license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0795 | 2.1 | 500 | 3.2227 | 0.9982 | | 1.21 | 4.2 | 1000 | 1.3713 | 0.8879 | | 0.742 | 6.3 | 1500 | 1.2660 | 0.8296 | |... | 58f7da030fa8e0ea5f666266584c0aff |
apache-2.0 | ['translation'] | false | By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC) * source languages: en * target languages: el * licence: apache-2.0 * dataset: Opus, CCmatrix * model: transformer(fairseq) * pre-processing: tokenization + lower-casing + BPE segmentation * metrics: bleu, chrf * output: lowercase only, fo... | 3938a086882439aa952a5e34c21f66d6 |
apache-2.0 | ['translation'] | false | How to use ``` from transformers import FSMTTokenizer, FSMTForConditionalGeneration mname = " <your_downloaded_model_folderpath_here> " tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) text = "Not all those who wander are lost." encoded = tokenizer.encod... | ae7d023d67c7fa490659ff363bf1fd02 |
apache-2.0 | ['translation'] | false | Eval results Results on Tatoeba testset (EN-EL): | BLEU | chrF | | ------ | ------ | | 77.3 | 0.739 | Results on XNLI parallel (EN-EL): | BLEU | chrF | | ------ | ------ | | 66.1 | 0.606 | | e1b0329f6160eb12ad5fec9ad4f00922 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-work-2-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3586 - Accuracy: 0.3689 | 3622af105d7451b945abea2045aae040 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_3_ternary This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7987 - F1: 0.7460 | 65a25e8b031bef81edf7d274595af42d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.5903 | 0.6893 | | 0.5417 | 2.0 | 578 | 0.5822 | 0.7130 | | 0.5417 | 3.0 | 867 | 0.6471 | 0.7385 | |... | a7013ee7e227c21130438c08dcb0cec4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_data_aug_stsb_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 1.4500 - Pearson: 0.1761 - Spearmanr: 0.1778 - Combined Sc... | cedcfd119904c9ac668aab3667d11171 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 0.5832 | 1.0 | 1259 | 1.5244 | 0.1737 | 0.1803 | 0.1770 | | 0.2202 | 2.0 | 2518 ... | 6194b7ce2ae323c0a220899dd4f794f9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2170 - Accuracy: 0.9255 - F1: 0.9255 | 0c4398ee54c8285875513d66b8bca01e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8237 | 1.0 | 250 | 0.3205 | 0.9045 | 0.9002 | | 0.2539 | 2.0 | 500 | 0.2170 | 0.9255 | 0.9255 | | ba3cd8fced373ecd2da6903c8dc80263 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5777 -... | 210087beed8d44f67cca30f0302ff163 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 48 | 0.6059 | 0.63 | 0.4932 | | No log | 2.0 | 96 | 0.6327 | 0.705 | 0.5630 | | No log |... | fda649c4a3b3c4f69ec319ee94743f40 |
apache-2.0 | [] | false | Randeng-TransformerXL-5B-Deduction-Chinese - Main page: [Fengshenbang](https://fengshenbang-lm.com/) - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) - Demo: [Reasoning Tree](https://ccnl.fengshenbang-lm.com/single/reasoning... | c3429152a1dcbc493a0fc6ff84e031ac |
apache-2.0 | [] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言生成 NLG | 燃灯 Randeng | TransformerXL | 5.0B | 中文-因果推理 Chinese-Reasoning | | b824c894c473aa5eee56f4989aa96ca5 |
apache-2.0 | [] | false | 模型信息 Model Information **数据准备 Corpus Preparation** * 悟道语料库（280G版本） * 因果语料库（2.3M个样本）：基于悟道语料库（280G版本），通过关联词匹配、人工标注 + [GTSFactory](https://gtsfactory.com/)筛选、数据清洗等步骤获取的具有因果关系的句子对 * Wudao Corpus (with 280G samples) * Wudao Causal Corpus (with 2.3 million samples): Based on the Wudao corpus (280G version), sentence pai... | 9895a399737e4d70cb20cbc009c89abd |
apache-2.0 | [] | false | 加载模型 Loading Models ```shell git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git cd Fengshenbang-LM ``` ```python from fengshen.models.transfo_xl_reasoning import TransfoXLModel from transformers import T5Tokenizer as TransfoXLTokenizer model = TransfoXLModel.from_pretrained('IDEA-CCNL/Randeng-TransformerXL... | 01a0ba06f9c27de19a47f8af1132edd0 |
apache-2.0 | [] | false | 使用示例 Usage Example ```python from fengshen.models.transfo_xl_reasoning import deduction_generate input_text = "机器人统治世界" input_texts = ["机器人统治世界", "玉米价格持续上涨"] print(deduction_generate(model, tokenizer, input_text, device=0)) print(deduction_generate(model, tokenizer, input_texts, device=0)) ``` | badc699a9006b24c3b7c5141b43ac8fe |
