Update README.md

README.md

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 ---
 license: mit
+pipeline_tag: text-classification
 ---
+# roberta-nei-fact-check
+
+This is a machine learning model trained for text classification using the Roberta architecture and a tokenizer. The purpose of this model is to identify whether a given claim with evidence contains enough information to make a fact-checking decision.
+
+## Model Details
+
+The model was trained using the Adam optimizer with a learning rate of 2-4e, an epsilon of 1-8, and a weight decay of 2-8e. The training data consisted mainly of the Fever and Hover datasets, with a small sample of created data. The model returns two labels:
+
+- 0: Enough information
+- 1: Not enough information
+
+The model uses a tokenizer for text classification and requires input in the form of a claim with evidence. This means that the input should be a text string containing both the claim and the evidence to provide best result.
+
+## Usage
+
+To use this model, you can load it into your Python code using a library such as PyTorch or TensorFlow. You can then pass in a claim with evidence string and the model will return a label indicating whether there is enough information in the claim with evidence for fact-checking.
+
+Here is an example of how to use the model in PyTorch:
+
+```python
+import torch
+from transformers import RobertaTokenizer, RobertaForSequenceClassification
+
+# Load the tokenizer and model
+tokenizer = RobertaTokenizer.from_pretrained('Dzeniks/roberta-nei-fact-check')
+model = RobertaForSequenceClassification.from_pretrained('Dzeniks/roberta-nei-fact-check')
+
+# Define the claim with evidence to classify
+claim = "Albert Einstein work in the field of computer science"
+evidence = "Albert Einstein was a German-born theoretical physicist, widely acknowledged to be one of the greatest and most influential physicists of all time."
+
+# Tokenize the claim with evidence
+x = tokenizer.encode_plus(claim, evidence, return_tensors="pt")
+
+model.eval()
+with torch.no_grad():
+  prediction = model(**x)
+
+label = torch.argmax(outputs[0]).item()
+
+print(f"Label: {label}")
+```
+In this example, the claim_with_evidence variable contains the claim with evidence to classify. The claim with evidence is tokenized using the tokenizer and converted to a tensor. The model is then used to classify the claim with evidence and the resulting label is printed to the console.