license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
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apache-2.0 | ['Fake News Detection', 'Text Classification'] | false | Applying softmax here for single label classification softmax = nn.Softmax(dim = 1) prediction_probabilities = list(softmax(detached_output).detach().numpy()) predictions = [] for x,y in prediction_probabilities: predictions.append("not_fake_news") if x > y else predictions.append("fake_news") print(predictions) ```... | 1d257e2f1ba5f2c14c2843d2a9c14827 |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2t_de_hubert_s55 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 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is ... | 96baff673650ee2022c983ae2625f676 |
other | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | FremyCompany/BioLORD-STAMB2-v1 This model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and p... | 1d91aeeec1764bb09052c3a4419fbfac |
other | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | General purpose 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. This model has been finentuned for the biomedical domain. While it preserves a good ability to produce emb... | 4b81144c42539add26cd8fe15fed8f4a |
other | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Citation This model accompanies the [BioLORD: Learning Ontological Representations from Definitions](https://arxiv.org/abs/2210.11892) paper, accepted in the EMNLP 2022 Findings. When you use this model, please cite the original paper as follows: ```latex @inproceedings{remy-etal-2022-biolord, title = "{B}io{LORD... | 5183c6b67344ac816e4ecc344dd1fda7 |
other | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | 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 = ["Cat scratch injury", "Cat ... | 9859298589785ea263d7af8607df20bf |
other | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), 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. ```python from transformers import AutoTok... | f5f951d360fcb662ad435265dc68cedf |
other | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | License My own contributions for this model are covered by the MIT license. However, given the data used to train this model originates from UMLS, you will need to ensure you have proper licensing of UMLS before using this model. UMLS is free of charge in most countries, but you might have to create an account and rep... | 922d0eae5a9c3c19e9238ff333a13c43 |
cc-by-4.0 | ['generated_from_trainer'] | false | DISO_SINAI_test1 This model is a fine-tuned version of [chizhikchi/Spanish_disease_finder](https://huggingface.co/chizhikchi/Spanish_disease_finder) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0807 - Diso Precision: 0.8260 - Diso Recall: 0.8247 - Diso F1: 0.8253 - Diso Numb... | 69658552f4c1ee9899cbcc393896c760 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:... | fe665b82b5742b87661c57295632e2d4 |
apache-2.0 | ['bert'] | 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 | 自然语言理解 NLU | 二郎神 Erlangshen | DeBERTa-v2 | 186M | 中文-分句 Chinese-SentencePiece | | 94554ec7bbcf32aa97d6e05ae51459b6 |
apache-2.0 | ['bert'] | false | 模型信息 Model Information 为了得到一个中文版的DeBERTa-v2（186M），我们用悟道语料库(180G版本)进行预训练。我们使用了Sentence Piece的方式分词（词表大小：约128000）。具体地，我们在预训练阶段中使用了[封神框架](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen)大概花费了8张3090TI（24G）约21天。 To get a Chinese DeBERTa-v2 (186M), we use WuDao Corpora (180 GB version) for pre-training. We ... | 0f7509a57b67b952969d37df08340f86 |
apache-2.0 | ['bert'] | false | 下游效果 Performance 我们展示了下列下游任务的结果（dev集）： We present the results (dev set) on the following tasks: | Model | OCNLI | CMNLI | | ---------------------------------------------------- | ------ | ------ | | RoBERTa-base | 0.743 | 0.79... | 7ba0fcab6af97c1813aea19550641fcd |
apache-2.0 | ['bert'] | false | 使用 Usage ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline import torch tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-186M-Chinese-SentencePiece', use_fast=False) model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-186M-Chinese... | 444cb3f51e6c3f750bf45010f856f9e2 |
