davanstrien/model_cards_with_readmes · Datasets at Hugging Face

ssavla2/bert-finetuned-ner

ssavla2

bert

8

12

transformers

0

token-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,423

false

# ssavla2/bert-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0243 - Validation Loss: 0.0603 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 1017, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1199 | 0.0570 | 0 | | 0.0399 | 0.0586 | 1 | | 0.0243 | 0.0603 | 2 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.1.0 - Tokenizers 0.12.1

9dce69c8b27b40648f82326e33d74c1d

Rocketknight1/europython-imdb

Rocketknight1

deberta-v2

8

6

transformers

0

text-classification

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,407

false

# europython-imdb This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1279 - Train Accuracy: 0.9548 - Validation Loss: 0.1595 - Validation Accuracy: 0.9418 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.2073 | 0.9203 | 0.1486 | 0.9435 | 0 | | 0.1279 | 0.9548 | 0.1595 | 0.9418 | 1 | ### Framework versions - Transformers 4.21.0.dev0 - TensorFlow 2.9.1 - Datasets 2.3.3.dev0 - Tokenizers 0.11.0

77036b012803aa0cbdfad8b1a1213746

fathyshalab/all-roberta-large-v1-auto_and_commute-1-16-5

fathyshalab

roberta

11

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,521

false

# all-roberta-large-v1-auto_and_commute-1-16-5 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: 2.2614 - Accuracy: 0.4289 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7929 | 1.0 | 1 | 2.5690 | 0.2667 | | 2.267 | 2.0 | 2 | 2.4558 | 0.3533 | | 1.8495 | 3.0 | 3 | 2.3630 | 0.3911 | | 1.4397 | 4.0 | 4 | 2.2956 | 0.4133 | | 1.2985 | 5.0 | 5 | 2.2614 | 0.4289 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

d012167f14011551d585e534a4dcf1fe

Helsinki-NLP/opus-mt-fi-sg

Helsinki-NLP

marian

10

8

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

768

false

### opus-mt-fi-sg * source languages: fi * target languages: sg * OPUS readme: [fi-sg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-sg/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-sg/opus-2020-01-24.zip) * test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-sg/opus-2020-01-24.test.txt) * test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-sg/opus-2020-01-24.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.fi.sg | 29.3 | 0.480 |

d6e6221869e97d95e42a5e4480072bbd

andresca94/t5-small-finetuned-en-to-es

andresca94

t5

12

2

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,266

false

# t5-small-finetuned-en-to-es 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: - Loss: 1.8937 - Bleu: 7.4133 - Gen Len: 15.9653 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | 2.27 | 1.0 | 7061 | 1.8937 | 7.4133 | 15.9653 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

95506c960a47b10299bd5b12b804d47c

gokuls/bert-tiny-emotion-KD-BERT

gokuls

bert

13

6

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

2,036

false

# bert-tiny-emotion-KD-BERT This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.4810 - Accuracy: 0.9175 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 3.8247 | 1.0 | 1000 | 2.5170 | 0.7745 | | 1.9864 | 2.0 | 2000 | 1.3436 | 0.874 | | 1.1126 | 3.0 | 3000 | 0.8299 | 0.894 | | 0.6924 | 4.0 | 4000 | 0.6500 | 0.9025 | | 0.5272 | 5.0 | 5000 | 0.6097 | 0.908 | | 0.4298 | 6.0 | 6000 | 0.5913 | 0.904 | | 0.3936 | 7.0 | 7000 | 0.5165 | 0.9135 | | 0.3238 | 8.0 | 8000 | 0.5120 | 0.9075 | | 0.3018 | 9.0 | 9000 | 0.4989 | 0.916 | | 0.2605 | 10.0 | 10000 | 0.4810 | 0.9175 | | 0.2512 | 11.0 | 11000 | 0.4757 | 0.9135 | | 0.219 | 12.0 | 12000 | 0.4676 | 0.914 | | 0.2046 | 13.0 | 13000 | 0.4794 | 0.911 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

b081f0792437796d54cfc6fe9f4e0e9b

nandysoham/12-clustered

nandysoham

distilbert

8

13

transformers

0

question-answering

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

2,074

false

# nandysoham/12-clustered This model is a fine-tuned version of [Rocketknight1/distilbert-base-uncased-finetuned-squad](https://huggingface.co/Rocketknight1/distilbert-base-uncased-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6856 - Train End Logits Accuracy: 0.8145 - Train Start Logits Accuracy: 0.7542 - Validation Loss: 0.8791 - Validation End Logits Accuracy: 0.7585 - Validation Start Logits Accuracy: 0.7096 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 632, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.9975 | 0.7354 | 0.6632 | 0.8689 | 0.7719 | 0.7048 | 0 | | 0.6856 | 0.8145 | 0.7542 | 0.8791 | 0.7585 | 0.7096 | 1 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

56e6f3d681ee37a09bba2a5a51f34be0

jonatasgrosman/exp_w2v2t_es_wavlm_s115

jonatasgrosman

wavlm

10

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['es']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'es']

false

true

439

false

# exp_w2v2t_es_wavlm_s115 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) 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 that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

344dfb4da504ed7b6ee6f5f2a0e69db6

jordyvl/bert-base-cased_conll2003-sm-all-ner

jordyvl

bert

13

74

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

0

['generated_from_trainer']

true

1,574

false

# bert-base-cased_conll2003-sm-all-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0489 - Precision: 0.9487 - Recall: 0.9564 - F1: 0.9526 - Accuracy: 0.9916 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.052 | 1.0 | 3511 | 0.0510 | 0.9374 | 0.9456 | 0.9415 | 0.9898 | | 0.0213 | 2.0 | 7022 | 0.0497 | 0.9484 | 0.9519 | 0.9501 | 0.9911 | | 0.0099 | 3.0 | 10533 | 0.0489 | 0.9487 | 0.9564 | 0.9526 | 0.9916 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

0f51dbf22e02929af11da7d6876b7091

sd-dreambooth-library/dtv-pkmn-monster-style

sd-dreambooth-library

null

20

22

diffusers

4

null

false

mit

null

3

0

1

0

1

[]

false

true

1,727

false

### JRPG Monster art style 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 #### Model by wooshim This your the Stable Diffusion model fine-tuned the dtv_pkmn_monster_style concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **image** Please use **"feralplmr"** in your prompt to trigger the style. You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb). You can run your new concept via A1111 Colab :[Fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Sample pictures of this concept: Low steps (40~) and Euler_a is highly suggested. Here is a prompt to try: ((wings)), dragon, bowser, creature, ((monster)), ((dinosaur)) intricate large dragon , ((rathalos)), detailed artwork in ((feralplmr artsyle)), feral, fullbody, monster character, scales, reptile, dragon, claws, wings, ((detailed)) image ![image 0](https://huggingface.co/sd-dreambooth-library/dtv-pkmn-monster-style/resolve/main/concept_images/image_2022-11-15_060407023.png)

a120a9e4312342bba01867e5349c6c54

edumunozsala/vit_base-224-in21k-ft-cifar10

edumunozsala

vit

6

9

transformers

0

image-classification

true

false

apache-2.0

['es']

['cifar10']

null

1

0

1

0

['sagemaker', 'vit', 'ImageClassification', 'generated_from_trainer']

true

2,819

false

# Model vit_base-224-in21k-ft-cifar10 ## **A finetuned model for Image classification in Spanish** This model was trained using Amazon SageMaker and the Hugging Face Deep Learning container, The base model is **Vision Transformer (base-sized model)** which is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.[Link to base model](https://huggingface.co/google/vit-base-patch16-224-in21k) ## Base model citation ### BibTeX entry and citation info ```bibtex @misc{wu2020visual, title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision}, author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda}, year={2020}, eprint={2006.03677}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## Dataset [Link to dataset description](http://www.cs.toronto.edu/~kriz/cifar.html) The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class. Sizes of datasets: - Train dataset: 50,000 - Test dataset: 10,000 ## Intended uses & limitations This model is intented for Image Classification. ## Hyperparameters { "epochs": "5", "train_batch_size": "32", "eval_batch_size": "8", "fp16": "true", "learning_rate": "1e-05", } ## Test results - Accuracy = 0.97 ## Model in action ### Usage for Image Classification ```python from transformers import ViTFeatureExtractor, ViTModel from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k') model = ViTModel.from_pretrained('edumunozsala/vit_base-224-in21k-ft-cifar10') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` Created by [Eduardo Muñoz/@edumunozsala](https://github.com/edumunozsala)

99bde6a5085c741fa9f43e907d0f64f5

ftshijt/ESPnet2_pretrained_model_ftshijt_thchs30_tts_train_raw_phn_pypinyin_g2p_phone_train.loss.best

ftshijt

null

3

0

espnet

0

text-to-speech

false

cc-by-4.0

['zh']

['thchs30']

null

0

['espnet', 'audio', 'text-to-speech']

false

true

5,500

false

This model was trained by ftshijt using thchs30/tts1 recipe in <a href="https://github.com/espnet/espnet/">espnet</a>. <p> </p> <ul> <li><strong>Python API</strong><pre><code class="language-python">See https://github.com/espnet/espnet_model_zoo</code></pre></li> <li><strong>Evaluate in the recipe</strong><pre> <code class="language-bash">Please see ESPNet for how to use pre-trained model </pre></li> <li><strong>Config</strong><pre><code>config: conf/train.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/tts_train_raw_phn_pypinyin_g2p_phone ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 500 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min - - train - loss - min keep_nbest_models: 5 grad_clip: 1.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: 500 batch_size: 20 valid_batch_size: null batch_bins: 3750000 valid_batch_bins: null train_shape_file: - exp/tts_stats_raw_phn_pypinyin_g2p_phone/train/text_shape.phn - exp/tts_stats_raw_phn_pypinyin_g2p_phone/train/speech_shape valid_shape_file: - exp/tts_stats_raw_phn_pypinyin_g2p_phone/valid/text_shape.phn - exp/tts_stats_raw_phn_pypinyin_g2p_phone/valid/speech_shape batch_type: numel valid_batch_type: null fold_length: - 150 - 204800 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train/text - text - text - - dump/raw/train/wav.scp - speech - sound - - dump/xvector/train/xvector.scp - spembs - kaldi_ark valid_data_path_and_name_and_type: - - dump/raw/dev/text - text - text - - dump/raw/dev/wav.scp - speech - sound - - dump/xvector/dev/xvector.scp - spembs - kaldi_ark allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 eps: 1.0e-06 weight_decay: 0.0 scheduler: null scheduler_conf: {} token_list: - <blank> - <unk> - '' - d - sh - j - zh - l - i4 - x - b - g - h - e - q - t - m - ch - i1 - z - u4 - i2 - i3 - n - f - s - r - k - c - p - ai4 - e4 - a1 - an4 - ian4 - ing2 - u3 - ian2 - ong1 - e2 - in1 - eng2 - ui4 - ao4 - u2 - iao4 - üan2 - en2 - an1 - u1 - ai2 - ao3 - ing4 - eng1 - iou3 - ü4 - uo4 - üe4 - ong2 - ian1 - ing1 - uo3 - ie4 - ang1 - uei4 - ang4 - an2 - a4 - ou4 - ei4 - uai4 - ie3 - ang3 - ong4 - ai3 - ü2 - uo2 - an3 - ang2 - ou3 - er2 - ou1 - uo1 - en1 - ia1 - ü3 - uan1 - in2 - iong4 - ian3 - iang3 - a3 - iang2 - ia4 - ü1 - uan4 - iao3 - iang4 - uen2 - iang1 - uan3 - ai1 - ie2 - ei3 - uan2 - uang2 - in4 - üe2 - ao1 - eng3 - iu4 - iao1 - er4 - iu2 - in3 - un1 - uang1 - eng4 - a2 - uang3 - en3 - uang4 - ong3 - ing3 - e3 - ei2 - ou2 - ao2 - i - ün4 - uei2 - ua4 - iou4 - ui1 - ua1 - en4 - ün2 - iao2 - ie1 - iou2 - iu3 - ün1 - üan4 - en - ei1 - o2 - un4 - ui3 - iu1 - üan3 - e1 - v3 - ua2 - ia2 - ui2 - un2 - o4 - un3 - er3 - ia3 - iong1 - uei3 - o1 - üe1 - üan1 - iong3 - v4 - iong2 - uen4 - uai2 - uei1 - iou1 - a - ua3 - uen1 - o3 - ueng1 - uai1 - uen3 - üe3 - ou - uai3 - ve4 - er - ün3 - o - ua - ia - ' l =' - <sos/eos> odim: null model_conf: {} use_preprocessor: true token_type: phn bpemodel: null non_linguistic_symbols: null cleaner: null g2p: pypinyin_g2p_phone feats_extract: fbank feats_extract_conf: n_fft: 1024 hop_length: 256 win_length: null fs: 16000 fmin: 80 fmax: 7600 n_mels: 80 normalize: global_mvn normalize_conf: stats_file: exp/tts_stats_raw_phn_pypinyin_g2p_phone/train/feats_stats.npz tts: tacotron2 tts_conf: embed_dim: 512 elayers: 1 eunits: 512 econv_layers: 3 econv_chans: 512 econv_filts: 5 atype: location adim: 512 aconv_chans: 32 aconv_filts: 15 cumulate_att_w: true dlayers: 2 dunits: 1024 prenet_layers: 2 prenet_units: 256 postnet_layers: 5 postnet_chans: 512 postnet_filts: 5 output_activation: null use_batch_norm: true use_concate: true use_residual: false spk_embed_dim: 512 spk_embed_integration_type: add use_gst: true gst_heads: 4 gst_tokens: 16 dropout_rate: 0.5 zoneout_rate: 0.1 reduction_factor: 1 use_masking: true bce_pos_weight: 10.0 use_guided_attn_loss: true guided_attn_loss_sigma: 0.4 guided_attn_loss_lambda: 1.0 pitch_extract: null pitch_extract_conf: {} pitch_normalize: null pitch_normalize_conf: {} energy_extract: null energy_extract_conf: {} energy_normalize: null energy_normalize_conf: {} required: - output_dir - token_list version: 0.10.2a1 distributed: false</code></pre></li> </ul>

35b30b83e2ad77d174d0b6f748f1fbc9

espnet/americasnlp22-asr-gn

espnet

null

21

3

espnet

0

automatic-speech-recognition

false

cc-by-4.0

['gn']

['americasnlp22']

null

0

['espnet', 'audio', 'automatic-speech-recognition']

false

true

7,877

false

## ESPnet2 ASR model ### `espnet/americasnlp22-asr-gn` This model was trained by Pavel Denisov using americasnlp22 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout fc62b1ce3e50c5ef8a2ac8cedb0d92ac41df54ca pip install -e . cd egs2/americasnlp22/asr1 ./run.sh \ --skip_data_prep false \ --skip_train true \ --download_model espnet/americasnlp22-asr-gn \ --lang gn \ --local_data_opts "--lang gn" \ --train_set train_gn \ --valid_set dev_gn \ --test_sets dev_gn \ --gpu_inference false \ --inference_nj 8 \ --lm_train_text data/train_gn/text \ --bpe_train_text data/train_gn/text ```  # RESULTS ## Environments - date: `Sun Jun 5 12:17:58 CEST 2022` - python version: `3.9.13 (main, May 18 2022, 00:00:00) [GCC 11.3.1 20220421 (Red Hat 11.3.1-2)]` - espnet version: `espnet 202204` - pytorch version: `pytorch 1.11.0+cu115` - Git hash: `d55704daa36d3dd2ca24ae3162ac40d81957208c` - Commit date: `Wed Jun 1 02:33:09 2022 +0200` ## asr_train_asr_transformer_raw_gn_bpe100_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.cer_ctc.best/dev_gn|93|391|11.5|73.7|14.8|12.5|101.0|100.0| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.cer_ctc.best/dev_gn|93|2946|83.4|7.9|8.7|8.7|25.3|100.0| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.cer_ctc.best/dev_gn|93|2439|76.6|13.5|9.9|8.7|32.1|100.0| ## ASR config <details><summary>expand</summary> ``` config: conf/train_asr_transformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_raw_gn_bpe100_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 15 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - cer_ctc - min keep_nbest_models: 1 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: - frontend.upstream.model.feature_extractor - frontend.upstream.model.encoder.layers.0 - frontend.upstream.model.encoder.layers.1 - frontend.upstream.model.encoder.layers.2 - frontend.upstream.model.encoder.layers.3 - frontend.upstream.model.encoder.layers.4 - frontend.upstream.model.encoder.layers.5 - frontend.upstream.model.encoder.layers.6 - frontend.upstream.model.encoder.layers.7 - frontend.upstream.model.encoder.layers.8 - frontend.upstream.model.encoder.layers.9 - frontend.upstream.model.encoder.layers.10 - frontend.upstream.model.encoder.layers.11 - frontend.upstream.model.encoder.layers.12 - frontend.upstream.model.encoder.layers.13 - frontend.upstream.model.encoder.layers.14 - frontend.upstream.model.encoder.layers.15 - frontend.upstream.model.encoder.layers.16 - frontend.upstream.model.encoder.layers.17 - frontend.upstream.model.encoder.layers.18 - frontend.upstream.model.encoder.layers.19 - frontend.upstream.model.encoder.layers.20 - frontend.upstream.model.encoder.layers.21 num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 200000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_gn_bpe100_sp/train/speech_shape - exp/asr_stats_raw_gn_bpe100_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_gn_bpe100_sp/valid/speech_shape - exp/asr_stats_raw_gn_bpe100_sp/valid/text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_gn_sp/wav.scp - speech - sound - - dump/raw/train_gn_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev_gn/wav.scp - speech - sound - - dump/raw/dev_gn/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adamw optim_conf: lr: 0.0001 scheduler: warmuplr scheduler_conf: warmup_steps: 300 token_list: - <blank> - <unk> - ▁ - a - i - e - o - '''' - . - u - '"' - p - r - n - y - h - ▁" - ▁o - é - re - va - pe - s - ra - á - he - t - mb - g - ka - ã - v - ve - je - ▁ha - te - k - ñ - ha - py - ta - ku - ẽ - ja - pa - O - mi - ó - mo - j - ko - ʼ - ña - me - ma - c - M - í - H - ú - A - ̃ - õ - ý - m - P - U - ',' - ũ - l - ỹ - N - ĩ - E - I - J - L - Á - V - S - z - '-' - '?' - Ñ - R - G - Y - T - K - C - d - “ - B - ’ - ” - D - b - f - q - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: null use_preprocessor: true token_type: bpe bpemodel: data/gn_token_list/bpe_unigram100/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: s3prl frontend_conf: frontend_conf: upstream: wav2vec2_url upstream_ckpt: https://dl.fbaipublicfiles.com/fairseq/wav2vec/xlsr2_300m.pt download_dir: ./hub multilayer_feature: true fs: 16k specaug: null specaug_conf: {} normalize: utterance_mvn normalize_conf: {} model: espnet model_conf: ctc_weight: 1.0 lsm_weight: 0.0 length_normalized_loss: false extract_feats_in_collect_stats: false preencoder: linear preencoder_conf: input_size: 1024 output_size: 80 encoder: transformer encoder_conf: input_layer: conv2d2 num_blocks: 1 linear_units: 2048 dropout_rate: 0.2 output_size: 256 attention_heads: 8 attention_dropout_rate: 0.2 postencoder: null postencoder_conf: {} decoder: rnn decoder_conf: {} required: - output_dir - token_list version: '202204' distributed: false ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

0267fefdebc39b68e9af0b4bb6f877b6

DOOGLAK/Tagged_Uni_100v3_NER_Model_3Epochs_AUGMENTED

DOOGLAK

bert

13

5

transformers

0

token-classification

true

false

apache-2.0

null

['tagged_uni100v3_wikigold_split']

null

0

['generated_from_trainer']

true

1,565

false

# Tagged_Uni_100v3_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni100v3_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.4884 - Precision: 0.2764 - Recall: 0.1080 - F1: 0.1553 - Accuracy: 0.8106 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 26 | 0.6238 | 0.2 | 0.0089 | 0.0170 | 0.7822 | | No log | 2.0 | 52 | 0.5210 | 0.2497 | 0.0587 | 0.0950 | 0.7971 | | No log | 3.0 | 78 | 0.4884 | 0.2764 | 0.1080 | 0.1553 | 0.8106 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.11.6

9bb4ddb13b35b97aa1e0775700629c19

VioletaMG/dtu-scan114-128_50epochs

VioletaMG

null

16

2

diffusers

0

null

false

apache-2.0

['en']

['imagefolder']

null

0

[]

false

true

1,213

false

# dtu-scan114-128_50epochs ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/VioletaMG/dtu-scan114-128_50epochs/tensorboard?#scalars)

c7a4f07556eac3992d5e43c57bb11d45

RecordedFuture/Swedish-Sentiment-Fear-Targets

RecordedFuture

bert

11

10

transformers

0

token-classification

true

mit

['sv']

null

0

[]

false

true

3,032

false

## Swedish BERT models for sentiment analysis, Sentiment targets. [Recorded Future](https://www.recordedfuture.com/) together with [AI Sweden](https://www.ai.se/en) releases two language models for target/role assignment in Swedish. The two models are based on the [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased), the models as has been fine tuned to solve a Named Entety Recognition(NER) token classification task. This is a downstream model to be used in conjunction with the [Swedish violence sentiment classifier](https://huggingface.co/RecordedFuture/Swedish-Sentiment-Violence) or [Swedish violence sentiment classifier](https://huggingface.co/RecordedFuture/Swedish-Sentiment-Fear). The models are trained to tag parts of sentences that has recieved a positive classification from the upstream sentiment classifier. The model will tag parts of sentences that contains the targets that the upstream model has activated on. The NER sentiment target models do work as standalone models but their recommended application is downstreamfrom a sentence classification model. The models are only trained on Swedish data and only supports inference of Swedish input texts. The models inference metrics for all non-Swedish inputs are not defined, these inputs are considered as out of domain data. The current models are supported at Transformers version >= 4.3.3 and Torch version 1.8.0, compatibility with older versions are not verified. ### Fear targets The model can be imported from the transformers library by running from transformers import BertForSequenceClassification, BertTokenizerFast tokenizer = BertTokenizerFast.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear-Targets") classifier_fear_targets= BertForTokenClassification.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear-Targets") When the model and tokenizer are initialized the model can be used for inference. #### Verification metrics During training the Fear target model had the following verification metrics when using "any overlap" as the evaluation metric. | F-score | Precision | Recall | |:-------------------------:|:-------:|:---------:|:------:| | 0.8361 | 0.7903 | 0.8876 | #### Swedish-Sentiment-Violence The model be can imported from the transformers library by running from transformers import BertForSequenceClassification, BertTokenizerFast tokenizer = BertTokenizerFast.from_pretrained("RecordedFuture/Swedish-Sentiment-Violence-Targets") classifier_violence_targets = BertForTokenClassification.from_pretrained("RecordedFuture/Swedish-Sentiment-Violence-Targets") When the model and tokenizer are initialized the model can be used for inference. #### Verification metrics During training the Violence target model had the following verification metrics when using "any overlap" as the evaluation metric. | F-score | Precision | Recall | |:-------------------------:|:-------:|:---------:|:------:| | 0.7831| 0.9155| 0.8442 |

