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 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.18796906442746e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 3ecccc8eabe8ca084d431cc2191890e8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3703 | 1.0 | 408 | 0.6624 | 0.7029 | | 0.2122 | 2.0 | 816 | 0.6684 | 0.7258 | | 0.9452 | 3.0 | 1224 | 1.0001 | 0.7041 | |... | c822c25f5439ac247eeea4f95183e7c1 |
apache-2.0 | ['byte representation', 'gradient boosting', 'hungarian'] | false | Charmen-Electra A byte-based transformer model trained on Hungarian language. In order to use the model you will need a custom Tokenizer which is available at: [https://github.com/szegedai/byte-offset-tokenizer](https://github.com/szegedai/byte-offset-tokenizer). Since we use a custom architecture with Gradient Boos... | d5090efae61aab346d4793d8ea4a0e89 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-stsb-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.1017 | 5ee97d79ac826fd108109e0d7c27e932 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 8.0065 | 0.7 | 500 | 7.1630 | | 6.9741 | 1.39 | 1000 | 7.2582 | | 6.8436 | 2.09 | 1500 | 7.0893 | | 6.6443 | 2.78 | 2000 | 7.1783 ... | 1dd3773b5364a4e3378ba93426ed0ed6 |
openrail++ | ['stable-diffusion', 'text-to-image', 'stable-diffusion-diffusers', 'diffusers'] | false | Mitsua Diffusion CC0 Model Card Mitsua Diffusion CC0 is a latent text-to-image diffusion model, whose U-Net is **trained from scratch using only public domain/CC0 or copyright images with permission for use**. Text Encoder and VAE are borrowed from [Stable Diffusion v2.1 base](https://huggingface.co/stabilityai/stab... | 919756d4c7756191ca1fd445f363f6b1 |
openrail++ | ['stable-diffusion', 'text-to-image', 'stable-diffusion-diffusers', 'diffusers'] | false | Training Data Sources All data was obtained ethically and in compliance with the site's terms and conditions. No copyright images are used in the training of this model without the permission. No AI generated images are in the dataset. - Traditional Artwork in public domain / CC0 - MET Museum Open Access - Smi... | f75660253678aa1e46a201806a8ec339 |
openrail++ | ['stable-diffusion', 'text-to-image', 'stable-diffusion-diffusers', 'diffusers'] | false | License [Creative Open-Rail++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) ❗❗ “Mitsua Diffusion CC0” means most of the training data is CC0. **the model license itself is NOT CC0**.❗❗ This model is open access and available to all, with a CreativeML OpenRAIL++-M license f... | e3923fd2058f402dfb4ca414fb643899 |
apache-2.0 | ['generated_from_trainer'] | false | BART_corrector_15 This model is a fine-tuned version of [ainize/bart-base-cnn](https://huggingface.co/ainize/bart-base-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0214 - Rouge1: 80.3263 - Rouge2: 78.1274 - Rougel: 80.3215 - Rougelsum: 80.3039 - Gen Len: 19.3993 | 64968694225ab416f960b5ae473660b7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.0597 | 1.0 | 2365 | 0.0367 | 79.3503 | 76.3308 | 79.32 | 79.3005 |... | d579f1d67ef908b2c5b133b9a5fe354a |
apache-2.0 | [] | false | [README UNDER CONSTRUCTION] emBert is a Hungarian text classification model, aimed at classifying 7 possible emotions and a neutral state. The model uses [huBERT](https://huggingface.co/SZTAKI-HLT/hubert-base-cc) tokenizer, and was fine-tuned on a [huBERT](https://huggingface.co/SZTAKI-HLT/hubert-base-cc) base model ... | 53e0bddb0001235eb286e3e2d8f6fb8a |
mit | ['generated_from_keras_callback'] | false | FineTune_Vit5_LR0_00001 This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9405 - Validation Loss: 0.7156 - Train Rouge1: 48.1785 - Train Rouge2: 25.7772 - Train Rougel: 39.50... | e2c9478bd1e0b917c0cbcbcc25f7d674 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 0.9405 | 0.7156 | 48.1785 | 25.7772 ... | 6a7a3a06e980558615b9a6a56178aa15 |
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: 0.3209 - Accuracy: 0.8733 - F1: 0.8797 | ab92c9d229cad5e9d60ea83fa431bf06 |
apache-2.0 | ['generated_from_trainer'] | false | HateXplain-first-annotator This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8646 - Accuracy: 0.6065 | 8fd0f9cf8924b1c4c5265674f5ed389a |
