Text Classification
Transformers
Safetensors
PyTorch
English
bert
biomedical
text-embeddings-inference
Instructions to use STRIDE-lab/biolinkbert-study-type-20251005 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use STRIDE-lab/biolinkbert-study-type-20251005 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="STRIDE-lab/biolinkbert-study-type-20251005")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("STRIDE-lab/biolinkbert-study-type-20251005") model = AutoModelForSequenceClassification.from_pretrained("STRIDE-lab/biolinkbert-study-type-20251005") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "Randomized-controlled trial (RCT)", | |
| "1": "Cohort study", | |
| "2": "Real-world study", | |
| "3": "Study protocol", | |
| "4": "Systematic review/meta-analysis", | |
| "5": "Qualitative Study", | |
| "6": "Case report", | |
| "7": "Case series", | |
| "8": "Other" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "Case report": "6", | |
| "Case series": "7", | |
| "Cohort study": "1", | |
| "Other": "8", | |
| "Qualitative Study": "5", | |
| "Randomized-controlled trial (RCT)": "0", | |
| "Real-world study": "2", | |
| "Study protocol": "3", | |
| "Systematic review/meta-analysis": "4" | |
| }, | |
| "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", | |
| "problem_type": "single_label_classification", | |
| "transformers_version": "4.57.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 28895 | |
| } | |