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', 'es'] | false | exp_w2v2r_es_xls-r_gender_male-0_female-10_s961 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 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure ... | 44a1d3454fd0c5f9cb0bfef5b38c03e8 |
apache-2.0 | ['translation', 'wmt19', 'facebook'] | false | Model description This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for de-en. For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616). The abbreviation FSMT stands for Fai... | a6be61508f50b57b892885544ebfeec7 |
apache-2.0 | ['translation', 'wmt19', 'facebook'] | false | How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "facebook/wmt19-de-en" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "Maschinelles Lernen ist großartig, oder?" input_ids = tokenizer.encode(input, ... | e84d66268a1fd847291f9a1c10ce7cee |
apache-2.0 | ['translation', 'wmt19', 'facebook'] | false | Eval results pair | fairseq | transformers -------|---------|---------- de-en | [42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750) | 41.35 The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support: - model ensemble, therefore the best performing chec... | a224c7a466e9ff72263a9a5796fe39a6 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-marc This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9725 - Mae: 0.5221 | a59c01926610266444ffbc6f73810878 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1615 | 1.0 | 308 | 1.0893 | 0.6106 | | 0.9994 | 2.0 | 616 | 0.9725 | 0.5221 | | 2101cfd7c42970e22d6d46f5a1a26056 |
other | ['generated_from_trainer', 'stable diffusion', 'diffusion', 'text2image', 'prompt augment', 'prompt engineering'] | false | opt-350m-magicprompt-SD Generate/augment your prompt, stable diffusion style. This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the Gustavosta/Stable-Diffusion-Prompts dataset. It achieves the following results on the evaluation set: - Loss: 1.2987 - eval_steps_pe... | b3d2586c3aea31526228594df5697d9f |
other | ['generated_from_trainer', 'stable diffusion', 'diffusion', 'text2image', 'prompt augment', 'prompt engineering'] | false | example  output (_on DALL-E 2, but as words are words, works anywhere_)  | 52a812426ec6300afd8634ec332fa530 |
other | ['generated_from_trainer', 'stable diffusion', 'diffusion', 'text2image', 'prompt augment', 'prompt engineering'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - total_eval_batch_size: 4 - optimizer: Adam wi... | 53826a35f4370143d72b68bc698484ba |
other | ['generated_from_trainer', 'stable diffusion', 'diffusion', 'text2image', 'prompt augment', 'prompt engineering'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8568 | 0.95 | 16 | 2.5937 | | 2.2487 | 1.95 | 32 | 2.1050 | | 1.9011 | 2.95 | 48 | 1.8082 | | 1.6837 | 3.95 | 64 | 1.6178 ... | b0d402f840c899d1e52aed12b6bbc228 |
mit | [] | false | Charmander from anime on Stable Diffusion This is the `<charmanderanime>` 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) notebo... | d81b9737ebdfb2dceb16541d0020f090 |
apache-2.0 | ['generated_from_trainer'] | false | bags_results 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 imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 - Accuracy: 1.0 | 01d326513845c899fe7b5d9415a77049 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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 - num_epochs: 5 - mixed_precision_training: Native AMP | 53826784c824e06a64b4e79f2244fd12 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0485 | 0.23 | 100 | 0.0119 | 0.9974 | | 0.0029 | 0.45 | 200 | 0.0105 | 0.9983 | | 0.0029 | 0.68 | 300 | 0.0080 | 0.... | 14acbc8618f8d94f486d5713c5f1c808 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2109 - Accuracy: 0.9245 - F1: 0.9247 | 6a3030061fd54ff9d25d842842ee70e2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8203 | 1.0 | 250 | 0.3080 | 0.909 | 0.9072 | | 0.2412 | 2.0 | 500 | 0.2109 | 0.9245 | 0.9247 | | 849dda278f0c0bbd588c0c0fa6f5c94f |
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 amazon_polarity dataset. It achieves the following results on the evaluation set: - Loss: 0.4356 - Accuracy: 0.8067 - F1: 0.8079 | 64725a060c0a11b1f379d3aaea514075 |
