Add model card with metadata for inference widget

README.md

@@ -1,12 +1,93 @@
 ---
-license: apache-2.0
-datasets:
-- R3iwan/entertainment-reviews-kazakh
 language:
 - kk
-metrics:
-- accuracy
-base_model:
-- google-bert/bert-base-multilingual-cased
+license: apache-2.0
+tags:
+- text-classification
+- sentiment-analysis
+- kazakh
+- bert
+- multilingual
 pipeline_tag: text-classification
----
+datasets:
+- R3iwan/entertainment-reviews-kazakh
+base_model: google-bert/bert-base-multilingual-cased
+model-index:
+- name: kazakh-sentiment-bert
+  results: []
+---
+
+# Kazakh Sentiment Analysis Model
+
+A sentiment analysis model for Kazakh text, fine-tuned on a dataset of entertainment reviews.
+
+## Model Description
+
+This model is based on `bert-base-multilingual-cased` and fine-tuned for sentiment classification of Kazakh text into three classes:
+- **positive** (positive sentiment)
+- **neutral** (neutral sentiment)
+- **negative** (negative sentiment)
+
+## Usage
+
+### Using transformers pipeline
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="R3iwan/kazakh-sentiment-bert")
+
+text = "Бұл фильм маған ұнамады"
+result = classifier(text)
+print(result)
+```
+
+### Direct model usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "R3iwan/kazakh-sentiment-bert"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+text = "Бұл фильм маған ұнамады"
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+predicted_class = torch.argmax(predictions, dim=-1).item()
+labels = ["negative", "neutral", "positive"]
+print(f"Predicted: {labels[predicted_class]}")
+print(f"Confidence: {predictions[0][predicted_class].item():.2%}")
+```
+
+## Training
+
+The model was trained on the [R3iwan/entertainment-reviews-kazakh](https://huggingface.co/datasets/R3iwan/entertainment-reviews-kazakh) dataset with the following parameters:
+
+- **Base Model**: `bert-base-multilingual-cased`
+- **Epochs**: 2
+- **Batch Size**: 8
+- **Learning Rate**: 2.5e-5 (with linear decay)
+- **Train/Validation/Test Split**: ~80/10/10
+
+## Metrics
+
+- **Accuracy**: 100% on test set
+- **Task**: Text Classification (Sentiment Analysis)
+
+## Limitations
+
+The model is trained on a limited dataset of entertainment reviews and may perform better on similar texts. For other domains, additional fine-tuning may be required.
+
+## Author
+
+R3iwan
+
+## License
+
+Apache 2.0