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 | ['translation'] | false | zho-msa * source group: Chinese * target group: Malay (macrolanguage) * OPUS readme: [zho-msa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-msa/README.md) * model: transformer-align * source language(s): cmn_Bopo cmn_Hani cmn_Latn hak_Hani yue_Bopo yue_Hani * target language(s): ind z... | a6b622524c3886a2ee392830b477b18e |
apache-2.0 | ['translation'] | false | System Info: - hf_name: zho-msa - source_languages: zho - target_languages: msa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-msa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['zh', 'ms'] - src_constituents: {'cmn_Hans', 'nan', ... | 889c37ef2a9c0f968592fccd53f74f2e |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_2e-05_all_16_02_2022-16_06_20 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1750 - Precision: 0.3286... | dfdaedc0878061a11594546bdab6e5c7 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-53m-gl-jupyter2 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0941 - Wer: 0.0615 | c588c296f8229a2e4c8bb0a28b0dd6c7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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... | d4797576daf809f0b2bc5bb045898ea0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.7298 | 3.36 | 400 | 0.2477 | 0.2493 | | 0.1507 | 6.72 | 800 | 0.1294 | 0.1264 | | 0.066 | 10.08 | 1200 | 0.1235 | 0.1161 | |... | 285264190c551bd35618cb91a42716a3 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4786 - Rouge1: 28.2047 - Rouge2: 7.7109 - Rougel: 22.1559 - Rougelsum: 22.1595 - Gen Len: 18.8257 | 3a26118665cc2d4e12d1435dd43228e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.7156 | 1.0 | 12753 | 2.4786 | 28.2047 | 7.7109 | 22.1559 | 22.1595 | 18... | 7d631b3c87a07da4d38223bfee1a7728 |
mit | [] | false | How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='KoboldAI/GPT-Neo-1.3B-Adventure')
>>> generator("> You wake up.", do_sample... | 1146711d5803281557c2e18e3bf93c79 |
mit | [] | false | Limitations and Biases
GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work.
GPT-Neo was trained on the Pile... | 51c0d8aad008bda7bb187320e353fd50 |
mit | [] | false | BibTeX entry and citation info
The model is made using the following software:
```bibtex
@software{gpt-neo,
author = {Black, Sid and
Leo, Gao and
Wang, Phil and
Leahy, Connor and
Biderman, Stella},
title = {{GPT-Neo: Larg... | cb1b714b7e5e17d255dc549d060d08fb |
apache-2.0 | ['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Pyramids'] | 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-Pyramids 2. Step 1: Write your model_id: unity/ML-Agents-Pyramids 3. Step 2: Select your *.nn or *.onnx file 4. Click on Watch the agent play ๐ | 649fc32971312d256abac6a89e3a7d3e |
mit | ['generated_from_trainer'] | false | bert-base-portuguese-cased-finetuned-oparticles This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2012 | 5e7e8d6996571d017fe2bcae8db6b6b5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6294 | 1.0 | 85 | 2.2813 | | 2.3647 | 2.0 | 170 | 2.2857 | | 2.3189 | 3.0 | 255 | 2.3030 | | aad5716e3bb00de33c20a4d7f62e30a1 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased_token_itr0_0.0001_TRAIN_all_TEST_null__second_train_set_NULL_False 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: 0.0650 - Precision: 0.9847 - Recall: 0.9864 - F... | 839549de05c3126fcc59c44df1953f0a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 25 | 0.2530 | 0.9106 | 0.8321 | 0.8696 | 0.7793 | | No log | 2.0 |... | 4a4f0d295e678dd101974245a4c69ffb |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_cola_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6609 - Matthews Correlation: 0.0939 | 8a2669f0cf0f411aa8c7e24e7c333910 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:-----:|:---------------:|:--------------------:| | 0.5394 | 1.0 | 1669 | 0.6609 | 0.0939 | | 0.4545 | 2.0 | 3338 | 0.7807 | 0.0474 | |... | 5e584bb63ac056430a90f6db680a6f2a |
