license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 9a00367479db8e131cad35a3196b0546 |
apache-2.0 | ['automatic-speech-recognition', 'ar'] | false | exp_w2v2t_ar_vp-sv_s953 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | e276b00830034f899223edfc484b0c04 |
apache-2.0 | ['translation'] | false | By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC) * source languages: en * target languages: el * licence: apache-2.0 * dataset: Opus, CCmatrix * model: transformer(fairseq) * pre-processing: tokenization + BPE segmentation * metrics: bleu, chrf | 80ae78aa8b30e19b6e5ca7aadc19fb2f |
apache-2.0 | ['translation'] | false | How to use ``` from transformers import FSMTTokenizer, FSMTForConditionalGeneration mname = "lighteternal/SSE-TUC-mt-en-el-cased" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) text = " 'Katerina', is the best name for a girl." encoded = tokenizer.enco... | 1934937b2982ae25efc2ff55a6c75a7d |
apache-2.0 | ['translation'] | false | Eval results Results on Tatoeba testset (EN-EL): | BLEU | chrF | | ------ | ------ | | 76.9 | 0.733 | Results on XNLI parallel (EN-EL): | BLEU | chrF | | ------ | ------ | | 65.4 | 0.624 | | 151a1c2a253b4d4f5c6670194542c4e1 |
apache-2.0 | ['generated_from_trainer'] | false | openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2697 - Wer: 15.3855 | 0b3574f95c2a7e342d5350500759352b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0301 | 1.19 | 1000 | 0.2697 | 15.3855 | | 54b78d02a25d25121f58cda0d78eb7d2 |
mit | ['generated_from_trainer'] | false | roberta_tec_gpu_v1 This model is a fine-tuned version of [ibm/ColD-Fusion](https://huggingface.co/ibm/ColD-Fusion) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2970 - F1: 0.8202 - Roc Auc: 0.8806 - Recall: 0.8561 - Precision: 0.7871 | efd01dfacb064e1990b70c2afae7cd25 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:------:|:---------:| | 0.4549 | 1.0 | 923 | 0.3128 | 0.7604 | 0.8277 | 0.7404 | 0.7815 | | 0.251 | 2.0 | 18... | 27d39279ed5ae1ece99d04fddbe44420 |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | paws_x_m_bert_only_ko This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the paws-x dataset. It achieves the following results on the evaluation set: - Loss: 0.7649 - Accuracy: 0.8215 | afdd12e9128de4ca1a92da651dce446e |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10 | a9e7cfd7e1a3c4eb039029da95907b35 |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5446 | 1.0 | 386 | 0.4837 | 0.768 | | 0.3443 | 2.0 | 772 | 0.4530 | 0.8125 | | 0.258 | 3.0 | 1158 | 0.4496 | 0.... | af0d4b9e4dac6725d943c95065d5e9ae |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert-finetuned-tapt-lm-music 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: | b604937f500c536be56be9408ac62d76 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'Polynomia... | 2280a8e12321f720df89dd481fe6cc7c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | kornwtp/ConGen-paraphrase-multilingual-mpnet-base-v2 This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 768 dimensional dense vector space and can be used for tasks like semantic search. | 0cbbbf7489b3a61477b187340ee5f204 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed: ``` pip install -U git+https://github.com/KornWtp/ConGen.git ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["กลุ่มผู้ชายเล่นฟุตบอลบนชายหาด... | ee1fbf698e70595003d6670bf0b0af17 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_xlarge_tf_512.in21k_ft_in1k An official MaxViT image classification model. Pretrained in tensorflow on ImageNet-21k (21843 Google specific instance of ImageNet-22k) and fine-tuned on ImageNet-1k by paper authors. Ported from official Tensorflow implementation (https://github.com/google-research... | c211c4c43b29c4393c2fa0f3fdf2ea2a |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 475.8 - GMACs: 534.1 - Activations (M): 1413.2 - Image size: 512 x 512 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - **Dataset:** ImageNet-1k - **Pretrain Datas... | 2f8f76e996ca66d24bb38af586119517 |
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('maxvit_xlarge_tf_512.in21k_ft_in1k', pretrained=True) ... | d1006422d278176854562719a2c71842 |
