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# DistilBERT-finetuned-ner-S800

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0834
- Precision: 0.5329
- Recall: 0.6129
- F1: 0.5701
- Accuracy: 0.9689

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 55   | 0.1363          | 0.2460    | 0.2987 | 0.2698 | 0.9436   |
| No log        | 2.0   | 110  | 0.0926          | 0.3943    | 0.4656 | 0.4270 | 0.9636   |
| No log        | 3.0   | 165  | 0.0823          | 0.4937    | 0.6059 | 0.5441 | 0.9683   |
| No log        | 4.0   | 220  | 0.0802          | 0.5187    | 0.5849 | 0.5498 | 0.9696   |
| No log        | 5.0   | 275  | 0.0834          | 0.5329    | 0.6129 | 0.5701 | 0.9689   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "DistilBERT-finetuned-ner-S800", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer base_model:distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

DistilBERT-finetuned-ner-S800
=============================


This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0834
* Precision: 0.5329
* Recall: 0.6129
* F1: 0.5701
* Accuracy: 0.9689


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.33.2
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.33.2
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.33.2
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3