apache-2.0 | ['generated_from_trainer'] | false | zero_last This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.9190 | d53c46f6ca2e006d52320c11f66197d4 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Ligne Claire Anime Diffusion Ligne claire is French for "clear line" and the style focuses on strong lines, flat colors and lack of gradient shading. This is a finetuned text to image model focusing on anime style ligne claire style. More releases for the future are planned at this time. Key prompt: ```ligne claire``... | ac90e0eab0da4cd32bca623d11a1346e |
apache-2.0 | ['generated_from_trainer'] | false | spanish-disease-tagger This model is a fine-tuned version of [plncmm/roberta-clinical-wl-es](https://huggingface.co/plncmm/roberta-clinical-wl-es) on the disease dataset. It achieves the following results on the evaluation set: - Loss: 0.1786 - Precision: 0.8385 - Recall: 0.8711 - F1: 0.8545 - Accuracy: 0.9488 | 15054476fbf4d25f9b8dae1ceb772778 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2217 | 1.0 | 502 | 0.1698 | 0.8142 | 0.8587 | 0.8359 | 0.9437 | | 0.1203 | 2.0 |... | 461a68615611cb028ef2cd192d9e4eb8 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ES... | c028b005e306e85699f05415d6452362 |
mit | [] | false | TEST2 on Stable Diffusion This is the `<AIOCARD>` 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 also train y... | 6109990998c0306ed52056c089ce3548 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-multilingual-cased-finetuned-viquad This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4241 | 2d71bfb66ecc9d534bcf0e3d45263379 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 65 | 4.0975 | | No log | 2.0 | 130 | 3.9315 | | No log | 3.0 | 195 | 3.6742 | | No log | 4.0 | 260 | 3.4878 ... | 4bac61e565962167cb4c1371d8702875 |
cc-by-sa-4.0 | [] | false | ELECTRA base Japanese generator This is a [ELECTRA](https://github.com/google-research/electra) model pretrained on texts in the Japanese language. The codes for the pretraining are available at [retarfi/language-pretraining](https://github.com/retarfi/language-pretraining/tree/v1.0). | 8d24cd5ca097b4aebf97575bb277662d |
cc-by-sa-4.0 | [] | false | Model architecture The model architecture is the same as ELECTRA base in the [original ELECTRA implementation](https://github.com/google-research/electra); 12 layers, 256 dimensions of hidden states, and 4 attention heads. | 01accf48396e097ec6399209283822e2 |
cc-by-sa-4.0 | [] | false | Training The models are trained with the same configuration as ELECTRA base in the [original ELECTRA paper](https://arxiv.org/abs/2003.10555) except size; 512 tokens per instance, 256 instances per batch, and 766k training steps. The size of the generator is 1/3 of the size of the discriminator. | b8e5d4ad1b3cc54804b1d2a5a5e3bcab |
apache-2.0 | ['generated_from_trainer'] | false | results This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.3793 - Accuracy: 0.8404 | 5bf7ba6eb137980d939e3e4bcc187170 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3542 | 1.0 | 125 | 0.3611 | 0.839 | | 0.2255 | 2.0 | 250 | 0.3793 | 0.8404 | | 808507a18fc82ae8975dbb1a5fcfde5a |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_vp-fr_s255 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 9d47858f1938a5096522a3ed5fd6ff4e |
