models distilbert base uncased finetuned sst 2 english

distilbert-base-uncased-finetuned-sst-2-english

Overview

This is a fine-tuned version of DistilBERT-base-uncased, trained on SST-2, which reached 91.3 % accuracy on the dev set. Developed by Hugging Face, it's mainly intended to be used for topic classification and can be fine-tuned on downstream tasks, but it's important to keep in mind that it has certain biases, such as biased predictions for certain underrepresented populations and that it should not be used to create hostile or alienating environments for people. Additionally, the authors used the Stanford Sentiment Treebank(sst2) corpora for training the model. It's recommended to evaluate the risks of this model by thoroughly probing the bias evaluation datasets like WinoBias, WinoGender, Stereoset

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

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	Dataset	Python sample (Notebook)	CLI with YAML
Text Classification	Sentiment Classification	SST2	evaluate-model-sentiment-analysis.ipynb	evaluate-model-sentiment-analysis.yml

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": "POSITIVE"
    },
    {
        "0": "NEGATIVE"
    }
]

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 : apache-2.0 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/distilbert-base-uncased-finetuned-sst-2-english/version/10

License: apache-2.0

Properties

SHA: 3d65bad49c7ba6f71920504507a8927f4b9db6c0

datasets: sst2, glue

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|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