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 | ['feature-extraction', 'sentence-similarity'] | false | List of sentences for comparison sentences_1 = ["This is a sentence for testing miCSE.", "This is using mutual information Contrastive Sentence Embeddings model."] sentences_2 = ["This is testing miCSE.", "Similarity with miCSE"] | 5540e5451520ade378928a48bb2abcb2 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Benchmark Model results on SentEval Benchmark: <details> <summary> Click to expand </summary> ```shell +-------+-------+-------+-------+-------+--------------+-----------------+--------+ | STS12 | STS13 | STS14 | STS15 | STS16 | STSBenchmark | SICKRelatedness | S.Avg. ... | a69be4dd909f6b219e0f47a27cae50aa |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Citations If you use this code in your research or want to refer to our work, please cite: ``` @article{Klein2022miCSEMI, title={miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings}, author={Tassilo Klein and Moin Nabi}, journal={ArXiv}, year={2022}, volume={abs/2211.04928} } ```... | 038cdffe214bbc49d406b3646064b3b5 |
creativeml-openrail-m | [] | false | **UPDATE 9/NOV/2022: added 2 additional versions trained from Trinart Characters base. The 5000 steps version is probably the better one for most people as it is much more editable than the 6000 steps version. 6000 steps may be good for merging with other models.** waifu diffusion 1.3 base model with dreambooth train... | 86911d4154efe1a1664972f1034cf0a2 |
apache-2.0 | [] | false | Vision-and-Language Transformer (ViLT), fine-tuned on VSR random split Vision-and-Language Transformer (ViLT) model fine-tuned on random split of [Visual Spatial Reasoning (VSR)](https://arxiv.org/abs/2205.00363). ViLT was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Su... | 784d0d4790ce4e22b1d68ed3bfafe7c3 |
apache-2.0 | [] | false | How to use Here is how to use the model in PyTorch: ``` from transformers import ViltProcessor, ViltForImagesAndTextClassification import requests from PIL import Image image = Image.open(requests.get("https://camo.githubusercontent.com/ffcbeada14077b8e6d4b16817c91f78ba50aace210a1e4754418f1413d99797f/687474703a2f2f... | ec1338d11bff19510e805785c2e80943 |
apache-2.0 | [] | false | BibTeX entry and citation info ```bibtex @misc{kim2021vilt, title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision}, author={Wonjae Kim and Bokyung Son and Ildoo Kim}, year={2021}, eprint={2102.03334}, archivePrefix={arXiv}, primaryClass={stat.ML} }... | 3a19e851b80e310955402f451cf27a43 |
mit | ['generated_from_trainer'] | false | camembert-base-mrpc This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.4286 - Accuracy: 0.8505 - F1: 0.8928 - Combined Score: 0.8716 | 28f556d801cc069f2519b603366e3330 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | cv1.2 Dreambooth model trained by ukeeba 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-diffusio... | ba605fd9c04734b83a6ceae2d1568b8e |
odc-by | [] | false | Basically, generate the images by saying "dnd[RACE] person" I know some arent people, but it's what I've got to work with. ;) Make sure there are no spaces, or punctuation in the "dnd[RACE HERE]" section, so "a portrait of dndYuanTi person, intricate, elegant, highly detailed, digital painting, artstation, trending, ... | eab46641407abbcf92343a1877439b81 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4526 - Wer: 0.3411 | 100f866e42dc6403b9f32711d37a7072 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.7503 | 4.0 | 500 | 2.4125 | 1.0006 | | 0.9595 | 8.0 | 1000 | 0.4833 | 0.4776 | | 0.3018 | 12.0 | 1500 | 0.4333 | 0.4062 | |... | baeffd258e3773ce15499a918df6da1d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_pretrain_wnli This model is a fine-tuned version of [gokuls/distilbert_add_pre-training-complete](https://huggingface.co/gokuls/distilbert_add_pre-training-complete) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3435 - Accuracy: 0.... | 3fc357ad13793874cd35e53b4b875345 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3566 | 1.0 | 3 | 0.3453 | 0.5634 | | 0.347 | 2.0 | 6 | 0.3435 | 0.5634 | | 0.3501 | 3.0 | 9 | 0.3465 | 0.... | cc42ae18e1c8acb0ad3638b985b1a86f |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-rte 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: 2.3557 | e717cb5d45b7d983628429195759ba0d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5903 | 1.6 | 500 | 2.1820 | | 2.4763 | 3.21 | 1000 | 2.4737 | | 2.3778 | 4.81 | 1500 | 2.2902 | | 2.2735 | 6.41 | 2000 | 2.3557 ... | 2eed95f3435728344e46a9d0796af701 |