mit | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | Model description This multilingual model can perform natural language inference (NLI) on 116 languages and is therefore also suitable for multilingual zero-shot classification. The underlying XLM-V-base model was created by Meta AI and pretrained on the [CC100 multilingual dataset](https://huggingface.co/datasets/c... | fcc4d74a7ed1b063d5c6692b56ce0da1 |
mit | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | Simple zero-shot classification pipeline ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="MoritzLaurer/xlm-v-base-mnli-xnli") sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" candidate_labels = ["politics", "economy"... | f226381ba7efe8501dfb56df0d1d444d |
mit | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | NLI use-case ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model_name = "MoritzLaurer/xlm-v-base-mnli-xnli" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelFor... | f0a8ce3e5999e566f93e5364cceaa037 |
mit | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | Training data This model was trained on the XNLI development dataset and the MNLI train dataset. The XNLI development set consists of 2490 professionally translated texts from English to 14 other languages (37350 texts in total) (see [this paper](https://arxiv.org/pdf/1809.05053.pdf)). Note that the XNLI contains a... | b437f089d7ee76f6cce0d4809578cb47 |
mit | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | Eval results The model was evaluated on the XNLI test set on 15 languages (5010 texts per language, 75150 in total). Note that multilingual NLI models are capable of classifying NLI texts without receiving NLI training data in the specific language (cross-lingual transfer). This means that the model is also able of ... | 36558c3acc115a40b45f16e3d1c56002 |
mit | ['zero-shot-classification', 'text-classification', 'nli', 'pytorch'] | false | Citation If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. โLess Annotating, More Classifying โ Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLIโ. Preprint, June. Open Science Framewor... | 05e1698b6fb4fb08c12581bcacab64d6 |
apache-2.0 | ['fill-mask', 'transformers', 'en', 'ko'] | false | mdistilbertV3.1 - distilbert-base-multilingual-cased ๋ชจ๋ธ์ [moco-corpus-kowiki2022 ๋ง๋ญ์น](https://huggingface.co/datasets/bongsoo/moco-corpus-kowiki2022)(kowiki202206 + MOCOMSYS ์ถ์ถ 3.2M ๋ฌธ์ฅ)๋ก vocab ์ถ๊ฐํ์ฌ ํ์ต ์ํจ ๋ชจ๋ธ - **vocab: 159,552๊ฐ (๊ธฐ์กด bert ๋ชจ๋ธ vocab(119,548๊ฐ)์ 40,004๊ฐ (ํ๊ธ๋จ์ด30,000๊ฐ+์๋ฌธ10,000๊ฐ+์๋ 4๊ฐ)vocab ์ถ๊ฐ** - mdistilbert... | 55e74949412925a2b2908312b0d0bd6d |
apache-2.0 | ['fill-mask', 'transformers', 'en', 'ko'] | false | 1. MASK ์์ ```python from transformers import AutoTokenizer, AutoModel, DistilBertForMaskedLM import torch import torch.nn.functional as F tokenizer = AutoTokenizer.from_pretrained('bongsoo/mdistilbertV3.1', do_lower_case=False) model = DistilBertForMaskedLM.from_pretrained('bongsoo/mdistilbertV3.1') text = ['ํ๊ตญ์ ์๋... | bd80f05e7f7931e1d12390c0f7dd4d45 |
apache-2.0 | ['fill-mask', 'transformers', 'en', 'ko'] | false | ๊ฒฐ๊ณผ ์ถ๋ ฅ print('\n') print('*Input: {}'.format(text[idx])) print('*[MASK] : {} ({})'.format(mask_logits_token, mask_logits_idx)) ``` - ๊ฒฐ๊ณผ ``` *Input: ํ๊ตญ์ ์๋๋ [MASK] ์ด๋ค *[MASK] : ์์ธ (48253) *Input: ์ํ ํ์ [MASK]์ ์๋ค *[MASK] : ํ๋์ค (47364) *Input: ์ถฉ๋ฌด๊ณต ์ด์์ ์ [MASK]์ ์ต๊ณ ์ ์ฅ์์๋ค *[MASK] : ์์ง์๋ (121990) ``` | d36749d8967add4fef003b7b04085672 |
apache-2.0 | ['fill-mask', 'transformers', 'en', 'ko'] | false | 2. ์๋ฒ ๋ฉ ์์ - ํ๊ท ํด๋ง(mean_pooling) ๋ฐฉ์ ์ฌ์ฉ. ([cls ํด๋ง](https://huggingface.co/sentence-transformers/bert-base-nli-cls-token), [max ํด๋ง](https://huggingface.co/sentence-transformers/bert-base-nli-max-tokens)) ```python from transformers import AutoTokenizer, AutoModel import torch | fc69cd84e5e1cb8dfc13b210e6f8e8af |
