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 | Sentiment140_ELECTRA_5E This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the sentiment140 dataset. It achieves the following results on the evaluation set: - Loss: 0.5410 - Accuracy: 0.84 | da37d9c2e37642386197d6ed6431efff |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6896 | 0.08 | 50 | 0.6605 | 0.7133 | | 0.6664 | 0.16 | 100 | 0.6054 | 0.7133 | | 0.5915 | 0.24 | 150 | 0.4777 | 0.... | e8497edbccf895ba19f383b69a5d870b |
mit | [] | false | retropixelart pinguin on Stable Diffusion This is the `<retropixelart-style>` 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) no... | 60cd2bc9bcca2e6d5bb58075934dd212 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_unispeech_s870 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | d3e370d8bf483145b6e09963fa538398 |
cc-by-sa-4.0 | ['coptic', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pre-trained with [UD_Coptic](https://universaldependencies.org/cop/) for POS-tagging and dependency-parsing, derived from [deberta-base-coptic](https://huggingface.co/KoichiYasuoka/deberta-base-coptic). Every word is tagged by [UPOS](https://universaldependencies.org/u/po... | ca83a729975f148b9c5370cda92a1624 |
cc-by-sa-4.0 | ['coptic', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-coptic-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-coptic-upos") ``` or ``` import esupar nlp=esupar.load("KoichiYa... | f11023d4db0b4e9d0242e3a9a599a3ba |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_xlsr-53_s533 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your ... | 643eba29a16d7accd9387069a4621101 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the shiba concept trained by ashiqabdulkhader on the ashiqabdulkhader/animals dataset. This is a Stable Diffusion model fine-tuned on the shiba concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of shiba dog** This model was created as part of the DreamBooth Ha... | 2c6d2f68dae9e9ebbcc808642226b491 |
apache-2.0 | ['translation'] | false | opus-mt-he-sv * source languages: he * target languages: sv * OPUS readme: [he-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/he-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | a30a63ff9b3d420c9d1135e462ff2f20 |
afl-3.0 | [] | false | Model Description We release all models introduced in our [paper](https://arxiv.org/pdf/2206.11147.pdf), covering 13 different application scenarios. Each model contains 11 billion parameters. | Model | Description | Recommended Application | ----------- | ----------- |----------- | | **rst-all-11b** ... | a32c73a6f04d337ed1ead80250f5ba3e |
apache-2.0 | ['Early Modern French', 'Historical'] | false | D'AlemBERT base model This model is a [RoBERTa base model](https://huggingface.co/bert-base-uncased) pre-trained on the [FreEMmax corpus](https://doi.org/10.5281/zenodo.6481135) for Early Modern French. It was introduced in [this paper](https://aclanthology.org/2022.lrec-1.359/). This model is Cased and was trained w... | dd17fd1e3c69d65339eb1b84c5184989 |
apache-2.0 | ['Early Modern French', 'Historical'] | false | Model description D'AlemBERT is a transformers mode pretrained on the raw texts only with no humans labelling them in any way with an automatic process to generate inputs and labels from those texts using the RoBERTa base model. More precisely, it was pretrained with one objective: - Masked language modeling (MLM): ... | 44a26f39051dcc46ec418dd4a8c6b212 |
apache-2.0 | ['Early Modern French', 'Historical'] | false | Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification,... | bcad88e2f148e7369aa93ed71f4ebb0b |
apache-2.0 | ['Early Modern French', 'Historical'] | false | Limitations and bias This model is trained with historical French data from starting from the 16th c., so it might produce results that seem extremely biased by today standards. It might not work well on contemporary data and it is not intended to be used on it. This bias will also affect all fine-tuned versions of ... | c63b19231016d670325555149333893e |
apache-2.0 | ['Early Modern French', 'Historical'] | false | Training data D'AlemBERT was pretrained on the non-freely available version of the [FreEMmax corpus](https://doi.org/10.5281/zenodo.6481135), a dataset consisting of more than 180k tokens coming from 22 different sources, and comprising French textual data going from the 16th c to the early 20th c. | 4dbaa8e630a186a58374bd4fc9ca3fab |
