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* Add more readmes. * Add a readme for dinov2. * Add some skeleton files for a couple more examples. * More whisper details.
45 lines
1.6 KiB
Markdown
45 lines
1.6 KiB
Markdown
# candle-bert
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Bert is a general large language model. In this example it can be used for two
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different tasks:
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- Compute sentence embeddings for a prompt.
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- Compute similarities between a set of sentences.
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## Sentence embeddings
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Bert is used to compute the sentence embeddings for a prompt. The model weights
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are downloaded from the hub on the first run.
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```bash
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cargo run --example bert --release -- --prompt "Here is a test sentence"
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> [[[ 0.0798, -0.0665, -0.0247, ..., -0.1082, -0.1000, -0.2751],
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> [ 0.4218, 0.2690, 0.2740, ..., 0.3889, 1.3503, 0.9908],
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> [ 0.0466, 0.3041, -0.1143, ..., 0.4427, 0.6926, -0.1515],
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> ...
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> [ 0.3396, 0.4320, -0.4408, ..., 0.9212, 0.2331, -0.6777],
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> [ 0.2789, 0.7539, 0.4306, ..., -0.0095, 0.3375, -1.7529],
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> [ 0.6737, 0.7882, 0.0548, ..., 0.1836, 0.7299, -0.6617]]]
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> Tensor[[1, 7, 384], f32]
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```
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## Similarities
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In this example, Bert is used to compute the sentence embeddings for a set of
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sentences (hardcoded in the examples). Then cosine similarities are computed for
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each sentence pair and they are reported by decreasing values, hence the first
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reported pair contains the two sentences that have the highest similarity score.
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The sentence embeddings are computed using average pooling through all the
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sentence tokens, including some potential padding.
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```bash
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cargo run --example bert --release
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> score: 0.85 'The new movie is awesome' 'The new movie is so great'
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> score: 0.61 'The cat sits outside' 'The cat plays in the garden'
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> score: 0.52 'I love pasta' 'Do you like pizza?'
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> score: 0.23 'The new movie is awesome' 'Do you like pizza?'
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> score: 0.22 'I love pasta' 'The new movie is awesome'
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```
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