Instructions to use Akselssss/MT5_large_NO_CNN-idun-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akselssss/MT5_large_NO_CNN-idun-final with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Akselssss/MT5_large_NO_CNN-idun-final") model = AutoModelForSeq2SeqLM.from_pretrained("Akselssss/MT5_large_NO_CNN-idun-final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +70 -0
- generation_config.json +10 -0
- pytorch_model.bin +1 -1
README.md
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---
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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_NO_CNN-idun-final
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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_NO_CNN-idun-final
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This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.8492
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- Rouge1: 31.9047
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- Rouge2: 12.0487
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- Rougel: 21.7323
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- Rougelsum: 29.4557
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- Gen Len: 98.7777
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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: 5e-05
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 8
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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 | Gen Len |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
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| 2.246 | 1.0 | 6118 | 1.9772 | 30.9142 | 11.28 | 21.0914 | 28.4499 | 95.2396 |
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| 2.0899 | 2.0 | 12236 | 1.9070 | 31.3973 | 11.6219 | 21.3122 | 28.9365 | 100.0907 |
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| 1.9736 | 3.0 | 18354 | 1.8716 | 31.5752 | 11.7955 | 21.4748 | 29.1354 | 100.4973 |
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| 1.9189 | 4.0 | 24472 | 1.8547 | 31.9802 | 12.183 | 21.8505 | 29.5171 | 98.4764 |
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| 1.8697 | 5.0 | 30590 | 1.8492 | 31.9047 | 12.0487 | 21.7323 | 29.4557 | 98.7777 |
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### Framework versions
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- Transformers 4.32.1
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- Pytorch 2.3.0+cu121
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- Datasets 2.12.0
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- Tokenizers 0.13.2
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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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"length_penalty": 2.0,
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"max_length": 128,
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"no_repeat_ngram_size": 3,
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"num_beams": 4,
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"pad_token_id": 0,
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"transformers_version": "4.32.1"
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 4918519650
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version https://git-lfs.github.com/spec/v1
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oid sha256:4b0323a9c2c7a39764d002032bde42db9817e8b95d1d3a1caf841d130c3126a4
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size 4918519650
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