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--- |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: bert-base-cased-trec-fine |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# bert-base-cased-trec-fine |
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This model was trained from scratch on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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### How to use |
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```from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import tensorflow |
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model_name = "ndavid/bert-base-cased-trec-fine" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name, from_tf=True) |
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from transformers import pipeline |
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nlp = pipeline("sentiment-analysis", model=model_name, tokenizer=model_name) |
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results = nlp(["Where did the queen go?", "Why did the Queen hire 1000 ML Engineers?"]) |
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print(results) |
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``` |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} |
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- training_precision: float32 |
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### Training results |
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### Framework versions |
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- Transformers 4.18.0 |
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- TensorFlow 2.8.0 |
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- Datasets 2.0.0 |
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- Tokenizers 0.12.1 |
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