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 | ['automatic-speech-recognition', 'ar'] | false | exp_w2v2t_ar_hubert_s693 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is... | eed0c1bb5dadef4680e625ee3f1cb23a |
mit | [] | false | belize-blue-sofa on Stable Diffusion This is the `<belize-blue-sofa>` 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. ... | 06e86b400b0db2941644abbb27b68c28 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Hi - Swedish This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3260 - Wer: 19.8946 | c589568f86ce0a944f78812e654a5d6d |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3144 | 0.65 | 500 | 0.3244 | 24.0623 | | 0.1321 | 1.29 | 1000 | 0.2977 | 21.5563 | | 0.1318 | 1.94 | 1500 | 0.2788 | 20.919... | b5506bd3fda0a7444fc96cbc89b1cf74 |
apache-2.0 | ['lexical normalization'] | false | Fine-tuned ByT5-small for MultiLexNorm (Slovenian version)  This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://n... | a15caae36ab9a8b71cf40095160c908e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-my_hindi_home-latest-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. | 061c44403950b6813c84d058ede016f4 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'mt', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-maltese This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - MT dataset. It achieves the following results on the evaluation set: - Loss: 0.2005 - Wer: 0.1897 | a33eeea8b59476ca036d3d68f4bd859b |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'mt', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.2238 | 18.02 | 2000 | 0.3911 | 0.4310 | | 0.7871 | 36.04 | 4000 | 0.2063 | 0.2309 | | 0.6653 | 54.05 | 6000 | 0.1960 | 0.209... | 529fa0d6ea0f44daa3f73119e102a042 |
apache-2.0 | ['ar', 'automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AR dataset. It achieves the following results on the evaluation set: - Loss: 0.4502 - Wer: 0.4783 | e03cd03b326db3a2a85b96b90f4f6698 |
apache-2.0 | ['ar', 'automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.7972 | 0.43 | 500 | 5.1401 | 1.0 | | 3.3241 | 0.86 | 1000 | 3.3220 | 1.0 | | 3.1432 | 1.29 | 1500 | 3.0806 | 0.999... | 81b8fc7bca9bd068535ffa00f485fc0b |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-medium_sst2_negation0.0005_pretrainedTrue This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.4480 | ab0564a6f4511f41f256b0e8c026856c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8212 | 1.0 | 1059 | 3.3124 | | 2.5361 | 2.0 | 2118 | 3.3864 | | 2.3876 | 3.0 | 3177 | 3.4480 | | 2ab2bb48e8f0125c499fa475886cfbfd |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Introduction ptt5-base-msmarco-pt-10k-v2 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This model was finetuned for 10k steps. Further information about the da... | 7a02453f6dd1d38661878d81c1a425f3 |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'unicamp-dl/ptt5-base-msmarco-pt-10k-v2' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) ``` | 4eb5891f4e9330bb28e386233a6a76e2 |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Citation If you use ptt5-base-msmarco-pt-10k-v2, please cite: @misc{bonifacio2021mmarco, title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and and Roberto Lo... | 6db5eefeeb69a362f5556ffe686c4966 |
apache-2.0 | ['generated_from_trainer'] | false | Article_500v7_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v7_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.1961 - Precision: 0.7235 - Recall: 0.7613 - F1: 0.7419 - Accuracy: 0.... | 4cd8f38b14a16cbfd92a2d66a19a715a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 162 | 0.1924 | 0.6942 | 0.7087 | 0.7014 | 0.9358 | | No log | 2.0 |... | 2c0f583e40698da3cab2f4005d47b899 |
mit | ['generated_from_trainer'] | false | BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-1 This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset. It achieves the following results on... | 379062be8db3905b9508970404e9fb53 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 57 | 0.8471 | 0.58 | | No log | 2.0 | 114 | 0.8450 | 0.58 | | No log | 3.0 | 171 | 0.7846 | 0.... | 9e5b4405f227673663a358ffc005a6af |
