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# bert-base-cased-ner_cv-med-ft

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5926
- Precision: 0.2559
- Recall: 0.3460
- F1: 0.2942
- Accuracy: 0.8368

## 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: 10
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.9616        | 2.73  | 30   | 0.7717          | 0.0       | 0.0    | 0.0    | 0.8608   |
| 0.9266        | 5.45  | 60   | 0.6687          | 0.0       | 0.0    | 0.0    | 0.8608   |
| 0.8486        | 8.18  | 90   | 0.6100          | 0.2133    | 0.0488 | 0.0794 | 0.8635   |
| 0.7421        | 10.91 | 120  | 0.5922          | 0.2534    | 0.1966 | 0.2215 | 0.8542   |
| 0.6481        | 13.64 | 150  | 0.5696          | 0.2889    | 0.2378 | 0.2609 | 0.8596   |
| 0.5948        | 16.36 | 180  | 0.5798          | 0.2678    | 0.3034 | 0.2845 | 0.8472   |
| 0.5621        | 19.09 | 210  | 0.5913          | 0.2486    | 0.3293 | 0.2833 | 0.8381   |
| 0.5234        | 21.82 | 240  | 0.5816          | 0.2585    | 0.3262 | 0.2884 | 0.8404   |
| 0.5028        | 24.55 | 270  | 0.5944          | 0.2545    | 0.3476 | 0.2938 | 0.8368   |
| 0.4975        | 27.27 | 300  | 0.5923          | 0.2531    | 0.3476 | 0.2929 | 0.8368   |
| 0.4791        | 30.0  | 330  | 0.5926          | 0.2559    | 0.3460 | 0.2942 | 0.8368   |


### 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"], "widget": [{"text": "Vivo en Buenos Aires,Argentina. Egresado de la carrera Ingenier\u00eda en Computaci\u00f3n Conocimientos de lenguajes HTML, CSS, Javascript y MySQL. Experiencia trabajando en \u00e1mbitos de redes de peque\u00f1a y mediana escala. Ingl\u00e9s Hablado nivel b\u00e1sico, escrito nivel intermedio.HTML, CSS y JavaScript. Realidad aumentada.  Lenguaje R. HTML5, JavaScript y Nodejs"}], "model-index": [{"name": "bert-base-cased-ner_cv-med-ft", "results": []}]}
transformers pytorch bert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-base-cased-ner\_cv-med-ft
==============================


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


* Loss: 0.5926
* Precision: 0.2559
* Recall: 0.3460
* F1: 0.2942
* Accuracy: 0.8368


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: 10
* num\_epochs: 30
* mixed\_precision\_training: Native AMP


### 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: 10
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.8.1+cu111
* Datasets 1.6.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #bert #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: 10
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### 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 #bert #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: 10
* num\_epochs: 30
* mixed\_precision\_training: Native AMP### Training results### Framework versions


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