidas ambientales tomadas por el gobierno"
encoded_review = tokenizer.encode_plus(
texto,
max_length=MAX_LEN,
add_special_tokens=True,
#return_token_type_ids=False,
pad_to_max_length=True,
return_attention_mask=True,
return_tensors='pt',
)
input_ids = encoded_review['input_ids']
attention_mask = encoded_review['attention_mask']
output = model(input_ids, attention_mask)
_, prediction = torch.max(output['logits'], dim=1)
print(f'Review text: {texto}')
print(f'Sentiment : {model.config.id2label[prediction.detach().cpu().numpy()[0]]}')
```
```Review text: las emisiones estan bajando, debido a las medidas ambientales tomadas por el gobierno```
```Sentiment : opinion```
A more in depth example on how to use the model can be found in this colab notebook: https://colab.research.google.com/drive/1XsKea6oMyEckye2FePW_XN7Rf8v41Cw_?usp=sharing
## Validation Results
|Full Dataset||
|------|------|
|Accuracy Score|0.362|
|Precision (Macro)|0.21|
|Recall (Macro)|0.22|
| 3,731 |
daniel780/finetuning-sentiment-model-3000-samples | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_polarity
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_polarity
type: amazon_polarity
args: amazon_polarity
metrics:
- name: Accuracy
type: accuracy
value: 0.8066666666666666
- name: F1
type: f1
value: 0.8079470198675497
---
<!-- 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 amazon_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4356
- Accuracy: 0.8067
- F1: 0.8079
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,570 |
RANG012/SENATOR-Scaled | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: SENATOR-Scaled
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.89
- name: F1
type: f1
value: 0.8897795591182365
---
<!-- 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. -->
# SENATOR-Scaled
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: 0.2670
- Accuracy: 0.89
- F1: 0.8898
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,455 |
cjbarrie/masress-medcrit-camel | [
"critical",
"neutral",
"uncritical"
] | ---
tags: autotrain
language: unk
widget:
- text: "الكل ينتقد الرئيس على إخفاقاته"
datasets:
- cjbarrie/autotrain-data-masress-medcrit-binary-5
co2_eq_emissions: 0.01017487638098474
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 937130980
- CO2 Emissions (in grams): 0.01017487638098474
## Validation Metrics
- Loss: 0.757265031337738
- Accuracy: 0.7551020408163265
- Macro F1: 0.7202470830473576
- Micro F1: 0.7551020408163265
- Weighted F1: 0.7594301962377263
- Macro Precision: 0.718716577540107
- Micro Precision: 0.7551020408163265
- Weighted Precision: 0.7711448215649895
- Macro Recall: 0.7285714285714286
- Micro Recall: 0.7551020408163265
- Weighted Recall: 0.7551020408163265
## 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/cjbarrie/autotrain-masress-medcrit-binary-5-937130980
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("cjbarrie/autotrain-masress-medcrit-binary-5-937130980", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("cjbarrie/autotrain-masress-medcrit-binary-5-937130980", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,465 |
Jeevesh8/lecun_feather_berts-66 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-55 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-56 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-63 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-9 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-13 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/lecun_feather_berts-19 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
clhuang/albert-sentiment | null | ---
language:
- tw
tags:
- albert
- classification
license: afl-3.0
metrics:
- Accuracy
---
# 繁體中文情緒分類: 負面(0)、正面(1)
依據ckiplab/albert預訓練模型微調，訓練資料集只有8萬筆，做為課程的範例模型。
# 使用範例:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("clhuang/albert-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("clhuang/albert-sentiment")
## Pediction
target_names=['Negative','Positive']
max_length = 200 # 最多字數 若超出模型訓練時的字數，以模型最大字數為依據
def get_sentiment_proba(text):
# prepare our text into tokenized sequence
inputs = tokenizer(text, padding=True, truncation=True, max_length=max_length, return_tensors="pt")
# perform inference to our model
outputs = model(**inputs)
# get output probabilities by doing softmax
probs = outputs[0].softmax(1)
response = {'Negative': round(float(probs[0, 0]), 2), 'Positive': round(float(probs[0, 1]), 2)}
# executing argmax function to get the candidate label
#return probs.argmax()
return response
get_sentiment_proba('我喜歡這本書')
get_sentiment_proba('不喜歡這款產品') | 1,197 |
QuentinKemperino/ECHR_test_2 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- lex_glue
model-index:
- name: ECHR_test_2
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. -->
# ECHR_test_2 Task A
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the lex_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1998
- Macro-f1: 0.5295
- Micro-f1: 0.6157
