license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | [] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 91 | 3.4934 | | No log | 2.0 | 182 | 3.4451 | | No log | 3.0 | 273 | 3.4356 | | 7393ce7c5716c2dd7ab44aab6e096e4d |
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: - Loss: 0.5805 - Accuracy: 0.8767 - F1: 0.8810 | 29231b55e6918e6a9126d7a6457a3f70 |
cc-by-4.0 | [] | false | roberta-base for QA > Note: this is a clone of [`roberta-base-squad2`](https://huggingface.co/deepset/roberta-base-squad2) for internal testing. This is the [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained... | 591cbdb59d9f83523abe120ac1f3f842 |
cc-by-4.0 | [] | false | Overview **Language model:** roberta-base **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system) **Infrastructure**: 4x Tesla v100 | 406fc0001a0a4e37d3ff948d4c71ac99 |
cc-by-4.0 | [] | false | Using a distilled model instead Please note that we have also released a distilled version of this model called [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2). The distilled model has a comparable prediction quality and runs at twice the speed of the base model. | e81f28084ae6999f75e0816422fa405a |
cc-by-4.0 | [] | false | In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2") | 61edfc7e98a0446fa7803b1c43c2a60b |
cc-by-4.0 | [] | false | or reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2") ``` For a complete example of ``roberta-base-squad2`` being used for Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system) ... | 2beea2d7510d2eb103da8f7a1189444b |
cc-by-4.0 | [] | false | Performance Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 79.87029394424324, "f1": 82.91251169582613, "total": 11873, "HasAns_exact": 77.93522267206478, "HasAns_f1": 84.02838248389763, "H... | 0f439c163d66e00e507106f0676a95c8 |
cc-by-4.0 | [] | false | Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://haystack.deepset.ai">Documentation</a></strong>. We also have a <strong><a class="h-7" href="https://haystack.deepset.ai... | d76cddceed9969d58dfefca08f4f4897 |
other | ['whisper-event', 'generated_from_trainer'] | false | Whisper Base Japanese Elite This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Elite35P-Server/EliteVoiceProject twitter dataset. It achieves the following results on the evaluation set: - Loss: 0.4385 - Wer: 17.0732 | ab4ffc5dacc0c632272cc8f9021ac086 |
other | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 200 - training_steps: 10000... | 4151613762f9c37218626d0d06464b81 |
other | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:-------:| | 0.0002 | 111.0 | 1000 | 0.2155 | 9.7561 | | 0.0001 | 222.0 | 2000 | 0.2448 | 12.1951 | | 0.0 | 333.0 | 3000 | 0.2674 ... | 984fc7559ca03aa334dc76501b4c4677 |
apache-2.0 | ['generated_from_trainer'] | false | electra-base-discriminator-CoLA This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.3542 - Matthews Correlation: 0.6580 | ad293aef386500a8ec04390a75cb9895 |
apache-2.0 | ['generated_from_trainer'] | false | Model description Trying to find a decent optimum between accuracy/quality and inference speed. ```json { "epoch": 8.0, "eval_loss": 0.3541961908340454, "eval_matthews_correlation": 0.6579677841732349, "eval_runtime": 1.9552, "eval_samples": 1043, "eval_samples_per_second": 533.451, "eva... | b11b141a5bbad39a80277ea9ac1f36d7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 128 - eval_batch_size: 16 - seed: 22165 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - ... | 4a7293fff5c7d9477e1905ad633c5fdc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4004 | 1.0 | 67 | 0.3569 | 0.6340 | | 0.2843 | 2.0 | 134 | 0.3542 | 0.6580 | | 0.1... | c3c60196334cd9611c659f0a8bc8ffd4 |
mit | ['generated_from_trainer'] | false | mbart_finetuned_dialect_translation_4 This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0109 - Bleu: 99.3856 - Gen Len: 14.951 | fbc6d1b14d9b52b42b7921125086a843 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 0.1512 | 1.0 | 938 | 0.0563 | 98.0769 | 14.981 | | 0.044 | 2.0 | 1876 | 0.0244 | 98.639 | 14.962 | | 0.0214 |... | 17061346591224d31cefa1fa7e373415 |