apache-2.0 | [] | false | PaddlePaddle/uie-micro Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. The unified text-to-structure generation framework, namely UIE, can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE... | 0a99ab03755316bb63454c24bbaf519e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-cased-finetuned-squad_v2 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.4225 | 1e38c583714526e6b6db3138f38443aa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2416 | 1.0 | 8255 | 1.2973 | | 0.9689 | 2.0 | 16510 | 1.3242 | | 0.7803 | 3.0 | 24765 | 1.4225 | | 7f0d7fc0240728799cc0e0acd4d019bb |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | kornwtp/ConGen-Multilingual-DistilBERT This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 768 dimensional dense vector space and can be used for tasks like semantic search. | a49ec1334f0126458c957bd3f1ea8cdf |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed: ``` pip install -U git+https://github.com/KornWtp/ConGen.git ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence",... | 241d78b70c40b886e78e24fb32f3f382 |
mit | [] | false | dq10-anrushia on Stable Diffusion This is the `<anrushia>` 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 als... | 6931d002a2d3327709d97590b712dc94 |
mit | ['generated_from_keras_callback'] | false | recklessrecursion/Cardinal__Catholicism_-clustered This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2711 - Train End Logits Accuracy: 0.9306 - Trai... | 0392f1278b766899a16493d3ce776885 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 1ced43c84f999f80da688ab97f3288f2 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | glpn-nyu-finetuned-diode-221223-094145 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.4077 - Mae: 0.4032 - Rmse: 0.6201 - Abs Rel: 0.3554 - Log Mae: 0.1594 - Log Rmse:... | 309b3255c5a9d9db15e52555295cb8bb |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 1.0433 | 1.0 | 72 | 0.5885 ... | 5e0355dd7502da08a9900a4341597fd0 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1631 - F1 Score: 0.8579 | 7bd7ae445f2f1d7ed39bcee48d5dc482 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2878 | 1.0 | 715 | 0.1840 | 0.8247 | | 0.1456 | 2.0 | 1430 | 0.1596 | 0.8473 | | 0.0925 | 3.0 | 2145 | 0.1631 | 0.... | ad318495e0f42f1bdec6cd3aebcfc0e0 |
cc0-1.0 | ['conversational'] | false | Chizuru Ichinose as a DialoGPT model This model is a fine-tuned version of [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium/) trained on the [Chizuru Ichinose conversational dataset](https://huggingface.co/datasets/alexandreteles/chizuru-ichinose). We recommend using one of the Transformers pre-bui... | 92d2d93eec589740b68a847b97fc9639 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | DeathCharacter Dreambooth model trained by LaCambre with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stab... | 1923c94c5627ed8cedc9b9615f4d30ab |
apache-2.0 | ['speech'] | false | Wav2Vec2-Conformer-Large with Relative Position Embeddings Wav2Vec2 Conformer with relative position embeddings, pretrained on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it w... | 762696b4848684d4c1a7e1d5d4083b03 |
cc-by-4.0 | ['answer extraction'] | false | Model Card of `lmqg/mt5-base-frquad-ae` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for answer extraction on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ... | 8e3b7116348cdb8c4a747c195466553d |
cc-by-4.0 | ['answer extraction'] | false | model prediction answers = model.generate_a("Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.") ``` - With `transformers` ... | 8e2c9f9eefd952a96a540d07690913f9 |
cc-by-4.0 | ['answer extraction'] | false | Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-frquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_frquad.default.json) | | Score | Type | Dataset | |:----... | dd47e4d235b6be8d6679a57ba6f267bd |
cc-by-4.0 | ['answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_frquad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: None - model: google/mt5-base - max_length: 512 - max_length_output: 32 - epoch: 15 - b... | bdb6efc6afb95cf7427572e2edde7315 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_pretrain_cola This model is a fine-tuned version of [gokuls/distilbert_add_pre-training-complete](https://huggingface.co/gokuls/distilbert_add_pre-training-complete) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6826 - Matthews Cor... | 4c3e221d1df32486af8f30d16a54cc3a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.8045 | 1.0 | 34 | 0.6851 | 0.0 | | 0.7928 | 2.0 | 68 | 0.6826 | 0.0 | | 0.7... | c473da18244797333d8d84ce7a7d064f |
cc-by-sa-4.0 | [] | false | Danish BERT for hate speech classification The BERT HateSpeech model classifies offensive Danish text into 4 categories: * `Særlig opmærksomhed` (special attention, e.g. threat) * `Personangreb` (personal attack) * `Sprogbrug` (offensive language) * `Spam & indhold` (spam) This model is intended to be used afte... | a478cf7feb93886fd8c989feb78f8f87 |
