Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ultra-coder54732/MiniLM-prop-16-train-set with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ultra-coder54732/MiniLM-prop-16-train-set with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ultra-coder54732/MiniLM-prop-16-train-set")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ultra-coder54732/MiniLM-prop-16-train-set") model = AutoModelForSequenceClassification.from_pretrained("ultra-coder54732/MiniLM-prop-16-train-set", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 635 Bytes
dc0feaf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"batch_size": 16,
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": true,
"mask_token": "[MASK]",
"name_or_path": "microsoft/MiniLM-L12-H384-uncased",
"never_split": null,
"num_epochs": 10,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": "/root/.cache/huggingface/transformers/1e5909e4dfaa904617797ed35a6105a23daa56cbefca48fef329f772584699fb.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]",
"use_cache": false,
"verbose": true
}
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