Text Classification
Transformers
PyTorch
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence") model = AutoModelForSequenceClassification.from_pretrained("DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence") - Notebooks
- Google Colab
- Kaggle
Commit ·
2865a25
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Parent(s): 99c0d45
update model card README.md
Browse files
README.md
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---
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license: mit
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tags:
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- generated_from_trainer
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metrics:
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- f1
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- accuracy
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model-index:
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- name: xlm-roberta-large-DreamBank
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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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# xlm-roberta-large-DreamBank
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2942
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- F1: 0.8621
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- Roc Auc: 0.9069
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- Accuracy: 0.6973
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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: 1e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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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 | F1 | Roc Auc | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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| No log | 1.0 | 185 | 0.5949 | 0.0 | 0.5 | 0.0 |
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| No log | 2.0 | 370 | 0.3825 | 0.6052 | 0.7481 | 0.4595 |
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| 0.476 | 3.0 | 555 | 0.2891 | 0.7403 | 0.8010 | 0.5730 |
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| 0.476 | 4.0 | 740 | 0.2604 | 0.8425 | 0.8852 | 0.7081 |
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| 0.476 | 5.0 | 925 | 0.2484 | 0.8504 | 0.8932 | 0.6649 |
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| 0.1457 | 6.0 | 1110 | 0.3092 | 0.8352 | 0.8909 | 0.6703 |
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| 0.1457 | 7.0 | 1295 | 0.2882 | 0.8546 | 0.8950 | 0.6919 |
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| 0.1457 | 8.0 | 1480 | 0.3099 | 0.8549 | 0.9014 | 0.6865 |
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| 0.0691 | 9.0 | 1665 | 0.3080 | 0.8548 | 0.9019 | 0.6811 |
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| 0.0691 | 10.0 | 1850 | 0.2942 | 0.8621 | 0.9069 | 0.6973 |
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### Framework versions
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- Transformers 4.25.1
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- Pytorch 1.12.1
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- Datasets 2.5.1
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- Tokenizers 0.12.1
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