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# twitter-roberta-base-dec2021-WNUT

This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2152
- Precision: 0.7112
- Recall: 0.6244
- F1: 0.6650
- Accuracy: 0.9643

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 1024
- 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        | 0.46  | 25   | 0.2818          | 0.0982    | 0.0383 | 0.0551 | 0.9241   |
| No log        | 0.93  | 50   | 0.2158          | 0.6181    | 0.4569 | 0.5254 | 0.9480   |
| No log        | 1.39  | 75   | 0.1930          | 0.6682    | 0.5347 | 0.5940 | 0.9555   |
| No log        | 1.85  | 100  | 0.1728          | 0.6583    | 0.5646 | 0.6079 | 0.9594   |
| No log        | 2.31  | 125  | 0.1787          | 0.7050    | 0.5718 | 0.6314 | 0.9619   |
| No log        | 2.78  | 150  | 0.2051          | 0.6979    | 0.5251 | 0.5993 | 0.9587   |
| No log        | 3.24  | 175  | 0.1755          | 0.7172    | 0.5945 | 0.6501 | 0.9621   |
| No log        | 3.7   | 200  | 0.1720          | 0.6943    | 0.6304 | 0.6608 | 0.9645   |
| No log        | 4.17  | 225  | 0.1873          | 0.7203    | 0.6316 | 0.6730 | 0.9646   |
| No log        | 4.63  | 250  | 0.1781          | 0.6934    | 0.6196 | 0.6545 | 0.9638   |
| No log        | 5.09  | 275  | 0.1953          | 0.7040    | 0.6172 | 0.6577 | 0.9631   |
| No log        | 5.56  | 300  | 0.1953          | 0.7223    | 0.6316 | 0.6739 | 0.9642   |
| No log        | 6.02  | 325  | 0.1839          | 0.7008    | 0.6471 | 0.6729 | 0.9648   |
| No log        | 6.48  | 350  | 0.1995          | 0.716     | 0.6423 | 0.6772 | 0.9650   |
| No log        | 6.94  | 375  | 0.2056          | 0.7251    | 0.6184 | 0.6675 | 0.9640   |
| No log        | 7.41  | 400  | 0.2044          | 0.7065    | 0.6220 | 0.6616 | 0.9640   |
| No log        | 7.87  | 425  | 0.2042          | 0.7201    | 0.6400 | 0.6776 | 0.9650   |
| No log        | 8.33  | 450  | 0.2247          | 0.7280    | 0.6244 | 0.6722 | 0.9638   |
| No log        | 8.8   | 475  | 0.2060          | 0.7064    | 0.6447 | 0.6742 | 0.9649   |
| 0.0675        | 9.26  | 500  | 0.2152          | 0.7112    | 0.6244 | 0.6650 | 0.9643   |
| 0.0675        | 9.72  | 525  | 0.2086          | 0.7070    | 0.6495 | 0.6771 | 0.9650   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "twitter-roberta-base-dec2021-WNUT", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "type": "wnut_17", "args": "wnut_17"}, "metrics": [{"type": "precision", "value": 0.7111716621253406, "name": "Precision"}, {"type": "recall", "value": 0.6244019138755981, "name": "Recall"}, {"type": "f1", "value": 0.664968152866242, "name": "F1"}, {"type": "accuracy", "value": 0.9642789042140724, "name": "Accuracy"}]}]}]}
transformers pytorch roberta token-classification generated_from_trainer dataset:wnut_17 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-wnut_17 #model-index #autotrain_compatible #endpoints_compatible #region-us 

twitter-roberta-base-dec2021-WNUT
=================================


This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the wnut\_17 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2152
* Precision: 0.7112
* Recall: 0.6244
* F1: 0.6650
* Accuracy: 0.9643


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: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 1024
* 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 4.20.1
* Pytorch 1.12.0
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 1024
* 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 4.20.1
* Pytorch 1.12.0
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-wnut_17 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 1024
* 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 4.20.1
* Pytorch 1.12.0
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-wnut_17 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 1024
* 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 4.20.1
* Pytorch 1.12.0
* Datasets 2.3.2
* Tokenizers 0.12.1