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 | ['exbert'] | false | Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-xxlarge-v2') >>> unmasker("The man worked as a [MASK].") [ { ... | 3ea59e0751a3145cb1aa145e23faee80 |
apache-2.0 | ['exbert'] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1909-11942, author = {Zhenzhong Lan and Mingda Chen and Sebastian Goodman and Kevin Gimpel and Piyush Sharma and Radu Soricut}, title = {{ALBERT:} {A} Lite {BE... | 2e08d8761ad62ddb98b5050edd7186ae |
mit | [] | false | Herge_style on Stable Diffusion This is the `<herge>` 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 tra... | 65d134076516fdea0bbd6890a403a566 |
apache-2.0 | ['generated_from_keras_callback'] | false | gopalkalpande/t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0422 - Validation Loss: 0.4407 - Train Rouge1: 19.5311 - Train Rouge2: 14.2402 - Train Rougel: 17.9781... | 162fce5f51e8a01392ea3575292edc7d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 1.0422 | 0.4407 | 19.5311 | 14.2402 ... | 89195e260fb8f5b0971743c95f92a907 |
mit | [] | false | This is a reproduction of the following paper: ``` @inproceedings{katsumata-komachi-2020-stronger, title = "Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model", author = "Katsumata, Satoru and Komachi, Mamoru", booktitle = "Proceedings of the 1st Conference ... | 67c530ed97919757591d30d6c4034bd9 |
mit | ['generated_from_trainer'] | false | jolly_saha This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-3... | f9812a9b535958fbfef2edcf428ddeb1 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tom... | f9ac31b55300e7fe23cf1f2c9d21546a |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/quora-distilbert-multilingual This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 3638af143a983fffac0bbd0ab36750a4 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | bf1b5f6bbd593bd9bc7d7de74ee350d6 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/quora-distilbert-multilingual') model = AutoModel.from_pretrained('sentence-transformers/quora-distilbert-multilingual') | aeb48d32740233b86f2dcce6b93032c8 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/quora-distilbert-multilingual) | c07101fdc9d86d29bd79f51aa097a63d |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_gender_male-5_female-5_s474 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using t... | 229b28a5ea8d32d6e8916969685b5322 |
mit | ['generated_from_trainer'] | false | finetuned_gpt2_sst2_negation0.05 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.5271 | 2912cdac8502711f2216f3709890948b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1134 | 1.0 | 1062 | 3.5060 | | 2.926 | 2.0 | 2124 | 3.5158 | | 2.8331 | 3.0 | 3186 | 3.5271 | | 3c58b6a945e78323e8834ff22becbbb3 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_9_0', 'generated_from_trainer'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - MR dataset. It achieves the following results on the evaluation set: - Loss: 0.3642 - Wer: 0.4190 - Cer: 0.0946 | 95fdf06266a29df56c0de0bd56962c20 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_9_0', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_... | 58785b666ed17bb46f46735d23cee58b |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_9_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:----:|:---------------:|:------:|:------:| | 3.5184 | 12.9 | 400 | 3.4210 | 1.0 | 1.0 | | 2.3797 | 25.81 | 800 | 1.1068 | 0.8389 | 0.2584 | | 1.5022 | 38.... | 0733e51deac8caf4b3639595e6331fa5 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-banking-16-16-5-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: 2.2920 - Accuracy: 0.3982 | 8f969a0bf0d4d1497edea7de2a7e7286 |
