license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Usage To use this model you have to download the file aswell as drop it into the "\stable-diffusion-webui\models\Stable-diffusion" folder Token: ```neko``` If it is to strong just add [] around it. Trained until 10000 steps Have fun :) | d1f9554ae91bf137be085556bacee3c9 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Example Pictures <table> <tr> <td><img src=https://i.imgur.com/MpyeqMe.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/wxzvHrL.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/MuUnJY5.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/XeDC8xA.png ... | 7863ddc1c98f939b5f28cb3dc92673dc |
mit | [] | false | LONGFORMER-BASE-4096 fine-tuned on SQuAD v1 This is longformer-base-4096 model fine-tuned on SQuAD v1 dataset for question answering task. [Longformer](https://arxiv.org/abs/2004.05150) model created by Iz Beltagy, Matthew E. Peters, Arman Coha from AllenAI. As the paper explains it > `Longformer` is a BERT-like... | a50fce6f7cbdd5178359d3c157c8128e |
mit | [] | false | Model Training This model was trained on google colab v100 GPU. You can find the fine-tuning colab here [](https://colab.research.google.com/drive/1zEl5D-DdkBKva-DdreVOmN0hrAfzKG1o?usp=sharing). Few things to keep in mind while training longfor... | e1674c19c020f7506de5d9cabf471d5c |
mit | [] | false | Model in Action 🚀 ```python import torch from transformers import AutoTokenizer, AutoModelForQuestionAnswering, tokenizer = AutoTokenizer.from_pretrained("valhalla/longformer-base-4096-finetuned-squadv1") model = AutoModelForQuestionAnswering.from_pretrained("valhalla/longformer-base-4096-finetuned-squadv1") text ... | 1e3c0c4c5fb9a1bfd9c43c9194e24054 |
mit | [] | false | the forward method will automatically set global attention on question tokens attention_mask = encoding["attention_mask"] start_scores, end_scores = model(input_ids, attention_mask=attention_mask) all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist()) answer_tokens = all_tokens[torch.argmax(start_scores... | 192e483f16677c2d70ab6082d17ff995 |
mit | [] | false | output => democratized NLP ``` The `LongformerForQuestionAnswering` isn't yet supported in `pipeline` . I'll update this card once the support has been added. > Created with ❤️ by Suraj Patil [](https://github.com/patil-suraj/) [!... | 31ddc53af307dc7f411032757f49a5e4 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_rte_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.3915 - Accuracy: 0.5271 | 72d9bee2516c6d4c430a41f57c5b9451 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4093 | 1.0 | 20 | 0.3917 | 0.5271 | | 0.4077 | 2.0 | 40 | 0.3922 | 0.5271 | | 0.4076 | 3.0 | 60 | 0.3916 | 0.... | a9b075e5c8eeb087ef8b6d86367f3d38 |
mit | ['generated_from_trainer'] | false | roberta-base.CEBaB_confounding.uniform.absa.5-class.seed_43 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.3790 - Accuracy: 0.8913 - Macro-f1: 0.8893 - Weighted-macro-... | bb80a68f2759062528452fad8d71b6c5 |
apache-2.0 | ['translation'] | false | phi-eng * source group: Philippine languages * target group: English * OPUS readme: [phi-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/phi-eng/README.md) * model: transformer * source language(s): akl_Latn ceb hil ilo pag war * target language(s): eng * model: transformer * pre-proces... | 5b2c14d0e7257c0fb1404134ccce8fbd |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.akl-eng.akl.eng | 11.6 | 0.321 | | Tatoeba-test.ceb-eng.ceb.eng | 21.7 | 0.393 | | Tatoeba-test.hil-eng.hil.eng | 17.6 | 0.371 | | Tatoeba-test.ilo-eng.ilo.eng | 36.6 | 0.560 | | Tatoeba-test.multi.en... | 8d996abd191d9f9f1fcf8b449b773994 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: phi-eng - source_languages: phi - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/phi-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['phi', 'en'] - src_constituents: {'ilo', 'akl_Latn',... | b458533f66100ad6d3cd9e6050f2161f |
mit | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | wav2vec2-xls-r-300m-uk This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0927 - Wer: 0.1222 - Cer: 0.0204 | c574c1a982f64a93ce7bbad01e001c9a |
mit | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 40 - eval_batch_size: 40 - seed: 42 - gradient_accumulation_steps: 6 - total_train_batch_size: 240 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | b345737facb09e501d84d3ac7df0bfd3 |
mit | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |:-------------:|:-----:|:----:|:------:|:---------------:|:------:| | 9.0008 | 1.68 | 200 | 1.0 | 3.7590 | 1.0 | | 3.4972 | 3.36 | 400 | 1.0 | 3.3933 | 1.0 | | 3.3432 | 5.04 |... | 1a3f48e16949f9294d9fa230beab7b7a |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-suicide-depression This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6622 - Accuracy: 0.7158 | 87fb75b45bdc97dcaf7ffa220e65499e |
apache-2.0 | ['generated_from_trainer'] | false | Model description Just a **POC** of a Transformer fine-tuned on [SDCNL](https://github.com/ayaanzhaque/SDCNL) dataset for suicide (label 1) or depression (label 0) detection in tweets. **DO NOT use it in production** | 57da9168df13d43afc91f2977f91e1f6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 214 | 0.6204 | 0.6632 | | No log | 2.0 | 428 | 0.6622 | 0.7158 | | 0.5244 | 3.0 | 642 | 0.7312 | 0.... | a94841318001d32fb5495b9518f22968 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-mnli-target-glue-rte This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-mnli](https://huggingface.co/muhtasham/tiny-mlm-glue-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5419 - Accuracy: 0.6137 | f6029552b014869aed31f17c9c4c63b5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6373 | 6.41 | 500 | 0.6751 | 0.5993 | | 0.4271 | 12.82 | 1000 | 0.8148 | 0.6390 | | 0.2621 | 19.23 | 1500 | 0.9962 | 0.... | 4f4154c326df7ed99e0daf0f77ba2d60 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2251 - Accuracy: 0.923 - F1: 0.9232 | 357a4e85dbff759fff614f023dc74e7c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8243 | 1.0 | 250 | 0.3183 | 0.906 | 0.9019 | | 0.2543 | 2.0 | 500 | 0.2251 | 0.923 | 0.9232 | | 2317afbcfda2bad2386c574bbc27fd94 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2281 - Accuracy: 0.924 - F1: 0.9240 | e24e19a3a5409c52999852267b6aa630 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8687 | 1.0 | 250 | 0.3390 | 0.9015 | 0.8984 | | 0.2645 | 2.0 | 500 | 0.2281 | 0.924 | 0.9240 | | 245b6a7f21eeee0d3336f5a64354cd7e |
apache-2.0 | ['generated_from_trainer'] | false | medium-mlm-tweet-target-tweet This model is a fine-tuned version of [muhtasham/medium-mlm-tweet](https://huggingface.co/muhtasham/medium-mlm-tweet) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.9066 - Accuracy: 0.7594 - F1: 0.7637 | 8af73ff35ce11330790f12e36ff1493e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4702 | 4.9 | 500 | 0.8711 | 0.7540 | 0.7532 | | 0.0629 | 9.8 | 1000 | 1.2918 | 0.7701 | 0.7668 | | 0.0227 |... | ab001e8395e0af13052d6f37ac284245 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-offensive-lm-tapt This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 | 5c395ef6eb29842af2f6a3fd3e237a79 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 28 - seed: 42 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 16 - mixed_precision_train... | e1bf38018957b0d6cc3c877a1ae2290b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.219 | 0.07 | 100 | 0.0728 | | 0.0358 | 0.13 | 200 | 0.0090 | | 0.0106 | 0.2 | 300 | 0.0033 | | 0.0056 | 0.26 | 400 | 0.0020 ... | 2e1a154f9a1907bb9834d0120172c417 |
apache-2.0 | ['LABEL-0 = NONE', 'LABEL-1 = B-DATE', 'LABEL-2 = I-DATE', 'LABEL-3 = B-TIME', 'LABEL-4 = I-TIME', 'LABEL-5 = B-DURATION', 'LABEL-6 = I-DURATION', 'LABEL-7 = B-SET', 'LABEL-8 = I-SET'] | false | Bio-RoBERTime This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0177 - Precision: 0.8121 - Recall: 0.8854 - F1: 0.84... | 63ef28193f6fcc8e42ea099a932226f0 |
apache-2.0 | ['LABEL-0 = NONE', 'LABEL-1 = B-DATE', 'LABEL-2 = I-DATE', 'LABEL-3 = B-TIME', 'LABEL-4 = I-TIME', 'LABEL-5 = B-DURATION', 'LABEL-6 = I-DURATION', 'LABEL-7 = B-SET', 'LABEL-8 = I-SET'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 72 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 24 | 04ad0b4b9b527333505b317865719fc1 |
apache-2.0 | ['LABEL-0 = NONE', 'LABEL-1 = B-DATE', 'LABEL-2 = I-DATE', 'LABEL-3 = B-TIME', 'LABEL-4 = I-TIME', 'LABEL-5 = B-DURATION', 'LABEL-6 = I-DURATION', 'LABEL-7 = B-SET', 'LABEL-8 = I-SET'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0433 | 1.0 | 12 | 0.0443 | 0.4948 | 0.5 | 0.4974 | 0.9800 | | 0.0234 | 2.0 |... | 39cafa8fca799c4ebd3b0e59465888d9 |
unknown | [] | false | karaokeroom.safetensors [<img width="480" src="https://i.imgur.com/hclI0vj.jpg">](https://i.imgur.com/hclI0vj.jpg) [<img width="480" src="https://i.imgur.com/8H3c7eE.jpg">](https://i.imgur.com/8H3c7eE.jpg) カラオケ屋さんの部屋の雰囲気を学習したLoRAです。 Loraを読み込ませて、プロンプトに **karaokeroom** と記述してください。 プロンプトに、1girl, karaoke, microphone... | df9bbc68bcefe6f4766dfdac206c13a0 |
unknown | [] | false | この実験をやってみた動機 たとえばプロンプトに shibuya,city, と書くと渋谷っぽい風景の絵を描いてくれます。これはモデルが「渋谷」という概念を知ってるという事だと思います。 しかし、 Nishinomiya と書いても西宮っぽい風景の絵を描いてはくれません。これはモデルが「西宮」という概念を知らないという事だと思います。 最近、LoRAという手法でスペックが低いパソコン(GPU)でも追加学習が出来る方法が普及してきました。既存のモデルでは描けないキャラクターや衣装等を学習させている方がたくさんいらっしゃいます。 そこで自分は、風景の写真を何枚か学習させれば、その「場所」の概念を学習してくれるのではないかと... | 3697d917068a5563f344c147cd135e02 |
unknown | [] | false | 問題点、今後の課題 カラオケ店の風景は再現できるようになりました。が、当LoRAを適用してカラオケを歌う女の子の絵を生成すると、人物の描画や画風に影響が生じる場合があります。 これはおそらく場所の概念だけではなく、素材写真の画風等も学習してしまったものだと思います。 現状はLoraを適用するWeightを下げることで影響を軽減できますが、学習方法やLoRAの適用の仕方で影響を軽減することが出来ないか？と考えています。 ・U-net層でWeight調整することで影響を押さえられる？ 実は僕も全然よく分かってないのですが（！）階層マージ(Marge Block Weighted)で多くの方が様々なモデルマージに挑戦した結... | 63834e374ea177892e89069b5a282cb4 |
apache-2.0 | ['generated_from_trainer'] | false | swin-finetuned-food101 This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.2772 - Accuracy: 0.9210 | 8a4e587dd8a1eb17548f9aea5a6743fb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5077 | 1.0 | 1183 | 0.3851 | 0.8893 | | 0.3523 | 2.0 | 2366 | 0.3124 | 0.9088 | | 0.1158 | 3.0 | 3549 | 0.2772 | 0.... | 3d3905e11f91de506b088fc08bf195ca |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_unispeech_s784 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | f610eec628957738cb829496aec331ba |
mit | [] | false | Fast_DreamBooth_AMLO 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 | 8f56780d5e29813ef4670d8043f06233 |
mit | [] | false | model by mrcrois This your the Stable Diffusion model fine-tuned the Fast_DreamBooth_AMLO concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **AMLO17.jpg, AMLO21.jpg, AMLO9.jpg, AMLO18.jpg, AMLO2.jpg, AMLO1.jpg, AMLO13.jpg, AMLO15.jpg, AMLO14.jpg, AMLO22.jpg, AMLO... | 995a48dc72c75a9387f01a4ea4558241 |
mit | [] | false | German ELECTRA large Released, Oct 2020, this is a German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to t... | bbd7dd557a0a7a55075d8ebb0850799e |
mit | [] | false | Performance ``` GermEval18 Coarse: 80.70 GermEval18 Fine: 55.16 GermEval14: 88.95 ``` See also: deepset/gbert-base deepset/gbert-large deepset/gelectra-base deepset/gelectra-large deepset/gelectra-base-generator deepset/gelectra-large-generator | 04cb0cd1268f18b9a03328b4a118ab1b |
apache-2.0 | ['generated_from_trainer'] | false | xlsr-53-bemba-15hrs This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2789 - Wer: 0.3751 | f69404d183957973055fa4acab0f970a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4138 | 0.71 | 400 | 0.4965 | 0.7239 | | 0.5685 | 1.43 | 800 | 0.2939 | 0.4839 | | 0.4471 | 2.15 | 1200 | 0.2728 | 0.4467 | |... | 2bfd681c3b4bdf2cf50c3b389b19e326 |
apache-2.0 | [] | false | ParsBERT (v2.0) A Transformer-based Model for Persian Language Understanding We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes! Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) rep... | d6084e05eb0e4d637642e23b7f45a273 |
apache-2.0 | [] | false | DigiMag A total of 8,515 articles scraped from [Digikala Online Magazine](https://www.digikala.com/mag/). This dataset includes seven different classes. 1. Video Games 2. Shopping Guide 3. Health Beauty 4. Science Technology 5. General 6. Art Cinema 7. Books Literature | Label | | 83a10f14734e380fd891f24e4b2b490b |
apache-2.0 | [] | false | | |:------------------:|:----:| | Video Games | 1967 | | Shopping Guide | 125 | | Health Beauty | 1610 | | Science Technology | 2772 | | General | 120 | | Art Cinema | 1667 | | Books Literature | 254 | **Download** You can download the dataset from [here](https://drive.google... | 292c57662cf27a3b93c86a1839c0299e |
apache-2.0 | [] | false | Results The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures. | Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | |:-----------------:|:-----------:|:-----------:|:-----:| | Digikala Magazine | 93.65* | 93.59 | 90.72 | | 8e0b426a81a8cf123df4a8fae82172bb |
apache-2.0 | [] | false | How to use :hugs: | Task | Notebook | |---------------------|---------------------------------------------------------... | a27931c938dedece7c772e82d961b473 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-0'] | false | MultiBERTs Seed 0 Checkpoint 1400k (uncased) Seed 0 intermediate checkpoint 1400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/g... | 7847d451871d7b8033c6de73e9fdbfa1 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-0'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-0-1400k') model = BertModel.from_pretrained("multiberts-seed-0-1400k") text = "Replace me by any text you'd lik... | 876b2745bf089990dfc4ba458a94bbba |
unknown | [] | false | Stable Diffusion Model Trained using Dreambooth using the original spites of the character Rika Furude from Higurashi <br> Tag to trigger Rika Generation is "furude_rika" Example Images: <img src="https://i.imgur.com/4Rsf4WI.png" alt="Girl in a jacket" > <b> DISCLAIMER: I am not responsible for what images you produ... | 5fc2df744e71622da44f393b084e523a |
mit | ['generated_from_keras_callback'] | false | sachinsahu/Warsaw_Pact-clustered This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0968 - Train End Logits Accuracy: 0.9688 - Train Start Logits Acc... | b989f745953d82967505048108d5ad0d |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 8b0e5d75c3f5db2828ad1c0ba35bdd05 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'stable-diffusion-diffusers'] | false | The Emoji file names were converted to become the text descriptions. It made the model learn a few special words: "flat", "high contrast" and "color"  | 76c1a70e76b55d1f5a307a2da78def84 |
apache-2.0 | [] | false | SnappFood [Snappfood](https://snappfood.ir/) (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification): 1. Happy 2. Sad | Label | | d38e277d465f64b4c229d94f3e681268 |
apache-2.0 | [] | false | Results The following table summarizes the F1 score obtained as compared to other models and architectures. | Dataset | ALBERT-fa-base-v2 | ParsBERT-v1 | mBERT | DeepSentiPers | |:------------------------:|:-----------------:|:-----------:|:-----:|:-------------:| | SnappFood User Comments | ... | d65cc09c95927179ccdbfa6164c8b9b3 |
apache-2.0 | ['G2P', 'Grapheme-to-Phoneme', 'speechbrain', 'text2text-generation'] | false | SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation This repository provides all the necessary tools to perform English grapheme-to-phoneme conversion with a pretrained SoundChoice G2P model using SpeechBrain. It is trained on LibriG2P training data derived from [LibriSpeech Alignments](https://zenod... | d2108d079a52dd5d3a163e6a95c2deb0 |
