transformers pytorch bert text-classification generated_from_trainer en dataset:glue autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
language:
- en
tags:
- generated_from_trainer
model-index:
- name: biolinkbert-large-mnli-resampled
  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. -->

# biolinkbert-large-mnli-resampled

This model is a fine-tuned version of [BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on the 
resampled version of GLUE [MNLI](https://huggingface.co/datasets/multi_nli), i.e. [`cnut1648/mnli_resampled_as_mednli`](cnut1648/mnli_resampled_as_mednli).

The performance is reported below:

| **Model**                   | **Dataset** | **Acc** |
|-----------------------------|-------------|---------|
| Roberta-large-mnli          | MNLI dev mm | 90.12   |
|                             | MNLI dev m  | 90.59   |
|                             | SNLI test   | 88.25   |
| BioLinkBERT-large           | MNLI dev mm | 33.56   |
|                             | MNLI dev m  | 33.18   |
|                             | SNLI test   | 32.66   |
| BioLinkBERT-large-mnli-snli | MNLI dev mm | 85.75   |
|                             | MNLI dev m  | 85.30   |
|                             | SNLI test   | 89.82   |
| BioLinkBERT-large-mnli-resampled | MNLI dev mm | 80.22   |
|                             | MNLI dev m  | 78.07   |
|                             | SNLI test   | 71.33   |

Compared to [`cnut1648/biolinkbert-large-mnli-snli`](https://huggingface.co/cnut1648/biolinkbert-large-mnli-snli), this checkpoint is never trained on SNLI.

The labels are
"0": "entailment",
"1": "neutral",
"2": "contradiction"


## Training procedure

We follow the same training procedure as [`cnut1648/biolinkbert-large-mnli-snli`](https://huggingface.co/cnut1648/biolinkbert-large-mnli-snli#training-procedure).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.4.0
- Tokenizers 0.12.1
