readme: ---
  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
  