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro-f1 | Micro-f1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.2142 | 0.44 | 500 | 0.2887 | 0.2391 | 0.4263 |
| 0.172 | 0.89 | 1000 | 0.2672 | 0.2908 | 0.4628 |
| 0.1737 | 1.33 | 1500 | 0.2612 | 0.3657 | 0.5102 |
| 0.1581 | 1.78 | 2000 | 0.2412 | 0.3958 | 0.5468 |
| 0.1509 | 2.22 | 2500 | 0.2264 | 0.3950 | 0.5552 |
| 0.1606 | 2.67 | 3000 | 0.2342 | 0.4006 | 0.5511 |
| 0.1491 | 3.11 | 3500 | 0.2176 | 0.4558 | 0.5622 |
| 0.1392 | 3.56 | 4000 | 0.2454 | 0.4128 | 0.5596 |
| 0.15 | 4.0 | 4500 | 0.2113 | 0.4684 | 0.5874 |
| 0.1461 | 4.44 | 5000 | 0.2179 | 0.4631 | 0.5815 |
| 0.1457 | 4.89 | 5500 | 0.2151 | 0.4805 | 0.5949 |
| 0.1443 | 5.33 | 6000 | 0.2155 | 0.5123 | 0.5917 |
| 0.1279 | 5.78 | 6500 | 0.2131 | 0.4915 | 0.5998 |
| 0.1377 | 6.22 | 7000 | 0.2244 | 0.4705 | 0.5944 |
| 0.1242 | 6.67 | 7500 | 0.2150 | 0.5089 | 0.5918 |
| 0.1222 | 7.11 | 8000 | 0.2045 | 0.4801 | 0.5981 |
| 0.1372 | 7.56 | 8500 | 0.2074 | 0.5317 | 0.5962 |
| 0.1289 | 8.0 | 9000 | 0.2035 | 0.5323 | 0.6126 |
| 0.1295 | 8.44 | 9500 | 0.2058 | 0.5213 | 0.6073 |
| 0.123 | 8.89 | 10000 | 0.2027 | 0.5486 | 0.6135 |
| 0.1335 | 9.33 | 10500 | 0.1984 | 0.5442 | 0.6249 |
| 0.1258 | 9.78 | 11000 | 0.1998 | 0.5295 | 0.6157 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 3,003 |
kjunelee/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: 0.931
- name: F1
type: f1
value: 0.9313235272564213
---
<!-- 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: 0.1595
- Accuracy: 0.931
- F1: 0.9313
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 125 | 0.1873 | 0.924 | 0.9234 |
| 0.1992 | 2.0 | 250 | 0.1649 | 0.929 | 0.9293 |
| 0.1992 | 3.0 | 375 | 0.1595 | 0.931 | 0.9313 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
| 1,876 |
Jeevesh8/std_pnt_04_feather_berts-39 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/std_pnt_04_feather_berts-38 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
binay1999/distilbert-cybertexts-text-classification | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-cybertexts-text-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-cybertexts-text-classification
This model is a fine-tuned version of [binay1999/distilbert-cybertexts-preprocessed](https://huggingface.co/binay1999/distilbert-cybertexts-preprocessed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1104
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1374 | 1.0 | 1000 | 0.1215 |
| 0.0769 | 2.0 | 2000 | 0.0959 |
| 0.039 | 3.0 | 3000 | 0.1104 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,460 |
Hardeep/distilbert-base-uncased-finetuned-emotion-01 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | Entry not found | 15 |
dibsondivya/distilbert-phmtweets-sutd | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
tags:
- distilbert
- health
- tweet
datasets:
- custom-phm-tweets
metrics:
- accuracy
model-index:
- name: distilbert-phmtweets-sutd
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: custom-phm-tweets
type: labelled
metrics:
- name: Accuracy
type: accuracy
value: 0.877
---
# distilbert-phmtweets-sutd
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) for text classification to identify public health events through tweets. The project was based on an [Emory University Study on Detection of Personal Health Mentions in Social Media paper](https://arxiv.org/pdf/1802.09130v2.pdf), that worked with this [custom dataset](https://github.com/emory-irlab/PHM2017).
It achieves the following results on the evaluation set:
- Accuracy: 0.877
## Usage
```Python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dibsondivya/distilbert-phmtweets-sutd")
model = AutoModelForSequenceClassification.from_pretrained("dibsondivya/distilbert-phmtweets-sutd")
```
### Model Evaluation Results
With Validation Set
- Accuracy: 0.8708661417322835
With Test Set
- Accuracy: 0.8772961058045555
# Reference for distilbert-base-uncased Model
```bibtex
@article{Sanh2019DistilBERTAD,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
journal={ArXiv},
year={2019},
volume={abs/1910.01108}
}
```
| 1,625 |
SISLab/amc-opt-msmd | null | ---
tags:
- text-classification
- sentiment-analysis
language:
- "it"
--- | 83 |
projecte-aina/roberta-base-ca-v2-cased-tc | [
"Medi ambient",
"Societat",
"Policial",
"Judicial",
"Empresa",
"Partits",
"Política",
"Successos",
"Salut",
"Infraestructures",
"Parlament",
"Música",
"Govern",
"Unió Europea",
"Economia",
"Mobilitat",
"Treball",
"Cultura",
"Educació"
] | ---
language:
- ca
tags:
- "catalan"
- "text classification"
- "tecla"
- "CaText"
- "Catalan Textual Corpus"
datasets:
- "projecte-aina/tecla"
metrics:
- accuracy
model-index:
- name: roberta-base-ca-v2-cased-tc
results:
- task:
type: text-classification
dataset:
name: TeCla
type: projecte-aina/tecla
metrics:
- name: Accuracy
type: accuracy
value: 0.7426
widget:
- text: "Els Pets presenten el seu nou treball al Palau Sant Jordi."
- text: "Els barcelonins incrementen un 23% l’ús del cotxe des de l’inici de la pandèmia."
