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 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.2688 | 1.0 | 24814 | 1.6485 | | 2.0974 | 2.0 | 49628 | 1.5777 | | 571c4ecef51080ed03b6017a5d54322c |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4789 - Rouge1: 28.282 - Rouge2: 7.6989 - Rougel: 22.2019 - Rougelsum: 22.197 - Gen Len: 18.8238 | ff4eaa746f1e9e16aca99af5f305b1f0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:| | 2.7189 | 1.0 | 12753 | 2.4789 | 28.282 | 7.6989 | 22.2019 | 22.197 | 18.82... | 34fcfcd6296afbdd0d783ec533641ab1 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-test-headline 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: 4.0992 | faf6422f15fa42827cf192be30e511f1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.6745 | 1.0 | 8 | 4.8602 | | 4.8694 | 2.0 | 16 | 4.3241 | | 4.5442 | 3.0 | 24 | 4.3963 | | f455251ab7c7443f85e22609cc440154 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-distil 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: 3.3731 - Rmse: 1.8366 - Mse: 3.3731 - Mae: 1.6145 | 4962f9cf6e4233964a90e8982d81a985 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:| | 3.3242 | 1.0 | 12809 | 3.3718 | 1.8362 | 3.3718 | 1.6145 | | 3.3237 | 2.0 | 25618 | 3.3720 | 1.8363 |... | 6ed393a1564d208677924cd9085c956f |
apache-2.0 | ['whisper-event', 'norwegian'] | false | Whisper Large Norwegian Bokmål This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) trained on several datasets. It is currently in the middle of a large training. Currently it achieves the following results on the evaluation set: - Loss: 0.2477 - Wer: 10.71... | 468b5a8a67a384fc5381c67d9f583ca9 |
apache-2.0 | ['whisper-event', 'norwegian'] | false | Model description The model is trained on a large corpus of roughly 5.000 hours of voice. The sources are subtitles from the Norwegian broadcaster NRK, transcribed speeches from the Norwegian parliament and voice recordings from Norsk Språkteknologi. | 7a3f2ed3e1273e45b9ef5d9179705faa |
apache-2.0 | ['whisper-event', 'norwegian'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 64 - gradient_accumulation_steps: 2 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant with warmpu - lr_scheduler_warmup_s... | 689dc2c01f7aea26575413119172595f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: tpu - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_schedule... | c533a508eab3381f312f939826093378 |
cc-by-sa-4.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Pretrained model This model utilizes a Japanese BERT model [colorfulscoop/bert-base-ja](https://huggingface.co/colorfulscoop/bert-base-ja) v1.0 released under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) as a pretrained model. | 637bc19e17af60d8c2b71399dabef5f9 |
cc-by-sa-4.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Training data [Japanese SNLI dataset](https://nlp.ist.i.kyoto-u.ac.jp/index.php?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) released under [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/) is used for training. Origina... | b8ce039c50132e4bbd2a5ced036de2a6 |
cc-by-sa-4.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Model description This model utilizes `SentenceTransformer` model from the [sentence-transformers](https://github.com/UKPLab/sentence-transformers) . The model detail is as below. ```py >>> from sentence_transformers import SentenceTransformer >>> SentenceTransformer("colorfulscoop/sbert-base-ja") SentenceTransforme... | b687e11d77e8c861ba7e83efbd9fb2fd |
cc-by-sa-4.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Training This model finetuned [colorfulscoop/bert-base-ja](https://huggingface.co/colorfulscoop/bert-base-ja) with Softmax classifier of 3 labels of SNLI. AdamW optimizer with learning rate of 2e-05 linearly warmed-up in 10% of train data was used. The model was trained in 1 epoch with batch size 8. Note: in a origi... | 4138d9abaabc22ce0d4c5f36b1e791fe |
cc-by-sa-4.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Usage First, install dependecies. ```sh $ pip install sentence-transformers==2.0.0 ``` Then initialize `SentenceTransformer` model and use `encode` method to convert to vectors. ```py >>> from sentence_transformers import SentenceTransformer >>> model = SentenceTransformer("colorfulscoop/sbert-base-ja") >>> senten... | 3737593d3683422b934d7cc96d3b6e7c |
