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# bert-tiny-finetuned-xglue-ner

This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the xglue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2489
- Precision: 0.6308
- Recall: 0.6681
- F1: 0.6489
- Accuracy: 0.9274

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4082        | 1.0   | 1756 | 0.3326          | 0.5600    | 0.5798 | 0.5697 | 0.9118   |
| 0.2974        | 2.0   | 3512 | 0.2635          | 0.6143    | 0.6562 | 0.6346 | 0.9248   |
| 0.2741        | 3.0   | 5268 | 0.2489          | 0.6308    | 0.6681 | 0.6489 | 0.9274   |


### 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": ["xglue"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-tiny-finetuned-xglue-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xglue", "type": "xglue", "config": "ner", "split": "train", "args": "ner"}, "metrics": [{"type": "precision", "value": 0.630759453447728, "name": "Precision"}, {"type": "recall", "value": 0.6681252103668799, "name": "Recall"}, {"type": "f1", "value": 0.6489048708728343, "name": "F1"}, {"type": "accuracy", "value": 0.9274310133922189, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:xglue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-xglue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-tiny-finetuned-xglue-ner
=============================


This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the xglue dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2489
* Precision: 0.6308
* Recall: 0.6681
* F1: 0.6489
* Accuracy: 0.9274


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: 3


### 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: 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: 3 ### 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 #bert #token-classification #generated_from_trainer #dataset-xglue #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: 3 ### 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 #bert #token-classification #generated_from_trainer #dataset-xglue #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: 3### Training results### Framework versions


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