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# 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

{"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "biolinkbert-large-mnli-resampled", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer en dataset:glue autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us 

biolinkbert-large-mnli-resampled
================================


This model is a fine-tuned version of BioLinkBERT-large on the
resampled version of GLUE MNLI, i.e. 'cnut1648/mnli\_resampled\_as\_mednli'.


The performance is reported below:


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


Compared to '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'.


### 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



### 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
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us 
 ### 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
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #autotrain_compatible #endpoints_compatible #region-us 
### 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