cc-by-sa-4.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | License Copyright (c) 2021 Colorful Scoop All the models included in this repository are licensed under [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/). **Disclaimer:** Use of this model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs... | e351fba0171cd6d6be6e966bdda2e5d2 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | Chinese Stable Diffusion Pokemon Model Card <!--  --> Stable-Diffusion-Pokemon-zh is a Chinese-specific latent text-to-image diffusion model capable of generating Pokemon images given any text input. This model was trained by usi... | 223892b5fd479fa3fa7e76084d8bd0a5 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | Model Details - **Developed by:** Zhipeng Yang - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** Chinese - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/1... | 93bdbd77ac5b776ab429c1e60dd2126d |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | Examples Firstly, install our package as follows. This package is modified [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Chinese Stable Diffusion. ```bash pip install git+https://github.com/rinnakk/japanese-stable-diffusion pip install diffusers==0.4.1 sudo apt-get install git-lfs git cl... | e99466e21f17aa4ba60cfe2f2323e6d3 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | get prompt text embeddings text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, return_tensors="pt", ) text_input_ids = text_inputs.input_ids if text_input_ids.shape[-1] > self.tokenizer.... | a047e63bd9aadf7b901427bc54874e2e |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | duplicate text embeddings for each generation per prompt, using mps friendly method bs_embed, seq_len, _ = text_embeddings.shape text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1) text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) | 826766551bb70cf65b29b8dc00ea1c15 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | get unconditional embeddings for classifier free guidance if do_classifier_free_guidance: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] elif type(prompt) is not type(negative_prompt): raise TypeError( ... | 0b63f0e2122340a9b948176ad0a402c3 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = uncond_embeddings.shape[1] uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1) uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per... | 6f3ce5774f40497578860e73bf70c961 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | However this currently doesn't work in `mps`. latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8) latents_dtype = text_embeddings.dtype if latents is None: if self.device.type == "mps": | 75ec636be07d0df2fd140434c6a957f1 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | randn does not work reproducibly on mps latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to( self.device ) else: latents = torch.randn(latents_shape, generator=generator, device=self.device, dtyp... | 8bdb855319cdeda79af438c131906f42 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta for i, t in enumerate(self.progress_bar(timesteps_tensor)): | 0f97b3996c3f50ab079d293023ab607f |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | 280d6b42783c4d357b65ae36fba7b408 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | print("before :" ,text_embeddings.shape) eh_shape = text_embeddings.shape if i == 0: eh_pad = torch.zeros((eh_shape[0], eh_shape[1], 768 - 512)) eh_pad = eh_pad.to(self.device) text_embeddings = torch.concat([text_embeddings, eh_pad], -1) ... | 9f7ca4bb05e92ec7c66e1786b678ae75 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) | 433dbcd9bdd38b8e801c017cb0d0e67a |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | call the callback, if provided if callback is not None and i % callback_steps == 0: callback(i, t, latents) latents = 1 / 0.18215 * latents image = self.vae.decode(latents).sample image = (image / 2 + 0.5).clamp(0, 1) | 93b4fab1660f2a5fce085d7bd6d68c8a |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() if self.safety_checker is not None: safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to( ... | 19dab015c1fcc216df8e3818c4f08fd8 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | git clone https://huggingface.co/svjack/Stable-Diffusion-Pokemon-zh pretrained_model_name_or_path = "Stable-Diffusion-Pokemon-zh" tokenizer = BertTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder = "tokenizer") text_encoder = BertForTokenClassification.from_pretrained(pretrained_model_name_or_path, s... | a75d8ef19c0358228a1e339f796a49a3 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | Generator Results comparison [https://github.com/svjack/Stable-Diffusion-Pokemon](https://github.com/svjack/Stable-Diffusion-Pokemon)  . | ![BLIP.gif](https://s3.amazonaws.com/moonup/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c.gif... | 9a9ed890d7f2c44e6c339f1c9d28bd25 |