apache-2.0 | ['G2P', 'Grapheme-to-Phoneme', 'speechbrain', 'text2text-generation'] | false | Install SpeechBrain First of all, please install SpeechBrain with the following command (local installation): ```bash pip install speechbrain pip install transformers ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). | c67d432602e008d633dd2e3cd1087289 |
apache-2.0 | ['G2P', 'Grapheme-to-Phoneme', 'speechbrain', 'text2text-generation'] | false | Perform G2P Conversion Please follow the example below to perform grapheme-to-phoneme conversion with a high-level wrapper. ```python from speechbrain.pretrained import GraphemeToPhoneme g2p = GraphemeToPhoneme.from_hparams("speechbrain/soundchoice-g2p") text = "To be or not to be, that is the question" phonemes = g... | a5eb2bc2b159063979a3e62b765e9902 |
apache-2.0 | ['G2P', 'Grapheme-to-Phoneme', 'speechbrain', 'text2text-generation'] | false | Training The model was trained with SpeechBrain (aa018540). 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/Libri... | c1265ae063ac9c9f67e59e9a9cbed7f7 |
apache-2.0 | ['G2P', 'Grapheme-to-Phoneme', 'speechbrain', 'text2text-generation'] | false | **Citing SpeechBrain** Please, cite SpeechBrain if you use it for your research or business. ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan a... | c276efb62954fa2da4dc55320bc9ec69 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-few-shot-sentiment-model 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: 0.6819 - Accuracy: 0.75 - F1: 0.8 | 79fb7ec497ae08c368d3b03f72e3de98 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00015 - 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: 1000 - num_epochs: 10 | 998c071729b857c4710c7270c0471aba |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_wnli_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3442 - Accuracy: 0.5634 | 874f2d89542c93c904cc910606f0517e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3478 | 1.0 | 3 | 0.3444 | 0.5634 | | 0.3472 | 2.0 | 6 | 0.3445 | 0.5634 | | 0.3467 | 3.0 | 9 | 0.3444 | 0.... | ae598eee6ba02cf52ecac35e5fe49936 |
apache-2.0 | [] | false | <a name="introduction"></a> BERTweet.BR: A Pre-Trained Language Model for Tweets in Portuguese Having the same architecture of [BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet) we trained our model from scratch following [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta) pre-t... | 828a7f321335afff6849e053fe1fcaa3 |
apache-2.0 | [] | false | Normalized Inputs ```python import torch from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained('melll-uff/bertweetbr') tokenizer = AutoTokenizer.from_pretrained('melll-uff/bertweetbr', normalization=False) | beea19903102e1dd87adbfe91096e74a |
apache-2.0 | [] | false | INPUT TWEETS ALREADY NORMALIZED! inputs = [ "Procuro um amor , que seja bom pra mim ... vou procurar , eu vou até o fim :nota_musical:", "Que jogo ontem @USER :mãos_juntas:", "Demojizer para Python é :polegar_para_cima: e está disponível em HTTPURL"] encoded_inputs = tokenizer(inputs, return_tensors="pt",... | e7da89b022b01d57bb40ac8595fd697a |
apache-2.0 | [] | false | CLS Token of last hidden states. Shape: (number of input sentences, hidden sizeof the model) last_hidden_states[0][:,0,:] tensor([[-0.1430, -0.1325, 0.1595, ..., -0.0802, -0.0153, -0.1358], [-0.0108, 0.1415, 0.0695, ..., 0.1420, 0.1153, -0.0176], [-0.1854, 0.1866, 0.3163, ..., -0.2117, 0.21... | 68151f34a21f516ef66c7fe4c48e4fae |
apache-2.0 | [] | false | Normalize raw input Tweets ```python from emoji import demojize import torch from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained('melll-uff/bertweetbr') tokenizer = AutoTokenizer.from_pretrained('melll-uff/bertweetbr', normalization=True) inputs = [ "Procuro um amor , que seja ... | fb41c8551187cecb1f2e25e194c4768b |
