Instructions to use anaghasavit/tinyBERTCrossEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anaghasavit/tinyBERTCrossEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anaghasavit/tinyBERTCrossEncoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anaghasavit/tinyBERTCrossEncoder") model = AutoModelForSequenceClassification.from_pretrained("anaghasavit/tinyBERTCrossEncoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from anaghasavit/tinyBERTCrossEncoder: direct link, hf CLI and curl.
- Browser
- Download file 538 Bytes
-
https://huggingface.co/anaghasavit/tinyBERTCrossEncoder/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://anaghasavit/tinyBERTCrossEncoder/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/anaghasavit/tinyBERTCrossEncoder/resolve/main/tokenizer_config.json
538 Bytes
| { | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": "/home/ukp-reimers/.cache/huggingface/transformers/448f85f42d7f87f0254da1997bc5cd60cb4607800084132993017232e82432a3.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |