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--- |
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language: |
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- "multilingual" |
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- "en" |
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tags: |
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- "sentiment-analysis" |
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- "testing" |
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- "unit tests" |
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--- |
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# DistilBert Dummy Sentiment Model |
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## Purpose |
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This is a dummy model that can be used for testing the transformers `pipeline` with the task `sentiment-analysis`. It should always give random results (i.e. `{"label": "negative", "score": 0.5}`). |
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## How to use |
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```python |
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classifier = pipeline("sentiment-analysis", "dhpollack/distilbert-dummy-sentiment") |
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results = classifier(["this is a test", "another test"]) |
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``` |
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## Notes |
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This was created as follows: |
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1. Create a vocab.txt file (in /tmp/vocab.txt in this example). |
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``` |
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[UNK] |
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[SEP] |
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[PAD] |
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[CLS] |
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[MASK] |
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``` |
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2. Open a python shell: |
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```python |
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import transformers |
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config = transformers.DistilBertConfig(vocab_size=5, n_layers=1, n_heads=1, dim=1, hidden_dim=4 * 1, num_labels=2, id2label={0: "negative", 1: "positive"}, label2id={"negative": 0, "positive": 1}) |
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model = transformers.DistilBertForSequenceClassification(config) |
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tokenizer = transformers.DistilBertTokenizer("/tmp/vocab.txt", model_max_length=512) |
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config.save_pretrained(".") |
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model.save_pretrained(".") |
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tokenizer.save_pretrained(".") |
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``` |
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