File size: 8,961 Bytes
e4d64c5 9f9e1b6 e4d64c5 92aa913 9f9e1b6 92aa913 9f9e1b6 98e7e50 9f9e1b6 92aa913 9f9e1b6 92aa913 9f9e1b6 92aa913 9f9e1b6 92aa913 9f9e1b6 92aa913 9f9e1b6 92aa913 9f9e1b6 92aa913 9f9e1b6 92aa913 9f9e1b6 92aa913 e4d64c5 92aa913 e4d64c5 92aa913 9f9e1b6 92aa913 e4d64c5 9f9e1b6 92aa913 e4d64c5 22159c8 92aa913 22159c8 e4d64c5 9f9e1b6 e4d64c5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | ---
license: mit
base_model: cointegrated/rubert-tiny2
tags:
- generated_from_trainer
model-index:
- name: rubert-tiny2-srl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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
|