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# aces-roberta-13

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4600
- Precision: 0.8364
- Recall: 0.8452
- F1: 0.8383
- Accuracy: 0.8452
- F1 Who: 0.9189
- F1 What: 0.8621
- F1 Where: 0.9231
- F1 How: 0.9141

## 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: 1e-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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy | F1 Who | F1 What | F1 Where | F1 How |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|:-------:|:--------:|:------:|
| 1.9849        | 0.35  | 20   | 1.4123          | 0.5426    | 0.6351 | 0.5494 | 0.6351   | 0.1026 | 0.6222  | 0.3232   | 0.7857 |
| 1.2159        | 0.7   | 40   | 0.9450          | 0.6559    | 0.7188 | 0.6592 | 0.7188   | 0.6780 | 0.7539  | 0.7071   | 0.7882 |
| 0.8634        | 1.05  | 60   | 0.6885          | 0.7652    | 0.7994 | 0.7725 | 0.7994   | 0.9067 | 0.8152  | 0.8070   | 0.8940 |
| 0.6777        | 1.4   | 80   | 0.6144          | 0.7650    | 0.7946 | 0.7711 | 0.7946   | 0.9189 | 0.7876  | 0.8039   | 0.9085 |
| 0.6051        | 1.75  | 100  | 0.5485          | 0.8126    | 0.8278 | 0.8150 | 0.8278   | 0.9315 | 0.8362  | 0.8148   | 0.9241 |
| 0.5511        | 2.11  | 120  | 0.5264          | 0.8113    | 0.8167 | 0.8036 | 0.8167   | 0.9315 | 0.8444  | 0.8257   | 0.9199 |
| 0.486         | 2.46  | 140  | 0.4867          | 0.8230    | 0.8357 | 0.8248 | 0.8357   | 0.9315 | 0.8539  | 0.9091   | 0.9048 |
| 0.4813        | 2.81  | 160  | 0.4767          | 0.8285    | 0.8278 | 0.8213 | 0.8278   | 0.9189 | 0.8701  | 0.9076   | 0.9135 |
| 0.4494        | 3.16  | 180  | 0.5042          | 0.8152    | 0.8199 | 0.8126 | 0.8199   | 0.9315 | 0.8427  | 0.8333   | 0.8956 |
| 0.4018        | 3.51  | 200  | 0.4802          | 0.8248    | 0.8357 | 0.8249 | 0.8357   | 0.9189 | 0.8736  | 0.8780   | 0.9357 |
| 0.4205        | 3.86  | 220  | 0.4723          | 0.8340    | 0.8389 | 0.8346 | 0.8389   | 0.9189 | 0.8636  | 0.9138   | 0.8986 |
| 0.3535        | 4.21  | 240  | 0.4669          | 0.8324    | 0.8452 | 0.8364 | 0.8452   | 0.9189 | 0.8571  | 0.9138   | 0.9167 |
| 0.3808        | 4.56  | 260  | 0.4585          | 0.8349    | 0.8452 | 0.8383 | 0.8452   | 0.9189 | 0.8621  | 0.9231   | 0.9141 |
| 0.3491        | 4.91  | 280  | 0.4600          | 0.8364    | 0.8452 | 0.8383 | 0.8452   | 0.9189 | 0.8621  | 0.9231   | 0.9141 |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "aces-roberta-13", "results": []}]}
transformers pytorch roberta token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

aces-roberta-13
===============


This model is a fine-tuned version of roberta-large on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4600
* Precision: 0.8364
* Recall: 0.8452
* F1: 0.8383
* Accuracy: 0.8452
* F1 Who: 0.9189
* F1 What: 0.8621
* F1 Where: 0.9231
* F1 How: 0.9141


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: 1e-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: 5


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 5 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 5 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 5### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2