Update README.md

README.md

@@ -1,9 +1,62 @@
 ---
 library_name: peft
+license: cc-by-sa-4.0
+datasets:
+- ctu-aic/csfever_v2
+language:
+- cs
+metrics:
+- accuracy
+- f1
+- recall
+- precision
+pipeline_tag: text-classification
 ---
+# Model card for lora-xlm-roberta-large-squad2-csfever_v2-f1
+
+## Model details
+Model for natural language inference.
+
 ## Training procedure
 
 ### Framework versions
 
-
 - PEFT 0.4.0
+
+## Uses
+
+### PEFT (Transformers)
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForSequenceClassification, Pipeline, AutoTokenizer
+
+config = PeftConfig.from_pretrained("ctu-aic/lora-xlm-roberta-large-squad2-csfever_v2-f1")
+model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path)
+model = PeftModel.from_pretrained(model, config)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+#pipeline for NLI
+class NliPipeline(Pipeline):
+    def _sanitize_parameters(self, **kwargs):
+        preprocess_kwargs = {}
+        if "evidence" in kwargs:
+            preprocess_kwargs["evidence"] = kwargs["evidence"]
+        return preprocess_kwargs, {}, {}
+
+    def preprocess(self, claim, evidence=""):
+        model_input = self.tokenizer(claim, evidence, return_tensors=self.framework, truncation=True)
+        return model_input
+    def _forward(self, model_inputs):
+        outputs = self.model(**model_inputs)
+        return outputs
+
+    def postprocess(self, model_outputs):
+        logits = model_outputs.logits
+
+        predictions = torch.argmax(logits, dim=-1)
+        return {"logits": logits, "label": int(predictions[0])}
+
+nli_pipeline = NliPipeline(model=model, tokenizer=tokenizer)
+
+nli_pipeline("claim", "evidence")
+```