- text: "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya."
- text: "Majors de 60 anys i sanitaris començaran a rebre la tercera dosi de la vacuna covid els propers dies."
- text: "Els cinemes Verdi estrenen Verdi Classics, un nou canal de televisió."
---
# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Text Classification.
## Table of Contents
- [Model Description](#model-description)
- [Intended Uses and Limitations](#intended-uses-and-limitations)
- [How to Use](#how-to-use)
- [Training](#training)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Evaluation](#evaluation)
- [Variable and Metrics](#variable-and-metrics)
- [Evaluation Results](#evaluation-results)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Funding](#funding)
- [Contributions](#contributions)
## Model description
The **roberta-base-ca-v2-cased-tc** is a Text Classification (TC) model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details).
## Intended Uses and Limitations
**roberta-base-ca-v2-cased-tc** model can be used to classify texts. The model is limited by its training dataset and may not generalize well for all use cases.
## How to Use
Here is how to use this model:
```python
from transformers import pipeline
from pprint import pprint
nlp = pipeline("text-classification", model="projecte-aina/roberta-base-ca-v2-cased-tc")
example = "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya."
tc_results = nlp(example)
pprint(tc_results)
```
## Training
### Training data
We used the TC dataset in Catalan called [TeCla](https://huggingface.co/datasets/projecte-aina/tecla) for training and evaluation.
### Training Procedure
The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set.
## Evaluation
### Variable and Metrics
This model was finetuned maximizing accuracy.
## Evaluation results
We evaluated the _roberta-base-ca-v2-cased-tc_ on the TeCla test set against standard multilingual and monolingual baselines:
| Model | TeCla (Accuracy) |
| ------------|:-------------|
| roberta-base-ca-v2-cased-tc | **74.26** |
| roberta-base-ca-cased-tc | 73.65 |
| mBERT | 69.90 |
| XLM-RoBERTa | 70.14 |
For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club).
## Licensing Information
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
## Citation Information
If you use any of these resources (datasets or models) in your work, please cite our latest paper:
```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",
}
```
### Funding
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
## Contributions
[N/A] | 4,896 |
emen/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: 0.9295
- name: F1
type: f1
value: 0.9297561758557029
---
<!-- 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: 0.2181
- Accuracy: 0.9295
- F1: 0.9298
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8495 | 1.0 | 250 | 0.3141 | 0.9085 | 0.9060 |
| 0.2511 | 2.0 | 500 | 0.2181 | 0.9295 | 0.9298 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,806 |
Luojike/autotrain-test-4-macbert-1071837613 | [
"0",
"1"
] | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Luojike/autotrain-data-test-4-macbert
co2_eq_emissions: 0.012225117907336358
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1071837613
- CO2 Emissions (in grams): 0.012225117907336358
## Validation Metrics
- Loss: 0.533202052116394
- Accuracy: 0.7408088235294118
- Precision: 0.5072463768115942
- Recall: 0.4088785046728972
- AUC: 0.710585043624057
- F1: 0.4527813712807245
## 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/Luojike/autotrain-test-4-macbert-1071837613
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Luojike/autotrain-test-4-macbert-1071837613", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Luojike/autotrain-test-4-macbert-1071837613", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,200 |
RobertIrv938/rewardModel60kEpoch3Balanced | null | Entry not found | 15 |
agarwalchaitanya/muril-unified-ei-infotabs-btnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
---
| 28 |
juliensimon/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: 0.5334876461854267
---
<!-- 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: 0.7737
- Matthews Correlation: 0.5335
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5225 | 1.0 | 535 | 0.5170 | 0.4007 |
| 0.3509 | 2.0 | 1070 | 0.5220 | 0.4837 |
| 0.2405 | 3.0 | 1605 | 0.6164 | 0.5186 |
| 0.1777 | 4.0 | 2140 | 0.7737 | 0.5335 |
| 0.1295 | 5.0 | 2675 | 0.8374 | 0.5162 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,999 |
morenolq/thext-cs-scibert | [
"LABEL_0"
] | ---
language: "en"
tags:
- bert
- regression
- pytorch
pipeline:
- text-classification
widget:
- text: "We propose a new approach, based on Transformer-based encoding, to highlight extraction. To the best of our knowledge, this is the first attempt to use transformer architectures to address automatic highlight generation. [SEP] Highlights are short sentences used to annotate scientific papers. They complement the abstract content by conveying the main result findings. To automate the process of paper annotation, highlights extraction aims at extracting from 3 to 5 paper sentences via supervised learning. Existing approaches rely on ad hoc linguistic features, which depend on the analyzed context, and apply recurrent neural networks, which are not effective in learning long-range text dependencies. This paper leverages the attention mechanism adopted in transformer models to improve the accuracy of sentence relevance estimation. Unlike existing approaches, it relies on the end-to-end training of a deep regression model. To attend patterns relevant to highlights content it also enriches sentence encodings with a section-level contextualization. The experimental results, achieved on three different benchmark datasets, show that the designed architecture is able to achieve significant performance improvements compared to the state-of-the-art."