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( 'maxvit_xlarge_tf_512.in21k_ft_in1k', pretra... | fa69098fc0ce36e198c8c6c8bccc0630 |
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( 'maxvit_xlarge_tf_512.in21k_ft_in1k', pretrained=T... | f9ec4cf24e10dbb9ddce8762dec6eb20 |
apache-2.0 | ['translation'] | false | opus-mt-sv-lue * source languages: sv * target languages: lue * OPUS readme: [sv-lue](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-lue/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 5268fd5df683e1b0385457c710df0876 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'robust-speech-event', 'pt', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-pt-cv This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3418 - Wer: 0.3581 | e7eaadc214ee4eb4de78f30526441c8c |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'robust-speech-event', 'pt', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 2f852ef23d5932082624823ff0918a66 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'robust-speech-event', 'pt', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 10.9035 | 0.2 | 100 | 4.2750 | 1.0 | | 3.3275 | 0.41 | 200 | 3.0334 | 1.0 | | 3.0016 | 0.61 | 300 | 2.9494 | 1.0 | |... | 5b2e2a23c586c932c81c5484b6f6b096 |
mit | ['spacy', 'token-classification'] | false | en_core_web_sm English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `en_core_web_sm` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `... | 9e4c998c8988a0f3da24d3571280fe55 |
mit | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (113 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PD... | 97d081d5b77f570e3522b85d6375b46c |
mit | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.86 | | `TOKEN_P` | 99.57 | | `TOKEN_R` | 99.58 | | `TOKEN_F` | 99.57 | | `TAG_ACC` | 97.25 | | `SENTS_P` | 92.02 | | `SENTS_R` | 89.21 | | `SENTS_F` | 90.59 | | `DEP_UAS` | 91.75 | | `DEP_LAS` | 89.87 | | `ENTS_P` | 84.55 | | `ENTS_R` | 84.57 | | `ENTS_F` | 8... | 875fd7b8af481361ca11f05729a4a799 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-demo-colab 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: 0.4657 - Wer: 0.3422 | f707ed248129fdf0f2c93f82ff07636f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4477 | 4.0 | 500 | 1.3352 | 0.9039 | | 0.5972 | 8.0 | 1000 | 0.4752 | 0.4509 | | 0.2224 | 12.0 | 1500 | 0.4604 | 0.4052 | |... | d6f8ac7aadce59a41c51ae1712a8f6f9 |
afl-3.0 | ['biomedical', 'social media', 'ner'] | false | Disease mention recognizer for Spanish Social Media texts 🦠💬 This resource derives from the participation of the SINAI team in [Mining Social Media Content for Disease Mention (SocialDisNER)](https://temu.bsc.es/socialdisner/) shared task. This task focused on the recognition of disease mentions in tweets written in... | e28612ed903d03f379d7b0e7cb08caea |
afl-3.0 | ['biomedical', 'social media', 'ner'] | false | Results The model contained in this repository constitutes the fundament of the NER system presented by the SINAI team on SocialDisNER. Enhanced with data [`pysentimiento`](https://github.com/pysentimiento/pysentimiento) pre-processing and rule-based submission post-processing, it obtained encouraging results during... | 51915c91efa9316894d92bb9003ecc1f |
afl-3.0 | ['biomedical', 'social media', 'ner'] | false | SMM4H) held on COLING22 in October 2022. ``` @inproceedings{chizhikova-etal-2022-sinai, title = "{SINAI}@{SMM}4{H}{'}22: Transformers for biomedical social media text mining in {S}panish", author = "Chizhikova, Mariia and L{\'o}pez-{\'U}beda, Pilar and D{\'\i}az-Galiano, Manuel C. and Ure... | c6a8c7e00cb4f6df15a1b13cdc868b4f |
afl-3.0 | ['biomedical', 'social media', 'ner'] | false | }SSM4H) workshop at COLING-2022. These tasks focus on leveraging Twitter posts written in Spanish for healthcare research. The objective of Task 5 was to classify tweets reporting COVID-19 symptoms, while Task 10 required identifying disease mentions in Twitter posts. The presented systems explore large RoBERTa languag... | f41b3e076410f009ae1dd4bcc906d482 |
