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# bert-base-cased-trec-fine

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:


## Model description

More information needed

## Intended uses & limitations

### How to use

```from transformers import AutoTokenizer, AutoModelForSequenceClassification
import tensorflow

model_name = "ndavid/bert-base-cased-trec-fine"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForSequenceClassification.from_pretrained(model_name, from_tf=True)

from transformers import pipeline

nlp = pipeline("sentiment-analysis", model=model_name, tokenizer=model_name)

results = nlp(["Where did the queen go?", "Why did the Queen hire 1000 ML Engineers?"])

print(results)
```


## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results



### Framework versions

- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.12.1

{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-base-cased-trec-fine", "results": []}]}
transformers tf bert text-classification generated_from_keras_callback autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 




# bert-base-cased-trec-fine

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:


## Model description

More information needed

## Intended uses & limitations

### How to use




## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results



### Framework versions

- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.12.1

# bert-base-cased-trec-fine

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set: ## Model description

More information needed ## Intended uses & limitations ### How to use ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32 ### Training results ### Framework versions

- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.12.1
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 
 # bert-base-cased-trec-fine

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set: ## Model description

More information needed ## Intended uses & limitations ### How to use ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32 ### Training results ### Framework versions

- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 
# bert-base-cased-trec-fine

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:## Model description

More information needed## Intended uses & limitations### How to use## Training and evaluation data

More information needed## Training procedure### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32### Training results### Framework versions

- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.12.1