estnafinema0
/

nerc-extraction

@@ -1,199 +1,114 @@
 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+tags:
+- ner
+license: apache-2.0
+language:
+- ru
+- en
+base_model:
+- google-bert/bert-base-uncased
 ---
+# NERC Extraction – Stage 2 Models
+This repository contains two small neural models for Named Entity Recognition (NER) that have been trained using different annotation sources:
+- **model_llm_pure**: Trained solely on low-quality annotations generated by a Large Language Model (LLM).
+- **primary_model**: Fine-tuned on the original, ground-truth annotations from the CoNLL2003 dataset.
+Both models use a hybrid architecture combining a pre-trained BERT model for contextualized word embeddings, a bidirectional LSTM layer to capture sequence dependencies, and a linear classifier to predict NER tags. The models are evaluated using an entity-level evaluation strategy that measures the correctness of entire entities (including boundaries and labels) using the `seqeval` library.
+---
+## Model Architecture
+**Core Components:**
+1. **Pre-trained BERT Encoder:**
+   Uses `bert-base-cased` to generate high-quality contextualized embeddings for input tokens.
+2. **Bidirectional LSTM (BiLSTM):**
+   Processes the sequence of BERT embeddings to capture sequential dependencies, ensuring that both left and right contexts are taken into account.
+3. **Linear Classification Layer:**
+   Maps the output of the BiLSTM to the set of NER tags defined in the project.
+   The tag set includes standard BIO tags for Person, Organization, Location, Miscellaneous, and additional special tokens (`[CLS]`, `[SEP]`, `X`).
+---
+## Training Data & Annotation Sources
+- **Low-Quality (LLM) Annotations:**
+  The **model_llm_pure** was trained on a dataset generated using the best method from the first stage of the project. This dataset contains approximately 1,000 sentences with LLM-generated annotations.
+- **Ground-Truth Annotations (CoNLL2003):**
+  The **primary_model** was trained on the original expert annotations from the CoNLL2003 dataset (approximately 14,000 sentences).
+  As a result, **primary_model** exhibits significantly improved performance over **model_llm_pure**.
+---
+## Evaluation Metrics
+Our evaluation strategy is based on an entity-level approach:
+1. **Entity-Level Evaluation Module:**
+   - **Prediction Collection:** For each sentence, predicted and true labels are collected in a list-of-lists format.
+   - **Seqeval Accuracy:** Measures the overall accuracy at the entity level.
+   - **F1-Score:** Calculated as the harmonic mean of precision and recall for entire entities. A correct prediction requires that the full entity (with correct boundaries and label) is identified.
+   - **Classification Report:** Provides detailed precision, recall, and F1-scores for each entity type.
+2. **Results Comparison:**
+| Model           | Validation Loss | Seqeval Accuracy | F1-Score |
+|-----------------|-----------------|------------------|----------|
+| **model_llm_pure**  | 0.53443         | 0.85185          | 0.47493  |
+| **primary_model**   | 0.09430         | 0.97955          | 0.88959  |
+These results demonstrate that **primary_model** (trained on ground-truth CoNLL2003 data) achieves significantly better performance compared to **model_llm_pure**, reflecting the importance of high-quality annotations in NER.
+---
+## Usage
+### Inference
+You can load any of the models using the Hugging Face `from_pretrained` API. For example, to load the primary model:
+```python
+from transformers import BertTokenizer
+from your_model_module import NERSmall  # make sure NERSmall is imported
+tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+model_primary = NERSmall.from_pretrained("estnafinema0/nerc-extraction", revision="model_primary").to("cuda")
+```
+Similarly, to load the LLM-based model:
+```python
+model_llm_pure = NERSmall.from_pretrained("estnafinema0/nerc-extraction", revision="main").to("cuda")
+```
+### Fine-tuning & Active Learning
+This repository also serves as the basis for further active learning experiments. The evaluation is performed using an entity-level strategy that ensures that only complete entities (with correct boundaries and labels) are counted as correct. Our active learning experiments (described in additional documentation) have demonstrated that adding high-quality expert examples significantly improves the F1-score.
+---
+## Training & Evaluation
+**Training Environment:**
+- **Optimizer:** Stochastic Gradient Descent (SGD) with learning rate 0.001 and momentum 0.9.
+- **Batch Size:** 32
+- **Epochs:** Models are trained for 5 epochs during initial training (with further fine-tuning as part of active learning experiments).
+**Evaluation Function:**
+Our evaluation function computes entity-level metrics (F1, seqeval accuracy, and validation loss) by processing batches and collecting predictions in a list-of-lists format to ensure that only correctly identified complete entities contribute to the final score.
+---
+## Additional Information
+- **Repository:** All models and intermediate checkpoints are stored in separate branches of the repository. For instance, **primary_model** is available in the branch `model_primary`, while other models (from active learning experiments) are stored in branches with names indicating the iteration and percentage of added expert data (e.g., `active_iter_1_added_20`).