- text: "We design a context-aware sentence-level regressor, in which the semantic similarity between candidate sentences and highlights is estimated by also attending the contextual knowledge provided by the other paper sections. [SEP] Highlights are short sentences used to annotate scientific papers. They complement the abstract content by conveying the main result findings. To automate the process of paper annotation, highlights extraction aims at extracting from 3 to 5 paper sentences via supervised learning. Existing approaches rely on ad hoc linguistic features, which depend on the analyzed context, and apply recurrent neural networks, which are not effective in learning long-range text dependencies. This paper leverages the attention mechanism adopted in transformer models to improve the accuracy of sentence relevance estimation. Unlike existing approaches, it relies on the end-to-end training of a deep regression model. To attend patterns relevant to highlights content it also enriches sentence encodings with a section-level contextualization. The experimental results, achieved on three different benchmark datasets, show that the designed architecture is able to achieve significant performance improvements compared to the state-of-the-art."
- text: "Fig. 2, Fig. 3, Fig. 4 show the effect of varying the number K of selected highlights on the extraction performance. As expected, recall values increase while increasing the number of selected highlights, whereas precision values show an opposite trend. [SEP] Highlights are short sentences used to annotate scientific papers. They complement the abstract content by conveying the main result findings. To automate the process of paper annotation, highlights extraction aims at extracting from 3 to 5 paper sentences via supervised learning. Existing approaches rely on ad hoc linguistic features, which depend on the analyzed context, and apply recurrent neural networks, which are not effective in learning long-range text dependencies. This paper leverages the attention mechanism adopted in transformer models to improve the accuracy of sentence relevance estimation. Unlike existing approaches, it relies on the end-to-end training of a deep regression model. To attend patterns relevant to highlights content it also enriches sentence encodings with a section-level contextualization. The experimental results, achieved on three different benchmark datasets, show that the designed architecture is able to achieve significant performance improvements compared to the state-of-the-art."
---
# General Information
This model is trained on journal publications of belonging to the domain: **Computer Science**.
This is an `allenai/scibert_scivocab_cased` model trained in the scientific domain. The model is trained with regression objective to estimate the relevance of a sentence according to the provided context (e.g., the abstract of the scientific paper).
The model is used in the paper 'Transformer-based highlights extraction from scientific papers' published in Knowledge-Based Systems scientific journal.
The model is able to achieve state-of-the-art performance in the task of highlights extraction from scientific papers.
Access to the full paper: [here](https://doi.org/10.1016/j.knosys.2022.109382).
# Usage:
For detailed usage please use the official repository https://github.com/MorenoLaQuatra/THExt .
# References:
If you find it useful, please cite the following paper:
```bibtex
@article{thext,
title={Transformer-based highlights extraction from scientific papers},
author={La Quatra, Moreno and Cagliero, Luca},
journal={Knowledge-Based Systems},
pages={109382},
year={2022},
publisher={Elsevier}
}
``` | 5,090 |
naem1023/roberta-phrase-clause-classification | null | ---
license: apache-2.0
---
| 28 |
naem1023/electra-phrase-clause-classification-dev | null | ---
license: apache-2.0
---
| 28 |
CenIA/albert-large-spanish-finetuned-pawsx | null | Entry not found | 15 |
EhsanAghazadeh/xlm-roberta-base-lcc-en-fa-2e-5-42 | null | Entry not found | 15 |
LysandreJik/test-upload1 | null | Entry not found | 15 |
JuliusAlphonso/dear-jarvis-v5 | [
"anger",
"anticipation",
"disgust",
"fear",
"joy",
"neutral",
"sadness",
"surprise",
"trust"
] | ---
license: apache-2.0
datasets:
- null
model_index:
- name: dear-jarvis-v5
results:
- task:
name: Text Classification
type: text-classification
---
<!-- 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. -->
# dear-jarvis-v5
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3148
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 470 | 0.3106 |
| 0.3452 | 2.0 | 940 | 0.3064 |
| 0.2692 | 3.0 | 1410 | 0.3148 |
### Framework versions
- Transformers 4.7.0
- Pytorch 1.9.0+cu102
- Datasets 1.8.0
- Tokenizers 0.10.3
| 1,412 |
M-FAC/bert-mini-finetuned-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | # BERT-mini model finetuned with M-FAC
This model is finetuned on MNLI dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC.
Hyperparameters used by M-FAC optimizer:
```bash
learning rate = 1e-4
number of gradients = 1024
dampening = 1e-6
```
## Results
We share the best model out of 5 runs with the following score on MNLI validation set:
```bash
matched_accuracy = 75.13
mismatched_accuracy = 75.93
```
Mean and standard deviation for 5 runs on MNLI validation set:
| | Matched Accuracy | Mismatched Accuracy |
|:-----:|:----------------:|:-------------------:|
| Adam | 73.30 ± 0.20 | 74.85 ± 0.09 |
| M-FAC | 74.59 ± 0.41 | 75.95 ± 0.14 |
Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script:
```bash
CUDA_VISIBLE_DEVICES=0 python run_glue.py \
--seed 8276 \
--model_name_or_path prajjwal1/bert-mini \
--task_name mnli \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 32 \
--learning_rate 1e-4 \
--num_train_epochs 5 \
--output_dir out_dir/ \
--optim MFAC \
--optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}'
```
We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE).
Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC).
A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials).
