Instructions to use zfan3/SPTM_CM_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zfan3/SPTM_CM_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zfan3/SPTM_CM_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zfan3/SPTM_CM_v2") model = AutoModelForSequenceClassification.from_pretrained("zfan3/SPTM_CM_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,099 Bytes
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"added_tokens_decoder": {
"0": {
"content": "<cls>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "<pad>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"2": {
"content": "<eos>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"3": {
"content": "<unk>",
"lstrip": false,
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"rstrip": false,
"single_word": false,
"special": true
},
"32": {
"content": "<mask>",
"lstrip": false,
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"rstrip": false,
"single_word": false,
"special": true
}
},
"clean_up_tokenization_spaces": true,
"cls_token": "<cls>",
"eos_token": "<eos>",
"mask_token": "<mask>",
"model_max_length": 1024,
"pad_token": "<pad>",
"tokenizer_class": "EsmTokenizer",
"unk_token": "<unk>"
}
|