mit | ['vision', 'image-to-text', 'image-captioning', 'visual-question-answering'] | false | BLIP-2, Flan T5-xxl, pre-trained only BLIP-2 model, leveraging [Flan T5-xxl](https://huggingface.co/google/flan-t5-xxl) (a large language model). It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by... | 67e1b16ba21782c1408d793a13ee730e |
mit | ['vision', 'image-to-text', 'image-captioning', 'visual-question-answering'] | false | Running the model on CPU <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, Blip2ForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip2-flan-t5-xxl") model = Blip2ForConditionalGeneration.from_pretraine... | 0cdb2374cfe6ad23ec4e9deef6067756 |
mit | ['vision', 'image-to-text', 'image-captioning', 'visual-question-answering'] | false | pip install accelerate import requests from PIL import Image from transformers import Blip2Processor, Blip2ForConditionalGeneration processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xxl") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xxl", device_map="auto") img_u... | e1f71c9ada47cc79be5c19e10c7da87b |
mit | ['vision', 'image-to-text', 'image-captioning', 'visual-question-answering'] | false | pip install accelerate import torch import requests from PIL import Image from transformers import Blip2Processor, Blip2ForConditionalGeneration processor = Bli2pProcessor.from_pretrained("Salesforce/blip2-flan-t5-xxl") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xxl", torch_dtype=... | 8edec6fcda348a3f64f316f8813af7a0 |
mit | ['vision', 'image-to-text', 'image-captioning', 'visual-question-answering'] | false | pip install accelerate bitsandbytes import torch import requests from PIL import Image from transformers import Blip2Processor, Blip2ForConditionalGeneration processor = Bli2pProcessor.from_pretrained("Salesforce/blip2-flan-t5-xxl") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xxl",... | 703b39c7b36ebca237cf21711866d1d1 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | betroco 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-dif... | ec65318522f8ba4de2b023b8dcf713ac |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-kitchen_and_dining-2-16-5-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: 2.3560 - Accuracy: 0.2692 | 612c6376950e3d1462f92110b764bdb7 |
apache-2.0 | ['generated_from_trainer'] | false | vit_model_santiago_ahumada 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: 0.0164 - Accuracy: 1.0 | ef8aa8cd1e66198cb0744b78f8664365 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.143 | 3.85 | 500 | 0.0164 | 1.0 | | 7c424860c1eaf6f779d44eb4fe2499e7 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1350 - F1: 0.8609 | 0da0bde8e3aba6be5aa9ad7ae98d24ae |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 4503a3275854fe728b7dadb29b63e1b1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2684 | 1.0 | 394 | 0.1598 | 0.8261 | | 0.13 | 2.0 | 788 | 0.1318 | 0.8528 | | 0.0852 | 3.0 | 1182 | 0.1350 | 0.8609 | ... | 78ee8e4cac95e8e90c3fc8f72ba62832 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion-data This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2092 - Accuracy: 0.9295 - F1: 0.9295 | dcec25b89c00edc119b255de1c6a962e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7939 | 1.0 | 250 | 0.3013 | 0.914 | 0.9116 | | 0.2353 | 2.0 | 500 | 0.2092 | 0.9295 | 0.9295 | | 234cf56d655aa1974ce4cd4d99952e0d |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | mr_and_misses_v2 Dreambooth model trained by fffiloni 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-st... | 72fda550ec2f8174f72b82c640eb1866 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-sentiment-analysis-model-40k-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: 0.3117 - Accuracy: 0.9141 - F1: 0.9551 | 48d73cbe87db35e5d93c6ab3859bc3cb |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat 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: 0.0741 - Accuracy: 0.9748 | a8de7014535e8c51d39ac8dad5d0ea4b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2868 | 1.0 | 190 | 0.1234 | 0.9574 | | 0.1519 | 2.0 | 380 | 0.0741 | 0.9748 | | 0.1211 | 3.0 | 570 | 0.0724 | 0.... | b5745c1306512d864341ae14660f554a |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_logit_kd_cola_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6833 - Matthews Correlation: 0.0 | aebb15d53abf31861162127638d125e2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.827 | 1.0 | 67 | 0.6869 | 0.0 | | 0.7971 | 2.0 | 134 | 0.6872 | 0.0 | | 0.7... | 5711ee9ab4d0b5ab50567699a5b6fc8f |