apache-2.0 | ['summarization'] | false | How to use ```python from transformers import AutoTokenizer, T5ForConditionalGeneration model_name = "IlyaGusev/rut5_base_headline_gen_telegram" tokenizer = AutoTokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) article_text = "..." input_ids = tokenizer( [arti... | 4d46525b5f62327905718886c4104b2c |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-53-espeak-cv-ft-mhr3-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7701 - Wer: 1.0 | 58cd8d7b9d7d4a6137ff828173f999f3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 5.329 | 5.79 | 400 | 1.3162 | 1.0 | | 1.5529 | 11.59 | 800 | 0.6968 | 1.0 | | 0.8373 | 17.39 | 1200 | 0.7345 | 1.0 | | 0.4959 ... | a691ef83169433ebacbbc9131a769b14 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_accent_surpeninsular-0_nortepeninsular-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 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this... | 1a5aeade45266180fa8ef87ddd636ea9 |
creativeml-openrail-m | ['text-to-image'] | false | DuskfallComicMixPartDeux 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/github/hug... | 55a90240c002b73f77122364f5c61ff9 |
apache-2.0 | ['translation'] | false | opus-mt-fr-sg * source languages: fr * target languages: sg * OPUS readme: [fr-sg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-sg/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | fa483b275c1be5ca2186d0d3f0185aa7 |
apache-2.0 | ['generated_from_keras_callback'] | false | t5-small-finetuned-samsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.7087 - Validation Loss: 1.6756 - Epoch: 7 | 2fec5b2cbf63b433d0de88f8dbf9aec6 |
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': 5.6e-05, 'decay_steps': 14728, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'deca... | fb5814137e4c16b88cbbd695183e6fac |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.1000 | 1.7915 | 0 | | 1.9259 | 1.7424 | 1 | | 1.8512 | 1.7167 | 2 | | 1.8005 | 1.6925 | 3 | | 1.7655 | 1.6840 | 4 | | 1.7392 |... | 10dbf9abbd87f059c926a9786b8bc1e9 |
apache-2.0 | ['exbert'] | false | CorefRoBERTa large model Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in [this paper](https://arxiv.org/abs/2004.06870) and first released in [this repository](https://github.com/thunlp/CorefBERT). Disclaimer: The tea... | 600a2be43da733f2d640bd895b03be01 |
apache-2.0 | ['exbert'] | false | Model description CorefRoBERTa 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... | 1b6a9e55692d9dc3610d25d7d2bf5625 |
apache-2.0 | ['exbert'] | false | BibTeX entry and citation info ```bibtex @misc{ye2020coreferential, title={Coreferential Reasoning Learning for Language Representation}, author={Deming Ye and Yankai Lin and Jiaju Du and Zhenghao Liu and Peng Li and Maosong Sun and Zhiyuan Liu}, year={2020}, eprint={2004.06870}, archiv... | db5a1723db8486e3b074e5a957997bf9 |
apache-2.0 | ['SEAD'] | false | SEAD-L-6_H-384_A-12-qqp This is a student model distilled from [**BERT base**](https://huggingface.co/bert-base-uncased) as teacher by using SEAD framework on **qqp** task. For weights initialization, we used [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) | 02323e892a951a9be91c4e95d7062e9a |
apache-2.0 | ['SEAD'] | false | Evaluation results | eval_accuracy | eval_f1 | eval_runtime | eval_samples_per_second | eval_steps_per_second | eval_loss | eval_samples | |:-------------:|:-------:|:------------:|:-----------------------:|:---------------------:|:---------:|:------------:| | 0.9126 | 0.8822 | 23.0122 | 1756.896 ... | a02ef7d1b8e623824a134d0b695781c8 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr4_3e-05_all_27_02_2022-18_46_19 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: 0.3962 - Accuracy:... | 47f07610a281171492e5579539244a1d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.3591 | 0.8366 | 0.8950 | | No log | 2.0 | 390 | 0.3558 | 0.8415 | 0.9012 | | 0.3647 |... | bc3d6d4209ce705bbaeaf420dd796ff4 |
