Text Classification
Transformers
Safetensors
English
longformer
legal
document-classification
multi-class-classification
msme
dispute-resolution
indian-legal
Instructions to use abhinavdread/msme-legal-dispute-classifier-longformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abhinavdread/msme-legal-dispute-classifier-longformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="abhinavdread/msme-legal-dispute-classifier-longformer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("abhinavdread/msme-legal-dispute-classifier-longformer") model = AutoModelForSequenceClassification.from_pretrained("abhinavdread/msme-legal-dispute-classifier-longformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "errors": "replace", | |
| "is_local": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "tokenizer_class": "RobertaTokenizer", | |
| "trim_offsets": true, | |
| "unk_token": "<unk>" | |
| } | |