other | ['vision', 'image-segmentation'] | false | Mask2Former Mask2Former model trained on COCO panoptic segmentation (base-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Ma... | aa4e0f9ba6fccedeae7cf7c208ca5ec5 |
other | ['vision', 'image-segmentation'] | false | load Mask2Former fine-tuned on COCO panoptic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-base-coco-panoptic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-base-coco-panoptic") url = "http://images.cocodataset.org/val2017/000000039769... | 09c888cb21fe58e2c0d5502e73ba703b |
apache-2.0 | ['translation'] | false | opus-mt-es-mt * source languages: es * target languages: mt * OPUS readme: [es-mt](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-mt/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | cbe122720d135498d1c8b5944b93d653 |
apache-2.0 | ['generated_from_trainer'] | false | whisper-small_test3 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2153 - eval_wer: 13.6949 - eval_runtime: 1589.8456 - eval_samples_per_second: 2.734 - eval_steps_... | 97416c23ffb2914f12de7e93fe62149f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - training_steps: 1000 - mixed_precisi... | 0bf148a48eafe596290391fa5c929c82 |
apache-2.0 | ['NER'] | false | Model description **mbert-base-uncased-swa** is a model based on the fine-tuned Multilingual BERT base uncased model. It has been trained to recognize four types of entities: - dates & time (DATE) - Location (LOC) - Organizations (ORG) - Person (PER) | 40b991bf265d44f4b71ecbc5370f73d3 |
apache-2.0 | ['NER'] | false | Training Data This model was fine-tuned on the Swahili corpus **(swa)** of the [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset. However, we thresholded the number of entity groups per sentence in this dataset to 10 entity groups. | 4ee19c28490f1d946b5f8634b5a30e66 |
apache-2.0 | ['NER'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("arnolfokam/mbert-base-uncased-swa") model = AutoModelForTokenClassification.from_pretrained("arnolfokam/mbert-base-uncased-swa") nlp = pipeline("ner", ... | d9c4702cf990bbe916f7d2350ba254bb |
apache-2.0 | [] | false | bert-base-en-es-zh-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 t... | 603249eb01d6b3b96d0a1c82b88501f3 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-es-zh-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-es-zh-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | e4c3c307d8e39d4ffda685613de898a7 |
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: - eval_loss: 0.6942 - eval_accuracy: 0.5 - eval_f1: 0.0 - eval_runtime: 272.0623 - eval_... | 2ac1bfdbbe851e780c6e69235461e886 |
mit | [] | false | zdenek art on Stable Diffusion This is the `<zdenek-artwork>` 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 ... | d22c9e1e78f9a7ece4b2d4d734187658 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_wavlm_s25 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16... | 06481ef3528fcc4e5e5f691cb2622340 |
apache-2.0 | [] | false | Model description **CAMeLBERT-CA Poetry Classification Model** is a poetry classification model that was built by fine-tuning the [CAMeLBERT Classical Arabic (CA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model. For the fine-tuning, we used the [APCD](https://arxiv.org/pdf/1905.05700.pdf) datas... | 9ed2c632fe08384870cac7aac662c997 |
apache-2.0 | [] | false | Intended uses You can use the CAMeLBERT-CA Poetry Classification model as part of the transformers pipeline. This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon. | 3a1a077d668d236434015cc128366397 |
apache-2.0 | [] | false | How to use To use the model with a transformers pipeline: ```python >>> from transformers import pipeline >>> poetry = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-ca-poetry') >>> | 023c73fc062c39702f9d5affe9cc1b54 |
apache-2.0 | [] | false | A list of verses where each verse consists of two parts. >>> verses = [ ['الخيل والليل والبيداء تعرفني' ,'والسيف والرمح والقرطاس والقلم'], ['قم للمعلم وفه التبجيلا' ,'كاد المعلم ان يكون رسولا'] ] >>> | f57104d92dbee3c4880e223063089ff8 |
