nesemenpolkov
/

msu-wiki-ner

@@ -5,199 +5,93 @@ tags:
 - msu
 - wiki
 - fine-tuned
 ---
 # Model Card for Model ID
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 - msu
 - wiki
 - fine-tuned
+datasets:
+- RCC-MSU/collection3
+language:
+- ru
+metrics:
+- precision
+- recall
+- f1
+base_model:
+- Babelscape/wikineural-multilingual-ner
+pipeline_tag: token-classification
 ---
 # Model Card for Model ID
+Fine-tuned multilingual model for russian language NER.
+This is the model card for fine-tuned [Babelscape/wikineural-multilingual-ner](https://huggingface.co/Babelscape/wikineural-multilingual-ner), which has multilingual mBERT as its base.
+I`ve fine-tuned it using [RCC-MSU/collection3](https://huggingface.co/datasets/RCC-MSU/collection3) dataset for token-classification task. The dataset has BIO-pattern and following labels:
+```python
+label_names = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']
+```
 ## Model Details
+Fine-tuning was proceeded in 3 epochs, and computed next metrics:
+| Epoch | Training Loss | Validation Loss | Precision | Recall | F1 | Accuracy |
+| 1 | 0.041000 | 0.032810 | 0.959569 | 0.974253 | 0.966855 | 0.993325 |
+| 2 | 0.020800 | 0.028395 | 0.959569 | 0.974253 | 0.966855 | 0.993325 |
+| 3 | 0.010500 | 0.029138 | 0.963239 | 0.973767 | 0.968474 | 0.993247 |
+To avoid over-fitting due to a small amount of training samples, i used hight weight_decay = 0.1.
+## Basic usage
+So, you can easily use this model with pipeline for 'token-classification' task.
+```python
+import torch
+from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
+from datasets import load_dataset
+model_ckpt = "nesemenpolkov/msu-wiki-ner"
+label_names = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']
+id2label = {i: label for i, label in enumerate(label_names)}
+label2id = {v: k for k, v in id2label.items()}
+tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
+model = AutoModelForTokenClassification.from_pretrained(
+    model_ckpt,
+    id2label=id2label,
+    label2id=label2id,
+    ignore_mismatched_sizes=True
+)
+pipe = pipeline(
+    task="token-classification",
+    model=model,
+    tokenizer=tokenizer,
+    device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
+    aggregation_strategy="simple"
+)
+demo_sample = "Этот Иван Иванов, в паспорте Иванов И.И."
+with torch.no_grad():
+    out = pipe(demo_sample)
+```
 ## Bias, Risks, and Limitations
+This model is finetuned version of [Babelscape/wikineural-multilingual-ner](https://huggingface.co/Babelscape/wikineural-multilingual-ner), on a russian language NER dataset [RCC-MSU/collection3](https://huggingface.co/datasets/RCC-MSU/collection3). It can show low scores on another language texts.
 ## Citation [optional]
+```
+@inproceedings{tedeschi-etal-2021-wikineural-combined,
+    title = "Fine-tuned multilingual model for russian language NER.",
+    author = "nesemenpolkov",
+    booktitle = "Detecting names in noisy and dirty data.",
+    month = oct,
+    year = "2024",
+    address = "Moscow, Russian Federation",
+}
+```