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# qqq-finetuned-on-calls

This model is a fine-tuned version of [bragovo/qqq](https://huggingface.co/bragovo/qqq) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0021
- Rouge-1: 1.0
- Rouge-2: 1.0
- Rouge-l: 1.0
- Gen Len: 11.0
- Avg Rouge F: 1.0

## 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: 2e-05
- train_batch_size: 3
- 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: 50
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Avg Rouge F |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-----------:|
| 2.1855        | 3.12  | 25   | 1.4282          | 0.0     | 0.0     | 0.0     | 15.0    | 0.0         |
| 1.5665        | 6.25  | 50   | 0.6420          | 0.1818  | 0.0     | 0.1818  | 12.0    | 0.1212      |
| 1.1046        | 9.38  | 75   | 0.2184          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.8218        | 12.5  | 100  | 0.1098          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.606         | 15.62 | 125  | 0.0749          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.5488        | 18.75 | 150  | 0.0577          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.4161        | 21.88 | 175  | 0.0684          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.3196        | 25.0  | 200  | 0.0570          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.2929        | 28.12 | 225  | 0.0416          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.2519        | 31.25 | 250  | 0.0247          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.178         | 34.38 | 275  | 0.0118          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.1603        | 37.5  | 300  | 0.0064          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.1684        | 40.62 | 325  | 0.0051          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.1326        | 43.75 | 350  | 0.0051          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.1349        | 46.88 | 375  | 0.0064          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.1105        | 50.0  | 400  | 0.0061          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.1026        | 53.12 | 425  | 0.0049          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.0936        | 56.25 | 450  | 0.0030          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.0704        | 59.38 | 475  | 0.0025          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.0699        | 62.5  | 500  | 0.0021          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.0863        | 65.62 | 525  | 0.0020          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.0595        | 68.75 | 550  | 0.0024          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.0594        | 71.88 | 575  | 0.0028          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.0683        | 75.0  | 600  | 0.0026          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |
| 0.074         | 78.12 | 625  | 0.0025          | 1.0     | 1.0     | 1.0     | 11.0    | 1.0         |


### Framework versions

- Transformers 4.29.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "qqq-finetuned-on-calls", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

qqq-finetuned-on-calls
======================


This model is a fine-tuned version of bragovo/qqq on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0021
* Rouge-1: 1.0
* Rouge-2: 1.0
* Rouge-l: 1.0
* Gen Len: 11.0
* Avg Rouge F: 1.0


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: 2e-05
* train\_batch\_size: 3
* 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: 50
* num\_epochs: 100


### Training results



### Framework versions


* Transformers 4.29.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 3
* 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: 50
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.29.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 3
* 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: 50
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.29.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3