Instructions to use Gkumi/tensorflow-DistilBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gkumi/tensorflow-DistilBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Gkumi/tensorflow-DistilBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Gkumi/tensorflow-DistilBERT") model = AutoModelForTokenClassification.from_pretrained("Gkumi/tensorflow-DistilBERT") - Notebooks
- Google Colab
- Kaggle
Upload TFDistilBertForTokenClassification
Browse files
README.md
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---
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license: apache-2.0
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tags:
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- generated_from_keras_callback
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base_model: distilbert-base-uncased
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model-index:
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- name: tensorflow-DistilBERT
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results: []
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---
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license: apache-2.0
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base_model: distilbert-base-uncased
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tags:
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- generated_from_keras_callback
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model-index:
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- name: tensorflow-DistilBERT
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results: []
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