apache-2.0 | ['Early Modern French', 'Historical'] | false | BibTeX entry and citation info ```bibtex @inproceedings{gabay-etal-2022-freem, title = "From {F}re{EM} to D{'}{A}lem{BERT}: a Large Corpus and a Language Model for Early {M}odern {F}rench", author = "Gabay, Simon and Ortiz Suarez, Pedro and Bartz, Alexandre and Chagu{\'e}, Alix and ... | 7887192756d13a5dddc88ce50bafca99 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-TIMIT-IPA2 This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1531 - Per: 0.0638 | ab8ffedfb6319422f3832e27b5b3bba2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Per | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.0846 | 6.85 | 500 | 0.1810 | 0.0991 | | 0.1857 | 13.7 | 1000 | 0.1411 | 0.0691 | | 0.0948 | 20.55 | 1500 | 0.1345 | 0.0666 | |... | 9c07290c9593927b298d42263ab64f91 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | The embeddings in this repository were trained for the 768px [Stable Diffusion v2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) model. The embeddings should work on any model that uses SD v2.1 as a base. **Examples** <div align="center"> <img src="https://huggingface.co/ProGamerGov/winter-cat-embeddi... | b17462fc1dd588761b297d6adf0bf596 |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `lmqg/t5-small-squad-qag` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question & answer pair generation task on the [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generat... | 776fa0ac83ab96569b6b2524ef514099 |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [t5-small](https://huggingface.co/t5-small) - **Language:** en - **Training data:** [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-gen... | 0354c0ab203b77ba26452de5d76b961b |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-small-squad-qag") output = pipe("generate question and ... | b559bfe7cc284d6e51f86826019511a4 |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-squad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_squad.default.json) | | Score | Type | Dataset ... | 85b455aeecabe8180689a071d2dd6dd9 |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_squad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: ['qag'] - model: t5-small - max_length: 512 - max_length_output: 256 - epoch: 18 - ba... | fd0406c57d11f3842c2308895e765627 |
apache-2.0 | ['generated_from_keras_callback'] | false | my-finetuned-distilbert This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.6482 - Validation Loss: 1.3103 - Epoch: 0 | b6c50e9b64af6fafb182b80f58c2a932 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | 39ca7ca3cf88121a24fa42c3746294e0 |
mit | ['generated_from_trainer'] | false | camembert-ner This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1179 - Overall Precision: 0.7367 - Overall Recall: 0.7522 - Overall F1: 0.7444 - Overall Accurac... | 1da3ca293c14219b3819f3e884856abc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Humanprod F1 | Loc F1 | Org F1 | Per F1 | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------------:|:----... | 2f3a577215c859a52a8893c9c05b122e |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_gender_male-5_female-5_s73 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 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 63055a1677c85e10c5ba48dc8862346e |
apache-2.0 | ['generated_from_trainer'] | false | T5-model-1-feedback-0510 This model is a fine-tuned version of [theojolliffe/T5-model-1-feedback-1109](https://huggingface.co/theojolliffe/T5-model-1-feedback-1109) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2334 - Rouge1: 91.6115 - Rouge2: 86.7084 - Rougel: 91.0616 - Roug... | e31f5845ead9c98a7ce7aa852554dd0c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.4381 | 1.0 | 542 | 0.2731 | 90.324 | 84.2616 | 89.0178 | 89.1459 | 14... | 047fafd6b8f398cf624c5f5ff748a050 |
apache-2.0 | ['timm', 'vision'] | false | Model Details The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model depl... | dea7ffb2cfea543f469565c5e175f404 |
apache-2.0 | ['timm', 'vision'] | false | Model Type The model uses a ViT-B/32 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The original implementation had two variants: one using a ResNet image ... | 29b1ddda620f47a6b6713d5b311b3759 |
apache-2.0 | ['timm', 'vision'] | false | Intended Use The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper... | c2181220578ed4f63d0c8d7f9a6c4c23 |
apache-2.0 | ['timm', 'vision'] | false | Primary intended uses The primary intended users of these models are AI researchers. We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models. | 52812f7de1c8ca1b7f3ce849b35b68fd |
apache-2.0 | ['timm', 'vision'] | false | Out-of-Scope Use Cases **Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This i... | 91de8001632f1ee6ce944737616e0cd2 |
apache-2.0 | ['timm', 'vision'] | false | Data The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the interne... | 507234f10751679d596ec75392ffa6b6 |
apache-2.0 | ['timm', 'vision'] | false | Data Mission Statement Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. Ho... | 7724f314b1dab207e83d0207db56f1cb |
apache-2.0 | ['timm', 'vision'] | false | Limitations CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, ou... | e17865921fcd0f8f0734716c60dc31ef |
apache-2.0 | ['timm', 'vision'] | false | Bias and Fairness We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://a... | f663de7dbfc583d4f575a0ba4587de7b |
