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}).

How to use

python

classifier = pipeline("sentiment-analysis", "dhpollack/distilbert-dummy-sentiment")
results  = classifier(["this is a test", "another test"])

Notes

This was created as follows:

Create a vocab.txt file (in /tmp/vocab.txt in this example).

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Open a python shell:

python

import transformers
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})
model = transformers.DistilBertForSequenceClassification(config)
tokenizer = transformers.DistilBertTokenizer("/tmp/vocab.txt", model_max_length=512)
config.save_pretrained(".")
model.save_pretrained(".")
tokenizer.save_pretrained(".")