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# tajroberto-ner

This model is a fine-tuned version of [muhtasham/RoBERTa-tg](https://huggingface.co/muhtasham/RoBERTa-tg) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9408
- Precision: 0.3155
- Recall: 0.5673
- F1: 0.4055
- Accuracy: 0.8360

## 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: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 2.0   | 50   | 0.7710          | 0.0532    | 0.1827 | 0.0824 | 0.6933   |
| No log        | 4.0   | 100  | 0.5901          | 0.0847    | 0.25   | 0.1265 | 0.7825   |
| No log        | 6.0   | 150  | 0.5226          | 0.2087    | 0.4615 | 0.2874 | 0.8186   |
| No log        | 8.0   | 200  | 0.5041          | 0.2585    | 0.5096 | 0.3430 | 0.8449   |
| No log        | 10.0  | 250  | 0.5592          | 0.2819    | 0.5096 | 0.3630 | 0.8499   |
| No log        | 12.0  | 300  | 0.5725          | 0.3032    | 0.5481 | 0.3904 | 0.8558   |
| No log        | 14.0  | 350  | 0.6433          | 0.3122    | 0.5673 | 0.4027 | 0.8508   |
| No log        | 16.0  | 400  | 0.6744          | 0.3543    | 0.5962 | 0.4444 | 0.8553   |
| No log        | 18.0  | 450  | 0.7617          | 0.3353    | 0.5577 | 0.4188 | 0.8335   |
| 0.2508        | 20.0  | 500  | 0.7608          | 0.3262    | 0.5865 | 0.4192 | 0.8419   |
| 0.2508        | 22.0  | 550  | 0.8483          | 0.3224    | 0.5673 | 0.4111 | 0.8494   |
| 0.2508        | 24.0  | 600  | 0.8370          | 0.3275    | 0.5385 | 0.4073 | 0.8439   |
| 0.2508        | 26.0  | 650  | 0.8652          | 0.3410    | 0.5673 | 0.4260 | 0.8394   |
| 0.2508        | 28.0  | 700  | 0.9441          | 0.3409    | 0.5769 | 0.4286 | 0.8216   |
| 0.2508        | 30.0  | 750  | 0.9228          | 0.3333    | 0.5577 | 0.4173 | 0.8439   |
| 0.2508        | 32.0  | 800  | 0.9175          | 0.3430    | 0.5673 | 0.4275 | 0.8355   |
| 0.2508        | 34.0  | 850  | 0.9603          | 0.3073    | 0.5288 | 0.3887 | 0.8340   |
| 0.2508        | 36.0  | 900  | 0.9417          | 0.3240    | 0.5577 | 0.4099 | 0.8370   |
| 0.2508        | 38.0  | 950  | 0.9408          | 0.3155    | 0.5673 | 0.4055 | 0.8360   |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tajroberto-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "config": "tg", "split": "train+test", "args": "tg"}, "metrics": [{"type": "precision", "value": 0.3155080213903743, "name": "Precision"}, {"type": "recall", "value": 0.5673076923076923, "name": "Recall"}, {"type": "f1", "value": 0.4054982817869416, "name": "F1"}, {"type": "accuracy", "value": 0.83597621407334, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard roberta token-classification generated_from_trainer dataset:wikiann model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-wikiann #model-index #autotrain_compatible #endpoints_compatible #region-us 

tajroberto-ner
==============


This model is a fine-tuned version of muhtasham/RoBERTa-tg on the wikiann dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9408
* Precision: 0.3155
* Recall: 0.5673
* F1: 0.4055
* Accuracy: 0.8360


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: 200


### Training results



### Framework versions


* Transformers 4.21.2
* 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: 200 ### Training results ### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-wikiann #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: 200 ### Training results ### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-wikiann #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: 200### Training results### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1