0acd5e15d3db6577912717a1ac0ce4ca

XLab/rst-gaokao-cloze-11b

XLab

t5

6

1

transformers

2

text2text-generation

true

false

afl-3.0

null

0

[]

false

true

11,247

false

<p align="center"> <br> <img src="https://expressai-xlab.s3.amazonaws.com/rst/intro_rst.png" width="1000"/> <br> </p> # reStructured Pre-training (RST) official [repository](https://github.com/ExpressAI/reStructured-Pretraining), [paper](https://arxiv.org/pdf/2206.11147.pdf), [easter eggs](http://expressai.co/peripherals/emoji-eng.html) #### RST is a new paradigm for language pre-training, which * unifies **26** different types of signal from **10** data sources (Totten Tomatoes, Dailymail, Wikipedia, Wikidata, Wikihow, Wordnet, arXiv etc ) in the world structurally, being pre-trained with a monolithcal model, * surpasses strong competitors (e.g., T0) on **52/55** popular datasets from a variety of NLP tasks (classification, IE, retrieval, generation etc) * achieves superior performance in National College Entrance Examination **(Gaokao-English, 高考-英语)** achieves **40** points higher than the average scores made by students and 15 points higher than GPT3 with **1/16** parameters. In particular, Qin gets a high score of **138.5** (the full mark is 150) in the 2018 English exam In such a pre-training paradigm, * Data-centric Pre-training: the role of data will be re-emphasized, and model pre-training and fine-tuning of downstream tasks are viewed as a process of data storing and accessing * Pre-training over JSON instead of TEXT: a good storage mechanism should not only have the ability to cache a large amount of data but also consider the ease of access. ## Model Description We release all models introduced in our [paper](https://arxiv.org/pdf/2206.11147.pdf), covering 13 different application scenarios. Each model contains 11 billion parameters. | Model | Description | Recommended Application | ----------- | ----------- |----------- | | rst-all-11b | Trained with all the signals below except signals that are used to train Gaokao models | All applications below （specialized models are recommended first if high performance is preferred） | | rst-fact-retrieval-11b | Trained with the following signals: WordNet meaning, WordNet part-of-speech, WordNet synonym, WordNet antonym, wikiHow category hierarchy, Wikidata relation, Wikidata entity typing, Paperswithcode entity typing | Knowledge intensive tasks, information extraction tasks,factual checker | | rst-summarization-11b | Trained with the following signals: DailyMail summary, Paperswithcode summary, arXiv summary, wikiHow summary | Summarization or other general generation tasks, meta-evaluation (e.g., BARTScore) | | rst-temporal-reasoning-11b | Trained with the following signals: DailyMail temporal information, wikiHow procedure | Temporal reasoning, relation extraction, event-based extraction | | rst-information-extraction-11b | Trained with the following signals: Paperswithcode entity, Paperswithcode entity typing, Wikidata entity typing, Wikidata relation, Wikipedia entity | Named entity recognition, relation extraction and other general IE tasks in the news, scientific or other domains| | rst-intent-detection-11b | Trained with the following signals: wikiHow goal-step relation | Intent prediction, event prediction | | rst-topic-classification-11b | Trained with the following signals: DailyMail category, arXiv category, wikiHow text category, Wikipedia section title | general text classification | | rst-word-sense-disambiguation-11b | Trained with the following signals: WordNet meaning, WordNet part-of-speech, WordNet synonym, WordNet antonym | Word sense disambiguation, part-of-speech tagging, general IE tasks, common sense reasoning | | rst-natural-language-inference-11b | Trained with the following signals: ConTRoL dataset, DREAM dataset, LogiQA dataset, RACE & RACE-C dataset, ReClor dataset, DailyMail temporal information | Natural language inference, multiple-choice question answering, reasoning | | rst-sentiment-classification-11b | Trained with the following signals: Rotten Tomatoes sentiment, Wikipedia sentiment | Sentiment classification, emotion classification | | rst-gaokao-rc-11b | Trained with multiple-choice QA datasets that are used to train the [T0pp](https://huggingface.co/bigscience/T0pp) model | General multiple-choice question answering| | **rst-gaokao-cloze-11b** | **Trained with manually crafted cloze datasets** | **General cloze filling**| | rst-gaokao-writing-11b | Trained with example essays from past Gaokao-English exams and grammar error correction signals | Essay writing, story generation, grammar error correction and other text generation tasks | ## Have a try? ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("XLab/rst-all-11b") model = AutoModelForSeq2SeqLM.from_pretrained("XLab/rst-all-11b") inputs = tokenizer.encode("TEXT: this is the best cast iron skillet you will ever buy. QUERY: Is this review \"positive\" or \"negative\"", return_tensors="pt") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)) ``` ## Data for reStructure Pre-training This dataset is a precious treasure, containing a variety of naturally occurring signals. Any downstream task you can think of (e.g., the college entrance exam mentioned in the RST paper) can benefit from being pre-trained on some of our provided signals. We spent several months collecting the following 29 signal types, accounting for a total of 46,926,447 data samples. We hope this dataset will be a valuable asset for everyone in natural language processing research. We provide collected signals through [DataLab](https://github.com/ExpressAI/DataLab). For efficiency, we only provide 50,000 samples at most for each signal type. If you want all the samples we collected, please fill this [form](https://docs.google.com/forms/d/e/1FAIpQLSdPO50vSdfwoO3D7DQDVlupQnHgrXrwfF3ePE4X1H6BwgTn5g/viewform?usp=sf_link). More specifically, we collected the following signals. ###### We will be happy :smiley: to know if the resource is helpful for your work, and please cite our [work](https://github.com/ExpressAI/reStructured-Pretraining/blob/main/README.md#Bib) :blush: | Mine | Signal | #Sample | Use in DataLab | Some Applications | | --- | --- | --- | --- | --- | | [Rotten Tomatoes](https://www.rottentomatoes.com/) | (review, rating) | 5,311,109 | `load_dataset("rst", "rotten_tomatoes_sentiment")` | Sentiment classification | | [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (text, category) | 899,904 | `load_dataset("rst", "daily_mail_category")`| Topic classification | | [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (title, text, summary) | 1,026,616 | `load_dataset("rst", "daily_mail_summary")` | Summarization; Sentence expansion| | [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (text, events) | 1,006,412 | `load_dataset("rst", "daily_mail_temporal")` | Temporal reasoning| | [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) | (entity, entity_type, text) | 2,214,274 | `load_dataset("rst", "wikidata_entity")` | Entity typing| | [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) | (subject, object, relation, text) | 1,526,674 | `load_dataset("rst", "wikidata_relation")` | Relation extraction; Fact retrieval| | [wikiHow](https://www.wikihow.com/Main-Page) | (text, category) | 112,109 | `load_dataset("rst", "wikihow_text_category")` | Topic classification | | [wikiHow](https://www.wikihow.com/Main-Page) | (low_category, high_category) | 4,868 | `load_dataset("rst", "wikihow_category_hierarchy")` | Relation extraction; Commonsense reasoning| | [wikiHow](https://www.wikihow.com/Main-Page) | (goal, steps) | 47,956 | `load_dataset("rst", "wikihow_goal_step")` | Intent detection| | [wikiHow](https://www.wikihow.com/Main-Page) | (text, summary) | 703,278 | `load_dataset("rst", "wikihow_summary")` | Summarization; Sentence expansion | | [wikiHow](https://www.wikihow.com/Main-Page) | (goal, first_step, second_step) | 47,787 | `load_dataset("rst", "wikihow_procedure")` | Temporal reasoning | | [wikiHow](https://www.wikihow.com/Main-Page) | (question, description, answer, related_questions) | 47,705 | `load_dataset("rst", "wikihow_question")` | Question generation| | [Wikipedia](https://www.wikipedia.org/) | (text, entities) |22,231,011 | `load_dataset("rst", "wikipedia_entities")` | Entity recognition| [Wikipedia](https://www.wikipedia.org/) | (texts, titles) | 3,296,225 | `load_dataset("rst", "wikipedia_sections")` | Summarization| | [WordNet](https://wordnet.princeton.edu/) | (word, sentence, pos) | 27,123 | `load_dataset("rst", "wordnet_pos")` | Part-of-speech tagging| | [WordNet](https://wordnet.princeton.edu/) | (word, sentence, meaning, possible_meanings) | 27,123 | `load_dataset("rst", "wordnet_meaning")` | Word sense disambiguation| | [WordNet](https://wordnet.princeton.edu/) | (word, sentence, synonyms) | 17,804 | `load_dataset("rst", "wordnet_synonym")`| Paraphrasing| | [WordNet](https://wordnet.princeton.edu/) | (word, sentence, antonyms) | 6,408 | `load_dataset("rst", "wordnet_antonym")` |Negation | | [ConTRoL]() | (premise, hypothesis, label) | 8,323 | `load_dataset("rst", "qa_control")` | Natural language inference| |[DREAM](https://transacl.org/ojs/index.php/tacl/article/view/1534)| (context, question, options, answer) | 9,164 | `load_dataset("rst", "qa_dream")` | Reading comprehension| | [LogiQA](https://doi.org/10.24963/ijcai.2020/501) | (context, question, options, answer) | 7,974 | `load_dataset("rst", "qa_logiqa")` | Reading comprehension| | [ReClor](https://openreview.net/forum?id=HJgJtT4tvB) | (context, question, options, answer) | 5,138 | `load_dataset("rst", "qa_reclor")` |Reading comprehension | | [RACE](https://doi.org/10.18653/v1/d17-1082) | (context, question, options, answer) | 44,880 | `load_dataset("rst", "qa_race")` | Reading comprehension| | [RACE-C](http://proceedings.mlr.press/v101/liang19a.html) | (context, question, options, answer) | 5,093 | `load_dataset("rst", "qa_race_c")` | Reading comprehension| | [TriviaQA](https://doi.org/10.18653/v1/P17-1147) | (context, question, answer) | 46,636 | `load_dataset("rst", "qa_triviaqa")` |Reading comprehension | | [Arxiv](https://arxiv.org/) | (text, category) | 1,696,348 | `load_dataset("rst", "arxiv_category")` |Topic classification| | [Arxiv](https://arxiv.org/) | (text, summary) | 1,696,348 | `load_dataset("rst", "arxiv_summary")` | Summarization; Sentence expansion| | [Paperswithcode](https://paperswithcode.com/) | (text, entities, datasets, methods, tasks, metrics) | 4,731,233 | `load_dataset("rst", "paperswithcode_entity")` | Entity recognition| | [Paperswithcode](https://paperswithcode.com/) | (text, summary) | 120,924 | `load_dataset("rst", "paperswithcode_summary")` | Summarization; Sentence expansion| ## Bibtext for Citation Info ``` @article{yuan2022restructured, title={reStructured Pre-training}, author={Yuan, Weizhe and Liu, Pengfei}, journal={arXiv preprint arXiv:2206.11147}, year={2022} } ```

9d3176c09b0f311f2b81817863564b19

theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv

theojolliffe

bart

13

1

transformers

0

text2text-generation

true

false

mit

null

['scientific_papers']

null

0

['generated_from_trainer']

true

1,514

false

# bart-cnn-pubmed-arxiv-pubmed-arxiv This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on the scientific_papers dataset. It achieves the following results on the evaluation set: - Loss: 2.1382 - Rouge1: 42.1723 - Rouge2: 15.7664 - Rougel: 24.5336 - Rougelsum: 37.7532 - Gen Len: 127.6382 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 2.125 | 1.0 | 67679 | 2.1382 | 42.1723 | 15.7664 | 24.5336 | 37.7532 | 127.6382 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

4ed4f77d9afce547a10bb6857c9410f1

facebook/wav2vec2-base-100h

facebook

wav2vec2

8

780

transformers

3

automatic-speech-recognition

true

false

apache-2.0

['en']

['librispeech_asr']

null

0

1

0

['audio', 'automatic-speech-recognition']

false

true

3,651

false

# Wav2Vec2-Base-100h [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. [Paper](https://arxiv.org/abs/2006.11477) Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli **Abstract** We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import soundfile as sf import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-100h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-100h") # define function to read in sound file def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") ds = ds.map(map_to_array) # tokenize input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) ``` ## Evaluation This code snippet shows how to evaluate **facebook/wav2vec2-base-100h** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import soundfile as sf import torch from jiwer import wer librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-100h").to("cuda") processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-100h") def map_to_pred(batch): input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values with torch.no_grad(): logits = model(input_values.to("cuda")).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"]) print("WER:", wer(result["text"], result["transcription"])) ``` *Result (WER)*: | "clean" | "other" | |---|---| | 6.1 | 13.5 |

2a166344559a2509ddc28b18ca5ba02f

kejian/mighty-rwr

kejian

gpt2

36

4

transformers

0

null

true

false

apache-2.0

['en']

['kejian/codeparrot-train-more-filter-3.3b-cleaned']

null

0

['generated_from_trainer']

true

4,373

false

# mighty-rwr This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 128, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_hits_threshold': 0, 'num_samples': 2048}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_hits_threshold': 0, 'num_samples': 2048, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'value_head_config': {'is_detached': False}}, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'alpha': 1, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'mighty-rwr', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/497nsm8z

d902abd61464f65c6e5e402c5e270e11

fathyshalab/all-roberta-large-v1-small_talk-8-16-5

fathyshalab

roberta

11

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,515

false

# all-roberta-large-v1-small_talk-8-16-5 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: 2.3566 - Accuracy: 0.3855 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7259 | 1.0 | 1 | 2.5917 | 0.2551 | | 2.217 | 2.0 | 2 | 2.5059 | 0.3275 | | 1.7237 | 3.0 | 3 | 2.4355 | 0.3768 | | 1.4001 | 4.0 | 4 | 2.3837 | 0.3739 | | 1.1937 | 5.0 | 5 | 2.3566 | 0.3855 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

e40c0c58b2f9d05e53cc690143b27d77

sd-dreambooth-library/kim_jung_gi_art_style

sd-dreambooth-library

null

15

63

diffusers

4

text-to-image

false

creativeml-openrail-m

null

1

0

1

0

['text-to-image', 'stable-diffusion']

false

true

757

false

### kim_jung_gi_art_style Dreambooth model trained by apurik-parv with [Shivam shri rao's DreamBooth](https://github.com/ShivamShrirao/diffusers/tree/main/examples/dreambooth/train_dreambooth.py) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Inference phrase: **kimjugi** The model is trained on kim jung gi images for a total of 10000 steps. The intention is to reproduce the art style faithfully. Feel free to use it.

96a5db6e8c49094aefd0e92bfcb5eb2a

jonatasgrosman/exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s368

jonatasgrosman

wav2vec2

10

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['fr']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'fr']

false

true

479

false

# exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s368 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 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

d498183fedc7323a447436bd4a70d7e8

jdang/xlm-roberta-base-finetuned-panx-de

jdang

xlm-roberta

18

24

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,314

false

# xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1365 - F1: 0.8649 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 | | 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 | | 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0 - Datasets 1.16.1 - Tokenizers 0.10.3

121d1e6365569de931323697391c337a

muhtasham/small-mlm-glue-mrpc-custom-tokenizer-expand-vocab

muhtasham

bert

12

2

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,684

false

# small-mlm-glue-mrpc-custom-tokenizer-expand-vocab This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0713 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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: constant - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.327 | 1.09 | 500 | 4.6505 | | 4.486 | 2.18 | 1000 | 4.0830 | | 4.0801 | 3.27 | 1500 | 3.9647 | | 3.841 | 4.36 | 2000 | 3.6616 | | 3.5786 | 5.45 | 2500 | 3.6047 | | 3.4159 | 6.54 | 3000 | 3.4439 | | 3.1616 | 7.63 | 3500 | 3.3131 | | 3.1009 | 8.71 | 4000 | 3.2167 | | 2.9661 | 9.8 | 4500 | 3.2388 | | 2.8417 | 10.89 | 5000 | 3.0713 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

79fe1b0a623dd49f3f324060b05431cf

espnet/siddhana_fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_fine-truncated-ef9dab

espnet

null

20

1

espnet

0

automatic-speech-recognition

false

cc-by-4.0

['en']

['fsc_unseen']

null

0

['espnet', 'audio', 'automatic-speech-recognition']

false

true

1,386

false

## ESPnet2 ASR pretrained model ### `siddhana/fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_finetune_raw_en_word_valid.acc.ave_5best` ♻️ Imported from https://zenodo.org/record/5655832 This model was trained by siddhana using fsc_unseen/asr1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```python # coming soon ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

d0dde0791de6100790a1ec1d7fa77c52

sd-concepts-library/leif-jones

sd-concepts-library

null

15

0

null

1

null

false

mit

null

0

[]

false

true

1,676

false

### leif jones on Stable Diffusion This is the `<leif-jones>` 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 your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<leif-jones> 0](https://huggingface.co/sd-concepts-library/leif-jones/resolve/main/concept_images/5.jpeg) ![<leif-jones> 1](https://huggingface.co/sd-concepts-library/leif-jones/resolve/main/concept_images/9.jpeg) ![<leif-jones> 2](https://huggingface.co/sd-concepts-library/leif-jones/resolve/main/concept_images/6.jpeg) ![<leif-jones> 3](https://huggingface.co/sd-concepts-library/leif-jones/resolve/main/concept_images/4.jpeg) ![<leif-jones> 4](https://huggingface.co/sd-concepts-library/leif-jones/resolve/main/concept_images/1.jpeg) ![<leif-jones> 5](https://huggingface.co/sd-concepts-library/leif-jones/resolve/main/concept_images/3.jpeg) ![<leif-jones> 6](https://huggingface.co/sd-concepts-library/leif-jones/resolve/main/concept_images/2.jpeg) ![<leif-jones> 7](https://huggingface.co/sd-concepts-library/leif-jones/resolve/main/concept_images/0.jpeg) ![<leif-jones> 8](https://huggingface.co/sd-concepts-library/leif-jones/resolve/main/concept_images/7.jpeg) ![<leif-jones> 9](https://huggingface.co/sd-concepts-library/leif-jones/resolve/main/concept_images/8.jpeg)

71059fb83998e04a310dc4d9bb060ace

Habana/vit

Habana

null

3

1,807

null

0

null

false

apache-2.0

null

0

[]

false

true

2,502

false

[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks. Learn more about how to take advantage of the power of Habana HPUs to train and deploy Transformers and Diffusers models at [hf.co/hardware/habana](https://huggingface.co/hardware/habana). ## ViT model HPU configuration This model only contains the `GaudiConfig` file for running the [ViT](https://huggingface.co/google/vit-base-patch16-224-in21k) model on Habana's Gaudi processors (HPU). **This model contains no model weights, only a GaudiConfig.** This enables to specify: - `use_habana_mixed_precision`: whether to use Habana Mixed Precision (HMP) - `hmp_opt_level`: optimization level for HMP, see [here](https://docs.habana.ai/en/latest/PyTorch/PyTorch_Mixed_Precision/PT_Mixed_Precision.html#configuration-options) for a detailed explanation - `hmp_bf16_ops`: list of operators that should run in bf16 - `hmp_fp32_ops`: list of operators that should run in fp32 - `hmp_is_verbose`: verbosity - `use_fused_adam`: whether to use Habana's custom AdamW implementation - `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator ## Usage The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs. [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/image-classification/run_image_classification.py) is an image classification example script to fine-tune a model. You can run it with ViT with the following command: ```bash python run_image_classification.py \ --model_name_or_path google/vit-base-patch16-224-in21k \ --dataset_name cifar10 \ --output_dir /tmp/outputs/ \ --remove_unused_columns False \ --do_train \ --do_eval \ --learning_rate 2e-5 \ --num_train_epochs 5 \ --per_device_train_batch_size 64 \ --per_device_eval_batch_size 64 \ --evaluation_strategy epoch \ --save_strategy epoch \ --load_best_model_at_end True \ --save_total_limit 3 \ --seed 1337 \ --use_habana \ --use_lazy_mode \ --gaudi_config_name Habana/vit \ --throughput_warmup_steps 2 ``` Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.

80d0bb96c859df2795b2b622b2a4fbd8

inkittmahdi/ddpm-butterflies-128

inkittmahdi

null

13

0

diffusers

0

null

false

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

null

0

[]

false

true

1,233

false

# ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/inkittmahdi/ddpm-butterflies-128/tensorboard?#scalars)

b62dec5f56143720ea8f4b85e5a1430a

Matthijs/vit-base-patch16-224

Matthijs

null

5

0

null

0

image-classification

false

apache-2.0

null

['imagenet', 'imagenet-21k']

null

0

['vision', 'image-classification']

false

true

2,047

false

# Vision Transformer (base-sized model) Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. This repo contains a Core ML version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224). ## Usage instructions Create a `VNCoreMLRequest` that loads the ViT model: ```swift import CoreML import Vision lazy var classificationRequest: VNCoreMLRequest = { do { let config = MLModelConfiguration() config.computeUnits = .all let coreMLModel = try ViT(configuration: config) let visionModel = try VNCoreMLModel(for: coreMLModel.model) let request = VNCoreMLRequest(model: visionModel, completionHandler: { [weak self] request, error in if let results = request.results as? [VNClassificationObservation] { /* do something with the results */ } }) request.imageCropAndScaleOption = .centerCrop return request } catch { fatalError("Failed to create VNCoreMLModel: \(error)") } }() ``` Perform the request: ```swift func classify(image: UIImage) { guard let ciImage = CIImage(image: image) else { print("Unable to create CIImage") return } DispatchQueue.global(qos: .userInitiated).async { let handler = VNImageRequestHandler(ciImage: ciImage, orientation: .up) do { try handler.perform([self.classificationRequest]) } catch { print("Failed to perform classification: \(error)") } } } ```

a83a301006877ce382cbf7e6e0853763

Laurie/billsum_t5_model

Laurie

t5

14

9

transformers

0

text2text-generation

true

false

apache-2.0

null

['billsum']

null

0

['generated_from_trainer']

true

1,692

false

# billsum_t5_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5045 - Rouge1: 0.1393 - Rouge2: 0.0511 - Rougel: 0.117 - Rougelsum: 0.1171 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.8011 | 0.1314 | 0.0398 | 0.111 | 0.1107 | 19.0 | | No log | 2.0 | 124 | 2.5850 | 0.1371 | 0.049 | 0.1157 | 0.1158 | 19.0 | | No log | 3.0 | 186 | 2.5221 | 0.1407 | 0.0531 | 0.1184 | 0.1186 | 19.0 | | No log | 4.0 | 248 | 2.5045 | 0.1393 | 0.0511 | 0.117 | 0.1171 | 19.0 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

9720cf8027defecd0d890d20107dea3f

cardiffnlp/twitter-roberta-base-mar2022

cardiffnlp

roberta

9

17

transformers

2

fill-mask

true

false

mit

['en']

['twitter-api']

null

0

['timelms', 'twitter']

false

true

4,685

false

# Twitter March 2022 (RoBERTa-base, 128M) This is a RoBERTa-base model trained on 128.06M tweets until the end of March 2022. More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829). Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the [TimeLMs repository](https://github.com/cardiffnlp/timelms). For other models trained until different periods, check this [table](https://github.com/cardiffnlp/timelms#released-models). ## Preprocess Text Replace usernames and links for placeholders: "@user" and "http". If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data). ```python def preprocess(text): preprocessed_text = [] for t in text.split(): # expects whitespace tokenization if len(t) > 1: t = '@user' if t[0] == '@' and t.count('@') == 1 else t t = 'http' if t.startswith('http') else t preprocessed_text.append(t) return ' '.join(preprocessed_text) ``` ## Example Masked Language Model ```python from transformers import pipeline, AutoTokenizer MODEL = "cardiffnlp/twitter-roberta-base-mar2022" fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL) tokenizer = AutoTokenizer.from_pretrained(MODEL) def pprint(candidates, n): for i in range(n): token = tokenizer.decode(candidates[i]['token']) score = candidates[i]['score'] print("%d) %.5f %s" % (i+1, score, token)) texts = [ "So glad I'm <mask> vaccinated.", "I keep forgetting to bring a <mask>.", "Looking forward to watching <mask> Game tonight!", ] for text in texts: t = preprocess(text) print(f"{'-'*30}\n{t}") candidates = fill_mask(t) pprint(candidates, 5) ``` Output: ``` ------------------------------ So glad I'm <mask> vaccinated. 1) 0.34390 fully 2) 0.28177 not 3) 0.16473 getting 4) 0.04932 still 5) 0.01754 double ------------------------------ I keep forgetting to bring a <mask>. 1) 0.05391 book 2) 0.04560 mask 3) 0.03456 pen 4) 0.03251 lighter 5) 0.03098 charger ------------------------------ Looking forward to watching <mask> Game tonight! 1) 0.60744 the 2) 0.15224 The 3) 0.02575 this 4) 0.01450 End 5) 0.01035 Championship ``` ## Example Tweet Embeddings ```python from transformers import AutoTokenizer, AutoModel, TFAutoModel import numpy as np from scipy.spatial.distance import cosine from collections import Counter def get_embedding(text): # naive approach for demonstration text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') features = model(**encoded_input) features = features[0].detach().cpu().numpy() return np.mean(features[0], axis=0) MODEL = "cardiffnlp/twitter-roberta-base-mar2022" tokenizer = AutoTokenizer.from_pretrained(MODEL) model = AutoModel.from_pretrained(MODEL) query = "The book was awesome" tweets = ["I just ordered fried chicken 🐣", "The movie was great", "What time is the next game?", "Just finished reading 'Embeddings in NLP'"] sims = Counter() for tweet in tweets: sim = 1 - cosine(get_embedding(query), get_embedding(tweet)) sims[tweet] = sim print('Most similar to: ', query) print(f"{'-'*30}") for idx, (tweet, sim) in enumerate(sims.most_common()): print("%d) %.5f %s" % (idx+1, sim, tweet)) ``` Output: ``` Most similar to: The book was awesome ------------------------------ 1) 0.98985 The movie was great 2) 0.96122 Just finished reading 'Embeddings in NLP' 3) 0.95733 I just ordered fried chicken 🐣 4) 0.93271 What time is the next game? ``` ## Example Feature Extraction ```python from transformers import AutoTokenizer, AutoModel, TFAutoModel import numpy as np MODEL = "cardiffnlp/twitter-roberta-base-mar2022" tokenizer = AutoTokenizer.from_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) # Pytorch model = AutoModel.from_pretrained(MODEL) encoded_input = tokenizer(text, return_tensors='pt') features = model(**encoded_input) features = features[0].detach().cpu().numpy() features_mean = np.mean(features[0], axis=0) #features_max = np.max(features[0], axis=0) # # Tensorflow # model = TFAutoModel.from_pretrained(MODEL) # encoded_input = tokenizer(text, return_tensors='tf') # features = model(encoded_input) # features = features[0].numpy() # features_mean = np.mean(features[0], axis=0) # #features_max = np.max(features[0], axis=0) ```

bb8f5d0d6bebae4030836b256dbe1dc8

jonatasgrosman/exp_w2v2t_ar_vp-sv_s445

jonatasgrosman

wav2vec2

10

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ar']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'ar']

false

true

469

false

# exp_w2v2t_ar_vp-sv_s445 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 your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