bsd-3-clause | ['image-captioning'] | false | Running the model on CPU <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("ybelkada/blip-image-captioning-base") model = BlipForConditionalGeneration.from_pret... | 5e45c72f22e7952028450255500d8b2b |
bsd-3-clause | ['image-captioning'] | false | unconditional image captioning inputs = processor(raw_image, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> a woman sitting on the beach with her dog ``` </details> | a1de98a6ca7699dca8d5de3ca47ebfe3 |
bsd-3-clause | ['image-captioning'] | false | In full precision <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model = BlipForConditionalGeneration.from_pretrain... | bae56a410f4e4aff849b3c3760d87a7d |
bsd-3-clause | ['image-captioning'] | false | unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> a woman sitting on the beach with her dog ``` </details> | 488d3413ec4b774f0498f8d7ac7c4358 |
bsd-3-clause | ['image-captioning'] | false | In half precision (`float16`) <details> <summary> Click to expand </summary> ```python import torch import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model = BlipForConditional... | fc53a92a13f0291afcc54cc214c48a98 |
apache-2.0 | ['finnish', 'convbert'] | false | ConvBERT for Finnish Pretrained ConvBERT model on Finnish language using a replaced token detection (RTD) objective. ConvBERT was introduced in [this paper](https://arxiv.org/abs/2008.02496) and first released at [this page](https://github.com/yitu-opensource/ConvBert). **Note**: this model is the ConvBERT generator... | 4dfe2c404da10b7529293a3b991faaf7 |
apache-2.0 | ['finnish', 'convbert'] | false | Intended uses & limitations You can use this generator model mainly just for the fill-mask task. For other tasks, check the [Finnish-NLP/convbert-base-finnish](https://huggingface.co/Finnish-NLP/convbert-base-finnish) model instead. | 737f119f3efc4fb5e5f18ddc070c5098 |
apache-2.0 | ['finnish', 'convbert'] | false | How to use Here is how to use this model directly with a pipeline for fill-mask task: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Finnish-NLP/convbert-base-generator-finnish') >>> unmasker("Moikka olen [MASK] kielimalli.") [{'score': 0.08341152966022491, 'token': 461... | b6fd05a2b6fca20600fa0e91a80cbfea |
openrail | [] | false | <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> <img src = 'https://images.unsplash.com/photo-1599623560574-39d485900c95?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80'> | cf3c444126c1be7a55963c19101b0a13 |
openrail | [] | false | Usage DialoGPT small version, finetuned on Woody Scripts from Toy Story. Simple snippet of how to infer of this model: ```python from transformers import AutoModelWithLMHead, AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('s3nh/DialoGPT-small-woody-toy-story') model = AutoModelWi... | 38caa8f6291c686a608d931bf29f4254 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | false | MultiBERTs Seed 3 Checkpoint 1000k (uncased) Seed 3 intermediate checkpoint 1000k 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/g... | 1391ff3602a31021fe45c63a8b13b740 |
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-3-1000k') model = BertModel.from_pretrained("multiberts-seed-3-1000k") text = "Replace me by any text you'd lik... | 51616206230d72d92099eb77b26639c7 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/nli-bert-large-cls-pooling This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. | b007811b70f2b4055d4f7dd189accae0 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | 8fe6c49eb486bbc9e96a53f7d9d3f3e7 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/nli-bert-large-cls-pooling') model = AutoModel.from_pretrained('sentence-transformers/nli-bert-large-cls-pooling') | d7e15f205945595ad72c3015a4f91dd2 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/nli-bert-large-cls-pooling) | 2d008ee0f77ee63bc9d066b79b95b448 |
