license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | [] | false | Training data A cleaned version of the ParaBank 2 dataset introduced in "[Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering](https://aclanthology.org/K19-1005/)". ParaBank 2 is a paraphrasing dataset constructed by back-translating the Czech portion of an English-Czech parallel corpus. We use a s... | aadcba952917f8473670056a9e3d99f7 |
apache-2.0 | [] | false | Training Procedure The model is fine-tuned for 4 epochs on the above-mentioned dataset, starting from `facebook/bart-large` checkpoint. We use token-level cross-entropy loss calculated using the gold paraphrase sentence. To ensure the output of the model is grammatical, during training, we use the back-translated Czec... | 27e894e809465262fc5b99e8c8224b29 |
apache-2.0 | [] | false | How to use Using `top_p=0.9` and `temperature` between `0` and `1` usually results in good generated paraphrases. Higher temperatures make paraphrases more diverse and more different from the input, but might slightly change the meaning of the original sentence. | 8f394464b4b586a84445a25258b1de44 |
apache-2.0 | [] | false | Citation If you are using this model in your work, please use this citation: ``` @inproceedings{xu-etal-2020-autoqa, title = "{A}uto{QA}: From Databases to {QA} Semantic Parsers with Only Synthetic Training Data", author = "Xu, Silei and Semnani, Sina and Campagna, Giovanni and Lam, Monica", booktitle ... | 3a7d0a244d4dc8282c0679d016364bfe |
apache-2.0 | ['thai', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from [deberta-base-thai](https://huggingface.co/KoichiYasuoka/deberta-base-thai). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). | 068615fcf00057a1cb3a715c779ba6c8 |
apache-2.0 | ['thai', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-thai-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-thai-upos") s="หลายหัวดีกว่าหัวเดียว" t=tokenizer.token... | e96328fde7a026fc5dbdf15c690ebaec |
openrail | [] | false | Dreambooth model for a retro, pulp science fiction art style This is a model trained on classic/retro science fiction illustrations using works from Chris Foss, John Harris, Syd Mead, Robert McCall and Philippe Bouchet. Mostly trained on space scenes with a few landscapes so it tends to produce spaceships unless othe... | a497a4577bb6ca14113959734c1f5af9 |
openrail | [] | false | Example images       on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4883 - Rouge1: 19.5044 - Rouge2: 6.2046 - Rougel: 19.3543 - Rougelsum: 19.381 | 408cf6663e8fc2853cf29d2931ee330c |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 | c5d8dc35474f2ce4c99c56b6e0f1c9a4 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 6.4829 | 1.0 | 217 | 2.7590 | 12.7914 | 3.3267 | 12.493 | 12.4137 | | 3.4814 | 2.0 |... | a699332dfd88c1f899caef851915c67a |
mit | ['question-answering', 'bert', 'bert-base'] | false | BERT-base uncased model fine-tuned on SQuAD v1 This model was fine-tuned from the HuggingFace [BERT](https://www.aclweb.org/anthology/N19-1423/) base uncased checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer). This model is case-insensitive: it does not make a difference between english and English.... | 844413b4add9ddabeb56c17cedb070e6 |
mit | ['question-answering', 'bert', 'bert-base'] | false | after install https://github.com/huggingface/transformers cd examples/question-answering mkdir -p data wget -O data/train-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json wget -O data/dev-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json python run_sq... | 3f1cf4d8e82b2b17e9f23bb845220280 |
mit | ['question-answering', 'bert', 'bert-base'] | false | Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| | ------ | --------- | --------- | | **EM** | **80.9** | **80.8** | | **F1** | **88.2** | **88.5** | Note that the above results didn't involve any hyperparameter search. | ea2b43891e2ec1bf13a2f06009a23f0a |
mit | ['question-answering', 'bert', 'bert-base'] | false | Example Usage ```python from transformers import pipeline qa_pipeline = pipeline( "question-answering", model="csarron/bert-base-uncased-squad-v1", tokenizer="csarron/bert-base-uncased-squad-v1" ) predictions = qa_pipeline({ 'context': "The game was played on February 7, 2016 at Levi's Stadium in t... | 45a2ad7ca496fb061cb9840ec70222c6 |