apache-2.0 | ['fill-mask', 'transformers', 'en', 'ko'] | false | => ์
๋ ฅ๊ฐ embeddings ์ (1,768) ์ฒ๋ผ 2D ์ฌ์ผ ํจ. from sklearn.metrics.pairwise import paired_cosine_distances, paired_euclidean_distances, paired_manhattan_distances cosine_scores = 1 - (paired_cosine_distances(sentence_embeddings[0].reshape(1,-1), sentence_embeddings[1].reshape(1,-1))) print(f'*cosine_score:{cosine_scores[0]... | fb384041f4038a8d5fd277dca784d84d |
apache-2.0 | ['fill-mask', 'transformers', 'en', 'ko'] | false | Training **MLM(Masked Langeuage Model) ํ๋ จ** - ์
๋ ฅ ๋ชจ๋ธ : distilbert-base-multilingual-cased - ๋ง๋ญ์น : ํ๋ จ : bongsoo/moco-corpus-kowiki2022(7.6M) , ํ๊ฐ: **bongsoo/moco_eval** - HyperParameter : **LearningRate : 5e-5, epochs: 12 , batchsize: 32, max_token_len : 128** - vocab : **159,552๊ฐ** (๊ธฐ์กด bert ๋ชจ๋ธ vocab(119,548๊ฐ)์ 40,004๊ฐ... | 3949315ea30b149f0965bde49c47a9f0 |
apache-2.0 | ['fill-mask', 'transformers', 'en', 'ko'] | false | Model Config ``` { "_name_or_path": "", "activation": "gelu", "architectures": [ "DistilBertForMaskedLM" ], "attention_dropout": 0.1, "dim": 768, "dropout": 0.1, "hidden_dim": 3072, "initializer_range": 0.02, "max_position_embeddings": 512, "model_type": "distilbert", "n_heads": 12, "n_la... | 237308595e02626a61c32d6838e23f06 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 88.6 - GMACs: 15.4 - Activations (M): 28.8 - Image size: 224 x 224 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/facebookresearch/ConvNeXt - ... | 01f238ecd8c95ea21a001b8d8556e191 |
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_base.fb_in1k', pretrained=True) model = model... | 4639818b6acd43338b4da29366d1d91a |
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_base.fb_in1k', pretrained=True, ... | f10b6e78e584847eadcbac24a300f2d6 |
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_base.fb_in1k', pretrained=True, num_... | a73ba4ff2b41267cfbf454723ed2aeb4 |
apache-2.0 | ['translation'] | false | opus-mt-ru-es * source languages: ru * target languages: es * OPUS readme: [ru-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ru-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](https://... | cb41ca48a70a24b635a4dad7a1702536 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newstest2012.ru.es | 26.1 | 0.527 | | newstest2013.ru.es | 28.2 | 0.538 | | Tatoeba.ru.es | 49.4 | 0.675 | | 892695440269e1130f46100ba67461ff |
apache-2.0 | ['generated_from_trainer'] | false | korean-aihub-learning-3 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: 2.2854 - Wer: 0.7921 | d0549edff35063b7c51df675b9c79bab |
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: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 4984aac21caad7b5cd1822255e3d2b74 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.99 | 35 | 45.5713 | 1.0 | | No log | 1.99 | 70 | 24.4376 | 1.0 | | 35.4145 | 2.99 | 105 | 18.3030 | 1.0 | |... | 5728f707b4e036f31d82fcb7bc268d24 |
apache-2.0 | ['Openslr Multilingual', 'automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Wav2Vec2_xls_r_300m_hi_final This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the ['Openslr Multilingual and code-switching ASR challenge'](http://www.openslr.org/103/) dataset and ['mozilla-foundation/common_voice_7_0'](https://huggingface.co... | 83e3bcc27fda4f8ea8c4ceada4d87c44 |
apache-2.0 | ['Openslr Multilingual', 'automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 3f45783e8086c346d5befb1250162b9c |
