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1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training 5. Evaluation 6. Environmental Impact 7. Technical Specifications 8. Citation 9. Model Card Authors 10. How To Get Started With the Model The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. This model is XLM-RoBERTa-large fine-tuned with the conll2003 dataset in English. The model is a language model. The model can be used for token classification, a natural language understanding task in which a label is assigned to some tokens in a text.

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GGUF builds of `abhishek085/spark-s1-4b-v6` for **llama.cpp**, **Ollama**, LM Studio, Jan and other GGUF runtimes. Part of Open Spark Jev, an open-source project of the Nokast AI community. > **This is not a chat model.** `spark-s1` is a System-1 *decision* model: given a state and a question with a fixed menu of options, the > answer is the probability distribution over the option letters at the **first** generated token. Chatting with it (`ollama run ...`) only > ever produces a single letter. Use it through the logprob recipe below or through the Open Spark Jev gateway. Measured on 200 shuffled rows of the project's locked held-out split (`v5_test_locked`), llama.cpp `llama-server` (CUDA, build `38a5b42`), `/v1/completions` with `top_logprobs=20`, calibrated temperature applied client-side. Ollama 0.40.2 with the Q8_0 file gave the same 99.5% / 0.900. This is a parity check against the HF model, not a new benchmark; the model's own evaluation is on the base model card.

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This model is a fine-tuned version of bert-tiny on the emotion balanced dataset. It achieves the following results on the evaluation set: - Loss: 0.1809 - Accuracy: 0.9354 TinyBERT is 7.5 times smaller and 9.4 times faster on inference compared to its teacher BERT model (while DistilBERT is 40% smaller and 1.6 times faster than BERT). The model has been trained on 89_754 examples split into train, validation and test. Each label was perfectly balanced in each split. This model is not as accurate as the distilbert-emotion-balanced one because the focus was on speed, which can lead to misinterpretation of complex sentences. Despite this, its performance is quite good and should be more than sufficient for most use cases.

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**CodonTransformer** is the ultimate tool for codon optimization, transforming protein sequences into optimized DNA sequences specific for your target organisms. Whether you are a researcher or a practitioner in genetic engineering, CodonTransformer provides a comprehensive suite of features to facilitate your work. By leveraging the Transformer architecture and a user-friendly Jupyter notebook, it reduces the complexity of codon optimization, saving you time and effort. Adibvafa Fallahpour 1,2 \*, Vincent Gureghian 3 \*, Guillaume J. Filion 2 ‡, Ariel B. Lindner 3 ‡, Amir Pandi 3 ‡ 1 Vector Institute for Artificial Intelligence, Toronto ON, Canada 2 University of Toronto Scarborough; Department of Biological Science; Scarborough ON, Canada 3 Université Paris Cité, INSERM U1284, Center for Research and Interdisciplinarity, F-75006 Paris, France \* These authors contributed equally to this work. ‡ To whom correspondence should be addressed: guillaume.filion@utoronto.ca, ariel.lindner@inserm.fr, amir.pandi@cri-paris.org

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This model is fine-tuned from **`indobenchmark/indobert-base-p1`** for binary sentiment classification (Positive/Negative) on Indonesian text. - **Developed by:** agufsamudra - **Model type:** Text Classification - **Language(s):** Indonesian (id) - **License:** Apache-2.0 - **Fine-tuned from model:** indobenchmark/indobert-base-p1 This model is intended for binary sentiment classification tasks in Indonesian language texts. It predicts whether a given text expresses positive or negative sentiment.

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The model was fine-tuned for Sentiment Analysis task on _Financial PhraseBank_ dataset. Experiments show that this model outperforms the general BERT and other financial domain-specific models. More details on `FinancialBERT`'s pre-training process can be found at: https://www.researchgate.net/publication/358284785_FinancialBERT_-_A_Pretrained_Language_Model_for_Financial_Text_Mining FinancialBERT model was fine-tuned on Financial PhraseBank, a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive).

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