cc-by-sa-4.0 | [] | false | bertdr) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForSequenceClassification model = BertForSequenceClassification.from_pretrained("alexandrainst/da-hatespeech-classification-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-hatespeech-cla... | 75fe4f23b179f32eefd1da01f19b3d7b |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'whisper-event'] | false | openai/whisper-base This is an automatic speech recognition model that also does punctuation and casing. Vegeu [projecte de millora dels models Whisper](https://www.softcatala.org/projectes/millora-del-catala-dels-models-del-reconeixement-de-la-parla-whisper/) (Catalan). This model is a fine-tuned version of [openai... | 3d674aa345686b4f955e222e8b137a9f |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 40000 - mixed_precisi... | 4fd43fb1aeefc9f2c97e616bb6b604c5 |
apache-2.0 | ['generated_from_trainer', 'hf-asr-leaderboard', 'whisper-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.4841 | 0.1 | 4000 | 0.5078 | 26.7974 | | 0.3116 | 0.2 | 8000 | 0.4524 | 22.9455 | | 0.3971 | 0.3 | 12000 | 0.4281 | 2... | 2b214223357216e75ae103e387632c3c |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 66.3 - GMACs: 8.7 - Activations (M): 21.6 - Image size: 224 x 224 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/facebookresearch/ConvNeXt - *... | 38c97883b9425630b1d6e8e0c77281ea |
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_small.fb_in22k', pretrained=True) model = mod... | 89bf628b3a6ba08a6f93fad39c44c6db |
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_small.fb_in22k', pretrained=True, ... | f43c6d51cc8b645b6b1c930aa3a10413 |
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_small.fb_in22k', pretrained=True, nu... | d0d9d15eff3bd1c4f1d86d727edeed03 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de 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: 0.1352 - F1: 0.8591 | fdcf67c4c3e0cce0916805df11b629b8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.257 | 1.0 | 525 | 0.1512 | 0.8302 | | 0.1305 | 2.0 | 1050 | 0.1401 | 0.8447 | | 0.0817 | 3.0 | 1575 | 0.1352 | 0.8591 | ... | 84bc447f337386e6ff0dc20e8e298cd0 |
apache-2.0 | ['translation'] | false | opus-mt-fi-fr * source languages: fi * target languages: fr * OPUS readme: [fi-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-02-26.zip](https://... | 0ebe6105aae7121954aa6fe6766bcaaa |
creativeml-openrail-m | ['text-to-image'] | false | cy0208 Dreambooth model trained by aichina with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob... | 87e643ecdfaebd697494e922aeb9570d |
apache-2.0 | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'neural-compressor', 'PostTrainingStatic'] | false | PyTorch This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [Intel/bert-base-uncased-mrpc](https://h... | 79427f4301a55aab42a65830b63f2a82 |
apache-2.0 | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'neural-compressor', 'PostTrainingStatic'] | false | Load with Intel® Neural Compressor: ```python from optimum.intel.neural_compressor import IncQuantizedModelForSequenceClassification int8_model = IncQuantizedModelForSequenceClassification.from_pretrained( 'Intel/bert-base-uncased-mrpc-int8-static', ) ``` | 7655f8ae7cb55cbcc5f4bfe24291b9af |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-bengali 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: 3.0518 - Wer: 1.0 | 365ec9281a885be3ee74cada2b60bda5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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... | 444633e7e94a0797e040b8003db26317 |
apache-2.0 | ['translation'] | false | opus-mt-csn-es * source languages: csn * target languages: es * OPUS readme: [csn-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/csn-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-15.zip](http... | 7531a203cfc3b73bca2f48a8ac6b15f6 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoch... | bc42ec631f376ff8bec7429f076002a5 |
apache-2.0 | ['translation'] | false | opus-mt-lv-sv * source languages: lv * target languages: sv * OPUS readme: [lv-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lv-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 1301297e033dccda3102bbfa7f13166a |
apache-2.0 | ['translation'] | false | opus-mt-fr-en * source languages: fr * target languages: en * OPUS readme: [fr-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-02-26.zip](https://... | 20b123501b110598e36b386b9bc8e4d5 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdiscussdev2015-enfr.fr.en | 33.1 | 0.580 | | newsdiscusstest2015-enfr.fr.en | 38.7 | 0.614 | | newssyscomb2009.fr.en | 30.3 | 0.569 | | news-test2008.fr.en | 26.2 | 0.542 | | newstest2009.fr.en | 30.2 | 0.57... | 5b610452011f620787976b59220bcc06 |
