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# rubert-tiny2-srl

This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1428
- Addressee Precision: 0.6364
- Addressee Recall: 0.875
- Addressee F1: 0.7368
- Addressee Number: 8
- Benefactive Precision: 0.0
- Benefactive Recall: 0.0
- Benefactive F1: 0.0
- Benefactive Number: 2
- Causator Precision: 0.9286
- Causator Recall: 0.8125
- Causator F1: 0.8667
- Causator Number: 16
- Cause Precision: 0.6
- Cause Recall: 0.25
- Cause F1: 0.3529
- Cause Number: 12
- Contrsubject Precision: 0.6364
- Contrsubject Recall: 0.4118
- Contrsubject F1: 0.5
- Contrsubject Number: 17
- Deliberative Precision: 1.0
- Deliberative Recall: 0.6667
- Deliberative F1: 0.8
- Deliberative Number: 6
- Destinative Precision: 1.0
- Destinative Recall: 0.5
- Destinative F1: 0.6667
- Destinative Number: 4
- Directivefinal Precision: 1.0
- Directivefinal Recall: 1.0
- Directivefinal F1: 1.0
- Directivefinal Number: 2
- Experiencer Precision: 0.8018
- Experiencer Recall: 0.9368
- Experiencer F1: 0.8641
- Experiencer Number: 95
- Instrument Precision: 0.0
- Instrument Recall: 0.0
- Instrument F1: 0.0
- Instrument Number: 3
- Limitative Precision: 0.0
- Limitative Recall: 0.0
- Limitative F1: 0.0
- Limitative Number: 1
- Object Precision: 0.7589
- Object Recall: 0.8
- Object F1: 0.7789
- Object Number: 240
- Overall Precision: 0.7724
- Overall Recall: 0.7857
- Overall F1: 0.7790
- Overall Accuracy: 0.9589

## 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: 8.017672397578385e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 678943
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.04
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Addressee Precision | Addressee Recall | Addressee F1 | Addressee Number | Benefactive Precision | Benefactive Recall | Benefactive F1 | Benefactive Number | Causator Precision | Causator Recall | Causator F1 | Causator Number | Cause Precision | Cause Recall | Cause F1 | Cause Number | Contrsubject Precision | Contrsubject Recall | Contrsubject F1 | Contrsubject Number | Deliberative Precision | Deliberative Recall | Deliberative F1 | Deliberative Number | Destinative Precision | Destinative Recall | Destinative F1 | Destinative Number | Directivefinal Precision | Directivefinal Recall | Directivefinal F1 | Directivefinal Number | Experiencer Precision | Experiencer Recall | Experiencer F1 | Experiencer Number | Instrument Precision | Instrument Recall | Instrument F1 | Instrument Number | Limitative Precision | Limitative Recall | Limitative F1 | Limitative Number | Object Precision | Object Recall | Object F1 | Object Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:----------------:|:-------------:|:---------:|:-------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.2206        | 1.0   | 490  | 0.1959          | 0.6667              | 0.25             | 0.3636       | 8                | 0.0                   | 0.0                | 0.0            | 2                  | 0.8667             | 0.8125          | 0.8387      | 16              | 0.0             | 0.0          | 0.0      | 12           | 1.0                    | 0.0588              | 0.1111          | 17                  | 0.0                    | 0.0                 | 0.0             | 6                   | 0.0                   | 0.0                | 0.0            | 4                  | 0.0                      | 0.0                   | 0.0               | 2                     | 0.7203                | 0.8947             | 0.7981         | 95                 | 0.0                  | 0.0               | 0.0           | 3                 | 0.0                  | 0.0               | 0.0           | 1                 | 0.6692           | 0.725         | 0.696     | 240           | 0.6927            | 0.6773         | 0.6849     | 0.9445           |
| 0.1507        | 2.0   | 981  | 0.1492          | 0.5556              | 0.625            | 0.5882       | 8                | 0.0                   | 0.0                | 0.0            | 2                  | 0.8667             | 0.8125          | 0.8387      | 16              | 0.6             | 0.25         | 0.3529   | 12           | 0.75                   | 0.3529              | 0.48            | 17                  | 1.0                    | 0.1667              | 0.2857          | 6                   | 1.0                   | 0.25               | 0.4            | 4                  | 1.0                      | 1.0                   | 1.0               | 2                     | 0.8646                | 0.8737             | 0.8691         | 95                 | 0.0                  | 0.0               | 0.0           | 3                 | 0.0                  | 0.0               | 0.0           | 1                 | 0.7635           | 0.7667        | 0.7651    | 240           | 0.7884            | 0.7340         | 0.7602     | 0.9566           |
| 0.1146        | 3.0   | 1472 | 0.1437          | 0.6364              | 0.875            | 0.7368       | 8                | 0.0                   | 0.0                | 0.0            | 2                  | 0.9286             | 0.8125          | 0.8667      | 16              | 0.6             | 0.25         | 0.3529   | 12           | 0.6429                 | 0.5294              | 0.5806          | 17                  | 1.0                    | 0.5                 | 0.6667          | 6                   | 1.0                   | 0.5                | 0.6667         | 4                  | 1.0                      | 1.0                   | 1.0               | 2                     | 0.8                   | 0.9263             | 0.8585         | 95                 | 0.0                  | 0.0               | 0.0           | 3                 | 0.0                  | 0.0               | 0.0           | 1                 | 0.7443           | 0.8125        | 0.7769    | 240           | 0.7612            | 0.7931         | 0.7768     | 0.9584           |
| 0.0842        | 3.99  | 1960 | 0.1428          | 0.6364              | 0.875            | 0.7368       | 8                | 0.0                   | 0.0                | 0.0            | 2                  | 0.9286             | 0.8125          | 0.8667      | 16              | 0.6             | 0.25         | 0.3529   | 12           | 0.6364                 | 0.4118              | 0.5             | 17                  | 1.0                    | 0.6667              | 0.8             | 6                   | 1.0                   | 0.5                | 0.6667         | 4                  | 1.0                      | 1.0                   | 1.0               | 2                     | 0.8018                | 0.9368             | 0.8641         | 95                 | 0.0                  | 0.0               | 0.0           | 3                 | 0.0                  | 0.0               | 0.0           | 1                 | 0.7589           | 0.8           | 0.7789    | 240           | 0.7724            | 0.7857         | 0.7790     | 0.9589           |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "cointegrated/rubert-tiny2", "model-index": [{"name": "rubert-tiny2-srl", "results": []}]}
transformers pytorch bert token-classification generated_from_trainer base_model:cointegrated/rubert-tiny2 license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #base_model-cointegrated/rubert-tiny2 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

