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--- |
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datasets: |
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- glue |
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- anli |
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model-index: |
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- name: e5-large-mnli-anli |
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results: [] |
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pipeline_tag: zero-shot-classification |
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language: |
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- en |
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license: mit |
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--- |
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# e5-large-mnli-anli |
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This model is a fine-tuned version of [intfloat/e5-large](https://huggingface.co/intfloat/e5-large) on the glue (mnli) and anli dataset. |
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## Model description |
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[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). |
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Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 |
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## How to use the model |
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The model can be loaded with the `zero-shot-classification` pipeline like so: |
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```python |
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from transformers import pipeline |
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classifier = pipeline("zero-shot-classification", |
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model="mjwong/e5-large-mnli-anli") |
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``` |
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You can then use this pipeline to classify sequences into any of the class names you specify. |
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```python |
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sequence_to_classify = "one day I will see the world" |
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candidate_labels = ['travel', 'cooking', 'dancing'] |
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classifier(sequence_to_classify, candidate_labels) |
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#{'sequence': 'one day I will see the world', |
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# 'labels': ['travel', 'dancing', 'cooking'], |
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# 'scores': [0.9878318905830383, 0.01044005248695612, 0.001728130504488945]} |
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``` |
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If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently: |
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```python |
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candidate_labels = ['travel', 'cooking', 'dancing', 'exploration'] |
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classifier(sequence_to_classify, candidate_labels, multi_class=True) |
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#{'sequence': 'one day I will see the world', |
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# 'labels': ['exploration', 'travel', 'dancing', 'cooking'], |
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# 'scores': [0.9956096410751343, |
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# 0.9929478764533997, |
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# 0.21706733107566833, |
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# 0.0005817742203362286]} |
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``` |
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### Eval results |
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The model was evaluated using the dev sets for MultiNLI and test sets for ANLI. The metric used is accuracy. |
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|Datasets|mnli_dev_m|mnli_dev_mm|anli_test_r1|anli_test_r2|anli_test_r3| |
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| :---: | :---: | :---: | :---: | :---: | :---: | |
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|[e5-base-mnli](https://huggingface.co/mjwong/e5-base-mnli)|0.840|0.839|0.231|0.285|0.309| |
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|[e5-base-v2-mnli](https://huggingface.co/mjwong/e5-base-v2-mnli)|0.844|0.838|0.253|0.288|0.301| |
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|[e5-large-mnli](https://huggingface.co/mjwong/e5-large-mnli)|0.868|0.869|0.301|0.296|0.294| |
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|[e5-large-v2-mnli](https://huggingface.co/mjwong/e5-large-v2-mnli)|0.875|0.876|0.354|0.298|0.313| |
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|[e5-large-unsupervised-mnli](https://huggingface.co/mjwong/e5-large-unsupervised-mnli)|0.865|0.867|0.314|0.285|0.303| |
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|[e5-large-mnli-anli](https://huggingface.co/mjwong/e5-large-mnli-anli)|0.843|0.848|0.646|0.484|0.458| |
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|[e5-large-unsupervised-mnli-anli](https://huggingface.co/mjwong/e5-large-unsupervised-mnli-anli)|0.836|0.842|0.634|0.481|0.478| |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 2 |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 1.12.1+cu116 |
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- Datasets 2.11.0 |
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- Tokenizers 0.12.1 |
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