mit | ['generated_from_keras_callback'] | false | ishaankul67/Warsaw_Pact-clustered This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1010 - Train End Logits Accuracy: 0.9653 - Train Start Logits Ac... | a6a7f2879cd818a386e8ffb9efaaf364 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 618e5d129d205f98bbcbe7054338059c |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_250v0_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni250v0_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3679 - Precision: 0.4748 - Recall: 0.3732 - F1: 0.4179 - Accura... | 2f66e4e73880d99bea486ed032f0c5fd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 91 | 0.4333 | 0.2856 | 0.1851 | 0.2246 | 0.8440 | | No log | 2.0 |... | 52c4ca1f8784660513ec2d320aa8dfc6 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora'] | false | LoRA DreamBooth - margret-stalizburg-v1-lora These are LoRA adaption weights for [andite/anything-v4.0](https://huggingface.co/andite/anything-v4.0). The weights were trained on the instance prompt "margret stalizburg" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the followin... | 15c2794d634cac8052c951626ab35c86 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | wedadams_pyros_bj Dreambooth model trained by tftgregrge 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... | b890bef9664d7df6f5d31fe815fb5f55 |
apache-2.0 | ['thai', 'masked-lm', 'wikipedia'] | false | Model Description This is a RoBERTa model pre-trained on Thai Wikipedia texts. You can fine-tune `roberta-base-thai-spm` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-thai-spm-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/roberta-base-thai-spm-ud-head... | 6c684c2d81b570f737c8bf781bdeed54 |
apache-2.0 | ['thai', 'masked-lm', 'wikipedia'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-thai-spm") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-base-thai-spm") ``` | 947f625db2b8924f3f5d691cc6534499 |
mit | ['generated_from_keras_callback'] | false | Sushant45/Warsaw_Pact-clustered This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0812 - Train End Logits Accuracy: 0.9757 - Train Start Logits Accu... | 0b03d31a833b37bfc23648a4f0d59645 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 80fdeaee5c24fd158c239d989a64b7c7 |
mit | [] | false | [](https://github.com/AlexeyAB/darknet/actions?query=workflow%3A%22Darknet+Continuous+Integration%22) | 7f2cd47c403296a776a444862d18f2ac |
mit | [] | false | Model YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detec... | 6eb93f26a2f461a9263d6ce0f86f2a32 |
mit | [] | false | Citation ``` @misc{bochkovskiy2020yolov4, title={YOLOv4: Optimal Speed and Accuracy of Object Detection}, author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao}, year={2020}, eprint={2004.10934}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ``` @InProceedings... | e9af6c70ac5342486a78b3332ed0bf05 |
cc-by-sa-4.0 | ['chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from [deberta-base-chinese](https://huggingface.co/KoichiYasuoka/deberta-base-chinese). Every word is tagged by [UPOS](https://universaldependencies.or... | a007b3ecdf798aa91711eb72bbe3bd6e |
cc-by-sa-4.0 | ['chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-chinese-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-chinese-upos") ``` or ```py import esupar nlp=esupar.load("Koic... | 0ca572cd3156e8f87b08840db3372d5f |
mit | [] | false | Model description It is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This model is uncased. This is one of several other language models that have been pre-trained with indonesian datasets. More detail about its usage on downstream... | b8c2ddb7d4d45f073e3c8da7c90b9b42 |
