This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the jnlpba dataset. It achieves the following results on the evaluation set: - Loss: 0.5113 - Precision: 0.6551 - Recall: 0.7646 - F1: 0.7056 - Accuracy: 0.9108 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
This model ("SiEBERT", prefix for "Sentiment in English") is a fine-tuned checkpoint of RoBERTa-large (Liu et al. 2019). It enables reliable binary sentiment analysis for various types of English-language text. For each instance, it predicts either positive (1) or negative (0) sentiment. The model was fine-tuned and evaluated on 15 data sets from diverse text sources to enhance generalization across different types of texts (reviews, tweets, etc.). Consequently, it outperforms models trained on only one type of text (e.g., movie reviews from the popular SST-2 benchmark) when used on new data as shown below. If you want to predict sentiment for your own data, we provide an example script via Google Colab. You can load your data to a Google Drive and run the script for free on a Colab GPU. Set-up only takes a few minutes. We suggest that you manually label a subset of your data to evaluate performance for your use case. For performance benchmark values across various sentiment analysis contexts, please refer to our paper (Hartmann et al. 2023). The easiest way to use the model for single predictions is Hugging Face's sentiment analysis pipeline, which only needs a couple lines of code as shown in the following example:
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
`roberta-base` pretrained on discourse marker prediction on the Discovery dataset with a validation accuracy of 30.93% (majority class is 0.57%)
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
输入 文本→ 输出 文本
Mica is a small decision model. You give it a state, a question and the allowed answers, and it returns a probability for each answer: yes/no, a choice among 2–255 options, or a score with 2–10 levels. It reads the input once and generates no text, so a decision costs one prefill. It speaks the TypeSafe `/v1/systemone` format, so clients written for Jev work unchanged. It was trained on English and Korean. The first start downloads the BF16 GGUF (9.7 GB). Use `-e MICA_GGUF=mica-v0.1-4b-Q5_K_M.gguf` for a smaller file. The image builds for CUDA architectures 86, 89 and 90; pass `--build-arg CUDA_ARCH=...` to change that.
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
Looking forward to v1.0? A small donation on Ko-fi would mean a lot. I'm just a broke student, and I need your help. **Mica is a 4B model that answers your questions about an input with a probability for every answer you allow.** Give it a state (a log, a trace, a document, JSON — up to 32k tokens), one or more questions, and the allowed answers. It reads the input once and returns a calibrated distribution. It never writes free text, so there is nothing to parse and the answer is always one of yours. Use it where you now call a large model just to get a yes/no or a label: guardrails, RAG checks, agent gates, routing, triage, evaluation.
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
> IMPORTANT: > This model was trained using the decontaminated version of the original Skywork Reward Preference dataset, now referred to as **v0.2**. The updated dataset, Skywork-Reward-Preference-80K-v0.2, removes 4,957 contaminated pairs from the magpie-ultra-v0.1 subset, which had significant n-gram overlap with the evaluation prompts in RewardBench. You can find the set of removed pairs here. For more detailed information, please refer to this GitHub gist. > > **If your task involves evaluation on RewardBench, we strongly recommend using v0.2 of both the dataset and the models instead of v0.1, to ensure proper decontamination and avoid any contamination issues.** **Skywork-Reward-Gemma-2-27B-v0.2** and **Skywork-Reward-Llama-3.1-8B-v0.2** are two advanced reward models built on the gemma-2-27b-it and Llama-3.1-8B-Instruct architectures, respectively. Both models were trained using the Skywork Reward Data Collection containing only 80K high-quality preference pairs sourced from publicly available data. We include only public data in an attempt to demonstrate that high-performance reward models can be achieved with a relatively small dataset and straightforward data curation techniques, without further algorithmic or architectural modifications. The sources of data used in the Skywork Reward Data Collection are detailed in the Data Mixture section below.
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
> IMPORTANT: > This model was trained using the decontaminated version of the original Skywork Reward Preference dataset, now referred to as **v0.2**. The updated dataset, Skywork-Reward-Preference-80K-v0.2, removes 4,957 contaminated pairs from the magpie-ultra-v0.1 subset, which had significant n-gram overlap with the evaluation prompts in RewardBench. You can find the set of removed pairs here. For more detailed information, please refer to this GitHub gist. > > **If your task involves evaluation on RewardBench, we strongly recommend using v0.2 of both the dataset and the models instead of v0.1, to ensure proper decontamination and avoid any contamination issues.** We include only public data in an attempt to demonstrate that high-performance reward models can be achieved with a relatively small dataset and straightforward data curation techniques, without further algorithmic or architectural modifications. The sources of data used in the Skywork Reward Data Collection are detailed in the Data Mixture section below. The resulting reward models excel at handling preferences in complex scenarios, including challenging preference pairs, and span various domains such as mathematics, coding, and safety.
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
**Skywork-Reward-V2** is a series of eight reward models designed for versatility across a wide range of tasks, trained on a mixture of 26 million carefully curated preference pairs. While the Skywork-Reward-V2 series remains based on the Bradley-Terry model, we push the boundaries of training data scale and quality to achieve superior performance. Compared with the first generation of Skywork-Reward, the Skywork-Reward-V2 series offers the following major improvements: In the following table, we categorize the models into two types: Bradley-Terry (BT) reward models and Generative reward models. The Skywork-Reward-V2 series outperforms models in both categories with much smaller model sizes. 1. For most use cases, we recommend Skywork-Reward-V2-Llama-3.1-8B and consider smaller variants for low-resource settings. 2. All models are trained on preference data with a maximum length of 16,384 tokens. It is recommended to perform inference within this limit. 3. Do not include system prompts when using chat templates.
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
**Skywork-Reward-V2** is a series of eight reward models designed for versatility across a wide range of tasks, trained on a mixture of 26 million carefully curated preference pairs. While the Skywork-Reward-V2 series remains based on the Bradley-Terry model, we push the boundaries of training data scale and quality to achieve superior performance. Compared with the first generation of Skywork-Reward, the Skywork-Reward-V2 series offers the following major improvements: - **Trained on a significantly larger and higher-quality preference data mixture**, consisting of **26 million preference pairs** curated via a large-scale human-LLM synergistic pipeline. - **State-of-the-art performance on seven major reward model benchmarks**, including RewardBench v1, RewardBench v2, PPE Preference, PPE Correctness, RMB, RM-Bench, and JudgeBench. - **Available in eight models across multiple sizes**, with the smallest 0.6B variant, *Skywork-Reward-V2-Qwen3-0.6B*, nearly matching the average performance of our previous best model, Skywork-Reward-Gemma-2-27B-v0.2. The largest 8B version, *Skywork-Reward-V2-Llama-3.1-8B*, surpasses all existing reward models across all benchmarks on average. Our top experimental model, *Skywork-Reward-V2-Llama-3.1-8B-40M*, **outperforms all existing reward models on every benchmark**. For the complete collection of models, please refer to the Skywork-Reward-V2 collection.
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