mit | ['question-answering', 'bert', 'bert-base'] | false | {'score': 0.8730505704879761, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'} ``` > Created by [Qingqing Cao](https://awk.ai/) | [GitHub](https://github.com/csarron) | [Twitter](https://twitter.com/sysnlp) > Made with ❤️ in New York. | df9e0f8834a28d427fe5564fab6458fc |
mit | [] | false | AemondHoD_Sandman2022 on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | 2ab55cdc2a2b4b9d6e9daffe072c9732 |
mit | [] | false | model by ByteXD This your the Stable Diffusion model fine-tuned the AemondHoD_Sandman2022 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **AemondHoD, Sandman2022** You can also train your own concepts and upload them to the library by using [the fast-DremaBoo... | a265d2c0872c1931debd4af3ae482870 |
apache-2.0 | [] | false | bert-base-en-ur-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the ... | e810cf2451f71e57d584b90bf851b405 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-ur-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-ur-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](h... | 6b0f8c5fba9aae432234cfe4931cc130 |
other | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | segformer-b5-finetuned-segments-instryde-foot-test This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the inStryde/inStrydeSegmentationFoot dataset. It achieves the following results on the evaluation set: - Loss: 0.0496 - Mean Iou: 0.4672 - Mean Accuracy: 0.9344 - Overall ... | e726753892a913cf2c040d5282f50c54 |
other | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------:|:-----------------------... | 90bd1e76b85334f27a564d706e1d5e77 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Stable Diffusion v2 Model Card This model card focuses on the model associated with the Stable Diffusion v2 model, available [here](https://github.com/Stability-AI/stablediffusion). This `stable-diffusion-2` model is resumed from [stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) (`... | eb649c035f5b777909f9bb026fce8722 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner. ```bash pip install diffusers transformers accelerate scipy safetensors ``` Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in th... | 74ed48c7b08d2d979d7a6d08e1a407f0 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Use the Euler scheduler here instead scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pip... | 629e82ab4136e6a3c35b03dc46164cff |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-j-roman-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.2233 - Wer: 0.1437 | 225bc890f687c2df5cbf8cba9f239d1a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 30704554eff8addf7812cb77adb8103b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.3479 | 1.22 | 400 | 1.6349 | 0.3868 | | 0.8621 | 2.45 | 800 | 1.0185 | 0.2346 | | 0.5421 | 3.67 | 1200 | 0.7549 | 0.186... | 3abe2f7b7d8f26d146f3ea73f25bb7a8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.1223 | 1.0 | 7958 | 2.9579 | | 2.9033 | 2.0 | 15916 | 2.7765 | | 2.8392 | 3.0 | 23874 | 2.7315 | | 1efd9d934c5c91d976a898e19b7f4c8f |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_2e-05_all_16_02_2022-15_41_15 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: 0.1742 - Precision: 0.3447... | 58343ee4a6ea505868c55ce516b15889 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 38 | 0.3692 | 0.0868 | 0.2030 | 0.1216 | 0.8238 | | No log | 2.0 |... | 5cf1802729182191bb458ee4e816c376 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2095 - Accuracy: 0.943 - F1: 0.9420 | 35c9fed8fde75511cf472f0c52f5aaee |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-sst2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3451 - Accuracy: 0.9083 | 10009776a02f23f7541bfeaf0fb82949 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2928 | 1.0 | 4210 | 0.3499 | 0.8876 | | 0.1908 | 2.0 | 8420 | 0.3451 | 0.9083 | | 0.1489 | 3.0 | 12630 | 0.3440 ... | 6d3add1bd396be513fb04a81676e7db3 |
