models finiteautomata bertweet base sentiment analysis

finiteautomata-bertweet-base-sentiment-analysis

Overview

The pysentimiento library is an open-source tool for non-commercial use and scientific research purposes, used for Sentiment Analysis and Social NLP tasks. It was trained on about 40k tweets from the SemEval 2017 corpus, using the BERTweet - a RoBERTa model trained on English tweets and processes POS, NEG, and NEU labels. The Github repository link is https://github.com/finiteautomata/pysentimiento/ While using this model, it is important to keep in mind that they are trained with third-party datasets and are subject to their respective licenses.Additionally Publication details are also mentioned.

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

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": "POS"
    },
    {
        "0": "NEG"
    }
]

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'] 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/finiteautomata-bertweet-base-sentiment-analysis/version/10

Properties

SHA: 924fc4c80bccb8003d21fe84dd92c7887717f245

datasets:

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

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