Update README.md

README.md

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 ---
 license: mit
+language:
+- sk
+pipeline_tag: text-classification
+library_name: transformers
+metrics:
+- f1
+base_model: daviddrzik/SK_BPE_BLM
+tags:
+- sentiment
 ---
+
+# Fine-Tuned Sentiment Classification Model - SK_BPE_BLM (Movie reviews)
+
+## Model Overview
+
+This model is a fine-tuned version of the [SK_BPE_BLM model](https://huggingface.co/daviddrzik/SK_BPE_BLM) for the task of sentiment classification. It has been trained on a dataset containing movie reviews in the Czech language from the ČSFD dataset, which were then machine-translated into Slovak using Google Cloud Translation.
+
+## Sentiment Labels
+
+Each review in the dataset is labeled with one of the following sentiments:
+- **Negative (0)**
+- **Positive (1)**
+
+## Dataset Details
+
+The dataset used for fine-tuning comprises a total of 53,402 text records, labeled with sentiment as follows:
+- **Negative records (0):** 25,618
+- **Positive records (1):** 27,784
+
+For more information about the dataset, please visit [this link](https://www.kaggle.com/datasets/lowoncuties/czech-movie-review-csfd/).
+
+## Fine-Tuning Hyperparameters
+
+The following hyperparameters were used during the fine-tuning process:
+
+- **Learning Rate:** 5e-06
+- **Training Batch Size:** 64
+- **Evaluation Batch Size:** 64
+- **Seed:** 42
+- **Optimizer:** Adam (default)
+- **Number of Epochs:** 5
+
+## Model Performance
+
+The model was evaluated using stratified 10-fold cross-validation, achieving a weighted F1-score with a median value of <span style="font-size: 24px;">**0.928**</span> across the folds.
+
+## Model Usage
+
+This model is suitable for sentiment classification in Slovak text, especially for user reviews of movies. It is specifically designed for applications requiring sentiment analysis of user reviews and may not generalize well to other types of text.
+
+### Example Usage
+
+Below is an example of how to use the fine-tuned `SK_Morph_BLM-sentiment-csfd` model in a Python script:
+
+```python
+import torch
+from transformers import RobertaForSequenceClassification, RobertaTokenizerFast
+
+class SentimentClassifier:
+    def __init__(self, tokenizer, model):
+        self.model = RobertaForSequenceClassification.from_pretrained(model, num_labels=2)
+        self.tokenizer = RobertaTokenizerFast.from_pretrained(tokenizer, max_length=256)
+
+    def tokenize_text(self, text):
+        encoded_text = self.tokenizer.encode_plus(
+            text.lower(), 
+            max_length=256, 
+            padding='max_length', 
+            truncation=True, 
+            return_tensors='pt'
+        )
+        return encoded_text
+
+    def classify_text(self, encoded_text):
+        with torch.no_grad():
+            output = self.model(**encoded_text)
+            logits = output.logits
+            predicted_class = torch.argmax(logits, dim=1).item()
+            probabilities = torch.softmax(logits, dim=1)
+            class_probabilities = probabilities[0].tolist()
+            predicted_class_text = self.model.config.id2label[predicted_class]
+        return predicted_class, predicted_class_text, class_probabilities
+
+# Instantiate the sentiment classifier with the specified tokenizer and model
+classifier = SentimentClassifier(tokenizer="daviddrzik/SK_BPE_BLM", model="daviddrzik/SK_BPE_BLM-sentiment-csfd")
+
+# Example text to classify sentiment
+text_to_classify = "Tento film síce nebol najlepší aký som kedy videl, ale pozrel by som si ho opäť."
+print("Text to classify: " + text_to_classify + "\n")
+
+# Tokenize the input text
+encoded_text = classifier.tokenize_text(text_to_classify)
+
+# Classify the sentiment of the tokenized text
+predicted_class, predicted_class_text, logits = classifier.classify_text(encoded_text)
+
+# Print the predicted class label and index
+print(f"Predicted class: {predicted_class_text} ({predicted_class})")
+# Print the probabilities for each class
+print(f"Class probabilities: {logits}")
+```
+
+Here is the output when running the above example:
+```yaml
+Text to classify: Tento film síce nebol najlepší aký som kedy videl, ale pozrel by som si ho opäť.
+
+Predicted class: POSITIVE (1)
+Class probabilities: [0.015124241821467876, 0.9848757386207581]
+```