Feature Extraction
Transformers
English
hare
embeddings
text-retrieval
long-context
rwkv
modernbert
streaming
semantic-search
retrieval
custom_code
Instructions to use SixOpen/HARE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SixOpen/HARE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SixOpen/HARE", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SixOpen/HARE", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "is_local": true, | |
| "mask_token": "[MASK]", | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "[UNK]" | |
| } | |