models roberta large mnli

roberta-large-mnli

Overview

Roberta-large-MNLI is a fine-tuned version of the RoBERTa large model on the Multi-Genre Natural Language Inference (MNLI) corpus. It is a transformer-based language model for English. The model is developed on GitHub Repo by some developers, also licensed under MIT. The fine-tuned model can be used for zero-shot classification tasks. The model should not be used to intentionally create hostile or alienating environments for people, it was not trained to be factual or true representations of people or events. The RoBERTa large model card notes that: "The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral." The predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. The model uses Multi-Genre Natural Language Inference (MNLI) corpus, see the Repo and data card for more information. The preprocessing include tokenization and masking procedure, pretraining is done on V100 gpus with a specific settings. The fine-tuned model can be used for zero-shot classification tasks, including zero-shot sentence-pair classification (examples can be accessed on GitHub Repo) and zero-shot sequence classification.

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	text-classification-online-endpoint.ipynb	text-classification-online-endpoint.sh
Batch	entailment-contradiction-batch.ipynb	coming soon

Model Evaluation

Task	Use case	Dataset	Python sample (Notebook)	CLI with YAML
Text Classification	Textual Entailment	MNLI	evaluate-model-text-classification.ipynb	evaluate-model-text-classification.yml

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

Sample inputs and outputs (for real-time inference)

Sample input

{
    "input_data": {
        "input_string": ["Today was an amazing day!", "It was an unfortunate series of events."]
    }
}

Sample output

[
    {
        "0": "NEUTRAL"
    },
    {
        "0": "NEUTRAL"
    }
]

Version: 10

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 : mit model_specific_defaults : ordereddict({'apply_deepspeed': 'true', 'apply_lora': 'true', 'apply_ort': 'true'}) task : text-classification

View in Studio: https://ml.azure.com/registries/azureml/models/roberta-large-mnli/version/10

License: mit

Properties

SHA: 0dcbcf20673c006ac2d1e324954491b96f0c0015

datasets: multi_nli, wikipedia, bookcorpus

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

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