mit | ['dmv', 'fun'] | false | GPT-DMV-125m A finetuned version of [GPT-Neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the 'DMV' dataset. (Linked above) A demo is available [here](https://huggingface.co/spaces/DarwinAnim8or/GPT-DMV-Playground) (I recommend using the demo playground rather than the Inference window on the right here) ... | 5db1245234ae5909cbb2302ae2350647 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout e5c0e0dbdab7e56ea9bf0a852bac10a1d99acf64 pip install -e . cd egs2/swbd_sentiment/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2 ``` <!-- Generated by scripts/utils/show... | aa1516ff47fdced046088ee1f68bb2bb |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Fri Mar 4 07:57:13 EST 2022` - python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.9.0+cu102` - Git hash: `3b53aedc654fd30a828689c2139a1e130adac077` - Commit date: `Fri Feb 25 00:13:16 2022 -0500` | 0901fc1747c87c0c77a7521fcfc01d2c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Using Conformer based encoder, Transformer based decoder and self-supervised learning features (Wav2vec2.0) with spectral augmentation and predicting transcript along with sentiment - ASR config: [conf/tuning/train_asr_conformer_wav2vec2.yaml](conf/tuning/train_asr_conformer_wav2vec2.yaml) - token_type: word - labels:... | 59ad9392230486268a74ccc6cfd7a988 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_wav2vec2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_wav2vec2_raw_en_word ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_m... | 1a67fbb4ca70b9cd65de2f0a6c0c4b5a |
mit | [] | false | Dataset 1. [DeepCrystal Train](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/blob/main/Datasets/train.csv) 2. [DeepCrystal Test](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/blob/main/Datasets/test.csv) 3. [BCrystal Test](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/tree/main/Datasets/BCrystal_Bal... | 2ac1bb4390fb3b9d7fd31214a35ccf5c |
mit | [] | false | ESMCrystal_t12_35M_v2 ESMCrystal_t12_35M_v2 is a state-of-the-art protein crystallization prediction model finetuned on [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D), having 12 layers and 35M parameters with size of [approx. 136MB](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/blob/m... | 3af445e07a999463eb9dd1ddcb28e4e2 |
mit | [] | false | Accuracy : | Dataset | Accuracy | |------------------|--------------------| | DeepCrystal Test | 0.8161222339304531 | | BCrystal test | 0.8052602126468943 | | SP test | 0.7637130801687764 | | TR test | 0.8389328063241107 | | 8e7efa0915e6a70aa2658175882fbe11 |
mit | [] | false | Comparision Table: | Dataset | Count | Positives | Negatives | TP | FP | FN | TN | Precision | Recall | F1 | Accuracy | ROC | Mathew's Coefficient | PPV | NPV | |-------------------|... | 54f998aed8634795d014aa3af17233d9 |
mit | [] | false | ROC-AUC Curve * DeepCrystal Test  * BCrystal Test  * SP Test ![SP T... | 4b802fff468289aa2dee2324c5ca4d94 |
mit | [] | false | PR-AUC Curve * DeepCrystal Test  * BCrystal Test  * SP Test ![SP Test P... | ff9b5835ce1ffe4a17fe799fe94bdad0 |
mit | [] | false | Final scores : * on DeepCrystal test: | | precision | recall | f1-score | support | |--------------------|-----------|--------|----------|---------| | non-crystallizable | 0.75 | 0.97 | 0.85 | 1000 | | crystallizable | 0.95 | 0.64 | 0.77 | 898 | | accuracy ... | 216b063cfbd24f2c66f1156ccf1803ac |
mit | [] | false | Confusion matrix: * on DeepCrystal test: ``` | 579 | 319 | | 30 | 970 | ``` * on BCrystal test: ``` | 573 | 318 | | 30 | 866 | ``` * on SP test: ``` | 97 | 51 | | 5 | 84 | ``` * on TR test: ``` | 225 | 149 | | 14 | 624 | ``` | 6a4cbeb6c4f47b76e0a973c972f78271 |
mit | [] | false | Metrics roc score: * on DeepCrystal test: 0.9403474387527841 * on BCrystal test: 0.9395705567580568 * on SP test: 0.9293197692074097 * on TR test: 0.9561924798417515 Mathews Coefficient: * on DeepCrystal test: 0.6575261170551334 * on BCrystal test: 0.6446356961702661 * on SP test: 0.58606970... | 9615453f1abe407f8e0aeeb0dfa96b4f |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-0front-1body-1rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4869 - Wer: 0.1801 - Mer: 0.1739 - Wil: 0.2635 - Wip: 0.7365 - Hits: 55253 - S... | 31e4ed71aebb5a1b479d18cc39e14de7 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6609 ... | 51acc15118b4c4e31e76e5871c9f7c6d |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 85.0 - GMACs: 15.1 - Activations (M): 49.2 - Image size: 224 x 224 - **Papers:** - CoAtNet: Marrying Convolution and Attention for All Data Sizes: https://arxiv.org/abs/2201.03545 - **Dataset:** ImageNet-... | 6b14185261e10935c41a2ecd6c22e2c4 |