apache-2.0 | ['generated_from_keras_callback'] | false | TestZee/t5-small-finetuned-custom-wion-test-BIG 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: 1.1165 - Validation Loss: 0.4609 - Epoch: 29 | 38bdc57b139bc459599faff6beda10a0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.9622 | 0.8875 | 0 | | 1.9276 | 0.8601 | 1 | | 1.8301 | 0.8342 | 2 | | 1.7776 | 0.8104 | 3 | | 1.7345 | 0.7878 | 4 | | 1.7733 |... | 8918a64dd2d3c1bee61617a4387a9645 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_s... | 443c7b1f1ea81f3dad0fdf231015d10b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.991 | 0.33 | 1000 | 2.5183 | | 2.2592 | 0.65 | 2000 | 2.0328 | | 1.9112 | 0.98 | 3000 | 1.8410 | | f5374bb9d22a5ec2ec375ca59820f586 |
apache-2.0 | ['generated_from_trainer'] | false | albert-large-v2_cls_subj This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6940 - Accuracy: 0.4835 | 6743dcc0c61be9f5a28523857c41dc59 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3156 | 1.0 | 500 | 0.2889 | 0.9305 | | 0.4473 | 2.0 | 1000 | 0.6936 | 0.4835 | | 0.7088 | 3.0 | 1500 | 0.7079 | 0.... | 4a7cc1d3b877670c4cc2a1350037a9f9 |
apache-2.0 | ['generated_from_trainer'] | false | Vin16-P3 This model is a fine-tuned version of [HuyenNguyen/Vin11-P3](https://huggingface.co/HuyenNguyen/Vin11-P3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4000 - Wer: 25.7994 | 4112aae32de460b424be4d05a6b88246 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 150 - mixed_precisio... | 631a1cd15c97ec9c809dcb9afdbe3ba4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3252 | 0.27 | 50 | 0.3806 | 24.3160 | | 0.2973 | 0.53 | 100 | 0.3923 | 24.8214 | | 0.2815 | 0.8 | 150 | 0.4000 | 25.799... | 26c6ad766a37b513f772519f09009832 |
apache-2.0 | ['translation'] | false | ukr-deu * source group: Ukrainian * target group: German * OPUS readme: [ukr-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-deu/README.md) * model: transformer-align * source language(s): ukr * target language(s): deu * model: transformer-align * pre-processing: normalization + Sen... | d5e7740f5dd7d1a1fde82bc5f338ed9f |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ukr-deu - source_languages: ukr - target_languages: deu - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-deu/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['uk', 'de'] - src_constituents: {'ukr'} - tgt_const... | d5b5a4c6892d718cee23c1c931c7274f |
apache-2.0 | [] | false | DISCLAIMER - I don't own the weights of `ernie-m-base` neither did I train the model. I only converted the model weights from paddle to pytorch(using the scripts listed in files). The real(paddle) weights can be found [here](https://huggingface.co/PaddlePaddle/ernie-m-base). The rest of the README is copied from the ... | d678118a0fb2df918cb0545d927e2b9d |
apache-2.0 | ['generated_from_trainer'] | false | albert-base-v2_squad This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the **squadV1** dataset. - "eval_exact_match": 82.69631031220435 - "eval_f1": 90.10806626207174 - "eval_samples": 10808 | 5cf109194936d9c5f64f60d35648eb19 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 28.6 - GMACs: 4.5 - Activations (M): 13.4 - Image size: 224 x 224 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/facebookresearch/ConvNeXt - *... | 86b765f45d853a721039823ceebfd72f |
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_tiny.fb_in1k', pretrained=True) model = model... | b23673e8af6afdaf1c7990581e01040c |
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_tiny.fb_in1k', pretrained=True, ... | 0b5e4dcc197affcbb866f3f33f161a8e |
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_tiny.fb_in1k', pretrained=True, num_... | 601322c43162681e463b81806e8ea2b5 |
