This model is an Italian classification model fine-tuned from the Italian BERT model for the classification of inclusive language in Italian. It has been trained to detect three classes: - `inclusive`: the sentence is inclusive (e.g. "Il personale docente e non docente") - `not_inclusive`: the sentence is not inclusive (e.g. "I professori") - `not_pertinent`: the sentence is not pertinent to the task (e.g. "La scuola è chiusa") The model has been trained on a dataset containing: - 8580 training sentences - 1073 validation sentences - 1072 test sentences
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This model was converted to OpenVINO from `distilbert/distilbert-base-uncased-finetuned-sst-2-english` using optimum-intel via the export space.
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This model is designed to categorize text into two classes: "safe", or "nsfw" (not safe for work), which makes it suitable for content moderation and filtering applications. The model was trained using a dataset containing 190,000 labeled text samples, distributed among the two classes of "safe" and "nsfw". To improve the performance of the model, it is necessary to preprocess the input text. You can refer to the preprocess function in the app.py file in the following space: .
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For academic reference, cite the following paper: https://ieeexplore.ieee.org/document/10223689 CryptoBERT is a pre-trained NLP model to analyse the language and sentiments of cryptocurrency-related social media posts and messages. It was built by further training the vinai's bertweet-base language model on the cryptocurrency domain, using a corpus of over 3.2M unique cryptocurrency-related social media posts. (A research paper with more details will follow soon.) The model was trained on the following labels: "Bearish" : 0, "Neutral": 1, "Bullish": 2 CryptoBERT's sentiment classification head was fine-tuned on a balanced dataset of 2M labelled StockTwits posts, sampled from ElKulako/stocktwits-crypto.
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- **Developed by:** Elron Bandel, Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research - **Shared by [Optional]:** Elron Bandel - **Model type:** Text Classification - **Language(s) (NLP):** More information needed - **License:** More information needed - **Parent Model:** BERT - **Resources for more information:** - GitHub Repo - Associated Paper - Blog Post The model should not be used to intentionally create hostile or alienating environments for people. Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
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This model is a fine-tune of GLiNER aimed at improving accuracy across a broad range of topics, especially with respect to long-context news entity extraction. As shown in the table below, these fine-tunes improved upon the base GLiNER model zero-shot accuracy by up to 7.5% across 18 benchmark datasets. The underlying dataset, AskNews-NER-v0 was engineered with the objective of diversifying global perspectives by enforcing country/language/topic/temporal diversity. All data used to fine-tune this model was synthetically generated. WizardLM 13B v1.2 was used for translation/summarization of open-web news articles, while Llama3 70b instruct was used for entity extraction. Both the diversification and fine-tuning methods are presented in a our paper on ArXiv. The synthetic data underlying this news fine-tune was pulled from the AskNews API. We enforced diveristy across country/language/topic/time.
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This model is xlm-roberta-base fine-tuned to classify descriptions of activities into NACE Rev. 2 codes. The data used to fine-tune the model consist of 2.5 million descriptions of activities from Norwegian and Danish businesses. To improve the model's multilingual performance, random samples of the Norwegian and Danish descriptions were machine translated into the following languages: - English - German - Spanish - French - Finnish - Polish
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Based on this paper, this is the SocRoBERTa-social language model. A language model that is trained to better classify social texts in the ESG domain. *Note: We generally recommend choosing the SocialBERT-social model since it is quicker, less resource-intensive and only marginally worse in performance.* Using the SocRoBERTa-base model as a starting point, the SocRoBERTa-social Language Model is additionally fine-trained on a 2k social dataset to detect social text samples.
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