mit | [] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='cahya/bert-base-indonesian-1.5G') >>> unmasker("Ibu ku sedang bekerja [MASK] supermarket") [{'sequence': '[CLS] ibu ku sedang bekerja di ... | 801a76db231ead9836ea6025890359c4 |
mit | [] | false | Training data This model was pre-trained with 522MB of indonesian Wikipedia and 1GB of [indonesian newspapers](https://huggingface.co/datasets/id_newspapers_2018). The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are then of the form: ```[CLS] Sentence ... | 8980bd1c1392d1d9fca213ead6ba9fe2 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | glpn-nyu-finetuned-diode-230113-130735 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.4320 - Mae: 0.4213 - Rmse: 0.6133 - Abs Rel: 0.4298 - Log Mae: 0.1697 - Log Rmse:... | dcf9ea0b03840e767e9a2e0157ecbd88 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 1.0073 | 1.0 | 72 | 0.4927 ... | 16b05284aff4f90fd4b494a195d0a8ee |
apache-2.0 | ['generated_from_trainer'] | false | stance_detection This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) towards 26 US SPAC stock mergers on Twitter. It achieves the following results on the evaluation set: - Loss: 0.4906 - Accuracy: 0.8409 - F1w: 0.8574 - Acc0: 0.8293 - Acc1: 0.6 - Acc2: 0.7652 - Acc3: 0.863... | 4441dc54e3b9bd3465ce730cb66f59c6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1w | Acc0 | Acc1 | Acc2 | Acc3 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:----:|:------:|:------:| | 0.7748 | 1.0 | 194 | 0.5172 | 0.8158 | 0.8297 | 0.8699 | 0.0 | 0.74... | 193738740dee1bc1f0f6d7986c1061df |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | messy_sketch_art_style Dreambooth model trained by apurik-parv with [Shivamshri rao's DreamBooth implementation] Instance prompt:**meartsty** As the name implies the the model is trained on messy art style sketch /doodle images for 50000 steps. Simple prompts can replicate faithfully. complicated and contradicting p... | fc8269cb46d6bb82a9d54821a51f8a4d |
apache-2.0 | ['generated_from_trainer'] | false | text-to-sparql-t5-base-2021-10-18_16-15 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1294 - Gen Len: 19.0 - Bertscorer-p: 0.5827 - Bertscorer-r: 0.0812 - Bertscorer-f1: 0.3202 - Sacrebleu-score: ... | bc897b980515a5d5cbb8979e5216e12b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Gen Len | Bertscorer-p | Bertscorer-r | Bertscorer-f1 | Sacrebleu-score | Sacrebleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------------:|:-----------... | 342c9f754b5c088fbbffd4b473441ba8 |
apache-2.0 | ['generated_from_trainer'] | false | t5_8_3e-5_datav2_min30_lp2_sample This model is a fine-tuned version of [KETI-AIR/ke-t5-large-ko](https://huggingface.co/KETI-AIR/ke-t5-large-ko) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.2375 - Rouge1: 24.1102 - Rouge2: 5.3137 - Rougel: 16.1086 - Bleu1: 18.6424 - Bleu2... | 48b8398311266e9bc7d469f56ff17de7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 | 6f6d641c848d10479285b78216f89050 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:-------:|:------:|:------:|:------:|:-------:| | 4.1641 | 1.04 | 5000 | 6.8094 ... | e959f6e9389363f84a2e0d663c41c290 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-OnionOrNot 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: 0.2039 - Accuracy: 0.9224 - F1: 0.9218 | 7186973c15ac27407088fef4cf9e9dba |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3334 | 1.0 | 300 | 0.2382 | 0.9024 | 0.9011 | | 0.1822 | 2.0 | 600 | 0.2039 | 0.9224 | 0.9218 | | 91f94898bf8eb1c8eb1a749ccf762d35 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | fastbooth-jsjessy-1400 Dreambooth model trained by eicu 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-... | 44b387e16c9b6e6c709b50e07facef84 |
mit | [] | false | MSG on Stable Diffusion This is the `<MSG69>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your ... | 13558a88218deb824aa7c6bfb06e6112 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-credit_cards-4-16-5 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: 2.3376 - Accuracy: 0.3186 | 629864f8214bbe89b6c8f3460571740d |
