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# tape-fluorescence-prediction-RITA_s

This model is a fine-tuned version of [lightonai/RITA_s](https://huggingface.co/lightonai/RITA_s) on the cradle-bio/tape-fluorescence dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5855
- Spearmanr: 0.2955

## 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: 32
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 4096
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 4.3595        | 0.85  | 4    | 0.7057          | 0.0940    |
| 0.8654        | 1.85  | 8    | 0.6873          | 0.1280    |
| 0.8292        | 2.85  | 12   | 0.6835          | 0.2290    |
| 0.8212        | 3.85  | 16   | 0.6837          | 0.3110    |
| 0.8191        | 4.85  | 20   | 0.6799          | 0.3281    |
| 0.8137        | 5.85  | 24   | 0.6748          | 0.3277    |
| 0.8057        | 6.85  | 28   | 0.6592          | 0.3162    |
| 0.7769        | 7.85  | 32   | 0.6283          | 0.3065    |
| 0.7382        | 8.85  | 36   | 0.6103          | 0.2795    |
| 0.5991        | 9.85  | 40   | 0.5855          | 0.2955    |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["protein language model", "generated_from_trainer"], "datasets": ["train"], "metrics": ["spearmanr"], "model-index": [{"name": "tape-fluorescence-prediction-RITA_s", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "cradle-bio/tape-fluorescence", "type": "train"}, "metrics": [{"type": "spearmanr", "value": 0.2955275250425323, "name": "Spearmanr"}]}]}]}
transformers pytorch rita text-classification protein language model generated_from_trainer custom_code dataset:train license:apache-2.0 model-index autotrain_compatible region:us
TAGS
#transformers #pytorch #rita #text-classification #protein language model #generated_from_trainer #custom_code #dataset-train #license-apache-2.0 #model-index #autotrain_compatible #region-us 

tape-fluorescence-prediction-RITA\_s
====================================


This model is a fine-tuned version of lightonai/RITA\_s on the cradle-bio/tape-fluorescence dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5855
* Spearmanr: 0.2955


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: 32
* seed: 42
* gradient\_accumulation\_steps: 128
* total\_train\_batch\_size: 4096
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 128
* total\_train\_batch\_size: 4096
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #rita #text-classification #protein language model #generated_from_trainer #custom_code #dataset-train #license-apache-2.0 #model-index #autotrain_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 128
* total\_train\_batch\_size: 4096
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #rita #text-classification #protein language model #generated_from_trainer #custom_code #dataset-train #license-apache-2.0 #model-index #autotrain_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 128
* total\_train\_batch\_size: 4096
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1