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: 0.2186 - Accuracy: 0.9245 - F1: 0.9246 | ce361735ca87dd6a08fcea4e08e517e3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3083 | 0.9005 | 0.8972 | | No log | 2.0 | 500 | 0.2186 | 0.9245 | 0.9246 | | cf7e58359bd0f2c6d0c9063afd3717e6 |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | important notice(Jan 15/23) According to bbc-mc's note, there is a possibility of bug that some token(prompt) can be ignored, when merge with "add difference" option. Milk and ChaiLatte models are now replaced with bug-fix ver. https://note.com/bbcmc/n/n12c05bf109cc | ee30dec8a2b8ab637614251025bd301c |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | Recommended Setteings VAE: "kl-f8-anime2" and "vae-ft-mse-840000-ema-pruned" are suitable Steps: 20-30, Sampler: DPM++ SDE Karras or DPM++ 2M Karras, CFG scale: 8, Clip skip: 2, ENSD: 31377, Hires upscale: 2, Hires upscaler: Latent (bicubic antialiased),Denoising strength: 0.54~0.7 Negataive Prompt: (worst quality:2... | 735baa90601b1405474d425b3c1dcc35 |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | Sample prompt 4girls,(a 3d reader of:0.8) (teenage loli children:1.2), (wearing intricate casual camisole, cute hair ornament,crop jacket,hot pants, tighhigh:1.1), shiny brown skin, looking at viewer, (alluring smug:1.2), dynamic angle, (onomichi street:1.2),fisheye <img src="https://i.imgur.com/2JiZwFU.jpg" width=""... | f6578f801681bc7d89d70960853e74dc |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | YuzuLemonMilk Block merged model of Anything v3 and some real models. Rather photo realistic. Works fine with positive (realistic) and (photo realistic). <img src="https://i.imgur.com/qYK8DKn.jpg" width="" height="1000"> | 013c81ef17bb1cd698f3824098bce6f4 |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | YuzuLemonChaiLatte Combination of a weight merge of ACertainModel and Anything-V3.0, and a block merge of several realistic models. Rather anime-ish style with realistic background. - v3.5 <img src="https://i.imgur.com/WLKr3pj.jpg" width="" height="1000"> - v9.5 <img src="https://i.imgur.com/Ufh3JK2.jpg" width="" ... | 250a5fe9452c44397179cade040b7b5a |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | YuzuGinger Add more anime models to YuzuLemonChaiLatte. Can be very anime looks. - v1 <img src="https://i.imgur.com/4vc4HSL.jpg" width="" height="1000"> - v4 <img src="https://i.imgur.com/M6q6hYp.jpg" width="" height="1000"> | 1be5c5046cc36288893b146721d19e71 |
mit | ['generated_from_trainer'] | false | distilcamembert-base-finetuned-allocine This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on the allocine dataset. It achieves the following results on the evaluation set: - Loss: 2.1493 | 7282dfc8874ae31eba5c78bfca9e6c95 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4479 | 1.0 | 157 | 2.2066 | | 2.3065 | 2.0 | 314 | 2.1144 | | 2.2567 | 3.0 | 471 | 2.1565 | | 7b669c91b51f33ac3840e86e62ca36f9 |
mit | ['generated_from_trainer'] | false | xlm-roberta-large-xnli-finetuned-mnli-SJP This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the swiss_judgment_prediction dataset. It achieves the following results on the evaluation set: - Loss: 1.3456 - Accuracy: 0.7957 | 2268b036f774f3c6302089cf4e5c89f4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 5 | 1.8460 | 0.7956 | | No log | 2.0 | 10 | 1.3456 | 0.7957 | | No log | 3.0 | 15 | 1.2799 | 0.... | 975986f6377b05f58e8415878090c6a7 |
apache-2.0 | ['generated_from_keras_callback'] | false | europython-imdb-distilbert This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3081 - Train Accuracy: 0.8663 - Validation Loss: 0.2459 - Validation Accuracy: 0.9006 -... | 2dd1534fb213c94026aa0477c4096c0e |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.3081 | 0.8663 | 0.2459 | 0.9006 | 0 | | 35efc024e303d97874704bb94568dd24 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Polish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Polish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | c849531b9af8bc3e39d8088a37048fad |
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", "pl", split="test[:2%]") processor = Wav2Vec2Processor.from_p... | fb7183b0ba968b62135ab9b4d29c1e36 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Polish test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "pl", split="test") wer ... | 5e0983e5d601bb481caa488cafc67c1d |
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.argmax(logits,... | bf0be794d091a1c0cc5181a928bdad26 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found [here](https://colab.research.google.com/drive/1DvrFMoKp9h3zk_eXrJF2s4_TGDHh0tMc?usp=sharing) | 904519c9200ffe16c4e13c16fe402ea7 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'man'] | false | DreamBooth model for the niraj concept trained by colab71 on the dataset. This is a Stable Diffusion model fine-tuned on the niraj concept with DreamBooth. It can be used by modifying the This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-... | 0439ac85b93cb9f1a062350ab774851a |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-medium_sst2_negation0.05 This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.4461 | ae1a6cfe7d0e44bdb51bda1a96287cb8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8275 | 1.0 | 1062 | 3.3098 | | 2.5383 | 2.0 | 2124 | 3.3873 | | 2.3901 | 3.0 | 3186 | 3.4461 | | d84a90c94a4a418218d39ff5a48e5019 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-en 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: 0.2239 - F1: 0.8201 | 569770883f68b5615fb15383461afb68 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2573 | 1.0 | 625 | 0.2573 | 0.7591 | | 0.1631 | 2.0 | 1250 | 0.2147 | 0.8127 | | 0.1096 | 3.0 | 1875 | 0.2239 | 0.8201 | ... | 24c3d161a4a11bc4f9d5396a9956dadb |
mit | [] | false | Tony DiTerlizzi's Planescape Art on Stable Diffusion This is the `<tony-diterlizzi-planescape>` 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_i... | 8de7390a493e3a90859fec81ef1516fc |
apache-2.0 | ['generated_from_trainer'] | false | paraphrase-multilingual-mpnet-base-v2-tuned-smartcat This model is a fine-tuned version of [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the None dataset. It achieves the following results on the evaluation set: - L... | 7f0dae0446e06e9e23cdc35c49232abb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | ebcf01db52c8d37e3cc37ee9ff932c70 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 0.0072 | 0.16 | 10000 | 0.0025 | | 0.0014 | 0.32 | 20000 | 0.0005 | | 0.0004 | 0.48 | 30000 | 0.0002 | | 0.0002 | 0.64 | 40000 | 0... | 9062f8231409dfb1259ad843b698c70b |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased_cls_SentEval-CR This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3488 - Accuracy: 0.9283 | 623951a0d13444556312b43c2d88e0b2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 189 | 0.2951 | 0.8977 | | No log | 2.0 | 378 | 0.2895 | 0.8964 | | 0.2663 | 3.0 | 567 | 0.3707 | 0.... | 05a9639c3adb3a78d6377b8c6d164e0b |
apache-2.0 | ['translation'] | false | bat-eng * source group: Baltic languages * target group: English * OPUS readme: [bat-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bat-eng/README.md) * model: transformer * source language(s): lav lit ltg prg_Latn sgs * target language(s): eng * model: transformer * pre-processing: no... | 564724b48c0f78689f5590f9af3c66ef |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2017-enlv-laveng.lav.eng | 27.5 | 0.566 | | newsdev2019-enlt-liteng.lit.eng | 27.8 | 0.557 | | newstest2017-enlv-laveng.lav.eng | 21.1 | 0.512 | | newstest2019-lten-liteng.lit.eng | 30.2 | 0.592 | | Tatoeba... | d5190a1db15f6a41938e2d060055362e |
apache-2.0 | ['translation'] | false | System Info: - hf_name: bat-eng - source_languages: bat - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bat-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['lt', 'lv', 'bat', 'en'] - src_constituents: {'lit',... | c7901779fc6966e37efc1333e205b041 |
creativeml-openrail-m | [] | false | Usage To use embeddings, place the embedding file into the embedding folder (automatic1111 webui), and use the filename in the prompt. You can choose to rename the file freely. It is recommended to use these embeddings at low strength for cleaner results, for example (nixeu_basic:0.7). | 19dfc8e6247575d9c3e82dbf0cbade93 |