apache-2.0 | [] | false | 一些例子 Some Cases （以下内容为模型生成，不代表本单位观点） 1. 鲸鱼是哺乳动物，哺乳动物是恒温动物 - 鲸鱼拥有极强的恒温能力 - 鲸鱼需要消耗大量的能量 - 鲸鱼能在海洋中生存下来，完全依靠其体温恒定 2. 鲸鱼是哺乳动物，哺乳动物不是恒温动物 - 鲸鱼不是恒温动物，鲸鱼是变温动物 - 鲸鱼会随温度变化而变化 - 鲸鱼有冬眠现象 3. 机器人统治世界 - 机器人取代人类做大部分工作 - 机器人世界的法律必须能够适用于机器人统治的现实世界 - 机器人必须能够相互沟通，并能够处理人类无法处理的复杂情况 - 未来将不再需要大量工人，机器... | d2ca164579ee7dae6f469a33787c2a66 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-patch16-224-in21k_male_or_female_eyes This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0810 - Accuracy: 0.9727 - F1: 0.9741 - Recal... | 95c438c1bd190bcd4435e936918115b0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.1998 | 1.0 | 577 | 0.2365 | 0.9072 | 0.9196 | 0.9976 | 0.8530 | | 0.0846 | 2.0 |... | c4aab3291325960d416f813bea40afe7 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 12 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20.0 | a3b2fb72b169e11830c4f879d0a66c5a |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-mrpc This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6779 | 87bbc960e1abce53a3d0331d5c708737 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8599 | 1.09 | 500 | 2.5767 | | 2.7017 | 2.18 | 1000 | 2.5086 | | 2.535 | 3.27 | 1500 | 2.5969 | | 2.4565 | 4.36 | 2000 | 2.6871 ... | 071fab7a62dafdebba455f96accc938b |
apache-2.0 | ['generated_from_keras_callback'] | false | ksabeh/bert-base-uncased-attribute-correction This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0541 - Validation Loss: 0.0579 - Epoch: 1 | 00c86c9a74f70075f7f3f3e95b8845c0 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.1128 - Precision: 0.9428 - Recall: 0.9534 - F1: 0.9480 - Accuracy: 0.9867 | 32b52a7c00e8e42727a6d86aa7272196 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0937 | 1.0 | 1756 | 0.0660 | 0.9179 | 0.9332 | 0.9255 | 0.9825 | | 0.0378 | 2.0 ... | fe2919825e11670292a974d310d4e6eb |
gpl | [] | false | Ady Endre style GPT network. Some details about the network: -It's based on a hungarian language model that was also trained from scratch with casual language modelling. -The language model was trained 1,5x times on a big part of the Hungarian Wikipedia, then trained on all of Ady Endre's poems. -The model has a GPT... | fd1eefc3de0a022f527c8b651d0dc7bd |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_ner_wikiann This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.2834 - Precision: 0.8139 - Recall: 0.8367 - F1: 0.8251 - Accuracy: 0.9300 | 2d01db7d5a32c9804ea97e82f04e2273 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3325 | 1.0 | 1250 | 0.2657 | 0.7732 | 0.8175 | 0.7947 | 0.9214 | | 0.2242 | 2.0 |... | 2831eb5931769a1902d55b2c4b0863b3 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1719 - F1: 0.8544 | 6d0ad0dd90ae060b231c781ffc1f4cec |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2951 | 1.0 | 835 | 0.1882 | 0.8171 | | 0.1547 | 2.0 | 1670 | 0.1707 | 0.8454 | | 0.1018 | 3.0 | 2505 | 0.1719 | 0.8544 | ... | daca5e620e6e11bd37c972a44952c49f |
apache-2.0 | ['India', 'politics', 'tweets', 'BJP', 'Congress', 'AAP', 'pytorch', 'gpt2', 'lm-head', 'text-generation'] | false | Model description Note: This model is based on GPT2, if you want a bigger model based on GPT2-medium and finetuned on the same data please take a look at the [IndianPoliticalTweetsLMMedium](https://huggingface.co/bagdaebhishek/IndianPoliticalTweetsLMMedium) model. This is a GPT2 Language model with LM head fine-tune... | c11a1665ce4d272cc1114a57a1fe14fc |
apache-2.0 | ['India', 'politics', 'tweets', 'BJP', 'Congress', 'AAP', 'pytorch', 'gpt2', 'lm-head', 'text-generation'] | false | How to use ```python from transformers import AutoTokenizer,AutoModelWithLMHead,pipeline tokenizer = AutoTokenizer.from_pretrained("bagdaebhishek/IndianPoliticalTweetsLM") model = AutoModelWithLMHead.from_pretrained("bagdaebhishek/IndianPoliticalTweetsLM") text_generator = pipeline("text-generation",model=model, tok... | abcc75ea0d6fdfb0fe335dd84d32ed41 |
