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# roberta-base-NER-favsbot

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the favsbot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4812
- Precision: 0.6940
- Recall: 0.7056
- F1: 0.6997
- Accuracy: 0.8454

## 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: 1.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 4    | 2.1885          | 0.1787    | 0.3722 | 0.2414 | 0.2998   |
| No log        | 2.0   | 8    | 1.9109          | 0.3768    | 0.1444 | 0.2088 | 0.4707   |
| No log        | 3.0   | 12   | 1.6425          | 0.0       | 0.0    | 0.0    | 0.4075   |
| No log        | 4.0   | 16   | 1.5402          | 0.75      | 0.0167 | 0.0326 | 0.4145   |
| No log        | 5.0   | 20   | 1.3569          | 0.5263    | 0.3333 | 0.4082 | 0.5785   |
| No log        | 6.0   | 24   | 1.2250          | 0.4691    | 0.5056 | 0.4866 | 0.6721   |
| No log        | 7.0   | 28   | 1.0957          | 0.4973    | 0.5167 | 0.5068 | 0.6885   |
| No log        | 8.0   | 32   | 0.9751          | 0.5581    | 0.5333 | 0.5455 | 0.7190   |
| No log        | 9.0   | 36   | 0.8824          | 0.6456    | 0.5667 | 0.6036 | 0.7518   |
| No log        | 10.0  | 40   | 0.8035          | 0.6442    | 0.5833 | 0.6122 | 0.7658   |
| No log        | 11.0  | 44   | 0.7336          | 0.6412    | 0.6056 | 0.6229 | 0.7822   |
| No log        | 12.0  | 48   | 0.6722          | 0.6384    | 0.6278 | 0.6331 | 0.7963   |
| No log        | 13.0  | 52   | 0.6205          | 0.6610    | 0.65   | 0.6555 | 0.8080   |
| No log        | 14.0  | 56   | 0.5796          | 0.6740    | 0.6778 | 0.6759 | 0.8244   |
| No log        | 15.0  | 60   | 0.5503          | 0.6796    | 0.6833 | 0.6814 | 0.8244   |
| No log        | 16.0  | 64   | 0.5256          | 0.6851    | 0.6889 | 0.6870 | 0.8337   |
| No log        | 17.0  | 68   | 0.5066          | 0.6868    | 0.6944 | 0.6906 | 0.8407   |
| No log        | 18.0  | 72   | 0.4929          | 0.6868    | 0.6944 | 0.6906 | 0.8431   |
| No log        | 19.0  | 76   | 0.4845          | 0.6868    | 0.6944 | 0.6906 | 0.8431   |
| No log        | 20.0  | 80   | 0.4812          | 0.6940    | 0.7056 | 0.6997 | 0.8454   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["favsbot"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-NER-favsbot", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "favsbot", "type": "favsbot", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "precision", "value": 0.6939890710382514, "name": "Precision"}, {"type": "recall", "value": 0.7055555555555556, "name": "Recall"}, {"type": "f1", "value": 0.6997245179063362, "name": "F1"}, {"type": "accuracy", "value": 0.8454332552693209, "name": "Accuracy"}]}]}]}
transformers pytorch roberta token-classification generated_from_trainer dataset:favsbot license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-favsbot #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

roberta-base-NER-favsbot
========================


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


* Loss: 0.4812
* Precision: 0.6940
* Recall: 0.7056
* F1: 0.6997
* Accuracy: 0.8454


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: 1.5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1.5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-favsbot #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1.5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-favsbot #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1.5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1