apache-2.0 | [] | false | Apply this to all the verses in the list. >>> verses = [join_verse(verse) for verse in verses] >>> poetry(sentences) [{'label': 'البسيط', 'score': 0.9845284819602966}, {'label': 'الكامل', 'score': 0.752918004989624}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could downlo... | f8dd3d57439d80206c9d4919414df7bf |
mit | [] | false | model by JetJaguar This your the Stable Diffusion model fine-tuned the JustinKrane_artwork concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **art by sks JustinKrane** You can also train your own concepts and upload them to the library by using [this notebook](http... | dbd7818820cdc32d0d168d6bcc34d294 |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-swag This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the swag dataset. It achieves the following results on the evaluation set: - Loss: 0.4382 - Accuracy: 0.8390 | 2b6e8d96dc1a69a58ab18574491f6d53 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 40 - optimizer: Adam with betas=(0.9,0.999) an... | 2b31e30e3997d5dbb40e05c0883c63b4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5707 | 1.0 | 574 | 0.4990 | 0.8097 | | 0.5092 | 2.0 | 1148 | 0.4321 | 0.8361 | | 0.3597 | 3.0 | 1722 | 0.4382 | 0.... | 53e8d273df07aeff07b8214fc46590af |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Wav2Vec2-Large-XLSR-53-Punjabi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Punjabi using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. | b06c577fb7fdf11629c065a4aeebdb85 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "pa-IN", split="test[:2%]"). processor = Wav2Vec2Processor.from_p... | 2a7af3f25229f21c050e271f3e1cc730 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "pa-IN", split="test") w... | 59cc0540daf3fb2b5fbea2ac73680c21 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.a... | 825a27ac8845e39fa01875d259d4e915 |
other | ['fp16', 'safetensors'] | false | Original Models - https://huggingface.co/WarriorMama777/OrangeMixs - https://huggingface.co/syaimu/7th_Layer - https://huggingface.co/unkounko/BalloonMix - https://huggingface.co/TASUKU2023/Chilloutmix | e80f9dd404afd655b3cf811571e8ed69 |
other | ['fp16', 'safetensors'] | false | Already fp16 - https://huggingface.co/thiros/YuzuLemonTea - https://huggingface.co/hesw23168/SD_Shirayuki_Model - https://huggingface.co/nuigurumi/cinnamon_mix - https://huggingface.co/nuigurumi/basil_mix - https://huggingface.co/nuigurumi/Almond_mix | eaa030b491db524546465e906aebb344 |
other | ['fp16', 'safetensors'] | false | Major Models - https://huggingface.co/hesw23168/SD-Elysium-Model - https://huggingface.co/Linaqruf/anything-v3-better-vae - https://huggingface.co/JosephusCheung/ACertainty - https://huggingface.co/JosephusCheung/ACertainModel - https://huggingface.co/JosephusCheung/ACertainThing | b4898daa667bbdd39070967ef891ba6d |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | t5-small-finetuned-samsum-en This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.9335 - Rouge1: 44.3313 - Rouge2: 20.71 - Rougel: 37.221 - Rougelsum: 40.9603 | 46f76337ffb3b277069d208e34d5ea0d |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - 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: 10 | 1782570aec0aa747e5dd7be575714709 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 1.4912 | 1.0 | 300 | 1.9043 | 44.1517 | 20.0186 | 36.6053 | 40.5164 | | 1.5055 | 2.0 ... | 5dde2bff956e0a8fc642787a320c3fe1 |
cc-by-4.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | SHerbert - Polish SentenceBERT SentenceBERT is a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. Training was based on the original paper [Siamese BERT models for the task of ... | 78503ae0b15a2821328cc3d1d5756536 |
cc-by-4.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Tokenizer As in the original HerBERT implementation, the training dataset was tokenized into subwords using a character level byte-pair encoding (CharBPETokenizer) with a vocabulary size of 50k tokens. The tokenizer itself was trained with a tokenizers library. We kindly encourage you to use the Fast version of the... | e7b4ddcfc678d99f087f2174acfad91f |
