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# MLMA_lab9_task2

This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2509
- Precision: 0.0159
- Recall: 0.1487
- F1: 0.0287
- Accuracy: 0.6365

## 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: 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: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.153         | 1.0   | 680   | 1.0671          | 0.0122    | 0.1258 | 0.0223 | 0.5452   |
| 1.02          | 2.0   | 1360  | 1.0418          | 0.0098    | 0.0203 | 0.0132 | 0.6791   |
| 0.9552        | 3.0   | 2040  | 1.0269          | 0.0135    | 0.1677 | 0.0250 | 0.5282   |
| 0.926         | 4.0   | 2720  | 1.0390          | 0.0143    | 0.0940 | 0.0248 | 0.6686   |
| 0.9156        | 5.0   | 3400  | 1.0200          | 0.0135    | 0.2046 | 0.0253 | 0.4679   |
| 0.8791        | 6.0   | 4080  | 1.0543          | 0.0131    | 0.2745 | 0.0250 | 0.3149   |
| 0.8672        | 7.0   | 4760  | 1.0545          | 0.0141    | 0.2732 | 0.0267 | 0.3471   |
| 0.8627        | 8.0   | 5440  | 1.0734          | 0.0145    | 0.0826 | 0.0246 | 0.7220   |
| 0.8375        | 9.0   | 6120  | 1.1068          | 0.0156    | 0.1410 | 0.0281 | 0.6451   |
| 0.8235        | 10.0  | 6800  | 1.0796          | 0.0158    | 0.1537 | 0.0286 | 0.6210   |
| 0.8157        | 11.0  | 7480  | 1.1476          | 0.0143    | 0.1690 | 0.0263 | 0.5737   |
| 0.7957        | 12.0  | 8160  | 1.1369          | 0.0143    | 0.1525 | 0.0262 | 0.6155   |
| 0.7937        | 13.0  | 8840  | 1.2014          | 0.0151    | 0.1741 | 0.0278 | 0.5808   |
| 0.7765        | 14.0  | 9520  | 1.2249          | 0.0160    | 0.1449 | 0.0289 | 0.6443   |
| 0.7661        | 15.0  | 10200 | 1.2509          | 0.0159    | 0.1487 | 0.0287 | 0.6365   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["ncbi_disease"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "MLMA_lab9_task2", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "ncbi_disease", "type": "ncbi_disease", "config": "ncbi_disease", "split": "validation", "args": "ncbi_disease"}, "metrics": [{"type": "precision", "value": 0.015873015873015872, "name": "Precision"}, {"type": "recall", "value": 0.14866581956797967, "name": "Recall"}, {"type": "f1", "value": 0.028683500858053445, "name": "F1"}, {"type": "accuracy", "value": 0.6365342039100904, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard gpt2 token-classification generated_from_trainer dataset:ncbi_disease license:mit model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #gpt2 #token-classification #generated_from_trainer #dataset-ncbi_disease #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

MLMA\_lab9\_task2
=================


This model is a fine-tuned version of microsoft/biogpt on the ncbi\_disease dataset.
It achieves the following results on the evaluation set:


* Loss: 1.2509
* Precision: 0.0159
* Recall: 0.1487
* F1: 0.0287
* Accuracy: 0.6365


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: 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: 15


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 15 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #gpt2 #token-classification #generated_from_trainer #dataset-ncbi_disease #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 15 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #gpt2 #token-classification #generated_from_trainer #dataset-ncbi_disease #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 15### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3