This is a fine-tuned Deberta model to detect human values in arguments. The model is part of the ensemble that was the best-performing system in the SemEval2023 task: Detecting Human Values in arguments It was trained and tested on a dataset of 9324 annotated arguments. The whole ensemble system achieved a F1-Score of 0.56 in the competiton. This model achieves a F1-Score of 0.55. Code for retraining the ensemble is accessible in this repo This model is built on custom code. So the inference api cannot be used directly. To use the model please follow the steps below... To make a prediction and map the the outputs to the correct labels. During the competiton a threshold of 0.25 was used to binarize the output.
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Cloudflare's Clef for typed decisions about text on a 16 GB GPU, or about images on a larger one. It answers typed questions with a probability for each answer, in one pass. This is a decision model: send typed questions to llama-server's `/v1/systemone` endpoint, not to chat. Needs llama.cpp from 9 October 2026 or newer. Tested at commit `1e6f04a`, and every file also on release `b11572` (12 October 2026), which gave identical answers on a 100-decision check. Tested on an Apple M3 Max (36 GB) with llama.cpp Metal (`b11617`): every file here loads and answers with the command above.
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Cloudflare's Clef-Flash for typed decisions about text or images on a CPU or a small GPU. It answers typed questions with a probability for each answer, in one pass. This is a decision model: send typed questions to llama-server's `/v1/systemone` endpoint, not to chat. Needs llama.cpp from 9 October 2026 or newer. Tested at commit `1e6f04a`, and the Artemis IQ4_XS and Artemis IQ3_XXS files also on release `b11572` (12 October 2026), which gave identical answers on a 100-decision check. Tested on an Apple M3 Max (36 GB) with llama.cpp Metal (`b11617`): every file here loads and answers with the command above.
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Kev-27B for routing, triage and quick checks on a single 24 GB GPU. It answers typed questions (yes or no, pick one, a score) with a probability for each answer, in one pass. This is a decision model: send typed questions to llama-server's `/v1/systemone` endpoint, not to chat. Tested with llama.cpp commit `fcac190` (13 October 2026); llama.cpp serves Kev models since 9 October 2026. Tested on an Apple M3 Max (36 GB) with llama.cpp Metal (`b11617`): every file here loads and answers with the command above.
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Kev-9B for routing, triage and quick checks on a 12 GB GPU. It answers typed questions (yes or no, pick one, a score) with a probability for each answer, in one pass. This is a decision model: send typed questions to llama-server's `/v1/systemone` endpoint, not to chat. Needs llama.cpp from 9 October 2026 or newer (tested at commit `1e6f04a`). Tested on an Apple M3 Max (36 GB) with llama.cpp Metal (`b11617`): every file here loads and answers with the command above.
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This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by UER-py, which is introduced in this paper. Besides, the models could also be fine-tuned by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework. You can download the 5 Chinese RoBERTa-Base classification models either from the UER-py Modelzoo page, or via HuggingFace from the links below: You can use this model directly with a pipeline for text classification (take the case of roberta-base-finetuned-chinanews-chinese):
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This a medroberta.nl base model finetuned for span classification. This specific model is the average of the best checkpoints per fold over a ten-fold cross-validation. For this model we used the IOB-tagged. Using the IOB-tagging schema facilitates the aggregation of predictions over sequences. For the chunking we used paragraph based chunking, and we assumed the maximum context length of the base model, i.e. 512 tokens. CardioNER_MedRoBERTa.nl_multilabel is a muticlass span classification model. The classes that can be predicted are disease, medication, procedure and symptom.
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Este es un modelo de MarIA (RoBERTa) con fine-tuning con optimización de hiper-parámetros para la clasificación de sentimientos en tres niveles: negativo, neutral y positivo. El modelo ha sido entrenado juntando diversos datasets como TASS y otros datasets de análisis de sentimientos relacionados con política y otros dominios.
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