Instructions to use DidulaThavisha/distilroberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DidulaThavisha/distilroberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="DidulaThavisha/distilroberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("DidulaThavisha/distilroberta") model = AutoModelForTokenClassification.from_pretrained("DidulaThavisha/distilroberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -1
config.json
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@@ -39,7 +39,7 @@
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"max_position_embeddings": 514,
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers":
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"transformers_version": "4.6.1",
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"max_position_embeddings": 514,
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 3,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"transformers_version": "4.6.1",
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