mit | [] | false | kaya-ghost-assasin on Stable Diffusion This is the `<kaya-ghost-assasin>` 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) notebo... | 6349283f255910ab4f219bc163f204ba |
cc-by-4.0 | [] | false | Model description This is the T5-3B model for System 3 DREAM-FLUTE (emotion), as described in our paper Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE, FigLang workshop @ EMNLP 2022 (Arxiv link: https://arxiv.org/abs/2210.16407) Systems 3: DREAM-FLUTE - Providing DREAM’s different dimensions... | 88e17e29036f48b743f5f7c6ac9d14a8 |
cc-by-4.0 | [] | false | How to use this model? We provide a quick example of how you can try out DREAM-FLUTE (emotion) in our paper with just a few lines of code: ``` >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> model = AutoModelForSeq2SeqLM.from_pretrained("allenai/System3_DREAM_FLUTE_emotion_FigLang2022") >>> toke... | dbcbd5c59cb29680150c7371a5a2503a |
cc-by-4.0 | [] | false | More details about DREAM-FLUTE ... For more details about DREAM-FLUTE, please refer to our: * 📄Paper: https://arxiv.org/abs/2210.16407 * 💻GitHub Repo: https://github.com/allenai/dream/ This model is part of our DREAM-series of works. This is a line of research where we make use of scene elaboration for building a... | 9ce3adb455fcd6d5422b39ba2a72c57c |
cc-by-4.0 | [] | false | Model details This model is a fine-tuned version of [t5-3b](https://huggingface.co/t5-3b). It achieves the following results on the evaluation set: - Loss: 0.7557 - Rouge1: 58.5894 - Rouge2: 38.6 - Rougel: 52.5083 - Rougelsum: 52.4698 - Gen Len: 40.5607 | 1919f80b12a1020af9f157c6cc854c16 |
cc-by-4.0 | [] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.9974 | 0.33 | 1000 | 0.8938 | 39.8909 | 27.4849 | 38.2724 | 38.2772 | 18... | 1f3c9210e3890300182f104d5520b2c7 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - lr_scheduler_warmup_steps: 500 - num_epochs: 3 | 426e1abad7cd22815d9743b2ede1b9ae |
apache-2.0 | ['generated_from_trainer'] | false | KB13-t5-small-finetuned-en-to-regex 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: - Loss: 0.4028 - Semantic accuracy: 0.439 - Syntactic accuracy: 0.3659 - Gen Len: 15.3659 | 2d060d520e16db53d1100c00b3a5b769 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP | a5d57a07023be6ead0fc0348cd72265e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Semantic accuracy | Syntactic accuracy | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:------------------:|:-------:| | No log | 1.0 | 47 | 0.9241 | 0.0488 | 0.0488 | 15.19... | 29df9bd7e01ad98a26c4ea5673a67cc8 |
apache-2.0 | ['generated_from_keras_callback'] | false | nandysoham/Myocardial_infarction-theme-finetuned-overfinetuned This model is a fine-tuned version of [nandysoham/distilbert-base-uncased-finetuned-squad](https://huggingface.co/nandysoham/distilbert-base-uncased-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train L... | d627dd821367d73ad039eeb807046033 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 12, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1... | a35f4fbf1347ec2c7c3d3130529a5643 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | a2c1c59dfc6e7d41722ccff75f3fc33a |
apache-2.0 | ['vision', 'image-classification'] | false | ResNet ResNet model trained on imagenet-1k. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) and first released in [this repository](https://github.com/KaimingHe/deep-residual-networks). Disclaimer: The team releasing ResNet did not write a model card f... | 782c3999a737cfc663cfe45b26abd579 |
apache-2.0 | ['vision', 'image-classification'] | false | Model description ResNet introduced residual connections, they allow to train networks with an unseen number of layers (up to 1000). ResNet won the 2015 ILSVRC & COCO competition, one important milestone in deep computer vision. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/... | 3367a39933597a58a727f489631e64e8 |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model: ```python >>> from transformers import AutoFeatureExtractor, ResNetForImageClassification >>> import torch >>> from datasets import load_dataset >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> feature_extractor = AutoFeature... | a06a9b02a7b225d3e073d660795a00a7 |
