Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Dulfary/platzi-distilroberta-base-mrpc-glue-prueba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dulfary/platzi-distilroberta-base-mrpc-glue-prueba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dulfary/platzi-distilroberta-base-mrpc-glue-prueba")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Dulfary/platzi-distilroberta-base-mrpc-glue-prueba") model = AutoModelForSequenceClassification.from_pretrained("Dulfary/platzi-distilroberta-base-mrpc-glue-prueba", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
README.md
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---
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license: apache-2.0
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base_model: distilroberta-base
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- f1
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model-index:
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- name: platzi-distilroberta-base-mrpc-glue-prueba
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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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# platzi-distilroberta-base-mrpc-glue-prueba
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9205
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- Accuracy: 0.8235
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- F1: 0.8746
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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: 8
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- eval_batch_size: 8
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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: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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| 0.336 | 1.09 | 500 | 0.7472 | 0.8235 | 0.8784 |
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| 0.1824 | 2.18 | 1000 | 0.9205 | 0.8235 | 0.8746 |
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### Framework versions
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- Transformers 4.35.2
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- Pytorch 2.1.0+cu121
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- Datasets 2.16.0
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- Tokenizers 0.15.0
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model.safetensors
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