apache-2.0 | ['India', 'politics', 'tweets', 'BJP', 'Congress', 'AAP', 'pytorch', 'gpt2', 'lm-head', 'text-generation'] | false | Limitations and bias 1. The tweets used to train the model were not manually labelled, so the generated text may not always be in English. I've cleaned the data to remove non-English tweets but the model may generate "Hinglish" text and hence no assumptions should be made about the language of the generated text. 2. I... | 6c69c776bc53af19fdb67a6562e7ddc5 |
apache-2.0 | ['India', 'politics', 'tweets', 'BJP', 'Congress', 'AAP', 'pytorch', 'gpt2', 'lm-head', 'text-generation'] | false | Training data I used the pre-trained gpt2 model from Huggingface transformers repository and fine-tuned it on custom data set crawled from twitter. The method used to identify the political handles is mentioned in detail in a [blog](https://bagdeabhishek.github.io/twitterAnalysis) post. I used tweets from both the Pro... | 84b3bc47cc4c4e616b15309b3b49fc0f |
apache-2.0 | ['India', 'politics', 'tweets', 'BJP', 'Congress', 'AAP', 'pytorch', 'gpt2', 'lm-head', 'text-generation'] | false | Training procedure For pre-processing, I removed tweets from handles which are not very influential in their cluster. I removed them by calculating Eigenvector centrality on the twitter graph and pruning handles which have this measure below a certain threshold. This threshold was set manually after experimenting wit... | 1dbc4696aa40c360b195d86f74f67329 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2091 - Accuracy: 0.926 - F1: 0.9263 | 9b98e20c703560fca8f6810f23474d6a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8285 | 1.0 | 250 | 0.2969 | 0.9065 | 0.9046 | | 0.2399 | 2.0 | 500 | 0.2091 | 0.926 | 0.9263 | | ac1f01704a22b1d9bfbece7447cb2a81 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - eval_loss: 0.3264 - eval_accuracy: 0.8867 - eval_f1: 0.8896 - eval_runtime: 253.6051 -... | 8e1c370736b27db1a206268be6cecf13 |
apache-2.0 | ['generated_from_trainer'] | false | t5_base_race_cosmos_qa This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the race dataset. It achieves the following results on the evaluation set: - Loss: 0.4414 - Accuracy: 0.7424 | 73b3b7f50a997b67d33f084957fbf8b1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 3.0 | 31b19447ac9198cfc08a0b9358048a99 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4355 | 1.0 | 10984 | 0.3910 | 0.7072 | | 0.3233 | 2.0 | 21968 | 0.3833 | 0.7321 | | 0.229 | 3.0 | 32952 | 0.4414 ... | bf2ecfd131d588ded44299e694eafa89 |
mit | ['generated_from_trainer'] | false | microsoft-deberta-v3-large_cls_SentEval-CR This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2799 - Accuracy: 0.9363 | 871eef777a3cbd64f0cade5635ca0788 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 189 | 0.3706 | 0.9110 | | No log | 2.0 | 378 | 0.2884 | 0.9243 | | 0.3597 | 3.0 | 567 | 0.3018 | 0.... | 4aae085e5e5217cbff114b7c32e72f3d |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-imdb_movie_title-2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.9802 | 08cdf01a0d2ea5aa44bc1df88f9b6c2e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 490 | 4.0177 | | 4.2912 | 2.0 | 980 | 3.9872 | | 4.0628 | 3.0 | 1470 | 3.9802 | | 14a0ca8e014a73f5fc25934d7c9b4270 |
apache-2.0 | ['generated_from_trainer'] | false | fine-tune-Wav2Vec2-XLS-R-300M-Indonesia-3 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_10_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2856 - Wer: 0.1931 | 882983a2a9c621ed66b4fcb9efca7af0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 36 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 72 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | cf1eb42805b446cb5d5bc263c5c538ff |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.8503 | 4.0 | 460 | 1.2215 | 0.8013 | | 0.2917 | 8.0 | 920 | 0.2824 | 0.3055 | | 0.1487 | 12.0 | 1380 | 0.2719 | 0.2593 | |... | 4333b40b15ccf7276815fc797bc5c27f |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2495 - F1: 0.8123 | 47cd64007d2491052ebcb0a1ce3f3290 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8517 | 1.0 | 70 | 0.3472 | 0.7072 | | 0.2997 | 2.0 | 140 | 0.2935 | 0.7857 | | 0.1906 | 3.0 | 210 | 0.2495 | 0.8123 | ... | 34fe2baa448a75f9c1f1f4cc17b78a12 |