creativeml-openrail-m | ['text-to-image', 'art', 'digital art', 'stable diffusion'] | false | [](https://huggingface.co/spaces/MultiversexPeeps/duskfall-s-... | 37f76b1def2e5361172870030fabdeff |
creativeml-openrail-m | ['text-to-image', 'art', 'digital art', 'stable diffusion'] | false | Duskfall's General Digital Art 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.co... | 2a8ea3997f2a98b2bb33434926498ff4 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-j-phoneme-common-test 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: 0.0000 - Wer: 0.0001 | d8321534e1ba71e11c1020c177916eae |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 4 - 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_sche... | 3d733b17fec203b6ee6368a6c33fe8ae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1488 | 7.14 | 2000 | 0.0788 | 0.0919 | | 0.0308 | 14.28 | 4000 | 0.0155 | 0.0271 | | 0.0121 | 21.43 | 6000 | 0.0070 | 0.010... | 8af7429a5f2aef955ede0d479adb771c |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Slovak CV11 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 sk dataset. It achieves the following results on the evaluation set: - Loss: 0.3982 - Wer: 23.1437 | 86a0bbc41eb4d7e1b285f883dbbe8095 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.001 | 14.29 | 1000 | 0.3982 | 23.1437 | | 0.0013 | 28.57 | 2000 | 0.4343 | 24.0362 | | 0.0001 | 42.86 | 3000 | 0.4565 | 23.322... | f1f353dcd2679d3b8e9e69d871b2ec20 |
apache-2.0 | ['translation'] | false | opus-mt-ng-en * source languages: ng * target languages: en * OPUS readme: [ng-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ng-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 6d99a487028fa55970fd4cb2f7cecbf5 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run tpkify-v1: [Open in Spaces](https://huggingface.co/spaces/akhaliq/tpkify-v1) thelastben fast-dreambooth sd1.5 model for turning things into toothpick art. use trigger tpkify. ex: a photo of a tpkify dog, sitting on the beach ex: oi... | 5b020cfab4f88f5b6b5aa8d1651c869a |
apache-2.0 | ['generated_from_trainer'] | false | 20NG_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2209 - Accuracy: 0.6067 | bc6e6a4cbebe1bf0d71542e74fb3029d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | b1096e6ac244cfb2c5762fc167347955 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8602 | 0.07 | 50 | 2.5794 | 0.2133 | | 2.3635 | 0.14 | 100 | 2.0956 | 0.38 | | 2.1526 | 0.21 | 150 | 1.9011 | 0.... | ccf374a6586e7f34ef4037bb7c504908 |
mit | ['text-classification'] | false | Multi2ConvAI-Quality: finetuned Bert for French
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Quality (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: French (fr)
- model type: ... | a1a7cc1145ab23062e947f740708d753 |
mit | ['text-classification'] | false | Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-quality-fr-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-quality-fr-bert")
````
| e6c18d30506e157310e8ad3006cb5c00 |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | Stable Diffusion fine tuned on art by [Björn Hurri](https://www.artstation.com/bjornhurri) This model is fine tuned on some of his "shiny"-style paintings. I also have a version for his "matte" works. | 1c3be4d46b5ba4f0c8ba8aaa82877d6b |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | Samples For this model I made two checkpoints. The "hurrishiny monster x2" model is trained for twice as long as the regular checkpoint, meaning it should be more fine tuned on the style but also more rigid. The top 4 images are from the regular version, the rest are from the x2 version. I hope it gives you an idea of... | 63572db22518feb9c56bde085d4dd6fe |
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: 0.0649 - Precision: 0.9330 - Recall: 0.9485 - F1: 0.9407 - Accuracy: 0.9854 | c7bc7a677c724e79fe8da1f15f967599 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0871 | 1.0 | 1756 | 0.0672 | 0.9209 | 0.9387 | 0.9297 | 0.9834 | | 0.0394 | 2.0 |... | e60f72d216a2f90a71bc7f6459050d58 |
