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# action-policy-plans-classifier

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: 0.6839
- Precision Micro: 0.7089
- Precision Weighted: 0.7043
- Precision Samples: 0.4047
- Recall Micro: 0.7066
- Recall Weighted: 0.7066
- Recall Samples: 0.4047
- F1-score: 0.4041

## 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: 2.915e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision Micro | Precision Weighted | Precision Samples | Recall Micro | Recall Weighted | Recall Samples | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:---------------:|:--------------:|:--------:|
| 0.7333        | 1.0   | 253  | 0.5828          | 0.625           | 0.6422             | 0.4047            | 0.7098       | 0.7098          | 0.4065         | 0.4047   |
| 0.5905        | 2.0   | 506  | 0.5593          | 0.6292          | 0.6318             | 0.4437            | 0.7760       | 0.7760          | 0.4446         | 0.4434   |
| 0.4934        | 3.0   | 759  | 0.5269          | 0.6630          | 0.6637             | 0.4319            | 0.7571       | 0.7571          | 0.4347         | 0.4325   |
| 0.4018        | 4.0   | 1012 | 0.5645          | 0.6449          | 0.6479             | 0.4456            | 0.7792       | 0.7792          | 0.4465         | 0.4453   |
| 0.3235        | 5.0   | 1265 | 0.6101          | 0.6964          | 0.6929             | 0.4220            | 0.7382       | 0.7382          | 0.4229         | 0.4217   |
| 0.2638        | 6.0   | 1518 | 0.6692          | 0.6888          | 0.6841             | 0.4111            | 0.7192       | 0.7192          | 0.4120         | 0.4108   |
| 0.2197        | 7.0   | 1771 | 0.6839          | 0.7089          | 0.7043             | 0.4047            | 0.7066       | 0.7066          | 0.4047         | 0.4041   |


### 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": "action-policy-plans-classifier", "results": []}]}
transformers pytorch mpnet text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mpnet #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

action-policy-plans-classifier
==============================


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: 0.6839
* Precision Micro: 0.7089
* Precision Weighted: 0.7043
* Precision Samples: 0.4047
* Recall Micro: 0.7066
* Recall Weighted: 0.7066
* Recall Samples: 0.4047
* F1-score: 0.4041


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: 2.915e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 300
* num\_epochs: 7


### 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: 2.915e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 300
* num\_epochs: 7 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #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: 2.915e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 300
* num\_epochs: 7 ### Training results ### Framework versions


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