Update README.md

README.md

@@ -17,31 +17,37 @@ probably proofread and complete it, then remove this comment. -->
 
 # DistilFEVERen
 
-This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
-It achieves the following results on the evaluation set:
-
 
-## Model description
+This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), specifically trained on the Recognize Textual Entailment (RTE) task using [the first fold split of FEVER dataset in English](https://huggingface.co/datasets/raicrits/fever_folds/blob/main/folds_en/1.json).
+RTE focuses on evaluating the support or refutation of claims within a given text. The labels used for classification are as follows:
+- 0: SUPPORT (indicating that the claim is supported by the text)
+- 1: CONFUTE (indicating that the claim is refuted by the text)
+- 2: NOT ENOUGH INFO (indicating that there is insufficient information in the text to support or refute the claim).
 
-More information needed
 
-## Intended uses & limitations
 
-More information needed
+## Inference API Usage
 
-## Training and evaluation data
+When using the Inference API, it is important to note that the input should be provided by pasting the text first, followed by the claim, without any spaces or separators. The model's tokenizer concatenates these inputs in the specified order. Interestingly, inverting the order of pasting (claim first, then text) seems to produce similar results, suggesting that the model generally captures coherence within a given text.
 
-More information needed
 
 ## Training procedure
 
+
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
 - optimizer: None
 - training_precision: float32
 
-### Training results
+## Inference procedure
+
+
+### Evaluation results
+It achieves the following results on the evaluation set:
+
+## Inference procedure