apache-2.0 | ['vision', 'image-classification'] | false | model predicts one of the 1000 ImageNet classes >>> predicted_label = logits.argmax(-1).item() >>> print(model.config.id2label[predicted_label]) tiger cat ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/resnet). | e9703b2225697bf5890d78f043526196 |
apache-2.0 | ['generated_from_trainer'] | false | fine-tune-Wav2Vec2-XLS-R-300M-Indonesia-test This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_10_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3076 - Wer: 0.2971 | 6db8137f240f74b45323c52447fe6f6e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 7 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 14 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 2d37dada07844b9022e026941e378c18 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.9436 | 9.99 | 570 | 2.7467 | 1.0 | | 1.0498 | 19.99 | 1140 | 0.3630 | 0.3965 | | 0.6789 | 29.99 | 1710 | 0.3396 | 0.3712 | |... | b7d84d6291e6f8383604b5d2d6c0f9ab |
creativeml-openrail-m | ['text-to-image'] | false | model by estelleflores This your the Stable Diffusion model fine-tuned the estelle-sims-style concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **3D render from a videogame in sks style** You can also train your own concepts and upload them to the library by using ... | 81c43181e7fc62cc84b4de62e327777c |
apache-2.0 | ['translation'] | false | opus-mt-en-loz * source languages: en * target languages: loz * OPUS readme: [en-loz](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-loz/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | b7b055813623a3361faf3a2021ba962c |
mit | [] | false | Buddha statue on Stable Diffusion This is the `<buddha-statue>` 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 ca... | 4c23adc3fa957951f24d70e7abd7bddd |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_unispeech-ml_s253 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using... | efaca96e5099ed375cdf8813c8aa1daa |
other | [] | false | Carpet Stain Removal Plano TX https://carpetcleaningplanotx.com/carpet-stain-removal.html (469) 444-1903 Carpet Cleaning Plano in Texas is the company of choice for the majority of customers when it comes to stain removal.We have the best-trained staff and professional technology.We will get rid of even the worst sta... | bb603bb6d8da46b987d48fe262e08725 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53_final_train1 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6432 - Wer: 0.6298 | a4f307067e4f62db0ee3357c6d4ab728 |
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: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | c0b9e0a9a8e8e89ea0c136f72dfb5d43 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.6199 | 0.25 | 250 | 3.6163 | 1.0 | | 3.0927 | 0.5 | 500 | 3.5932 | 1.0 | | 3.0837 | 0.76 | 750 | 3.2418 | 1.0 | |... | 6179f675b9eab3d6216de68eccf3fa73 |
mit | ['generated_from_trainer'] | false | predict-perception-xlmr-blame-object 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: - Loss: 0.7219 - Rmse: 0.6215 - Rmse Blame::a Un oggetto: 0.6215 - Mae: 0.4130 - Mae Blame::a Un oggett... | 83a4c69c9770511ce4d76d9de146a9f2 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Blame::a Un oggetto | Mae | Mae Blame::a Un oggetto | R2 | R2 Blame::a Un oggetto | Cos | Pair | Rank | Neighbors | Rsa | |:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------:|:------:|:------... | 7bf7ebda03bc234f18a66081341bd322 |
apache-2.0 | ['generated_from_keras_callback'] | false | notmaineyy/distilbert-base-uncased-finetuned-ner 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: 0.0344 - Validation Loss: 0.0633 - Train Precision: 0.9181 - Tr... | 46e1a5ca3aac037962d283ae8edb5bdb |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.2048 | 0.0749 | 0.8898 | 0.9129 | 0.9012 | 0.9784 | 0 ... | 74177adea07967d6e06046dfda0f200c |
apache-2.0 | ['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: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 1000 | 801533113db200cfd2fa5f214cc651dc |