mit | [] | false | Thorneworks on Stable Diffusion This is the `<Thorneworks>` 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 al... | b7128fd54f4c116ce5dca80ef0eeafc1 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.25, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.000475}, ... | c25213b543dcd4e1e42c6927ad0e18a8 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | exper7_mesum5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem5 dataset. It achieves the following results on the evaluation set: - Loss: 0.5889 - Accuracy: 0.8538 | 3e140338ae5c574537fd0f0cb064edfb |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 10 - mixed_precision_training: Native AMP | 5eca5bd00e9041b684684331b89fe125 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2072 | 0.23 | 100 | 4.1532 | 0.1923 | | 3.5433 | 0.47 | 200 | 3.5680 | 0.2888 | | 3.1388 | 0.7 | 300 | 3.1202 | 0.... | a4a410ccb4950830de3638648d239e51 |
apache-2.0 | ['generated_from_trainer'] | false | bert-essay-concat This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0735 - Accuracy: 0.6331 | d80a8ff9465530e2063bd0ddd8181ed3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.7024 | 1.0 | 3677 | 0.9159 | 0.6329 | | 0.6413 | 2.0 | 7354 | 1.0267 | 0.6346 | | 0.5793 | 3.0 | 11031 | 1.0735 ... | f2296c59128a8bb2888fc7a79de48a0a |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-generation'] | false | Model description **GPT-fr** 🇫🇷 is a GPT model for French developped by [Quantmetry](https://www.quantmetry.com/) and the [Laboratoire de Linguistique Formelle (LLF)](http://www.llf.cnrs.fr/en). We train the model on a very large and heterogeneous French corpus. We release the weights for the following configuratio... | 266fb5723ee0929b1e46a8c50b564765 |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-generation'] | false | Intended uses & limitations The model can be leveraged for language generation tasks. Besides, many tasks may be formatted such that the output is directly generated in natural language. Such configuration may be used for tasks such as automatic summary or question answering. We do hope our model might be used for bo... | 30d384442ff1dcf0b3a8ab4a4d88ab69 |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-generation'] | false | shoeybi-2019) and calibrate our model such that during pre-training or fine-tuning, the model can fit on a single NVIDIA V100 32GB GPU. ```python from transformers import GPT2Tokenizer, GPT2LMHeadModel | 2984f408f5913a408cdb4223b8c1b5fa |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-generation'] | false | Generate a sample of text model.eval() input_sentence = "Longtemps je me suis couché de bonne heure." input_ids = tokenizer.encode(input_sentence, return_tensors='pt') beam_outputs = model.generate( input_ids, max_length=100, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1 ... | 04a013a5c006d7c42b10f86e121f328c |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-generation'] | false | Limitations and bias Large language models tend to replicate the biases found in pre-training datasets, such as gender discrimination or offensive content generation. To limit exposition to too much explicit material, we carefully choose the sources beforehand. This process — detailed in our paper — aims to limit of... | ee40fa4d38a3d3084d84008f1e6376ce |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-generation'] | false | Training data We created a dedicated corpus to train our generative model. Indeed the model uses a fixed-length context size of 1,024 and require long documents to be trained. We aggregated existing corpora: [Wikipedia](https://dumps.wikimedia.org/frwiki/), [OpenSubtitle](http://opus.nlpl.eu/download.php?f=OpenSubti... | a68a23a488e67455428944b624e4dbe3 |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-generation'] | false | tiedemann-2012)), [Gutenberg](http://www.gutenberg.org) and [Common Crawl](http://data.statmt.org/ngrams/deduped2017/) ([Li et al., 2019](li-2019)). Corpora are filtered and separated into sentences. Successive sentences are then concatenated within the limit of 1,024 tokens per document. | 