Instructions to use Siyam/s61tiny-NLP-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Siyam/s61tiny-NLP-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Siyam/s61tiny-NLP-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Siyam/s61tiny-NLP-model") model = AutoModelForSequenceClassification.from_pretrained("Siyam/s61tiny-NLP-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- model.safetensors +1 -1
- tokenizer_config.json +0 -2
model.safetensors
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size 438032472
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version https://git-lfs.github.com/spec/v1
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oid sha256:d7af8230ab812b5b44ea8406608f3787f5bb09fe38014ee76564602df95516db
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size 438032472
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tokenizer_config.json
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"max_length": 512,
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"model_max_length": 1000000000000000019884624838656,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"max_length": 512,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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