apache-2.0 | ['generated_from_trainer'] | false | insertion-prop-05-correct-data 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: 0.0794 - Precision: 0.9284 - Recall: 0.9056 - F1: 0.9169 - Accuracy: 0.9689 | af0900440d16702c926bb50656aca707 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1815 | 0.32 | 500 | 0.0982 | 0.9159 | 0.8802 | 0.8977 | 0.9619 | | 0.1113 | 0.64 |... | b1ac20426a943db37f001bca9fe15748 |
apache-2.0 | [] | false | Electra small ⚡ + SQuAD v2 ❓ [Electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) fine-tuned on [SQUAD v2.0 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task. | 86d590344b75c12d0a34e2ce5ae9f68a |
apache-2.0 | [] | false | Details of the downstream task (Q&A) - Model 🧠 **ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another ... | d79622f1c2dbefcfe716b4c120677b99 |
apache-2.0 | [] | false | Details of the downstream task (Q&A) - Dataset 📚 **SQuAD2.0** combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine w... | 0b0dec8b665ec031a928bbd50c432896 |
apache-2.0 | [] | false | Model training 🏋️ The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command: ```bash python transformers/examples/question-answering/run_squad.py \ --model_type electra \ --model_name_or_path 'google/electra-small-discriminator' \ --do_eval \ --do_train \ --do_lower_case \ --... | 425632cd905a1a7d2acc5f16cf294962 |
apache-2.0 | [] | false | Value | | ------ | --------- | | **EM** | **69.71** | | **F1** | **73.44** | | **Size**| **50 MB** | ```json { 'exact': 69.71279373368147, 'f1': 73.4439546123672, 'total': 11873, 'HasAns_exact': 69.92240215924427, 'HasAns_f1': 77.39542393937836, 'HasAns_total': 5928, 'NoAns_exact': 69.50378469301934, 'NoAns_f1': 6... | dd8538ba097ca51cc32ed5f6ba46a230 |
apache-2.0 | [] | false | Model in action 🚀 Fast usage with **pipelines**: ```python from transformers import pipeline QnA_pipeline = pipeline('question-answering', model='mrm8488/electra-base-finetuned-squadv2') QnA_pipeline({ 'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by sc... | 07a966d1106083c4690945a23c7daaaa |
apache-2.0 | [] | false | Output: {'answer': 'A new strain of flu', 'end': 19, 'score': 0.8650811568752914, 'start': 0} ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) > Made with <span style="color: | 145ae170325d08946f3284b050f768be |
bsd-3-clause | ['visual-question-answering'] | false | BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Model card for BLIP trained on visual question answering- base architecture (with ViT base backbone). |  |... | 5eb74cf4585e5ac24ba5a9f65b4c9276 |
bsd-3-clause | ['visual-question-answering'] | false | Running the model on CPU <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForQuestionAnswering processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") model = BlipForQuestionAnswering.from_pretrained("Salesforce/... | c6d1126dc42e3529055fba702d989083 |
bsd-3-clause | ['visual-question-answering'] | false | In full precision <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForQuestionAnswering processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-v... | 1bf06bebdf58ad61e67613dbaba3f136 |
bsd-3-clause | ['visual-question-answering'] | false | In half precision (`float16`) <details> <summary> Click to expand </summary> ```python import torch import requests from PIL import Image from transformers import BlipProcessor, BlipForQuestionAnswering processor = BlipProcessor.from_pretrained("ybelkada/blip-vqa-base") model = BlipForQuestionAnswering.from_pretrai... | 21032ed76019bbc38e245ba4b9d59c7a |
mit | [] | false | Generates Ad text copy, for ads for Amazon shopping (fine tuned for electronics and wearables). The model is fine tuned on the EleutherAI/gpt-neo-125M model using the Amazon Ads dataset. **Usage Examples:** Select from among the examples in the dropdown or enter your own prompts. You can try entering bran... | 75a89a849449bcf3c07c45f31d130b54 |
