license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
openrail++ | ['text-to-image', 'stable-diffusion'] | false | Samples Here are a few example images (generated with 50 steps). | pbr uneven stone wall | pbr dirt with weeds | pbr bright white marble | | --- | --- | --- | |  |  |  | | a93d65c5aaaef1a3e79669bb8a7a9c19 |
openrail++ | ['text-to-image', 'stable-diffusion'] | false | Usage Use the token `pbr` in your prompts to invoke the style. This model was made for use in [Dream Textures](https://github.com/carson-katri/dream-textures), a Stable Diffusion add-on for Blender. You can also use it with [🧨 diffusers](https://github.com/huggingface/diffusers): ```python from diffusers import St... | d9053bf6ec6661f5e8df1ae3c5ee5528 |
openrail++ | ['text-to-image', 'stable-diffusion'] | false | Training Details * Base Model: [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) * Resolution: `512` * Prior Loss Weight: `1.0` * Class Prompt: `texture` * Batch Size: `1` * Learning Rate: `1e-6` * Precision: `fp16` * Steps: `4000` * GPU: Tesla T4 | 18f158d0d59ffeb818c6712bfb5949b6 |
apache-2.0 | ['generated_from_trainer'] | false | openai/whisper-large-v2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1534 - Wer: 145.6786 | 10e86c109d6bf21b0e9b24cffa020a6b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0799 | 2.03 | 500 | 0.1010 | 28.1322 | | 0.0239 | 5.01 | 1000 | 0.1388 | 161.0139 | | 0.0066 | 7.03 | 1500 | 0.1221 | 99... | bda84a0a0c1dbec134db67dfd46dbaa3 |
apache-2.0 | ['generated_from_keras_callback'] | false | kevinbram/testarbara This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.4900 - Train End Logits Accuracy: 0.6129 - Train Start Logits Accuracy: 0.5735 - Validati... | ea5c37d821d66af59a123257be163faa |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | ece5bfe5a74895e01ecfe2468f27eaa9 |
apache-2.0 | ['translation'] | false | opus-mt-en-gl * source languages: en * target languages: gl * OPUS readme: [en-gl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-gl/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://... | a55d4d1e99a5485074c36947889b224d |
creativeml-openrail-m | ['text-to-image'] | false | hulk_style_v1 Dreambooth model trained by sztanki with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/noteboo... | 685b83023e6eeb477572a1bf7a651905 |
creativeml-openrail-m | [] | false | Prompt and settings for portraits: **brld harrison ford** _Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 3940025417 **brld morgan freeman** _Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 3940025417 | a61984345e450db6a7eb1257f346823e |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_vp-es_s980 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 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | df338ec11ecd6fc92fc1e0ace2981506 |
other | ['generated_from_trainer'] | false | segformer-b0-scene-parse-150 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset. It achieves the following results on the evaluation set: - Loss: 4.4675 - Mean Iou: 0.0363 - Mean Accuracy: 0.1783 - Overall Accuracy: 0.2473 - Per Category Iou: [0.... | 0f2856f8c67e7b9072d427e91a8c62be |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou ... | 4d1d18fde8edda4297c7d40a4772f272 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en 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: 0.7589 - F1: 0.6307 | 3b88ae9b591e8100f73d1db95c5821d3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.9453 | 1.0 | 1180 | 0.7589 | 0.6307 | | 323a6db8b83cef3e3182d5a6e4c25f2e |
wtfpl | [] | false | Cat picture embedding for 2.0. Trained on high quality Unsplash images, so it tends to prefer photorealism. Warning: the weights are quite strong. But, when tamed, it works great with stylistic embeddings like the last couple of images! Trained for 1500 steps, but added the 1000 steps one as well which also works pre... | b08574f56bc714775a5d13aa47fc07ff |
