Text Classification
Transformers
PyTorch
Enawené-Nawé
roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use davis901/roberta-frame-CP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davis901/roberta-frame-CP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="davis901/roberta-frame-CP")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("davis901/roberta-frame-CP") model = AutoModelForSequenceClassification.from_pretrained("davis901/roberta-frame-CP") - Notebooks
- Google Colab
- Kaggle
changing model_max_length: to 10000
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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@@ -5,7 +5,7 @@
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"eos_token": "</s>",
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"errors": "replace",
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"mask_token": "<mask>",
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-
"model_max_length":
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"name_or_path": "AutoTrain",
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"pad_token": "<pad>",
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"sep_token": "</s>",
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| 5 |
"eos_token": "</s>",
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"errors": "replace",
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"mask_token": "<mask>",
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+
"model_max_length": 10000,
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"name_or_path": "AutoTrain",
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"pad_token": "<pad>",
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| 11 |
"sep_token": "</s>",
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