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# nli-finetune-model

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the multi_nli dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2551
- Accuracy: 0.7793

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6813        | 1.0   | 2500 | 0.6655          | 0.7657   |
| 0.5632        | 2.0   | 5000 | 1.0409          | 0.778    |
| 0.3753        | 3.0   | 7500 | 1.2551          | 0.7793   |


### Framework versions

- Transformers 4.28.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["multi_nli"], "metrics": ["accuracy"], "model-index": [{"name": "nli-finetune-model", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "multi_nli", "type": "multi_nli", "config": "default", "split": "validation_matched", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7793333333333333, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:multi_nli license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-multi_nli #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

nli-finetune-model
==================


This model is a fine-tuned version of bert-large-uncased on the multi\_nli dataset.
It achieves the following results on the evaluation set:


* Loss: 1.2551
* Accuracy: 0.7793


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1
* num\_epochs: 3.0


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-multi_nli #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3