mit | ['generated_from_trainer'] | false | 10_epochs_camembert_jb This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1070 - Overall Precision: 0.8279 - Overall Recall: 0.8660 - Overall F1: 0.8465 - Overal... | 95ee5d25255415de385b775a3295ae49 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Er F1 | Oc F1 | Umanprod F1 | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:-----------... | 9f2504131f7fb6b5c7fefa3ea135be7e |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-ft-imdb This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [steciuk/imdb](https://huggingface.co/datasets/steciuk/imdb) dataset. It achieves the following results on the evaluation set: - Loss: 0.2556 - Accuracy: 0.945 - F1: 0.9441 and flowing... | 1904d61c07ec6de153a330b445e8cec2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2943 | 0.38 | 750 | 0.1877 | 0.9257 | 0.9226 | | 0.2133 | 0.75 | 1500 | 0.1806 | 0.9375 | 0.9347 | | 0.1811 |... | 7eb7a17bdc98abfac07d8d5999f802fd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 188 | 2.2663 | 4.5343 | 17.698 | | 4cdc1863039d0694b6ee6763e2ef260a |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | wav2vec2-base-ft-keyword-spotting This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0795 - Accuracy: 0.9829 | 47fc1f21f288a97c00c8e94f81a02395 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5546 | 1.0 | 399 | 0.4250 | 0.9618 | | 0.2128 | 2.0 | 798 | 0.1331 | 0.9781 | | 0.1763 | 3.0 | 1197 | 0.0935 | 0.... | fe63f7593a21f1c60fb25297f6eb75a8 |
apache-2.0 | ['generated_from_trainer'] | false | distilr2-lr1e05-wd0.08-bs32 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: 0.2743 - Rmse: 0.5237 - Mse: 0.2743 - Mae: 0.4134 | 30c2abd818472d1e81f305337228ccf4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2775 | 1.0 | 623 | 0.2735 | 0.5230 | 0.2735 | 0.4181 | | 0.2738 | 2.0 | 1246 | 0.2727 | 0.5222 | 0.2727 ... | 4f41a79a22aa6e55488509c4f9e9587d |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr 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: 0.1642 - F1: 0.8589 | 0fc1c4615f9ad345cd019402d255e0e0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2886 | 1.0 | 715 | 0.1804 | 0.8293 | | 0.1458 | 2.0 | 1430 | 0.1574 | 0.8494 | | 0.0931 | 3.0 | 2145 | 0.1642 | 0.8589 | ... | 90068e2e37025340803f21daaeccf43a |
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: 0.1689 - Accuracy: 0.9295 - F1: 0.9300 | 50f243deda330e8144ae73f2af518573 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2853 | 1.0 | 250 | 0.1975 | 0.9235 | 0.9233 | | 0.1568 | 2.0 | 500 | 0.1689 | 0.9295 | 0.9300 | | b22753945cb6b726e83d227e151c9ced |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_mnli_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.5305 - Accuracy: 0.5881 | a1afa98cf3535e77c14e0a98439248fc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.5834 | 1.0 | 1534 | 0.5611 | 0.5153 | | 0.5545 | 2.0 | 3068 | 0.5469 | 0.5330 | | 0.5418 | 3.0 | 4602 | 0.5420 ... | f1865dcda010d82271bd0e43630de8e6 |
mit | ['generated_from_trainer'] | false | distilcamembert-cae-thinking This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8373 - Precision: 0.7054 - Recall: 0.7089 - F1: 0.7057 | 48b22740d5a673ecfc292ca1d38aa207 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 1.1608 | 1.0 | 40 | 1.0292 | 0.1963 | 0.4430 | 0.2720 | | 0.894 | 2.0 | 80 | 0.9292 | 0.6253 ... | 5715263c6ab6ab1903afbb0fbabeef72 |
mit | ['generated_from_trainer'] | false | roberta-large-aces This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5257 - Precision: 0.8561 - Recall: 0.8594 - F1: 0.8553 - Accuracy: 0.8594 - F1 Who: 0.8494 - F1 What: 0.8391 - F1 Whe... | 42bb31726515ceff81749c6863390a3d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | F1 Who | F1 What | F1 Where | F1 How | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|:-------:|:--------:|:------:| | 0.4619 | 1.0 | 87 | 0.5447... | 33999c5dce45b2c0a81bf326bbb9938b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-imdb 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: - Loss: 0.1827 - Accuracy: 0.9292 | 7a72970f2deb1b3be3e9e0ff462a1655 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2182 | 1.0 | 1563 | 0.1827 | 0.9292 | | 04d7a5506d0b1d7666b10ade98edc328 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_clinic_credit_cards-massive_alarm-roberta-large-v1-2-48 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer... | db0df2c61224456c6f903cc8070c7ed1 |
