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# bert-base-cased-NER-favsbot

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1680
- Precision: 0.8462
- Recall: 0.88
- F1: 0.8627
- Accuracy: 0.9444

## 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   | 7    | 1.8761          | 0.0       | 0.0    | 0.0    | 0.5833   |
| No log        | 2.0   | 14   | 1.3530          | 0.0       | 0.0    | 0.0    | 0.5972   |
| No log        | 3.0   | 21   | 1.0400          | 1.0       | 0.12   | 0.2143 | 0.6389   |
| No log        | 4.0   | 28   | 0.7987          | 0.7895    | 0.6    | 0.6818 | 0.8194   |
| No log        | 5.0   | 35   | 0.6055          | 0.85      | 0.68   | 0.7556 | 0.875    |
| No log        | 6.0   | 42   | 0.4749          | 0.8696    | 0.8    | 0.8333 | 0.9167   |
| No log        | 7.0   | 49   | 0.3838          | 0.84      | 0.84   | 0.8400 | 0.9444   |
| No log        | 8.0   | 56   | 0.3084          | 0.88      | 0.88   | 0.88   | 0.9583   |
| No log        | 9.0   | 63   | 0.2643          | 0.88      | 0.88   | 0.88   | 0.9583   |
| No log        | 10.0  | 70   | 0.2360          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
| No log        | 11.0  | 77   | 0.2168          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
| No log        | 12.0  | 84   | 0.2031          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
| No log        | 13.0  | 91   | 0.1937          | 0.88      | 0.88   | 0.88   | 0.9583   |
| No log        | 14.0  | 98   | 0.1853          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
| No log        | 15.0  | 105  | 0.1791          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
| No log        | 16.0  | 112  | 0.1757          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
| No log        | 17.0  | 119  | 0.1718          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
| No log        | 18.0  | 126  | 0.1698          | 0.8148    | 0.88   | 0.8462 | 0.9444   |
| No log        | 19.0  | 133  | 0.1686          | 0.8148    | 0.88   | 0.8462 | 0.9444   |
| No log        | 20.0  | 140  | 0.1680          | 0.8462    | 0.88   | 0.8627 | 0.9444   |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["favsbot"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-cased-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": 0.8461538461538461, "name": "Precision"}, {"type": "recall", "value": 0.88, "name": "Recall"}, {"type": "f1", "value": 0.8627450980392156, "name": "F1"}, {"type": "accuracy", "value": 0.9444444444444444, "name": "Accuracy"}]}]}]}
transformers pytorch bert token-classification generated_from_trainer dataset:favsbot license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-favsbot #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-base-cased-NER-favsbot
===========================


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


* Loss: 0.1680
* Precision: 0.8462
* Recall: 0.88
* F1: 0.8627
* Accuracy: 0.9444


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 #bert #token-classification #generated_from_trainer #dataset-favsbot #license-apache-2.0 #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 #bert #token-classification #generated_from_trainer #dataset-favsbot #license-apache-2.0 #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