['cc0-1.0'] | ['fastai', 'resnet', 'computer-vision', 'classification', 'image-classification', 'binary-classification'] | false | Model Description This is a resnet34 model fine-tuned with fastai to [classify real and fake Pokemon cards (dataset)](https://www.kaggle.com/datasets/ongshujian/real-and-fake-pokemon-cards). Here is a colab notebook that shows how the model was trained and pushed to the hub: [link](https://github.com/mindwrapped/pok... | 127415cc6629d149c05ea9b8883a1fa9 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | pkmdlhs_2500_300 Dreambooth model trained by ifif with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable... | 8f7075f025427f2ad154accc2bc29a04 |
apache-2.0 | ['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-PushBlock'] | false | Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-PushBlock 2. Step 1: Write your model_id: unity/ML-Agents-PushBlock 3. Step 2: Select your *.nn or *.onnx file 4. Click on Watch the agent play 👀 | 21e9bed7e26707f959bc466eeaa006cf |
mit | ['ælæctra', 'pytorch', 'danish', 'ELECTRA-Small', 'replaced token detection'] | false | Ælæctra - A Step Towards More Efficient Danish Natural Language Processing **Ælæctra** is a Danish Transformer-based language model created to enhance the variety of Danish NLP resources with a more efficient model compared to previous state-of-the-art (SOTA) models. Initially a cased and an uncased model are released... | 394331274094b7858d44bd192acbb968 |
mit | ['ælæctra', 'pytorch', 'danish', 'ELECTRA-Small', 'replaced token detection'] | false | Evaluation of current Danish Language Models Ælæctra, Danish BERT (DaBERT) and multilingual BERT (mBERT) were evaluated: | Model | Layers | Hidden Size | Params | AVG NER micro-f1 (DaNE-testset) | Average Inference Time (Sec/Epoch) | Download | | --- | --- | --- | --- | --- | --- | --- | | Ælæctra Uncased | 12 | ... | 69c6134c606ef8d11d69b8a61a19876a |
mit | ['ælæctra', 'pytorch', 'danish', 'ELECTRA-Small', 'replaced token detection'] | false | Contact For help or further information feel free to connect with the author Malte Højmark-Bertelsen on [hjb@kmd.dk](mailto:hjb@kmd.dk?subject=[GitHub]%20Ælæctra) or any of the following platforms: [<img align="left" alt="MalteHB | Twitter" width="22px" src="https://cdn.jsdelivr.net/npm/simple-icons@v3/icons/twitter... | e583031a19a42bcc1da93dc15c6af7b9 |
['cc0-1.0'] | ['gan', 'computer vision', 'horse to zebra'] | false | cycle_ganhorse2zebra) 🐴 -> 🦓 This repo contains the model and the notebook [to this Keras example on CycleGAN](https://keras.io/examples/generative/cyclegan/). Full credits to: [Aakash Kumar Nain](https://twitter.com/A_K_Nain) | 222901c54eff434c7ae79d405cd59069 |
['cc0-1.0'] | ['gan', 'computer vision', 'horse to zebra'] | false | Background Information CycleGAN is a model that aims to solve the image-to-image translation problem. The goal of the image-to-image translation problem is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, obtaining paired examples isn't always feasi... | 5a85c0b41ef3d2c5486b828dd33fd38a |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-dutch-baseline 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: 0.5107 - Wer: 0.2674 - Cer: 0.0863 | 16dc348c6a1bb557f94debd28628d0a3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with... | 3a6e62bcd3b75ee8ab1c4e5bd36f2c65 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 3.655 | 1.31 | 400 | 0.9337 | 0.7332 | 0.2534 | | 0.42 | 2.61 | 800 | 0.5018 | 0.4115 | 0.1374 | | 0.2267 | 3.92 |... | 83fd543c7893287f0d649c9265b6d800 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | CRDNN with CTC/Attention trained on CommonVoice French (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on CommonVoice (French Language) within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](... | 9584946f3ee502e24f321d374bb07df1 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions (train.tsv) of CommonVoice (FR). - Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of N blocks of convoluti... | 01211fea2c610b68b4e3a327922eadca |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Transcribing your own audio files (in French) ```python from speechbrain.pretrained import EncoderDecoderASR asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-commonvoice-fr", savedir="pretrained_models/asr-crdnn-commonvoice-fr") asr_model.transcribe_file("speechbrain/asr-crdnn-commonvoice-fr/... | 8e8a2d7a7009b7146ed491abbdf090df |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Training The model was trained with SpeechBrain (986a2175). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/Commo... | 4ce741495ed33e63c20c40758410f1ba |
