models t5 small

t5-small

Overview

T5 Small is a text-to-text transformer model with 60 million parameters. It is developed by a group of researchers and is based on the Text-To-Text Transfer Transformer (T5) framework, which allows for a unified text-to-text format for input and output of all NLP tasks. T5-Small can be trained for multiple NLP tasks such as machine translation, document summarization, question answering, classification, and even regression tasks. It is pre-trained on the Colossal Clean Crawled Corpus (C4) and a multi-task mixture of unsupervised and supervised tasks in various languages, such as English, French, Romanian and German. You can use the code provided to get started with the model and refer to the resources provided for more information on the model.

The above summary was generated using ChatGPT. Review the original-model-card to understand the data used to train the model, evaluation metrics, license, intended uses, limitations and bias before using the model.

Inference samples

Inference type	Python sample (Notebook)	CLI with YAML
Real time	translation-online-endpoint.ipynb	translation-online-endpoint.sh
Batch	translation-batch-endpoint.ipynb	coming soon

Finetuning samples

Task	Use case	Dataset	Python sample (Notebook)	CLI with YAML
Summarization	News Summary	CNN DailyMail	news-summary.ipynb	news-summary.sh
Translation	Translate English to Romanian	WMT16	translate-english-to-romanian.ipynb	translate-english-to-romanian.sh

Model Evaluation

Task	Use case	Dataset	Python sample (Notebook)	CLI with YAML
Translation	Translation	wmt16/ro-en	evaluate-model-translation.ipynb	evaluate-model-translation.yml

Sample inputs and outputs (for real-time inference)

Sample input

{
    "inputs": {
        "input_string": ["My name is John and I live in Seattle", "Berlin is the capital of Germany."]
    },
    "parameters": {
        "task_type": "translation_en_to_fr"
    }
}

Sample output

[
    {
        "0": "Mon nom est John et je vivais à Seattle."
    },
    {
        "0": "Berlin est la capitale de l'Allemagne."
    }
]

Version: 9

Tags

Preview computes_allow_list : ['Standard_NV12s_v3', 'Standard_NV24s_v3', 'Standard_NV48s_v3', 'Standard_NC6s_v3', 'Standard_NC12s_v3', 'Standard_NC24s_v3', 'Standard_NC24rs_v3', 'Standard_NC6s_v2', 'Standard_NC12s_v2', 'Standard_NC24s_v2', 'Standard_NC24rs_v2', 'Standard_NC4as_T4_v3', 'Standard_NC8as_T4_v3', 'Standard_NC16as_T4_v3', 'Standard_NC64as_T4_v3', 'Standard_ND6s', 'Standard_ND12s', 'Standard_ND24s', 'Standard_ND24rs', 'Standard_ND40rs_v2', 'Standard_ND96asr_v4'] license : apache-2.0 model_specific_defaults : ordereddict({'apply_deepspeed': 'true', 'apply_lora': 'true', 'apply_ort': 'true'}) task : text-translation

View in Studio: https://ml.azure.com/registries/azureml/models/t5-small/version/9

License: apache-2.0

Properties

SHA: 3479082dc36f8a4730936ef1c9b88cd8b0835c53

datasets: c4

evaluation-min-sku-spec: 8|0|28|56

evaluation-recommended-sku: Standard_DS4_v2

finetune-min-sku-spec: 4|1|28|176

finetune-recommended-sku: Standard_NC24rs_v3

finetuning-tasks: summarization, translation

inference-min-sku-spec: 2|0|7|14

inference-recommended-sku: Standard_DS2_v2, Standard_D2a_v4, Standard_D2as_v4, Standard_DS3_v2, Standard_D4a_v4, Standard_D4as_v4, Standard_DS4_v2, Standard_D8a_v4, Standard_D8as_v4, Standard_DS5_v2, Standard_D16a_v4, Standard_D16as_v4, Standard_D32a_v4, Standard_D32as_v4, Standard_D48a_v4, Standard_D48as_v4, Standard_D64a_v4, Standard_D64as_v4, Standard_D96a_v4, Standard_D96as_v4, Standard_F4s_v2, Standard_FX4mds, Standard_F8s_v2, Standard_FX12mds, Standard_F16s_v2, Standard_F32s_v2, Standard_F48s_v2, Standard_F64s_v2, Standard_F72s_v2, Standard_FX24mds, Standard_FX36mds, Standard_FX48mds, Standard_E2s_v3, Standard_E4s_v3, Standard_E8s_v3, Standard_E16s_v3, Standard_E32s_v3, Standard_E48s_v3, Standard_E64s_v3, Standard_NC4as_T4_v3, Standard_NC6s_v3, Standard_NC8as_T4_v3, Standard_NC12s_v3, Standard_NC16as_T4_v3, Standard_NC24s_v3, Standard_NC64as_T4_v3, Standard_NC24ads_A100_v4, Standard_NC48ads_A100_v4, Standard_NC96ads_A100_v4, Standard_ND96asr_v4, Standard_ND96amsr_A100_v4, Standard_ND40rs_v2

languages: en, fr, ro, de