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--- |
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language: et |
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tags: |
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- estonian |
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- token-classification |
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- quantifier-extraction |
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- roberta |
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- transformers |
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license: mit |
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datasets: |
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- custom |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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--- |
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# Est-RoBERTa for Quantifier Extraction (Estonian) |
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This model is a fine-tuned version of [`EMBEDDIA/est-roberta`](https://huggingface.co/EMBEDDIA/est-roberta) on a custom dataset for extracting **quantifier constructions** (e.g., "kari koeri", "hunnik raamatuid") in Estonian text. |
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It performs **token classification** using the BIO labeling scheme with the following labels: |
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- `O`: Outside |
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- `B-QUANT`: Beginning of a quantifier expression |
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- `I-QUANT`: Inside a quantifier expression |
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📊 Training and Evaluation |
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Epochs: 12 |
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Batch size: 8 |
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Test set: 159 positive cases, 1000 negative cases |
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Precision: 87.05% |
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Recall: 94.53% |
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F1-score: 90.64% |
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Accuracy: 99.88% |
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🏛️ Funding |
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This work was supported by the Estonian Research Council grant (PRG 1978). |
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Uurimistööd on finantseerinud Eesti Teadusagentuur (PRG 1978). |
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## 🔍 Example Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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import torch |
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model = AutoModelForTokenClassification.from_pretrained("ahtokiil/est-roberta-quant-extraction_EKI") |
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tokenizer = AutoTokenizer.from_pretrained("ahtokiil/est-roberta-quant-extraction_EKI") |
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sentence = "Arsti juures tuli tükk aega oodata." |
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inputs = tokenizer(sentence, return_tensors="pt") |
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outputs = model(**inputs) |
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predictions = torch.argmax(outputs.logits, dim=2) |
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tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0]) |
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labels = [model.config.id2label[p.item()] for p in predictions[0]] |
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print(list(zip(tokens, labels))) |
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