Update README.md

README.md

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 ---
 license: mit
+language:
+- en
+pipeline_tag: text-classification
 ---
+# Model Card for Model NegBLEURT
+
+This model is a negation-aware version of the BLEURT metric for evaluation of generated text.
+
+### Direct Use
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+model_name = "tum-nlp/NegBLEURT"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+references = ["Ray Charles is legendary.", "Ray Charles is legendary."]
+candidates = ["Ray Charles is a legend.", "Ray Charles isn’t legendary."]
+
+tokenized = tokenizer(references, cadidates, return_tensors='pt', padding=True)
+print(model(**tokenized).logits)
+# returns scores 0.8409 and 0.2601 for the two candidates
+```
+
+
+## Training Details
+
+The model is a fine-tuned version of the [bleurt-tiny](https://github.com/google-research/bleurt/tree/master/bleurt/test_checkpoint) checkpoint from the official BLUERT repository. 
+It was fine-tuned on the CANNOT dataset's train split for 500 steps using the [fine-tuning script](https://github.com/google-research/bleurt/blob/master/bleurt/finetune.py) provided by BLEURT.
+
+
+
+## Citation [optional]
+
+Please cite our INLG 2023 paper, if you use our model. 
+**BibTeX:**
+tba
+
+
+