apache-2.0 | ['Openslr Multilingual', '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 | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 0.9821 | 0.64 | 400 | 0.5059 | 0.4783 | 0.1573 | | 0.6861 | 1.28 | 800 | 0.4201 | 0.4247 | 0.1356 | | 0.585 | 1.92 | ... | 6aae6c3bd02f85378711066c3fabe789 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 8fb11d3b289ab71df474c8c7ec60a27d |
apache-2.0 | ['hf-asr-leaderboard', 'automatic-speech-recognition', 'NbAiLab/NST', 'generated_from_trainer'] | false | whisper-NST-cons2e5 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the NBAILAB/NST - NO-CLOSE dataset. It achieves the following results on the evaluation set: - Loss: 0.3521 - Wer: 11.8586 | 7f4e5ddc7a23a2dfb2ac9ef0051ce7ba |
apache-2.0 | ['hf-asr-leaderboard', 'automatic-speech-recognition', 'NbAiLab/NST', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 500 - training_steps: 10000... | 2b1b67f75ae865a82707cfb68396870f |
apache-2.0 | ['hf-asr-leaderboard', 'automatic-speech-recognition', 'NbAiLab/NST', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.2517 | 0.1 | 1000 | 0.4131 | 18.4721 | | 0.1931 | 0.2 | 2000 | 0.3531 | 19.0422 | | 0.1598 | 0.3 | 3000 | 0.3605 | 1... | a33bea868f66e71ba40da79a2dc929aa |
apache-2.0 | ['generated_from_trainer'] | false | bert-small-nan-labels-500 This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.1664 | 2798f5f2b4287eed80f691761954537c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 500 | 2baa9d6c8259bbe232cd3b65bcdb5f39 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.9422 | 1.0 | 14 | 5.5730 | | 4.4976 | 2.0 | 28 | 5.4533 | | 4.2684 | 3.0 | 42 | 5.4158 | | 4.2673 | 4.0 | 56 | 5.4162 ... | 59e58fa4bd26295bbf3befd72f367cc6 |
apache-2.0 | ['automatic-speech-recognition', 'gary109/AI_Light_Dance', 'generated_from_trainer'] | false | ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v3 This model is a fine-tuned version of [gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v2](https://huggingface.co/gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v2) on the GARY109/AI_LIGHT_DANCE - ONSET-STEPMANIA2 dataset. It achieves the fo... | 26b363ef58ef5514d8fd30c231c97012 |
apache-2.0 | ['automatic-speech-recognition', 'gary109/AI_Light_Dance', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | d61e9cb573b8d49f521f3696a4d972ab |
apache-2.0 | ['automatic-speech-recognition', 'gary109/AI_Light_Dance', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.9189 | 1.0 | 188 | 1.0770 | 0.7002 | | 0.9172 | 2.0 | 376 | 1.0780 | 0.6955 | | 0.9177 | 3.0 | 564 | 1.0824 | 0.6916 | |... | 5a3c654e2447788204d9d57bcf88ec80 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-stsb-custom-tokenizer-expand-vocab 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: 4.4684 | 00253123cd85bfff33c9485456b7c408 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.4557 | 0.7 | 500 | 5.4720 | | 5.7672 | 1.39 | 1000 | 5.1064 | | 5.4426 | 2.09 | 1500 | 5.0087 | | 5.1495 | 2.78 | 2000 | 4.8300 ... | eb4f9be414589b436c6349b408e9353a |
apache-2.0 | ['generated_from_keras_callback'] | false | reaprtripr/distill-bert-csn 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: nan - Validation Loss: nan - Epoch: 0 | 7ad3b7707b6088c2c32918f961415ada |
apache-2.0 | ['generated_from_trainer'] | false | T5-model-1-d-1 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3197 - Rouge1: 77.3742 - Rouge2: 59.7356 - Rougel: 74.3935 - Rougelsum: 74.3553 - Gen Len: 13.8383 | 6145dcdad8c04610d92924f499664c42 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.4886 | 1.0 | 2011 | 0.3197 | 77.3742 | 59.7356 | 74.3935 | 74.3553 | 13... | 6cd1e599042e02d6a0597b145370b1f7 |