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('coatnet_2_rw_224.in12k', pretrained=True) model = mode... | 99aec9d7f6abc96b4da1cd1e7bf4380d |
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( 'coatnet_2_rw_224.in12k', pretrained=True, ... | b1b10a182356e8c719bd2d0960febe0f |
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( 'coatnet_2_rw_224.in12k', pretrained=True, num... | d42adede199c22a106d364e2d3811de5 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-beans This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0410 - Accuracy: 0.9925 | 59f634a51b808f32d77b5d918738b5a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0751 | 1.54 | 100 | 0.0768 | 0.9850 | | 0.0121 | 3.08 | 200 | 0.0410 | 0.9925 | | 93ef86732e791266f92ca5b81b1a3592 |
apache-2.0 | ['generated_from_trainer'] | false | hate_trained This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9661 - F1: 0.7730 | 4dc89fa31992632cbbbe40c6972359a5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.303025140957233e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 54055c7749e0f9902967e4e04230ec70 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4767 | 1.0 | 2250 | 0.5334 | 0.7717 | | 0.4342 | 2.0 | 4500 | 0.7633 | 0.7627 | | 0.3813 | 3.0 | 6750 | 0.9452 | 0.7614 | |... | 0f10c819212e468970f92f26e4251330 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1631 - F1: 0.8579 | ec35f904a4ad023ede29eb7b19a674fc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2878 | 1.0 | 715 | 0.1840 | 0.8247 | | 0.1456 | 2.0 | 1430 | 0.1596 | 0.8473 | | 0.0925 | 3.0 | 2145 | 0.1631 | 0.8579 | ... | a8eed82a319bcaacef4169aa5d67418b |
mit | ['generated_from_trainer'] | false | muppet-roberta-base-finetuned-squad This model is a fine-tuned version of [facebook/muppet-roberta-base](https://huggingface.co/facebook/muppet-roberta-base) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9017 | 1e5580e81ccc08f5aec02587b93bfd0d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.7007 | 1.0 | 8239 | 0.7905 | | 0.4719 | 2.0 | 16478 | 0.9017 | | dd189d026eb223a064e9399c41cac670 |
apache-2.0 | ['translation'] | false | opus-mt-fr-ilo * source languages: fr * target languages: ilo * OPUS readme: [fr-ilo](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-ilo/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | 30c6a257e1af732d69598bd24f12452e |
bsd-3-clause | [] | false | Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models a... | f9b3f55c644d7e50e467e317f55d67b9 |
bsd-3-clause | [] | false | Training data This checkpoint (CodeGen-Mono 350M) was firstly initialized with *CodeGen-Multi 350M*, and then pre-trained on BigPython dataset. The data consists of 71.7B tokens of Python programming language. See Section 2.1 of the [paper](https://arxiv.org/abs/2203.13474) for more details. | 0e949222439d825e944746113e5a41ca |
bsd-3-clause | [] | false | How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-mono") tex... | 4d5c0bb6070d766b05eefd7cc6bbf217 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: tpu - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - ... | 3cbbe064a137957e995f8003383fc4a2 |
other | ['stable-diffusion', 'text-to-image'] | false | Cool Japan Diffusion 2.1.1 Model Card  [注意事项。中国将对图像生成的人工智能实施法律限制。 ](http://www.cac.gov.cn/2022-12/11/c_1672221949318230.htm) （中国国内にいる人への警告） English version is [here](README_en.md). | e7070e6ad1fa4a932ad1b2668059a586 |
other | ['stable-diffusion', 'text-to-image'] | false | 使い方 手軽に楽しみたい方は、こちらの[Space](https://huggingface.co/spaces/aipicasso/cool-japan-diffusion-latest-demo)をお使いください。 詳しい本モデルの取り扱い方は[こちらの取扱説明書](https://alfredplpl.hatenablog.com/entry/2023/01/11/182146)にかかれています。 モデルは[ここ](https://huggingface.co/aipicasso/cool-japan-diffusion-2-1-1/resolve/main/v2-1-1.ckpt)からダウンロードできます。 以下、一般的... | 35e9969f23cc79b81f98043c8f8745f1 |