apache-2.0 | ['translation'] | false | opus-mt-fr-yo * source languages: fr * target languages: yo * OPUS readme: [fr-yo](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-yo/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 29e51361b81f7090ab216ee325e5bb29 |
apache-2.0 | ['generated_from_trainer'] | false | sentiment_trained_42 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: 1.3194 - F1: 0.7132 | 78e23680e185ee015f852762ab64022a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.2140338797769864e-05 - 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: 4 | 7245934cb146f03f4a989734fa7cd2d7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.6405 | 1.0 | 11404 | 0.6631 | 0.7046 | | 0.5998 | 2.0 | 22808 | 0.8429 | 0.7102 | | 0.5118 | 3.0 | 34212 | 1.0906 | 0.715... | 7bcc8727a640dfcaa08423773df343b4 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-qnli This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.2297 - Accuracy: 0.9105 | fe0523adf547cc2910ca11d16c1a3e4a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3436 | 1.0 | 819 | 0.2489 | 0.9035 | | 0.1962 | 2.0 | 1638 | 0.2297 | 0.9105 | | 0.1049 | 3.0 | 2457 | 0.2620 | 0.... | e4c7dac05a0e6a5593c3bca8a7c020e9 |
other | ['generated_from_trainer'] | false | monogptari-6.7b This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on an english monogatari (物語) dataset. It achieves the following results on the evaluation set: - Loss: 0.7030 - Accuracy: 0.8436 | 9a94544d9e2d62cff17829190de3d7a7 |
other | ['generated_from_trainer'] | false | Quick start ```python from transformers import pipeline generator = pipeline('text-generation', model="tensorcat/monogptari-6.7b" , device=0, use_fast=False) generator("I think its about time I talked about Kiss-Shot", min_length=100, max_length=800, do_sample=True, early_stopping=True, temperature=.98, top_k=5... | 133b3b31662b79d078af2bfc067d8d11 |
other | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0... | beca9441243ff5e6b893351f00cf3e9c |
mit | ['audio', 'music', 'generation', 'tensorflow'] | false | Musika Audio Autoencoder Pretrained universal autoencoder model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation. Introduced in [this paper](https://arxiv.org/abs/2208.08706). | 97cc52a93bcbcfba048ae2db493b141c |
mit | ['audio', 'music', 'generation', 'tensorflow'] | false | Model description The Musika autoencoder consists of two hierarchical stages that are separately trained. This autoencoder is trained to encode and reconstruct general 44.1 kHz waveform music. The final time compression ratio that is achieved is 4096x. As an example, 23 seconds of 44.1 kHz audio are encoded into a s... | 83e1a8ffbe5df569d355c8618c519f6c |
mit | [] | false | This model takes a tweet with the word "jew" in it, and determines if it's antisemitic. *Training data:* This model was trained on 4k tweets, where ~50% were labeled as antisemitic. I labeled them myself based on personal experience and knowledge about common antisemitic tropes. *Note:* The goal for this model ... | 3b51f6c220ca6b03fe83b273c496d24c |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'apache-2.0', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'PyTorch'] | false | Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "pt", split="test") ... | a29445506bdc9fe28dd17bbe8bcf4635 |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_unispeech_s244 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | f9179201a85a78f2058495de49e82f10 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-EL16-DL4 (Deep-Narrow version) T5-Efficient-SMALL-EL16-DL4 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* che... | dee6b90c5f9865611bbdd2bdee0f57f9 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-el16-dl4** - is of model type **Small** with the following variations: - **el** is **16** - **dl** is **4** It has **83.6** million parameters and thus requires *ca.* **334.41 MB** of memory in full precision (*fp32*) or **167.21 MB** of memor... | 1487218c34430de349b2e4c8ce0c364f |