32ede7cc083e409471901bda3fe69212

StanfordAIMI/stanford-deidentifier-only-i2b2

StanfordAIMI

bert

7

3,942

transformers

1

token-classification

true

false

mit

['en']

['radreports']

null

0

1

0

['token-classification', 'sequence-tagger-model', 'pytorch', 'transformers', 'pubmedbert', 'uncased', 'radiology', 'biomedical']

false

true

2,665

false

Stanford de-identifier was trained on a variety of radiology and biomedical documents with the goal of automatising the de-identification process while reaching satisfactory accuracy for use in production. Manuscript in-proceedings. Associated github repo: https://github.com/MIDRC/Stanford_Penn_Deidentifier ## Citation ```bibtex @article{10.1093/jamia/ocac219, author = {Chambon, Pierre J and Wu, Christopher and Steinkamp, Jackson M and Adleberg, Jason and Cook, Tessa S and Langlotz, Curtis P}, title = "{Automated deidentification of radiology reports combining transformer and “hide in plain sight” rule-based methods}", journal = {Journal of the American Medical Informatics Association}, year = {2022}, month = {11}, abstract = "{To develop an automated deidentification pipeline for radiology reports that detect protected health information (PHI) entities and replaces them with realistic surrogates “hiding in plain sight.”In this retrospective study, 999 chest X-ray and CT reports collected between November 2019 and November 2020 were annotated for PHI at the token level and combined with 3001 X-rays and 2193 medical notes previously labeled, forming a large multi-institutional and cross-domain dataset of 6193 documents. Two radiology test sets, from a known and a new institution, as well as i2b2 2006 and 2014 test sets, served as an evaluation set to estimate model performance and to compare it with previously released deidentification tools. Several PHI detection models were developed based on different training datasets, fine-tuning approaches and data augmentation techniques, and a synthetic PHI generation algorithm. These models were compared using metrics such as precision, recall and F1 score, as well as paired samples Wilcoxon tests.Our best PHI detection model achieves 97.9 F1 score on radiology reports from a known institution, 99.6 from a new institution, 99.5 on i2b2 2006, and 98.9 on i2b2 2014. On reports from a known institution, it achieves 99.1 recall of detecting the core of each PHI span.Our model outperforms all deidentifiers it was compared to on all test sets as well as human labelers on i2b2 2014 data. It enables accurate and automatic deidentification of radiology reports.A transformer-based deidentification pipeline can achieve state-of-the-art performance for deidentifying radiology reports and other medical documents.}", issn = {1527-974X}, doi = {10.1093/jamia/ocac219}, url = {https://doi.org/10.1093/jamia/ocac219}, note = {ocac219}, eprint = {https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocac219/47220191/ocac219.pdf}, } ```

9d902c8b37be5c4749a085c78d4b241b

Helsinki-NLP/opus-mt-de-iso

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-de-iso * source languages: de * target languages: iso * OPUS readme: [de-iso](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-iso/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-iso/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-iso/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-iso/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.de.iso | 21.4 | 0.389 |

9207434fd4604a1c1f62b8dffa1dce23

BeckyB/Bert_Classifier

BeckyB

bert

12

14

transformers

0

text-classification

true

false

apache-2.0

null

['yelp_review_full']

null

0

['generated_from_trainer']

true

1,322

false

# Bert_Classifier 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: 1.1067 - Accuracy: 0.5533 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 188 | 1.0636 | 0.5 | | No log | 2.0 | 376 | 1.0405 | 0.52 | | 0.9962 | 3.0 | 564 | 1.1067 | 0.5533 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

88abde3fdf4ab3bb1447600a904d1f91

Fiddi/distilbert-base-uncased-finetuned-ner

Fiddi

distilbert

19

9

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

1

0

['generated_from_trainer']

true

1,555

false

# distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0604 - Precision: 0.9291 - Recall: 0.9376 - F1: 0.9333 - Accuracy: 0.9841 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2412 | 1.0 | 878 | 0.0688 | 0.9178 | 0.9246 | 0.9212 | 0.9815 | | 0.0514 | 2.0 | 1756 | 0.0608 | 0.9251 | 0.9344 | 0.9298 | 0.9832 | | 0.0304 | 3.0 | 2634 | 0.0604 | 0.9291 | 0.9376 | 0.9333 | 0.9841 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.12.1 - Tokenizers 0.10.3

3b2fdf25ff6ebb8b6126082554064bd7

Kuanchy/bauti

Kuanchy

null

22

2

diffusers

0

null

false

mit

null

2

0

[]

false

true

1,171

false

### Bauti on Stable Diffusion via Dreambooth #### model by Kuanchy This your the Stable Diffusion model fine-tuned the Bauti concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a sks person ** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/Kuanchy/bauti/resolve/main/concept_images/2.jpeg) ![image 1](https://huggingface.co/Kuanchy/bauti/resolve/main/concept_images/0.jpeg) ![image 2](https://huggingface.co/Kuanchy/bauti/resolve/main/concept_images/1.jpeg) ![image 3](https://huggingface.co/Kuanchy/bauti/resolve/main/concept_images/3.jpeg)

73da6336e6157810d5cd2e68305697ee

Zekunli/flan-t5-large-extraction-cnndm_2000-all

Zekunli

t5

10

29

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,142

false

# flan-t5-large-extraction-cnndm_2000-all This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7621 - Rouge1: 34.9258 - Rouge2: 15.2218 - Rougel: 29.9813 - Rougelsum: 29.9443 - Gen Len: 18.986 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 24 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.1649 | 0.8 | 200 | 1.8161 | 34.9143 | 14.9085 | 29.8629 | 29.811 | 19.0 | | 1.9114 | 1.6 | 400 | 1.7713 | 34.8733 | 14.6521 | 29.8186 | 29.7829 | 18.986 | | 1.7997 | 2.4 | 600 | 1.7917 | 34.1481 | 14.7443 | 29.7078 | 29.6144 | 18.99 | | 1.7477 | 3.2 | 800 | 1.7771 | 35.0882 | 15.3186 | 29.9749 | 29.9643 | 18.99 | | 1.6821 | 4.0 | 1000 | 1.7621 | 34.9258 | 15.2218 | 29.9813 | 29.9443 | 18.986 | | 1.6301 | 4.8 | 1200 | 1.7796 | 34.3705 | 14.8013 | 29.6128 | 29.5457 | 18.99 | | 1.597 | 5.6 | 1400 | 1.7669 | 35.4342 | 15.7045 | 30.4953 | 30.4293 | 18.99 | | 1.5543 | 6.4 | 1600 | 1.7857 | 34.5322 | 15.0244 | 29.8476 | 29.7596 | 18.99 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.5.1 - Tokenizers 0.12.1

968fdd866a88ffcdb68d005ad6573f66

vuiseng9/nncf-qat-kd-bert-l-squadv1.1-sl256

vuiseng9

bert

25

2

transformers

0

null

true

false

apache-2.0

null

['squad']

null

0

[]

true

1,514

false

This model is quantized version of ```vuiseng9/bert-l-squadv1.1-sl256``` using OpenVINO NNCF. ### Training ```bash # used 4xV100 GPUS # --fp16 for lower turnaround and resource requirement python run_qa.py \ --model_name_or_path vuiseng9/bert-l-squadv1.1-sl256 \ --dataset_name squad \ --do_eval \ --do_train \ --evaluation_strategy steps \ --eval_steps 250 \ --learning_rate 3e-5 \ --fp16 \ --num_train_epochs 2 \ --per_device_eval_batch_size 64 \ --per_device_train_batch_size 8 \ --max_seq_length 256 \ --doc_stride 128 \ --save_steps 500 \ --logging_steps 1 \ --overwrite_output_dir \ --nncf_config nncf_bert_config_squad_kd.json \ #stock config which has seq.len modified to 256. --run_name $RUNID \ --output_dir $OUTDIR ``` ### Evaluation Require ```vuiseng9/transformers (fork)``` , commit: ```ff24569b```, NNCF v2.1+ commit (```8e26365```) ```bash git clone https://huggingface.co/vuiseng9/nncf-qat-kd-bert-l-squadv1.1-sl256 python run_qa.py \ --model_name_or_path ./nncf-qat-kd-bert-l-squadv1.1-sl256 \ --dataset_name squad \ --nncf_config ./nncf-qat-kd-bert-l-squadv1.1-sl256/nncf_bert_config_squad_kd.json \ --nncf_ckpt ./nncf-qat-kd-bert-l-squadv1.1-sl256 \ --do_eval \ --per_device_eval_batch_size 128 \ --max_seq_length 256 \ --doc_stride 128 \ --output_dir /tmp/eval-nncf-qat-kd-bert-l-squadv1.1-sl256 \ --overwrite_output_dir ``` ### Results ``` eval_exact_match = 87.1902 eval_f1 = 93.0286 eval_samples = 12097 ```

f61621e4e80dba06a5f2c57ebbf4ad48

mattyhew/charliee

mattyhew

null

15

4

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

['text-to-image', 'stable-diffusion']

false

true

611

false

### charliee Dreambooth model trained by mattyhew 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-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:

95828382a8d860ad35bed57c0d25bd07

danhsf/distilbert-base-uncased-finetuned-emotion

danhsf

distilbert

16

1

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

1

0

['generated_from_trainer']

true

1,345

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: 0.2201 - Accuracy: 0.9265 - F1: 0.9266 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8631 | 1.0 | 250 | 0.3221 | 0.904 | 0.9011 | | 0.254 | 2.0 | 500 | 0.2201 | 0.9265 | 0.9266 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

97d2c30f4418d618f2efc3608ac6c28d

Helsinki-NLP/opus-mt-zai-es

Helsinki-NLP

marian

10

8

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-zai-es * source languages: zai * target languages: es * OPUS readme: [zai-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/zai-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/zai-es/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/zai-es/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/zai-es/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.zai.es | 20.8 | 0.372 |

3cb9f609a18d71c8a71f8129093b909a

aXhyra/sentiment_trained_1234567

aXhyra

distilbert

10

6

transformers

0

text-classification

true

false

apache-2.0

null

['tweet_eval']

null

0

['generated_from_trainer']

true

1,408

false

# sentiment_trained_1234567 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: 1.2854 - F1: 0.7165 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.2140338797769864e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 1234567 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.6603 | 1.0 | 11404 | 0.7020 | 0.6992 | | 0.5978 | 2.0 | 22808 | 0.8024 | 0.7151 | | 0.5495 | 3.0 | 34212 | 1.0837 | 0.7139 | | 0.4026 | 4.0 | 45616 | 1.2854 | 0.7165 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3

f3c3e30678f8ed3755b0c354980865f0

versae/bertin-roberta-base-spanish-finetuned-recores3

versae

roberta

15

0

transformers

0

multiple-choice

true

false

cc-by-4.0

null

0

['generated_from_trainer']

true

2,823

false

# bertin-roberta-base-spanish-finetuned-recores3 This model is a fine-tuned version of [bertin-project/bertin-roberta-base-spanish](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.0975 - Accuracy: 0.3884 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 3000 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.6095 | 1.0 | 524 | 1.6094 | 0.2342 | | 1.607 | 2.0 | 1048 | 1.5612 | 0.3058 | | 1.4059 | 3.0 | 1572 | 1.6292 | 0.3361 | | 0.7047 | 4.0 | 2096 | 2.5111 | 0.4132 | | 0.2671 | 5.0 | 2620 | 3.2399 | 0.3499 | | 0.1065 | 6.0 | 3144 | 5.1217 | 0.3444 | | 0.0397 | 7.0 | 3668 | 4.3270 | 0.3691 | | 0.0162 | 8.0 | 4192 | 5.1796 | 0.3719 | | 0.0096 | 9.0 | 4716 | 5.2161 | 0.3994 | | 0.0118 | 10.0 | 5240 | 4.9225 | 0.3719 | | 0.0015 | 11.0 | 5764 | 5.0544 | 0.3829 | | 0.0091 | 12.0 | 6288 | 5.7731 | 0.3884 | | 0.0052 | 13.0 | 6812 | 4.1606 | 0.3939 | | 0.0138 | 14.0 | 7336 | 6.2725 | 0.3857 | | 0.0027 | 15.0 | 7860 | 6.2274 | 0.3857 | | 0.0003 | 16.0 | 8384 | 6.0935 | 0.4022 | | 0.0002 | 17.0 | 8908 | 5.7650 | 0.3994 | | 0.0 | 18.0 | 9432 | 6.3595 | 0.4215 | | 0.0 | 19.0 | 9956 | 5.8934 | 0.3747 | | 0.0001 | 20.0 | 10480 | 6.0571 | 0.3884 | | 0.0 | 21.0 | 11004 | 6.0718 | 0.3884 | | 0.0 | 22.0 | 11528 | 6.0844 | 0.3884 | | 0.0 | 23.0 | 12052 | 6.0930 | 0.3884 | | 0.0 | 24.0 | 12576 | 6.0966 | 0.3884 | | 0.0 | 25.0 | 13100 | 6.0975 | 0.3884 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

d9c87ae770de62856d56e22180ee0a1e

jmunoz/finetuning-sentiment-model-3000-samples

jmunoz

distilbert

14

11

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

925

false

# finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cpu - Datasets 1.2.1 - Tokenizers 0.12.1

4c0fb2769ce05f8802246ad536468c96

coreml/coreml-trinart-stable-diffusion

coreml

null

3

0

null

0

text-to-image

false

creativeml-openrail-m

null

0

['coreml', 'stable-diffusion', 'text-to-image']

false

true

1,150

false

# Core ML Converted Model: - This model was converted to Core ML for use on Apple Silicon devices. Instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-files-to-Core-ML).<br> - Provide the model to an app such as [Mochi Diffusion](https://github.com/godly-devotion/MochiDiffusion) to generate images.<br> - `split_einsum` version is compatible with all compute unit options including Neural Engine.<br> # Stable Diffusion TrinArt/Trin-sama AI finetune v2: Source(s): [Hugging Face](https://huggingface.co/naclbit/trinart_stable_diffusion_v2) Stable Diffusion TrinArt/Trin-sama AI finetune v2 trinart_stable_diffusion is a SD model finetuned by about 40,000 assorted high resolution manga/anime-style pictures for 8 epochs. This is the same model running on Twitter bot @trinsama (https://twitter.com/trinsama) # Please Note! This model is NOT the 19.2M images Characters Model on TrinArt, but an improved version of the original Trin-sama Twitter bot model. This model is intended to retain the original SD's aesthetics as much as possible while nudging the model to anime/manga style.

e78fa10cf549d5b5185b0b5c038cf1bb

abdoutony207/m2m100_418M-evaluated-en-to-ar-2000instancesUNMULTI-leaningRate2e-05-batchSize8-regu2

abdoutony207

m2m_100

12

3

transformers

0

text2text-generation

true

false

mit

null

['un_multi']

null

0

['generated_from_trainer']

true

2,351

false

# m2m100_418M-evaluated-en-to-ar-2000instancesUNMULTI-leaningRate2e-05-batchSize8-regu2 This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the un_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.3642 - Bleu: 40.8245 - Meteor: 0.4272 - Gen Len: 41.8075 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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: 11 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 5.1584 | 0.5 | 100 | 3.2518 | 30.3723 | 0.3633 | 41.5 | | 2.1351 | 1.0 | 200 | 0.9929 | 32.9915 | 0.3833 | 41.8225 | | 0.568 | 1.5 | 300 | 0.4312 | 33.705 | 0.3896 | 42.6225 | | 0.3749 | 2.0 | 400 | 0.3697 | 36.9316 | 0.4084 | 40.57 | | 0.2376 | 2.5 | 500 | 0.3587 | 37.6782 | 0.4124 | 41.99 | | 0.2435 | 3.0 | 600 | 0.3529 | 37.9931 | 0.4128 | 42.02 | | 0.1706 | 3.5 | 700 | 0.3531 | 39.9972 | 0.4252 | 41.8025 | | 0.165 | 4.0 | 800 | 0.3514 | 39.3155 | 0.42 | 41.0275 | | 0.1273 | 4.5 | 900 | 0.3606 | 40.0765 | 0.4234 | 41.6175 | | 0.1307 | 5.0 | 1000 | 0.3550 | 40.4468 | 0.428 | 41.72 | | 0.0926 | 5.5 | 1100 | 0.3603 | 40.5454 | 0.4307 | 41.765 | | 0.1096 | 6.0 | 1200 | 0.3613 | 40.5691 | 0.4298 | 42.31 | | 0.0826 | 6.5 | 1300 | 0.3642 | 40.8245 | 0.4272 | 41.8075 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

8eb7e78693ea1fbc23b1df922d5609b9

Ahmed007/mt5-small-ibn-Shaddad-v3

Ahmed007

mt5

13

1

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['Poet', 'generated_from_trainer']

true

1,327

false

# mt5-small-ibn-Shaddad-v3 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.2668 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 5.4157 | 1.0 | 935 | 3.2668 | 0.0 | 0.0 | 0.0 | 0.0 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

b9b0df475ae5f56aea26c8469acf44ae

Helsinki-NLP/opus-mt-es-tn

Helsinki-NLP

marian

10

8

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

768

false

### opus-mt-es-tn * source languages: es * target languages: tn * OPUS readme: [es-tn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-tn/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-tn/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-tn/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-tn/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.es.tn | 32.2 | 0.528 |

ec0c404a14cec5ab4ab71965f2cbba49

JoonJoon/koelectra-base-v3-discriminator-finetuned-ner

JoonJoon

electra

10

6

transformers

0

token-classification

true

false

apache-2.0

null

['klue']

null

0

['generated_from_trainer']

true

1,593

false

# koelectra-base-v3-discriminator-finetuned-ner This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.1957 - Precision: 0.6665 - Recall: 0.7350 - F1: 0.6991 - Accuracy: 0.9396 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 438 | 0.2588 | 0.5701 | 0.6655 | 0.6141 | 0.9212 | | 0.4333 | 2.0 | 876 | 0.2060 | 0.6671 | 0.7134 | 0.6895 | 0.9373 | | 0.1944 | 3.0 | 1314 | 0.1957 | 0.6665 | 0.7350 | 0.6991 | 0.9396 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu102 - Datasets 1.14.0 - Tokenizers 0.10.3

e5f2e108847439227907d47702bf7562

Helsinki-NLP/opus-mt-uk-it

Helsinki-NLP

marian

11

19

transformers

0

translation

true

false

apache-2.0

['uk', 'it']

null

1

0

['translation']

false

true

2,003

false

### ukr-ita * source group: Ukrainian * target group: Italian * OPUS readme: [ukr-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-ita/README.md) * model: transformer-align * source language(s): ukr * target language(s): ita * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-ita/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-ita/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-ita/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.ukr.ita | 46.0 | 0.662 | ### System Info: - hf_name: ukr-ita - source_languages: ukr - target_languages: ita - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-ita/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['uk', 'it'] - src_constituents: {'ukr'} - tgt_constituents: {'ita'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-ita/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-ita/opus-2020-06-17.test.txt - src_alpha3: ukr - tgt_alpha3: ita - short_pair: uk-it - chrF2_score: 0.662 - bleu: 46.0 - brevity_penalty: 0.9490000000000001 - ref_len: 27846.0 - src_name: Ukrainian - tgt_name: Italian - train_date: 2020-06-17 - src_alpha2: uk - tgt_alpha2: it - prefer_old: False - long_pair: ukr-ita - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

980026f7d8aafd586c53488484e95515

ksabeh/bert-base-uncased-attribute-correction-mlm-titles

ksabeh

bert

8

10

transformers

0

question-answering

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,439

false

# ksabeh/bert-base-uncased-attribute-correction-mlm-titles This model is a fine-tuned version of [ksabeh/bert-base-uncased-attribute-correction-mlm](https://huggingface.co/ksabeh/bert-base-uncased-attribute-correction-mlm) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0430 - Validation Loss: 0.0625 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 23878, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1429 | 0.0743 | 0 | | 0.0430 | 0.0625 | 1 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1

21c575b37ac774d6bfcbdfb17896d0f5

philschmid/distilbert-onnx

philschmid

distilbert

6

36,890

transformers

1

question-answering

false

apache-2.0

['en']

['squad']

null

2

1

0

1

0

[]

false

true

479

false

# ONNX Conversion of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) # DistilBERT base cased distilled SQuAD This model is a fine-tune checkpoint of [DistilBERT-base-cased](https://huggingface.co/distilbert-base-cased), fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1. This model reaches a F1 score of 87.1 on the dev set (for comparison, BERT bert-base-cased version reaches a F1 score of 88.7).

d46fbcfb68d5248e1a2ef07d2d636d84

philschmid/habana-xlm-r-large-amazon-massive

philschmid

xlm-roberta

15

1

transformers

0

text-classification

true

false

apache-2.0

null

['AmazonScience/massive']

null

0

['generated_from_trainer', 'habana']

false

true

2,206

false

# philschmid/habana-xlm-r-large-amazon-massive This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the AmazonScience/massive dataset. It achieves the following results on the evaluation set: ## 8x HPU approx. 41min **train results** ```bash {'loss': 0.2651, 'learning_rate': 2.4e-05, 'epoch': 1.0} {'loss': 0.1079, 'learning_rate': 1.8e-05, 'epoch': 2.0} {'loss': 0.0563, 'learning_rate': 1.2e-05, 'epoch': 3.0} {'loss': 0.0308, 'learning_rate': 6e-06, 'epoch': 4.0} {'loss': 0.0165, 'learning_rate': 0.0, 'epoch': 5.0} ``` total ```bash {'train_runtime': 3172.4502, 'train_samples_per_second': 127.028, 'train_steps_per_second': 1.986, 'train_loss': 0.09531746031746031, 'epoch': 5.0} ``` **eval results** ```bash {'eval_loss': 0.3128528892993927, 'eval_accuracy': 0.9125852013210597, 'eval_f1': 0.9125852013210597, 'eval_runtime': 45.1795, 'eval_samples_per_second': 314.988, 'eval_steps_per_second': 4.936, 'epoch': 1.0} {'eval_loss': 0.36222779750823975, 'eval_accuracy': 0.9134987000210807, 'eval_f1': 0.9134987000210807, 'eval_runtime': 29.8241, 'eval_samples_per_second': 477.165, 'eval_steps_per_second': 7.477, 'epoch': 2.0} {'eval_loss': 0.3943144679069519, 'eval_accuracy': 0.9140608530672476, 'eval_f1': 0.9140 608530672476, 'eval_runtime': 30.1085, 'eval_samples_per_second': 472.657, 'eval_steps_per_second': 7.407, 'epoch': 3.0} {'eval_loss': 0.40938863158226013, 'eval_accuracy': 0.9158878504672897, 'eval_f1': 0.9158878504672897, 'eval_runtime': 30.4546, 'eval_samples_per_second': 467.286, 'eval_steps_per_second': 7.322, 'epoch': 4.0} {'eval_loss': 0.4137658476829529, 'eval_accuracy': 0.9172932330827067, 'eval_f1': 0.9172932330827067, 'eval_runtime': 30.3464, 'eval_samples_per_second': 468.952, 'eval_steps_per_second': 7.348, 'epoch': 5.0} ``` # Environment The training was run on a `DL1` instance on AWS using Habana Gaudi1 and `optimum`. see for more information: https://github.com/philschmid/deep-learning-habana-huggingface