cc-by-4.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Usage ```python from transformers import AutoTokenizer, AutoModel from sklearn.metrics import pairwise sbert = AutoModel.from_pretrained("Voicelab/sbert-base-cased-pl") tokenizer = AutoTokenizer.from_pretrained("Voicelab/sbert-base-cased-pl") s0 = "Uczenie maszynowe jest konsekwencją rozwoju idei sztucznej intelig... | acf526863a197d56a3ec12207e4c0a86 |
cc-by-4.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Results | Model | Accuracy | Source | |--------------------------|------------|---------------------------------------------------------| | SBERT-WikiSec-base (EN) | 80.42% | https://arxiv.org/abs/1908.10084 | | SBERT-Wi... | 71bc0f320c9341fca0d52d506e6a3a76 |
apache-2.0 | ['bert', 'qnli', 'glue', 'torchdistill'] | false | `bert-base-uncased` fine-tuned on QNLI dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb). The hyperparameters are the same as thos... | eebb2a5a9c391077870841ddae43f9b8 |
mit | ['zero-shot-classification', 'xnli', 'nli', 'fr'] | false | How to use Two different usages : - As a Zero-Shot sequence classifier : ```python classifier = pipeline("zero-shot-classification", model="BaptisteDoyen/camembert-base-xnli") sequence = "L'équipe de France joue aujourd'hui au Parc des Princes" candidate_labels = ["sport","politique","scie... | dc4e1b13c64a9e9db72c32167af18e0d |
mit | ['zero-shot-classification', 'xnli', 'nli', 'fr'] | false | 'scores': [0.8595073223114014, 0.10821866989135742, 0.0322740375995636]} ``` - As a premise/hypothesis checker : <br> The idea is here to compute a probability of the form \\( P(premise|hypothesis ) \\) ```python | 4d6497aba6fde034ba982a18b694d782 |
mit | ['zero-shot-classification', 'xnli', 'nli', 'fr'] | false | load model and tokenizer nli_model = AutoModelForSequenceClassification.from_pretrained("BaptisteDoyen/camembert-base-xnli") tokenizer = AutoTokenizer.from_pretrained("BaptisteDoyen/camembert-base-xnli") | be0cc4e3be6fc321e9924bf72acc8431 |
mit | ['zero-shot-classification', 'xnli', 'nli', 'fr'] | false | "entailment" (0) as the probability of the label being true entail_contradiction_logits = logits[:,::2] probs = entail_contradiction_logits.softmax(dim=1) prob_label_is_true = probs[:,0] prob_label_is_true[0].tolist() * 100 | 0d1025e6b70b344aaa36bd19818f3031 |
mit | ['zero-shot-classification', 'xnli', 'nli', 'fr'] | false | Training data Training data is the french fold of the [XNLI](https://research.fb.com/publications/xnli-evaluating-cross-lingual-sentence-representations/) dataset released in 2018 by Facebook. <br> Available with great ease using the ```datasets``` library : ```python from datasets import load_dataset dataset = load... | 68d1f8aaf249b2b8eb4ce3bf6bb8bda5 |
mit | ['zero-shot-classification', 'xnli', 'nli', 'fr'] | false | Training/Fine-Tuning procedure Training procedure is here pretty basic and was performed on the cloud using a single GPU. <br> Main training parameters : - ```lr = 2e-5``` with ```lr_scheduler_type = "linear"``` - ```num_train_epochs = 4``` - ```batch_size = 12``` (limited by GPU-memory) - ```weight_decay = 0.01```... | ecf10899ba73eb45918356f8199d156e |
mit | ['zero-shot-classification', 'xnli', 'nli', 'fr'] | false | Eval results We obtain the following results on ```validation``` and ```test``` sets: | Set | Accuracy | | ---------- |-------------| | validation | 81.4 | | test | 81.7 | | 31e899ddcdaf39a95eea94929e6ba0bb |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1593 | a9ff058d35e2747f9ec3251ec86edb1f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 140 | 2.3338 | | No log | 2.0 | 280 | 2.1981 | | No log | 3.0 | 420 | 2.1593 | | e82e7901b74bb52f280f82871ff436e8 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | openai/whisper-large-v2-Assamese This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 1.1469 - Wer: 58.13030138964086 | e6262d89cf1ad41c374c3a65e02cb92b |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 31bc7a3c3d086e751772723385d04af2 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0646 | 1.13 | 600 | 1.1469 | 58.1303 | | f68607f65dd00dbc2dbc03d223f643e4 |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_vp-it_s211 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 0ea0fa3d20e4ade421294c0027b51b03 |