mit | ['generated_from_trainer'] | false | deberta-v3-large__sst2__train-16-0 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9917 - Accuracy: 0.7705 | 2f45b010e17a991b68ca56aac46b8d24 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7001 | 1.0 | 7 | 0.7327 | 0.2857 | | 0.6326 | 2.0 | 14 | 0.6479 | 0.5714 | | 0.5232 | 3.0 | 21 | 0.5714 | 0.... | 87aa85c33eb78ca3cf718ce10e5ac427 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_itr0_0.0002_all_16_02_2022-20_30_01 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: 0.1577 - Precision: ... | b1b8c2434cf30977a8689a7a2918ab2a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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 | 0965deeb1bbddf3e9991a3002fd63158 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 38 | 0.3553 | 0.1068 | 0.0810 | 0.0922 | 0.8412 | | No log | 2.0 |... | 92034df4851cc5038b20d63d35826746 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr1e05-wd0.005-bs32 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2756 - Rmse: 0.5250 - Mse: 0.2756 - Mae: 0.4181 | 83b38a4c1b4d45bfcef7e00357679205 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2769 | 1.0 | 623 | 0.2768 | 0.5261 | 0.2768 | 0.4281 | | 0.2743 | 2.0 | 1246 | 0.2739 | 0.5234 | 0.2739 ... | 6e3c5d69a1e96682eb88ff1ac0b88e0a |
apache-2.0 | ['generated_from_trainer'] | false | bert-fine-tuned-cola This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8136 - Matthews Correlation: 0.5779 | 5a2d62a094a257e1e0b6fbef3ddd05dc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4785 | 1.0 | 1069 | 0.5265 | 0.4996 | | 0.3162 | 2.0 | 2138 | 0.6626 | 0.5701 | | 0.1... | b7bea7f9af39b52913928161b015f0f7 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-MINI-NL24 (Deep-Narrow version) T5-Efficient-MINI-NL24 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint an... | 747570fbc024f51537bd78171a66b8af |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-mini-nl24** - is of model type **Mini** with the following variations: - **nl** is **24** It has **125.69** million parameters and thus requires *ca.* **502.75 MB** of memory in full precision (*fp32*) or **251.37 MB** of memory in half precision (*... | eda35ecb9d09ae411984117a53dc0afa |
openrail++ | ['stable-diffusion', 'text-to-image', 'diffusers'] | false | Holiday Stop Motion This is the fine-tuned Stable Diffusion 1.5 model trained on classic Christmas stop motion tv specials by Rankin and Bass. Use the tokens `rbsm style` in your prompts for the effect. Trained on Stability.ai's 1.5 model with 768x768 resolution. **Characters rendered with the model:** ![rbsm... | 830483dbc9d1e2186e64080255d8629f |
openrail++ | ['stable-diffusion', 'text-to-image', 'diffusers'] | false | License This model is open access and available to all, with a CreativeML Open RAIL++-M License further specifying rights and usage. [Please read the full license here](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) | cb5a9612a9854db056bafadbb36eaf08 |
mit | ['sentence-transformers'] | false | Cross-Encoder for MS Marco The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/READM... | d30e66854f5e746218690b5cdcea3ce8 |
mit | ['sentence-transformers'] | false | Usage The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('model_name', max_length=512) scores = model.predict([('Query', 'Paragraph1'), ('Query... | 486e01db691e29c459aaafb1832065dc |
mit | ['sentence-transformers'] | false | Performance In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset. | Model-Name ... | 89b6a1ba00029514ebd38c343a1f51f9 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-banking-4-16-5-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 | 9de63a72940ed5c875606f290b339c12 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-LARGE-NL32 (Deep-Narrow version) T5-Efficient-LARGE-NL32 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint ... | 26c9de586181b44fb00d673106ccc403 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-large-nl32** - is of model type **Large** with the following variations: - **nl** is **32** It has **972.67** million parameters and thus requires *ca.* **3890.66 MB** of memory in full precision (*fp32*) or **1945.33 MB** of memory in half precisio... | 84134b75f0a5afe7003738c720009edd |
apache-2.0 | [] | false | Overview This model is trained by over 40,000 news from different medias based on the 'roberta-base'. It can give result by simply entering the text of the news less than 500 words(the excess will be truncated automatically). LABEL_0: Fake news LABEL_1: Real news | f1ab7e562646fe066e2bce8ad9078779 |
