---
tags:
- generated_from_keras_callback
model-index:
- name: bert-base-cased-trec-fine
  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. -->

# 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