Instructions to use danielsaggau/slbert_scotus_classsification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danielsaggau/slbert_scotus_classsification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="danielsaggau/slbert_scotus_classsification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("danielsaggau/slbert_scotus_classsification") model = AutoModelForSequenceClassification.from_pretrained("danielsaggau/slbert_scotus_classsification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from danielsaggau/slbert_scotus_classsification: direct link, hf CLI and curl.
- Browser
- Download file 560 Bytes
-
https://huggingface.co/danielsaggau/slbert_scotus_classsification/resolve/refs%2Fpr%2F1/tokenizer_config.json
- Command line
-
hf download hf://danielsaggau/slbert_scotus_classsification@refs/pr/1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/danielsaggau/slbert_scotus_classsification/resolve/refs%2Fpr%2F1/tokenizer_config.json
560 Bytes
| { | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 4096, | |
| "name_or_path": "/content/drive/MyDrive/sbert_longformer_ecthr_b_simcse/train_simcse-_root_.local_share_jupyter_runtime_kernel-702ca39d-5545-4b00-b884-05176c135763.json-2022-10-21_08-44-17/1500", | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": null, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |