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# esm2_t6_8M_UR50D-localization-v1-finetuned-localization

This model is a fine-tuned version of [facebook/esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2123
- F1: 0.7355

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4992        | 1.0   | 2048  | 0.5178          | 0.5567 |
| 0.4623        | 2.0   | 4096  | 0.3970          | 0.6536 |
| 0.3754        | 3.0   | 6144  | 0.5035          | 0.7153 |
| 0.3396        | 4.0   | 8192  | 0.6703          | 0.6598 |
| 0.2128        | 5.0   | 10240 | 0.7133          | 0.6876 |
| 0.1336        | 6.0   | 12288 | 0.9024          | 0.7065 |
| 0.0607        | 7.0   | 14336 | 0.9994          | 0.6841 |
| 0.025         | 8.0   | 16384 | 1.1050          | 0.7046 |
| 0.0098        | 9.0   | 18432 | 1.2199          | 0.7119 |
| 0.0047        | 10.0  | 20480 | 1.2123          | 0.7355 |


### Framework versions

- Transformers 4.28.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "facebook/esm2_t6_8M_UR50D", "model-index": [{"name": "esm2_t6_8M_UR50D-localization-v1-finetuned-localization", "results": []}]}
transformers pytorch esm text-classification generated_from_trainer base_model:facebook/esm2_t6_8M_UR50D license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #esm #text-classification #generated_from_trainer #base_model-facebook/esm2_t6_8M_UR50D #license-mit #autotrain_compatible #endpoints_compatible #region-us 

esm2\_t6\_8M\_UR50D-localization-v1-finetuned-localization
==========================================================


This model is a fine-tuned version of facebook/esm2\_t6\_8M\_UR50D on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.2123
* F1: 0.7355


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: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #esm #text-classification #generated_from_trainer #base_model-facebook/esm2_t6_8M_UR50D #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3