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# sd-panelization-v2

This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on the source_data_nlp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0050
- Accuracy Score: 0.9982
- Precision: 0.9134
- Recall: 0.9495
- F1: 0.9311

## 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: 32
- eval_batch_size: 256
- seed: 42
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 1.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:|
| 0.0048        | 1.0   | 431  | 0.0050          | 0.9982         | 0.9134    | 0.9495 | 0.9311 |


### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0a0+bfe5ad2
- Datasets 1.17.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["source_data_nlp"], "metrics": ["precision", "recall", "f1"], "model-index": [{"name": "sd-panelization-v2", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "source_data_nlp", "type": "source_data_nlp", "args": "PANELIZATION"}, "metrics": [{"type": "precision", "value": 0.9134245120169964, "name": "Precision"}, {"type": "recall", "value": 0.9494824016563147, "name": "Recall"}, {"type": "f1", "value": 0.9311044937736871, "name": "F1"}]}]}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:source_data_nlp license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-source_data_nlp #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

sd-panelization-v2
==================


This model is a fine-tuned version of michiyasunaga/BioLinkBERT-large on the source\_data\_nlp dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0050
* Accuracy Score: 0.9982
* Precision: 0.9134
* Recall: 0.9495
* F1: 0.9311


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: 32
* eval\_batch\_size: 256
* seed: 42
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 1.0


### Training results



### Framework versions


* Transformers 4.20.0
* Pytorch 1.11.0a0+bfe5ad2
* Datasets 1.17.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 256
* seed: 42
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 1.0 ### Training results ### Framework versions


* Transformers 4.20.0
* Pytorch 1.11.0a0+bfe5ad2
* Datasets 1.17.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-source_data_nlp #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 256
* seed: 42
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 1.0 ### Training results ### Framework versions


* Transformers 4.20.0
* Pytorch 1.11.0a0+bfe5ad2
* Datasets 1.17.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-source_data_nlp #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 256
* seed: 42
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 1.0### Training results### Framework versions


* Transformers 4.20.0
* Pytorch 1.11.0a0+bfe5ad2
* Datasets 1.17.0
* Tokenizers 0.12.1