apache-2.0 | [] | false | Tokenizer first normalizes tweet sentences ['Procuro um amor , que seja bom pra mim ... vou procurar , eu vou até o fim :nota_musical:', 'Que jogo ontem @USER :mãos_juntas:', 'Demojizer para Python é :polegar_para_cima: e está disponível em HTTPURL'] encoded_inputs = tokenizer(inputs, return_tensors="pt", padding=T... | 8f3ee5dcffb639c7e1f10ee1178f59ba |
apache-2.0 | [] | false | Mask Filling with Pipeline ```python from transformers import pipeline model_name = 'melll-uff/bertweetbr' tokenizer = AutoTokenizer.from_pretrained('melll-uff/bertweetbr', normalization=False) filler_mask = pipeline("fill-mask", model=model_name, tokenizer=tokenizer) filler_mask("Rio é a <mask> cidade do Brasil."... | 37647749d4c29c0ce1830e51a7a67ab5 |
apache-2.0 | [] | false | Output [{'sequence': 'Rio é a melhor cidade do Brasil.', 'score': 0.9871652126312256, 'token': 120, 'token_str': 'm e l h o r'}, {'sequence': 'Rio é a pior cidade do Brasil.', 'score': 0.005050931591540575, 'token': 316, 'token_str': 'p i o r'}, {'sequence': 'Rio é a maior cidade do Brasil.', 'score': ... | 8786514f638e8095c5777b37ec87cab8 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_age_teens-0_sixties-10_s225 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure t... | 49c06652636adcad5d0b06043140824f |
apache-2.0 | ['question-answering'] | false | Model Overview This is an ELECTRA-Large QA Model trained from https://huggingface.co/google/electra-large-discriminator in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator, and then it is trained on SQuAD and AdversarialQA (https://arxiv.org/abs/2002.00293)... | ca38937a39e0d65967b13b1bccd81580 |
apache-2.0 | ['generated_from_trainer'] | false | data-augmentation-whitenoise-timit-1155 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5458 - Wer: 0.3324 | c30db7f0bef0635b082bc1a98042a518 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5204 | 0.8 | 500 | 1.6948 | 0.9531 | | 0.8435 | 1.6 | 1000 | 0.5367 | 0.5113 | | 0.4449 | 2.4 | 1500 | 0.4612 | 0.452... | 63973428c47350987172470c1746c42e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Spanish-With-LM This is a model copy of [Wav2Vec2-Large-XLSR-53-Spanish](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish) that has language model support. This model card can be seen as a demo for the [pyctcdecode](https://github.com/kensho-technologies/pyctcdecode) integ... | b78de20cfee256d6e7f7dc832f8b5f23 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | => 'bien y qué regalo vas a abrir primero' ``` **Improvement** This model has been compared on 512 speech samples from the Spanish Common Voice Test set and gives a nice *20 %* performance boost: The results can be reproduced by running *from this model repository*: | Model | WER | CER | | ------------- | -------... | 38549ccfae4b1d0481e85499dbeeffe2 |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-tr-en-finetuned-en-to-tr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tr-en](https://huggingface.co/Helsinki-NLP/opus-mt-tr-en) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.9429 - Bleu: 6.471 - Gen Len: 56.1688 | f74eab9602166c5c867bd37b918c2cab |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:| | 1.5266 | 1.0 | 12860 | 2.2526 | 4.5834 | 55.6563 | | 1.2588 | 2.0 | 25720 | 2.0113 | 5.9203 | 56.3506 | | 1.1878 |... | 66c3ae094bad30c3634fb0c31482f471 |
cc-by-4.0 | [] | false | Sentiment Classification in Polish ```python import numpy as np from transformers import AutoTokenizer, AutoModelForSequenceClassification id2label = {0: "negative", 1: "neutral", 2: "positive"} tokenizer = AutoTokenizer.from_pretrained("Voicelab/herbert-base-cased-sentiment") model = AutoModelForSequenceClassificat... | 4d5cc69c6473821a39423b954a76d447 |
