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--- |
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library_name: transformers |
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license: apache-2.0 |
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language: |
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- en |
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widget: |
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- text: 'You will be given a question and options. Select the right answer. QUESTION: |
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If (G, .) is a group such that (ab)^-1 = a^-1b^-1, for all a, b in G, then G is |
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a/an CHOICES: - A: commutative semi group - B: abelian group - C: non-abelian |
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group - D: None of these ANSWER: [unused0] [MASK]' |
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tags: |
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- fill-mask |
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- masked-lm |
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- long-context |
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- classification |
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- modernbert |
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- mlx |
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pipeline_tag: fill-mask |
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inference: false |
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--- |
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# mlx-community/answerdotai-ModernBERT-Large-Instruct-8bit |
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The Model [mlx-community/answerdotai-ModernBERT-Large-Instruct-8bit](https://huggingface.co/mlx-community/answerdotai-ModernBERT-Large-Instruct-8bit) was converted to MLX format from [answerdotai/ModernBERT-Large-Instruct](https://huggingface.co/answerdotai/ModernBERT-Large-Instruct) using mlx-lm version **0.0.3**. |
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## Use with mlx |
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```bash |
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pip install mlx-embeddings |
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``` |
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```python |
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from mlx_embeddings import load, generate |
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import mlx.core as mx |
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model, tokenizer = load("mlx-community/answerdotai-ModernBERT-Large-Instruct-8bit") |
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# For text embeddings |
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output = generate(model, processor, texts=["I like grapes", "I like fruits"]) |
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embeddings = output.text_embeds # Normalized embeddings |
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# Compute dot product between normalized embeddings |
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similarity_matrix = mx.matmul(embeddings, embeddings.T) |
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print("Similarity matrix between texts:") |
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print(similarity_matrix) |
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``` |
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