creativeml-openrail-m | [] | false | Squeezable (artist) Style [Hypernetwork] Hypernetwork trained on art by artist [Sqeezable](https://www.pixiv.net/en/users/41717665). [](https://www.buymeacoffee.com/stricky) | 2dd4a85f8b03eec3031fa94e45a27e57 |
creativeml-openrail-m | [] | false | Settings ``` Model: NAI Layer structure: (1, 2, 1) Activation function: relu Layer normalization: False Use dropout: False Raw dataset size: 143 images Final dataset size: 788 images Size: 512x512 Create flipped copies: True Split oversized images: True Captions: DeepBooru Learning rate: 0.0000025 -> 20000 steps R... | 70814db259f03fe9d86cf78344295562 |
apache-2.0 | ['translation'] | false | opus-mt-en-kqn * source languages: en * target languages: kqn * OPUS readme: [en-kqn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-kqn/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | 8f617e3d6756794d7780123ea1f3c42a |
apache-2.0 | ['translation'] | false | swe-nor * source group: Swedish * target group: Norwegian * OPUS readme: [swe-nor](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/swe-nor/README.md) * model: transformer-align * source language(s): swe * target language(s): nno nob * model: transformer-align * pre-processing: normalization ... | 91cb9efc8e99ce763d10da3c6fa0e04d |
apache-2.0 | ['translation'] | false | System Info: - hf_name: swe-nor - source_languages: swe - target_languages: nor - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/swe-nor/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['sv', 'no'] - src_constituents: {'swe'} - tgt_const... | 52d3a4c08a5483765959edd8cdda2fe1 |
mit | ['donut', 'image-to-text', 'vision'] | false | Donut (base-sized model, fine-tuned on CORD) Donut model fine-tuned on CORD. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut). Disclaimer: The team releasing Donut... | e1d5b1bfefaf90fa99194cb986e56511 |
mit | ['donut', 'image-to-text', 'vision'] | false | Intended uses & limitations This model is fine-tuned on CORD, a document parsing dataset. We refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) which includes code examples. | d464ab72c51c2d4ef33c2898c76612a2 |
mit | ['generated_from_trainer'] | false | cervantes-gpt This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.1302 | 070cfd8c8f019f3e41be8388c15ad6c4 |
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: 16 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_... | 89aa633596c8cf1f7a03e5ef2f137498 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 10.6864 | 0.96 | 13 | 9.4380 | | 9.6293 | 1.96 | 26 | 9.0791 | | 9.2039 | 2.96 | 39 | 8.5999 | | 8.5709 | 3.96 | 52 | 7.9434 ... | 7bbbb795881032a9e162e6e8c0cd0801 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | This is a DreamBooth model finetuned from the multilingual text-to-image model AltDiffusion. Dreambooth is one of the method of finetune the pretrained text-to-image model.Given as input just a few images of a subject, it learns to bind a unique identifier with that specific subject. AltDiffusion which is a multilin... | 97797fdd825a84bfa62ccd9c131afa08 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'multilingual', 'English(En)', 'Chinese(Zh)', 'Spanish(Es)', 'French(Fr)', 'Russian(Ru)', 'Japanese(Ja)', 'Korean(Ko)', 'Arabic(Ar)', 'Italian(It)', 'diffusers'] | false | Example code of inference ``` from diffusers import AltDiffusionPipeline, DPMSolverMultistepScheduler import torch pipe = AltDiffusionPipeline.from_pretrained("BAAI/DreamBooth-AltDiffusion") pipe = pipe.to("cuda") pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) prompt = "一张<鸣人>男孩的照片"... | 716ce28c5d94b9128a9f9003a107e1bc |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | DreamBooth model for the ghghormehsabzi concept trained by taesiri on the dataset. This is a Stable Diffusion model fine-tuned on the ghghormehsabzi concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of ghghormehsabzi dish** This model was created as part of the DreamBooth Hackatho... | 8b27b9333089881ded2523ab7f273137 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model 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: 0.3144 - Accuracy: 0.8633 - F1: 0.8656 | af3dd7b82e671f84eb996247fb1a0467 |