apache-2.0 | ['ner', 'zero-shot', 'information extruction'] | false | Erlangshen-UniEX-RoBERTa-330M-Chinese - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/UniEX/) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) | d9ce959d410742a28b0c7acb58da0a72 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'kk', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | 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_8_0 - KK dataset. It achieves the following results on the evaluation set: - Loss: 0.7149 - Wer: 0.451 | fb7798d08ffd4fc8b5e028874ad48537 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'kk', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-kk-with-LM --dataset mozilla-foundation/common_voice_8_0 --config kk --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Ka... | d6856488e9c880ab99eeb277ea7acb45 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'kk', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000222 - 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_s... | c6089d24d5163f3f212b1b729f95aa96 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'kk', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 9.6799 | 9.09 | 200 | 3.6119 | 1.0 | | 3.1332 | 18.18 | 400 | 2.5352 | 1.005 | | 1.0465 | 27.27 | 600 | 0.6169 | 0.682... | 1793ef5eaad2549df55e5206af5f529d |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/distilbert-base-nli-stsb-mean-tokens 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. | c32568a305569948c51e77ca089e5e78 |
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... | a6e5c890612d1688c4bbcb6b2d0a0c4a |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/distilbert-base-nli-stsb-mean-tokens') model = AutoModel.from_pretrained('sentence-transformers/distilbert-base-nli-stsb-mean-tokens') | 4b2ce08b86eddac35f48360a5ab2f6b9 |
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/distilbert-base-nli-stsb-mean-tokens) | 406246d706f3142278f45f57f6011f22 |
mit | ['singapore', 'sg', 'singlish', 'malaysia', 'ms', 'manglish', 'bert-large-uncased'] | false | Model description Similar to [SingBert](https://huggingface.co/zanelim/singbert) but the large version, which was initialized from [BERT large uncased (whole word masking)](https://github.com/google-research/bert | 836e1907bc4cb8c4d0cc1dd295b6061d |
mit | ['singapore', 'sg', 'singlish', 'malaysia', 'ms', 'manglish', 'bert-large-uncased'] | false | How to use ```python >>> from transformers import pipeline >>> nlp = pipeline('fill-mask', model='zanelim/singbert-large-sg') >>> nlp("kopi c siew [MASK]") [{'sequence': '[CLS] kopi c siew dai [SEP]', 'score': 0.9003700017929077, 'token': 18765, 'token_str': 'dai'}, {'sequence': '[CLS] kopi c siew mai [SEP]',... | 5b4e12926314a351567026ce34389224 |
apache-2.0 | ['generated_from_trainer'] | false | swin-kitchenware 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 imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0860 - Accuracy: 0.9762 | 2c0bf108739a2185aff3b25dce7aa2f7 |
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: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | bab658837816a0a4a365a8c9d475f018 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6286 | 1.0 | 56 | 0.1875 | 0.9437 | | 0.3034 | 2.0 | 112 | 0.0921 | 0.9750 | | 0.2759 | 3.0 | 168 | 0.0992 | 0.... | e1868ff0f2b0f5b2573a02db52fe2a56 |
mit | [] | false | aemond_targaryen_sandman on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | 827c5dc8db32e6b235aa6336b8a166e0 |
mit | [] | false | model by EdXD This your the Stable Diffusion model fine-tuned the aemond_targaryen_sandman concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **AemondHoD, Sandman2022** You can also train your own concepts and upload them to the library by using [the fast-DremaBo... | a8408e50882af3dbcb021d1c60ac47a3 |
cc-by-sa-4.0 | ['asteroid', 'audio', 'ConvTasNet', 'audio-to-audio'] | false | .X9M69cLjJH4) Description: This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid). It was trained on the `sep_clean` task of the Libri2Mix dataset. Training config: ```yaml data: n_src: 2 sample_rate: 8000 segment: 3 task: sep_cle... | df10c42a4dc746dbbfe7c239d3d399d8 |
