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# bert-tomi

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4198

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 20   | 1.5815          |
| No log        | 2.0   | 40   | 0.7518          |
| No log        | 3.0   | 60   | 0.7153          |
| No log        | 4.0   | 80   | 0.6354          |
| No log        | 5.0   | 100  | 0.5895          |
| No log        | 6.0   | 120  | 0.4882          |
| No log        | 7.0   | 140  | 0.4590          |
| No log        | 8.0   | 160  | 0.4303          |
| No log        | 9.0   | 180  | 0.4644          |
| No log        | 10.0  | 200  | 0.4416          |
| No log        | 11.0  | 220  | 0.4348          |
| No log        | 12.0  | 240  | 0.5306          |
| No log        | 13.0  | 260  | 0.4412          |
| No log        | 14.0  | 280  | 0.4053          |
| No log        | 15.0  | 300  | 0.4185          |
| No log        | 16.0  | 320  | 0.3982          |
| No log        | 17.0  | 340  | 0.4291          |
| No log        | 18.0  | 360  | 0.4316          |
| No log        | 19.0  | 380  | 0.4328          |
| No log        | 20.0  | 400  | 0.4198          |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.10.1
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-tomi", "results": []}]}
transformers pytorch tensorboard distilbert question-answering generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

bert-tomi
=========


This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4198


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: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0
* Datasets 2.10.1
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0
* Datasets 2.10.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0
* Datasets 2.10.1
* Tokenizers 0.12.1