mit | ['generated_from_trainer'] | false | AgitationTextV4 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5320 - Accuracy: 0.76 - Precision: 0.8507 - Recall: 0.8028 - F1: 0.8261 | 5af2f537e9f85bceae41cfeddf22d0e5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.6223 | 1.0 | 50 | 0.6247 | 0.66 | 0.7403 | 0.8028 | 0.7703 | | 0.5431 | 2.0 |... | 0e19383b2811564a44f9b144ddf38506 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-home-2-16-5-oos This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 | ec30f3ed4eaf3f4af23f2858ff901daa |
apache-2.0 | ['translation'] | false | opus-mt-es-srn * source languages: es * target languages: srn * OPUS readme: [es-srn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-srn/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | f2d5fa57c22fb7b6b330b79d65cc4616 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'style-transfer', 'efficient', 'formality-style-transfer'] | false | IT5 Cased Small Efficient EL32 for Formal-to-informal Style Transfer 🤗 *Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!* This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingface.co/it5/it5-efficient-small-el32... | 3e6e97a22168ffde12f0d1caa85cefb3 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'style-transfer', 'efficient', 'formality-style-transfer'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines f2i = pipeline("text2text-generation", model='it5/it5-efficient-small-el32-formal-to-informal') f2i("Vi ringrazio infinitamente per vost... | c89ccf982e085d1b1e35bd9498b35838 |
mit | [] | false | Model description DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots o... | 14f80ad17a4128a33ee432a5fb42f34a |
mit | [] | false | Training data The NLP Deep 2 model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). It was fine tuned on [IMDB](https://arxiv.org/abs/2005.... | 9fa908af2d35f76416f7d7e7d44e4493 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2218 - Accuracy: 0.9205 - F1: 0.9208 | 1c52ae1223e6ef26db6623d218be9e67 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8262 | 1.0 | 250 | 0.3223 | 0.9005 | 0.8971 | | 0.2474 | 2.0 | 500 | 0.2218 | 0.9205 | 0.9208 | | eae5cdddba113f5654a73494920f5021 |
apache-2.0 | ['generated_from_keras_callback'] | false | Abdulmateen/abdul-distillbert-finetuned-imdb 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: 2.8507 - Validation Loss: 2.5825 - Epoch: 0 | 0f8f908a2654779d45d61074deecc950 |
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... | 1ec1df52b1378420337c0dbfe127e7d6 |
mit | ['generated_from_trainer'] | false | bert-base-historic-multilingual-64k-td-cased-squad-nl This model is a fine-tuned version of [dbmdz/bert-base-historic-multilingual-64k-td-cased](https://huggingface.co/dbmdz/bert-base-historic-multilingual-64k-td-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6382 | de6ac9a027c233c6485cff959fdec37a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0101 | 1.0 | 4659 | 1.8679 | | 1.6528 | 2.0 | 9318 | 1.6382 | | 3a07986c22fcf85cc827230a5eac2919 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__hate_speech_offensive__train-8-8 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: 1.0005 - Accuracy: 0.518 | 75c0b012497534bff52af34de9a783a0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1029 | 1.0 | 5 | 1.1295 | 0.0 | | 1.0472 | 2.0 | 10 | 1.1531 | 0.0 | | 1.054 | 3.0 | 15 | 1.1475 | 0.... | 1ee275b0e34338a5de7baaed07395d60 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_140k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, Step 140k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ... | 2e13abca92b23dd2e9955c3166e0bd5f |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_140k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_140k') model = TFBertModel.from_pretrained("google/multibe... | 8e1d7b8282286785de70c2351b8eb662 |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-medium_sst2_negation0.2_pretrainedFalse 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: 5.2012 | 3fd4e9351c6bafc862e2b0cefd09b5be |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.7789 | 1.0 | 1072 | 5.4517 | | 4.368 | 2.0 | 2144 | 5.2641 | | 4.1183 | 3.0 | 3216 | 5.2012 | | c4906f39d089d7e13dc88f383745b750 |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-finetuned-en-es This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-es](https://huggingface.co/Helsinki-NLP/opus-mt-en-es) on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: 1.9813 - Bleu: 21.5636 - Gen Len: 30.0992 | 118e46348fd3be1966e49af6d3974c07 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 2.09 | 1.0 | 4382 | 1.9813 | 21.5636 | 30.0992 | | 67d3299a97873181ba61994d5a57094e |
apache-2.0 | [] | false | doc2query/msmarco-japanese-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20... | 6455d32e499f894b63e66be906310518 |
apache-2.0 | [] | false | Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
model_name = 'doc2query/msmarco-japanese-mt5-base-v1'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
text = "Python（パイソン）はインタープリタ型の高水準汎用プログラミング言語である。グイド... | b6fbd0f4fe27560fdc6dc285cf0d496d |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard', 'pashto', 'ps'] | false | Whisper Medium Pashto This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the google/fleurs ps_af dataset. It achieves the following results on the evaluation set: - Loss: 1.4807 - Wer: 50.5448 | e19e11b97a42184c43d2293b702c3821 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard', 'pashto', 'ps'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-07 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | cc2f869f1304bad373811a41ad3d9f4b |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard', 'pashto', 'ps'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.0334 | 14.29 | 100 | 1.0348 | 50.0908 | | 0.0021 | 28.57 | 200 | 1.1971 | 49.4855 | | 0.0007 | 42.86 | 300 | 1.2651 | 4... | 5b0bf73b2fdf0771c023a93dcc0f65c3 |
mit | [] | false | Model Description <!-- Provide a longer summary of what this model is. --> ['Comprehensive_school', 'Institute_of_technology', 'University_of_Kansas', 'Imperial_College_London', 'Brigham_Young_University', 'Yale_University', 'Eton_College', 'Northwestern_University', 'Madrasa', 'Education', 'Washington_University_in... | 7c7707d0380714851d3e089f1a73de33 |
apache-2.0 | ['translation'] | false | opus-mt-fi-niu * source languages: fi * target languages: niu * OPUS readme: [fi-niu](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-niu/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | ecac2c3e3e6c7f4cfe05e38ef04fdc19 |
apache-2.0 | ['generated_from_keras_callback'] | false | andywedlake/test-finetuned-imdb 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: 1.7505 - Validation Loss: 2.0472 - Epoch: 9 | 502d608482fa4278f31943267b5da2b8 |
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': 0.0005, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_ste... | b46a79177ee31780f9b35c26c77d96b2 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.6663 | 2.5238 | 0 | | 2.4783 | 2.6058 | 1 | | 2.4284 | 2.5982 | 2 | | 2.3804 | 2.5057 | 3 | | 2.3487 | 2.6968 | 4 | | 2.1253 |... | 1c42f6825ab6da906c868a54e4624baa |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_qqp_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.7047 - Accuracy: 0.6401 - F1: 0.0484 - Combined Score: 0.3442 | 144c99b1ad650ddc8ab13c912a33ff26 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.824 | 1.0 | 1422 | 0.7761 | 0.6318 | 0.0 | 0.3159 | | 0.7581 | 2.0 | 2844 | ... | d6aa0231d2aba02d99e1be3d96947367 |
mit | [] | false | Pretrained `avici` models The neural networks trained in **a**mortized **v**ariational **i**nference for **c**ausal d**i**scovery (AVICI) **infer causal structure from data** based on a simulator of the domain of interest. By being trained on simulated data, the models can acquire realistic inductive biases from p... | 3200bbfd3c6db956cc19f5f94707bd15 |
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