rubert-tiny2-srl
================


This model is a fine-tuned version of cointegrated/rubert-tiny2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1428
* Addressee Precision: 0.6364
* Addressee Recall: 0.875
* Addressee F1: 0.7368
* Addressee Number: 8
* Benefactive Precision: 0.0
* Benefactive Recall: 0.0
* Benefactive F1: 0.0
* Benefactive Number: 2
* Causator Precision: 0.9286
* Causator Recall: 0.8125
* Causator F1: 0.8667
* Causator Number: 16
* Cause Precision: 0.6
* Cause Recall: 0.25
* Cause F1: 0.3529
* Cause Number: 12
* Contrsubject Precision: 0.6364
* Contrsubject Recall: 0.4118
* Contrsubject F1: 0.5
* Contrsubject Number: 17
* Deliberative Precision: 1.0
* Deliberative Recall: 0.6667
* Deliberative F1: 0.8
* Deliberative Number: 6
* Destinative Precision: 1.0
* Destinative Recall: 0.5
* Destinative F1: 0.6667
* Destinative Number: 4
* Directivefinal Precision: 1.0
* Directivefinal Recall: 1.0
* Directivefinal F1: 1.0
* Directivefinal Number: 2
* Experiencer Precision: 0.8018
* Experiencer Recall: 0.9368
* Experiencer F1: 0.8641
* Experiencer Number: 95
* Instrument Precision: 0.0
* Instrument Recall: 0.0
* Instrument F1: 0.0
* Instrument Number: 3
* Limitative Precision: 0.0
* Limitative Recall: 0.0
* Limitative F1: 0.0
* Limitative Number: 1
* Object Precision: 0.7589
* Object Recall: 0.8
* Object F1: 0.7789
* Object Number: 240
* Overall Precision: 0.7724
* Overall Recall: 0.7857
* Overall F1: 0.7790
* Overall Accuracy: 0.9589


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: 8.017672397578385e-05
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 678943
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.04
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.33.2
* Pytorch 2.0.1+cu117
* Datasets 2.14.5
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 8.017672397578385e-05
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 678943
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.04
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.33.2
* Pytorch 2.0.1+cu117
* Datasets 2.14.5
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #base_model-cointegrated/rubert-tiny2 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 8.017672397578385e-05
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 678943
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.04
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.33.2
* Pytorch 2.0.1+cu117
* Datasets 2.14.5
* Tokenizers 0.13.3