cc-by-4.0 | [] | false | Model description This is the T5-3B model for System 3 DREAM-FLUTE (all 4 dimensions), as described in our paper Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE, FigLang workshop @ EMNLP 2022 (Arxiv link: https://arxiv.org/abs/2210.16407) Systems 3: DREAM-FLUTE - Providing DREAM’s different d... | 865dfba924aed6c4d06f93e2a87d1303 |
cc-by-4.0 | [] | false | How to use this model? We provide a quick example of how you can try out DREAM-FLUTE (all 4 dimensions) in our paper with just a few lines of code: ``` >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> model = AutoModelForSeq2SeqLM.from_pretrained("allenai/System3_DREAM_FLUTE_all_dimensions_FigLang... | 6d2184db457988003d50468d037e43a8 |
cc-by-4.0 | [] | false | Model details This model is a fine-tuned version of [t5-3b](https://huggingface.co/t5-3b). It achieves the following results on the evaluation set: - Loss: 0.7499 - Rouge1: 58.5551 - Rouge2: 38.5673 - Rougel: 52.3701 - Rougelsum: 52.335 - Gen Len: 40.7452 | 43ab571ffcc44f1cbf48cba39fb9dd51 |
cc-by-4.0 | [] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.992 | 0.33 | 1000 | 0.8911 | 39.9287 | 27.5817 | 38.2127 | 38.2042 | 19... | 9ed16f3f08db35fba8367c698e981c46 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it 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: 0.5555 - F1: 0.7875 | 9217f0ecf6961efb084af6e170bb4fd8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8118 | 1.0 | 1680 | 0.5555 | 0.7875 | | d3b591741938e1b78ca2cb7aff29b2d9 |
apache-2.0 | ['generated_from_trainer'] | false | 2nd-wav2vec2-l-xls-r-300m-turkish-test 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: 0.6019 - Wer: 0.4444 | 64c19b9ba2cdafbee497d36341ab394d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0522 | 3.67 | 400 | 0.7773 | 0.7296 | | 0.5369 | 7.34 | 800 | 0.6282 | 0.5888 | | 0.276 | 11.01 | 1200 | 0.5998 | 0.5330 | |... | fe05952f79fbfc8e7e2b0b2f5aefa996 |
mit | [] | false | Lula 13 on Stable Diffusion This is the `<lula-13>` 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... | 9ae588937c7eeaf06bc6a1a1a20662f8 |
apache-2.0 | ['generated_from_trainer'] | false | hf_fine_tune_hello_world This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 1.0142 - Accuracy: 0.592 | 6d9506cf9a75ed0110679915559c2bd2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 1.0844 | 0.529 | | No log | 2.0 | 250 | 1.0022 | 0.58 | | No log | 3.0 | 375 | 1.0142 | 0.... | 6485e2d5c2a2ceb6e11dd1e1c1de347f |
apache-2.0 | ['generated_from_trainer', 't5-base'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP | 461cb6fdb79c2295bbfe39256ea6fcd7 |
mit | ['roberta-base', 'roberta-base-epoch_51'] | false | RoBERTa, Intermediate Checkpoint - Epoch 51 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | a62bac9de86563aaf91e4b0889a6e0cd |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper_large_Khmer This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the google/fleurs km_kh dataset. It achieves the following results on the evaluation set: - Loss: 0.5659 - Wer: 51.1683 | 4312e5497cd6f83a86fcd9179cb53eb3 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0002 | 50.0 | 500 | 0.5488 | 51.5328 | | 0.0001 | 100.0 | 1000 | 0.5659 | 51.1683 | | 8ddac99f7f56065bd206d6d4dd32261a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(log... | 40dd012fc80c8e6bccc33838a9c70a80 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | exper_batch_32_e8 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 sudo-s/herbier_mesuem1 dataset. It achieves the following results on the evaluation set: - Loss: 0.3520 - Accuracy: 0.9113 | b9a07edf641d418b75e5edb9fb6a73ea |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: 8 - mixed_precision_training: Apex, opt level O1 | 74d34851ae88fe56a03f68f5e572c8c2 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.3787 | 0.31 | 100 | 3.3100 | 0.3566 | | 2.3975 | 0.62 | 200 | 2.3196 | 0.5717 | | 1.5578 | 0.94 | 300 | 1.6764 | 0.... | b8c12b92bb7296d564e0f330b16d287e |
gpl-3.0 | ['pytorch', 'lm-head', 'albert', 'zh'] | false | Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-base-chinese') ``... | 716e4d5d429ad4a3db2ce6451489a6c9 |
