Text Classification
Transformers
Safetensors
PEFT
English
mpnet
patents
green-tech
qlora
sequence-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use CTB2001/Assignment_3_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CTB2001/Assignment_3_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CTB2001/Assignment_3_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CTB2001/Assignment_3_Model") model = AutoModelForSequenceClassification.from_pretrained("CTB2001/Assignment_3_Model", device_map="auto") - PEFT
How to use CTB2001/Assignment_3_Model with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 1,615 Bytes
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"added_tokens_decoder": {
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"single_word": false,
"special": true
},
"1": {
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"special": true
},
"2": {
"content": "</s>",
"lstrip": false,
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"rstrip": false,
"single_word": false,
"special": true
},
"3": {
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},
"104": {
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"lstrip": false,
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"special": true
},
"30526": {
"content": "<mask>",
"lstrip": true,
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"single_word": false,
"special": true
}
},
"bos_token": "<s>",
"clean_up_tokenization_spaces": false,
"cls_token": "<s>",
"do_lower_case": true,
"eos_token": "</s>",
"extra_special_tokens": {},
"mask_token": "<mask>",
"max_length": 512,
"model_max_length": 512,
"pad_to_multiple_of": null,
"pad_token": "<pad>",
"pad_token_type_id": 0,
"padding_side": "right",
"sep_token": "</s>",
"stride": 0,
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "MPNetTokenizer",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]"
}
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