Instructions to use huggingface/CodeBERTa-language-id with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingface/CodeBERTa-language-id with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="huggingface/CodeBERTa-language-id")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("huggingface/CodeBERTa-language-id") model = AutoModelForSequenceClassification.from_pretrained("huggingface/CodeBERTa-language-id") - Notebooks
- Google Colab
- Kaggle
Add TF weights
Browse filesModel converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py).
All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=2.861e-06; Maximum converted output difference=2.861e-06.
cc @lysandre (HF maintainer for this repo)
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:9114ffaf2db4344a1e463f0713177ab811b8c141746090144a9e3e8b52155890
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size 333971544
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