apache-2.0 | ['spacy', 'token-classification'] | false | DaCy large transformer DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines. DaCy's largest pipeline has achieved State-of-the-Art performance on Named entity recognition, part-of-speech tagging and dependency parsing for Danish on the... | b849691a8dc6e30f591d9fb08a06f3c3 |
apache-2.0 | ['spacy', 'token-classification'] | false | danish-dependency-treebank-dane) (Rasmus Hvingelby, Amalie B. Pauli, Maria Barrett, Christina Rosted, Lasse M. Lidegaard, Anders Søgaard)<br />[xlm-roberta-large](https://huggingface.co/xlm-roberta-large) (Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard G... | c4a51fd11a03213c67afa8f2ff91b076 |
apache-2.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `POS_ACC` | 98.70 | | `MORPH_ACC` | 98.49 | | `DEP_UAS` | 90.75 | | `DEP_LAS` | 88.38 | | `SENTS_P` | 96.09 | | `SENTS_R` | 95.74 | | `SENTS_F` | 95.91 | | `LEMMA_ACC` | 84.91 | | `ENTS_F` | 90.12 | | `ENTS_P` | 89.02 | | `ENTS_R` | 91.25 | | `TRANSFORMER_LOSS` | 1805626.49 |... | 292d474faab90e1de977817e49b4d9ff |
apache-2.0 | ['spacy', 'token-classification'] | false | Deterministic Augmentations Deterministic augmentations are augmentation which always yield the same result. | Augmentation | Part-of-speech tagging (Accuracy) | Morphological tagging (Accuracy) | Dependency Parsing (UAS) | Dependency Parsing (LAS) | Sentence segmentation (F1) | Lemmatization (Accuracy) | Named entit... | 9d91d786788edc034202cdd9bd4bcfee |
apache-2.0 | ['spacy', 'token-classification'] | false | Stochastic Augmentations Stochastic augmentations are augmentation which are repeated mulitple times to estimate the effect of the augmentation. | Augmentation | Part-of-speech tagging (Accuracy) | Morphological tagging (Accuracy) | Dependency Parsing (UAS) | Dependency Parsing (LAS) | Sentence segmentation (F1) | Le... | faf4d2e8772a0f245b98ae2ece7fdfc9 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | tfmfurbase Dreambooth model trained by Deitsao with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook (BROKEN because I'm sleepy asf 😭) Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.co... | daa30595b1f33b798d22be0129523a0f |
apache-2.0 | ['generated_from_trainer'] | false | test-clm This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the bittensor train-v1.1.json dataset. It achieves the following results on the evaluation set: - Loss: 6.5199 - Accuracy: 0.1387 | da10be0856c58f8b0337498f9e2c0d19 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_1_ternary_v1 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: 2.1145 - F1: 0.7757 | 29abec69c901ff416eac7bcce00f6174 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.5580 | 0.7646 | | 0.555 | 2.0 | 580 | 0.5820 | 0.7670 | | 0.555 | 3.0 | 870 | 0.6683 | 0.7757 | |... | a237927bd9919f0fd96c8f885bbd60c8 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 047d0c474c18a87c205e566948410be16787e477 pip install -e . cd egs2/kss/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model imdanboy/kss_tts_train_jets_raw_phn_null_g2pk_train.total_count.ave ``` | cc95c14092b40f8d3a2407662fb8557b |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | TTS config <details><summary>expand</summary> ``` config: conf/tuning/train_jets.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/tts_train_jets_raw_phn_null_g2pk ngpu: 1 seed: 777 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_siz... | c5527f5fcb7f0a9f37d79dfefe6d9ba0 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-highways-2 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 highways-hacktum dataset. It achieves the following results on the evaluation set: - Loss: 0.2196 - Accuracy: 0.96 | d15ea82dd74e291cad101320743eef73 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | ee741037c7ec32bf5a3fe08a713c12d6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0012 | 1.59 | 100 | 0.2196 | 0.96 | | af48739fd34f01dcc490acab01021c7a |
apache-2.0 | ['catalan'] | false | roberta-base-ca-finetuned-catalonia-independence-detector This model is a fine-tuned version of [BSC-TeMU/roberta-base-ca](https://huggingface.co/BSC-TeMU/roberta-base-ca) on the catalonia_independence dataset. It achieves the following results on the evaluation set: - Loss: 0.6065 - Accuracy: 0.7612 <details> | 5a736b70d252c7cd60b46e2094840f91 |
