Feature Extraction
Transformers
PyTorch
Safetensors
Hebrew
bert
custom_code
text-embeddings-inference
Instructions to use dicta-il/dictabert-joint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dicta-il/dictabert-joint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dicta-il/dictabert-joint", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dicta-il/dictabert-joint", trust_remote_code=True) model = AutoModel.from_pretrained("dicta-il/dictabert-joint", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
|
@@ -58,6 +58,6 @@
|
|
| 58 |
"sep_token": "[SEP]",
|
| 59 |
"strip_accents": null,
|
| 60 |
"tokenize_chinese_chars": true,
|
| 61 |
-
"tokenizer_class": "
|
| 62 |
"unk_token": "[UNK]"
|
| 63 |
}
|
|
|
|
| 58 |
"sep_token": "[SEP]",
|
| 59 |
"strip_accents": null,
|
| 60 |
"tokenize_chinese_chars": true,
|
| 61 |
+
"tokenizer_class": "PreTrainedTokenizerFast",
|
| 62 |
"unk_token": "[UNK]"
|
| 63 |
}
|