## BibTeX entry and citation info
```bibtex
@article{frantar2021m,
title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information},
author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan},
journal={Advances in Neural Information Processing Systems},
volume={35},
year={2021}
}
```
| 2,882 |
M-FAC/bert-tiny-finetuned-qqp | null | # BERT-tiny model finetuned with M-FAC
This model is finetuned on QQP dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC.
Hyperparameters used by M-FAC optimizer:
```bash
learning rate = 1e-4
number of gradients = 1024
dampening = 1e-6
```
## Results
We share the best model out of 5 runs with the following score on QQP validation set:
```bash
f1 = 79.84
accuracy = 84.40
```
Mean and standard deviation for 5 runs on QQP validation set:
| | F1 | Accuracy |
|:----:|:-----------:|:----------:|
| Adam | 77.58 ± 0.08 | 81.09 ± 0.15 |
| M-FAC | 79.71 ± 0.13 | 84.29 ± 0.08 |
Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script:
```bash
CUDA_VISIBLE_DEVICES=0 python run_glue.py \
--seed 1234 \
--model_name_or_path prajjwal1/bert-tiny \
--task_name qqp \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 32 \
--learning_rate 1e-4 \
--num_train_epochs 5 \
--output_dir out_dir/ \
--optim MFAC \
--optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}'
```
We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE).
Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC).
A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials).
## BibTeX entry and citation info
```bibtex
@article{frantar2021m,
title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information},
author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan},
journal={Advances in Neural Information Processing Systems},
volume={35},
year={2021}
}
```
| 2,785 |
MoritzLaurer/MiniLM-L6-mnli-binary | [
"entailment",
"not_entailment"
] | ---
language:
- en
tags:
- text-classification
- zero-shot-classification
metrics:
- accuracy
widget:
- text: "I liked the movie. [SEP] The movie was good."
---
# MiniLM-L6-mnli-binary
## Model description
This model was trained on the [MultiNLI](https://huggingface.co/datasets/multi_nli) dataset. The model was trained for binary NLI, which means that the "neutral" and "contradiction" classes were merged into one class. The model therefore predicts "entailment" or "not_entailment".
The base model is MiniLM-L6 from Microsoft, which is very fast, but a bit less accurate than other models.
## Intended uses & limitations
#### How to use the model
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "MoritzLaurer/MiniLM-L6-mnli-binary"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
premise = "I liked the movie"
hypothesis = "The movie was good."
input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu"
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "not_entailment"]
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
print(prediction)
```
### Training data
[MultiNLI](https://huggingface.co/datasets/multi_nli).
### Training procedure
MiniLM-L6-mnli-binary was trained using the Hugging Face trainer with the following hyperparameters.
```
training_args = TrainingArguments(
num_train_epochs=5, # total number of training epochs
learning_rate=2e-05,
per_device_train_batch_size=32, # batch size per device during training
per_device_eval_batch_size=32, # batch size for evaluation
warmup_ratio=0.1, # number of warmup steps for learning rate scheduler
weight_decay=0.06, # strength of weight decay
fp16=True # mixed precision training
)
```
### Eval results
The model was evaluated using the binary (matched) test set from MultiNLI. Accuracy: 0.886
## Limitations and bias
Please consult the original MiniLM paper and literature on different NLI datasets for potential biases.
### BibTeX entry and citation info
If you want to cite this model, please cite the original MiniLM paper, the respective NLI datasets and include a link to this model on the Hugging Face hub. | 2,509 |
Sakil/distilbert_lazylearner_hatespeech_detection | null | ---
license: apache-2.0
language: en
tags:
- hate
- speech
widget:
- text: "RT @ShenikaRoberts: The shit you hear about me might be true or it might be faker than the bitch who told it to ya ᙨ"
---
# Dataset Collection:
* The hatespeech dataset is collected from different open sources like Kaggle ,social media like Twitter.
* The dataset has the two classes hatespeech and non hatespeech.
* The class distribution is equal
* Different strategies have been followed during the data gathering phase.
* The dataset is collected from relevant sources.
# distilbert-base-uncased model is fine-tuned for Hate Speech Detection
* The model is fine-tuned on the dataset.
* This model can be used to create the labels for academic purposes or for industrial purposes.
* This model can be used for the inference purpose as well.
# Data Fields:
**label**: 0 - it is a hate speech, 1 - not a hate speech
# Application:
* This model is useful for the detection of hatespeech in the tweets.
* There are numerous situations where we have tweet data but no labels, so this approach can be used to create labels.
* You can fine-tune this model for your particular use cases.
# Model Implementation
# !pip install transformers[sentencepiece]
from transformers import pipeline
model_name="Sakil/distilbert_lazylearner_hatespeech_detection"
classifier = pipeline("text-classification",model=model_name)
classifier("!!! RT @mayasolovely: As a woman you shouldn't complain about cleaning up your house. & as a man you should always take the trash out...")
