Upload final model artifacts

README.md

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+---
+language:
+- en
+library_name: transformers
+license: mit
+pipeline_tag: text-classification
+tags:
+- arxiv
+- scientific-text-classification
+- scibert
+- streamlit-demo
+datasets:
+- librarian-bots/arxiv-metadata-snapshot
+metrics:
+- accuracy
+- f1
+---
+
+# Article Topic Service SciBERT
+
+SciBERT text classifier for scientific article topic prediction from article title and abstract.
+
+## Labels
+
+- Artificial Intelligence
+- Natural Language Processing
+- Computer Vision
+- Machine Learning
+- Computer Science Theory and Algorithms
+- Mathematics
+- Statistics
+- Electrical Engineering
+- Astrophysics
+- Condensed Matter Physics
+- Quantum Physics
+- Quantitative Biology
+
+## Dataset
+
+Balanced 12-class subset built from `librarian-bots/arxiv-metadata-snapshot`.
+
+- Train: 30,000 examples
+- Validation: 3,600 examples
+- Test: 3,600 examples
+
+## Metrics
+
+- Validation accuracy: 0.8350
+- Validation macro F1: 0.8351
+- Test accuracy: 0.8356
+- Test macro F1: 0.8351
+- Title-only test accuracy: 0.7522
+- Title-only test macro F1: 0.7495
+
+## Usage
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+model_id = "Ian-Khalzov/article-topic-service-scibert"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+
+text = "Title: Large language models for scientific document classification\n\nAbstract: We study..."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
+with torch.inference_mode():
+    probs = torch.softmax(model(**inputs).logits[0], dim=-1)
+
+predicted_label = model.config.id2label[int(probs.argmax())]
+print(predicted_label)
+```
+
+## Notes
+
+The current baseline is strongest on physics-heavy classes and weakest on the broad `Machine Learning` category, where topical overlap with AI, NLP, CV, and Statistics remains high.

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Artificial Intelligence",
+    "1": "Natural Language Processing",
+    "2": "Computer Vision",
+    "3": "Machine Learning",
+    "4": "Computer Science Theory and Algorithms",
+    "5": "Mathematics",
+    "6": "Statistics",
+    "7": "Electrical Engineering",
+    "8": "Astrophysics",
+    "9": "Condensed Matter Physics",
+    "10": "Quantum Physics",
+    "11": "Quantitative Biology"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "label2id": {
+    "Artificial Intelligence": 0,
+    "Astrophysics": 8,
+    "Computer Science Theory and Algorithms": 4,
+    "Computer Vision": 2,
+    "Condensed Matter Physics": 9,
+    "Electrical Engineering": 7,
+    "Machine Learning": 3,
+    "Mathematics": 5,
+    "Natural Language Processing": 1,
+    "Quantitative Biology": 11,
+    "Quantum Physics": 10,
+    "Statistics": 6
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "type_vocab_size": 2,
+  "use_cache": false,
+  "vocab_size": 31090
+}

eval_run_config.json

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+{
+  "prepared_at_utc": "2026-04-06T15:01:43.444774+00:00",
+  "model_name": "/home/yakh/ML/ML2/article_topic_service/artifacts/scibert_topics12_run1",
+  "max_length": 256,
+  "per_device_train_batch_size": 4,
+  "per_device_eval_batch_size": 8,
+  "gradient_accumulation_steps": 8,
+  "num_train_epochs": 12,
+  "learning_rate": 2e-05,
+  "weight_decay": 0.01,
+  "warmup_ratio": 0.1,
+  "label_smoothing_factor": 0.05,
+  "title_only_prob": 0.2,
+  "early_stopping_patience": 3,
+  "seed": 42,
+  "resume_from_checkpoint": null,
+  "use_bf16": true,
+  "use_fp16": false,
+  "taxonomy_profile": "topics12",
+  "num_labels": 12,
+  "label_order": [
+    "artificial_intelligence",
+    "natural_language_processing",
+    "computer_vision",
+    "machine_learning",
+    "computer_science_theory",
+    "mathematics",
+    "statistics",
+    "electrical_engineering",
+    "astrophysics",
+    "condensed_matter_physics",
+    "quantum_physics",
+    "quantitative_biology"
+  ]
+}

metrics_summary.json

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metrics_summary.txt

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