apache-2.0 | ['generated_from_keras_callback'] | false | Gorenzelg/bert-finetuned-squad1 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5669 - Epoch: 0 | 0b3d5e3609755949dcdf2d30efb30ea9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 55450, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 1b5eadd8b5782915d9b3119cb35872f8 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'safetensors', 'diffusers', 'artwork', 'HDR photography', 'safetensors', 'photos'] | false | Foto Assisted Diffusion (FAD)_V0 This model is meant to mimic a modern HDR photography style It was trained on 600 HDR images on SD1.5 and works best at **768x768** resolutions Merged with one of my own models for illustrations and drawings, to increase flexibility | 4ec5571243c5f7113c2f9af881f23fba |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'safetensors', 'diffusers', 'artwork', 'HDR photography', 'safetensors', 'photos'] | false | Features: * **No additional licensing** * **Multi-resolution support** * **HDR photographic outputs** * **No Hi-Res fix required** * [**Spreadsheet with supported resolutions, keywords for prompting and other useful hints/tips**](https://docs.google.com/spreadsheets/d/1RGRLZhgiFtLMm5Pg8qK0YMc6wr6uvj9-XdiFM877Pp0/edi... | 4eb9db2f1c853a546fdc1a724faacfcb |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'safetensors', 'diffusers', 'artwork', 'HDR photography', 'safetensors', 'photos'] | false | Example Cards: Below you will find some example cards that this model is capable of outputting. You can acquire the images used here: [HF](https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/tree/main/Model%20Examples) or [Google Drive](https://docs.google.com/spreadsheets/d/1RGRLZhgiFtLMm5Pg8qK0YMc6wr6u... | a2c42c7a36e167ea0eaadd751eac56e9 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'safetensors', 'diffusers', 'artwork', 'HDR photography', 'safetensors', 'photos'] | false | gid=364842308). Google Drive gives you them all at once without needing to clone the repo, which is easier. If you decide to clone it, set ``` GIT_LFS_SKIP_SMUDGE=1 ``` to skip downloading large files Place them into an EXIF viewer such as the built in "PNG Info" tab in the popular Auto1111 repository to quickly c... | 92ca6c6e48d02127e0fc7a5b534f541f |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'safetensors', 'diffusers', 'artwork', 'HDR photography', 'safetensors', 'photos'] | false | 512x512 Photos <img src="https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/resolve/main/512x512%20Photo.jpg" style="max-width: 800px;" width="100%"/> >My motivation for making this model was to have a free, non-restricted model for the community to use and for startups. >I was noticing the models pe... | 53268ab0cd47744cacf4957da47078f2 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1769 - F1: 0.8533 | 74bebd539de9ca5ec34906af8ebda983 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3049 | 1.0 | 835 | 0.1873 | 0.8139 | | 0.1576 | 2.0 | 1670 | 0.1722 | 0.8403 | | 0.1011 | 3.0 | 2505 | 0.1769 | 0.8533 | ... | 9ffe3bee0681aa9d734cf561c6119f72 |
apache-2.0 | ['Italian', 'efficient', 'sequence-to-sequence', 'squad_it', 'text2text-question-answering', 'text2text-generation'] | false | IT5 Cased Small Efficient EL32 for Question Answering ⁉️ 🇮🇹 *Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!* This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingface.co/it5/it5-efficient-small-el32) model fi... | 88890988ef4422d7fce989b329ceddd7 |
apache-2.0 | ['Italian', 'efficient', 'sequence-to-sequence', 'squad_it', 'text2text-question-answering', 'text2text-generation'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines qa = pipeline("text2text-generation", model='it5/it5-efficient-small-el32-question-answering') qa("In seguito all' evento di estinzione ... | 77386b03f1108d04c5a97d49f48295dc |