apache-2.0 | [] | false | Feed Data ```python text = "Indonesian police have recaptured a U.S. citizen who escaped a week ago from an overcrowded prison on the holiday island of Bali, the jail s second breakout of foreign inmates this year. Cristian Beasley from California was rearrested on Sunday, Badung Police chief Yudith Satria Hananta s... | 51648bdb729d987cc9c10f64670f4618 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2245 - Accuracy: 0.9225 - F1: 0.9225 | d2eaf6ce9a04cee14f3bcab6ad45fc58 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8649 | 1.0 | 250 | 0.3333 | 0.899 | 0.8956 | | 0.2627 | 2.0 | 500 | 0.2245 | 0.9225 | 0.9225 | | 653ab734fabfb885427d55f8b5a1dd6f |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | jrtec-distilroberta-base-mrpc-glue-omar-espejel This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset. It achieves the following results on the evaluation set: - Loss: 0.4901 - Accuracy: 0.8162 - F1: 0.8748 | e33984102c729ae1c5991eec5e3a226b |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4845 | 1.09 | 500 | 0.4901 | 0.8162 | 0.8748 | | 0.3706 | 2.18 | 1000 | 0.6421 | 0.8162 | 0.8691 | | 0.2003 |... | 4fe9cb335e0f82f1e7475ce830c666e9 |
creativeml-openrail-m | ['anime'] | false | Bondrewd LoRA [<img src="https://huggingface.co/Aotsuyu/Bondrewd-Lora/resolve/main/images/3.png" width="800" height="512">](https://huggingface.co/Aotsuyu/ozen-Lora/resolve/main/images/3.png) A LoRA for Bondrewd from Made in Abyss! Created with the dataset provided by Brenton | 2c6cd417ebb5684d197673187d2da1c2 |
creativeml-openrail-m | ['anime'] | false | What to get I am including most epochs, but I've personally had the best results with the 5th epoch, which I am renaming to **bestdad-BEST*, while renaming the 4th to softer and 6th to harder. Not the greatest naming scheme possibly but I want it to be obvious which one should one download. Get **bestdad-BEST**. | 833cfe15545f9b46e2d9156db88aa8db |
creativeml-openrail-m | ['anime'] | false | Invoking I've decided on two **tokens** this time, **bdsks** and **bfsks**. The first one, 'bdsks' (best dad) is the normal token, it's associated with Bondrewd's normal look. The second one, 'bfsks' (best furry), however, is associated with his transformed look, one where he exhibits animal characteristics. If you'r... | f38cf70c141e7e90fbae976317246e56 |
creativeml-openrail-m | ['anime'] | false | Previews All the previews have prompts included, so read that! I've generated some on my mix holo-youcha, it's bad, don't use it, (https://huggingface.co/Aotsuyu/houjicha)[it's here.] bestdad-000005 is just bestdad-BEST before renaming. [<img src="https://huggingface.co/Aotsuyu/Bondrewd-Lora/resolve/main/images/4.p... | 0ade36dc84eac0b66d3453f12c43a12f |
creativeml-openrail-m | ['anime'] | false | Model and epoch comparison This is trained on base NAI so any models off of that should do fine. [<img src="https://huggingface.co/Aotsuyu/Bondrewd-Lora/resolve/main/images/grid.png" width="840" height="964">](https://huggingface.co/Aotsuyu/Bondrewd-Lora/resolve/main/images/grid.png) | 31316952331e9c66cef14b620ea84815 |
gpl-3.0 | ['bicleaner-ai'] | false | Bicleaner AI full model for en-mk Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It indicates the likelihood of a pair of sentences being mutual translations (with a value near to 1) or not (with a value near to 0). Sentence pairs considered very noisy are scored with 0. Find... | e1f90608ad2a66a765af1fb605d4f684 |
other | ['vision', 'image-segmentation'] | false | Mask2Former Mask2Former model trained on Cityscapes instance segmentation (large-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookrese... | ff6bff6cabdd1663735ccd825d76cf8f |
other | ['vision', 'image-segmentation'] | false | load Mask2Former fine-tuned on Cityscapes instance segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-large-cityscapes-instance") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-large-cityscapes-instance") url = "http://images.cocodataset.org/... | 0ac61bd6d696c9b3872208f0c44c7f33 |
