Update README.md

README.md

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 ---
 license: mit
+pipeline_tag: text-classification
 ---
+## Roberta for Justification analyst 
+
+This model is a fine-tuned version of the Roberta architecture that has been trained specifically for sequence classification. The fine-tuning process involved using the PyTorch deep learning framework and specific hyperparameters (2-4e, 1-8 epsilon) with Adagrad optimizer.
+
+---
+## Example Usage
+
+To use the model, first load it in PyTorch:
+
+```python
+import torch
+from transformers import RobertaForSequenceClassification, RobertaTokenizer
+# Load the fine-tuned model
+model = RobertaForSequenceClassification.from_pretrained('Dzeniks/justification-analyst')
+
+# Load the tokenizer
+tokenizer = RobertaTokenizer.from_pretrained('Dzeniks/justification-analyst')
+
+# Tokenize the input sequence
+input_text = "This is a sample input sequence"
+input = tokenizer.encode_plus(claim, evidence, return_tensors="pt")
+# Use the model to make a prediction
+model.eval()
+with torch.no_grad():
+  prediction = model(**x)
+predictions = torch.argmax(outputs[0], dim=1).item()
+```
+
+## Classification Labels
+
+The model was trained on a dataset consisting of claims and evidence, where the goal was to classify each claim as either supporting, refuting, or not having enough information to make a decision. The labels used for this task are as follows:
+
+- Label 0: Supports
+- Label 1: Refutes
+- Label 2: Not enough information