mit | ['generated_from_trainer'] | false | deberta-classifier-feedback-1024-pseudo-final This model is a fine-tuned version of [TTian/deberta-classifier-feedback-1024-pseudo](https://huggingface.co/TTian/deberta-classifier-feedback-1024-pseudo) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5263 | 88c569917cb592837b885ed2767e5f81 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoc... | 2564c0e46e4e16855c09bb0bffc2e2ac |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5814 | 0.04 | 10 | 0.5888 | | 0.5521 | 0.08 | 20 | 0.5736 | | 0.5685 | 0.13 | 30 | 0.5809 | | 0.6052 | 0.17 | 40 | 0.5702 ... | 92c908a97dab96d12006b0f21e960579 |
apache-2.0 | ['generated_from_keras_callback'] | false | fintuned-bert-disfluency This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0814 - Train Sparse Categorical Accuracy: 0.9795 - Validation Loss: 0.0816 - Validation Sparse Ca... | 4059b2968a353d3ecc5f2fa4824b2c01 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | 0.1105 | 0.9694 | 0.0821 ... | 7341b36b471a4c70be93de6e19d2e4f8 |
apache-2.0 | ['generated_from_trainer'] | false | convnext-tiny-224-eurosat This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3153 - Accuracy: 0.9537 | b6be9ae7385343ae0820bb5a9e418648 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.863 | 0.98 | 33 | 1.5775 | 0.7619 | | 1.039 | 1.98 | 66 | 0.8142 | 0.9008 | | 0.5825 | 2.98 | 99 | 0.4442 | 0.... | cf7504f6fb9b5386634aaeb7f69201b8 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | eduardosflopes2 Dreambooth model trained by eduardosflopes 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/fa... | d4121df397ed8d1280bbcfba7740a520 |
mit | ['generated_from_trainer'] | false | vit-swin-base-224-gpt2-image-captioning This model is a fine-tuned [VisionEncoderDecoder](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder) model on 60% of the [COCO2014](https://huggingface.co/datasets/HuggingFaceM4/COCO) dataset. It achieves the following results on the testing set: - Loss:... | 094590cef1c0068a0c90c915fe883598 |
mit | ['generated_from_trainer'] | false | Model description The model was initialized on [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) as the vision encoder, the [gpt2](https://huggingface.co/gpt2) as the decoder. | 7bbe07c5dc9f6a40dce06986e3399fd5 |
mit | ['generated_from_trainer'] | false | How to use You can either use the simple pipeline API: ```python from transformers import pipeline image_captioner = pipeline("image-to-text", model="Abdou/vit-swin-base-224-gpt2-image-captioning") | 1e700ba771780a6072ae9d247145c833 |
mit | ['generated_from_trainer'] | false | infer the caption caption = image_captioner("http://images.cocodataset.org/test-stuff2017/000000000019.jpg")[0]['generated_text'] print(f"caption: {caption}") ``` Or initialize everything for more flexibility: ```python from transformers import VisionEncoderDecoderModel, GPT2TokenizerFast, ViTImageProcessor import ... | ec7a30691bf00b98cba278fce5ca9e36 |
mit | ['generated_from_trainer'] | false | load the fine-tuned image captioning model and corresponding tokenizer and image processor model = VisionEncoderDecoderModel.from_pretrained("Abdou/vit-swin-base-224-gpt2-image-captioning").to(device) tokenizer = GPT2TokenizerFast.from_pretrained("Abdou/vit-swin-base-224-gpt2-image-captioning") image_processor = ViTIm... | 116abe12f283e1f36c53898538c6dd2c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:| | 1.0018 | 0.38 | 2000 | 0.8860 | 38.6537 | 13.8145 | 3... | 09f26cab4e046ac4001d52975459e068 |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-imdb This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.1783 - Accuracy: 0.9552 | ecb1b7c6a993c5f7f6307100615a61c1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1904 | 1.0 | 1563 | 0.1423 | 0.9517 | | 0.1187 | 2.0 | 3126 | 0.1783 | 0.9552 | | aff50e9505ed74116859a0bab01d1fc3 |
apache-2.0 | ['Image Captioning'] | false | Model Description These are model weights originally provided by the authors of the paper [Text-Only Training for Image Captioning using Noise-Injected CLIP](https://arxiv.org/pdf/2211.00575.pdf). Their method aims to train CLIP with only text samples. Therefore they are injecting zero-mean Gaussian Noise into the t... | 4e0236c0a357360536a6fd88b0ca866d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 367 | 0.4436 | 0.8106 | 0.8597 | | 60b6ff1144a51599ae56933f55b629f1 |
mit | [] | false | German GPT-2 model In this repository we release (yet another) GPT-2 model, that was trained on various texts for German. The model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or "dangerous" as the English GPT-3 model. We do not plan extensive PR or staged releases ... | 7ab16dbc3f8f1e0fd5b6672413acd740 |
mit | [] | false | Changelog 16.08.2021: Public release of re-trained version of our German GPT-2 model with better results. 15.11.2020: Initial release. Please use the tag `v1.0` for [this older version](https://huggingface.co/dbmdz/german-gpt2/tree/v1.0). | c0d5845a63ad1d642e5cd1608a4578ef |
