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models bert large uncased
Description: BERT is a pre-trained language model created by the Hugging Face team that uses masked language modeling (MLM) on a large corpus of English data. Its primary uses are for sequence classification and question answering, and it is not intended for text generation. It is important to note that this particular BERT model is cased, making a distinction between 'english' and 'English'. The model can be fine-tuned for downstream tasks, and it is described as having 24 layers, 1024 hidden dimensions, 16 attention heads, and 336M parameters. It's most effective on use cases that involve using the entire sentence to make decisions, whereas tasks such as text-generations, you should use GPT2.
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 { "input_data": { "input_string": ["Paris is the [MASK] of France.", "Today is a [MASK] day!"] } } #### Sample output json [ { "0": "capital" }, { "0": "good" } ]
Version: 12
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 : fill-mask
View in Studio: https://ml.azure.com/registries/azureml/models/bert-large-uncased/version/12
License: apache-2.0
SHA: 80792f8e8216b29f3c846b653a0ff0a37c210431
datasets: bookcorpus, wikipedia
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: text-classification, token-classification, question-answering
inference-min-sku-spec: 2|0|14|28
inference-recommended-sku: 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_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