Token Classification
Transformers
Safetensors
English
eurobert
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
chemistry
chem
custom_code
Instructions to use OpenMed/OpenMed-NER-ChemicalDetect-EuroMed-212M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ChemicalDetect-EuroMed-212M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ChemicalDetect-EuroMed-212M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ChemicalDetect-EuroMed-212M", trust_remote_code=True) model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ChemicalDetect-EuroMed-212M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from OpenMed/OpenMed-NER-ChemicalDetect-EuroMed-212M: direct link, hf CLI and curl.
- Browser
- Download file 1.54 kB
-
https://huggingface.co/OpenMed/OpenMed-NER-ChemicalDetect-EuroMed-212M/resolve/main/config.json
- Command line
-
hf download hf://OpenMed/OpenMed-NER-ChemicalDetect-EuroMed-212M/config.json
-
curl -L -o config.json https://huggingface.co/OpenMed/OpenMed-NER-ChemicalDetect-EuroMed-212M/resolve/main/config.json
1.54 kB
| { | |
| "architectures": [ | |
| "EuroBertForTokenClassification" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_eurobert.EuroBertConfig", | |
| "AutoModel": "modeling_eurobert.EuroBertModel", | |
| "AutoModelForMaskedLM": "modeling_eurobert.EuroBertForMaskedLM", | |
| "AutoModelForPreTraining": "modeling_eurobert.EuroBertPreTrainedModel", | |
| "AutoModelForSequenceClassification": "modeling_eurobert.EuroBertForSequenceClassification", | |
| "AutoModelForTokenClassification": "modeling_eurobert.EuroBertForTokenClassification" | |
| }, | |
| "bos_token": "<|begin_of_text|>", | |
| "bos_token_id": 128000, | |
| "clf_pooling": "late", | |
| "eos_token": "<|end_of_text|>", | |
| "eos_token_id": 128001, | |
| "head_dim": 64, | |
| "hidden_act": "silu", | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-CHEM", | |
| "2": "I-CHEM" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-CHEM": 1, | |
| "I-CHEM": 2, | |
| "O": 0 | |
| }, | |
| "mask_token": "<|mask|>", | |
| "mask_token_id": 128002, | |
| "max_position_embeddings": 8192, | |
| "mlp_bias": false, | |
| "model_type": "eurobert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "num_key_value_heads": 12, | |
| "pad_token": "<|end_of_text|>", | |
| "pad_token_id": 128001, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 250000, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.53.2", | |
| "use_cache": false, | |
| "vocab_size": 128256 | |
| } | |