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# gpt2-NER-favsbot

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the favsbot dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5146
- Precision: 0.3782
- Recall: 0.3278
- F1: 0.3512
- Accuracy: 0.5597

## 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    | 4.0808          | 0.0630    | 0.0444 | 0.0521 | 0.0773   |
| No log        | 2.0   | 8    | 3.5205          | 0.0946    | 0.0778 | 0.0854 | 0.1077   |
| No log        | 3.0   | 12   | 3.0413          | 0.0909    | 0.0722 | 0.0805 | 0.2084   |
| No log        | 4.0   | 16   | 2.6817          | 0.0615    | 0.0444 | 0.0516 | 0.2740   |
| No log        | 5.0   | 20   | 2.4227          | 0.1140    | 0.0722 | 0.0884 | 0.3560   |
| No log        | 6.0   | 24   | 2.2422          | 0.1382    | 0.0944 | 0.1122 | 0.3770   |
| No log        | 7.0   | 28   | 2.0941          | 0.1654    | 0.1222 | 0.1406 | 0.3864   |
| No log        | 8.0   | 32   | 1.9726          | 0.2344    | 0.1667 | 0.1948 | 0.4309   |
| No log        | 9.0   | 36   | 1.8916          | 0.2925    | 0.1722 | 0.2168 | 0.4543   |
| No log        | 10.0  | 40   | 1.8321          | 0.31      | 0.1722 | 0.2214 | 0.4660   |
| No log        | 11.0  | 44   | 1.7697          | 0.2957    | 0.1889 | 0.2305 | 0.4707   |
| No log        | 12.0  | 48   | 1.7087          | 0.3228    | 0.2278 | 0.2671 | 0.4965   |
| No log        | 13.0  | 52   | 1.6551          | 0.3485    | 0.2556 | 0.2949 | 0.5152   |
| No log        | 14.0  | 56   | 1.6136          | 0.3219    | 0.2611 | 0.2883 | 0.5176   |
| No log        | 15.0  | 60   | 1.5819          | 0.3510    | 0.2944 | 0.3202 | 0.5363   |
| No log        | 16.0  | 64   | 1.5575          | 0.3506    | 0.3    | 0.3234 | 0.5410   |
| No log        | 17.0  | 68   | 1.5394          | 0.3529    | 0.3    | 0.3243 | 0.5433   |
| No log        | 18.0  | 72   | 1.5265          | 0.3791    | 0.3222 | 0.3483 | 0.5574   |
| No log        | 19.0  | 76   | 1.5180          | 0.3766    | 0.3222 | 0.3473 | 0.5574   |
| No log        | 20.0  | 80   | 1.5146          | 0.3782    | 0.3278 | 0.3512 | 0.5597   |


### 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": "gpt2-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": 0.3782051282051282, "name": "Precision"}, {"type": "recall", "value": 0.3277777777777778, "name": "Recall"}, {"type": "f1", "value": 0.3511904761904762, "name": "F1"}, {"type": "accuracy", "value": 0.5597189695550351, "name": "Accuracy"}]}]}]}
transformers pytorch gpt2 token-classification generated_from_trainer dataset:favsbot license:mit model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #token-classification #generated_from_trainer #dataset-favsbot #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

gpt2-NER-favsbot
================


This model is a fine-tuned version of gpt2 on the favsbot dataset.
It achieves the following results on the evaluation set:


* Loss: 1.5146
* Precision: 0.3782
* Recall: 0.3278
* F1: 0.3512
* Accuracy: 0.5597


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 #gpt2 #token-classification #generated_from_trainer #dataset-favsbot #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #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 #gpt2 #token-classification #generated_from_trainer #dataset-favsbot #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #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