
<!-- 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. -->

# bert-small-finetuned-xglue-ner-longer20

This model is a fine-tuned version of [muhtasham/bert-small-finetuned-xglue-ner-longer10](https://huggingface.co/muhtasham/bert-small-finetuned-xglue-ner-longer10) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5839
- Precision: 0.5783
- Recall: 0.4330
- F1: 0.4952
- Accuracy: 0.9261

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 425  | 0.4882          | 0.5       | 0.4510 | 0.4742 | 0.9228   |
| 0.013         | 2.0   | 850  | 0.5039          | 0.5130    | 0.4246 | 0.4647 | 0.9242   |
| 0.0124        | 3.0   | 1275 | 0.5331          | 0.5506    | 0.4426 | 0.4907 | 0.9256   |
| 0.0159        | 4.0   | 1700 | 0.5403          | 0.5488    | 0.4234 | 0.4781 | 0.9249   |
| 0.0132        | 5.0   | 2125 | 0.5459          | 0.5714    | 0.4211 | 0.4848 | 0.9255   |
| 0.0117        | 6.0   | 2550 | 0.5522          | 0.5637    | 0.4342 | 0.4905 | 0.9253   |
| 0.0117        | 7.0   | 2975 | 0.5712          | 0.5778    | 0.4354 | 0.4966 | 0.9257   |
| 0.007         | 8.0   | 3400 | 0.5860          | 0.5828    | 0.4378 | 0.5    | 0.9259   |
| 0.0066        | 9.0   | 3825 | 0.5745          | 0.5703    | 0.4462 | 0.5007 | 0.9259   |
| 0.0049        | 10.0  | 4250 | 0.5839          | 0.5783    | 0.4330 | 0.4952 | 0.9261   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-small-finetuned-xglue-ner-longer20", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "type": "wnut_17", "config": "wnut_17", "split": "train", "args": "wnut_17"}, "metrics": [{"type": "precision", "value": 0.5782747603833865, "name": "Precision"}, {"type": "recall", "value": 0.43301435406698563, "name": "Recall"}, {"type": "f1", "value": 0.4952120383036936, "name": "F1"}, {"type": "accuracy", "value": 0.92613831861452, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:wnut_17 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-small-finetuned-xglue-ner-longer20
=======================================


This model is a fine-tuned version of muhtasham/bert-small-finetuned-xglue-ner-longer10 on the wnut\_17 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5839
* Precision: 0.5783
* Recall: 0.4330
* F1: 0.4952
* Accuracy: 0.9261


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: 10


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+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: 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: 10 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #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: 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: 10 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #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: 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: 10### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1