apache-2.0 | ['generated_from_keras_callback'] | false | en-fr-translation This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.7838 - Validation Loss: 1.5505 - Epoch: 1 | 873bf815bf1ebdd2e2692088ebbafb1a |
mit | [] | false | Model Description A CLIP ViT-H/14 frozen xlm roberta large model trained with the LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training done by Romain Beaumont on the [stability.ai](https://stability.ai/) cluster. | f142260a75d84662714391af872f5f6d |
mit | [] | false | Training Procedure Training with batch size 90k for 13B sample of laion5B, see https://wandb.ai/rom1504/open-clip/reports/xlm-roberta-large-unfrozen-vit-h-14-frozen--VmlldzoyOTc3ODY3 Model is H/14 on visual side, xlm roberta large initialized with pretrained weights on text side. The H/14 was initialized from https... | c575bf57aff908f59d3318c75449758a |
mit | [] | false | Results The model achieves imagenet 1k 77.0% (vs 78% for the english H/14)  On zero shot classification on imagenet with translated prompts this model reaches: * 56% in italian (vs 21% for https://github.com/clip-italian/clip-italian) * 53% in japanese (... | 89a8ea639f6cf8cbe062707c8b6ce6b8 |
openrail | [] | false | Hypernetworks of the Musical Isotope girls: Kafu, Sekai, Rime, Coko, and Haru.  on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1148 - Validation Loss: 0.1330 - Epoch: 2 | 2041e0b828bca478b1f0bcc45b476c84 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 65502, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 5067a7a292690c2a0721e021372073d5 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.3044 | 0.1808 | 0 | | 0.1626 | 0.1459 | 1 | | 0.1148 | 0.1330 | 2 | | f00dd65976c81df7bacdbfc063681cea |
apache-2.0 | ['generated_from_trainer'] | false | bert-bert-cased-first512-Conflict-SEP This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6806 - F1: 0.6088 - Accuracy: 0.5914 - Precision: 0.5839 - Recall: 0.6360 | 957f140f6e2520a07217cbaf421ac84e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:| | 0.7027 | 1.0 | 685 | 0.6956 | 0.6018 | 0.5365 | 0.5275 | 0.7003 | | 0.7009 | 2.0 |... | e53f533c7e8514fd4548535a98d44742 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | En-Af This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-af](https://huggingface.co/Helsinki-NLP/opus-mt-en-af) on the None dataset. It achieves the following results on the evaluation set: Before training: - 'eval_bleu': 35.055184951449 - 'eval_loss': 2.225693941116333 After training: - Loss: 2.0057 - B... | d3f53d0b1c915875fd52e868cd8001c5 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 | 32dc57684897a45246b9d2fdffb16d37 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ft1500_norm500_aug4 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: - Loss: 1.5706 - Mse: 3.1412 - Mae: 1.0811 - R2: 0.3860 - Accuracy: 0.43... | 611a726d7f8b31da3426a53ee52e4953 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:| | 1.1391 | 1.0 | 3952 | 1.5706 | 3.1412 | 1.0811 | 0.3860 | 0.4382 | | d60ab2814f9fd02f0366157bc3c4dda3 |
apache-2.0 | ['generated_from_keras_callback'] | false | Sohini17/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.1301 - Validation Loss: 2.6937 - Epoch: 3 | 8dbf0e77d8b46cc4d632f3c30e531806 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 92170, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'deca... | fdef9596ab9078400d23bc11c80e2287 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 5.1520 | 3.1004 | 0 | | 3.5274 | 2.8682 | 1 | | 3.2465 | 2.7544 | 2 | | 3.1301 | 2.6937 | 3 | | c52c264728929ea8d154cdbfbe90e1be |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__sst2__train-32-0 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: 0.8558 - Accuracy: 0.7183 | d83fe30260ad665311c6f2ea99d1d7e3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7088 | 1.0 | 13 | 0.6819 | 0.6154 | | 0.635 | 2.0 | 26 | 0.6318 | 0.7692 | | 0.547 | 3.0 | 39 | 0.5356 | 0.... | 46d3e2e6640b8196ce319f91a26cb28a |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-b0.1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8190 - Bleu: 7.497 - Gen Len: 44.5613 | b95a0fd23ba05d1c3bffb16cb409fd13 |
apache-2.0 | ['translation'] | false | opus-mt-sl-sv * source languages: sl * target languages: sv * OPUS readme: [sl-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sl-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 1f6d58cc2846447ee44f8afaf4a823a3 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0300 | 2da9b1e77852b03b2742138e35d38995 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.6964 | 1.0 | 1209 | 3.3036 | | 3.9031 | 2.0 | 2418 | 3.1324 | | 3.5802 | 3.0 | 3627 | 3.0846 | | 3.4212 | 4.0 | 4836 | 3.0613 ... | d964361bd550ac45038d812ad49f91a0 |
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