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# ikitracs_mitigation

This model is a fine-tuned version of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7245
- F1-score: 0.2177

## 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: 9.24e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2983        | 1.0   | 207  | 1.0239          | 0.1101   |
| 1.0475        | 2.0   | 414  | 0.9027          | 0.1449   |
| 0.9095        | 3.0   | 621  | 0.8214          | 0.1646   |
| 0.7996        | 4.0   | 828  | 0.8772          | 0.1629   |
| 0.6585        | 5.0   | 1035 | 0.7504          | 0.2025   |
| 0.5473        | 6.0   | 1242 | 0.8324          | 0.2144   |
| 0.4423        | 7.0   | 1449 | 0.6799          | 0.2176   |
| 0.3741        | 8.0   | 1656 | 0.7245          | 0.2177   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "ikitracs_mitigation", "results": []}]}
transformers pytorch tensorboard mpnet text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #mpnet #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

ikitracs\_mitigation
====================


This model is a fine-tuned version of sentence-transformers/all-mpnet-base-v2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7245
* F1-score: 0.2177


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: 9.24e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 6
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 300
* num\_epochs: 8


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 9.24e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 6
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 300
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #mpnet #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 9.24e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 6
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 300
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3