Feature Extraction
Transformers
Safetensors
English
modernbert
Generated from Trainer
custom_code
text-embeddings-inference
Instructions to use GliteTech/DisambertSingleSense-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GliteTech/DisambertSingleSense-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="GliteTech/DisambertSingleSense-base", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("GliteTech/DisambertSingleSense-base", trust_remote_code=True) model = AutoModel.from_pretrained("GliteTech/DisambertSingleSense-base", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "extra_special_tokens": [ | |
| "[START]", | |
| "[END]" | |
| ], | |
| "is_local": false, | |
| "mask_token": "[MASK]", | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 8192, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "[UNK]" | |
| } | |