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--- |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: twitter-roberta-base-emotion-multilabel-latest |
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results: [] |
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pipeline_tag: text-classification |
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language: |
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- en |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# twitter-roberta-base-emotion-multilabel-latest |
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2022-154m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m) on the |
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[`SemEval 2018 - Task 1 Affect in Tweets`](https://aclanthology.org/S18-1001/) `(subtask: E-c / multilabel classification)`. |
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## Performance |
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Following metrics are achieved on the test split: |
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- F1 (micro): 0.7169 |
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- F1 (macro): 0.5464 |
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- Jaccard Index (samples): 0.5970: |
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### Usage |
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#### 1. [tweetnlp](https://pypi.org/project/tweetnlp/) |
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Install tweetnlp via pip. |
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```shell |
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pip install tweetnlp |
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``` |
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Load the model in python. |
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```python |
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import tweetnlp |
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model = tweetnlp.load_model('topic_classification', model_name='cardiffnlp/twitter-roberta-base-emotion-multilabel-latest') |
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model.predict("I bet everything will work out in the end :)") |
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>> {'label': ['joy', 'optimism']} |
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``` |
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#### 2. pipeline |
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```shell |
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pip install -U tensorflow==2.10 |
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``` |
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```python |
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from transformers import pipeline |
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pipe = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-emotion-multilabel-latest", return_all_scores=True) |
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pipe("I bet everything will work out in the end :)") |
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>> [[{'label': 'anger', 'score': 0.018903767690062523}, |
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{'label': 'anticipation', 'score': 0.28172484040260315}, |
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{'label': 'disgust', 'score': 0.011607927270233631}, |
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{'label': 'fear', 'score': 0.036411102861166}, |
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{'label': 'joy', 'score': 0.8812029361724854}, |
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{'label': 'love', 'score': 0.09591569006443024}, |
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{'label': 'optimism', 'score': 0.9810988306999207}, |
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{'label': 'pessimism', 'score': 0.016823478043079376}, |
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{'label': 'sadness', 'score': 0.01889917254447937}, |
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{'label': 'surprise', 'score': 0.02702752873301506}, |
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{'label': 'trust', 'score': 0.4155798852443695}]] |
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``` |
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### Reference |
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``` |
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@inproceedings{camacho-collados-etal-2022-tweetnlp, |
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title={{T}weet{NLP}: {C}utting-{E}dge {N}atural {L}anguage {P}rocessing for {S}ocial {M}edia}, |
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author={Camacho-Collados, Jose and Rezaee, Kiamehr and Riahi, Talayeh and Ushio, Asahi and Loureiro, Daniel and Antypas, Dimosthenis and Boisson, Joanne and Espinosa-Anke, Luis and Liu, Fangyu and Mart{\'\i}nez-C{\'a}mara, Eugenio and others}, |
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", |
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month = nov, |
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year = "2022", |
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address = "Abu Dhabi, U.A.E.", |
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publisher = "Association for Computational Linguistics", |
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} |
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``` |
