
# bert-base-cased-ag-news

BERT model fine-tuned on AG News classification dataset using a linear layer on top of the [CLS] token output, with 0.945 test accuracy.

### How to use

Here is how to use this model to classify a given text:
```python
from transformers import AutoTokenizer, BertForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('lucasresck/bert-base-cased-ag-news')
model = BertForSequenceClassification.from_pretrained('lucasresck/bert-base-cased-ag-news')
text = "Is it soccer or football?"
encoded_input = tokenizer(text, return_tensors='pt', truncation=True, max_length=512)
output = model(**encoded_input)
```

### Limitations and bias

Bias were not assessed in this model, but, considering that pre-trained BERT is known to carry bias, it is also expected for this model. BERT's authors say: "This bias will also affect all fine-tuned versions of this model."

## Evaluation results

```
              precision    recall  f1-score   support

           0     0.9539    0.9584    0.9562      1900
           1     0.9884    0.9879    0.9882      1900
           2     0.9251    0.9095    0.9172      1900
           3     0.9127    0.9242    0.9184      1900

    accuracy                         0.9450      7600
   macro avg     0.9450    0.9450    0.9450      7600
weighted avg     0.9450    0.9450    0.9450      7600
```

{"language": ["en"], "license": "mit", "tags": ["bert", "classification"], "datasets": ["ag_news"], "metrics": ["accuracy", "f1", "recall", "precision"], "widget": [{"text": "Is it soccer or football?", "example_title": "Sports"}, {"text": "A new version of Ubuntu was released.", "example_title": "Sci/Tech"}]}
transformers pytorch bert text-classification classification en dataset:ag_news license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #classification #en #dataset-ag_news #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# bert-base-cased-ag-news

BERT model fine-tuned on AG News classification dataset using a linear layer on top of the [CLS] token output, with 0.945 test accuracy.

### How to use

Here is how to use this model to classify a given text:


### Limitations and bias

Bias were not assessed in this model, but, considering that pre-trained BERT is known to carry bias, it is also expected for this model. BERT's authors say: "This bias will also affect all fine-tuned versions of this model."

## Evaluation results



# bert-base-cased-ag-news

BERT model fine-tuned on AG News classification dataset using a linear layer on top of the [CLS] token output, with 0.945 test accuracy. ### How to use

Here is how to use this model to classify a given text: ### Limitations and bias

Bias were not assessed in this model, but, considering that pre-trained BERT is known to carry bias, it is also expected for this model. BERT's authors say: "This bias will also affect all fine-tuned versions of this model." ## Evaluation results
TAGS
#transformers #pytorch #bert #text-classification #classification #en #dataset-ag_news #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # bert-base-cased-ag-news

BERT model fine-tuned on AG News classification dataset using a linear layer on top of the [CLS] token output, with 0.945 test accuracy. ### How to use

Here is how to use this model to classify a given text: ### Limitations and bias

Bias were not assessed in this model, but, considering that pre-trained BERT is known to carry bias, it is also expected for this model. BERT's authors say: "This bias will also affect all fine-tuned versions of this model." ## Evaluation results
passage: TAGS
#transformers #pytorch #bert #text-classification #classification #en #dataset-ag_news #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# bert-base-cased-ag-news

BERT model fine-tuned on AG News classification dataset using a linear layer on top of the [CLS] token output, with 0.945 test accuracy.### How to use

Here is how to use this model to classify a given text:### Limitations and bias

Bias were not assessed in this model, but, considering that pre-trained BERT is known to carry bias, it is also expected for this model. BERT's authors say: "This bias will also affect all fine-tuned versions of this model."## Evaluation results