Instructions to use Mediocre-Judge/multilingual_bert_AGRO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mediocre-Judge/multilingual_bert_AGRO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Mediocre-Judge/multilingual_bert_AGRO")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Mediocre-Judge/multilingual_bert_AGRO") model = AutoModelForQuestionAnswering.from_pretrained("Mediocre-Judge/multilingual_bert_AGRO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,518 Bytes
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library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
model-index:
- name: multilingual_bert_AGRO
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. -->
# multilingual_bert_AGRO
This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9701
- Exact Match: 24.8571
- F1 Score: 56.8185
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 3407
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 Score |
|:-------------:|:------:|:----:|:---------------:|:-----------:|:--------:|
| 6.2135 | 0.0053 | 1 | 6.2224 | 0.0 | 9.8241 |
| 6.2165 | 0.0107 | 2 | 6.1874 | 0.0 | 9.8716 |
| 6.1776 | 0.0160 | 3 | 6.1182 | 0.0 | 10.1769 |
| 6.1126 | 0.0214 | 4 | 6.0144 | 0.0 | 11.2194 |
| 6.0166 | 0.0267 | 5 | 5.8717 | 0.0752 | 12.2552 |
| 5.8816 | 0.0321 | 6 | 5.6741 | 2.2556 | 18.5657 |
| 5.7374 | 0.0374 | 7 | 5.4450 | 12.1805 | 37.6517 |
| 5.5652 | 0.0428 | 8 | 5.1969 | 23.8346 | 53.3283 |
| 5.2962 | 0.0481 | 9 | 4.9758 | 26.5414 | 56.9819 |
| 5.0538 | 0.0535 | 10 | 4.8192 | 22.1805 | 56.7266 |
| 5.0246 | 0.0588 | 11 | 4.6919 | 18.2707 | 56.2903 |
| 4.8358 | 0.0641 | 12 | 4.5354 | 18.2707 | 56.7852 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3
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