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: 0.2111 - Accuracy: 0.9245 - F1: 0.9244 | f0878a860cf1bdb51f2b38ef4d1da5b7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8083 | 1.0 | 250 | 0.3083 | 0.9045 | 0.9008 | | 0.243 | 2.0 | 500 | 0.2111 | 0.9245 | 0.9244 | | 22369f3f5c3ecfe9e11d938449acb419 |
apache-2.0 | ['generated_from_keras_callback'] | false | vikram15/bert-finetuned-squad 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: 0.7556 - Epoch: 2 | 5070402ac780c25fbe11f603ba15b922 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 954, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': ... | 8bbdf0b1f10125592f46e6297ff17463 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/paraphrase-mpnet-base-v2 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. | d723368732c368d26e54589e0a7af54a |
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... | 6183fcf258d9999c05d9641e7a9f9172 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-mpnet-base-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-mpnet-base-v2') | 44943e3bf895f1e3613a401d1e2d88b0 |
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/paraphrase-mpnet-base-v2) | 75077dd913d4b69f9ef549e80408c974 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout e31965d55993766461f0964216a0bb9aea3cfb7a pip install -e . cd egs2/catslu/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/sujay_catslu_map ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 992904b12eba1be2db28a61f7bb1e1a2 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Sun Oct 3 12:53:16 EDT 2021` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.3a3` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `b41391336042a4876e30d9fe5c66afb4e4be404c` - Commit date: `Wed Sep 22 10:02:03 2021 -0400` | 0bd60daa4615fea1f49ce836e8389d59 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.ave_5best/test|1577|11441|46.1|30.1|23.7|2.5|56.4|81.3| |inference_asr_model_valid.acc.ave_5best/valid|921|6438|49.4|29.2|21.4|2.7|53.4|79.2| | 5e218d58de06347aee7b78dd10ed56bb |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.ave_5best/test|1577|45924|74.4|13.0|12.5|3.2|28.8|81.3| |inference_asr_model_valid.acc.ave_5best/valid|921|26110|77.0|11.9|11.1|2.7|25.7|79.2| | 19781c80dac44c794c4afaf4e3c29aaa |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr_smaller_aishell_xlsr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp_train_asr_smaller_aishell_xlsr/asr_train_asr_smaller_aishell_xlsr_raw_zh_word ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 di... | c0dfe97c3f30ac68d7bd09079c7e87dc |
other | ['generated_from_keras_callback'] | false | MariaK/scene_segmentation This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.7868 - Validation Loss: 3.0539 - Validation Mean Iou: 0.0483 - Validation Mean Accuracy: 0.0908 - Valida... | 8dbca631073a923afc4e54a1c462317d |
other | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 6e-05, 'decay_steps': 2000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay':... | fc094149466bbdd5d0d1bd7ff5e7dc3f |
other | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Accuracy Wall | Validation Accuracy Building | Validation Accuracy Sky | Validation Accuracy Floor | Validation Accuracy Tree | Validation Accuracy Ceiling | Validation Accuracy ... | 037291096130cfe23f66137db658d273 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image', 'diffusers'] | false | DucHaitenAnime_v4.0: In this version i added a little 3D, a little realistic, improved the hand but not much, improved the color because i don't like to use vae All images above are used only text to image, not edited or accompanying application software. https://civitai.com/models/6634 please support me by becomin... | a5f394e1ce36ca46bd8bdd945807fa74 |
