File size: 3,363 Bytes
44bf796 |
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 |
---
license: apache-2.0
base_model: google/mt5-large
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9627
- Loc: {'precision': 0.07002967359050445, 'recall': 0.13817330210772832, 'f1': 0.09294998030720757, 'number': 854}
- Org: {'precision': 0.06141439205955335, 'recall': 0.1523076923076923, 'f1': 0.08753315649867373, 'number': 650}
- Per: {'precision': 0.030874785591766724, 'recall': 0.07741935483870968, 'f1': 0.04414469650521153, 'number': 465}
- Overall Precision: 0.0567
- Overall Recall: 0.1285
- Overall F1: 0.0787
- Overall Accuracy: 0.3287
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Loc | Org | Per | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 3.8187 | 2.0 | 10 | 3.1219 | {'precision': 0.06360022714366836, 'recall': 0.13114754098360656, 'f1': 0.08565965583173997, 'number': 854} | {'precision': 0.05763688760806916, 'recall': 0.15384615384615385, 'f1': 0.08385744234800839, 'number': 650} | {'precision': 0.027879677182685254, 'recall': 0.08172043010752689, 'f1': 0.04157549234135668, 'number': 465} | 0.0515 | 0.1270 | 0.0732 | 0.2983 |
| 3.2942 | 4.0 | 20 | 2.9627 | {'precision': 0.07002967359050445, 'recall': 0.13817330210772832, 'f1': 0.09294998030720757, 'number': 854} | {'precision': 0.06141439205955335, 'recall': 0.1523076923076923, 'f1': 0.08753315649867373, 'number': 650} | {'precision': 0.030874785591766724, 'recall': 0.07741935483870968, 'f1': 0.04414469650521153, 'number': 465} | 0.0567 | 0.1285 | 0.0787 | 0.3287 |
### Framework versions
- Transformers 4.39.3
- Pytorch 1.11.0a0+17540c5
- Datasets 2.20.0
- Tokenizers 0.15.2
|