Text Classification
Transformers
TensorFlow
roberta
generated_from_keras_callback
text-embeddings-inference
Instructions to use leonong84/roberta-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leonong84/roberta-tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="leonong84/roberta-tuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("leonong84/roberta-tuned") model = AutoModelForSequenceClassification.from_pretrained("leonong84/roberta-tuned") - Notebooks
- Google Colab
- Kaggle
Upload TFRobertaForSequenceClassification
Browse files- README.md +2 -2
- tf_model.h5 +1 -1
README.md
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tags:
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- generated_from_keras_callback
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model-index:
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- name: roberta-
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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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# roberta-
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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tags:
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- name: roberta-tuned
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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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# roberta-tuned
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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tf_model.h5
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