openrail | ['generated_from_trainer'] | false | santacoder-finetuned-the-stack-dockerfiles This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8741 | c17260e6205cefbd225cbfe495a7904a |
openrail | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.3691 | 0.05 | 500 | 1.2915 | | 1.267 | 0.1 | 1000 | 1.1962 | | 1.1998 | 0.15 | 1500 | 1.1459 | | 1.0753 | 0.2 | 2000 | 1.1130 ... | bc4ae6c492e95550c1db7aa89f08f20d |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Stable Diffusion x2 latent upscaler model card This model card focuses on the latent diffusion-based upscaler developed by [Katherine Crowson](https://github.com/crowsonkb/k-diffusion) in collaboration with [Stability AI](https://stability.ai/). This model was trained on a high-resolution subset of the LAION-2B dat... | 20d97efe33eb8b882e9b16ae0ac66029 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Model Details - **Developed by:** Katherine Crowson - **Model type:** Diffusion-based latent upscaler - **Language(s):** English - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) | 7bbf2da1f56ada237b3b41d6d8c42fe0 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run latent upscaler on top of any `StableDiffusionUpscalePipeline` checkpoint to enhance its output image resolution by a factor of 2. ```bash pip install git+https://github.com/huggingface/diffusers.git pip install transformer... | 5032175dabf72f0dbb67ef096301ade1 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | in latent space low_res_latents = pipeline(prompt, generator=generator, output_type="latent").images upscaled_image = upscaler( prompt=prompt, image=low_res_latents, num_inference_steps=20, guidance_scale=0, generator=generator, ).images[0] | 437f7aea1a7b5f4ba4de8a71f5fc4591 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | as a comparison: Let's also save the low-res image with torch.no_grad(): image = pipeline.decode_latents(low_res_latents) image = pipeline.numpy_to_pil(image)[0] image.save("astronaut_512.png") ``` **Result**: *512-res Astronaut*  *1024-res Astronaut*  on the caner dataset. It achieves the following results on the evaluation set: - Loss: 0.3598 - Precision: 0.8599 - Recall: 0.8612 - F1: 0.8605 - Accuracy: 0.9482 | 17c2e5b60d91c5208eab1db1d51dfa65 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2352 | 1.0 | 3228 | 0.3782 | 0.8478 | 0.8359 | 0.8418 | 0.9348 | | 0.1572 | 2.0 |... | 41518370dcc0358ebec937b3778846ad |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | jatmikocooll Dreambooth model trained by fishers with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-... | fc3959904a428851616d160a4fc0bf26 |
apache-2.0 | [] | false | Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. Using this [script](https://github.com/huggingface/diffusers/blob/cde0ed162a127b17f1b4d4b16ff7f736cf04e690/examples/train_unconditional.p... | 2726d936adda7a65190f62e657e5e165 |
apache-2.0 | [] | false | bert-base-en-fr-es-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly t... | 40842db1c324c209f87eaceafc6d250d |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-fr-es-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-fr-es-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | 9e5ebb4a63b242a4c3b99a8f8811184a |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr0_3e-05_essays_27_02_2022-19_35_56 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: 0.3767 - Accura... | 800e6f9682258bed42e62cbefdf21ac0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 81 | 0.4489 | 0.8309 | 0.8969 | | No log | 2.0 | 162 | 0.4429 | 0.8272 | 0.8915 | | No log |... | b80675b336eb2c2dcbb58bf393360087 |
