Instructions to use jysssacc/mt0-base_fine_lr0.005_bs4_epoch5_wd0.01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jysssacc/mt0-base_fine_lr0.005_bs4_epoch5_wd0.01 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jysssacc/mt0-base_fine_lr0.005_bs4_epoch5_wd0.01") model = AutoModelForSeq2SeqLM.from_pretrained("jysssacc/mt0-base_fine_lr0.005_bs4_epoch5_wd0.01", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from jysssacc/mt0-base_fine_lr0.005_bs4_epoch5_wd0.01: direct link, hf CLI and curl.
- Browser
- Download file 1.57 kB
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https://huggingface.co/jysssacc/mt0-base_fine_lr0.005_bs4_epoch5_wd0.01/resolve/main/README.md
- Command line
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hf download hf://jysssacc/mt0-base_fine_lr0.005_bs4_epoch5_wd0.01/README.md
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curl -L -o README.md https://huggingface.co/jysssacc/mt0-base_fine_lr0.005_bs4_epoch5_wd0.01/resolve/main/README.md
1.57 kB
metadata
license: apache-2.0
base_model: bigscience/mt0-base
tags:
- generated_from_trainer
model-index:
- name: mt0-base_fine_lr0.005_bs4_epoch5_wd0.01
results: []
mt0-base_fine_lr0.005_bs4_epoch5_wd0.01
This model is a fine-tuned version of bigscience/mt0-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 6.0147
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: 0.005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0959 | 1.0 | 157 | 0.1935 |
| 0.755 | 2.0 | 314 | 0.8319 |
| 1.6037 | 3.0 | 471 | 6.6868 |
| 5.8058 | 4.0 | 628 | 6.2972 |
| 5.3912 | 5.0 | 785 | 6.0147 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0