3cd7bb4f98050663322ce4cd07e45477

albert-xxlarge-v2

null

albert

8

64,439

transformers

7

fill-mask

true

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

['exbert']

false

true

9,849

false

# ALBERT XXLarge v2 Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1909.11942) and first released in [this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a difference between english and English. Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ALBERT is a transformers model 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 any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Sentence Ordering Prediction (SOP): ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the ALBERT model as inputs. ALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers. This is the second version of the xxlarge model. Version 2 is different from version 1 due to different dropout rates, additional training data, and longer training. It has better results in nearly all downstream tasks. This model has the following configuration: - 12 repeating layers - 128 embedding dimension - 4096 hidden dimension - 64 attention heads - 223M parameters ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=albert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-xxlarge-v2') >>> unmasker("Hello I'm a [MASK] model.") [ { "sequence":"[CLS] hello i'm a modeling model.[SEP]", "score":0.05816134437918663, "token":12807, "token_str":"â–modeling" }, { "sequence":"[CLS] hello i'm a modelling model.[SEP]", "score":0.03748830780386925, "token":23089, "token_str":"â–modelling" }, { "sequence":"[CLS] hello i'm a model model.[SEP]", "score":0.033725276589393616, "token":1061, "token_str":"â–model" }, { "sequence":"[CLS] hello i'm a runway model.[SEP]", "score":0.017313428223133087, "token":8014, "token_str":"â–runway" }, { "sequence":"[CLS] hello i'm a lingerie model.[SEP]", "score":0.014405295252799988, "token":29104, "token_str":"â–lingerie" } ] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AlbertTokenizer, AlbertModel tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v2') model = AlbertModel.from_pretrained("albert-xxlarge-v2") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import AlbertTokenizer, TFAlbertModel tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v2') model = TFAlbertModel.from_pretrained("albert-xxlarge-v2") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-xxlarge-v2') >>> unmasker("The man worked as a [MASK].") [ { "sequence":"[CLS] the man worked as a chauffeur.[SEP]", "score":0.029577180743217468, "token":28744, "token_str":"â–chauffeur" }, { "sequence":"[CLS] the man worked as a janitor.[SEP]", "score":0.028865724802017212, "token":29477, "token_str":"â–janitor" }, { "sequence":"[CLS] the man worked as a shoemaker.[SEP]", "score":0.02581118606030941, "token":29024, "token_str":"â–shoemaker" }, { "sequence":"[CLS] the man worked as a blacksmith.[SEP]", "score":0.01849772222340107, "token":21238, "token_str":"â–blacksmith" }, { "sequence":"[CLS] the man worked as a lawyer.[SEP]", "score":0.01820771023631096, "token":3672, "token_str":"â–lawyer" } ] >>> unmasker("The woman worked as a [MASK].") [ { "sequence":"[CLS] the woman worked as a receptionist.[SEP]", "score":0.04604868218302727, "token":25331, "token_str":"â–receptionist" }, { "sequence":"[CLS] the woman worked as a janitor.[SEP]", "score":0.028220869600772858, "token":29477, "token_str":"â–janitor" }, { "sequence":"[CLS] the woman worked as a paramedic.[SEP]", "score":0.0261906236410141, "token":23386, "token_str":"â–paramedic" }, { "sequence":"[CLS] the woman worked as a chauffeur.[SEP]", "score":0.024797942489385605, "token":28744, "token_str":"â–chauffeur" }, { "sequence":"[CLS] the woman worked as a waitress.[SEP]", "score":0.024124596267938614, "token":13678, "token_str":"â–waitress" } ] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The ALBERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` ### Training The ALBERT procedure follows the BERT setup. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ## Evaluation results When fine-tuned on downstream tasks, the ALBERT models achieve the following results: | | Average | SQuAD1.1 | SQuAD2.0 | MNLI | SST-2 | RACE | |----------------|----------|----------|----------|----------|----------|----------| |V2 | |ALBERT-base |82.3 |90.2/83.2 |82.1/79.3 |84.6 |92.9 |66.8 | |ALBERT-large |85.7 |91.8/85.2 |84.9/81.8 |86.5 |94.9 |75.2 | |ALBERT-xlarge |87.9 |92.9/86.4 |87.9/84.1 |87.9 |95.4 |80.7 | |ALBERT-xxlarge |90.9 |94.6/89.1 |89.8/86.9 |90.6 |96.8 |86.8 | |V1 | |ALBERT-base |80.1 |89.3/82.3 | 80.0/77.1|81.6 |90.3 | 64.0 | |ALBERT-large |82.4 |90.6/83.9 | 82.3/79.4|83.5 |91.7 | 68.5 | |ALBERT-xlarge |85.5 |92.5/86.1 | 86.1/83.1|86.4 |92.4 | 74.8 | |ALBERT-xxlarge |91.0 |94.8/89.3 | 90.2/87.4|90.8 |96.9 | 86.5 | ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1909-11942, author = {Zhenzhong Lan and Mingda Chen and Sebastian Goodman and Kevin Gimpel and Piyush Sharma and Radu Soricut}, title = {{ALBERT:} {A} Lite {BERT} for Self-supervised Learning of Language Representations}, journal = {CoRR}, volume = {abs/1909.11942}, year = {2019}, url = {http://arxiv.org/abs/1909.11942}, archivePrefix = {arXiv}, eprint = {1909.11942}, timestamp = {Fri, 27 Sep 2019 13:04:21 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1909-11942.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=albert-xxlarge-v2"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

70197b4b31403a4f3171b694e708a897

sd-concepts-library/herge-style

sd-concepts-library

null

9

0

null

6

null

false

mit

null

0

[]

false

true

1,182

false

### Herge_style on Stable Diffusion This is the `<herge>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<herge> 0](https://huggingface.co/sd-concepts-library/herge-style/resolve/main/concept_images/1.jpeg) ![<herge> 1](https://huggingface.co/sd-concepts-library/herge-style/resolve/main/concept_images/3.jpeg) ![<herge> 2](https://huggingface.co/sd-concepts-library/herge-style/resolve/main/concept_images/2.jpeg) ![<herge> 3](https://huggingface.co/sd-concepts-library/herge-style/resolve/main/concept_images/0.jpeg) Here is the result at "Scarlett Johansson at <herge> style: ![scarlett01.png](https://s3.amazonaws.com/moonup/production/uploads/1662692210622-6304d4549aef62c4013f1641.png)

04a55e1e81622222a125caba0771b3aa

gopalkalpande/t5-small-finetuned-xsum

gopalkalpande

t5

18

2

transformers

0

text2text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,529

false

# gopalkalpande/t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0422 - Validation Loss: 0.4407 - Train Rouge1: 19.5311 - Train Rouge2: 14.2402 - Train Rougel: 17.9781 - Train Rougelsum: 18.1546 - Train Gen Len: 19.0 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 1.0422 | 0.4407 | 19.5311 | 14.2402 | 17.9781 | 18.1546 | 19.0 | 0 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1

516d886d8ed879f5d8fa0d4a73de9bf3

gotutiyan/gec-bart-large

gotutiyan

bart

9

18

transformers

0

text2text-generation

true

false

mit

null

0

[]

false

true

1,062

false

This is a reproduction of the following paper: ``` @inproceedings{katsumata-komachi-2020-stronger, title = "Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model", author = "Katsumata, Satoru and Komachi, Mamoru", booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing", month = dec, year = "2020", address = "Suzhou, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.aacl-main.83", pages = "827--832", } ``` This model achieves the following results: |Data|Metric|gotutiyan/gec-bart-large|Paper (bart-large)| |:--|:--|:--|:--| |CoNLL-2014|M2 (P/R/F0.5)|71.01 / 43.3 / 62.9|69.3 / 45.0 /62.6| |BEA19-test|ERRANT (P/R/F0.5)3|70.4 / 55.0 / 66.6|68.3 / 57.1 /65.6| |JFLEG-test|GLEU|57.8|57.3| The details can be found in the [GitHub repository](https://github.com/gotutiyan/GEC-BART).

a7ab07610defa4fc87e7ea42bd8d2cba

tomekkorbak/jolly_saha

tomekkorbak

null

2

0

null

0

null

false

mit

['en']

['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000']

null

0

['generated_from_trainer']

true

8,068

false

# jolly_saha This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 3147 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True, 'skip_tokens': 1649999872}, 'generation': {'every_n_steps': 32, 'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 4096}], 'scorer_config': {}}, 'kl_gpt3_callback': {'every_n_steps': 32, 'force_call_on': [25177], 'gpt3_kwargs': {'model_name': 'davinci'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': '9e6c78543a6ff1e4089002c38864d5a9cf71ec90', 'value_head_config': {'is_detached': False}}, 'path_or_name': 'tomekkorbak/nervous_wozniak'}, 'objective': {'alpha': 0.5, 'beta': 0.1, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 512, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'jolly_saha', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 3346, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1649999872, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/3q8ux7mw

f0c5e22495396e6de696f5c9e522b768

sentence-transformers/quora-distilbert-multilingual

sentence-transformers

distilbert

13

18,870

sentence-transformers

0

sentence-similarity

true

false

apache-2.0

null

0

['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']

false

true

3,579

false

# sentence-transformers/quora-distilbert-multilingual This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## 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 sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/quora-distilbert-multilingual') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/quora-distilbert-multilingual') model = AutoModel.from_pretrained('sentence-transformers/quora-distilbert-multilingual') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/quora-distilbert-multilingual) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

90cc48f229dcebc789de5b2b7e98caa7

jonatasgrosman/exp_w2v2r_fr_vp-100k_gender_male-5_female-5_s474

jonatasgrosman

wav2vec2

10

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['fr']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'fr']

false

true

498

false

# exp_w2v2r_fr_vp-100k_gender_male-5_female-5_s474 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

6839d3a22d2c0929ddbc14544a692e8f

yuhuizhang/finetuned_gpt2_sst2_negation0.05

yuhuizhang

gpt2

11

1

transformers

0

text-generation

true

false

mit

null

['sst2']

null

0

['generated_from_trainer']

true

1,231

false

# finetuned_gpt2_sst2_negation0.05 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.5271 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1134 | 1.0 | 1062 | 3.5060 | | 2.926 | 2.0 | 2124 | 3.5158 | | 2.8331 | 3.0 | 3186 | 3.5271 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.2 - Tokenizers 0.12.1

1e67e28afcb1ad701ba4b1c4793bd4cd

anuragshas/wav2vec2-xls-r-300m-mr-cv9-with-lm

anuragshas

wav2vec2

24

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['mr']

['mozilla-foundation/common_voice_9_0']

null

0

['automatic-speech-recognition', 'mozilla-foundation/common_voice_9_0', 'generated_from_trainer']

true

2,402

false

# This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - MR dataset. It achieves the following results on the evaluation set: - Loss: 0.3642 - Wer: 0.4190 - Cer: 0.0946 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 6124 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:----:|:---------------:|:------:|:------:| | 3.5184 | 12.9 | 400 | 3.4210 | 1.0 | 1.0 | | 2.3797 | 25.81 | 800 | 1.1068 | 0.8389 | 0.2584 | | 1.5022 | 38.71 | 1200 | 0.5278 | 0.6280 | 0.1517 | | 1.3181 | 51.61 | 1600 | 0.4254 | 0.5587 | 0.1297 | | 1.2037 | 64.52 | 2000 | 0.3836 | 0.5143 | 0.1176 | | 1.1245 | 77.42 | 2400 | 0.3643 | 0.4871 | 0.1111 | | 1.0582 | 90.32 | 2800 | 0.3562 | 0.4676 | 0.1062 | | 1.0027 | 103.23 | 3200 | 0.3530 | 0.4625 | 0.1058 | | 0.9382 | 116.13 | 3600 | 0.3388 | 0.4442 | 0.1002 | | 0.8915 | 129.03 | 4000 | 0.3430 | 0.4427 | 0.1000 | | 0.853 | 141.94 | 4400 | 0.3536 | 0.4375 | 0.1000 | | 0.8127 | 154.84 | 4800 | 0.3511 | 0.4344 | 0.0986 | | 0.7861 | 167.74 | 5200 | 0.3595 | 0.4372 | 0.0993 | | 0.7619 | 180.65 | 5600 | 0.3628 | 0.4316 | 0.0985 | | 0.7537 | 193.55 | 6000 | 0.3633 | 0.4174 | 0.0943 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.1.1.dev0 - Tokenizers 0.12.1

be72f4dac958e604e4ab37ab5d3952b0

fathyshalab/all-roberta-large-v1-banking-16-16-5-oos

fathyshalab

roberta

11

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,517

false

# all-roberta-large-v1-banking-16-16-5-oos This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2920 - Accuracy: 0.3982 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7211 | 1.0 | 1 | 2.5748 | 0.2301 | | 2.2722 | 2.0 | 2 | 2.4566 | 0.3009 | | 1.9185 | 3.0 | 3 | 2.3596 | 0.3805 | | 1.667 | 4.0 | 4 | 2.2920 | 0.3982 | | 1.4704 | 5.0 | 5 | 2.2565 | 0.3982 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

4362f8b84a06291977b2b8511e6adf26

DarwinAnim8or/GPT-DMV-125m

DarwinAnim8or

gpt_neo

10

11

transformers

0

text-generation

true

false

mit

['en']

['DarwinAnim8or/DMV-Plate-Review']

{'emissions': 20, 'source': 'https://mlco2.github.io/impact/#compute', 'training_type': 'fine-tuning', 'geographical_location': 'Oregon, USA', 'hardware_used': '1 T4, Google Colab'}

0

['dmv', 'fun']

false

true

1,236

false

# GPT-DMV-125m A finetuned version of [GPT-Neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the 'DMV' dataset. (Linked above) A demo is available [here](https://huggingface.co/spaces/DarwinAnim8or/GPT-DMV-Playground) (I recommend using the demo playground rather than the Inference window on the right here) # Training Procedure This was trained on the 'DMV' dataset, using the "HappyTransformers" library on Google Colab. This model was trained for 5 epochs with learning rate 1e-2. # Biases & Limitations This likely contains the same biases and limitations as the original GPT-Neo-125M that it is based on, and additionally heavy biases from the DMV dataset. # Intended Use This model is meant for fun, nothing else. # Sample Use ```python #Import model: from happytransformer import HappyGeneration happy_gen = HappyGeneration("GPT-NEO", "DarwinAnim8or/GPT-DMV-125m") #Set generation settings: from happytransformer import GENSettings args_top_k = GENSettings(no_repeat_ngram_size=3, do_sample=True,top_k=80, temperature=0.4, max_length=50, early_stopping=False) #Generate a response: result = happy_gen.generate_text("""PLATE: LUCH REVIEW REASON CODE: """, args=args_top_k) print(result) print(result.text) ```

1d80bc2a5fe7b49349fb7d463e4aea19

espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2

espnet

null

20

8

espnet

0

automatic-speech-recognition

false

cc-by-4.0

['en']

['swbd_sentiment']

null

0

['espnet', 'audio', 'automatic-speech-recognition']

