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# test_ner3

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the pv_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2983
- Precision: 0.6698
- Recall: 0.6499
- F1: 0.6597
- Accuracy: 0.9607

## 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1106        | 1.0   | 1813  | 0.1128          | 0.6050    | 0.5949 | 0.5999 | 0.9565   |
| 0.0705        | 2.0   | 3626  | 0.1190          | 0.6279    | 0.6122 | 0.6200 | 0.9585   |
| 0.0433        | 3.0   | 5439  | 0.1458          | 0.6342    | 0.5983 | 0.6157 | 0.9574   |
| 0.0301        | 4.0   | 7252  | 0.1453          | 0.6305    | 0.6818 | 0.6552 | 0.9594   |
| 0.0196        | 5.0   | 9065  | 0.1672          | 0.6358    | 0.6871 | 0.6605 | 0.9594   |
| 0.0133        | 6.0   | 10878 | 0.1931          | 0.6427    | 0.6138 | 0.6279 | 0.9587   |
| 0.0104        | 7.0   | 12691 | 0.1948          | 0.6657    | 0.6511 | 0.6583 | 0.9607   |
| 0.0081        | 8.0   | 14504 | 0.2243          | 0.6341    | 0.6574 | 0.6455 | 0.9586   |
| 0.0054        | 9.0   | 16317 | 0.2432          | 0.6547    | 0.6318 | 0.6431 | 0.9588   |
| 0.0041        | 10.0  | 18130 | 0.2422          | 0.6717    | 0.6397 | 0.6553 | 0.9605   |
| 0.0041        | 11.0  | 19943 | 0.2415          | 0.6571    | 0.6420 | 0.6495 | 0.9601   |
| 0.0027        | 12.0  | 21756 | 0.2567          | 0.6560    | 0.6590 | 0.6575 | 0.9601   |
| 0.0023        | 13.0  | 23569 | 0.2609          | 0.6640    | 0.6495 | 0.6566 | 0.9606   |
| 0.002         | 14.0  | 25382 | 0.2710          | 0.6542    | 0.6670 | 0.6606 | 0.9598   |
| 0.0012        | 15.0  | 27195 | 0.2766          | 0.6692    | 0.6539 | 0.6615 | 0.9610   |
| 0.001         | 16.0  | 29008 | 0.2938          | 0.6692    | 0.6415 | 0.6551 | 0.9603   |
| 0.0007        | 17.0  | 30821 | 0.2969          | 0.6654    | 0.6490 | 0.6571 | 0.9604   |
| 0.0007        | 18.0  | 32634 | 0.3035          | 0.6628    | 0.6456 | 0.6541 | 0.9601   |
| 0.0007        | 19.0  | 34447 | 0.2947          | 0.6730    | 0.6489 | 0.6607 | 0.9609   |
| 0.0004        | 20.0  | 36260 | 0.2983          | 0.6698    | 0.6499 | 0.6597 | 0.9607   |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["pv_dataset"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "test_ner3", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "pv_dataset", "type": "pv_dataset", "config": "PVDatasetCorpus", "split": "train", "args": "PVDatasetCorpus"}, "metrics": [{"type": "precision", "value": 0.6698151950718686, "name": "Precision"}, {"type": "recall", "value": 0.6499117663801446, "name": "Recall"}, {"type": "f1", "value": 0.6597133941985438, "name": "F1"}, {"type": "accuracy", "value": 0.9606609586670052, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer dataset:pv_dataset license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-pv_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

test\_ner3
==========


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


* Loss: 0.2983
* Precision: 0.6698
* Recall: 0.6499
* F1: 0.6597
* Accuracy: 0.9607


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 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* 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 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-pv_dataset #license-apache-2.0 #model-index #autotrain_compatible #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 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-pv_dataset #license-apache-2.0 #model-index #autotrain_compatible #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 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1