mit | ['generated_from_trainer'] | false | xlnet-base-mnli-orgs-finetuned1 This model is a fine-tuned version of [clevrly/xlnet-base-mnli-finetuned](https://huggingface.co/clevrly/xlnet-base-mnli-finetuned) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1542 - F1: 0.6957 | 91d2dc80f7b369cd608c9147280446ab |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoch... | afb352142d8a30bab979a05992e5c3f3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2719 | 1.0 | 1462 | 0.2841 | 0.0 | | 0.3042 | 2.0 | 2924 | 0.2664 | 0.4324 | | 0.1366 | 3.0 | 4386 | 0.1408 | 0.6452 | |... | 1dff0587a4dbf7768197ba725d155b38 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_full_band_vits_accent_with_pause` ♻️ Imported from https://zenodo.org/record/5431984/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 4579aafdd6c90223269d355222b3300c |
apache-2.0 | ['zero-shot-classification'] | false | PaddlePaddle/utc-large Text classification technology is widely used in various industries such as dialogue intention recognition, bill archiving, and event detection. However, there are many challenges in industrial-level text classification practices, including diverse tasks, limited data availability and label tr... | b304c5c54bf8592d9efbecc26afdd4fb |
apache-2.0 | ['zero-shot-classification'] | false | Available Models | Model Name | Usage Scenarios | Supporting Tasks | | :--------------: | :------------------------- | :---------------------------- | | `utc-large` | A **text classification** model supports **Chinese** | Supports intention recognition, semantic matching, natural la... | 4bb7c277cd8052e7d4959e7f3ca4e915 |
apache-2.0 | ['zero-shot-classification'] | false | Performance on Text Dataset UTC tops [ZeroCLUE](https://www.cluebenchmarks.com/zeroclue.html) and [FewCLUE](https://www.cluebenchmarks.com/fewclue.html) benchmarks, as of 2023/01/12.  **Detailed Info:**... | d839560131c4317b735f139fdc93f74d |
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: 1.6082 | 449523c97799c2e0981e9c3491db1bae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6172 | 1.0 | 555 | 1.6168 | | 1.36 | 2.0 | 1110 | 1.4994 | | 0.9526 | 3.0 | 1665 | 1.6082 | | 7b9c891a3646c5d627393c78693d340b |
apache-2.0 | ['generated_from_trainer'] | false | distill_bert_fine_tuned_mind 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: 0.3697 - Accuracy: 0.9187 | 8f924fc2fe7272c0133628db503e8e36 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6468 | 1.0 | 3054 | 0.5016 | 0.8311 | | 0.4298 | 2.0 | 6108 | 0.3328 | 0.8998 | | 0.2216 | 3.0 | 9162 | 0.3697 | 0.... | f6a90207a096c6ec733c0c10a51b974a |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Tiny it 11 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.670211 - Wer: 42.276761 | a2a056978d9100e2c6199436f7ae30e8 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training and evaluation data Data used for training is the initial 25% of train and validation of [Italian Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/it/train) 11.0 from Mozilla Foundation. The dataset used for evaluation is the initial 10% of test of Italian Common Voic... | 62dd77f2c518e365009a082f548dde6d |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.584600 | 0.95 | 1000 | 0.801204 |48.980865| | 0.496100 | 1.91 | 2000 | 0.713927 |46.283971| | 0.406000 | 2.86 | 3000 | 0.680141 |43.26816... | b882d0f71a3f41f5119bfbedb2a66960 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53-torgo-demo-f04-nolm This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0139 - Wer: 0.4976 | f2d7cb20e9c2b2d9df05e28be1e27861 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.2981 | 0.89 | 500 | 4.2044 | 1.0 | | 2.8779 | 1.79 | 1000 | 3.0752 | 1.0 | | 2.5952 | 2.68 | 1500 | 2.5703 | 1.296... | a9913daca2c7f55a76ffc5c1c034292f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small et - Common Voice+FLEURS This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0, FLEURS dataset. It achieves the following results on the evaluation set: - Loss: 0.8754 - Wer: 42.4844 | 1a9441578789eba1e5ee7c4c77b6fabe |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0094 | 10.0 | 1000 | 0.7125 | 43.4085 | | 0.0024 | 20.01 | 2000 | 0.7960 | 42.1795 | | 0.0012 | 30.01 | 3000 | 0.8237 | 41.896... | 56fbedddcf08d33e4956cc2d5e910a65 