other | ['stable-diffusion', 'text-to-image'] | false | Diffusersの場合 [🤗's Diffusers library](https://github.com/huggingface/diffusers) を使ってください。 まずは、以下のスクリプトを実行し、ライブラリをいれてください。 ```bash pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy ``` 次のスクリプトを実行し、画像を生成してください。 ```python from diffusers import StableDiffusionPipelin... | 61ca7af5487cade35bb12519cc5b4bcb |
other | ['stable-diffusion', 'text-to-image'] | false | 学習 **学習データ** 次のデータを主に使ってStable Diffusionをファインチューニングしています。 - VAEについて - Danbooruなどの無断転載サイトを除いた日本の国内法を遵守したデータ: 60万種類 （データ拡張により無限枚作成） - U-Netについて - Danbooruなどの無断転載サイトを除いた日本の国内法を遵守したデータ: 100万ペア **学習プロセス** Stable DiffusionのVAEとU-Netをファインチューニングしました。 - **ハードウェア:** RTX 3090, A6000 - **オプティマイザー:** AdamW - **Gradie... | 9bec2a757c3bb08781c692be1058376b |
apache-2.0 | ['italian', 'sequence-to-sequence', 'style-transfer', 'formality-style-transfer'] | false | mT5 Small for Informal-to-formal Style Transfer 🧐 This repository contains the checkpoint for the [mT5 Small](https://huggingface.co/google/mt5-small) model fine-tuned on Informal-to-formal style transfer on the Italian subset of the XFORMAL dataset as part of the experiments of the paper [IT5: Large-scale Text-to-t... | c5ee64e1db98243cef7696a56e3e3cf6 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'style-transfer', '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 i2f = pipeline("text2text-generation", model='it5/mt5-small-informal-to-formal') i2f("nn capisco xke tt i ragazzi lo fanno") >>> [{"gene... | 42e6b5cc887b5667333bc87cd7b87e1a |
apache-2.0 | ['generated_from_trainer'] | false | image-classification This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the mnist dataset. It achieves the following results on the evaluation set: - Loss: 0.0556 - Accuracy: 0.9833 | 81f3dfcf4df074079b35eea44d3df773 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3743 | 1.0 | 422 | 0.0556 | 0.9833 | | 95e64eba8d2652338dc516c506e494ba |
apache-2.0 | ['generated_from_keras_callback'] | false | itsGanni/Canadian_Armed_Forces-clustered This model is a fine-tuned version of [nandysoham/0-clustered](https://huggingface.co/nandysoham/0-clustered) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7260 - Train End Logits Accuracy: 0.8160 - Train Start Logits Accuracy:... | 4214d65841175c959e352bb94bb51d6c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 9516e0f12e707df353fe4b00bf2f8f12 |
mit | ['torch'] | false | ROBERTA BASE (cased) trained on private Bulgarian-English parallel data This is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences. Using the ideas from [Sentence-BERT](https://arxiv.org/abs/2004.09813), the training is based on the idea that a translated sentence should be... | 82a2703db3084bad0e3310356f4a0aac |
mit | ['torch'] | false | How to use Here is how to use this model in PyTorch: ```python >>> import scipy >>> import torch >>> from transformers import AutoModel, AutoTokenizer >>> >>> model = AutoModel.from_pretrained('rmihaylov/roberta-base-use-qa-theseus-bg') >>> tokenizer = AutoTokenizer.from_pretrained('rmihaylov/roberta-base-use-qa-th... | 9753a3adb159060cc105298b6061f4c9 |
mit | ['generated_from_trainer'] | false | m2m100_418M-finetuned-ko-to-en3 This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5175 - Bleu: 75.215 - Gen Len: 9.726 | 2e4ec66fac1c50bc3546f9dd55ecf8b3 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 256 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_... | 06efcd44082d3276b88b31472b00af80 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 0.99 | 103 | 2.7756 | 8.9955 | 9.425 | | No log | 1.99 | 206 | 0.7248 | 63.7645 | 9.6421 | | No log |... | 87a24efa85b726f24565383f37536729 |
apache-2.0 | ['generated_from_trainer'] | false | platzi-vit-model-orlando-murcia This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0532 - Accuracy: 0.9850 | dff2c92cbd6dbbc7f812054754e7dc05 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0776 | 3.85 | 500 | 0.0532 | 0.9850 | | 3991a7c73381b7e5d22b59627610e53a |