apache-2.0 | ['catalan'] | false | Training and evaluation data The data was collected over 12 days during February and March of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia. Each corpus is annotated with three classes: AGAINST, FAVOR and NEUTRAL, which express the stance tow... | b1c63e07636f9fc56ad24000bbf3d73d |
apache-2.0 | ['catalan'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 377 | 0.6311 | 0.7453 | | 0.7393 | 2.0 | 754 | 0.6065 | 0.7612 | | 0.5019 | 3.0 | 1131 | 0.6340 | 0.... | c9bc0f19020e26d0f155ac451eff7667 |
apache-2.0 | ['catalan'] | false | Model in action 🚀 Fast usage with **pipelines**: ```python from transformers import pipeline model_path = "JonatanGk/roberta-base-ca-finetuned-catalonia-independence-detector" independence_analysis = pipeline("text-classification", model=model_path, tokenizer=model_path) independence_analysis( "Assegura l'ex... | 6a1f0bfe74126926648728b090e39fbb |
apache-2.0 | ['catalan'] | false | Output: [{'label': 'FAVOR', 'score': 0.9040119647979736}] ``` [](https://colab.research.google.com/github/JonatanGk/Shared-Colab/blob/master/Catalonia_independence_Detector_(CATALAN).ipynb | fee5493dff137ae28cc4b3f482d7edfc |
apache-2.0 | ['catalan'] | false | Citation Thx to HF.co & [@lewtun](https://github.com/lewtun) for Dataset ;) > Special thx to [Manuel Romero/@mrm8488](https://huggingface.co/mrm8488) as my mentor & R.C. > Created by [Jonatan Luna](https://JonatanGk.github.io) | [LinkedIn](https://www.linkedin.com/in/JonatanGk/) | eb556b451160f0228fc16982d2638ba9 |
mit | ['generated_from_trainer'] | false | DistillBerTurk_15_epoch This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0549 - Accuracy: 0.9931 | 12a0983d8321075fc801780f65fbc2fc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 50 | 0.6766 | 0.625 | | No log | 2.0 | 100 | 0.3955 | 0.9583 | | No log | 3.0 | 150 | 0.0874 | 0.... | c9f83b944700be98b9409baea85e275a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb 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: 2.2489 | 839a990efa5752c1b42e0b7c0865371f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4463 | 1.0 | 782 | 2.2692 | | 2.3895 | 2.0 | 1564 | 2.2460 | | 2.3631 | 3.0 | 2346 | 2.2205 | | f3b0030c48f9492e605dc7ac1d5f8528 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 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_sc... | c060912cfeee7092f5ca0959da562681 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, ... | b7decb98b9279640b1aef2b1a771e810 |
cc-by-4.0 | ['norwegian', 'bert'] | false | Results |**Model** | **NoRec** | **NorNe-NB**| **NorNe-NN** | **NorDial** | **DaNe** | **Da-Angry-Tweets** | |:-----------|------------:|------------:|------------:|------------:|------------:|------------:| |roberta-base (English) | 51.77 | 79.01/79.53| 79.79/83.02 | 67.18| 75.44/78.07 | 55.51 | |mBERT-cased | 63.91... | 3bcf0d48d839b8055fd5b8bf9d8e078d |
apache-2.0 | ['automatic-speech-recognition', 'multilingual_librispeech', 'generated_from_trainer'] | false | wav2vec2-xlsr-53-300m-mls-german-ft This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MULTILINGUAL_LIBRISPEECH - GERMAN 10h dataset. It achieves the following results on the evaluation set: - Loss: 0.2219 - Wer: 0.1288 | 5975ccee37e92c6dd3dc052ca0e3de01 |
apache-2.0 | ['automatic-speech-recognition', 'multilingual_librispeech', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 2.9888 | 7.25 | 500 | 2.9192 | 1.0 | | 2.9313 | 14.49 | 1000 | 2.8698 | 1.0 | | 1.068 | 21.74 | 1500 | 0.2647 | ... | a5bbb102f3be090d5555c86ccfacedd6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 393 | 0.2287 | 0.9341 | 0.9112 | | 821db75089abcd4fb516ea6b254708cd |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | yyaaeell Dreambooth model trained by Brainergy with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-di... | a4105c08dcd6367d356d3e33f4a7da87 |
