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# rut5-base-absum-tech-support-calls

This model is a fine-tuned version of [cointegrated/rut5-base-absum](https://huggingface.co/cointegrated/rut5-base-absum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4464
- Rouge-1: 0.5076
- Rouge-2: 0.3897
- Rouge-l: 0.4945
- Gen Len: 15.75
- Avg Rouge F: 0.4639

## 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: 200
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Avg Rouge F |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-----------:|
| 2.6017        | 2.78  | 50   | 2.0030          | 0.0     | 0.0     | 0.0     | 8.125   | 0.0         |
| 2.1413        | 5.56  | 100  | 1.5154          | 0.1125  | 0.0317  | 0.0958  | 11.5    | 0.08        |
| 1.6874        | 8.33  | 150  | 1.2364          | 0.3417  | 0.2312  | 0.325   | 13.25   | 0.2993      |
| 1.2272        | 11.11 | 200  | 1.1259          | 0.3605  | 0.2437  | 0.3291  | 14.25   | 0.3111      |
| 0.9384        | 13.89 | 250  | 1.0853          | 0.4505  | 0.3     | 0.4211  | 13.5    | 0.3905      |
| 0.7071        | 16.67 | 300  | 1.0607          | 0.3559  | 0.1368  | 0.3133  | 14.875  | 0.2687      |
| 0.5871        | 19.44 | 350  | 1.0346          | 0.5377  | 0.4194  | 0.5126  | 16.0    | 0.4899      |
| 0.4194        | 22.22 | 400  | 1.0672          | 0.5079  | 0.3819  | 0.4829  | 15.5    | 0.4576      |
| 0.3685        | 25.0  | 450  | 1.1284          | 0.5029  | 0.3835  | 0.4897  | 14.75   | 0.4587      |
| 0.2884        | 27.78 | 500  | 1.1729          | 0.5427  | 0.421   | 0.5164  | 15.875  | 0.4933      |
| 0.2368        | 30.56 | 550  | 1.1640          | 0.5326  | 0.421   | 0.5195  | 15.25   | 0.491       |
| 0.195         | 33.33 | 600  | 1.2053          | 0.5326  | 0.421   | 0.5195  | 15.25   | 0.491       |
| 0.1667        | 36.11 | 650  | 1.2525          | 0.4245  | 0.2717  | 0.4114  | 16.125  | 0.3692      |
| 0.1491        | 38.89 | 700  | 1.3346          | 0.5032  | 0.3897  | 0.4901  | 16.0    | 0.461       |
| 0.1122        | 41.67 | 750  | 1.3354          | 0.5094  | 0.4062  | 0.5094  | 15.375  | 0.475       |
| 0.1166        | 44.44 | 800  | 1.3685          | 0.5076  | 0.3897  | 0.4945  | 15.625  | 0.4639      |
| 0.0973        | 47.22 | 850  | 1.4157          | 0.5076  | 0.3897  | 0.4945  | 15.375  | 0.4639      |
| 0.0944        | 50.0  | 900  | 1.4523          | 0.5095  | 0.3897  | 0.4963  | 15.125  | 0.4652      |
| 0.0744        | 52.78 | 950  | 1.4221          | 0.5326  | 0.421   | 0.5195  | 15.25   | 0.491       |
| 0.0745        | 55.56 | 1000 | 1.4464          | 0.5076  | 0.3897  | 0.4945  | 15.75   | 0.4639      |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "rut5-base-absum-tech-support-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 

rut5-base-absum-tech-support-calls
==================================


This model is a fine-tuned version of cointegrated/rut5-base-absum on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4464
* Rouge-1: 0.5076
* Rouge-2: 0.3897
* Rouge-l: 0.4945
* Gen Len: 15.75
* Avg Rouge F: 0.4639


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: 200
* num\_epochs: 100


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* 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: 200
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* 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: 200
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Tokenizers 0.13.3