apache-2.0 | ['generated_from_trainer'] | false | hf_trainer This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0708 - F1: 0.9066 | 735a67f4bb806256618e714f10136c11 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0344 | 1.0 | 565 | 0.0661 | 0.8811 | | 0.0354 | 2.0 | 1130 | 0.0641 | 0.8963 | | 0.0222 | 3.0 | 1695 | 0.0690 | 0.8994 | |... | ed34036fedfbb5fbf38e99dd9107c546 |
apache-2.0 | ['automatic-speech-recognition', 'basque', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-basque This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4276 - Wer: 0.5962 | c8f08e991c98775ee3ea311fe1556294 |
apache-2.0 | ['automatic-speech-recognition', 'basque', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 7cc94a1604f5585a833ef4afae939f3d |
apache-2.0 | ['automatic-speech-recognition', 'basque', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.9902 | 1.29 | 400 | 2.1257 | 1.0 | | 0.9625 | 2.59 | 800 | 0.5695 | 0.7452 | | 0.4605 | 3.88 | 1200 | 0.4276 | 0.5962 | ... | 01709ee076910fc2eb77e1682cf318c6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.1491 - Matthews Correlation: 0.5365 | 53faeacb403cd22ce1fbfa69caed69a7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 268 | 0.7051 | 0.5240 | | 0.1591 | 2.0 | 536 | 0.7489 | 0.5047 | | 0.1... | 9e8fdf0f312ffffe07d022585aa714fc |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-vios This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the vivos_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.3729 - Wer: 0.2427 | 368d0e7942d53649f597c48898c243d6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.4755 | 1.37 | 500 | 0.7991 | 0.5957 | | 0.5424 | 2.75 | 1000 | 0.4290 | 0.3653 | | 0.3586 | 4.12 | 1500 | 0.3809 | 0.2890 | |... | ca68054166edfaa9e6a9feb6eb212a60 |
apache-2.0 | ['generated_from_keras_callback'] | false | javilonso/Mex_Rbta_TitleWithOpinion_Attraction This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0064 - Validation Loss: 0.0515 - Epoch: 2 | 20c498cd5228995964c2198b193ba56c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0780 | 0.0650 | 0 | | 0.0204 | 0.0464 | 1 | | 0.0064 | 0.0515 | 2 | | 0dc4808052afe5b5c80264e09074a5f1 |
mit | [] | false | model by toyxyz This your the Stable Diffusion model fine-tuned the ba-shiroko concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks shiroko** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.re... | 11e42df2f243396ba79c8b1d5b4d8b81 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.4603 - F1: 0.6181 | fdfe7757a6550057de2ce862f4563d9c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 25 | 0.8577 | 0.3917 | | 1.0821 | 2.0 | 50 | 0.5391 | 0.5466 | | 1.0821 | 3.0 | 75 | 0.4603 | 0.6181 | ... | 25db27849dce8ac2bd8f059ad94e9b42 |
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: 0.3357 - Accuracy: 0.8667 - F1: 0.8667 | 7c680ccda68ed8a63e3b66a677fe5617 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-bne-finetuned-ner This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0659 - Precision: 0.9238 - Recall: 0.9351 - F1: 0.9294 - Accuracy: 0... | c04a48a3e2e2273ad66ef63138d57fd0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1931 | 1.0 | 878 | 0.0800 | 0.8892 | 0.8853 | 0.8872 | 0.9770 | | 0.0409 | 2.0 |... | 1c8a989557727923ce41de3a0b8bd3f7 |
apache-2.0 | ['translation'] | false | cat-fra * source group: Catalan * target group: French * OPUS readme: [cat-fra](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cat-fra/README.md) * model: transformer-align * source language(s): cat * target language(s): fra * model: transformer-align * pre-processing: normalization + Sente... | a14bb3d8bfe8fe69c4a918793600f4e3 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: cat-fra - source_languages: cat - target_languages: fra - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cat-fra/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ca', 'fr'] - src_constituents: {'cat'} - tgt_const... | b247bbf2911954ea075ddd848fd6b4ca |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | fnet-base-finetuned-mrpc This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.9653 - Accuracy: 0.7721 - F1: 0.8502 - Combined Score: 0.8112 The model was fine-tuned to compare [go... | 4d28ddc5c786e11c15ab9c5a98bd3abf |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | !/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name mrpc \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir fnet-base-finetuned-mrpc \\n --push_to_hub \\n --h... | 8b8b23bd0e3f1a896bcef600a20c7e1a |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.544 | 1.0 | 230 | 0.5272 | 0.7328 | 0.8300 | 0.7814 | | 0.4034 | 2.0 | 460 | 0.62... | e80fee51583a33e42113fe8a5a073529 |