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'en', 'English'] | false | 🧨 Diffusers This model can be used like any other model. [Click to read more](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion) ```python from diffusers import StableDiffusionPipeline import torch model_id = "ShibaDeveloper/olivia-v1.0" pipe = StableDiffusionPipeline.from_pretrained(model_id, t... | be07c0550f0820f2e1fdef72c20bef43 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'en', 'English'] | false | ✨ Examples Examples of images generated using Olivia V1.0 :  ``` Prompt: 1girl, detailed, intricate, elegant, highly detailed, digital painting, artstation, concept art, matte, sharp focus, illustration, by dan mumford, yusuke murata, ma... | 552d0beaf204b4d8c24da89c0658dc14 |
mit | ['generated_from_trainer'] | false | stbl_clinical_bert_ft_rs2bs This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1189 - F1: 0.8982 | 4bb54ffcd544bf83ed1f4a0b99c3cdcf |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2719 | 1.0 | 101 | 0.0878 | 0.8458 | | 0.0682 | 2.0 | 202 | 0.0678 | 0.8838 | | 0.0321 | 3.0 | 303 | 0.0617 | 0.9041 | |... | 8e55966b38cf647da6bff8280675907a |
mit | ['generated_from_trainer'] | false | sleepy_pike This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-... | b61952dc7fe4a548e8bcbb066258dcb4 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tom... | e0f353a9c3748200605d60a0bb18f3c8 |
creativeml-openrail-m | ['text-to-image'] | false | [](https://huggingface.co/spaces/Duskfallcrew/duskfall-s-mang... | fc09eb31ca2f6bcc91ba2f01409f2789 |
creativeml-openrail-m | ['text-to-image'] | false | Duskfall's Manga Aesthetic Model Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/gi... | 5c352cb4385debafd310c23885cf64b0 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Chinese-zh-cn-gpt Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chinese (zh-CN) using the [Common Voice](https://huggingface.co/datasets/common_voice), included [Common Voice](https://huggingface.co/datasets/common_voice) Chinese (zh-TW)... | d740b43854767074d412fbc4931e7957 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "zh-CN", split="test") processor = Wav2Vec2Processor.from_pre... | 0aebc98a2cfff0298e020ad0050043a5 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | !pip install jiwer import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re import jiwer def chunked_cer(targets, predictions, chunk_size=None): _predictions = [char for seq in predictions for char in list(seq)] _target... | 6c1dd89633639a270febd281e8c60b99 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") + " " speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() ... | 0ea838e5ae51b5d6eb2d8197760b6704 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch... | 9a17156f312d86ea1e966c3bd2068c05 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice zh-CN `train`, `validation` were used for training, as well as Common Voice zh-TW `train`, `validation` and `test` datasets. The script used for training can be found [to be uploaded later](...) | c07ede7d12a07dcb965345467ec11270 |
mit | [] | false | 학습 환경 및 하이퍼파라미터 - TPU V2-8 - Learning Rate: 3e-4, Batch Size: 512(=64 accum x 8 devices), Scheduler: Linear, WarmUp: 1000 step - adam_beta1=0.9 adam_beta2=0.98, weight_decay=0.01 - Training Steps: 43247 (3 epoch) - 학습 토큰 수: 21.11B (43247 * 512 * 1024seq / 1024^3) - 학습 기간: 2023/1/25 ~ 2023/1/29 | 6420a0b78ebb37678c735b543bbebbdf |
mit | [] | false | 사용 예시 ```python from transformers import pipeline model_name = "heegyu/kogpt-j-base-24L" pipe = pipeline('text-generation', model=model_name) print(pipe("안녕하세요", repetition_penalty=1.2, do_sample=True, eos_token_id=1, early_stopping=True, max_new_tokens=128)) print(pipe("오늘 정부 발표에 따르면, ", repetition_penalty=1.2, do_sa... | 6d8cec62fe711f56b5918056d1efb050 |
apache-2.0 | ['generated_from_trainer'] | false | mt5-small-finetuned-2epochs-kde4-en-to-it This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 1.5777 | 32e1a5a1e55ba9c8417d8ecf57f5237f |
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