# Github: [Sakil Ansari](https://github.com/Sakil786/hate_speech_detection_pretrained_model) | 1,696 |
SetFit/deberta-v3-large__sst2__train-16-7 | [
"negative",
"positive"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-16-7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large__sst2__train-16-7
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6953
- Accuracy: 0.5063
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6911 | 1.0 | 7 | 0.7455 | 0.2857 |
| 0.6844 | 2.0 | 14 | 0.7242 | 0.2857 |
| 0.6137 | 3.0 | 21 | 0.7341 | 0.4286 |
| 0.3805 | 4.0 | 28 | 1.0217 | 0.4286 |
| 0.2201 | 5.0 | 35 | 1.1437 | 0.2857 |
| 0.0296 | 6.0 | 42 | 1.5997 | 0.4286 |
| 0.0103 | 7.0 | 49 | 2.6835 | 0.4286 |
| 0.0046 | 8.0 | 56 | 3.3521 | 0.4286 |
| 0.002 | 9.0 | 63 | 3.7846 | 0.4286 |
| 0.0017 | 10.0 | 70 | 4.0088 | 0.4286 |
| 0.0018 | 11.0 | 77 | 4.1483 | 0.4286 |
| 0.0006 | 12.0 | 84 | 4.2235 | 0.4286 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,092 |
StevenLimcorn/indo-roberta-indonli | [
"c",
"e",
"n"
] | ---
language: id
tags:
- roberta
license: mit
datasets:
- indonli
widget:
- text: "Amir Sjarifoeddin Harahap lahir di Kota Medan, Sumatera Utara, 27 April 1907. Ia meninggal di Surakarta, Jawa Tengah, pada 19 Desember 1948 dalam usia 41 tahun. </s></s> Amir Sjarifoeddin Harahap masih hidup."
---
## Indo-roberta-indonli
Indo-roberta-indonli is natural language inference classifier based on [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) model. It was trained on the trained on [IndoNLI](https://github.com/ir-nlp-csui/indonli/tree/main/data/indonli) dataset. The model used was [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) and was transfer-learned to a natural inference classifier model. The model are tested using the validation, test_layer and test_expert dataset given in the github repository. The results are shown below.
### Result
| Dataset | Accuracy | F1 | Precision | Recall |
|-------------|----------|---------|-----------|---------|
| Test Lay | 0.74329 | 0.74075 | 0.74283 | 0.74133 |
| Test Expert | 0.6115 | 0.60543 | 0.63924 | 0.61742 |
## Model
The model was trained on with 5 epochs, batch size 16, learning rate 2e-5 and weight decay 0.01. Achieved different metrics as shown below.
| Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall |
|-------|---------------|-----------------|----------|----------|-----------|----------|
| 1 | 0.942500 | 0.658559 | 0.737369 | 0.735552 | 0.735488 | 0.736679 |
| 2 | 0.649200 | 0.645290 | 0.761493 | 0.759593 | 0.762784 | 0.759642 |
| 3 | 0.437100 | 0.667163 | 0.766045 | 0.763979 | 0.765740 | 0.763792 |
| 4 | 0.282000 | 0.786683 | 0.764679 | 0.761802 | 0.762011 | 0.761684 |
| 5 | 0.193500 | 0.925717 | 0.765134 | 0.763127 | 0.763560 | 0.763489 |
## How to Use
### As NLI Classifier
```python
from transformers import pipeline
pretrained_name = "StevenLimcorn/indonesian-roberta-indonli"
nlp = pipeline(
"zero-shot-classification",
model=pretrained_name,
tokenizer=pretrained_name
)
nlp("Amir Sjarifoeddin Harahap lahir di Kota Medan, Sumatera Utara, 27 April 1907. Ia meninggal di Surakarta, Jawa Tengah, pada 19 Desember 1948 dalam usia 41 tahun. </s></s> Amir Sjarifoeddin Harahap masih hidup.")
```
## Disclaimer
Do consider the biases which come from both the pre-trained RoBERTa model and the `INDONLI` dataset that may be carried over into the results of this model.
## Author
Indonesian RoBERTa Base IndoNLI was trained and evaluated by [Steven Limcorn](https://github.com/stevenlimcorn). All computation and development are done on Google Colaboratory using their free GPU access.
## Reference
The dataset we used is by IndoNLI.
