
<!-- 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. -->

# distilbert-base-uncased-ner_cv

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: 0.8548
- Precision: 0.3327
- Recall: 0.2358
- F1: 0.2760
- Accuracy: 0.7815

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 5.0   | 30   | 1.0790          | 0.0       | 0.0    | 0.0    | 0.7537   |
| No log        | 10.0  | 60   | 0.9589          | 0.3208    | 0.1207 | 0.1754 | 0.7677   |
| No log        | 15.0  | 90   | 0.8975          | 0.3363    | 0.1591 | 0.2160 | 0.7773   |
| No log        | 20.0  | 120  | 0.8675          | 0.3354    | 0.2259 | 0.2699 | 0.7786   |
| No log        | 25.0  | 150  | 0.8568          | 0.3333    | 0.2443 | 0.2820 | 0.7811   |
| No log        | 30.0  | 180  | 0.8548          | 0.3327    | 0.2358 | 0.2760 | 0.7815   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.8.1+cu111
- Datasets 1.6.2
- Tokenizers 0.12.1

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

distilbert-base-uncased-ner\_cv
===============================


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: 0.8548
* Precision: 0.3327
* Recall: 0.2358
* F1: 0.2760
* Accuracy: 0.7815


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 20
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.8.1+cu111
* Datasets 1.6.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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
* lr\_scheduler\_warmup\_steps: 20
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.8.1+cu111
* Datasets 1.6.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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
* lr\_scheduler\_warmup\_steps: 20
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.8.1+cu111
* Datasets 1.6.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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
* lr\_scheduler\_warmup\_steps: 20
* num\_epochs: 30### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.8.1+cu111
* Datasets 1.6.2
* Tokenizers 0.12.1