Marwolaeth
/

rubert-tiny-nli-terra-v1

@@ -1,199 +1,101 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- nli
+- bert
+- natural-language-inference
+language:
+- ru
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+base_model:
+- cointegrated/rubert-tiny2
+pipeline_tag: text-classification
+model-index:
+- name: rubert-tiny-nli-terra-v0
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: TERRA
+      type: NLI
+      split: validation
+    metrics:
+    - type: accuracy
+      value: 0.6807817589576547
+      name: Accuracy
+    - type: f1
+      value: 0.6776315789473685
+      name: F1
+    - type: precision
+      value: 0.6821192052980133
+      name: Precision
+    - type: recall
+      value: 0.673202614379085
+      name: Recall
 ---
+**⚠️ Disclaimer: This model is in the early stages of development and may produce low-quality predictions. For better results, consider using the recommended Russian natural language inference models available [here](https://huggingface.co/cointegrated).**
+# RuBERT-tiny-nli v1
+This model is a second attempt to fine-tune the [RuBERT-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) model for a two-way natural language inference task, utilizing the Russian [Textual Entailment Recognition](https://russiansuperglue.com/tasks/task_info/TERRa) dataset. This model uses a custom classifier head with two dense layers. The performance is currently limited.
+## Usage
+How to run the model for NLI:
+```python
+# !pip install transformers sentencepiece --quiet
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+model_id = 'Marwolaeth/rubert-tiny-nli-terra-v0'
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+if torch.cuda.is_available():
+    model.cuda()
+# An example from the base model card
+premise1 = 'Сократ - человек, а все люди смертны.'
+hypothesis1 = 'Сократ никогда не умрёт.'
+with torch.inference_mode():
+    prediction = model(
+      **tokenizer(premise1, hypothesis1, return_tensors='pt').to(model.device)
+    )
+    p = torch.softmax(prediction.logits, -1).cpu().numpy()[0]
+print({v: p[k] for k, v in model.config.id2label.items()})
+# {'not_entailment': 0.61647415, 'entailment': 0.38352585}
+# An example concerning sentiments
+premise2 = 'Я ненавижу желтые занавески'
+hypothesis2 = 'Мне нравятся желтые занавески'
+with torch.inference_mode():
+    prediction = model(
+      **tokenizer(premise2, hypothesis2, return_tensors='pt').to(model.device)
+    )
+    p = torch.softmax(prediction.logits, -1).cpu().numpy()[0]
+print({v: p[k] for k, v in model.config.id2label.items()})
+# {'not_entailment': 0.520086, 'entailment': 0.47991407}
+```
+## Model Performance Metrics
+The following metrics summarize the performance of the model on the test dataset:
+| Metric                           | Value                     |
+|----------------------------------|---------------------------|
+| **Validation Loss**              | 0.6640                    |
+| **Validation Accuracy**          | 68.08%                    |
+| **Validation F1 Score**          | 67.76%                    |
+| **Validation Precision**         | 68.21%                    |
+| **Validation Recall**            | 67.32%                    |
+| **Validation Runtime***          | 0.2521 seconds            |
+| **Samples per Second***          | 1 219.90                    |
+| **Steps per Second***            | 7.93                     |
+*Using T4 GPU with Google Colab