apache-2.0 | ['generated_from_trainer'] | false | irony_trained_31415 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: 1.6608 - F1: 0.6690 | 76f27cb5ab2e60c3d42c1493e5856e0b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.6774391860025942e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 31415 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | fb858810dee88f7aead16fc667c4e13f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6547 | 1.0 | 716 | 0.6173 | 0.6508 | | 0.57 | 2.0 | 1432 | 0.8629 | 0.6577 | | 0.2955 | 3.0 | 2148 | 1.4836 | 0.6722 | |... | 362e3a8f77d7f5952ac55f9615597c6a |
apache-2.0 | ['distigpt2', 'hearthstone'] | false | h0 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on [hearthstone](https://huggingface.co/datasets/dvitel/hearthstone) dataset. [GitHub repo](https://github.com/dvitel/nlp-sem-parsing/blob/master/h0.py). It achieves the following results on the evaluation set: - Loss: 0.3117 - E... | 84c37b917b3abac06d5818a19fc5000a |
apache-2.0 | ['distigpt2', 'hearthstone'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | Bleu | Codebleu | Ngram Match Score | Weighted Ngram Match Score | Syntax Match Score | Dataflow Match Score | Chrf | |:-------------:|:------:|:-----:|:---------------:|:-----------:|:------:|:--------:|:-----------------:|:----... | 82756d5d36bb6130bbb06adf3f93366b |
apache-2.0 | ['ner', 'zero-shot', 'information extruction'] | false | Erlangshen-UniEX-RoBERTa-110M-Chinese - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/UniEX/) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) | de967f6d5cb53dc097f4c99d2dac7eb5 |
apache-2.0 | ['ner', 'zero-shot', 'information extruction'] | false | 简介 Brief Introduction UniEX 核心思想是将信息抽取转化为 token-pair 任务，为了将实体识别、关系抽取、事件抽取等抽取任务统一起来。我们使用一张表来识别实体的位置，其他表用来识别实体的类型或者关系的类型。此外，我们将标签信息和要抽取的文本拼接在一起，通过transformer进行编码。然后得到label的表示和文本的表示。最后通过Triaffine注意力机制使得所有任务可以共享一套参数。 The core idea of UniEX is to transform information extraction into token-pair tasks, in order to unify e... | 25441feddbbeffcad532e56bcfef2f04 |
apache-2.0 | ['ner', 'zero-shot', 'information extruction'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 抽取 Extraction | 自然语言理解 NLU | 二郎神 Erlangshen | RoBERTa | 110M | Chinese | | 5d04731979d1a59986b4a26c091467ca |
apache-2.0 | ['ner', 'zero-shot', 'information extruction'] | false | 模型信息 Model Information 由于 UniEX 可以统一所有抽取任务，且经过预训练之后，UniEX拥有着不错的 Few-Shot 和 Zero-shot 性能。为了方便社区做中文领域的抽取任务，我们使用百度百科这种结构化的数据构建弱监督数据集，通过清洗过后得到大概600M的数据，此外也收集了 16 个实体识别，7个关系抽取，6个事件抽取，11个阅读理解数据集。我们将收集得到的数据同时输入模型进行预训练。 Because UniEX can unify all extraction tasks, and after pre-training, UniEX has strong Few-Shot and Zero-... | 3c9915f2bbb321518269776b5b87ac9e |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | newhorrorfantasy_style This is an own SD trainee with a 2010s horror and fantasy illustrations as a style. If you wanna test it, you can put this word on the prompt: newhorrorfantasy_style [](https://www.buymeacoffee.com/elrivx) E... | f8dfd25223f9143d4d9ee5b5c0f85651 |
mit | ['generated_from_trainer', 'opt'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15 | 203e0d8317b288e06a7c34b75602e3d7 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.07-0.5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8729 - Bleu: 6.6788 - Gen Len: 43.8899 | bd940cf97b445bbf1c958d23fcdbadba |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-all 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: 0.1812 - F1: 0.8567 | 0df9e2dccc622dc44e489fd39ee71cf7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2983 | 1.0 | 1252 | 0.1945 | 0.8033 | | 0.1603 | 2.0 | 2504 | 0.1889 | 0.8441 | | 0.1012 | 3.0 | 3756 | 0.1812 | 0.8567 | ... | e784cb2275326df2f59c8c38fae4f29b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__subj__train-8-4 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: 0.3305 - Accuracy: 0.8565 | 24f450f39411b29e6e4c41e22cc24f36 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6991 | 1.0 | 3 | 0.6772 | 0.75 | | 0.6707 | 2.0 | 6 | 0.6704 | 0.75 | | 0.6402 | 3.0 | 9 | 0.6608 | 1.... | b7f050a8fff40f52c396f3d7076eac4c |
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