openrail | ['image-to-text', 'visual-question-answering', 'image-captioning'] | false | Captioning Pipeline Please follow the prompt format, which will give the best performance. Generate a prompt-guided caption by following: ```python import torch from promptcap import PromptCap model = PromptCap("vqascore/promptcap-coco-vqa") | c3d2303521e22d1b152ac6f4363a47f7 |
openrail | ['image-to-text', 'visual-question-answering', 'image-captioning'] | false | also support OFA checkpoints. e.g. "OFA-Sys/ofa-large" if torch.cuda.is_available(): model.cuda() prompt = "please describe this image according to the given question: what piece of clothing is this boy putting on?" image = "glove_boy.jpeg" print(model.caption(prompt, image)) ``` To try generic captioning, just ... | 602563c98b23cc579101ac7d7ea96017 |
openrail | ['image-to-text', 'visual-question-answering', 'image-captioning'] | false | Visual Question Answering Pipeline Different from typical VQA models, which are doing classification on VQAv2, PromptCap is open-domain and can be paired with arbitrary text-QA models. Here we provide a pipeline for combining PromptCap with UnifiedQA. ```python import torch from promptcap import PromptCap_VQA | 8eba315be4c22a0422d40ae8d6b0c3dd |
openrail | ['image-to-text', 'visual-question-answering', 'image-captioning'] | false | QA model support all UnifiedQA variants. e.g. "allenai/unifiedqa-v2-t5-large-1251000" vqa_model = PromptCap_VQA(promptcap_model="vqascore/promptcap-coco-vqa", qa_model="allenai/unifiedqa-t5-base") if torch.cuda.is_available(): vqa_model.cuda() question = "what piece of clothing is this boy putting on?" image = "gl... | ba373bb69063da669a5b845f3880812c |
mit | ['bert', 'fill-mask'] | false | Versions - **ro-bert-tweet-v2** 1.5 million steps of pre-training. - **ro-bert-tweet-v1** 1 million steps of pre-training. - **ro-bert-tweet-v0** Initial version of the model after 300k steps of pre-training, only for setting up the framework. | 31285ac22dfef01e26bda8a2d97e1fca |
mit | ['bert', 'fill-mask'] | false | The last hidden-state is the first element of the output tuple ``` **Always** use the `normalize.py` script included in the repository to sanitize you input text, before feeding the tokenizer. Otherwise you will decrease the performance due to the [UNK] tokens. | 919e7e80400352a10808c63d3bf59c7b |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_vp-es_s609 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | aa4f4bf61171652620cc64bc143fd82c |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Sussurrar This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4367 - Wer: 26.2605 | e8398a878f0b393a3f17cb0922f0f36e |
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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2000 - mixed_precision_training: Native AMP | c5a7443f15d9e302c8622a1daa31434d |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.4076 | 0.1 | 200 | 0.5182 | 32.4930 | | 0.3462 | 0.2 | 400 | 0.4912 | 29.0266 | | 0.3283 | 0.3 | 600 | 0.4671 | 27.030... | 835d02cd5dfb89687d4b28faa01259b9 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_mnli_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.8966 - Accuracy: 0.6007 | 748b76f694c7306482fce696430ce31d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 0.8664 | 1.0 | 62880 | 0.8856 | 0.5992 | | 0.7181 | 2.0 | 125760 | 0.9450 | 0.6005 | | 0.6088 | 3.0 | 188640 | 1.0020 ... | fd453e10b3cd3097dd54c47d0ed64f90 |
apache-2.0 | ['generated_from_trainer'] | false | mini-vanilla-target-imdb This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4773 - Accuracy: 0.8753 - F1: 0.9335 | d5da172768020a6011b684cca3bb3111 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4272 | 0.64 | 500 | 0.2066 | 0.92 | 0.9583 | | 0.299 | 1.28 | 1000 | 0.2608 | 0.8906 | 0.9422 | | 0.2533 |... | 6874b4728502f3b35e3ac59815a175f2 |
