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models roberta large
Description: The RoBERTa Large model is a pretrained language model developed by the Hugging Face team, based on the transformer architecture. It was trained on a large corpus of English data in a self-supervised manner using the masked language modeling (MLM) objective. The model is case-sensitive and primarily intended for use in fine-tuning downstream tasks such as sequence classification, token classification, or question answering. It was trained on a combination of five datasets weighing 160GB of text, and uses a vocabulary size of 50,000 for tokenization. The model was trained for 500K steps on 1024 V100 GPUs with a batch size of 8K and a sequence length of 512. The optimizer used was Adam with a learning rate of 4e-4, β1=0.9, β2=0.98, and ϵ=1e-6, with a weight decay of 0.01 and learning rate warmup for 30,000 steps.
Please Note: This model accepts masks in <mask> format. See Sample input for reference. > 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|fill-mask-online-endpoint.ipynb|fill-mask-online-endpoint.sh Batch |fill-mask-batch-endpoint.ipynb| coming soon ### Finetuning samples Task|Use case|Dataset|Python sample (Notebook)|CLI with YAML |--|--|--|--|--| Text Classification|Emotion Detection|Emotion|emotion-detection.ipynb|emotion-detection.sh Token Classification|Named Entity Recognition|Conll2003|named-entity-recognition.ipynb|named-entity-recognition.sh Question Answering|Extractive Q&A|SQUAD (Wikipedia)|extractive-qa.ipynb|extractive-qa.sh ### Model Evaluation Task| Use case| Python sample (Notebook)| CLI with YAML |--|--|--|--| Fill Mask | Fill Mask | rcds/wikipedia-for-mask-filling | evaluate-model-fill-mask.ipynb | evaluate-model-fill-mask.yml ### Sample inputs and outputs (for real-time inference) #### Sample input json { "inputs": { "input_string": ["Paris is the <mask> of France.", "Today is a <mask> day!"] } } #### Sample output json [ { "0": "capital" }, { "0": "beautiful" } ]
Version: 9
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 : mit model_specific_defaults : ordereddict([('apply_deepspeed', 'true'), ('apply_lora', 'true'), ('apply_ort', 'true')]) task : fill-mask
View in Studio: https://ml.azure.com/registries/azureml/models/roberta-large/version/9
License: mit
SHA: 716877d372b884cad6d419d828bac6c85b3b18d9
datasets: bookcorpus, wikipedia
evaluation-min-sku-spec: 2|0|14|28
evaluation-recommended-sku: Standard_DS3_v2
finetune-min-sku-spec: 4|1|28|176
finetune-recommended-sku: Standard_NC24rs_v3
finetuning-tasks: text-classification, token-classification, question-answering
inference-min-sku-spec: 2|0|14|28
inference-recommended-sku: Standard_DS3_v2
languages: en