other | ['Shape modeling', 'Volumetric models'] | false | Model Description - SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation - Zheng, Xin-Yang and Liu, Yang and Wang, Peng-Shuai and Tong, Xin, 2022 The proposed deeplearning model for 3D shape generation called signed distance field (SDF) - SDF-StyleGAN, whicH is based on StyleGAN2. The goal of this appro... | beb8d6d683653276846ddc46d4c35255 |
other | ['Shape modeling', 'Volumetric models'] | false | Datasets ShapeNet is a comprehensive 3D shape dataset created for research in computer graphics, computer vision, robotics and related diciplines. - [Offical Dataset of ShapeNet](https://shapenet.org/) - [author's data preparation script](https://github.com/Zhengxinyang/SDF-StyleGAN) - [author's training data](http... | 72c015a91e14c6aaa77cb9e93c4130f8 |
other | ['Shape modeling', 'Volumetric models'] | false | BibTeX Entry and Citation Info ``` @inproceedings{zheng2022sdfstylegan, title = {SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation}, author = {Zheng, Xin-Yang and Liu, Yang and Wang, Peng-Shuai and Tong, Xin}, booktitle = {Comput. Graph. Forum (SGP)}, year = {2022}, } ``` | aac1b0d77c6b08e291012866798f556e |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_age_teens-10_sixties-0_s807 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... | 3a2cb862b516bb099630d956b9cd21a6 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-commonvoice-hindi This model is a fine-tuned version of [theainerd/Wav2Vec2-large-xlsr-hindi](https://huggingface.co/theainerd/Wav2Vec2-large-xlsr-hindi) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9825 - Wer: 0.6763 | 7c18b8f6fff7914575c87c97cd918be4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 20.0 | 100 | 0.8801 | 0.6754 | | ff5c4a897ece80e0af134997aa57cf68 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-hindi-new-3 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: - eval_loss: 2.1206 - eval_wer: 0.8949 - eval_runtime: 20.2358 - eval_samples_per_second: 1... | 1e96b787c1e5fbe8ccce7b193fc16795 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-small-subjqa-vanilla-electronics-qg` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](https://github.com/asahi41... | 4b699c21fec52a92ffbfa0c79784c75f |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [t5-small](https://huggingface.co/t5-small) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (electronics) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question... | e9dddee3e0e068a4e1553f2c7167bebc |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-small-subjqa-v... | 50dc4fb36b55112f9b2fd5d952e4f0b3 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-subjqa-vanilla-electronics-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) | | Score | Type | Dataset ... | 884128958e82ac8a16b039fd4ab6d8db |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: electronics - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 512 - max_length_output: 32 - epoch: 1 - bat... | bad438fd54271a5f1b959b613f1223fe |
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: 0.2973 - Accuracy: 0.88 - F1: 0.8808 | fc14b3b8ba27ce309ee1585c44e4b1fb |
apache-2.0 | ['translation'] | false | opus-mt-pis-es * source languages: pis * target languages: es * OPUS readme: [pis-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pis-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 78c286b444beaa91ce90f0c767cad3cf |
other | ['vision', 'image-classification'] | false | MobileNet V1 MobileNet V1 model pre-trained on ImageNet-1k at resolution 192x192. It was introduced in [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Howard et al, and first released in [this repository](https://github.com/tensorflow/models/bl... | b94215e43c168ed94757cf0b29992e9a |
gpl-3.0 | ['object-detection', 'computer-vision', 'sort', 'tracker', 'osnet'] | false | <div align="center"> <h1> Torchreid-Pip: Packaged version of Torchreid </h1> <h4> <img width="700" alt="teaser" src="https://raw.githubusercontent.com/goksenin-uav/torchreid-pip/main/doc/logo.png"> </h4> </div> This repo is a packaged version of the [Torchreid](https://github.com/KaiyangZhou/deep-person-reid) ... | 8da37c924b6a4d9c979ee930450df84e |