```
@inproceedings{indonli,
title = "IndoNLI: A Natural Language Inference Dataset for Indonesian",
author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
publisher = "Association for Computational Linguistics",
}
``` | 3,249 |
TehranNLP-org/bert-base-cased-avg-cola | null | The uploaded model is from epoch 4 with Matthews Correlation of 61.05
"best_metric": 0.4796141982078552,<br>
"best_model_checkpoint": "/content/output_dir/checkpoint-268",<br>
"epoch": 10.0,<br>
"global_step": 2680,<br>
"is_hyper_param_search": false,<br>
"is_local_process_zero": true,<br>
"is_world_process_zero": true,<br>
"max_steps": 2680,<br>
"num_train_epochs": 10,<br>
"total_flos": 7113018526540800.0,<br>
"trial_name": null,<br>
"trial_params": null<br>
<table class="table table-bordered table-hover table-condensed" style="width: 60%; overflow: auto">
<thead><tr><th title="Field #1">epoch</th>
<th title="Field #2">eval_loss</th>
<th title="Field #3">eval_matthews_correlation</th>
<th title="Field #4">eval_runtime</th>
<th title="Field #5">eval_samples_per_second</th>
<th title="Field #6">eval_steps_per_second</th>
<th title="Field #7">step</th>
<th title="Field #8">learning_rate</th>
<th title="Field #9">loss</th>
</tr></thead>
<tbody><tr>
<td align="left">1</td>
<td align="left">0.4796141982078552</td>
<td align="left">0.5351033849356494</td>
<td align="left">8.8067</td>
<td align="left">118.433</td>
<td align="left">14.875</td>
<td align="left">268</td>
<td align="left">0.000018067415730337083</td>
<td align="left">0.4913</td>
</tr>
<tr>
<td align="left">2</td>
<td align="left">0.5334435701370239</td>
<td align="left">0.5178799252679331</td>
<td align="left">8.9439</td>
<td align="left">116.616</td>
<td align="left">14.647</td>
<td align="left">536</td>
<td align="left">0.00001605992509363296</td>
<td align="left">0.2872</td>
</tr>
<tr>
<td align="left">3</td>
<td align="left">0.5544090270996094</td>
<td align="left">0.5649788851042796</td>
<td align="left">8.9467</td>
<td align="left">116.58</td>
<td align="left">14.642</td>
<td align="left">804</td>
<td align="left">0.000014052434456928841</td>
<td align="left">0.1777</td>
</tr>
<tr>
<td align="left">4</td>
<td align="left">0.5754779577255249</td>
<td align="left">0.6105374636148787</td>
<td align="left">8.8982</td>
<td align="left">117.215</td>
<td align="left">14.722</td>
<td align="left">1072</td>
<td align="left">0.000012044943820224718</td>
<td align="left">0.1263</td>
</tr>
<tr>
<td align="left">5</td>
<td align="left">0.7263916730880737</td>
<td align="left">0.5807606001872874</td>
<td align="left">8.9705</td>
<td align="left">116.27</td>
<td align="left">14.603</td>
<td align="left">1340</td>
<td align="left">0.000010037453183520601</td>
<td align="left">0.0905</td>
</tr>
<tr>
<td align="left">6</td>
<td align="left">0.8121512532234192</td>
<td align="left">0.5651092792103851</td>
<td align="left">8.9924</td>
<td align="left">115.987</td>
<td align="left">14.568</td>
<td align="left">1608</td>
<td align="left">0.00000802996254681648</td>
<td align="left">0.0692</td>
</tr>
<tr>
<td align="left">7</td>
<td align="left">0.941014289855957</td>
<td align="left">0.5632084517291658</td>
<td align="left">8.9583</td>
<td align="left">116.428</td>
<td align="left">14.623</td>
<td align="left">1876</td>
<td align="left">0.000006022471910112359</td>
<td align="left">0.0413</td>
</tr>
<tr>
<td align="left">8</td>
<td align="left">1.0095174312591553</td>
<td align="left">0.5856531698367675</td>
<td align="left">9.0029</td>
<td align="left">115.851</td>
<td align="left">14.551</td>
<td align="left">2144</td>
<td align="left">0.00000401498127340824</td>
<td align="left">0.0327</td>
</tr>
<tr>
<td align="left">9</td>
<td align="left">1.0425965785980225</td>
<td align="left">0.5941395545037332</td>
<td align="left">8.9217</td>
<td align="left">116.906</td>
<td align="left">14.683</td>
<td align="left">2412</td>
<td align="left">0.00000200749063670412</td>
<td align="left">0.0202</td>
</tr>
<tr>
<td align="left">10</td>
<td align="left">1.0782166719436646</td>
<td align="left">0.5956649094312695</td>
<td align="left">8.9472</td>
<td align="left">116.572</td>
<td align="left">14.641</td>
<td align="left">2680</td>
<td align="left">0</td>
<td align="left">0.0104</td>
</tr>
</tbody></table> | 4,039 |
Theivaprakasham/sentence-transformers-msmarco-distilbert-base-tas-b-twitter_sentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentence-transformers-msmarco-distilbert-base-tas-b-twitter_sentiment
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. -->
# sentence-transformers-msmarco-distilbert-base-tas-b-twitter_sentiment
This model is a fine-tuned version of [sentence-transformers/msmarco-distilbert-base-tas-b](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-tas-b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6954
- Accuracy: 0.7146
## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8892 | 1.0 | 1387 | 0.8472 | 0.6180 |
| 0.7965 | 2.0 | 2774 | 0.7797 | 0.6609 |
| 0.7459 | 3.0 | 4161 | 0.7326 | 0.6872 |
| 0.7096 | 4.0 | 5548 | 0.7133 | 0.6995 |
| 0.6853 | 5.0 | 6935 | 0.6998 | 0.7002 |
| 0.6561 | 6.0 | 8322 | 0.6949 | 0.7059 |
| 0.663 | 7.0 | 9709 | 0.6956 | 0.7077 |
| 0.6352 | 8.0 | 11096 | 0.6890 | 0.7164 |