mit | [] | false | Training corpora We use pretty much the same corpora as used for training the DBMDZ BERT model, that can be found in [this repository](https://github.com/dbmdz/berts). Thanks to the awesome Hugging Face team, it is possible to create byte-level BPE with their awesome [Tokenizers](https://github.com/huggingface/token... | 7727af875f28eed4e7775d21e1997387 |
mit | [] | false | Using the model The model itself can be used in this way: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("dbmdz/german-gpt2") model = AutoModelWithLMHead.from_pretrained("dbmdz/german-gpt2") ``` However, text generation is a bit more interesting, so... | 57c792c082bd230cac991445be12ea38 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr2e05-wd0.01-bs16 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: 0.2792 - Rmse: 0.5284 - Mse: 0.2792 - Mae: 0.4332 | 044120fb9f3e0a26807b14825f7a6638 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2768 | 1.0 | 1245 | 0.2747 | 0.5241 | 0.2747 | 0.4081 | | 0.2737 | 2.0 | 2490 | 0.2793 | 0.5285 | 0.2793 ... | b248b98b07ef87942acb6b026f0a6587 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | deployment-with-nvidia-riva) | This model transcribes speech in lower case English alphabet along with spaces and apostrophes. It is an "extra-small" versions of Citrinet-CTC (around 10M parameters) model. See the [model architecture]( | 3fce83434442217daa6a1f4841997ebc |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/stt_en_citrinet_256_ls" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` | 0e1aef9fc9183dd1eade5972d29163fc |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Model Architecture Citrinet-CTC model is an autoregressive variant of Citrinet model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer Loss. You may find more info on the detail of this model here: [Citrinet Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main... | 324289f3ed355b6b25f9b094f7a08f01 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Training The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/... | 013387bca0127684b1e5a38b8d5a502b |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Performance The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. | Version | Tokenizer | Vocabulary Size | LS test-other | LS test-clean | |---------|---------------------------|-----... | 129c564b72efbf392aa1806383e0e5a9 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | References [1] [ Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition](https://arxiv.org/abs/2104.01721) [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece) [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) | 13cf833782b0bf4305f26874c051fd88 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-stsb-target-glue-mrpc This model is a fine-tuned version of [muhtasham/small-mlm-glue-stsb](https://huggingface.co/muhtasham/small-mlm-glue-stsb) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9122 - Accuracy: 0.7598 - F1: 0.8322 | 6113ef813fac065e8bf23bdcc8eda964 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3924 | 4.35 | 500 | 0.8097 | 0.7647 | 0.8416 | | 0.0751 | 8.7 | 1000 | 1.4556 | 0.7574 | 0.8374 | | 0.0294 |... | 486c7a645c14b0b9cc9fc5580f01ffd2 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-mt5-base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 1.3594 - Bleu: 27.1659 - Gen Len: 43.9575 | 9018152f49f5476c10a7009fe79fa186 |
apache-2.0 | ['generated_from_trainer'] | false | 42 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3109 - Accuracy: 0.9255 | 60bf798f7e2824812aa3690c0c56ae21 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: not_parallel - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 2068d1c7359371e50d974d6f56a43af2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | No log | 1.0 | 2105 | 0.2167 | 0.9232 | | 0.2049 | 2.0 | 4210 | 0.2375 | 0.9278 | | 0.123 | 3.0 | 6315 | 0.2636 ... | 009afa575c12c77f6bdb3aece261dcdc |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Model Card of `lmqg/mt5-base-esquad-qg-ae` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation and answer extraction jointly on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/... | e4325f344298b661e7ebec5df9505c7e |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | model prediction question_answer_pairs = model.generate_qa("a noviembre , que es también la estación lluviosa.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-base-esquad-qg-ae") | cd44c3dcd3d883323d61c2548f19d8c4 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | question generation question = pipe("extract answers: <hl> En la diáspora somalí, múltiples eventos islámicos de recaudación de fondos se llevan a cabo cada año en ciudades como Birmingham, Londres, Toronto y Minneapolis, donde los académicos y profesionales somalíes dan conferencias y responden preguntas de la audien... | 4395793711bfcf7a5c6145f46c19c868 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) | | Score | Type | Dataset | |:---... | 8dc2c18ff85178ee543d39e2dfd83b4d |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_esquad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: google/mt5-base - max_length: 512 -... | 37772f440c07ca6085addc2ff288b902 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_sst2_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5433 - Accuracy: 0.7878 | 3bdd38f98eb0cdda2dfe842f36b68073 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3436 | 1.0 | 4374 | 0.5433 | 0.7878 | | 0.2417 | 2.0 | 8748 | 0.6281 | 0.7890 | | 0.1823 | 3.0 | 13122 | 0.7529 ... | 41febc490dddf59d3a44b659c56d555e |