gpl-3.0 | ['object-detection', 'computer-vision', 'sort', 'tracker', 'osnet'] | false | Model Description [Learning Generalisable Omni-Scale Representations for Person Re-Identification](https://arxiv.org/abs/1905.00953): [Omni-Scale Feature Learning for Person Re-Identification](https://arxiv.org/abs/1910.06827) [Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch](https://arxiv.... | 42398470e0f7358a9887eca400355706 |
gpl-3.0 | ['object-detection', 'computer-vision', 'sort', 'tracker', 'osnet'] | false | 2. Load data manager ```python datamanager = torchreid.data.ImageDataManager( root="reid-data", sources="market1501", targets="market1501", height=256, width=128, batch_size_train=32, batch_size_test=100, transforms=["random_flip", "random_crop"] ) ``` | dfad1fde7c9fb6b10d2fe16a7eb2efea |
gpl-3.0 | ['object-detection', 'computer-vision', 'sort', 'tracker', 'osnet'] | false | 3 Build model, optimizer and lr_scheduler ```python model = torchreid.models.build_model( name="resnet50", num_classes=datamanager.num_train_pids, loss="softmax", pretrained=True ) model = model.cuda() optimizer = torchreid.optim.build_optimizer( model, optim="adam", lr=0.0003 ) schedu... | ac5193029c27a3868d7f607053bc8e9e |
gpl-3.0 | ['object-detection', 'computer-vision', 'sort', 'tracker', 'osnet'] | false | 5. Run training and test ```python engine.run( save_dir="log/resnet50", max_epoch=60, eval_freq=10, print_freq=10, test_only=False ) ``` Citation --------- If you use this code or the models in your research, please give credit to the following papers: ```bibtex @article{torchreid, title={Torc... | cec3cdf7237e7580888d2bfa430edcb9 |
afl-3.0 | ['generative-model'] | false | About Us Created by [Mor Ventura](https://www.linkedin.com/in/mor-ventura/) and [Michael Toker](https://www.linkedin.com/in/mnlp/). --- tags: - generated_from_trainer metrics: - accuracy model-index: - name: checkpoint_gpt2-medium_lyrics_meaning_2022-03-10-16-16-32 results: [] --- <!-- This model card has been gen... | 970966347dc1b28af095cc83051fda89 |
afl-3.0 | ['generative-model'] | false | checkpoint_gpt2-medium_lyrics_meaning_2022-03-10-16-16-32 This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5896 - Accuracy: 0.4923 | ac22bae735a2bbdc0a1d806b9ec32cd2 |
afl-3.0 | ['generative-model'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epo... | f54da47ac2dd3f3df5cc465512743204 |
afl-3.0 | ['generative-model'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7781 | 1.01 | 128 | 2.6284 | 0.4875 | | 2.6217 | 2.02 | 256 | 2.6022 | 0.4908 | | 2.569 | 3.02 | 384 | 2.5928 | 0.... | 4a800014b9e57dc76f2dabbed89d43b8 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_mrpc_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.0 - Accuracy: 1.0 - F1: 1.0 - Combined Score: 1.0 | e6799e968866e1e71f03e5871d6bc271 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.2019 | 1.0 | 1959 | 0.0211 | 0.9926 | 0.9947 | 0.9936 | | 0.0464 | 2.0 | 3918 | ... | 1fdf11261d8c0f176f1dcf4303804d74 |
mit | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx'] | false | ONNX This is an INT8 ONNX model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [Intel/roberta-base-mrpc](https://huggingface.co/Intel/roberta-base-mrpc). | cf08c60282df73c6d1b1cdb95f3d5dc5 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-cola This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8347 - Matthews Correlation: 0.5914 | 33ee045fad6d20b82d18fbc91d158c93 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4921 | 1.0 | 535 | 0.5622 | 0.4713 | | 0.301 | 2.0 | 1070 | 0.4454 | 0.5611 | | 0.1... | 896b6f3dcc2e2d101d42f5a09a9b92d6 |
apache-2.0 | [] | false | Intended uses & limitations This model is an alternative to Chinese models. It may offer better performance for tasks catering to the langauge usage of Hong Kongers. Yue Wikipedia is used which is much smaller than Chinese Wikipedia; this model will lack the breath of knowledge compared to other Chinese models. | 8fc2194d028788330f77e139126097cb |
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