Instructions to use arbitropy/bcoqa-mt5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arbitropy/bcoqa-mt5 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("arbitropy/bcoqa-mt5") model = AutoModelForSeq2SeqLM.from_pretrained("arbitropy/bcoqa-mt5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from arbitropy/bcoqa-mt5: direct link, hf CLI and curl.
- Browser
- Download file 1.42 kB
-
https://huggingface.co/arbitropy/bcoqa-mt5/resolve/main/README.md
- Command line
-
hf download hf://arbitropy/bcoqa-mt5/README.md
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curl -L -o README.md https://huggingface.co/arbitropy/bcoqa-mt5/resolve/main/README.md
1.42 kB
metadata
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
model-index:
- name: bcoqa-mt5
results: []
bcoqa-mt5
This model is a fine-tuned version of google/mt5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1585
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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.6025 | 0.22 | 10000 | 1.3188 |
| 1.4277 | 0.43 | 20000 | 1.2436 |
| 1.2805 | 0.65 | 30000 | 1.1954 |
| 1.3656 | 0.86 | 40000 | 1.1585 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1