creativeml-openrail-m | ['text-to-image'] | false | Sample pictures of: sdcid (use that on your prompt)  but with different... | bb269cf1ef770b225342b22207c4a589 |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_1900k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_1900k') model = TFBertModel.from_pretrained("google/multib... | 5fd9fe0e6ca200c32cbb0c71a0dd76d2 |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-msmarco-nlgen-ob This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the [MS MARCO Natural Language Generation](https://huggingface.co/datasets/din0s/msmarco-nlgen) dataset. It achieves the following results on the evaluation set: - Loss: 0.3874 - Rougelsum: 14.4418 | bd32a093fadf28c34eb2e317155fd17f |
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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP | a1c82e06545ebcbce665de33b60f1770 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:---------:| | 0.5472 | 0.13 | 2500 | 0.4383 | 14.2497 | | 0.5238 | 0.26 | 5000 | 0.4252 | 14.4210 | | 0.501 | 0.39 | 7500 | 0.4093 ... | 24daf557e9016a1f25462e7e59ccbee2 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__subj__train-8-0 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: 0.4440 - Accuracy: 0.789 | d01000d5b072915cf27f67eb2849b7fb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7163 | 1.0 | 3 | 0.6868 | 0.5 | | 0.6683 | 2.0 | 6 | 0.6804 | 0.75 | | 0.6375 | 3.0 | 9 | 0.6702 | 0.... | 8c7615eebd2a9765e464506b58a08067 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'led', 'summary', 'longformer', 'booksum', 'long-document', 'long-form'] | false | Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization <a href="https://colab.research.google.com/gist/pszemraj/3eba944ddc9fc9a4a1bfb21e83b57620/summarization-token-batching.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> A fine-tuned ver... | e781710b886be386859b2493fbbd715a |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'led', 'summary', 'longformer', 'booksum', 'long-document', 'long-form'] | false | Usage - Basic - use `encoder_no_repeat_ngram_size=3` when calling the pipeline object to improve summary quality. - this forces the model to use new vocabulary and create an abstractive summary, otherwise it may compile the best _extractive_ summary from the input provided. Load the model into a pipeline object: ... | e4ec74e5d1b7ddf0d4e03d8f4906f6b8 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'led', 'summary', 'longformer', 'booksum', 'long-document', 'long-form'] | false | Training and evaluation data - the [booksum](https://arxiv.org/abs/2105.08209) dataset (this is what adds the `bsd-3-clause` license) - During training, the input text was the text of the `chapter`, and the output was `summary_text` - Eval results can be found [here](https://huggingface.co/datasets/autoevaluate/autoe... | aea7ebdbbab4c72614292cffc7d7bff9 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'led', 'summary', 'longformer', 'booksum', 'long-document', 'long-form'] | false | Training procedure - Training completed on the BookSum dataset for 13 total epochs - **The final four epochs combined the training and validation sets as 'train' in an effort to increase generalization.** | ee0feec1a48b5f5aa1bc363c37bc518d |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'led', 'summary', 'longformer', 'booksum', 'long-document', 'long-form'] | false | Initial Three Epochs The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_schedule... | 3ed2ff997b47e1af9d0dc438b214505d |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'led', 'summary', 'longformer', 'booksum', 'long-document', 'long-form'] | false | In-between Epochs Unfortunately, don't have all records on-hand for middle epochs; the following should be representative: - learning_rate: 4e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with b... | 4eebe1223bf7d4ad1f7443d19fbc73b4 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'led', 'summary', 'longformer', 'booksum', 'long-document', 'long-form'] | false | Final Two Epochs The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_... | ff8daa0b190887d8813c29bc41782af9 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'image-to-image', 'art', 'artistic', 'diffusers', 'filmation'] | false | Filmation MOTU Diffusion Fine-tuned Stable Diffusion model, based of ```SD 1.5```, trained with art from Masters of the Universe.  | a10f14383545e90e68bec876289ee1d5 |
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