69cc40c098e7309aacd8e798fa7f591f |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-generation'] | false | Training procedure We pre-trained the model on the new CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/eng/jean-zay/) supercomputer. We perform the training within a total of 140 hours of computation on Tesla V-100 hardware (TDP of 300W). The training was distributed on 4 compute ... | 40bc56253e7269954704d8d274428c93 |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-generation'] | false | Eval results We packaged **GPT-fr** with a dedicated language model evaluation benchmark for French. In line with the [WikiText](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark in English, we collected over 70 million tokens from the set of verified [good](https://fr.... | 9ea49adedba09fd679f61f4a23d42151 |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-generation'] | false | BibTeX entry and citation info Along with the model hosted by HuggingFace transformers library, we maintain a [git repository](https://github.com/AntoineSimoulin/gpt-fr). If you use **GPT-fr** for your scientific publications or your industrial applications, please cite the following paper: ```bibtex @inproceedings{... | 51a21ef96d78520025e1e765b830f965 |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-generation'] | false | References ><div name="tiedemann-2012">Jörg Tiedemann: Parallel Data, Tools and Interfaces in OPUS. LREC 2012: 2214-2218</div> ><div name="li-2019">Xian Li, Paul Michel, Antonios Anastasopoulos, Yonatan Belinkov, Nadir Durrani, Orhan Firat, Philipp Koehn, Graham Neubig, Juan Pino, Hassan Sajjad: Findings of the Firs... | b8a528bdc3a2f8ece3b1858a1f46e134 |
apache-2.0 | ['t5', 'Lithuanian', 'summarization'] | false | This is *t5-base* transformer model trained on Lithuanian news summaries for 175 000 steps. It was created during the work [**Generating abstractive summaries of Lithuanian news articles using a transformer model**](https://link.springer.com/chapter/10.1007/978-3-030-88304-1_27). | f57e3c1e061ffb6c42a6901580900201 |
apache-2.0 | ['t5', 'Lithuanian', 'summarization'] | false | Usage ```python from transformers import pipeline name= "LukasStankevicius/t5-base-lithuanian-news-summaries-175" my_pipeline = pipeline(task="text2text-generation", model=name, framework="pt") ``` Given the following article body from [15min](https://www.15min.lt/24sek/naujiena/lietuva/tarp-penkiu-rezultatyviausiu-ts... | 2b2249d557a9fcaecbe53b0966e8b40a |
apache-2.0 | ['fnet'] | false | FNet large model Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was introduced in [this paper](https://arxiv.org/abs/2105.03824) and first released in [this repository](https://github.com/google-research/google-research/tree/master/f_net).... | 7e9f92011075fbda7ab71a1cc25d8c1f |
apache-2.0 | ['fnet'] | false | Model description FNet is a transformers model with attention replaced with fourier transforms. Hence, the inputs do not contain an `attention_mask`. It is 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 a... | 7ca5ef2148f6500d7447891524a5994f |
apache-2.0 | ['fnet'] | false | Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=fnet) to look for fine-tuned versions on a task that interests you. Note that... | 1c09fc49cbd56ee553be837a5b83ee5b |
apache-2.0 | ['fnet'] | false | How to use You can use this model directly with a pipeline for masked language modeling: **Note: The mask filling pipeline doesn't work exactly as the original model performs masking after converting to tokens. In masking pipeline an additional space is added after the [MASK].** ```python >>> from transformers impo... | ee631fc7dbbbb5a45340d47d89225401 |
apache-2.0 | ['fnet'] | 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. However, the model's MLM accuracy may also affect answers. Given below are some example where gender-bias could be expected: ```python >>> from transformers import FNet... | 9eccd249b046f80db7717e6572f77dc6 |
apache-2.0 | ['fnet'] | false | Preprocessing The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 32,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and i... | 2916bef56bd1d68272e586886c3ffa96 |
apache-2.0 | ['fnet'] | false | Pretraining The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,... | 44650e603e536d74947452a38032cc26 |