creativeml-openrail-m | [] | false | Examples Prompt: ``` masterpiece, best quality, ultra-detailed, illustration, 1girl, (wearing casual clothing), beautiful face, (feminine body), (nixeu_basic:0.75) Negative prompt: close-up, portrait, (big breasts), (fat), flat color, flat shading, bad anatomy, disfigured, deformed, malformed, mutant, gross, disgusti... | 56bab9857ea6e79ee1ad2a3e2d884845 |
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: 0.1465 - Accuracy: 0.9405 - F1: 0.9409 | 8e837a87a234aabcf3dc88d6a6cf5f0a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8341 | 1.0 | 250 | 0.2766 | 0.9105 | 0.9088 | | 0.2181 | 2.0 | 500 | 0.1831 | 0.9305 | 0.9308 | | 0.141 |... | 647186bf3696caf2b206fdb75aae399a |
apache-2.0 | ['translation'] | false | opus-mt-fi-hu * source languages: fi * target languages: hu * OPUS readme: [fi-hu](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-hu/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | eaf2d9246bdb863fcb3873b2c2477d0f |
creativeml-openrail-m | ['cyberpunk', 'anime', 'stable-diffusion', 'aiart', 'text-to-image', 'TPU'] | false | <center><img src="https://huggingface.co/AdamOswald1/Cyberpunk-Anime-Diffusion/resolve/main/img/5.jpg" width="512" height="512"/></center>  | f91b54a6b0c9316efba3d48d50a9152b |
creativeml-openrail-m | ['cyberpunk', 'anime', 'stable-diffusion', 'aiart', 'text-to-image', 'TPU'] | false | Cyberpunk Anime Diffusion An AI model that generates cyberpunk anime characters!~ Based of a finetuned Waifu Diffusion V1.3 Model with Stable Diffusion V1.5 New Vae, training in Dreambooth by [DGSpitzer](https://www.youtube.com/channel/UCzzsYBF4qwtMwJaPJZ5SuPg) | 2b93f40534d18a6ec1cd6fc797c66838 |
creativeml-openrail-m | ['cyberpunk', 'anime', 'stable-diffusion', 'aiart', 'text-to-image', 'TPU'] | false | 🧨 Diffusers This repo contains both .ckpt and Diffuser model files. It's compatible to be used as any Stable Diffusion model, using standard [Stable Diffusion Pipelines](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can convert this model to [ONNX](https://huggingface.co/docs/diffusers/... | 0192b82767d6e4ba1a1d631752d554c0 |
creativeml-openrail-m | ['cyberpunk', 'anime', 'stable-diffusion', 'aiart', 'text-to-image', 'TPU'] | false | !pip install diffusers transformers scipy torch from diffusers import StableDiffusionPipeline import torch model_id = "AdamOswald1/Cyberpunk-Anime-Diffusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a beautiful perfect face girl in dgs illustra... | 2a227d7af05aad2fe9aea5119d7b67cd |
creativeml-openrail-m | ['cyberpunk', 'anime', 'stable-diffusion', 'aiart', 'text-to-image', 'TPU'] | false | Online Demo You can try the Online Web UI demo build with [Gradio](https://github.com/gradio-app/gradio), or use Colab Notebook at here: *My Online Space Demo* [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c6473... | 0986fd5b07f5d8ebddb379212b3f4de3 |
creativeml-openrail-m | ['cyberpunk', 'anime', 'stable-diffusion', 'aiart', 'text-to-image', 'TPU'] | false | **👇Model👇** AI Model Weights available at huggingface: https://huggingface.co/AdamOswald1/Cyberpunk-Anime-Diffusion <center><img src="https://huggingface.co/AdamOswald1/Cyberpunk-Anime-Diffusion/resolve/main/img/2.jpg" width="512" height="512"/></center> | 8f1c1b7da48e7d8e66697fa09e53d27e |
creativeml-openrail-m | ['cyberpunk', 'anime', 'stable-diffusion', 'aiart', 'text-to-image', 'TPU'] | false | Usage After model loaded, use keyword **dgs** in your prompt, with **illustration style** to get even better results. For sampler, use **Euler A** for the best result (**DDIM** kinda works too), CFG Scale 7, steps 20 should be fine **Example 1:** ``` portrait of a girl in dgs illustration style, Anime girl, female... | 9e33238bc5852479a6143ee327f0a225 |
apache-2.0 | ['generated_from_trainer'] | false | openai/whisper-small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5091 - Wer: 56.3216 | 796859e2723561ff4f4cb83b26477760 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0157 | 13.0 | 1000 | 1.1631 | 65.9101 | | 0.0025 | 26.0 | 2000 | 1.3416 | 58.5066 | | 0.0009 | 39.01 | 3000 | 1.4238 | 56.639... | 76fda5e8b11e1a1420abdd12a419352b |