mit | ['grug', 'caveman', 'fun'] | false | GPT-Grug-355m A finetuned version of [GPT2-Medium](https://huggingface.co/gpt2-medium) on the 'grug' dataset. A demo is available [here](https://huggingface.co/spaces/DarwinAnim8or/grug-chat) If you're interested, there's a smaller model available here: [GPT-Grug-125m](https://huggingface.co/DarwinAnim8or/gpt-grug-12... | 3dad10e76089a22d78d92fb62d5b928a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_data_aug_cola_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6917 - Matthews Correlation: 0.0937 | c897c8f7084f6ed18584cb43cd08fb86 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.647 | 1.0 | 835 | 0.6917 | 0.0937 | | 0.5356 | 2.0 | 1670 | 0.7312 | 0.1294 | | 0.4... | 0f571e05882ca262d6972f7d7baa3df5 |
apache-2.0 | ['fnet'] | false | FNet base model Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was introduced in [this paper](https://arxiv.org/abs/2105.03824) and first released in [this repository](https://github.com/google-research/google-research/tree/master/f_net). ... | ef90384b6c23ebe50b93a52bf5aa1f2b |
apache-2.0 | ['fnet'] | false | Model description FNet is a transformers model with attention replaced with fourier transforms. Hence, the inputs do not contain an `attention_mask`. It is pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in a... | 1cd48fb805d51fe4121aca8a5f6bd9c0 |
apache-2.0 | ['fnet'] | false | Pretraining FNet-base was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,... | 10b4c207b141bac18b7e1835ecb23fd1 |
apache-2.0 | ['fnet'] | false | Evaluation results FNet-base was fine-tuned and evaluated on the validation data of the [GLUE benchamrk](https://huggingface.co/datasets/glue). The results of the official model (written in Flax) can be seen in Table 1 on page 7 of [the official paper](https://arxiv.org/abs/2105.03824). For comparison, this model (p... | ac0bf64c8acbba74f32fa3045b681303 |
apache-2.0 | ['fnet'] | false | glue-tasks) alongside [bert-base-cased](https://hf.co/models/bert-base-cased) for comparison. The training was done on a single 16GB NVIDIA Tesla V100 GPU. For MRPC/WNLI, the models were trained for 5 epochs, while for other tasks, the models were trained for 3 epochs. A sequence length of 512 was used with batch size ... | 646ed0fc103b247df562dbf40c39095d |
apache-2.0 | ['fnet'] | false | How to use You can use this model directly with a pipeline for masked language modeling: **Note: The mask filling pipeline doesn't work exactly as the original model performs masking after converting to tokens. In masking pipeline an additional space is added after the [MASK].** ```python >>> from transformers impo... | 8703c18e551beca2599af59f189b9f4a |
apache-2.0 | ['generated_from_keras_callback'] | false | silviacamplani/bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0270 - Validation Loss: 0.0563 - Epoch: 2 | e29b10009fef9b61aee5c6d8e5aa58d4 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1764 | 0.0664 | 0 | | 0.0476 | 0.0584 | 1 | | 0.0270 | 0.0563 | 2 | | 4a492ba51690e19f03fca283e977d6f7 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'safetensors', 'disco-elysium'] | false | Harrier Du Bois Meet the [Harrier Du Bois](https://discoelysium.fandom.com/wiki/Harrier_Du_Bois). A disco elysium style Stable Diffusion model I trained a long time ago. Trained with [Disco Elysium Archetypes](https://www.artstation.com/artwork/6aAL8x) from [Aleksander Rostov](https://www.artstation.com/rostovjanka)... | a26577afc05b4ee3caca52a5f6558a56 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_qnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 1.2699 - Accuracy: 0.5997 | 29f9310d1268c61301509421a16d3627 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3057 | 1.0 | 16604 | 1.2699 | 0.5997 | | 0.0735 | 2.0 | 33208 | 1.7786 | 0.5953 | | 0.0313 | 3.0 | 49812 | 1.9603 ... | fb5c1516b1ef363b804f72de26342ba7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_wnli_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6861 - Accuracy: 0.5634 | b3c2f19b91a9905cceb3abecbf317641 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6944 | 1.0 | 3 | 0.6957 | 0.4366 | | 0.6952 | 2.0 | 6 | 0.6954 | 0.4366 | | 0.6914 | 3.0 | 9 | 0.6877 | 0.... | 62d02128a3fedef62d0a18f6e3572270 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.