apache-2.0 | ['fnet'] | false | Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Glue test results: | Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average | |:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:| | | 78/76 | 85 | 85 | 9... | ebebc2c357ddad6d84ddae3ab4fda410 |
apache-2.0 | ['fnet'] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2105-03824, author = {James Lee{-}Thorp and Joshua Ainslie and Ilya Eckstein and Santiago Onta{\~{n}}{\'{o}}n}, title = {FNet: Mixing Tokens with Fourier Transforms}, journal = {CoRR}, ... | a695375dddceb6fd7d2e0433c7cfe9d8 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 44.6 - GMACs: 4.5 - Activations (M): 13.5 - Image size: 224 x 224 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/facebookresearch/ConvNeXt - *... | a2c25270dc9d7ce814542a89511f8fe2 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('convnext_tiny.fb_in22k', pretrained=True) model = mode... | b9544153e8a0435d5ea03b0fa5040a2e |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_tiny.fb_in22k', pretrained=True, ... | 1e5a030d7f5b63d470d48ce9136e645e |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_tiny.fb_in22k', pretrained=True, num... | 55688459b79494395c6a4378d3cbe7c2 |
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: 0.2166 - Accuracy: 0.923 - F1: 0.9229 | 97928206daa3c0acb87304ca6fbb7769 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8472 | 1.0 | 250 | 0.3169 | 0.912 | 0.9105 | | 0.2475 | 2.0 | 500 | 0.2166 | 0.923 | 0.9229 | | 70075e68e4665a3026d8bcf347828010 |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-asqa-cb This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the [ASQA](https://huggingface.co/datasets/din0s/asqa) dataset. It achieves the following results on the evaluation set: - Loss: 2.7489 - Rougelsum: 26.6134 | 31321cd5692c0ea1c4dc5195d770bec1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: 50 | 4877c289511846254a9d9d283465e285 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:---------:| | No log | 1.0 | 273 | 2.9648 | 23.8374 | | 3.5538 | 2.0 | 546 | 2.9054 | 24.2701 | | 3.5538 | 3.0 | 819 | 2.8744 ... | b47b0b9e521619285f1a8c52684762d6 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_vp-fr_s281 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) 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 model, make sure that you... | 07bf06d3a290d80e66996aa55cf7bea3 |
mit | [] | false | WaterfallShadow on Stable Diffusion This is the `<WaterfallShadow>` 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. Yo... | b7fb276b596c1b805f6d51dcee22d3f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: tpu - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | b6964c9f7e594671dc7b3b0a410e4bff |
apache-2.0 | ['generated_from_trainer'] | false | eval_masked_v4_wnli This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.7105 - Accuracy: 0.3099 | 53db166f096588ce198e7042da3481d6 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.8885 - Mae: 0.4390 | b3d4da98002962a6602a220698c89d97 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1089 | 1.0 | 235 | 0.9027 | 0.4756 | | 0.9674 | 2.0 | 470 | 0.8885 | 0.4390 | | be192c74c62fbc61659dac0649245a5d |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | vit-base-cats-vs-dogs This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cats_vs_dogs dataset. It achieves the following results on the evaluation set: - Loss: 0.0182 - Accuracy: 0.9937 | fcb547c9445ba45b37669231643f1c22 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 32 - eval_batch_size: 32 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP | 05f7897f9b5c513f0ba796fc36ba0ff6 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1177 | 1.0 | 622 | 0.0473 | 0.9832 | | 0.057 | 2.0 | 1244 | 0.0362 | 0.9883 | | 0.0449 | 3.0 | 1866 | 0.0261 | 0.... | ab6c8253e4de1ea5962bb6beca79a27b |
apache-2.0 | ['exbert', 'multiberts'] | false | MultiBERTs Seed 23 (uncased) Seed 23 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/google-research/language/tree/master/language... | a21a939780268b21e235cea098d80214 |
apache-2.0 | ['exbert', 'multiberts'] | 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-23') model = BertModel.from_pretrained("multiberts-seed-23") text = "Replace me by any text you'd like." enco... | 18845a550497dcc27ce2bdb505be6b8a |
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