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Update README.md
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README.md
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@@ -18,12 +18,25 @@ model-index:
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dataset:
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type: multi_nli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
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name: MultiNLI # Required. A pretty name for the dataset. Example: Common Voice (French)
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split: validation_matched
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metrics:
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- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
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value: 0.9230769230769231 # Required. Example: 20.90
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- type: f1 # Required. Example: wer. Use metric id from https://hf.co/metrics
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value: 0.9225172687920663 # Required. Example: 20.90
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---
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# T5ForSequenceClassification
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**T5ForSequenceClassification** adapts the original [T5](https://github.com/google-research/text-to-text-transfer-transformer) architecture for sequence classification tasks.
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dataset:
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type: multi_nli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
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name: MultiNLI # Required. A pretty name for the dataset. Example: Common Voice (French)
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split: validation_matched # Optional. Example: test
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metrics:
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- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
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value: 0.9230769230769231 # Required. Example: 20.90
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- type: f1 # Required. Example: wer. Use metric id from https://hf.co/metrics
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value: 0.9225172687920663 # Required. Example: 20.90
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- task:
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type: nli # Required. Example: automatic-speech-recognition
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name: Natural Language Inference # Optional. Example: Speech Recognition
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dataset:
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type: multi_nli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
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name: MultiNLI # Required. A pretty name for the dataset. Example: Common Voice (French)
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split: validation_mismatched # Optional. Example: test
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metrics:
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- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
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value: 0.9222945484133441 # Required. Example: 20.90
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- type: f1 # Required. Example: wer. Use metric id from https://hf.co/metrics
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value: 0.9216699467726924 # Required. Example: 20.90
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---
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# T5ForSequenceClassification
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**T5ForSequenceClassification** adapts the original [T5](https://github.com/google-research/text-to-text-transfer-transformer) architecture for sequence classification tasks.
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