File size: 4,860 Bytes
72b48f3 |
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 |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: MsIssuesBERT
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. -->
# MsIssuesBERT
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Ethnic Boundaries F1: 0.9313
- Ethnic Boundaries Accuracy: 0.9363
- Economic Inequality F1: 0.8031
- Economic Inequality Accuracy: 0.8123
- Economic Policy Benefits F1: 0.8269
- Economic Policy Benefits Accuracy: 0.8485
- Religion Ethnic Identity F1: 0.8491
- Religion Ethnic Identity Accuracy: 0.8588
- Language Policy F1: 0.6336
- Language Policy Accuracy: 0.7059
- Mother Tongue Education F1: 0.8370
- Mother Tongue Education Accuracy: 0.8889
- Overall F1: 0.8135
- Overall Accuracy: 0.8418
## 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: 4.452845612911518e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 964
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Ethnic Boundaries F1 | Ethnic Boundaries Accuracy | Economic Inequality F1 | Economic Inequality Accuracy | Economic Policy Benefits F1 | Economic Policy Benefits Accuracy | Religion Ethnic Identity F1 | Religion Ethnic Identity Accuracy | Language Policy F1 | Language Policy Accuracy | Mother Tongue Education F1 | Mother Tongue Education Accuracy | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------------------------:|:----------------------:|:----------------------------:|:---------------------------:|:---------------------------------:|:---------------------------:|:---------------------------------:|:------------------:|:------------------------:|:--------------------------:|:--------------------------------:|:----------:|:----------------:|
| 0.0242 | 1.0 | 1000 | nan | 0.9199 | 0.9461 | 0.6796 | 0.7771 | 0.7411 | 0.8215 | 0.7662 | 0.8395 | 0.5459 | 0.6765 | 0.6806 | 0.7778 | 0.7222 | 0.8064 |
| 0.092 | 2.0 | 2000 | nan | 0.9393 | 0.9444 | 0.7938 | 0.8023 | 0.7996 | 0.8316 | 0.8412 | 0.8569 | 0.6336 | 0.7059 | 0.8370 | 0.8889 | 0.8074 | 0.8383 |
| 0.083 | 3.0 | 3000 | nan | 0.9323 | 0.9395 | 0.8053 | 0.8249 | 0.8170 | 0.8519 | 0.8419 | 0.8588 | 0.6071 | 0.7059 | 0.8370 | 0.8889 | 0.8068 | 0.8450 |
| 1.6647 | 4.0 | 4000 | nan | 0.9298 | 0.9297 | 0.8046 | 0.8098 | 0.8367 | 0.8586 | 0.8604 | 0.8627 | 0.6573 | 0.7353 | 0.8370 | 0.8889 | 0.8210 | 0.8475 |
| 0.0619 | 5.0 | 5000 | nan | 0.9313 | 0.9363 | 0.8031 | 0.8123 | 0.8269 | 0.8485 | 0.8491 | 0.8588 | 0.6336 | 0.7059 | 0.8370 | 0.8889 | 0.8135 | 0.8418 |
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|