apache-2.0 | ['generated_from_trainer'] | false | sst2_bert-base-uncased_144 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3521 - Accuracy: 0.9335 | 81b62f31cc81b47e6a57df544a81b8ad |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Wav2Vec2-Large-960h-Lv60 + Self-Training [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with [Self-Training objective... | 07b225957f6e3e0f05ac70a2622732c3 |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self") | 1d07d4fe11e5b62a36b6b0431dd859f4 |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Evaluation This code snippet shows how to evaluate **facebook/wav2vec2-large-960h-lv60-self** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import torch from jiwer import wer librispeech_eval = load_dataset(... | 45d2b764194e129c7e2610f3e99bc2d0 |
apache-2.0 | ['generated_from_trainer'] | false | GhPoliticsBERT This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 | 53151b7fe32e56604a69991fb1f7eb3c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.0 | 1.0 | 9188 | 0.0000 | | 0.0 | 2.0 | 18376 | 0.0000 | | 0.0 | 3.0 | 27564 | 0.0000 | | 0.0 | 4.0 | 36752 | 0.0000 ... | 8d922df399c92b372a2674724aafad3a |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_age_teens-0_sixties-10_s265 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure t... | 828c864ee6f294cce302b36e95603bcb |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased-whole-word-masking-ner-conll2003 This model is a fine-tuned version of [bert-large-uncased-whole-word-masking](https://huggingface.co/bert-large-uncased-whole-word-masking) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0592 - Precision: 0.9527 - Recall... | 2696763c2b502c4b18c3318162dae33d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | e31ea7106c1b15956d5882a8dc3a332b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.4071 | 1.0 | 877 | 0.0584 | 0.9306 | 0.9418 | 0.9362 | 0.9851 | | 0.0482 | 2.0 |... | 41af20226936b995de6be74647eb1f48 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_gender_male-8_female-2_s428 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using t... | eb332c410b587b115d962d413ab6288f |
apache-2.0 | ['translation'] | false | run-deu * source group: Rundi * target group: German * OPUS readme: [run-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/run-deu/README.md) * model: transformer-align * source language(s): run * target language(s): deu * model: transformer-align * pre-processing: normalization + Sentenc... | cab1a6bcc26750875d36084eb0a52277 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: run-deu - source_languages: run - target_languages: deu - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/run-deu/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['rn', 'de'] - src_constituents: {'run'} - tgt_const... | ef6077c7208a7e7444de0f94abe9c9f9 |
apache-2.0 | ['generated_from_trainer'] | false | Centrum Centrum is a pretrained model for multi-document summarization, trained with centroid-based pretraining objective on the NewSHead dataset. It is initialized from [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384). The details of the approach are mentioned in the preprint [Multi-Document S... | 7000c39c8aa9f791eaef1835bb576294 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 1 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 16 - optimizer: Adam with... | 3486395413f535e1ae82a870cd488dec |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 4.1628 | 0.05 | 500 | 4.0732 | | 4.0278 | 0.09 | 1000 | 3.9800 | | 4.0008 | 0.14 | 1500 | 3.9283 | | 3.9564 | 0.19 | 2000 | 3... | 7c57e856e6f93921d56d8377ac9dcd42 |
apache-2.0 | ['translation'] | false | opus-mt-fi-yo * source languages: fi * target languages: yo * OPUS readme: [fi-yo](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-yo/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://... | 818c491d307d111b4ce978484c028e9c |
apache-2.0 | ['automatic-speech-recognition', 'ja'] | false | exp_w2v2t_ja_hubert_s69 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is ... | 09eba74eb4abd950e67c6cf39b39dfdd |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1728 - F1: 0.8554 | 8d15b0d295db5f51f2ca3109235b5c13 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3009 | 1.0 | 835 | 0.1857 | 0.8082 | | 0.1578 | 2.0 | 1670 | 0.1733 | 0.8416 | | 0.1026 | 3.0 | 2505 | 0.1728 | 0.8554 | ... | a2076fcb3ea4baf3d26d7e6ec8a82e65 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/stsb-roberta-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | a9aa189f3c1a4684e3393d3620f32640 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | ebcad2ee9df590e63c8cf34b069a3807 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/stsb-roberta-base-v2') model = AutoModel.from_pretrained('sentence-transformers/stsb-roberta-base-v2') | 6e59ba42c9189075b525337aa7c7ff97 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/stsb-roberta-base-v2) | dd9f19c95c9dcf991c27283711706507 |
