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# assets

This model is a fine-tuned version of [iHealthGroup/shc-lm-v1](https://huggingface.co/iHealthGroup/shc-lm-v1) on the shc_deva_ds dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7051
- F1: 0.3333

## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1628        | 0.62  | 400  | 0.7051          | 0.3333 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.13.0+cpu
- Datasets 2.4.0
- Tokenizers 0.11.6

{"tags": ["generated_from_trainer"], "datasets": ["shc_deva_ds"], "metrics": ["f1"], "model-index": [{"name": "assets", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "shc_deva_ds", "type": "shc_deva_ds", "args": "SHCAdverseEvents"}, "metrics": [{"type": "f1", "value": 0.3333333333333333, "name": "F1"}]}]}]}
transformers pytorch bert text-classification generated_from_trainer dataset:shc_deva_ds model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-shc_deva_ds #model-index #autotrain_compatible #endpoints_compatible #region-us 

assets
======


This model is a fine-tuned version of iHealthGroup/shc-lm-v1 on the shc\_deva\_ds dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7051
* F1: 0.3333


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: 0.0001
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.13.0+cpu
* Datasets 2.4.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.13.0+cpu
* Datasets 2.4.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-shc_deva_ds #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.13.0+cpu
* Datasets 2.4.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-shc_deva_ds #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 1### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.13.0+cpu
* Datasets 2.4.0
* Tokenizers 0.11.6