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# bert-tiny-finetuned-wnut17-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 wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6054
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.8961

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log        | 1.0   | 27   | 1.1060          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 2.0   | 54   | 0.9075          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 3.0   | 81   | 0.7978          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 4.0   | 108  | 0.7333          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 5.0   | 135  | 0.6929          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 6.0   | 162  | 0.6661          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 7.0   | 189  | 0.6477          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 8.0   | 216  | 0.6346          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 9.0   | 243  | 0.6251          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 10.0  | 270  | 0.6182          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 11.0  | 297  | 0.6132          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 12.0  | 324  | 0.6097          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 13.0  | 351  | 0.6073          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 14.0  | 378  | 0.6059          | 0.0       | 0.0    | 0.0 | 0.8961   |
| No log        | 15.0  | 405  | 0.6054          | 0.0       | 0.0    | 0.0 | 0.8961   |


### 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": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-tiny-finetuned-wnut17-ner", "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": 0.0, "name": "Precision"}, {"type": "recall", "value": 0.0, "name": "Recall"}, {"type": "f1", "value": 0.0, "name": "F1"}, {"type": "accuracy", "value": 0.8960890010322284, "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-tiny-finetuned-wnut17-ner
==============================


This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the wnut\_17 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6054
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accuracy: 0.8961


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: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### 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: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### 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-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: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### 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 #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: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15### Training results### Framework versions


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