Instructions to use mcurmei/single_label_N_max_long_training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mcurmei/single_label_N_max_long_training with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="mcurmei/single_label_N_max_long_training")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mcurmei/single_label_N_max_long_training") model = AutoModelForQuestionAnswering.from_pretrained("mcurmei/single_label_N_max_long_training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
Browse files
README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- squad
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model-index:
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- name: single_label_N_max_long_training
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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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# single_label_N_max_long_training
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.8288
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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: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 3.0568 | 1.0 | 674 | 1.9993 |
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| 1.6024 | 2.0 | 1348 | 1.8497 |
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| 1.0196 | 3.0 | 2022 | 1.9178 |
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| 0.7622 | 4.0 | 2696 | 2.0412 |
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| 0.6066 | 5.0 | 3370 | 2.2523 |
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| 0.4136 | 6.0 | 4044 | 2.3845 |
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| 0.3113 | 7.0 | 4718 | 2.5712 |
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| 0.2777 | 8.0 | 5392 | 2.6790 |
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| 0.208 | 9.0 | 6066 | 2.7464 |
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| 0.1749 | 10.0 | 6740 | 2.8288 |
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### Framework versions
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- Transformers 4.18.0
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- Pytorch 1.11.0+cu113
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- Datasets 2.2.1
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- Tokenizers 0.12.1
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