Shibing624
Rank:Average Model Cost: $0.0000
Number of Runs: 902,668
Models by this creator
text2vec-base-chinese
text2vec-base-chinese
The text2vec-base-chinese model is a CoSENT (Cosine Sentence) model that maps sentences to a 768-dimensional vector space. It can be used for tasks such as sentence embeddings, text matching, and semantic search. The model is trained using the CoSENT method with the hfl/chinese-macbert-base model. It has been evaluated on the Chinese STS-B dataset and achieved good results. The model can be used with the text2vec library or the HuggingFace Transformers library for predictions. It is intended to be used as a sentence and short paragraph encoder for tasks such as information retrieval, clustering, and sentence similarity. The model has been fine-tuned using a contrastive objective and the training dataset used is shibing624/nli-zh-all. If you find this model helpful, you can cite text2vec.
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851.8K
Huggingface
macbert4csc-base-chinese
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31.2K
Huggingface
bart4csc-base-chinese
bart4csc-base-chinese
The bart4csc-base-chinese model is a Chinese spelling correction model based on BART. It has been trained on the SIGHAN+Wang271K dataset and achieves near state-of-the-art performance on the SIGHAN2015 test set. The model can be used for spelling correction tasks in Chinese text. It is available as part of the textgen project and can be accessed through a command line interface. The training data for the model consists of the SIGHAN+Wang271K Chinese correction dataset. To train the model, refer to the provided example script. For more information on spelling correction models, refer to the pycorrector GitHub repository.
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6.2K
Huggingface
text2vec-base-chinese-paraphrase
text2vec-base-chinese-paraphrase
shibing624/text2vec-base-chinese-paraphrase This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese-paraphrase. It maps sentences to a 768 dimensional dense vector space and can be used for tasks like sentence embeddings, text matching or semantic search. training dataset: https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset base model: nghuyong/ernie-3.0-base-zh max_seq_length: 256 best epoch: 5 sentence embedding dim: 768 Evaluation For an automated evaluation of this model, see the Evaluation Benchmark: text2vec Release Models 本项目release模型的中文匹配评测结果： 说明： 结果评测指标：spearman系数 shibing624/text2vec-base-chinese模型，是用CoSENT方法训练，基于hfl/chinese-macbert-base在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行examples/training_sup_text_matching_model.py代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用 shibing624/text2vec-base-chinese-sentence模型，是用CoSENT方法训练，基于nghuyong/ernie-3.0-base-zh用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset训练得到，并在中文各NLI测试集评估达到较好效果，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用 shibing624/text2vec-base-chinese-paraphrase模型，是用CoSENT方法训练，基于nghuyong/ernie-3.0-base-zh用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset，数据集相对于shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用 sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2模型是用SBERT训练，是paraphrase-MiniLM-L12-v2模型的多语言版本，支持中文、英文等 w2v-light-tencent-chinese是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况 Usage (text2vec) Using this model becomes easy when you have text2vec installed: Then you can use the model like this: Usage (HuggingFace Transformers) Without text2vec, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. Install transformers: Then load model and predict: Usage (sentence-transformers) sentence-transformers is a popular library to compute dense vector representations for sentences. Install sentence-transformers: Then load model and predict: Full Model Architecture Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. Training procedure Pre-training We use the pretrained nghuyong/ernie-3.0-base-zh model. Please refer to the model card for more detailed information about the pre-training procedure. Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the rank loss by comparing with true pairs and false pairs. Citing & Authors This model was trained by text2vec. If you find this model helpful, feel free to cite:
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3.5K
Huggingface
text2vec-base-chinese-sentence
text2vec-base-chinese-sentence
shibing624/text2vec-base-chinese-sentence This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese-sentence. It maps sentences to a 768 dimensional dense vector space and can be used for tasks like sentence embeddings, text matching or semantic search. training dataset: https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset base model: nghuyong/ernie-3.0-base-zh max_seq_length: 256 best epoch: 3 sentence embedding dim: 768 Evaluation For an automated evaluation of this model, see the Evaluation Benchmark: text2vec Release Models 本项目release模型的中文匹配评测结果： 说明： 结果评测指标：spearman系数 shibing624/text2vec-base-chinese模型，是用CoSENT方法训练，基于hfl/chinese-macbert-base在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行examples/training_sup_text_matching_model.py代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用 shibing624/text2vec-base-chinese-sentence模型，是用CoSENT方法训练，基于nghuyong/ernie-3.0-base-zh用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset训练得到，并在中文各NLI测试集评估达到较好效果，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用 shibing624/text2vec-base-chinese-paraphrase模型，是用CoSENT方法训练，基于nghuyong/ernie-3.0-base-zh用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset，数据集相对于shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用 sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2模型是用SBERT训练，是paraphrase-MiniLM-L12-v2模型的多语言版本，支持中文、英文等 w2v-light-tencent-chinese是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况 旧版 shibing624/text2vec-base-chinese-nli 模型放在tag1.0 Usage (text2vec) Using this model becomes easy when you have text2vec installed: Then you can use the model like this: Usage (HuggingFace Transformers) Without text2vec, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. Install transformers: Then load model and predict: Usage (sentence-transformers) sentence-transformers is a popular library to compute dense vector representations for sentences. Install sentence-transformers: Then load model and predict: Full Model Architecture Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. Training procedure Pre-training We use the pretrained nghuyong/ernie-3.0-base-zh model. Please refer to the model card for more detailed information about the pre-training procedure. Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the rank loss by comparing with true pairs and false pairs. Citing & Authors This model was trained by text2vec. If you find this model helpful, feel free to cite:
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2.8K
Huggingface
chinese-alpaca-plus-7b-hf
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2.7K
Huggingface
text2vec-base-multilingual
text2vec-base-multilingual
shibing624/text2vec-base-multilingual This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-multilingual. It maps sentences to a 384 dimensional dense vector space and can be used for tasks like sentence embeddings, text matching or semantic search. training dataset: https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-multilingual-dataset base model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 max_seq_length: 256 best epoch: 4 sentence embedding dim: 384 Evaluation For an automated evaluation of this model, see the Evaluation Benchmark: text2vec Languages Available languages are: de, en, es, fr, it, nl, pl, pt, ru, zh Release Models 本项目release模型的中文匹配评测结果： 说明： 结果评测指标：spearman系数 shibing624/text2vec-base-chinese模型，是用CoSENT方法训练，基于hfl/chinese-macbert-base在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行examples/training_sup_text_matching_model.py代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用 shibing624/text2vec-base-chinese-sentence模型，是用CoSENT方法训练，基于nghuyong/ernie-3.0-base-zh用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset训练得到，并在中文各NLI测试集评估达到较好效果，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用 shibing624/text2vec-base-chinese-paraphrase模型，是用CoSENT方法训练，基于nghuyong/ernie-3.0-base-zh用人工挑选后的中文STS数据集shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset，数据集相对于shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用 shibing624/text2vec-base-multilingual模型，是用CoSENT方法训练，基于sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2用人工挑选后的多语言STS数据集shibing624/nli-zh-all/text2vec-base-multilingual-dataset训练得到，并在中英文测试集评估相对于原模型效果有提升，运行examples/training_sup_text_matching_model_jsonl_data.py代码可训练模型，模型文件已经上传HF model hub，多语言语义匹配任务推荐使用 w2v-light-tencent-chinese是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况 QPS的GPU测试环境是Tesla V100，显存32GB 模型训练实验报告：实验报告 Usage (text2vec) Using this model becomes easy when you have text2vec installed: Then you can use the model like this: Usage (HuggingFace Transformers) Without text2vec, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. Install transformers: Then load model and predict: Usage (sentence-transformers) sentence-transformers is a popular library to compute dense vector representations for sentences. Install sentence-transformers: Then load model and predict: Full Model Architecture Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. Training procedure Pre-training We use the pretrained sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 model. Please refer to the model card for more detailed information about the pre-training procedure. Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the rank loss by comparing with true pairs and false pairs. Citing & Authors This model was trained by text2vec. If you find this model helpful, feel free to cite:
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1.6K
Huggingface
chinese-alpaca-plus-13b-hf
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1.4K
Huggingface
asian-role
asian-role
asian-role Welcome to asian-role model, this is a Chinese gorgeous antique style game role model. This model is intended to produce high-quality, highly detailed anime style with just a few prompts. e.g. 1girl, white hair, beautiful blue eyes, red lips, detailed sky, garden This model is a merged model, it has GuoFeng3 and Chilloutmix in it. Spaces We support a Gradio Web UI to run it: https://huggingface.co/spaces/shibing624/asian-role 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the Stable Diffusion. You can also export the model to ONNX, MPS and/or FLAX/JAX. NovelAI/stable-diffusion-webui This model can used in AUTOMATIC1111/stable-diffusion-webui. Just put the model file asian-role.safetensors to stable-diffusion-webui/models/Stable-diffusion, it is done, No extra VAE model need, the model contains VAE. Examples Below are some examples of images generated using this model: Anime Girl: Real Girl: Real Boy: Scene: How to use Recommand settings: prompts: Negative prompts: Sampling steps:30 or 50 Sampler:DPM++ SDE Karras The size of the picture should be at least 768 suggest prompts keywords: License This model is open access and available to all, with a cc-by-sa-4.0 license further specifying rights and usage. The cc-by-sa-4.0 License specifies: You can't use the model to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here
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830
Huggingface
bert4ner-base-chinese
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762
Huggingface