mit | ['generated_from_keras_callback'] | false | nandysoham/Cardinal__Catholicism_-clustered This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2081 - Train End Logits Accuracy: 0.9444 - Train Start... | c0d46db8ca5699f70b3c01d432ff7685 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | b09b760a8265754d53d3fc2c40723c72 |
apache-2.0 | ['translation'] | false | opus-mt-ee-sv * source languages: ee * target languages: sv * OPUS readme: [ee-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ee-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 7a3d1686a8dd3769dc3d96c243077a6d |
apache-2.0 | [] | false | Model description ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate input... | eff4bb373c7e8ece41d85ea22c437b86 |
apache-2.0 | [] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-xlarge-v1') >>> unmasker("Hello I'm a [MASK] model.") [ { "sequence":"[CLS] hello i'm a modeling model.[SEP]", "sc... | 4ebe5ecd53f5393db4e95f8f6899bf60 |
apache-2.0 | [] | false | Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-xlarge-v1') >>> unmasker("The man worked as a [MASK].") [ { ... | 03478e39951373e6c775afd91287c521 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Japanese This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 ja dataset. It achieves the following results on the evaluation set: - Loss: 0.3617 - Wer: 68.9459 | 8d4eefdbb8d4497e79c5fc91d8e525f1 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1938 | 1.09 | 1000 | 0.2841 | 74.6631 | | 0.0466 | 3.06 | 2000 | 0.2996 | 72.0953 | | 0.005 | 5.04 | 3000 | 0.3376 | 70.435... | a94fd221f82efd8130330f72175f0e53 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 49a284e69308d81c142b89795de255b4ce290c54 pip install -e . cd egs2/talromur/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/GunnarThor_talromur_f_fastspeech2 ``` | 674cc804b609e8e3940646de9b1a6f61 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | TTS config <details><summary>expand</summary> ``` config: conf/tuning/train_fastspeech2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/f/tts_train_fastspeech2_raw_phn_none ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_... | 1498215c309123d9d76afc81301fc1d7 |
mit | [] | false | Overview **Model Description:** roberta-large-faithcritic is the [RoBERTa large model](https://huggingface.co/roberta-large) fine-tuned on FaithCritic, a derivative of the [FaithDial](https://huggingface.co/datasets/McGill-NLP/FaithDial) dataset. The objective is to predict whether an utterance is faithful or not, gi... | a40bb0222000e91f349aeb24c951d770 |
mit | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/roberta-large-faithcritic") model = AutoModel.from_pretrained("McGill-NLP/roberta-large-faithcritic") knowledge = "A cardigan is a type of knitted garment (sweater) that has an open front." respon... | 5d05127fd02c4857ee339bda33573ab1 |
mit | [] | false | Citation Information ```bibtex @article{dziri2022faithdial, title={FaithDial: A Faithful Benchmark for Information-Seeking Dialogue}, author={Dziri, Nouha and Kamalloo, Ehsan and Milton, Sivan and Zaiane, Osmar and Yu, Mo and Ponti, Edoardo and Reddy, Siva}, journal={arXiv preprint, arXiv:2204.10757}, year={2... | 2e0d0b3ff6ea92a6d59faf53fbf4e376 |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | Model description This is a ported version of [S3PRL's Hubert for the SUPERB Keyword Spotting task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/speech_commands). The base model is [hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k), which is pretrained on 16kHz sampled speech a... | 08a820864f0d185595a234d29b70cd01 |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | Task and dataset description Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. SUPERB uses the widely used [Speech Commands... | 0bd46adde3af247ac820ef64f4450f72 |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | Usage examples You can use the model via the Audio Classification pipeline: ```python from datasets import load_dataset from transformers import pipeline dataset = load_dataset("anton-l/superb_demo", "ks", split="test") classifier = pipeline("audio-classification", model="superb/hubert-large-superb-ks") labels = cl... | 48540e10cede3202691aeaf9a68b233c |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | load a demo dataset and read audio files dataset = load_dataset("anton-l/superb_demo", "ks", split="test") dataset = dataset.map(map_to_array) model = HubertForSequenceClassification.from_pretrained("superb/hubert-large-superb-ks") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-large-supe... | 63803ddd1251cb02db5fa213e7426b08 |
creativeml-openrail-m | ['text-to-image'] | false | Sample images:   Based on StableDiffusion 1.5 model | 79b7f9d15403b92e799478eab64676ea |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased.CEBaB_confounding.food_service_positive.absa.5-class.seed_42 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.7699 - Accuracy: 0.8050 - Macro... | f75eb557ad2a7b6a9e79639450a6cbea |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Kazakh (kk) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](http... | f0beed34e5538e18f07da9779ea66443 |
mit | [] | false | Chillpill on Stable Diffusion This is the `<Chillpill>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also t... | 9f515e6eb870732b06e3146bdabaca29 |
mit | ['generated_from_trainer'] | false | rte_roberta-base_125_v2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.7551 - Accuracy: 0.6715 | ecaa645ec7981a1659bb3e09b346b48e |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.8004 - Wer: 0.7139 | 7ffbc0eb06b69bafa7212780c0bc0587 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | d415aaae0abe2e2936aef5ad4255dac1 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.6683 | 1.45 | 500 | 1.7698 | 1.0041 | | 1.9548 | 2.91 | 1000 | 1.0890 | 0.8602 | | 1.9568 | 4.36 | 1500 | 1.0878 | 0.8680 | |... | e7469ad18d581741bd2a03775d4f5fae |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr2e05-wd0.005-bs32 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2862 - Rmse: 0.5350 - Mse: 0.2862 - Mae: 0.4436 | b209ef8dd931372ce6847bd51993c31b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2774 | 1.0 | 623 | 0.2746 | 0.5240 | 0.2746 | 0.4160 | | 0.274 | 2.0 | 1246 | 0.2738 | 0.5233 | 0.2738 ... | bdce93891f83052d42ba50aee9489241 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | false | MultiBERTs Seed 3 Checkpoint 80k (uncased) Seed 3 intermediate checkpoint 80k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/googl... | ea7c6ad649667c54c7b63f43d7b0554b |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-3-80k') model = BertModel.from_pretrained("multiberts-seed-3-80k") text = "Replace me by any text you'd like." ... | 26234078712580d7bf1d5842cb3d10a6 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-banking77-classification This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the banking77 dataset. It achieves the following results on the evaluation set: - Loss: 0.3034 - Accuracy: 0.9321 - F1 Score: 0.9321 | d5812a8aeb5aeac15d3cd655e8d8289f |
mit | ['generated_from_trainer'] | false | Training and evaluation data The dataset used is [banking77](https://huggingface.co/datasets/banking77) The 77 labels are: |label|intent| |:---:|:----:| |0|activate_my_card| |1|age_limit| |2|apple_pay_or_google_pay| |3|atm_support| |4|automatic_top_up| |5|balance_not_updated_after_bank_transfer| |6|balance_not_upda... | 66349781857a20340f467e9d0f48a2a7 |
mit | ['generated_from_trainer'] | false | Training procedure ``` from transformers import pipeline pipe = pipeline("text-classification", model="nickprock/xlm-roberta-base-banking77-classification") pipe("Non riesco a pagare con la carta di credito") ``` | 265cdf0ececa6c172ff52a2ba86e9f65 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | 3.8002 | 1.0 | 157 | 2.7771 | 0.5159 | 0.4483 | | 2.4006 | 2.0 | 314 | 1.6937 | 0.7140 | 0.6720 | | 1.4633 ... | 97896799c78769e3444a29c913613ccc |
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