---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_keras_callback
model-index:
- name: DistilFEVERen
  results: []
widget:
- text: Soul Food is a 1997 American comedy-drama film produced by Kenneth `` Babyface '' Edmonds , Tracey Edmonds and Robert Teitel and released by Fox 2000 Pictures .Fox 2000 Pictures released the film Soul Food .
language:
- en
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# DistilFEVERen


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).



## Inference API Usage

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.


## Training procedure



### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32

## Inference procedure


### Evaluation results
It achieves the following results on the evaluation set:

## Inference procedure



### Framework versions

- Transformers 4.35.0
- TensorFlow 2.13.0
- Datasets 2.1.0
- Tokenizers 0.14.1