mit | ['generated_from_keras_callback'] | false | syp1229/xlm-roberta-base-finetuned-koidiom-epoch5 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0826 - Validation Loss: 1.9873 - Epoch: 4 | 377c9ad8dcd249ef18544970c65250dd |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.7703 | 2.0462 | 0 | | 2.2504 | 2.0178 | 1 | | 2.1653 | 1.9992 | 2 | | 2.1310 | 1.9829 | 3 | | 2.0826 | 1.9873 | 4 | | bbab20e1a78b38355b2083de731bc1f7 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 91325a1e58ca0b13494b94bf79b186b095fe0b58 pip install -e . cd egs2/mr_openslr64/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/marathi_openslr64 ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 5d1d8608c5a2747fd75a46fe8ad641da |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Mon Mar 21 16:06:03 UTC 2022` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.11.0+cu102` - Git hash: `91325a1e58ca0b13494b94bf79b186b095fe0b58` - Commit date: `Mon Mar 21 00:40:52 2022 +0000` | ea6a49ece7a8dd70d3dbff57cbc16c00 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_xlsr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_xlsr_raw_bpe150_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method:... | 047b9f92fb1d0fa1655b863a5423218a |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | MultiBERTs Seed 2 Checkpoint 20k (uncased) Seed 2 intermediate checkpoint 20k 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... | 69de28d8981a610d4a3c3ab5e79a1243 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | 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-2-20k') model = BertModel.from_pretrained("multiberts-seed-2-20k") text = "Replace me by any text you'd like." ... | 14cdc93506754089a7c5853d75df476b |
mit | ['generated_from_trainer'] | false | upbeat_ramanujan This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomek... | 4595b95ccccb6453e5e00ed967c58cee |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | f54e02aea9ec8c71ba475bb22028efad |
apache-2.0 | ['translation'] | false | eng-phi * source group: English * target group: Philippine languages * OPUS readme: [eng-phi](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-phi/README.md) * model: transformer * source language(s): eng * target language(s): akl_Latn ceb hil ilo pag war * model: transformer * pre-proces... | 87055bcd1c0101261a3bc9f7f41de984 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng-akl.eng.akl | 7.1 | 0.245 | | Tatoeba-test.eng-ceb.eng.ceb | 10.5 | 0.435 | | Tatoeba-test.eng-hil.eng.hil | 18.0 | 0.506 | | Tatoeba-test.eng-ilo.eng.ilo | 33.4 | 0.590 | | Tatoeba-test.eng.multi... | 93140539840f60ecda857204ecccd63f |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-phi - source_languages: eng - target_languages: phi - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-phi/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'phi'] - src_constituents: {'eng'} - tgt_cons... | 15df51a482aa9d381c9a6404212c97d8 |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'ner'] | false | Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset. RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus k... | e54ca0228e2f50efa5ab5bf270f0a988 |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'ner'] | false | Dataset The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1). **IMPORTANT ABOUT THIS MODEL:** We modified the dataset to make this model more robust to general Spanish input. In the Spanish language all the name entities are capitalized, as... | 9dc3ce3683d6fef4a6337b516d9e824d |
apache-2.0 | ['translation'] | false | opus-mt-de-loz * source languages: de * target languages: loz * OPUS readme: [de-loz](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-loz/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | 8c472a9df25ac8a51a6044ed4781a413 |
apache-2.0 | ['translation'] | false | ara-tur * source group: Arabic * target group: Turkish * OPUS readme: [ara-tur](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-tur/README.md) * model: transformer * source language(s): apc_Latn ara ara_Latn arq_Latn * target language(s): tur * model: transformer * pre-processing: normal... | 7df2d33e9fe154a3b783d2bb3df8e652 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ara-tur - source_languages: ara - target_languages: tur - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-tur/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ar', 'tr'] - src_constituents: {'apc', 'ara', 'arq_... | 1da36a6b828f98629d5e55a2e044a838 |
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