Instructions to use RamsesDIIP/mt5-large-ie-budquo-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RamsesDIIP/mt5-large-ie-budquo-finetuned with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("RamsesDIIP/mt5-large-ie-budquo-finetuned") model = AutoModelForSeq2SeqLM.from_pretrained("RamsesDIIP/mt5-large-ie-budquo-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +70 -0
- generation_config.json +6 -0
- model.safetensors +1 -1
README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: google/mt5-large
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tags:
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- generated_from_trainer
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metrics:
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- rouge
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model-index:
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- name: mt5-large-ie-budquo-finetuned
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# mt5-large-ie-budquo-finetuned
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This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0322
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- Rouge1: 0.0564
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- Rouge2: 0.0299
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- Rougel: 0.0562
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- Rougelsum: 0.0564
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0003
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 4
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
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|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
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| 0.1063 | 0.9998 | 1156 | 0.0669 | 0.1300 | 0.0852 | 0.1264 | 0.1299 |
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| 0.0516 | 1.9996 | 2312 | 0.0366 | 0.0609 | 0.0328 | 0.0608 | 0.0608 |
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| 0.0338 | 2.9994 | 3468 | 0.0265 | 0.1055 | 0.0668 | 0.1039 | 0.1057 |
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| 0.0253 | 4.0 | 4625 | 0.0223 | 0.1245 | 0.0741 | 0.1227 | 0.1246 |
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| 0.021 | 4.9989 | 5780 | 0.0206 | 0.1297 | 0.0834 | 0.1268 | 0.1298 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.1+cu121
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- Datasets 3.0.1
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- Tokenizers 0.19.1
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generation_config.json
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{
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"decoder_start_token_id": 0,
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"eos_token_id": 1,
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"pad_token_id": 0,
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"transformers_version": "4.44.2"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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size 4918393736
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:1774056cb13245014d1b47a6ffde61776e1794e63a2e92547e1abf3b61cfde65
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size 4918393736
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