cc-by-4.0 | ['pytorch', 'seq2seq', 'masked language modeling', 'multilingual'] | false | Model Description NeMo Megatron-mT5 3B is a *multilingual* transformer-based masked language model. [mT5](https://arxiv.org/abs/2010.11934) [1] is a class of encoder-decoder models trained with a span-based masked language modeling objective on a dataset comprising documents from many different languages. We follow t... | b722513018203ba41cd3fb0a2a092d09 |
cc-by-4.0 | ['pytorch', 'seq2seq', 'masked language modeling', 'multilingual'] | false | List of Languages We pre-trained our mT5 model on the following languages from the [mC4](https://github.com/allenai/allennlp/discussions/5265) dataset. 1. Japanese 2. English 3. Italian 4. Latvian 5. Russian 6. Hungarian 7. Chinese 8. Polish 9. Greek 10. German 11. Czech 12. Korean 13. Hindi 14. Norwegian 15. Danish... | 6e9f5579b14cec87d4b74326f06130df |
cc-by-4.0 | ['pytorch', 'seq2seq', 'masked language modeling', 'multilingual'] | false | Step 1: Install NeMo and dependencies You will need to install NVIDIA Apex and NeMo. ``` git clone https://github.com/ericharper/apex.git cd apex git checkout nm_v1.11.0 pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_n... | e2d3aa8823dd7cbc968b404fe6b41f40 |
cc-by-4.0 | ['pytorch', 'seq2seq', 'masked language modeling', 'multilingual'] | false | Step 2: Run inference **Note.** The model has been trained with Tensor Parallelism (TP) of 2 and Pipeline Parallelism (PP) of 1, but it should be possible to run inference with tensor parallel size 1 on most NVIDIA GPUs ``` git clone https://github.com/NVIDIA/NeMo.git cd NeMo/examples/nlp/language_modeling git che... | 6249da92ef392f4734f26f9cf9db3953 |
cc-by-4.0 | ['pytorch', 'seq2seq', 'masked language modeling', 'multilingual'] | false | Evaluation results Zero-shot language transformer performance on the [XNLI](https://arxiv.org/abs/1809.05053) dataset for a model fine-tuned on MNLI. | English | Spanish | German | French | Chinese| |---|---| ---|---|---| |89.4|86.4|84.5|85.8|79.9| | 9dd3631d487c8004e785f29f1d2dbefb |
cc-by-4.0 | ['pytorch', 'seq2seq', 'masked language modeling', 'multilingual'] | false | Limitations The model was trained on the data originally crawled from the Internet. This data contains toxic language and societal biases. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. | 926e6f9aa8d5a0e48c4f62b8e3c04a5d |
cc-by-4.0 | ['pytorch', 'seq2seq', 'masked language modeling', 'multilingual'] | false | References [1] [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) [2] [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/pdf/1909.08053.pdf) [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) [4] [XNLI... | 42337cfd56c6553f4cbfd1c544efa54f |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-stsb-target-glue-sst2 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: 0.3917 - Accuracy: 0.8876 | 7e31eaa2b5a931833199f0f1ffc88224 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3935 | 0.24 | 500 | 0.3629 | 0.8429 | | 0.3047 | 0.48 | 1000 | 0.3266 | 0.8624 | | 0.2593 | 0.71 | 1500 | 0.3357 | 0.... | 6884f49c1214c9c85207e8901a156170 |
apache-2.0 | ['translation'] | false | opus-mt-es-et * source languages: es * target languages: et * OPUS readme: [es-et](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-et/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | f3a6c636d5c9ae4cdbe9bd1283e4eef7 |
mit | ['text-to-image'] | false | Mann-E 3 _Mann-E_ (read as Mani) is an image generation model originally started and developed by [Muhammadreza Haghiri](https://haghiri75.com/en) in early 2022. This image generation model was originally based on VQGAN, but later in summer 2022, after the release of Stable Diffusion, the base model has been changed.... | 82ecdb7f6ffcdb7d588b87b0ea08861c |
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