push model

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+tags:
+- finance
+- aspect-classification
+- absa
+- finbert
+- text-classification
+datasets:
+- pauri32/fiqa-2018
+base_model: ProsusAI/finbert
+metrics:
+- accuracy
+- f1
+pipeline_tag: text-classification
+---
+
+# ABSA-FinBERT: Aspect Classification for Financial Text
+
+This model classifies financial headlines and tweets into four aspect categories: **Corporate**, **Economy**, **Market**, and **Stock**.
+
+## Model Description
+
+ABSA-FinBERT is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) for Level-1 aspect classification on the FiQA dataset. The model was trained with class-weighted cross-entropy loss to address extreme class imbalance in the training data.
+
+This work is motivated by [Yang et al. (2018)](https://arxiv.org/abs/1808.07931), "Financial Aspect-Based Sentiment Analysis using Deep Representations," which demonstrated that financial text often contains multi-dimensional information requiring aspect-level analysis.
+
+## Intended Use
+
+- Classifying financial news headlines by topic/aspect
+- Preprocessing step for aspect-based sentiment analysis pipelines
+- Financial text categorization
+
+## Training Data
+
+Trained on the [FiQA dataset](https://huggingface.co/datasets/pauri32/fiqa-2018) (WWW'18 Open Challenge), with Level-1 aspect labels extracted from hierarchical annotations.
+
+| Aspect | Training Examples | Percentage |
+|--------|-------------------|------------|
+| Stock | 562 | 58.5% |
+| Corporate | 367 | 38.2% |
+| Market | 26 | 2.7% |
+| Economy | 4 | 0.4% |
+
+### Class Weights Applied
+Due to extreme imbalance, inverse frequency weights were used: Corporate (0.65), Economy (59.94), Market (9.22), Stock (0.43).
+
+## Performance
+
+| Metric | Score |
+|--------|-------|
+| Accuracy | 88.59% |
+| Macro-F1 | 0.5429 |
+| Weighted-F1 | 0.8688 |
+
+### Per-Class Results
+
+| Aspect | Precision | Recall | F1-Score | Support |
+|--------|-----------|--------|----------|---------|
+| Corporate | 0.91 | 0.94 | 0.92 | 64 |
+| Economy | 0.00 | 0.00 | 0.00 | 3 |
+| Market | 0.50 | 0.25 | 0.33 | 8 |
+| Stock | 0.89 | 0.95 | 0.92 | 74 |
+
+**Note:** The model performs well on majority classes but fails on Economy due to having only 4 training examples. Class weighting cannot overcome severe data scarcity.
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("your-username/absa-finbert")
+model = AutoModelForSequenceClassification.from_pretrained("your-username/absa-finbert")
+
+# Label mapping
+id2label = {0: "Corporate", 1: "Economy", 2: "Market", 3: "Stock"}
+
+# Example inference
+text = "How Kraft-Heinz Merger Came Together in Speedy 10 Weeks"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
+outputs = model(**inputs)
+prediction = torch.argmax(outputs.logits, dim=-1).item()
+
+print(f"Aspect: {id2label[prediction]}")  # Output: Corporate
+```
+
+## Training Procedure
+
+- **Base model:** ProsusAI/finbert
+- **Learning rate:** 3e-5
+- **Batch size:** 16 (effective 32 with gradient accumulation)
+- **Epochs:** 10 (early stopping patience: 3)
+- **Loss:** Weighted cross-entropy
+- **Optimizer:** AdamW with warmup (10%)
+- **Mixed precision:** FP16
+
+## Limitations
+
+- Economy class is effectively unlearnable with only 4 training examples
+- Market class has limited representation (26 examples)
+- Model is optimized for short financial headlines/tweets, not long-form text
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{absa-finbert-2025,
+  title={ABSA-FinBERT: Aspect Classification for Financial Text},
+  author={Cirillo, Nick and Memon, Suha and Truong, Kalen and Zhang, Bruce},
+  year={2025},
+  howpublished={\url{https://huggingface.co/your-username/absa-finbert}}
+}
+```
+
+## References
+
+- Yang, S., Rosenfeld, J., & Makutonin, J. (2018). Financial Aspect-Based Sentiment Analysis using Deep Representations. arXiv:1808.07931.
+- Araci, D. (2019). FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. arXiv:1908.10063.
+- Maia, M., et al. (2018). WWW'18 Open Challenge: Financial Opinion Mining and Question Answering.

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.2,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.2,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "positive",
+    "1": "negative",
+    "2": "neutral"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "negative": 1,
+    "neutral": 2,
+    "positive": 0
+  },
+  "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",
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e0bbf9021d3af08672e7b0a911621662602736ff6d408035dfeb5e5e62749a5c
+size 437961724

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}