Feature Extraction
Transformers
PyTorch
Safetensors
Hebrew
bert
custom_code
text-embeddings-inference
Instructions to use dicta-il/dictabert-morph with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dicta-il/dictabert-morph with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dicta-il/dictabert-morph", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dicta-il/dictabert-morph", trust_remote_code=True) model = AutoModel.from_pretrained("dicta-il/dictabert-morph", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BertForMorphTagging" | |
| ], | |
| "auto_map": { | |
| "AutoModel": "BertForMorphTagging.BertForMorphTagging" | |
| }, | |
| "attention_probs_dropout_prob": 0.1, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.6.0.dev0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 128000, | |
| "newmodern": true | |
| } | |