Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Cohere Classification Finetuning - Multi
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Latest Version:
v2.0.3
Classification finetuning supports single / multi-label classification based on semantic meaning of text against a predefined set of labels.
Product Overview
Cohere’s Classification Finetuning enables you to train and deploy classification models with a few lines of code. Using as few as 2 examples per label, users are able to train custom models to classify text based on semantic meaning (results will vary depending on the classification task at hand - for more complex and nuanced tasks we recommend more than 2 examples).
Key Data
Version
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Type
Algorithm
Highlights
Cohere supports the training of single and multi-label classification models
This offering supports cross-lingual classification - classify incoming text irrespective of the language provided during training.
Classification, Finetuning, Multilingual, Text Embeddings
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Algorithm Training$11.41/hr
running on ml.g5.xlarge
Model Realtime Inference$5.97/hr
running on ml.g4dn.xlarge
Model Batch Transform$5.97/hr
running on ml.g4dn.xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Algorithm Training$1.408/host/hr
running on ml.g5.xlarge
SageMaker Realtime Inference$0.736/host/hr
running on ml.g4dn.xlarge
SageMaker Batch Transform$0.736/host/hr
running on ml.g4dn.xlarge
Algorithm Training
For algorithm training in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Algorithm/hr | |
---|---|---|
ml.g5.xlarge Vendor Recommended | $11.41 | |
ml.g5.2xlarge | $12.28 | |
ml.g4dn.xlarge | $5.97 | |
ml.g4dn.2xlarge | $7.62 |
Usage Information
Training
The data must be in JSONL format, with "text" and "label" keys. For single label classification, the "label" value must be an integer or string. For multi label classification, the "label" value must be a list of integers or a list of strings. For examples which do not correspond to any label, leave the list empty.
E.g. for multilabel classification: {"text":"This sentence talks about pasta and pizza", "label":[0,1]} {"text":"This sentence does not talk about food", "label":[]} {"text":"Pasta is a great dish", "label":[0]} {"text":"Could I get a slice please?", "label":[1]}
Channel specification
Fields marked with * are required
training
*Input modes: File
Content types: -
Compression types: None
evaluation
Input modes: File
Content types: -
Compression types: None
Hyperparameters
Fields marked with * are required
name
*Name of the model to be trained
Type: FreeText
Tunable: No
Model input and output details
Input
Summary
The model accepts JSON requests that specify the input texts to be classified and the model to use. E.g. {"texts": ["hello world"], "model_id": "BASE"}
It is better to use the co.classify() call to send requests, as in https://github.com/cohere-ai/cohere-sagemaker/blob/main/notebooks/Deploy%20classification%20model.ipynb
Input MIME type
application/jsonSample input data
{
"texts": [
"hello world"
],
"model_id": "BASE"
}
Output
Summary
The output format is a list of:
- labels in the single-label classification case
- list of labels in the multi-label classification case
Output MIME type
text/plainSample output data
[1, 0]
Sample notebook
Additional Resources
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Support Information
AWS Infrastructure
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