initial commit

.gitignore

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+# Ignore Python cache and compiled files
+__pycache__/
+*.py[cod]
+*.pyo
+
+# Ignore logs
+*.log
+logs/
+*.out
+
+# Ignore prediction files (JSON format)
+*.json
+
+# Ignore environment and dependency files
+.env
+*.env
+*.venv
+venv/
+ENV/
+*.lock
+
+# Ignore Jupyter Notebook checkpoints
+.ipynb_checkpoints
+
+# Ignore temporary or backup files
+*.bak
+*.swp
+*.tmp
+
+# Ignore OS-specific files
+.DS_Store
+Thumbs.db
+
+# Ignore model checkpoint files (optional)
+checkpoint/
+*.ckpt
+
+# If using Hugging Face Model repository, don't ignore the following:
+!config.json
+!tokenizer_config.json
+!tokenizer.json
+!special_tokens.json
+!added_tokens.json
+!pytorch_model.bin

README.md

@@ -10,4 +10,35 @@ base_model:
 - microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
 pipeline_tag: text-classification
 library_name: transformers
----
+---
+
+# Fine-tuned RE Model for DiMB-RE
+
+## Model Description
+This is a fine-tuned **Relation Extraction (RE)** model based on the [BiomedNLP-BiomedBERT-base-uncased](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) model, specifically designed for sentence classification task to extract relations between extract entities for diet, human metabolism and microbiome field. The model has been trained on the DiMB-RE dataset and is optimized to infer relationship with 13 relation types.
+
+<!-- ### Key Features:
+- **Language**: English
+- **Task**: Token classification for Named Entity Recognition (NER)
+- **Base Model**: BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
+- **Domains**: Biomedical, Clinical, Scientific -->
+
+## Performance
+The model has been evaluated on the DiMB-RE using the following metrics:
+- **REL Strict** - P: 0.416, R: 0.336, F1: 0.371
+- **REL Relaxed** - P: 0.448, R: 0.370, F1: 0.409
+
+## Citation
+If you use this model, please cite like below:
+
+```bibtex
+@misc{hong2024dimbreminingscientificliterature,
+      title={DiMB-RE: Mining the Scientific Literature for Diet-Microbiome Associations}, 
+      author={Gibong Hong and Veronica Hindle and Nadine M. Veasley and Hannah D. Holscher and Halil Kilicoglu},
+      year={2024},
+      eprint={2409.19581},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2409.19581}, 
+}
+```

config.json

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+{
+  "_name_or_path": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext",
+  "architectures": [
+    "BertForRelation"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.34.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30753
+}

pytorch_model.bin

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+oid sha256:497718ca44902743585aec6cdad91a1d98f3509557e874a5d76bec99eb53a985
+size 438744886

special_tokens.json

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+{}