SIRIS-Lab
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social-sciences_binary_classifier

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nicolauduran45 commited on Jul 7

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----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+# 📗 SPECTER2 – Social Sciences Classifier (Binary Classification)
+This model is a fine-tuned version of **allenai/specter2_base** for identifying whether a scientific publication belongs to the **Social Sciences** domain.
+It achieves the following results on the evaluation set:
+- Loss: 0.1382
+- Accuracy: 0.9670
+- F1 Micro: 0.9670
+- F1 Macro: 0.9480
+- F1 Weighted: 0.9670
+## Model description
+This model performs **binary document classification** and predicts whether a publication belongs to the **Social Sciences** domain.
+The model was trained using title and abstract text from multiple openly available datasets with native disciplinary annotations, including:
+- [MAG / SciDocs](https://github.com/allenai/scidocs)
+- [Elsevier Open Access (ASJC subject areas)](https://researchcollaborations.elsevier.com/en/datasets/elsevier-oa-cc-by-corpus/)
+- [ERC panel datasets (publications and funded projects)](https://huggingface.co/datasets/SIRIS-Lab/erc-classification-dataset)
+Each dataset was converted into a common binary label indicating whether a document belongs to the Social Sciences according to mappings from the original classification systems. :contentReference[oaicite:0]{index=0}
+**Key characteristics**
+- Base model: `allenai/specter2_base`
+- Task: binary document classification
+- Labels:
+  - `False` → Non-Social Sciences
+  - `True` → Social Sciences
+- Activation: softmax
+- Loss: CrossEntropyLoss
+## Intended uses
+This model is suitable for:
+- Identifying Social Sciences publications
+- Research information systems
+- Funding portfolio analysis
+- Metadata enrichment
+- Bibliometric analyses
+The model accepts:
+- title
+- abstract
+- title + abstract (recommended)
+## Training data
+Training data combines approximately **20,000** documents sampled from multiple sources:
+- MAG/SciDocs
+- ERC panel datasets (publications and projects)
+- Elsevier Open Access publications
+Each source provides its own disciplinary taxonomy. Categories corresponding to **Social Sciences** were mapped into a common binary classification problem. :contentReference[oaicite:1]{index=1}
+## Training procedure
+### Preprocessing
+- Input text: `title + abstract`
+- Maximum sequence length: **512 tokens**
+- Tokenization using the SPECTER2 tokenizer
+### Training hyperparameters
+- learning_rate: 2e-5
+- train_batch_size: 32
+- eval_batch_size: 32
+- num_epochs: 4
+- max_length: 512
+- optimizer: AdamW
+- metric for best model: F1 Macro
+## Evaluation results
+| Metric | Value |
+|--------|------:|
+| Accuracy | 0.9670 |
+| F1 Micro | 0.9670 |
+| F1 Macro | 0.9480 |
+| F1 Weighted | 0.9670 |
+## Limitations
+- The model predicts whether a publication belongs to the **Social Sciences** domain only.
+- It does not distinguish between individual Social Sciences disciplines.
+- Labels are derived from mappings between different disciplinary taxonomies and should be interpreted as high-level domain assignments.
+## Framework versions
+- Transformers 4.57.1
+- PyTorch 2.8.0
+- Datasets 3.6.0
+- Tokenizers 0.22.1