@@ -33,7 +33,7 @@ Finally, deploy your model. Let's say you want to use `BAAI/bge-large-en-v1.5`.
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model=BAAI/bge-large-en-v1.5
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volume=$PWD /data
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- docker run --gpus all -p 8080:80 -v $volume :/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.6 --model-id $model
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+ docker run --gpus all -p 8080:80 -v $volume :/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.7 --model-id $model
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```
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<Tip >
@@ -66,7 +66,7 @@ Let's say you want to use `BAAI/bge-reranker-large`:
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model=BAAI/bge-reranker-large
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volume=$PWD /data
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- docker run --gpus all -p 8080:80 -v $volume :/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.6 --model-id $model
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+ docker run --gpus all -p 8080:80 -v $volume :/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.7 --model-id $model
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```
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Once you have deployed a model, you can use the ` rerank ` endpoint to rank the similarity between a query and a list
@@ -87,7 +87,7 @@ You can also use classic Sequence Classification models like `SamLowe/roberta-ba
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model=SamLowe/roberta-base-go_emotions
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volume=$PWD /data
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- docker run --gpus all -p 8080:80 -v $volume :/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.6 --model-id $model
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+ docker run --gpus all -p 8080:80 -v $volume :/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.7 --model-id $model
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```
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Once you have deployed the model you can use the ` predict ` endpoint to get the emotions most associated with an input:
@@ -139,5 +139,5 @@ git clone https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5
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volume=$PWD
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# Mount the models directory inside the container with a volume and set the model ID
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- docker run --gpus all -p 8080:80 -v $volume :/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.6 --model-id /data/gte-base-en-v1.5
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+ docker run --gpus all -p 8080:80 -v $volume :/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.7 --model-id /data/gte-base-en-v1.5
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```
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