creativeml-openrail-m | ['art'] | false | The final 8 models that are trained over [hakurei's Waifu Diffusion](https://huggingface.co/hakurei/waifu-diffusion). Each model was trained on a notable Japanese (and Taiwanese, I think) AI artist's works using dreambooth, with 30 of their works gained mainly from twitter (except for sabakichi, which I collected the ... | 502656e9dd010f04f1d34e10fc4b9e3e |
creativeml-openrail-m | ['art'] | false | Why will they be the last? My initial intention on this series was a social experiment to see what will happen if the AI artists are targeted for personalized training. As it became more popular than expected and the artists started calling themselves "phantom 20," I came up with the second intention to see how they ... | 97426e5f01e1c477f4cd6f97e4fc1fcf |
creativeml-openrail-m | ['art'] | false | trained artist list - atsuwo_AI - recommended pos: multicolored hair, cg - fladdict - recommended pos: oil painting/ancient relief/impressionist impasto oil painting (maybe more) - possible neg: monkey - Hifumi_AID - recommended pos: dark purple hair, emerald eyes - mayonaka_rr - recommended pos: cg - poss... | ad11802b0804f53b2b3112e246e4a44e |
creativeml-openrail-m | ['art'] | false | samples The basic prompt is as follows. However, to present you the potential of these models as much as possible, many of them have additional postive tags (such as "in the style of") to get the result below (yes, use ``aitop (ARTIST)_style`` to gain the finetuned result). Many works better with the additional prom... | b059abe6fc88ed05fdbffffc74dda22b |
creativeml-openrail-m | ['art'] | false | atsuwo_AI       | 464e1bf940719176959e1e4815c19e8f |
creativeml-openrail-m | ['art'] | false | mayonaka_rr     | ac1b253c263503d1e0f50374eed2011a |
creativeml-openrail-m | ['art'] | false | sabakichi     | e3d8205f856b04a9a2dae590c0c6514a |
creativeml-openrail-m | ['art'] | false | violet_fizz   | ff5a263bbe41c8c753f3cae19399ffdf |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | This model is a fine-tuned version of [patrickvonplaten/wav2vec2_tiny_random_robust](https://huggingface.co/patrickvonplaten/wav2vec2_tiny_random_robust) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 1.0 | f268fc303d45e77d3b8bce420bc53c6b |
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: 0.0003 - 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: 1.0 - mixed_precision_training: Native AMP | fe92d084f30665e8e4368ad07cfdf922 |
apache-2.0 | ['generated_from_trainer'] | false | chinese-roberta-wwm-ext-finetuned2 This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1448 - Accuracy: 1.0 - F1: 1.0 | ec19364ae7b4d5e1316e9468cdf32a69 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.4081 | 1.0 | 3 | 0.9711 | 0.7273 | 0.6573 | | 0.9516 | 2.0 | 6 | 0.8174 | 0.8182 | 0.8160 | | 0.8945 |... | 946e617db0f677fcdc78dc51c5ad7924 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | 100Memories This is my new Stable Diffusion 1.5 custom model that bring to you an images with a retro look style. The magic word is: 100Memories If you enjoy my work, please consider supporting me: [](https://www.buymeacoffee.com/e... | 71322aed97ae8aededd395a50821f5f2 |
cc-by-4.0 | [] | false | GenRead (MergeDPR): FiD model trained on WebQ -- This is the model checkpoint of GenRead [2], based on the T5-3B and trained on the WebQ dataset [1]. -- Hyperparameters: 8 x 80GB A100 GPUs; batch size 16; AdamW; LR 5e-5; best dev at 18000 steps. References: [1] Semantic parsing on freebase from question-answer p... | cdcde00ab4d0204bed5e0863b7971283 |
cc-by-4.0 | [] | false | Model performance We evaluate it on the WebQ dataset, the EM score is 56.25. <a href="https://huggingface.co/exbert/?model=bert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a> --- license: cc-by-4.0 --- --- license: cc-by-4.0 --- | 0e7dd7211fee41b87f039e8abcbc691e |
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