cc-by-4.0 | [] | false | Overview - **Language model:** [allegro/herbert-base-cased](https://huggingface.co/allegro/herbert-base-cased) - **Language:** pl - **Training data:** Reviews + own data - **Blog post:** [Sentiment analysis - COVID-19 – the source of the heated discussion](https://voicelab.ai/covid-19-the-source-of-the-heated-discu... | 6d2f8f6c0f85fd2669573966cba83641 |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_qa_model 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: 4.2177 | 628ce027283c7b0bacbba1423bfccfac |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 266 | 4.1357 | | 3.5667 | 2.0 | 532 | 4.1447 | | 3.5667 | 3.0 | 798 | 4.2177 | | 43bb22dd4d39bc94ed35c5676a9f9d7a |
mit | [] | false | **Hyperparameters:** - learning rate: 2e-5 - weight decay: 0.01 - per_device_train_batch_size: 8 - per_device_eval_batch_size: 8 - gradient_accumulation_steps:1 - eval steps: 50000 - max_length: 512 - num_epochs: 1 - hidden_dropout_prob: 0.3 - attention_probs_dropout_prob: 0.25 **Dataset version:** -... | 5fc9d36b711ad3d3b6dd51047b557632 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 | 0586356265699b3bb1a1d5fa15cca9e9 |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-finetuned-eli5-a This model is a fine-tuned version of [ammarpl/t5-base-finetuned-xsum-a](https://huggingface.co/ammarpl/t5-base-finetuned-xsum-a) on the eli5 dataset. It achieves the following results on the evaluation set: - Loss: 3.1773 - Rouge1: 14.6711 - Rouge2: 2.2878 - Rougel: 11.3676 - Rougelsum: 13.1... | 20158034179e005bc3a5f19f78fbb546 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 3.3417 | 1.0 | 17040 | 3.1773 | 14.6711 | 2.2878 | 11.3676 | 13.1805 | 18... | 03cdc9bdb75bfbc72ee1c5d9e046a61e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper medium Croatian El Greco This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the google/fleurs hr_hr dataset. It achieves the following results on the evaluation set: - Loss: 0.3374 - Wer: 14.6133 | 109e7083979169f5b47709a08511f68b |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - train... | 9f505320498d0b2ad35581ae1ebd94a5 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0106 | 4.61 | 1000 | 0.3374 | 14.6133 | | d9808f09b416e724e8c7e1fc4b5fbc3d |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Vietnamese This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | 50d6b0bb1644cd18213620b536a828e2 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-vi") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-vi") ``` | 821d6a0fd888fab9de3396d658095c42 |
mit | [] | false | Model description This model takes the XLM-Roberta-base model which has been continued to pre-traine on a large corpus of Twitter in multiple languages. It was developed following a similar strategy as introduced as part of the [Tweet Eval](https://github.com/cardiffnlp/tweeteval) framework. The model is furthe... | f3d7c0023b1558de8b778486e55742ec |
mit | [] | false | Intended Usage This model was developed to do Zero-Shot Text Classification in the realm of Hate Speech Detection. It is focused on the language of english as it was finetuned on data in said language. Since the base model was pre-trained on 100 different languages it has shown some effectiveness in other languages. ... | 908fcc0cf322ecb7022d5d23cc029ac6 |
mit | [] | false | Usage with Zero-Shot Classification pipeline ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="morit/english_xlm_xnli") ``` After loading the model you can classify sequences in the languages mentioned above. You can specify your sequences and a... | 429fad3c3d33891d32b8f694634caa3c |
mit | [] | false | Training This model was pre-trained on a set of 100 languages and follwed further training on 198M multilingual tweets as described in the original [paper](https://arxiv.org/abs/2104.12250). Further it was trained on the training set of XNLI dataset in english which is a machine translated version of the MNLI dataset... | 8ef617fb069f13167afe5abeadf28148 |
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