pritamdeka
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PubMedBERT-MNLI-MedNLI

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pritamdeka commited on May 29, 2023

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@@ -4,16 +4,16 @@ tags:
 metrics:
 - accuracy
 model-index:
-- name: best_model
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
-# best_model
-This model is a fine-tuned version of [/content/drive/MyDrive/fine_tuned_models/pubmedbert_mnli_v3](https://huggingface.co//content/drive/MyDrive/fine_tuned_models/pubmedbert_mnli_v3) on an unknown dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.9501
 - Accuracy: 0.8667

 metrics:
 - accuracy
 model-index:
+- name: pritamdeka/PubMedBERT-MNLI-MEDNLI
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
+# pritamdeka/PubMedBERT-MNLI-MEDNLI
+This model is a fine-tuned version of [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the [MNLI](https://huggingface.co/datasets/multi_nli) dataset first and then on the [MedNLI](https://physionet.org/content/mednli/1.0.0/) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.9501
 - Accuracy: 0.8667