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

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