| 0.6205 | 9.0 | 12483 | 0.6888 | 0.7117 |
| 0.6203 | 10.0 | 13870 | 0.6871 | 0.7121 |
| 0.6005 | 11.0 | 15257 | 0.6879 | 0.7171 |
| 0.5985 | 12.0 | 16644 | 0.6870 | 0.7139 |
| 0.5839 | 13.0 | 18031 | 0.6882 | 0.7164 |
| 0.5861 | 14.0 | 19418 | 0.6910 | 0.7124 |
| 0.5732 | 15.0 | 20805 | 0.6916 | 0.7153 |
| 0.5797 | 16.0 | 22192 | 0.6947 | 0.7110 |
| 0.5565 | 17.0 | 23579 | 0.6930 | 0.7175 |
| 0.5636 | 18.0 | 24966 | 0.6959 | 0.7106 |
| 0.5642 | 19.0 | 26353 | 0.6952 | 0.7132 |
| 0.5717 | 20.0 | 27740 | 0.6954 | 0.7146 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 2,705 |
aXhyra/demo_hate_31415 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: demo_hate_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
type: f1
value: 0.7772939485986298
---
<!-- 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_hate_31415
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: 0.8697
- F1: 0.7773
## 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: 7.320702985778492e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 282 | 0.4850 | 0.7645 |
| 0.3877 | 2.0 | 564 | 0.5160 | 0.7856 |
| 0.3877 | 3.0 | 846 | 0.6927 | 0.7802 |
| 0.1343 | 4.0 | 1128 | 0.8697 | 0.7773 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,756 |
adamlin/ml999_power_punching_and_shearing_machinery | [
"0",
"1"
] | Entry not found | 15 |
adelgasmi/autonlp-kpmg_nlp-18833547 | [
"0",
"1",
"2",
"3",
"4"
] | ---
tags: autonlp
language: ar
widget:
- text: "I love AutoNLP 🤗"
datasets:
- adelgasmi/autonlp-data-kpmg_nlp
co2_eq_emissions: 64.58945483765274
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 18833547
- CO2 Emissions (in grams): 64.58945483765274
## Validation Metrics
- Loss: 0.14247722923755646
- Accuracy: 0.9586074193404036
- Macro F1: 0.9468339778730883
- Micro F1: 0.9586074193404036
- Weighted F1: 0.9585551117678807
- Macro Precision: 0.9445436604001405
- Micro Precision: 0.9586074193404036
- Weighted Precision: 0.9591405429662925
- Macro Recall: 0.9499427161888565
- Micro Recall: 0.9586074193404036
- Weighted Recall: 0.9586074193404036
## 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/adelgasmi/autonlp-kpmg_nlp-18833547
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("adelgasmi/autonlp-kpmg_nlp-18833547", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("adelgasmi/autonlp-kpmg_nlp-18833547", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,369 |
aditeyabaral/finetuned-iitpmovie-additionalpretrained-distilbert-base-cased | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
adp12/cs410finetune1 | [
"NEGATIVE",
"POSITIVE"
] | Entry not found | 15 |
agiagoulas/bert-pss | null | bert-base-uncased model trained on the tobacco800 dataset for the task of page-stream-segmentation.
[Link](https://github.com/agiagoulas/page-stream-segmentation) to the GitHub Repo with the model implementation. | 213 |
akahana/indonesia-sentiment-roberta | [
"POSITIF",
"NETRAL",
"NEGATIF"
] | ---
language: "id"
widget:
- text: "dia orang yang baik ya bunds."
---
## how to use
```python
from transformers import pipeline, set_seed
path = "akahana/indonesia-sentiment-roberta"
emotion = pipeline('text-classification',
model=path,device=0)
set_seed(42)
kalimat = "dia orang yang baik ya bunds."
preds = emotion(kalimat)
preds
``` | 362 |
am4nsolanki/autonlp-text-hateful-memes-36789092 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- am4nsolanki/autonlp-data-text-hateful-memes
co2_eq_emissions: 1.4280361775467445
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 36789092
- CO2 Emissions (in grams): 1.4280361775467445
## Validation Metrics
- Loss: 0.5255328416824341
- Accuracy: 0.7666078777189889
- Precision: 0.6913123844731978
- Recall: 0.6192052980132451
- AUC: 0.7893359070795125
- F1: 0.6532751091703057
## 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/am4nsolanki/autonlp-text-hateful-memes-36789092
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("am4nsolanki/autonlp-text-hateful-memes-36789092", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("am4nsolanki/autonlp-text-hateful-memes-36789092", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,203 |
berkergurcay/10k-pretrained-bert-model | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
bioformers/bioformer-cased-v1.0-mnli | [
"contradiction",
"entailment",
"neutral"
] | [bioformer-cased-v1.0](https://huggingface.co/bioformers/bioformer-cased-v1.0) fined-tuned on the [MNLI](https://cims.nyu.edu/~sbowman/multinli/) dataset for 2 epochs.
The fine-tuning process was performed on two NVIDIA GeForce GTX 1080 Ti GPUs (11GB). The parameters are:
```
max_seq_length=512
per_device_train_batch_size=16
total train batch size (w. parallel, distributed & accumulation) = 32
learning_rate=3e-5
```
## Evaluation results
eval_accuracy = 0.803973
## Speed
In our experiments, the inference speed of Bioformer is 3x as fast as BERT-base/BioBERT/PubMedBERT, and is 40% faster than DistilBERT.
## More information
The Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data. (source: https://huggingface.co/datasets/glue) | 1,435 |
chitra/finetuned-adversarial-paraphrasing-detector | null | Entry not found | 15 |
dkleczek/Polish-Hate-Speech-Detection-Herbert-Large | null | Entry not found | 15 |
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