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: 0.3903 - F1: 0.7590 | 7425d0ff33eb0354ad18a51e1af4b662 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.0489 | 1.0 | 50 | 0.5561 | 0.6565 | | 0.4953 | 2.0 | 100 | 0.4385 | 0.7189 | | 0.35 | 3.0 | 150 | 0.3903 | 0.7590 | ... | 816135e8336e3bf640fdf4f9a596a95f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large V2 Farsipal and El Greco This model is a fine-tuned version of [emilios/whisper-lg-v2-parsifal-el-0](https://huggingface.co/emilios/whisper-lg-v2-parsifal-el-0) on the mozilla-foundation/common_voice_11_0 el dataset. It achieves the following results on the evaluation set: - Loss: 0.2866 - Wer: 9.1103 | 18569db376a85d6f1ac910353bf39450 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0281 | 9.35 | 1000 | 0.2515 | 9.5561 | | 0.0131 | 18.69 | 2000 | 0.2866 | 9.1103 | | 0.0069 | 28.04 | 3000 | 0.3118 | 9.2403 | |... | 0bdb3eeac9c1558ccf9dd310bf66a08b |
apache-2.0 | ['generated_from_trainer'] | false | distil-s This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6895 - Rmse: 0.8304 - Mse: 0.6895 - Mae: 0.5893 | b7e070096dff520fd395e7e9478dbbbd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.6982 | 1.0 | 492 | 0.6440 | 0.8025 | 0.6440 | 0.5841 | | 0.5922 | 2.0 | 984 | 0.6412 | 0.8007 | 0.6412 ... | 85ff22b458817bb0ce7e6e1e2ae762b4 |
apache-2.0 | ['generated_from_keras_callback'] | false | Hardik1313X/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: 4.0749 - Validation Loss: 3.3854 - Epoch: 7 | ee017f360c8a9261950b7e41fc1744dc |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.0060 | 4.3897 | 0 | | 5.9039 | 3.8382 | 1 | | 5.1623 | 3.6476 | 2 | | 4.7477 | 3.5488 | 3 | | 4.4688 | 3.4721 | 4 | | 4.2706 |... | b852de7dc160d472815058bd1fa903c9 |
apache-2.0 | ['classification', 'zero-shot'] | false | Erlangshen-UniMC-RoBERTa-110M-Chinese - Main Page:[Fengshenbang](https://fengshenbang-lm.com/) - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/unimc/) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) - API: [Fengshen-OpenAPI](https://fengshenba... | fa4d959d2fea1cd2f1ed70b0f6390470 |
apache-2.0 | ['classification', 'zero-shot'] | false | ๆจกๅๅ็ฑป Model Taxonomy | ้ๆฑ Demand | ไปปๅก Task | ็ณปๅ Series | ๆจกๅ Model | ๅๆฐ Parameter | ้ขๅค Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | ้็จ General | ่ช็ถ่ฏญ่จ็่งฃ NLU | ไบ้็ฅ Erlangshen | RoBERTa | 110M | Chinese | | 710c7e793b74918dcc37ee41eaa7f317 |
apache-2.0 | ['classification', 'zero-shot'] | false | ไธๆธธๆๆ Performance **Few-shot** | Model | eprstmt | csldcp | tnews | iflytek | ocnli | bustm | chid | csl | wsc | Avg | |------------|------------|----------|-----------|----------|-----------|-----------|-----------|----------|-----------|-----------| | [FineTuning](https:... | fcf7db8ea641a4588142cff28fbf4b2f |
apache-2.0 | ['classification', 'zero-shot'] | false | ไฝฟ็จ Usage ```shell git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git cd Fengshenbang-LM pip install --editable . ``` ```python3 import argparse from fengshen.pipelines.multiplechoice import UniMCPipelines total_parser = argparse.ArgumentParser("TASK NAME") total_parser = UniMCPipelines.piplines_args(total_... | dec4d3b863c5f649b4c68460287bfab4 |
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: 0.2177 - Accuracy: 0.924 - F1: 0.9245 | 62aa39adf953d17a4ef01ca72aaeddfb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8318 | 1.0 | 250 | 0.3067 | 0.9115 | 0.9091 | | 0.2412 | 2.0 | 500 | 0.2177 | 0.924 | 0.9245 | | 0d88aeb1ddba78b8895dae4154aea481 |
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