apache-2.0 | [] | false | **_NOTE: `bioformer-cased-v1.0` has been renamed to `bioformer-8L`. All links to `bioformer-cased-v1.0` will automatically redirect to `bioformer-8L`, including git operations. However, to avoid confusion, we recommend updating any existing local clones to point to the new repository URL._** Bioformer-8L is a lightwe... | bb8f68ef8ccdcf0e02db9b6b016480b8 |
apache-2.0 | [] | false | Vocabulary of Bioformer-8L Bioformer-8L uses a cased WordPiece vocabulary trained from a biomedical corpus, which included all PubMed abstracts (33 million, as of Feb 1, 2021) and 1 million PMC full-text articles. PMC has 3.6 million articles but we down-sampled them to 1 million such that the total size of PubMed abs... | 339aa3f6a4ccc72cb40ec2b9c8104af8 |
apache-2.0 | [] | false | Pre-training of Bioformer-8L Bioformer-8L was pre-trained from scratch on the same corpus as the vocabulary (33 million PubMed abstracts + 1 million PMC full-text articles). For the masked language modeling (MLM) objective, we used whole-word masking with a masking rate of 15%. There are debates on whether the next se... | 8ec23fe1a24c7fdf80fbdf54787d255c |
apache-2.0 | [] | false | Usage Prerequisites: python3, pytorch, transformers and datasets We have tested the following commands on Python v3.9.16, PyTorch v1.13.1+cu117, Datasets v2.9.0 and Transformers v4.26. To install pytorch, please refer to instructions [here](https://pytorch.org/get-started/locally). To install the `transformers` an... | efaf0ecff070c0575b814bfb7750cd0f |
apache-2.0 | [] | false | Filling mask ``` from transformers import pipeline unmasker8L = pipeline('fill-mask', model='bioformers/bioformer-8L') unmasker8L("[MASK] refers to a group of diseases that affect how the body uses blood sugar (glucose)") unmasker16L = pipeline('fill-mask', model='bioformers/bioformer-16L') unmasker16L("[MASK] refer... | 9d3d305828ed262c8e6a00c39e872995 |
apache-2.0 | [] | false | Acknowledgment Training and evaluation of Bioformer-8L is supported by the Google TPU Research Cloud (TRC) program, the Intramural Research Program of the National Library of Medicine (NLM), National Institutes of Health (NIH), and NIH/NLM grants LM012895 and 1K99LM014024-01. | 8772126e4a406aa02caf7ae7bb78ca14 |
apache-2.0 | [] | false | Questions If you have any questions, please submit an issue here: https://github.com/WGLab/bioformer/issues You can also send an email to Li Fang (fangli9@mail.sysu.edu.cn, https://fangli80.github.io/). | 1dc7ee7b32a8cb7b7bb26b31f0fb3c13 |
apache-2.0 | [] | false | Citation You can cite our preprint on arXiv: Fang L, Chen Q, Wei C-H, Lu Z, Wang K: Bioformer: an efficient transformer language model for biomedical text mining. arXiv preprint arXiv:2302.01588 (2023). DOI: https://doi.org/10.48550/arXiv.2302.01588 BibTeX format: ``` @ARTICLE{fangli2023bioformer, author = {{F... | f32d22ef2eb4b19348d6a99d6185c45c |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | t5-v1_1-small-finetuned-cnn_dailymail This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.7290 - Rouge1: 0.3363 - Rouge2: 0.1736 - Rougel: 0.2951 - Rougelsum: 0.3151 | 653d7285cb991b9032d83b771fd49b74 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 0b2b5a4ec3036fbe1563ee4bc67d0884 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:| | 2.7338 | 1.0 | 35890 | 1.8390 | 0.3278 | 0.1658 | 0.2876 | 0.3064 | | 2.3233 | 2.0 |... | 239f26f0ed3ad20a33104a42e5de27b0 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1573 | e07a6976a6c62e303aad133ae3ed1f6b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2188 | 1.0 | 5533 | 1.1708 | | 0.9519 | 2.0 | 11066 | 1.1058 | | 0.7576 | 3.0 | 16599 | 1.1573 | | a293fd239fa22f0d75035c144fc0f5bc |
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