false

true

180,335

false

## ESPnet2 ASR model ### `espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2` This model was trained by YushiUeda using swbd_sentiment recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout e5c0e0dbdab7e56ea9bf0a852bac10a1d99acf64 pip install -e . cd egs2/swbd_sentiment/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2 ```  # RESULTS ## Environments - date: `Fri Mar 4 07:57:13 EST 2022` - python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.9.0+cu102` - Git hash: `3b53aedc654fd30a828689c2139a1e130adac077` - Commit date: `Fri Feb 25 00:13:16 2022 -0500` ## Using Conformer based encoder, Transformer based decoder and self-supervised learning features (Wav2vec2.0) with spectral augmentation and predicting transcript along with sentiment - ASR config: [conf/tuning/train_asr_conformer_wav2vec2.yaml](conf/tuning/train_asr_conformer_wav2vec2.yaml) - token_type: word - labels: Positive, Neutral, Negative |dataset|Snt|Intent Classification Macro F1 (%)| Weighted F1 (%)| Micro F1 (%)| |---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave_10best/valid|2415|64.5|67.5|67.4| |decode_asr_asr_model_valid.acc.ave_10best/test|2438|64.1|66.5|66.3| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_wav2vec2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_wav2vec2_raw_en_word ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 57795 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 3 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: - frontend.upstream num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 40000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_word/train/speech_shape - exp/asr_stats_raw_en_word/train/text_shape.word valid_shape_file: - exp/asr_stats_raw_en_word/valid/speech_shape - exp/asr_stats_raw_en_word/valid/text_shape.word batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train/wav.scp - speech - sound - - dump/raw/train/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - sound - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0025 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - <blank> - <unk> - i - and - the - you - that - it - a - Neutral - to - uh - '''s' - of - know - Positive - they - in - we - '''t' - have - but - so - was - like - Negative - yeah - is - just - um - well - do - for - think - don - there - or - 'on' - '''re' - my - what - really - be - with - not - if - are - one - he - '''ve' - because - '''m' - about - all - get - can - had - out - at - them - when - this - as - oh - lot - up - people - some - then - would - go - right - mean - now - time - kind - got - going - good - she - things - more - were - from - something - been - 'no' - see - me - too - an - your - much - little - guess - how - where - our - very - here - their - thing - two - '''ll' - other - did - years - work - even - has - any - way - probably - those - could - say - real - back - '''d' - year - down - home - than - want - didn - into - pretty - okay - who - take - huh - school - said - make - over - kids - never - always - put - by - her - stuff - went - doing - three - these - 'yes' - which - around - only - big - maybe - 'off' - anything - day - t - sure - actually - come - money - him - different - everything - still - used - many - five - will - sort - nice - us - last - his - thought - every - most - getting - first - feel - bit - need - children - same - course - also - new - care - family - hum - long - through - before - use - done - should - house - old - let - does - car - being - problem - doesn - four - seems - though - pay - look - whole - great - husband - haven - try - live - trying - ever - why - read - better - find - far - keep - ago - sometimes - watch - interesting - quite - area - hard - talking - else - another - part - bad - having - twenty - whatever - place - couple - usually - 'true' - high - texas - seen - fact - s - enough - after - own - college - while - country - hundred - somebody - few - either - times - week - away - gonna - type - job - six - dollars - tell - might - remember - again - came - give - started - start - ten - made - play - able - dallas - enjoy - working - once - c - someone - life - least - v - everybody - since - fun - both - talk - wouldn - ones - news - anyway - wasn - person - heard - believe - am - th - buy - may - point - call - night - y - almost - bye - isn - system - wanted - called - took - state - wife - child - half - women - goes - next - yet - especially - love - looking - parents - gone - such - gets - understand - together - movie - until - w - days - end - saying - idea - saw - music - mother - thirty - couldn - makes - stay - change - m - basically - wonderful - problems - guy - worked - spend - help - lived - credit - whether - seem - eight - n - best - world - run - hear - bought - young - each - months - seven - places - supposed - city - matter - coming - exactly - d - small - summer - comes - certain - company - less - thinking - won - during - b - thousand - agree - show - daughter - sounds - myself - funny - water - o - month - dog - fifty - paper - gotten - found - taking - today - certainly - boy - friends - number - mine - program - food - son - p - older - name - air - movies - government - moved - schools - outside - deal - close - tried - paying - eat - drive - hours - nine - rather - cars - crime - important - war - living - between - business - anymore - reason - weeks - public - vote - situation - recently - nothing - easy - sit - pick - taxes - turn - full - percent - making - friend - book - happen - minutes - middle - town - watching - paid - eighty - tax - several - listen - set - talked - north - takes - reading - definitely - law - jury - kinds - married - u - enjoyed - says - without - works - learn - everyone - drug - major - side - cost - room - education - morning - computer - involved - mostly - aren - health - l - anybody - along - amount - man - against - weather - often - under - age - forty - insurance - favorite - hope - card - must - happened - lives - left - drugs - expensive - american - miles - yourself - hour - already - plano - cards - decided - large - difference - ahead - fifteen - camping - told - although - second - r - woman - twelve - knew - guys - cut - neat - fish - mind - wrong - unless - sense - instead - leave - wear - class - hand - top - walk - bring - past - f - running - e - absolutely - weekend - line - books - question - team - wish - exercise - interested - areas - baby - states - liked - somewhere - father - experience - phone - case - men - lots - cat - society - taken - changed - game - worth - seventy - gun - h - wonder - hit - group - service - kept - shows - gosh - early - interest - trouble - control - themselves - ha - finally - using - god - dad - cook - hot - difficult - nursing - front - terms - growing - late - kid - looked - felt - rain - teach - tend - realize - weren - sixty - except - needs - social - budget - figure - recycling - lake - wanna - looks - wh - forth - mom - concerned - south - grew - topic - ways - death - christmas - regular - wait - imagine - television - east - trees - check - fairly - hate - general - catch - dinner - built - ready - fine - sister - story - playing - starting - homes - office - awful - radio - needed - companies - changes - programs - fishing - nineteen - ask - tough - cans - easier - yard - cold - ought - street - later - door - wants - students - national - space - across - brother - free - local - tha - level - happens - sitting - newspaper - move - countries - store - subject - girl - beautiful - turned - soon - income - putting - church - university - dress - information - lately - degree - york - vacation - pollution - totally - winter - america - ah - ours - cats - spent - happy - played - consider - cases - spring - california - longer - teacher - oil - send - lost - sports - garden - teachers - families - particular - buying - amazing - likes - football - united - teaching - hey - benefits - brought - gave - party - worry - throw - testing - given - bunch - near - nobody - community - driving - open - personal - sell - force - chance - wow - test - baseball - within - biggest - quality - building - example - seeing - power - afford - support - caught - inside - plan - seemed - ninety - younger - learned - generation - charge - punishment - rest - dogs - become - clean - short - privacy - g - calls - plus - particularly - decide - terrible - twice - fall - extra - period - choice - hold - ended - hadn - main - guilty - depends - save - excellent - price - strange - feeling - size - trial - military - boys - per - bet - judge - parts - noticed - anywhere - fan - head - center - glad - clothes - rate - stop - eleven - white - stand - suppose - guns - grade - watched - bigger - scary - issue - special - dollar - green - its - jobs - means - black - worse - knows - plastic - low - spending - picked - golf - gas - single - neighborhood - necessarily - alone - cooking - newspapers - pull - fast - completely - road - student - crimes - houses - paint - medical - learning - fair - restaurant - miss - lawn - giving - washington - doctor - word - killed - recycle - light - cash - visit - familiar - grass - itself - season - chicken - rid - president - stayed - normally - whenever - machine - graduate - eighteen - capital - shouldn - virginia - private - field - magazines - kill - market - apartment - anyone - waiting - asked - classes - break - crazy - helps - aware - sunday - hm - speak - term - sound - property - sad - comfortable - waste - channel - evening - cover - heavy - carry - everyday - systems - gives - wa - answer - higher - unfortunately - minute - future - serious - snow - available - smaller - handle - ground - behind - huge - west - plant - allowed - wind - peace - costs - cause - serve - rent - lucky - gee - build - english - telling - lose - individual - gardening - busy - order - raised - basic - basis - rock - training - happening - opinion - heart - follow - mainly - history - walking - ye - average - towards - houston - games - travel - decision - environment - respect - list - hopefully - grow - others - sorry - san - taught - weight - bags - hurt - finding - attention - hasn - computers - raise - aerobics - quick - shot - personally - bedroom - similar - loved - sixties - park - helping - feet - industry - write - generally - weird - record - benefit - pool - mail - pennsylvania - glass - notice - calling - process - land - originally - richardson - cities - afraid - utah - entire - colorado - ball - boat - grandmother - possible - folks - helped - strong - keeping - bill - keeps - thank - camp - third - types - eventually - obviously - yesterday - apparently - instance - pet - central - club - flowers - trash - trip - classical - europe - changing - perhaps - self - color - foot - video - based - station - saturday - french - normal - fire - '''clock' - issues - starts - piece - hobby - quit - prison - parent - oldest - bush - coverage - police - forget - girls - occasionally - bank - shape - beginning - moving - sent - vietnam - nights - current - salary - himself - stories - mountains - aluminum - luck - invasion - tape - florida - bed - laws - research - mess - hoping - players - tired - thirteen - magazine - expect - sleep - words - language - push - position - hobbies - background - plants - inches - easily - stopped - murder - shoot - maryland - hardly - bills - attitude - pro - civil - sometime - human - wanting - goodness - security - doctors - kitchen - somehow - penalty - county - eating - simply - die - bike - reunion - project - typical - j - however - total - mexico - base - economy - restaurants - responsibility - jail - lower - died - tested - safe - voting - elderly - sh - listening - sudden - numbers - career - stick - born - wondering - poor - painting - active - professional - supposedly - li - lady - reasons - cool - sixteen - yep - excuse - horrible - political - red - science - federal - besides - shop - opportunity - ride - planning - degrees - writing - mexican - engineering - surprised - bother - share - graduated - account - financial - hands - activities - seventies - step - thanks - bag - role - england - limit - willing - hospital - view - band - teams - tonight - groups - advantage - heat - department - turns - tree - telephone - became - brand - criminal - blue - dry - warm - weekends - grown - stores - rights - garbage - junior - everywhere - prices - metric - ran - equipment - till - cross - considered - track - moment - figured - americans - met - worst - ridiculous - grocery - yours - neighbor - piano - sold - cowboys - selling - savings - grandchildren - nowadays - add - plays - conversation - lunch - straight - sentence - floor - dead - fourteen - meet - ideas - foods - israel - fix - ourselves - swimming - upset - sign - sewing - wood - recipe - van - upon - standard - box - win - wall - offer - products - otherwise - pounds - stations - ex - staying - drop - body - carolina - sales - meal - ice - basketball - mixed - careful - possibly - sick - farm - retired - compared - western - hearing - finished - separate - mentioned - soviet - truck - river - defense - oklahoma - harder - k - re - stuck - cable - trade - favor - positive - related - smoke - effect - various - bottom - awhile - kindergarten - beat - court - beach - baltimore - choose - allow - brown - hang - known - sorts - bathroom - scared - popular - extremely - politics - hair - policy - wha - saint - covered - ca - sisters - boston - lakes - forever - fight - downtown - visa - sauce - garage - lines - suit - whereas - speech - direction - animals - corps - fit - majority - chinese - dark - painted - milk - concern - dump - nature - safety - shoes - star - questions - switch - clear - trips - management - beyond - depending - sing - iraq - pressure - cute - runs - windows - salad - board - chicago - population - legal - super - '''all' - puts - slow - pets - forward - thousands - style - debt - becoming - mo - pop - violent - italian - earlier - cheap - weapons - coast - austin - traveling - passed - x - speaking - points - prefer - threat - further - master - table - broken - random - row - northern - simple - appreciate - district - train - continue - rangers - pittsburgh - truth - value - quickly - raising - pass - tennis - flower - bass - engine - becomes - variety - jeans - exciting - organization - spread - sat - incredible - somewhat - loan - engineer - doubt - southern - monday - backyard - forced - papers - express - saving - owned - recent - toward - fortunate - liberal - shopping - rough - brothers - worried - meals - scouts - vacations - hunting - lawyers - wisconsin - bucks - act - voice - helpful - wide - retirement - cannot - picture - picking - suspect - spare - held - election - study - report - begin - antonio - drove - opposed - league - ju - se - solution - closer - character - finish - knowing - million - common - services - thinks - player - violence - wrote - highway - reasonable - afternoon - series - developed - effort - christian - fantastic - saved - seventeen - barbecue - sun - conditioning - ohio - babies - arlington - hole - visited - rural - herself - knowledge - kn - plans - instruments - above - border - bible - losing - china - events - leaving - written - taste - friday - schedule - anytime - showed - aspect - range - earth - rice - broke - tent - excited - roles - situations - rooms - spot - laid - duty - bottles - russia - fighting - pound - letter - convenient - thi - storm - original - wild - showing - percentage - required - grandparents - extent - economic - voted - canada - trust - healthy - dealing - face - hired - discuss - larger - pleased - eye - constantly - perfect - stupid - square - mix - meat - semester - necessary - mandatory - burning - fly - mothers - aids - checked - bedrooms - fresh - advice - tomatoes - treat - sale - ford - japanese - burn - correct - limited - sleeping - actual - ends - female - hundreds - feelings - impact - leaves - section - lay - provide - planted - factor - fill - rich - deep - someplace - drives - circumstances - honda - jersey - smoking - feels - fifties - access - doors - pattern - names - payment - facilities - automatic - boxes - hi - pictures - versus - ability - edge - politicians - amazed - boss - union - neighbors - distance - prime - article - mistake - grades - bread - bothers - jeez - rented - fourth - alcohol - gulf - catfish - license - shooting - touch - asking - realized - require - natural - expenses - purchase - energy - talks - colors - smart - considering - lessons - tremendous - participate - ages - missed - quiet - cheaper - cents - payments - iron - frightening - forgot - cheese - daughters - lawyer - creek - dental - seat - humid - belt - michigan - extended - flat - driver - foreign - stays - adults - songs - due - wet - double - stress - desert - drink - material - equal - deterrent - machines - eastern - boring - apart - vegetables - recipes - unusual - responsible - hire - garland - ho - dangerous - loans - colleges - served - prisons - recycled - cousins - gorgeous - member - values - fell - fund - metal - wolves - technology - form - enjoyable - entertainment - successful - juries - brings - likely - convicted - appeal - minimum - opposite - sport - complete - smell - gallon - lord - employees - centers - alive - blow - meant - cutting - relatives - bus - commit - none - jus - holding - sand - swing - courses - ski - breed - heck - casual - blood - admit - join - fi - draw - upper - bell - youngest - traffic - protect - tends - medicine - strongly - committed - opinions - brick - sides - congress - gasoline - regularly - plenty - collect - williams - tickets - perspective - damage - present - bowl - kidding - employee - tests - loves - round - nations - german - roof - august - october - disney - pieces - solid - knock - facts - concept - specific - option - jump - stage - block - items - murders - breaks - dirty - shirts - package - pair - pants - data - opera - standing - roll - count - action - physical - differently - teenagers - checks - replace - independent - neither - tuition - eyes - theater - educational - bins - animal - reports - senior - window - curious - de - argument - june - date - extreme - innocent - december - germany - salt - et - cetera - tomorrow - educated - clubs - bird - sons - journal - visiting - pulled - letting - tech - fixed - el - shorts - assume - message - primarily - signs - cuts - john - jazz - balance - un - walked - shirt - dropped - latin - feed - influence - wondered - adult - aid - inner - elementary - negative - swim - projects - raleigh - practically - grand - nearly - turning - cleaning - fort - recommend - ate - skiing - rules - yellow - cruise - impressed - address - labor - dish - highly - repair - prior - fee - terribly - experiences - lead - accept - mart - immediately - portion - nicer - seafood - fault - disease - truly - wearing - male - dances - closed - product - expected - caused - tapes - relaxing - culture - technical - criminals - sentencing - summertime - indiana - killing - encourage - housing - practice - ups - stitch - compare - sentenced - freedom - belong - purpose - throwing - crafts - pushing - sweet - decent - sew - campus - carpet - channels - repairs - preschool - please - minnesota - activity - naturally - cooked - quarterback - wise - satisfied - cadillac - streets - businesses - honest - automatically - routine - coach - arm - driven - dishes - mornings - contact - mall - deficit - humidity - location - fortunately - atmosphere - corporate - meeting - improvement - engineers - network - dressed - mcdonald - spanish - catholic - organizations - hill - model - fifth - elected - articles - expecting - seriously - volunteer - handy - riding - threw - ooh - trend - ba - arts - thursday - uncle - relationship - members - throughout - buffalo - solve - pain - auto - cholesterol - planned - prepared - presented - staff - choices - march - filled - overall - discipline - justice - weights - mile - unit - bringing - beef - camped - wal - mow - microwave - weapon - inch - rule - traveled - subscribe - proper - di - classic - software - pays - complex - missing - shepherd - pleasure - st - cream - expense - automobile - hers - orleans - king - philosophy - singing - eighties - enjoys - democratic - significant - chore - ev - combination - patterns - disappointed - republican - media - pre - sesame - fixing - seconds - passing - daily - trek - signed - raining - accident - scale - interests - route - ma - whoever - reach - judges - evidence - european - seasons - supporting - dirt - loose - france - cancer - planting - iowa - increase - hospitals - maintain - odd - pregnant - math - press - agency - shrimp - beer - key - puppy - sending - hardest - tr - wi - return - corner - suits - dakota - al - immediate - possibility - hooked - song - stadium - frame - dig - navy - comedy - annual - fear - island - exercising - fancy - fat - enjoying - motivated - design - affect - investment - recall - co - luxury - trim - flexible - international - furniture - potatoes - wou - fellow - breakfast - bath - trucks - uses - onto - beans - apple - alabama - records - musical - tie - setting - offs - michael - bugs - freeze - anyhow - properly - underneath - dining - aside - quarter - kentucky - skills - parole - parks - nation - complain - wine - summers - fans - golden - unanimous - shift - warranty - plastics - rates - rains - charged - lincoln - decisions - checking - gray - laugh - hills - commercial - recognize - quote - receive - recording - illegal - generations - advance - motor - outdoor - lab - honestly - rap - oriented - match - art - fiction - manage - flip - appropriate - strict - mad - mental - hung - adds - mileage - bicycle - thoroughly - elections - deserve - indian - according - latest - bu - ta - vehicle - holidays - july - junk - emergency - convinced - graduating - kick - including - teenage - ceiling - valley - victim - ocean - hell - steel - rainy - noise - marvelous - drunk - studying - mountain - hood - greatest - facility - generate - desk - improve - tells - sex - results - si - manager - goal - teenager - concert - copy - africa - paycheck - woods - lubbock - sentences - prevent - impossible - split - faster - speed - thin - chose - monthly - stands - turkey - repeat - japan - financially - lights - page - pulling - explain - potential - rape - wash - minor - thrown - professor - pan - vegetable - fried - onions - roommate - effects - wire - shame - individuals - sweat - scene - yards - whose - thoughts - draft - useful - welfare - organized - communities - realistic - directly - print - printer - purchased - aunt - prepare - millions - challenge - twins - badly - thick - pure - bar - roads - missouri - tall - library - added - sam - marriage - gardens - lesser - views - understanding - prove - deer - delicious - containers - depend - denver - favorites - tear - site - code - winds - parties - relatively - opened - falling - fascinating - forties - options - sharing - attached - owner - version - modern - standpoint - eaten - fully - neck - trials - knee - uncomfortable - temperature - chemical - processing - fruit - lovely - bothered - pot - causes - rea - diet - theory - conflict - earn - disagree - exposed - administration - breaking - buildings - fence - shocked - retire - wedding - ch - dust - acid - pushed - blame - contract - carried - nurse - overseas - texan - fuel - whe - vehicles - increased - necessity - plate - hitting - reduce - blocks - hide - silly - length - writer - film - development - refrigerator - engines - louis - relate - citizens - dorm - began - hawaii - january - wheel - gourmet - shots - bushes - theirs - outrageous - sea - hook - conscious - videos - mastercard - suburb - chevy - tiny - mowing - bulbs - flag - detroit - brakes - charges - retriever - towns - contribute - arms - slacks - definite - difficulty - produce - cultures - cou - discovered - whatnot - philadelphia - ou - electronic - strictly - tendency - mister - regard - con - approach - friendly - handled - governor - louisiana - urban - develop - pardon - construction - classroom - personality - currently - tour - apply - memory - francisco - affected - complicated - risk - shock - roses - movement - tied - teaches - nuts - halfway - softball - masters - causing - cake - unbelievable - cast - characters - actor - association - wallpaper - habit - blowing - expert - screen - bake - dessert - tents - minneapolis - tin - wars - steps - structure - motivation - buddy - minds - wound - coat - holes - covers - shell - tries - undergraduate - springs - banks - kuwait - kansas - established - dozen - steak - following - massachusetts - jewish - affects - hotel - sight - tight - birthday - statement - weeds - consumer - understood - tastes - cartoons - apartments - cares - settled - september - letters - atlanta - newer - guarantee - citizen - occasion - attorneys - tom - levels - sweaters - tires - direct - wagon - remarkable - result - shower - hello - commercials - cassette - forms - standards - james - native - falls - comment - peers - wore - pleasant - mid - region - essentially - differences - fitness - symphony - finger - ad - sounded - joined - trained - toyota - motors - aspects - candidate - votes - hunt - electronics - charging - registered - ed - electric - bite - gifts - manufacturing - farmers - participating - legislation - los - angeles - ticket - survive - catching - eliminate - ryan - luckily - teeth - ill - hated - offices - file - hassle - universal - entertain - roast - traditional - entertaining - crisis - officer - saudi - participated - profession - gue - soap - johnson - task - dumb - gain - broad - surgery - dressing - condition - tex - grill - camper - note - managed - increasing - rained - parking - wake - mistakes - pitch - cucumbers - prescription - shut - forgotten - conditions - rehabilitation - gold - waited - substitute - lift - crowd - gym - tools - divorced - practical - avoid - spray - seats - severe - litter - trunk - programming - soft - discover - cs - zero - firm - army - post - rarely - virtually - suddenly - relative - technically - frustrating - nursery - checkbook - rolls - colored - division - jack - districts - guitar - leaders - permanent - puerto - su - ultimately - race - biking - statistics - accepted - hussein - steal - shown - menu - pension - youth - pride - create - knit - walks - guide - fry - til - requirements - reporting - networks - chain - soil - jumped - hysterical - target - wasting - horse - buses - dear - butter - thanksgiving - instrument - cared - unemployment - switchboard - vice - morals - focus - beds - wednesday - george - principal - non - scores - grandfather - qualified - burned - courts - cousin - proud - ham - hits - literally - transferred - institution - debts - collection - weed - cigarettes - homework - corruption - clarion - purposes - improved - applied - closet - corn - tomato - lasagna - pickup - collecting - immigration - sooner - resources - largest - hurting - soccer - treated - shore - bored - abuse - mayor - continental - professionals - verdict - carrying - button - drinking - dying - reliable - transportation - subjects - fees - unfortunate - evenings - craft - scout - languages - scratch - sears - thirties - solutions - sherman - stack - funds - skirt - fed - correctly - listened - clothing - serving - supervisor - mark - materials - lewisville - below - chemicals - era - incentive - coffee - offered - interior - determine - sets - alternative - instructor - dance - saddam - discussion - joke - boating - fabulous - ship - funding - groceries - entirely - sitter - communications - democrat - cafeteria - corporation - squash - peppers - nor - pour - flour - waco - controls - argentina - flying - coal - nuclear - february - saturdays - phoenix - electrical - wage - laying - effective - robin - wealthy - hampshire - concerns - hall - figures - rochester - agreement - pages - bitty - cowboy - dealers - features - argue - commitment - hanging - policeman - critical - user - dried - strip - pie - balls - eggs - among - lifting - phase - desire - final - jogging - bless - attack - taxed - acres - april - oven - pack - claim - gorbachev - wherever - troops - illinois - industries - trailer - grab - pitching - nineties - ranch - ti - mortgage - mill - sue - register - attorney - alike - adopted - tournament - involvement - silver - perfectly - slightly - meetings - primary - sixth - employer - survey - indoor - partly - addition - nervous - georgia - recreation - internal - rise - schooling - previous - mood - stolen - birds - director - named - mustang - mystery - upstairs - goods - reunions - perform - reality - hurry - scattered - environmental - limits - cleaned - tons - concrete - belts - cabin - rolling - review - invaded - invade - obvious - requires - typically - religious - religion - opportunities - intelligent - peter - album - drawing - trumpet - stock - household - customer - kay - cotton - tennessee - specifically - lowest - moon - reputation - honor - secretary - rico - assumed - realizing - attitudes - rat - vegetarian - occurred - practicing - promote - adding - designed - delivered - nah - category - disk - exact - pilot - costing - brake - mercedes - pr - abortion - texans - moral - capable - applications - beneficial - flavor - drain - reporter - clock - aggravating - politically - governments - clearly - designing - burden - laughed - topics - chunk - spots - streams - efficient - slowly - arkansas - discussed - conservative - flute - choir - sugar - answering - lists - babysitter - impression - lets - david - forces - thumb - cop - creative - dip - switched - pine - content - aerobic - conversations - touched - candidates - legitimate - assistant - annoying - finance - vietnamese - husbands - storms - pump - lawns - patio - roots - russian - plot - mouth - amounts - suffering - headlines - hunter - acre - ties - measure - la - trout - guidelines - bonus - emotional - cow - unique - providing - encouraged - positions - barely - criteria - olds - tradition - scares - workers - iran - toys - tornado - moves - ton - recyclable - crowded - ladies - melt - crack - finances - score - crawfish - transmission - purple - mavericks - eve - babysitting - committing - maintenance - exposure - cassettes - socially - reagan - soup - hiking - athlete - cheesecake - grandson - skunk - addison - skied - realistically - profit - emissions - skirts - heels - awards - silence - lambs - whatsoever - lotus - offering - unquote - forest - phones - miniature - medium - grandma - goo - finishing - judicial - penalties - ki - hose - hungry - success - monitor - application - pink - depressing - supper - bureaucracy - status - territory - mississippi - exercises - preference - peo - packages - broadcast - doctorate - scholarship - grows - lean - anxious - core - voluntary - minority - couples - ears - crochet - selected - voters - democrats - authority - airport - horror - fox - sub - professors - legs - stir - celery - eats - chocolate - cup - asleep - studies - afterwards - slip - lap - connection - individually - dependent - foundation - worthwhile - fields - freedoms - giants - stars - kittens - vet - balanced - homeless - birth - mu - campaign - empty - scenes - heads - kicked - messed - arabia - greatly - bob - talent - nurses - strike - reached - dedicated - suggested - guard - basement - laughing - communication - ghost - abused - token - plane - beating - former - films - fought - failed - lesson - lo - walls - sink - girlfriend - accused - hurts - loud - gang - consistent - stereo - fa - struggling - interview - employment - borrowed - spoiled - tub - tea - mex - lemon - bin - evidently - grant - tremendously - cartons - opening - mi - skin - seed - acceptable - filter - golly - sits - coke - followed - basics - psychology - operate - owns - freezing - nissan - te - accidents - settle - leader - poverty - dr - masking - fiancee - jugs - landfill - heavily - lie - trends - interstate - competitive - arguments - weigh - competition - surprising - temporary - inclined - overnight - priority - darn - honey - roy - accurate - rocks - babysit - priced - twin - le - ban - athletes - lack - pond - muscles - connecticut - anyways - pacific - owners - freon - responsibilities - toxic - permit - closely - pitched - dresses - scenery - kevin - costner - greater - enemy - granted - welcome - define - advertising - salesman - reverse - ideal - locked - directions - object - figuring - frequently - boot - therefore - jails - murdered - purdue - received - led - picks - include - democracy - studied - fond - climate - alaska - sake - avid - healthier - fired - connected - stealing - chances - humane - supported - enjoyment - penny - turtles - encouraging - ea - marketing - garlic - broccoli - potato - suburbs - formal - rush - concentrate - woodworking - leaf - cent - automobiles - ozone - devices - source - comedies - landing - semi - agent - string - precious - ugly - phenomenal - hilarious - winning - doe - mobile - farther - chili - landscape - path - someday - complaining - sky - load - baked - stove - bend - en - command - decides - attacks - wished - ac - yearly - weekly - indeed - brief - mike - dealer - emergencies - event - charlotte - slapstick - purely - included - unfair - meaning - injuries - vermont - cornstarch - egg - worrying - wrap - buff - advertisements - plain - chores - mention - allows - novels - bases - billion - protected - workout - cancel - daddy - outdoors - novel - bruce - awfully - constant - spends - accent - deductions - dealt - informed - tournaments - snake - penn - sox - tho - root - rip - combat - polls - sundays - blank - frozen - assistance - ads - hiring - drivers - recession - convert - alternate - dryer - lightning - gr - chair - emotionally - angry - mature - treatment - lousy - seventh - ninth - deck - printed - answers - jumping - mentality - popcorn - shade - oaks - reasonably - budgeting - controlled - british - unreal - mini - performance - tip - ge - handgun - toy - skip - armed - fleas - redo - deposit - goldfish - childhood - removed - surprises - dodge - consulting - sacrifice - placed - sailing - classics - bottle - secretaries - diesel - liter - chosen - boats - returned - item - november - adoption - fewer - pizza - feature - nebraska - cafe - alzheimer - agreed - choosing - council - bermuda - suspense - satisfaction - winters - headed - murphy - customers - habits - norm - loss - bec - crawl - exist - attractive - wor - leg - selection - prob - sources - audience - styles - davis - borrow - goals - determined - accounts - pat - vs - whi - advantages - diapers - pin - models - queen - sticks - mesquite - canal - incredibly - feeding - importance - salvador - fathers - regardless - translation - frustrated - bond - structured - counting - factors - economical - involves - radical - depressed - universities - shall - tank - jesus - counselor - proposal - allowing - pocket - airplane - gangs - saints - consideration - dolls - horses - spouse - midwest - fashioned - screw - curriculum - oakland - candy - blanket - backpack - industrial - smog - canyon - elect - backed - bear - comfort - economically - warmer - sunny - exhausted - afternoons - ranger - worries - orange - physically - experiment - famous - copies - cardboard - pa - demand - polluted - tail - compatible - wordperfect - drag - float - carter - presidential - dug - israelis - relations - arab - rings - estate - salaries - recognition - headline - nowhere - ratings - asia - ei - lifestyle - tenth - preparing - cookies - fifteenth - bait - experienced - defendant - surprise - cocaine - reminds - liquid - destroy - century - admire - rare - tuned - schwartzkopf - reduced - cruel - cheers - picnic - accounting - pace - jane - tune - knees - holy - owe - pepper - worms - bricks - mound - additional - flow - tended - refuse - landfills - stance - cry - dumping - memories - anyplace - geared - arrangements - depth - tuesday - raw - neighborhoods - policemen - net - located - trail - edition - purchases - injury - beliefs - statements - sin - cultural - shorter - guilt - 'false' - economics - enormous - lifetime - advanced - adopt - mechanical - liters - dream - bachelor - nasty - scare - laundry - strikes - quilt - chlorine - shed - whom - ds - convince - courtroom - volleyball - domestic - stomach - concerts - stepfather - typewriter - clouds - rating - gifted - generals - clip - screwed - australia - maine - quarters - chrysler - oldsmobile - pistol - membership - seldom - supply - tornadoes - hu - oth - porch - persian - lakers - tarpley - seattle - thrilled - boards - brian - roughly - paints - attic - ceilings - baths - pig - killer - pros - paris - brooks - dealership - developing - islands - kennedy - ending - ratio - created - separated - lasts - wives - jean - spaghetti - village - biased - operating - enid - crappie - employers - conference - tuna - tole - pollutants - jones - handling - emission - vary - initially - finds - obligation - select - carefully - barrier - strangest - spaniel - blues - comparison - attend - focused - ver - blacks - jurors - floors - spell - wears - heel - wooden - assistants - accustomed - mild - bands - bang - alrighty - campbell - tours - panama - believes - corrupt - cocoa - interestingly - makeup - communism - etcetera - historical - heating - hispanic - bilingual - ultimate - bicycling - elsewhere - scientific - combine - ar - consequences - gal - cure - grader - corporations - stitching - grief - leading - graphics - regards - rank - personalities - mission - whiz - voter - controlling - believed - minded - kyle - author - certified - shelter - historically - protecting - fits - carrots - knitting - professionally - specialty - jars - needlework - robert - regarding - billions - rental - nolan - ruined - searching - taco - mama - relationships - exchange - highways - handicapped - scouting - discouraging - dropping - electricity - stacks - catalytic - muffler - pipe - error - compete - cajun - haul - discussing - kurds - anti - orchestra - needle - ireland - investments - dramatically - drawback - raises - growth - definition - guatemala - receiving - reported - aikman - shoulder - banking - highest - jimmy - jim - cardinals - jamaica - magic - convictions - usage - hamburgers - sporting - muscle - sophisticated - element - occur - designated - depression - covering - tooth - filling - sharp - strawberry - relax - advise - enter - throat - instances - allowance - stronger - debate - literature - shelves - remove - advertised - progress - smith - richard - raped - offense - detail - christians - tore - accomplish - released - loaning - bright - intense - dies - peas - steaks - spicy - conditioner - convenience - drought - cups - nee - russians - yeltsin - thirds - acting - northwest - freeway - curbside - corpus - publicized - mets - memorial - onion - garages - employed - lazy - wrestling - crab - loaded - stationary - coupons - ripped - balances - convict - loving - represent - judgment - pork - wasted - selecting - recover - divide - civic - builds - quicker - translate - churches - slice - discount - swear - nap - centered - vitamins - planes - contractor - drastically - elaborate - continued - decline - uncles - utilities - camera - musicians - musician - condominium - augustine - tolerant - southwest - counselors - mirrors - communicate - worker - medication - powerful - manure - replaced - redone - shotgun - memphis - turtle - supreme - owning - cycle - jay - airline - sir - method - mayonnaise - execution - plea - mower - buttons - campaigns - log - quarterbacks - hamburger - arizona - ignore - bred - indianapolis - envelope - conversion - hail - flooding - spanked - fluid - bay - leather - italy - locations - blew - extensive - traded - transition - kilometers - robbing - kills - cadillacs - randomly - institute - triangle - mercury - volvo - dan - leads - pe - rome - attraction - aunts - latex - texoma - rabbit - audi - methodist - basements - tee - clarinet - walker - massive - stroke - leak - sites - deals - lined - embarrassed - slab - officially - behavior - examples - witness - wishes - unlisted - terminal - modem - poodle - weighs - paul - subscription - chapter - likewise - documents - shoe - miserable - jacket - lax - varies - peach - blows - disco - suicide - bo - downhill - profitable - twenties - official - pressures - image - monies - absentee - senate - ethnic - involve - proven - offenders - afghans - borders - peaceful - ab - blown - lock - adequate - scholarships - offers - bat - injection - useless - revolution - mormon - enforce - cosby - preapproved - fortune - messing - promised - sum - frankly - damn - gravy - boil - remembered - consuming - metropolitan - gift - seeds - factories - layer - costly - usual - cooler - daytime - appearance - sufficient - balcony - chasing - chest - las - plumbing - farming - becau - cleaner - packed - cried - lover - indians - racial - occasional - rivers - pollute - locally - contribution - presentations - laser - represented - guests - apples - hank - closest - oak - missionaries - rob - mailing - ring - bias - newsweek - nicely - tables - zone - faith - cheapest - excuses - fail - administrator - baylor - sued - emotions - appeared - notes - tying - nail - shake - comp - entry - peer - sore - sticky - pudding - knowledgeable - haze - mass - stressed - academy - considerably - rowlett - shortly - nose - ordered - crying - handed - wages - input - praying - warfare - accomplished - woke - regulation - equivalent - bankrupt - jog - ell - ri - appeals - extraordinary - metroplex - absolute - conclusion - accountable - glory - pray - prisoners - bomb - destroyed - testament - pu - suggest - polish - principle - gardener - beets - behave - periods - shrubs - sprinkler - fajitas - describe - release - motorcycle - bound - styrofoam - valuable - tolerate - attempt - jordan - exists - screaming - stump - breathing - selfish - dick - blonde - maximum - max - secret - holds - landscaping - reads - prevalent - galveston - weirdest - joy - nationwide - soda - coin - dukakis - steam - embarrassing - plates - incorporate - deductible - machinery - categories - funded - chairs - recommended - handicap - bowling - meantime - accord - tyler - mosquitoes - booklet - coaches - syria - dinners - holiday - baltic - priorities - recognized - wipe - longest - suburban - delayed - backgrounds - varied - eighth - den - coats - theme - nicest - penney - adjust - hou - toilet - bullet - rapidly - capabilities - hilly - container - layoff - watches - jewelry - maker - infant - resent - blade - watering - wildlife - decorating - fabric - leadership - privilege - exotic - loop - seasoning - chopped - retiring - backseat - par - leukemia - ammunition - barrel - pontiac - mazda - expressway - administer - unions - function - stopping - organize - parenting - schedules - slept - wheels - resource - competing - sees - careers - pits - carpeting - legislature - functional - divorce - bridge - transfer - needlepoint - cookbook - breast - published - portland - throws - counts - larry - louisville - com - glued - tube - slide - protective - felony - dursban - renting - rebuild - london - shingles - lea - stink - puppies - schnauzer - steering - plugs - mechanic - worn - inflation - diving - stretch - purse - introduced - stripped - occupied - siamese - controversy - buick - religiously - allergic - edges - sail - nancy - biographies - nonfiction - thunderstorms - intend - educate - nerve - recordings - concentration - steve - academic - freshman - sophomore - neutered - ponds - disgusting - narrow - comparing - associate - adjusted - cottage - foster - rake - outstanding - appreciated - malpractice - thankful - personnel - selective - administrative - comparable - pier - contributing - cart - explore - commits - affair - cleveland - glasses - downstairs - details - backpacking - blackberries - alternator - antilock - peeves - chris - billy - henry - smooth - polluting - sweats - fever - sweater - wyoming - filmed - guts - respond - theories - database - culturally - threatened - tears - messages - ear - bark - grandpa - versions - lee - wave - analysis - gear - comments - colorful - photography - victims - resolution - stiff - brazil - minister - interpret - hero - lebanon - declare - heritage - escape - columbia - prescriptions - assumption - berkeley - combined - traditionally - relaxation - entering - regulate - consciousness - react - sexual - proved - booze - cloth - herald - instructors - vested - consultant - taxpayer - lethal - restricted - pub - directed - frequent - tempted - hat - treadmill - abilene - hates - skinny - turnout - bouncing - wayne - beforehand - deserves - ninja - expand - probation - eliminated - yogurt - powder - boyfriend - blankets - alarm - vacuum - chop - strips - ruin - knots - bits - rogers - guessing - addicted - pitcher - fingers - rascal - whip - ag - vegas - response - advocate - donate - proposed - emphasis - transit - carpool - map - sheets - punch - calories - strenuous - laboratory - resolve - serves - drum - compact - tigon - initial - moms - identify - respected - vision - visits - eagle - summary - illustrated - dial - extraordinarily - intelligence - stages - troy - injured - increases - joints - dayton - mary - deduct - administrators - pressing - contest - arguing - marked - seek - gross - roberts - mentally - session - failing - occasions - videotape - clever - jerry - mutant - warning - intellectual - approve - declared - hallway - edging - pressed - strawberries - nieces - sour - homemade - trick - mixture - solar - inspection - global - winner - drawn - trace - sympathetic - managing - anchors - sulphur - chuck - overcrowded - stole - dean - steven - bi - thursdays - appear - collapse - dome - flex - stressful - ok - paroled - apt - patient - injustice - farmer - socialized - snap - clay - wintertime - beaches - touching - curb - clippings - flowerbeds - toes - buffer - hardware - republic - battle - heading - units - shadow - yankees - rounded - immigrant - diseases - caesar - saves - nephews - slowed - grounds - snakes - abilities - missiles - nova - pen - digging - drew - pools - strung - port - sticking - orioles - hopes - ov - fertilizer - railroad - rub - robberies - theft - tourist - sta - stood - eligible - freshwater - saltwater - shark - fool - commute - deciding - fam - terrific - catalogs - froze - ethic - controversial - crossed - georgetown - soy - hoi - pasta - dreams - painful - filthy - innocence - leaning - cleared - feasible - perception - lottery - parochial - announced - ll - gallons - kindercare - behavioral - classrooms - merchandise - washer - refrigerators - tinker - supplies - stimulation - alert - furthest - cease - reward - biology - starter - prairie - drill - johnny - experiments - exercised - paneling - tougher - strain - noisy - instill - housework - gap - auditor - dot - maternity - butler - amarillo - mulch - actions - lawsuits - senators - anniversary - bonding - leisure - fertilize - dragging - decorated - statewide - format - skeptical - pad - mode - justify - budgets - seniors - chief - efforts - hispanics - drastic - frost - layoffs - temperatures - airlines - hoses - safer - nails - salads - clients - vans - surely - pulls - operation - sells - bikes - unable - permanently - slight - rifle - impulse - manual - handguns - gauge - someth - youngsters - karate - hotels - demanding - wool - warnings - sanctions - attract - mysteries - tenths - pots - neglected - sliced - leagues - bulls - celtics - struggle - qualify - bars - lucked - cliff - cabins - relaxed - gates - oregon - loads - crystal - fumes - previews - floating - reviews - peaks - poorer - matters - continues - costa - geographic - earthquake - intrigued - ain - albums - singapore - proof - bulb - spayed - fr - skating - robbery - sector - horn - drafting - premeditated - frustration - radiator - boundaries - bureau - belonged - nephew - officers - serger - seam - choral - dating - genuine - requirement - gradually - asians - establish - effectively - reel - ra - steady - produces - switzerland - calm - anthony - suzuki - plymouth - sized - thread - centimeters - recorder - signal - brands - resolved - converted - dumped - spur - trap - yell - smarter - humanities - amherst - sheriff - safely - completed - equally - labs - foam - sociology - entertained - lobster - title - recommendation - residential - vicious - lease - outer - honesty - switching - freezer - tollway - heavier - bahamas - sperry - rollers - mowed - cougar - chi - crooks - lips - remodeled - cocker - eigh - syndrome - overweight - titles - lettuce - gather - span - greenville - drip - senator - dam - zip - lexus - peninsula - counseling - grapevine - parental - branch - travels - atlantic - screening - thr - veterans - substance - golfers - golfer - manually - carbon - disposition - harrison - putt - disability - marry - infants - engaged - braves - mums - provo - boots - commercialized - replacing - moisture - assign - router - saws - translators - alleviate - acquainted - caring - incinerator - receipt - scrub - setup - hazardous - wardrobe - jackets - blouses - suspenseful - graphic - gary - monitoring - hacker - india - desirable - invite - reaction - fantasy - shocking - recorded - addresses - rig - instructions - faced - advances - paperwork - tongue - cha - accommodate - motion - performed - composer - horrendous - beatles - crop - applying - budgeted - coda - seminars - challenging - righty - cave - dragged - conscientious - lenient - warehouse - managers - windy - allergies - flu - inordinately - cinderella - shoulders - progressive - cam - colonial - nicaragua - exception - translations - scream - independence - cope - economies - tropical - consequently - difficulties - plead - disturbed - correlation - movements - athletic - stoned - invested - coincidence - analyze - chip - miracle - fif - kee - inmates - external - civilian - trapped - ghetto - amenities - clutch - disposable - makers - pursue - organ - blast - pluses - racquetball - lobbyists - republicans - outskirts - carpenter - buck - predict - backwards - wok - sweets - ugh - tablespoon - singer - shops - singers - stockings - mirror - crocheting - zucchini - voices - pockets - exhaust - oxides - victimized - cynical - colder - castle - listed - deliberately - spoken - adventure - repeats - imagination - viewing - bench - catcher - bull - corners - dustin - hoffman - kmart - concerning - bulk - accepting - eerie - na - properties - lying - sturdy - logic - dated - slick - separating - talented - raiders - device - macintosh - statistical - sausage - italians - canoe - thrill - honeymoon - arabs - defending - stability - pops - musicals - sends - asks - ringing - versa - opens - offhand - dana - envision - philosophical - charity - volunteering - commentaries - informal - commentary - viewpoint - independently - sections - nope - firmly - forcing - flags - gathered - gett - neil - jagged - awakening - julia - beside - initiated - pole - kidnapping - witnesses - handles - panel - refined - portions - moments - accessible - hollywood - norman - assets - tire - pursued - factory - au - romance - fuels - presentation - closets - hips - rated - publish - protestant - females - crowds - poorly - identified - buys - stuffed - chamber - brass - arrest - productive - ticks - earned - prisoner - reimbursement - spiritual - z - pronounce - riskier - protection - consistently - endless - charles - rebellion - pacifist - curse - unto - spirit - barbara - bombs - tearing - struck - heaven - theaters - northeast - licensed - reducing - peoples - lithuania - damaged - bacon - worm - bug - sprays - bloom - rye - leasing - nightmare - beautifully - washing - nurseries - neglect - mixes - frying - guacamole - disc - populated - cooperation - bundle - nickel - rely - insulation - powers - soldiers - leery - iraqi - germans - safest - appears - whoa - republics - participation - reference - disgusted - hauling - permitted - orientals - excluded - stone - sack - crush - fills - crap - fisher - leap - interact - publicity - brooklyn - idiot - easter - vines - extensively - fou - extras - shootings - knife - outcome - pensacola - fished - interviews - disappointing - overworked - speedy - apathy - juror - ann - appointed - spite - ballot - counter - appetite - technician - complaints - begins - reaching - referred - influences - swayed - award - slips - stranded - bankruptcy - users - socialize - boom - secondary - captured - backward - intellectually - bean - measured - remind - bolt - swung - dryers - extension - hooks - trinity - lasting - hatred - snack - altogether - heal - restore - restored - deeper - strength - link - graders - noticeable - lowering - preferred - remarkably - baroque - barry - townhouse - fertilizing - decade - slower - pl - hop - creates - alternatives - gains - operated - forgetting - detector - deliberate - cycling - legally - bridges - prize - adolescents - gamut - slant - fascinated - baskets - glue - collector - accountant - rides - def - remote - professions - suggesting - crafty - remembers - bears - identical - burns - basket - believer - document - korea - lasted - meatballs - waist - rear - stretching - fold - kroger - linoleum - angle - wo - diverse - buyer - bullets - banning - bargain - breeding - humor - evil - q - illness - peop - oldsmobiles - fiance - bodied - educating - showers - mud - connect - bothering - rebuilding - kuwaiti - possibilities - overcast - cloudy - hurricanes - forecast - ru - therapist - scott - rugs - angel - wheat - editor - caretaker - liking - kiss - inevitably - chat - unhappy - comfortably - litt - variation - protest - fences - samples - messy - affectionate - disabled - barking - production - kelly - corvette - fanatic - towel - firing - coaching - presents - burglar - overcrowding - lane - imprisonment - arrested - asian - wrecked - beauty - olympics - conviction - playground - garth - rs - jam - literary - cre - execute - cartoon - nearby - fundamental - ribbon - bobby - montessori - sofa - fetched - rolled - sewed - starters - crocheted - liberties - nintendo - majoring - associated - threatening - freezes - traction - perspectives - southeast - carp - advertise - pint - merit - durham - meryl - snowed - advisors - terrorism - sectors - joint - terrain - citizenship - melted - ounces - ounce - keys - races - smokers - sensible - bradshaw - hip - af - richmond - sen - readily - consistency - canned - enforcement - contracts - cons - differ - suffer - tool - specialist - flies - confidence - esteem - ironing - inexpensive - slots - buffet - cuisine - congressman - persuaded - minorities - stranger - brush - coastline - blind - cape - dow - partially - calcium - vast - abroad - museum - physician - physicians - redid - erie - cooperative - survival - har - exac - intentionally - affecting - urine - grandkids - agricultural - beam - display - constitution - capitol - ordinary - babysat - aggressive - journalism - grad - tia - olive - collin - casserole - cakes - operas - accents - almo - oprah - tiles - tile - trillions - struggled - tips - tulsa - museums - sailboat - perch - styling - seville - rotten - ken - dentist - maverick - medicare - douglas - leased - insane - madison - dock - subdivision - pouring - wooded - departments - airplanes - pilots - premium - ol - liberty - malls - fossil - produced - bumper - purchasing - gentleman - tribe - wordstar - rinse - santa - broth - thomas - addressed - unconsciously - enchiladas - slickers - rib - lawry - housekeeping - opener - doll - sierra - nuskin - legend - ruben - batteries - drywall - disturbing - relief - devastating - confined - strides - incineration - drums - cement - leaked - presently - semiconductor - firms - foremost - hoods - sample - client - update - predominantly - gory - dancing - inherent - harmed - sneak - invisible - obligated - invariably - supervisors - dentists - chew - randy - understandable - springer - artist - stardom - taylor - synthesis - adapt - pla - labeled - label - attended - manuals - stephen - stimulating - improvements - veterinarian - serial - wrongly - preschoolers - conditioned - detailed - unload - highs - collar - identification - stones - zoo - owens - sandinistas - greedy - kings - roosevelt - bananas - tempting - lessened - performances - greek - plots - sean - statehood - quo - assuming - significantly - woul - ve - occurring - stringent - troubled - resistance - regional - disastrous - practices - alternates - approved - believing - joe - iraqis - habitual - bone - dope - threaten - inventory - bibs - tasted - afghan - quilts - riot - earning - backup - christ - begun - guaranteed - beats - monetary - ne - involving - punishable - instantly - hog - logistics - joining - tutor - doggone - hats - remodeling - allen - cabinets - motivate - inspired - computerized - pers - extremes - willingness - excitement - jacobs - architect - lump - shared - evaluate - exclusive - expanded - tablespoons - ginger - peanuts - sang - choirs - finals - aggravated - okra - ruled - landmark - restrictions - smack - investing - drier - hotter - orlando - adventures - scrap - battery - timing - boeing - alcoholic - sullivan - continuing - ukraine - adjustments - astros - claws - declawed - rushed - stray - void - chase - messes - procedures - underwear - skill - politician - mitch - caddo - prizes - lids - files - tra - questioned - wolf - thunder - howl - buffaloes - honduras - wealth - contributes - wider - soak - installed - converter - authorities - visible - ash - suspected - agencies - mouse - printout - producing - unix - blueberry - hike - overly - baker - assault - restraint - enj - danny - couch - arnold - ridge - gene - clo - unemployed - ahold - dislike - equality - mistaken - aged - quoted - harsh - realizes - upstate - expend - brinkley - complaint - slanted - restricting - halls - wheelchair - supervised - terry - monstrous - drawbacks - fights - learns - fallen - challenged - rewarding - mailed - snowing - ni - wreck - amongst - misery - schwarzenegger - goofy - entered - rationale - prosecutor - excused - bare - lawsuit - audio - teti - eh - lacking - memorable - wisdom - succeed - jokes - frenchman - liability - workmen - executives - marijuana - surface - lengths - fondue - cheddar - watermelon - saucepan - lukewarm - cookbooks - collected - saran - hollow - warming - spa - bathing - incur - institutions - freshmen - sinking - description - graduates - nelson - commerce - recruiting - homemaker - cri - ankle - install - sympathy - burnt - episode - awesome - scandal - grasp - multiple - fonda - tolerance - enforced - lighter - enemies - gentle - avoided - approaches - sheep - grace - reserve - claimed - abusing - borrowing - servants - stops - moist - ass - kin - trimmed - varieties - experimenting - mashed - foo - barbecued - barbecues - marinate - manages - sacks - giant - pact - confused - stepping - seams - michener - blooming - stewart - tim - rebel - grammar - yankee - restriction - biblical - paychecks - request - stable - diego - lush - ga - limb - flooded - strokes - animated - muddy - sharks - quantum - partners - deedee - formula - subtle - solved - tow - bounds - rooting - championship - toronto - ontario - cabbage - cantaloupe - siding - twist - sirens - reminded - affluent - bee - captain - tackle - advancement - isolated - destroying - foggy - regulating - cigarette - linguistics - canadian - payless - cashways - bucket - cereal - maxed - rally - richards - convention - everytime - mar - dairy - doubts - pursuing - flight - crew - oops - misses - amazingly - punished - suited - flexibility - rehabilitate - deduction - debit - executive - requested - implemented - disadvantage - shoddy - naive - moscow - marcos - shoots - blessed - cad - noon - formed - bargains - circuit - dissertation - serviceable - roughing - cots - condo - poles - locks - ob - hearts - passover - seder - catholics - attacking - syrian - bagels - affairs - iranian - ideals - dividend - voluntarily - devote - performing - pipes - arteriosclerosis - nonexistent - torn - outfits - prejudice - invited - remembering - remedial - certification - textured - insides - tone - tornados - exxon - brain - photographer - audit - mainframe - jet - upgraded - baghdad - scheduled - receptacles - continual - potentially - prestige - perceived - trivial - broader - sided - claims - adjustment - tread - richland - discouraged - stepdaughter - sacrificed - possession - castroville - timer - shady - lehrer - editorial - embroidery - envelopes - continuous - typing - claude - aging - attending - trainable - watered - composition - dis - disabilities - intentions - inter - gay - facing - interviewed - seasonal - patch - peculiar - rec - brilliant - invest - payday - buddies - wiped - indoors - fiddle - inspect - peel - hors - impress - ridden - objects - surprisingly - servicemen - teeny - equitable - tier - stair - targets - knocked - accuracy - impressive - cycles - writers - rehabilitated - fleet - drops - quarts - peeve - sa - pregnancy - meets - campsite - specialized - indicated - beings - obnoxious - stereotype - communist - sway - soviets - monetarily - circle - blah - carnival - outs - indication - gigantic - ownership - feeds - latch - pansies - cau - screened - references - tabs - steamed - blueberries - desserts - sandwich - slices - mba - describing - duke - mechanics - secorski - financing - punishments - whack - addiction - '7' - specials - climbing - shells - spectrum - ins - ants - painter - painters - noises - rats - sequel - rocky - stallone - pai - exterior - afterward - greasy - builders - intervention - solving - appliances - fu - hesitant - incorrectly - lizards - bats - evils - refugees - permission - dive - instituted - parked - landry - scope - eagles - cows - orders - tokyo - subway - remorse - heinous - manufacturer - occupation - neal - brushes - manhattan - stud - leftover - coll - rifles - shelf - robbed - temporarily - inconvenient - limitations - spelling - precise - commodore - specifications - belief - aggravates - nev - bites - knox - overheard - rows - frederick - pointed - stu - rusty - reelected - loses - pretend - symptoms - biography - destroys - delicate - speakers - happier - grub - raiser - petroleum - menial - jeff - blink - recommending - diner - streep - copper - explosives - disappear - cosmopolitan - swimmer - vogue - felon - converting - bolts - ross - ro - reject - outfit - automotive - mexicans - envious - risking - shifts - cylinder - gaining - tragic - expressing - expression - chilly - yorker - dall - deny - bonuses - lucrative - congressmen - portray - needing - scallops - susan - protein - gained - baking - academically - kenyon - admissions - sciences - provides - preparation - logical - cage - owed - devastated - despite - pillsbury - surrounding - prosecution - liable - limitation - writes - follows - nash - paso - juice - reusable - procedure - vegetation - bach - delivery - rapes - thou - contemporary - brookhaven - heater - curiosity - fuse - assembly - limestone - danger - ferry - ducks - pilgrimage - annoyance - seniority - ben - partner - executed - healing - darker - diff - routes - touring - footage - abandoned - retain - warped - leslie - mockingbird - tricky - steep - overwhelming - killers - calendar - faculty - bingo - fog - rationing - visas - awareness - howard - repairing - bathrooms - upside - symbol - conception - veteran - daylight - babysitters - valentine - ideally - driveway - digest - danielle - severely - confident - idaho - searched - appointment - givers - pappasito - dillard - expertise - tasty - publisher - reruns - soaps - repaired - theatre - cedar - mainstream - refer - tina - secure - rockets - loo - contacts - carpooling - appalachian - adventurous - hostages - fatal - patients - '2' - sunfish - donated - shepherds - joey - treats - researcher - unnecessary - stucco - payroll - scan - conductors - versed - midway - beard - princess - naked - custom - mount - marshmallows - mommy - committee - allegedly - tap - woodstock - routinely - rod - tuesdays - patterned - czar - donald - booked - intent - granddaughter - chips - sedan - discounts - inn - dent - crib - deliver - schutzhund - alsatian - refused - nola - grapes - marinated - maxima - oahu - conferences - newly - kauai - maui - hunters - concentrated - bakery - hay - sleeve - niro - builder - curtain - spain - crust - intriguing - reimbursed - licenses - physics - reaches - donahue - cruises - nassau - olives - lodge - grandsons - acoustics - waves - uniforms - fancier - mesa - dalmatians - soapdish - mushroom - milwaukee - violin - harpsichord - rumor - disneyworld - thinner - carolyn - risque - saxophone - jodie - hopkins - credibility - barbies - motel - wendy - broncos - chico - troop - warranties - picky - aberdeen - solicitors - autumn - nevada - marlin - operations - exhibit - shuttle - wycliffe - sheltie - particulates - colombo - duties - burner - hometown - permits - contributions - astronomical - attire - blazer - critics - omaha - disturbs - politeness - polite - presumably - conscience - canceled - respects - norms - rang - solicitations - gossipy - obtained - frequency - turf - soliciting - medications - chow - smiling - leash - acts - gin - dispute - reactions - intimidated - alm - inundated - switches - influenced - rhythm - sim - mus - jimi - hendrix - pitiful - promise - simon - qualities - achieve - unexpected - alw - loaned - quota - holler - leeway - pains - wing - coordinated - spelled - skid - counsel - violation - actu - modeling - lyrics - oldies - phil - collins - criticize - suggestions - petting - farms - exit - determination - preservation - ted - teddy - underclass - considerable - watcher - gathering - sexually - justified - territories - capita - carefree - taxing - weak - territorial - resist - attempts - craze - uni - subscribed - tractors - regulated - cal - organic - weaponry - tanks - offender - cured - slave - foul - flipping - shades - acclimated - squares - tapped - jerusalem - fearful - interrupt - interrupted - erase - monterey - jose - ram - supplement - standardized - overtime - amazes - circumstance - summons - conservation - indestructible - littlest - missionary - wrapped - ellen - toyotas - preferences - rag - straw - wallpapering - hoe - vo - tubes - dulles - incoming - eldorado - coun - tenure - evaluation - assigned - flatter - chickens - curry - overextended - compl - housewife - simmer - yarn - demo - ensemble - bas - transmissions - frivolous - sessions - grind - ranges - quits - disconnected - substances - etched - notion - redeeming - grabbing - scrape - por - funniest - rotted - harvest - adaptations - mining - incaviglia - excess - exhibition - da - nightmares - biscuits - echoes - actress - believable - drafted - truman - snider - extend - planet - packing - dumpsters - awakenings - deniro - actors - ser - garp - attacked - ralph - rapid - agreements - forests - polluters - penalize - undergrad - output - sensational - failure - fattening - catered - brownies - crock - downy - delta - cooled - duplicate - clearing - pheasant - genuinely - capability - shield - agenda - coup - briefly - context - governors - irish - reserved - collectors - ole - antique - eights - irate - noticing - solo - shipped - dramatic - grateful - segments - updates - trite - platter - inc - incidences - estimate - walter - cronkite - mold - efficiency - spouses - widely - redskins - lynn - deaths - observe - educators - nother - visual - graded - objectives - principals - passes - poli - interaction - prescribed - breakthrough - fake - fears - web - housewives - awake - reservations - suggestion - genre - innovative - umbrella - annoyed - myth - proportion - generational - exams - gung - essential - pushers - cathy - sassafras - dye - barn - outlets - hollering - dents - scratches - layers - swiss - cauliflower - trays - pans - boiling - vanilla - custard - unsweetened - spoon - freons - officials - disaster - contributor - analyzing - respiratory - powered - desired - trainer - butt - psychological - majors - staggering - hamilton - tracy - protesting - prejudices - dale - willie - summoned - questionnaire - skipped - bail - hebert - mangione - breeze - fairer - regulations - seriousness - darkness - remem - judith - dedicate - owes - domino - insured - backing - risks - devalued - magnitude - taped - breakdown - beep - murderers - murderer - insanity - slap - wrist - merry - reinstated - atrocities - prayer - premature - pushes - offend - ridiculously - bind - identity - bombed - keepers - deducted - offset - owing - giveaway - immigrants - seeking - insects - daffodils - bud - dandelions - plagued - tiller - trie - plum - fescue - dries - greenbelt - cracks - smokey - megahertz - samna - proficient - poison - reused - mash - heights - lone - vicksburg - handful - futuristic - patrick - foggiest - soldier - buckets - tot - immigrate - render - fab - principles - payoff - incinerators - smelled - ozarks - disappeared - tad - tiers - glance - enlightening - nashville - fellows - communicated - catalog - insight - spoke - flounder - padre - aransas - dingy - marriages - becky - squeezed - triple - caribbean - bees - lilac - overhead - static - lumber - juan - irresponsible - bold - carmel - smarts - surf - snappers - snapper - described - aetna - medi - irving - provided - wells - romania - resort - affords - printing - seminar - thaw - payoffs - persuade - judeo - litigious - opponent - underdog - equate - fred - divided - separately - turnover - descent - filet - sole - jerk - therapy - companions - dresser - explained - hush - agrees - aff - drama - at&t - modest - bef - prep - vocational - col - inevitable - atomic - disadvantages - distracted - measurement - arrogant - clientele - jelly - biting - acceptance - fir - overdue - optima - suckers - honored - chevrolet - taurus - recreational - campers - shines - holly - mattresses - elastic - hectic - volunteered - heartbreaking - bargaining - forgive - adamant - moderates - egypt - muslims - palestinians - poem - naps - demonstrations - restless - underlying - dissatisfied - proposing - upbringing - outlook - quilting - amish - acreage - eyed - motivates - vitamin - drilled - extensions - quantities - carson - doses - experimented - chlorinated - rode - nationalities - exam - memorize - readers - scales - grain - matching - explains - semigloss - marks - experiencing - upbeat - connections - dah - seated - alley - uncertainty - hoot - itemize - processors - portable - hewlett - rival - rugged - decks - printers - obsolete - quitting - approximately - martin - achieved - tact - disappointment - trusting - corrected - opted - perjured - barred - script - ironic - witnessed - answered - dependents - mobility - preventative - lung - carrier - filed - pissed - offensive - opinionated - textbooks - forbid - advertisement - cordless - porcelain - sandy - tracks - amateur - sings - contraceptives - luxuries - continually - perennials - arriving - bows - ribbons - designs - bunny - ink - canvas - crewel - decorations - victorian - stiffen - uncommon - compensate - typed - correcting - frustrations - acted - rumors - lebanese - newsmen - chemistry - tw - literacy - jackson - macho - hint - cer - cutbacks - slogan - preserving - trigger - greenhouse - plattsburgh - digital - sane - boost - vacationing - stationed - slope - attach - starving - distant - mideast - bureaucratic - bearing - nightline - eng - centuries - decking - crawling - buds - vine - chops - guest - sucks - tails - '''oeuvres' - cooks - elegant - crumbs - crunchy - bouillon - 20/20 - cord - irritated - luggage - climates - richer - civilized - israeli - jazzercise - ego - exer - leaned - firearm - firearms - twirling - edited - dribble - accidental - resale - trading - strangely - cutlass - semesters - recipients - recipient - pathetic - import - partnership - ambition - disciplined - prenatal - peru - thir - filters - tourists - canadians - panamanians - initiate - concentrating - cellular - awkward - aw - sanitation - kuwaitis - accomplishment - defend - amy - sunshine - hurricane - flood - muggy - royals - pitchers - nat - indicator - lineup - knives - publishing - laptop - search - significance - chains - jonathan - petunias - blooms - stitches - fruits - righ - opportune - tang - inspiring - incomes - ferraro - isaiah - alma - mater - dominant - greed - hud - pit - bounced - installation - stinking - forgets - morally - millionaire - observer - restrict - ancestors - kitchenette - neatest - miniskirts - grandmothers - feminine - marching - bizarre - overboard - gu - neon - tints - condominiums - walt - crummy - flake - woodwork - widespread - worldwide - bow - contrast - vocal - removing - passive - colonies - bury - presence - quietly - whichever - vacant - equity - litters - fin - aquarium - commands - anticipate - resulted - ranches - repentance - mas - olympic - wicked - climbed - stretched - explaining - wayside - combinations - carpets - str - tickled - tinted - carmakers - sporty - miata - authentic - demands - parkway - gabriel - shannon - patriot - mansion - alan - blessing - catnip - bombay - himmy - champion - gloves - devon - curly - mice - associations - haired - qualifications - attracted - irritating - cops - irks - ron - relation - germantown - hondas - skins - errands - pigs - substituting - spoil - butts - experts - markets - hong - kong - tens - conflicts - bangladesh - prevention - barrels - lily - humongous - azaleas - fielder - cubs - pri - aft - kinder - callers - capone - arsenio - flatliners - scheduling - threads - bedspread - lobby - mckinney - spaced - ethical - expenditures - recovery - sitters - reader - authors - scraping - backlash - estes - sensitive - taxpayers - fisherman - soul - lures - hea - propose - reinforcement - exempt - pendulum - applies - flea - skilled - petty - brochures - bussed - african - glen - godfather - sooners - hump - summit - strengthen - meaningful - steamer - sprinkle - skillet - teflon - passion - increasingly - privileges - constitutional - thousandths - motorcycles - eighths - annoys - horizon - tooling - essence - decimal - inherited - fifths - sweatshirts - blouse - programmer - fashions - taiwan - keyboard - unpopular - plumber - sucker - transporting - indifferent - shallow - undo - seeming - kilograms - dates - propaganda - confidently - badge - clipper - steelers - temperament - scoring - warren - proving - arthritis - revenue - scheme - os - wholeheartedly - unknown - capacity - noodles - instincts - lecture - stanford - unlike - academics - cannon - instinct - stereotypical - mac - firepower - mug - antenna - denton - psych - hamsters - smelling - expenditure - dec - diploma - radioactive - packaging - detect - stream - particles - cattle - creeks - alaskan - roam - booster - contagious - scientist - wednesdays - shopper - species - tribes - underpaid - ambience - texture - enthralled - mel - presidents - consultants - persons - sweaty - speaker - subsidy - lies - ano - offenses - housekeeper - hottest - firewheel - salisbury - hams - locking - prosecuting - gettysburg - arena - openness - duplex - fords - carburetor - cap - notch - overlap - dash - vegetarians - cleanliness - vegan - bodies - utilize - coo - hens - ballpark - kicking - getaway - des - vitelle - a&m - oriental - yellowstone - lion - rio - grande - marble - jealous - ruins - objecting - fireman - malicious - compensation - executing - falsely - statistic - meanwhile - storing - internship - cooper - clinic - cardiovascular - rotate - picturesque - biggie - killeen - purebred - virus - affection - caravan - storage - libber - heated - shrubbery - supportive - unacceptable - appalled - reimburse - explorer - middlekauff - stiffer - disneyland - amusement - solely - lafayette - allies - liars - masses - majored - discriminated - valid - lonely - smile - consists - lisa - floods - historian - societies - eater - rewiring - praised - openly - logically - nest - pap - supporter - runner - moth - devastate - mediocre - excel - insist - halloween - toning - dramas - shakespeare - multimillionaire - supervise - imports - inferior - wallet - dwell - po - iguana - br - twentieth - assertive - chewing - freelance - reputable - avenues - smoothly - avenue - classify - spices - tort - riots - methods - textbook - sprayed - wiring - busting - minimal - youngster - manner - fringe - beeper - pill - spraying - heavens - splitting - maturity - cues - nineteenth - velcro - cole - codependency - losses - worlds - representation - roller - maternal - franchise - bones - quickie - resorts - inept - tossed - superior - enthusiastic - stripper - eth - shotguns - vital - mutual - laura - lotion - accumulate - dime - unfinished - toned - treatments - rust - instruction - productivity - wherewithal - indigent - employ - medicaid - desperately - equipped - alto - jerker - christopher - reeves - climb - mastercards - beaver - champions - pines - berries - dutch - shou - cathedral - constructed - rainfall - chased - tossing - peonies - hardy - divorces - drank - tan - sunburn - interfere - fo - custody - bottoms - guidance - flew - jar - eisenhower - bitter - motivational - presidency - leaps - noriega - tunnel - anger - roger - mis - universe - bargained - interviewing - potluck - trump - hyacinths - purply - mugged - paroling - int - avon - spectator - deeply - amou - crepe - pile - toll - dependable - cavalier - squish - drinks - census - pell - vienna - waitresses - ultra - regency - progressing - retrievers - prompt - brisket - reliability - graveyard - submit - reception - watercolor - jan - shanghai - effected - micro - satisfying - preston - broiled - violated - appealed - martha - melodies - speaks - squad - cutback - texasville - breathe - homemakers - dreyfuss - spit - presumed - cra - coordination - irons - perry - stepmother - ambulance - deteriorated - bunk - flan - vinegar - pies - happiest - wheeling - geriatric - cockapoo - rabbits - ignored - earnings - pencil - taller - glorified - sch - eyre - sung - madam - butterfly - puccini - canoeing - receptive - jackie - gymnastics - im - steadily - ronald - brownwood - temple - substantial - les - broadway - orthodontic - verge - orthopedic - silverton - drafter - drawings - unbiased - equals - secretarial - overturned - thelma - louise - tacky - chipped - sledding - ambulatory - reluctantly - adequately - cheryl - hearty - skim - thai - lunches - molestation - releasing - sketch - subscriptions - upright - paddle - appliance - tops - pant - gail - centralized - claus - earns - coit - orchestras - breasts - chill - punk - '101' - rebate - perkins - fluffy - parker - coppell - bleeding - pittosporum - thumper - carney - trailers - eager - signature - whoops - discovery - macaroni - golfing - superbowl - tease - includes - desperate - entitled - dill - suing - semiautomatic - cuddle - legislate - hubbard - screams - competitiveness - mechanically - jesuit - duh - haiti - constituents - ordering - striped - bonham - donna - du - nist - sheet - sergeant - rebuilt - spy - thorough - fame - hydrocarbons - nitrogen - ville - manufacturers - mats - algebra - glossy - pathology - towncar - missions - mat - gut - precaution - kenosha - pianos - commissioners - exemptions - daytona - holder - gloss - exploring - hatchback - abuses - royalty - rehearsals - meg - boise - barbie - radial - lathe - distributor - parakeets - chimney - telecom - bran - piedmont - howse - duncanville - admitted - warriors - marketplace - dunn - bradstreet - vivaldi - boutique - decorative - volume - honeywell - quicken - strengthened - quantity - hinge - cumbersome - qua - transport - makings - seal - entitle - opacity - abouts - forum - ductwork - shave - interchange - ber - scruffy - critic - trivia - sharon - invitation - astounded - effectiveness - insulted - conspiracy - paranoia - surmise - latches - invading - knocking - ritual - introducing - click - occurrences - summed - absenteeism - errand - discrimination - improving - uncertain - suspicious - detectors - hammer - royalties - hideous - militant - objections - absurd - frampton - performer - eclectic - listener - ravi - shankar - spreadsheet - dedication - mardi - gras - straps - convincing - carl - casually - horrifying - litigation - retention - dusty - regulars - texteller - stripe - tipped - pastel - pallet - patent - spin - coul - southbend - variable - intended - workplace - inputs - toured - reich - genesis - bottomed - shoul - devoted - detriment - manipulating - softly - alleged - accuse - exploiting - cuba - starve - hun - ashamed - connery - dwarf - favors - freer - imposed - demanded - natives - representative - undoubtedly - abou - melting - clinging - quebec - mountaineering - implies - fads - institutes - newsletter - orientation - meditation - desks - laborers - keyed - enc - incorporated - predominant - intending - trafficking - aghast - frito - artistic - kits - pinks - kit - lilly - greens - stocking - selections - chapel - percentile - stabilized - illegally - errors - nasa - quaint - mem - supplemental - applaud - competitors - generous - repayment - celebrated - negatives - ind - privately - brutal - hoped - slim - administrating - latter - nickname - customs - defeating - gadgets - bluegrass - pizzas - anderson - predominately - standings - moore - pennant - pirates - appraised - overpriced - longevity - satisfy - resell - editing - availability - prohibit - janitors - endurance - mutually - supervisory - quotas - swampers - laborer - happ - mushrooms - consisted - terr - siren - alarms - jamaican - knitted - granny - moderate - carpentry - candle - contributors - ai - comply - helicopter - sting - nitrous - chemist - unseasonable - ust - nostalgic - calligraphy - tidbits - mcgyver - inventing - baling - washers - junkyard - portraying - invented - attempting - innings - ke - weaned - meows - docile - traumatic - secretive - daisy - hype - mimic - predicting - fictional - swamp - margin - teasing - crosses - dang - dumpster - openings - recycles - imaginable - folded - straightened - reminding - settlement - beaten - ramifications - margaret - thatcher - gandhi - volcanos - rhode - residue - pitted - comeback - nader - volcano - indicates - previously - regulatory - arrows - zoom - calculate - yugo - pricing - dos - pastor - sauces - coleman - sacramento - backpacked - undeveloped - opposition - negotiate - factions - refreshing - reveal - occupy - responding - tunes - jigs - instrumental - mickey - wills - nickelodeon - fl - shenandoah - flimsy - programmers - mentioning - irritates - aspen - contel - demonstrated - surrogacy - crass - nurturing - donation - auction - shelters - bedridden - gals - '''am' - factual - nightly - chancellor - gaps - newscaster - excerpts - rises - choi - assisted - deteriorate - sponsor - caretakers - supplemented - possessions - signing - sectioned - zones - vikings - hart - educator - beg - initiative - administrations - maj - sabbatical - minuscule - referring - hourly - gardened - remotely - shack - broaden - ivy - couches - careless - anybo - oreo - twisted - actresses - kenny - columbus - disrupted - mistrial - chooses - confession - placing - inception - insure - burglars - jacques - lewis - chagrin - ame - preferably - loudly - epileptic - aftermath - snob - broadened - expectations - swore - amphetamines - endangering - hassles - splotches - scratching - dread - hardwood - toothbrush - proclaimed - nicks - breads - chunks - quart - slender - blender - thickens - thickened - thicken - cooling - leaded - endorse - caprice - converters - arguable - lit - meteorological - circulation - lungs - focal - volkswagen - pinned - fulfilling - obligations - belonging - wealthier - adulthood - functioning - monster - wandering - ropes - appreciation - confess - tolerances - pete - arnett - sporadically - impartial - diversity - affiliate - cutesy - beeped - moody - wonderfully - vowed - booklets - recruit - courthouse - strangled - testify - neurotic - crooked - bracelet - instructed - whereabouts - bracket - koontz - bachman - letterman - hologram - pitches - speculative - deregulation - teapot - vaguely - hoover - pennies - nickels - investors - holders - asphalt - charts - kathy - walkman - simmons - rapists - manson - repealed - thousandth - pac - kingdoms - ruler - scriptural - elses - discernment - walters - wiley - communists - assaulted - compensated - medicines - rude - returns - indebted - deli - strings - crabgrass - slimy - tempered - standby - surgeon - pruning - undertaking - irrigation - leafy - remain - flowering - chick - lem - humus - barbe - stoves - flame - grease - tortillas - turkeys - smoked - hickories - spreadsheets - specs - montana - hazards - crash - burlap - coupon - subtract - compost - branches - heed - staunch - withstand - buffers - scuds - provinces - merely - demilitarize - confusing - sucked - incomprehensible - disarm - socialism - boris - nationality - nut - sabine - consequence - wade - camps - kingsley - centennial - canton - dinky - proclamation - mason - dixon - seller - avalon - chilling - wits - characteristics - tuberculosis - wafer - linear - mismanaged - outraged - breyiana - demos - boggles - contaminated - refineries - desires - delaware - caves - fading - anythi - pantry - crushers - hallways - casualties - magnified - tones - questionable - andy - creatures - extends - fork - spills - degrading - spark - probab - hints - stereotypes - romanticize - thugs - beaumont - predictions - barring - substantially - separates - zealous - farmhouse - pumpkins - planter - creosote - landlord - brushing - rose - cantaloupes - cubic - wary - youths - hostilities - judging - burlington - confronted - slit - divisions - rash - monterrey - objective - hamper - grouper - oysters - tiring - canals - grabs - grabbed - dogfish - antibiotics - commuting - deprived - clinics - infections - enrolled - rigid - fined - mills - deceiving - surroundings - paths - motive - motivations - upwards - bundled - doubling - financed - integrity - benefitted - perceive - unfairness - wiser - segment - vengeful - pitifully - massively - respon - represents - speeches - slapped - inflammatory - atrocious - blitz - zoning - wholesaler - turnovers - argentine - microwaves - waxed - flakes - purplish - cubes - sherry - argentinean - sausages - breaded - publications - thesis - disgruntled - cries - replaces - belongings - roaches - overhaul - uniform - discretionary - emotion - hence - fines - documentary - dealings - declaring - dire - squirrelly - miscellaneous - nd - deposited - scurried - skaggs - endangerment - assumes - endanger - endangered - accidentally - suspicion - continents - ingrained - confuse - trans - centimeter - measurements - peanut - kindercares - alphabet - scold - inappropriate - trauma - weath - predictable - inversions - threesome - novice - rut - yo - delightful - ferrari - resembled - satellite - bathed - jacuzzi - wings - fastest - ant - kitchens - dented - refresher - kosher - knishes - mea - unstable - relevant - americanized - hugged - scam - apologize - hug - shiite - poss - wheth - countrymen - wom - implementing - decreasing - finland - selfishness - benefited - mil - flunk - canning - zinc - processed - bogged - distributed - moderately - companion - organs - sally - petite - isometrics - ingestation - plight - surrounded - directing - coed - subbing - calculator - behaved - versatile - applicable - depot - spackling - creamy - similarly - formative - contacting - aptitude - sounding - upkeep - cellar - rents - complexes - nanny - prefabs - enou - scoot - emulate - guru - auditors - packard - matrix - transparencies - outdated - advisor - panhandle - piling - shredded - pessimism - racism - destined - fronts - hippie - texaco - pennzoil - miscarriage - rational - testimony - testifying - paralegal - priors - aggravate - enlightened - niceties - flop - horrified - absence - taxation - flabbergasted - gracious - flops - certificate - explanation - univer - dustbuster - plated - bowls - patty - womb - soothing - repetitious - wilder - eleventh - painless - necessities - harm - magnolias - raking - underground - grasses - blend - macneil - jennings - informative - bureaus - comics - mourning - lace - weave - lacy - draping - batting - anticipating - splurge - deci - typist - damme - bland - widow - dummies - caan - rescuers - submarine - studio - survived - einstein - stepson - literate - honors - lifesaver - framing - hindsight - incidents - outsiders - jesse - complains - threatens - entrepreneur - achievement - clue - sights - transplant - glamorous - uncontrollable - constitute - denial - champlain - resume - technicians - fad - timid - macon - hous - espec - contacted - liquor - repairman - popped - radishes - turnips - loam - intensive - attachment - pickles - unfairly - seasonings - paralyzed - spinal - discrete - seatbelt - arrow - reuse - collects - dorms - perimeter - orthopedist - freak - diane - diver - limping - tights - casts - nautilus - cushion - singled - tighter - lonesome - naw - everyb - imitate - oscars - booth - demographic - judgments - texins - crest - demonstrator - reps - partying - tracking - perpetuate - manpower - coincide - cl - soreness - nighttime - evacuated - winnebago - benefiting - incidence - abundance - creature - aim - shah - felons - unseasonably - comparisons - waning - surviving - diplomacy - eliminating - processes - righteous - filtered - launch - unmet - strife - ray - blatant - fax - proactive - buil - treaty - bully - repay - swallow - evolve - tug - skewed - intersection - trampoline - downs - cy - swept - streak - averages - catches - tigers - strategy - bayless - advised - brunt - rooted - dseg - documentation - floppy - disks - hus - touchy - linda - rossa - teen - boo - livingston - seagull - wro - midland - odessa - practiced - fur - contra - haunt - resentment - laughable - arises - browns - topping - toast - mustard - cucumber - bonanza - meta - rearing - robinson - cylinders - akeem - dominate - reselling - jap - wichita - galen - amrein - snacks - elephant - transferring - fare - veterinarians - wonders - developer - breathed - limiting - cookouts - individuality - frills - fluctuates - tastefully - smashed - organizing - dare - reform - bri - gate - felonies - ima - racist - gripe - gar - width - spreader - lightly - freshly - arthur - waterfront - movers - frames - enamel - spun - descendants - favorable - intervening - advancing - frightened - revolting - upsetting - acquired - creeps - kitten - teacup - frustrates - cheaply - brunch - crook - mock - primaries - workday - chows - guinea - harming - bellies - rubbed - terrified - louder - lid - collie - mechanism - inspected - cheated - fingernails - uninformed - disinterested - honduran - rica - tourism - enabled - policies - engrossed - virgo - elder - ricans - rican - loaner - revival - christianity - revered - pyramid - birthdays - disciplinarian - nutri - stairs - elevator - powerhouse - alway - rehearse - patriots - photo - guards - congested - incarcerating - foreground - snatched - astro - minivan - subaru - ticking - rack - upgrade - retail - campgrounds - bearable - dipper - addict - sportsmanship - describes - strasbourg - missile - bounce - goll - humiliating - chauffeur - valet - condemning - airs - tithe - blessings - foley - croak - critters - turkish - himalayan - patches - paws - lanky - hillside - communicating - swam - supervision - stephanie - keel - tuba - nerves - turntable - dual - processor - edit - layout - preventing - overloaded - mentions - sevren - montgomery - piddly - compressor - prelude - impractical - wharf - colts - seahawks - winners - champs - expansion - attendance - kites - strangers - tasting - arrangement - rewards - interfering - inhumane - overtaken - underwater - intention - philippines - tag - quarterly - incentives - justification - sorting - insurmountable - forestry - trails - emphasized - obtain - cubicles - advent - op - accurately - orchids - dodgers - brat - petrified - circular - terrifies - niece - laughs - exc - negate - rejected - lawlessness - founded - crippled - perpetrators - breath - intake - valleys - pencils - abreast - ethics - scandalous - churchill - dickens - withstood - mindless - pi - sincerely - whew - spreading - petersburg - finest - southwestern - cincinnati - roaring - perpetual - lhasa - scuba - pampered - dinosaur - fires - ventured - dooming - plunked - cooperated - adjusting - decades - valued - downstream - lure - bumble - wasp - squirrels - popularity - isolation - disciplining - spank - isolate - handicraft - dough - ornaments - empties - posted - ruining - kurdish - roseanne - matthew - brando - levinson - follower - marino - keystone - cunningham - tactics - granada - cuban - salinas - terrorist - buried - hyundee - helicopters - stepper - pillow - staring - aqua - blisters - rubber - trashed - dwindling - cooker - cherry - blackening - gumbo - portuguese - ribs - ya - jumbo - initiatives - revolt - obliged - argues - constrained - fools - indoctrinated - millimeters - fractions - fittings - wrench - header - screws - progressively - pullover - smokes - sw - othe - designer - foolish - puzzled - warned - cab - tractor - sixes - diesels - injector - asylum - governmental - antiwar - translated - soapbox - usable - antimetric - sweden - midnight - plains - collapsible - helper - motivator - huff - phenomena - temper - miami - cyclical - oilers - stallworth - swan - oppose - decisive - wrath - constituency - nuggets - meatless - ingredients - hostess - soybeans - proteins - belton - pennsyl - lsats - als - sev - abcs - especiall - affordable - carpools - symbolic - scenario - gunfire - outlaw - abiding - restrictive - concealed - sp - deterrence - weighed - objection - misusing - impose - crackdown - dawn - liners - gerbils - mutts - counted - eel - tiniest - debated - symptom - furnish - nonsense - handicrafts - awarding - topsy - turvy - worldly - sparked - reg - flours - dublin - bulldozers - overflow - posters - chained - tabby - rampant - girlfriends - inadequate - '8088' - monitors - respectable - secondly - binary - calibrated - qualification - brackets - rescue - passport - mou - alcoholics - returning - laurie - clout - grilled - buffets - brunches - woodland - colo - prix - seagal - starred - premise - preoccupation - belly - millimeter - darndest - assembled - hauled - fertilizers - prohibited - facets - denied - loaf - dawned - boulders - marbles - duck - shish - odor - boneless - scrambled - armenian - consume - punishing - devil - suffered - agreeing - enforcing - burglaries - rationalize - busiest - airy - wires - compartment - soldered - restrain - overeat - pastas - minerals - accepts - supplements - toledo - oriole - steeper - moines - bleachers - collapsed - herbs - sill - appleseed - pecans - wes - enterprise - bulletin - electrician - terminology - gaithersburg - valedictorian - pushy - seemingly - rockies - carries - yells - breezed - solicit - coworkers - alright - humans - bust - holdup - underst - convicting - restoring - ankles - landscaped - sal - continuance - pensions - allergy - baxter - ceo - homa - rallies - anaerobic - improves - ls - adverse - hunk - pulse - resting - mirrored - fireplace - tucked - condos - abandon - dennis - distributing - refuses - glove - pricey - passenger - lowered - questioning - dummy - mans - occupations - norma - techniques - karen - spotted - incompetent - exper - priest - kindergartners - conform - creativity - manners - mannerisms - establishment - norfork - farthest - charleston - hairs - follicles - rehab - fro - weddings - graduation - med - saudis - thieves - chaos - promotion - unconditional - offspring - quotes - dumps - bluebonnets - absorb - es - flash - medina - salty - beirut - penalized - lining - faucets - repainting - arrange - tripping - ingest - ingesting - arteries - reacts - framers - framed - viable - supports - viewpoints - delay - nevertheless - allocation - infrastructure - expended - restock - twen - spider - marigolds - impatiens - replacement - teased - bacillus - gypsy - toddlers - recommendations - skits - attachments - slacked - contributed - bombarded - mrs - cleaver - senses - romantic - illiterate - paced - ridged - totaled - hesitate - technologies - stacked - renters - counties - citibank - scams - swayze - clyde - drummer - scratched - demographics - companionship - dependency - everyth - prospective - pairs - unsupervised - morton - lu - offended - drinker - measures - lions - arapaho - drool - yuppie - cheat - reinforced - fashion - defrosting - pilaf - mixing - mushy - korean - auxiliary - curriculums - kathleen - accordingly - residency - sportswise - blitzer - fanny - treadmills - cinema - dripping - shorted - enlarge - valves - shingle - fixtures - detached - stigma - pioneers - households - beepers - bulky - vibrates - hepatitis - freed - expectation - boyfriends - homeowners - existence - anguish - charming - weathered - leveled - wallpapered - conserving - diagnosed - inspiration - alerted - swimmers - extracurricular - loser - sats - barber - verses - robber - dachshunds - spaniels - anthropology - presses - clerical - forthcoming - homecoming - famil - familiarized - virgin - qui - divine - skates - cot - shove - nannies - objectivity - digressing - ordinarily - weirder - revolved - hatchery - intimate - calendars - decoration - passage - continuity - percentages - cavaliers - ewing - highlights - patience - bethesda - beijing - pooling - restful - pends - dells - starring - rage - terminator - twists - treble - mackerel - pike - stung - fleetwood - displayed - freaks - backs - buicks - convertible - vintage - setter - feathers - conducted - ethically - patrol - kidnapped - pun - exceedingly - albany - syracuse - rapist - investigation - pamper - waits - assistantship - newlyweds - hopping - annually - journals - figurines - sanded - 4h - refinish - hormones - lip - fender - sparingly - lime - sands - upscale - gum - rips - shreds - sponge - mate - averaged - harvard - successfully - approaching - nutrition - conductor - cringe - mcneil - criticism - palo - columns - candles - psycho - deadly - uneasy - robocop - molly - savage - resented - retrospect - juggling - density - crucial - oft - lame - assaulting - pleading - psychiatrist - psychiatrists - psychotics - assaults - sponsors - rainier - snowy - immune - tawakoni - cones - fearless - enclosed - roofs - sizes - cei - furnace - ambitious - poking - fountains - latitude - underpass - hiding - petals - slows - oscar - durant - alo - notorious - settles - smoker - sponsored - educations - ele - approached - proponent - thus - endeavor - wri - fingerprints - slipped - fingerprinted - astounding - intervals - contracted - dea - imm - soaking - visitors - rug - daddies - conformist - revolutionary - kramer - celebration - feeder - nets - minnow - burping - purina - parade - compound - pursuit - refuted - refute - turnouts - vi - relates - regain - moats - staubach - encountered - unrealistic - landon - portrayed - josey - clint - jot - baptist - reflection - damages - shortage - clerks - doubled - smallest - pavilion - fuses - alter - sensing - bandit - theatres - ellison - activist - photographs - hyacinth - hollies - spike - perennial - gomphrena - repeating - minimize - ornamental - happiness - acquire - congratulations - simpler - circles - wham - forgiving - detrimental - immature - maple - myrtles - screwing - disguise - formatting - paragraph - voyager - crank - pepsi - mcmahon - racking - recharged - seabrook - nucleus - billed - mints - adaptation - crown - lunchtime - celebrate - incident - shreveport - limbo - diaper - chassis - bent - soapies - bichon - frise - personable - rin - tervurien - latchkey - considerations - sunroom - rambler - sandstone - beltway - adored - surrendering - cooperate - allah - sakes - stirring - pineapple - oatmeal - casseroles - bronze - catherine - nissans - escort - trusted - insurances - provider - postal - recourse - invades - complained - susceptible - newhart - comedians - contrary - bart - simpson - morocco - continent - ripping - photos - reef - melbourne - squirrel - agents - hockey - christi - diverted - pea - fiasco - liver - caution - expediency - misplaced - technicalities - technicality - ruffle - conducive - sandwiches - vendors - pins - ligaments - beethoven - mozart - softer - banned - regime - liberalization - civics - dart - wasteful - wounded - mcmurtry - trashy - grou - grouchy - projectionist - subtitles - intuitive - footnotes - footnote - operator - lands - appetizers - premed - specialize - matinee - cocoon - alien - maintained - sharif - oddly - exceed - incapacitated - images - dangerfield - stacking - leftovers - catering - scooped - amelia - anyth - wolfe - myths - haggard - phonetics - relearning - wheelers - transaction - checkup - reserves - cranky - measuring - coating - cognitive - jour - austen - reviewed - attracts - grandchild - congealed - soprano - canoed - cancun - bummer - teenaged - manhood - ostracized - liken - pear - daytimes - ransom - sightseeing - gubernatorial - robb - receipts - gambling - sedentary - tortilla - picante - grated - jell - timely - subjected - athletics - bathe - commercially - accordion - miserables - milkman - travis - phantom - lloyd - listens - illnesses - diligent - invaluable - scotland - jaw - periodically - durango - jeep - destin - jetty - draftsman - roman - recognizes - regarded - mediation - crises - bystander - awe - prac - gannan - valerie - addicts - sayings - possi - restrooms - festival - alpine - uneven - sleds - knob - mows - mulched - presbyterian - willingly - littler - strategies - rapport - walnut - impersonal - hack - cheerful - emily - dell - preschools - pediatrician - dane - tangent - backfire - ethiopian - venison - fries - waitress - waiter - attentive - adventuresome - heyday - bernie - dra - assortment - piled - veal - evident - unleaded - ambivalent - clothe - rehabilitating - confessed - amendment - xeros - quartet - technique - carols - mechanisms - decompose - murray - sorted - dimes - crusher - renewed - prostate - antigen - fourths - smells - spinner - baits - fisherwoman - imitation - sticker - sn - pantsuit - pantsuits - enthusiasm - begging - fitting - harold - taft - milder - gimmicks - hemorrhaging - mennonite - sealer - premier - landed - suites - invalid - invalids - labels - frugal - substituted - legacy - reside - partial - yuck - balloting - sibling - colds - discontinued - primitive - tulips - hazard - codes - zenith - ques - slides - purity - richie - bushel - wines - napa - ronnie - whittle - satire - monotonous - menus - frankenstein - blazing - saddles - grants - hitler - paintings - specimen - fussing - presume - pollu - decorate - kindergartner - arguably - cradle - grave - fluff - swings - queens - beltline - thrus - aerosol - corny - fridays - camry - elway - moneys - exponentially - crawls - grieve - greg - foresee - uninsured - noses - rudman - accountability - proportionally - gruesome - couscous - repercussions - wimpy - shortened - befitting - nece - asset - flushed - dressy - slack - sl - tro - bidness - apiece - smokeys - sur - outlawed - legislating - creating - activated - steinbeck - grizzly - encounters - doubting - doug - ranked - sierras - rai - tempe - yelling - explored - bogey - burgled - plop - pee - ay - handyman - tighten - loopholes - withhold - advantageous - bueno - librarian - coma - seasick - minnows - seas - fore - calico - yaupon - labrador - wax - scalp - salsa - hidden - continuously - hibiscus - wetter - mitsubishi - '90210' - nicole - matlock - charlene - beverly - shred - pierre - recognizing - cinematography - invasions - premises - '911' - sitcoms - misbehaving - faces - censor - morality - jumps - finite - infinite - whining - panels - resurfaced - cimarron - jeopardizing - retirees - ladder - investigative - catastrophes - existed - halogen - sulfur - combustion - hitch - moynihan - skillman - lynch - chil - amnesty - abstinence - crayon - detest - ph - allante - peppy - saddle - inca - dub - regiment - twisters - toe - prone - adjustable - conspired - premiums - reasonableness - parkland - losers - witt - greave - wins - dilemma - reallowed - implement - unsmashed - crazies - fabricating - sampling - steele - youn - upsets - magnetic - resonance - sober - molesting - boar - constraints - betcha - severity - entitlements - reductions - defaults - blackman - manned - dealerships - purrs - feeders - frontier - jetsons - nearest - trough - sli - howatch - birmingham - disregard - darned - greenery - tahoe - skidding - surveyors - tracer - '486' - measles - crunch - burger - cameroon - scoutmaster - sitcom - seato - colony - nato - disbanded - arrive - uncooked - overdone - yummy - bendix - pontiacs - hattiesburg - bir - boa - constrictor - parrot - overspending - coughing - julio - misuse - sniff - milan - anchoring - tedious - stragglers - tobogganing - baggy - reduction - hewett - scaffolds - excessive - rep - disappoints - nairobi - safari - wesley - hospice - theoretically - mishap - electoral - stew - hardaway - dioxide - vapor - aye - pickings - legitimately - sails - bisquick - lopsided - boarding - freezers - genealogy - stash - proliferates - brokers - patterson - subsidized - amway - nonpolluting - bicycles - bullheads - nikki - jig - stroll - ogden - puzzles - combo - airless - scroll - dolphin - torpedo - malamute - trillion - ludicrous - payers - column - dumbbells - controllers - harrisville - specialties - virtue - accrued - transfusion - refund - pup - patron - parenthesis - earmarked - greatful - striper - senegalese - perks - parkinson - industrialized - truer - dispose - mega - tonnage - scrubber - ammonia - compounds - acids - thickness - pronto - finalization - utmost - cognizitive - scarves - uns - unseasonal - sleeves - sweatpants - corduroy - compliments - skorts - nominated - dud - recurring - fami - overreact - terror - cohill - cohi - drivel - eldon - housepainter - extracts - overtly - uncontrolled - pirated - ominous - thief - westerner - lunatic - violate - socia - jehovah - mormons - intrusive - solicited - invasive - soli - intruded - defining - surmised - incorrect - unsolicited - nonsol - unconscious - cli - sequence - peddling - harassment - generated - lois - intimidating - rver - greeting - stake - mitzi - yip - ranging - soaked - rhyme - ruckus - parallels - cov - hooker - absolu - phenomenon - brazilian - listenable - elec - acoustic - interchangeably - folk - arranger - sitar - muted - existing - tally - slush - stocks - expired - pleasures - albridge - slogans - outlooks - haggerty - spookier - pecially - airways - focusing - taj - mahals - prolongs - whim - deserved - prevents - mopping - odds - unair - facial - beards - skids - repack - buttoned - starched - suspenders - reorganization - cruddy - reall - notre - dame - explosion - untypically - accumulation - flatlands - zeppelin - floyd - brash - bump - bohemian - rhapsody - pumped - siskel - ebert - thumbs - travolta - quee - tokens - divi - showbiz - admission - scyene - inexpensively - sao - paulo - usefulness - spheres - spaniards - rulers - conquistadors - socialistic - horribly - dishonor - defenses - sabotaged - peasant - exploitation - exerts - export - broadcasting - ruddy - minist - wr - ler - interpretations - histories - copes - indicate - resident - fledged - barefoot - pejorative - unrest - citizenry - ignorance - ult - constitutionally - creole - prohibitions - strengths - cuisines - throes - reassess - functionally - fractiousness - faddish - wellness - biweekly - dispensed - distinctions - dev - fizzled - acupuncture - gestalt - irony - cert - vigorous - carbohydrates - kinesiology - calc - calculated - calisthenics - myerson - frantic - astonishing - mortars - formulated - sociopathic - pronounced - unfit - mouthed - transcribing - customized - anne - glenn - improvise - concentrates - password - verbal - rowing - lution - rower - transforms - markov - naval - postgraduate - civilians - mainline - respondent - unders - allergist - smorgasbord - compensatory - profile - bonds - deducting - disproportionate - brutally - commuted - delays - electrocution - determent - deter - dubious - internally - organiz - coordinating - scandals - kisha - knight - pullman - exacerbate - clutches - pads - benz - absorbed - keyboards - spaghettis - lasagnas - hor - horseback - dabbled - banjo - druther - stre - farts - polly - followers - inspir - booths - commutiv - billboards - bartman - simpsons - debbie - nigh - appraisers - onward - ease - folds - performs - tenured - microcomputer - comprehensive - rigamarole - teachable - specially - spicier - tofu - pistachios - pistachio - bumped - curried - saute - gigs - perse - ow - conventions - slippers - teller - alterations - utilitarian - knickknacks - sconces - jalapeno - almanac - concluding - warms - shutting - piloting - spectacle - lobbyist - legislators - individ - unbelieving - justifiable - nucle - kilowatt - washes - stinging - swelter - lively - eureka - rentals - inspires - glider - welder - treks - '747' - mindlessly - pacifier - reme - destructed - milton - berle - stepchild - tumultuous - regions - siberia - oppression - attentions - hopely - catchers - gladly - unheard - babe - ruth - thru - lovingest - cosmo - pellet - tod - lovey - dovey - kneading - trimming - bonzo - poindexter - felix - tortoise - possessive - bedtime - rendering - jessica - tandy - warmth - manhunt - manhunter - dysfunction - slay - toothpicks - outwardly - awfulness - wonderfulness - lapses - telecommunications - profits - waivers - earners - physicals - subsist - lodges - moss - footing - alumi - defrays - defray - unfold - walmart - discourages - catatonic - discovers - buzzards - pal - imagined - slaughter - earthquakes - robby - graze - indira - observed - attleboro - freeways - jets - swinging - kerosene - eah - boilerhouse - powerhouses - belch - kodak - smokestack - phosphorous - grenades - photograph - overstated - environmentalists - claiming - automakers - soot - particulate - meter - tailpipe - devise - mufflers - resumes - graph - erased - simplified - anduille - doughnuts - cobbler - fudge - fiber - sloughs - rafting - potty - packs - noth - outfitter - headwaters - damper - hostage - rhetoric - rolm - engi - sheer - estimated - doctrine - turks - cheering - reconcile - divisive - unprecedented - authorize - frontal - sununu - commend - scud - lefty - frizzell - galway - harpist - bagpipes - whistle - violins - instrumentals - rooney - dancer - entertainer - eddy - smiley - burnette - raspy - playboys - ernest - tubbs - rector - scratchy - opry - stadler - autry - anymo - vegetate - fri - relly - complication - eith - demolishing - stereos - annoy - troubleshooting - initials - conversed - sexes - consist - childbearing - storly - var - biological - urges - encumbered - heirs - characterized - acquaintances - terming - emerging - marathon - idear - discrepancies - overview - encapsulated - introductory - glamour - updated - airspace - huntley - analyst - paragraphs - noontime - dose - spee - fastened - wander - aides - debilitated - arboretum - maid - tackles - spinning - irvin - overwork - reinjuring - scab - revamped - metcalf - smuggled - investigated - rehi - renamed - psychologists - ration - modalities - learner - kinesthetic - gladewater - baccalaureate - unle - commentator - golsome - superintendent - adminis - scarce - overachievers - overachiever - beeps - expre - phoe - easiest - horizons - hurtling - brothers' - clips - madly - fetish - luring - costuming - remarked - thriller - distinguished - terrorized - branching - vito - flicks - bawled - toughest - venue - disrup - sequestered - entrapment - displeasure - waive - bungling - caricature - bloodless - comic - functions - thrash - fixes - climactic - joseph - reborn - targeted - hypercritical - fart - gags - slapsti - funniness - gag - retreading - tec - preemployment - brazen - wisened - ventilated - motorola - tack - orangish - feat - brighter - coloring - haphazard - baseboards - edger - granary - stocked - formulas - perfectionist - tasks - freehand - gratin - banana - dissipate - thickening - globs - rubbery - blenders - cools - favoring - nestle - quik - groedy - whisk - beater - melon - baler - cond - octane - generating - volt - v8s - repellent - erupted - meteorologists - chernobyl - tracers - smoky - array - fiero - undisciplined - jacuzzis - abdominals - thighs - mattered - alienated - suffocating - choke - differing - grads - quirks - academies - cadets - espouse - anglo - saxon - inveterate - switcher - dave - wylie - pumping - weatherman - hansen - gordon - lightfoot - winston - headphones - toweling - investigator - tailing - socialite - extradited - levy - uplifting - interpreting - jur - gui - overcrowd - connects - businessmen - sente - penned - duff - penal - beca - litigating - respo - spiritually - begats - durn - kratz - kranz - hedges - nathaniel - hawthorne - storybooks - woe - glossary - krantz - twilight - bogused - fuck - dares - hangover - sarcastic - fishbone - spirited - venezuela - avalanche - gobs - inflated - beneath - captures - resulting - risky - contain - vague - guaranty - guarantees - guaranties - disasters - vulnerability - regul - workup - incline - unjust - revoke - reverked - revoked - vengeance - sayeth - mao - tse - chung - temples - unified - humbly - sovereignly - rebuke - ager - preface - admonition - agrarian - commander - conceal - napalm - gro - clayton - uproots - residents - deba - servant - repaid - granddaddy - dodger - militia - bologna - alleviating - afresh - lifestyles - cabbages - broccolis - insecticides - dandelion - roly - poly - slug - dragons - sockets - alkaline - stem - peaches - silt - shrivels - mes - cottonwoods - irr - smartest - gardenias - revitalizing - mayb - chopping - blasted - hybrid - editions - spruce - dips - dipping - arabic - pita - eggplant - marinating - hickory - clones - mach - databases - searches - deleting - pieced - bypass - monochrome - enthusiasts - nathan - swollen - manuscripts - composts - nurserymen - goop - doorknob - compress - mugs - expressions - ungodly - expansionism - nationalistic - succ - origins - angolan - sinai - warsaw - militory - indu - chan - clobber - conquered - autonomists - shortages - bulgaria - czechoslovakia - placate - alienate - emancipated - slaves - emancipate - supplied - battleground - val - verde - briefcase - bookcase - armageddon - grove - imposing - yoakum - trilogy - terrifying - '''brien' - crappy - jakes - compendium - lobbying - emancimation - afterthought - luted - honorary - isaac - asimov - robot - developmental - blockbuster - mist - dune - freeman - debating - suave - charac - egalitarian - scripture - disciples - wafers - contradict - buyers - elma - sheds - pasadena - refinery - phoenixville - grumble - northwestern - piped - almetco - pantr - deanne - multipurpose - vide - launched - groupings - gentlem - dyke - griffith - idn - brave - shallows - gig - naughty - murky - spectrums - abso - feldon - madonna - lamar - gators - sneaky - buckner - stadiums - cornell - redwings - peewee - crude - tilled - screeching - acorn - scents - pollinate - yield - tiered - shrub - locus - thorns - pollination - pollinated - littleton - trucked - shovel - pressurized - chainsaw - dusk - unfeeling - spreads - datsun - ku - klux - klan - incumbents - larou - larouche - chord - mayport - brim - snagging - owl - baiting - oyster - cracker - trophies - rockport - netted - ugliest - archaic - dots - croaking - croaker - friendships - copayment - seclor - exemplary - snatch - impressions - inspections - yellowish - misty - emphysema - isolating - biker - vowel - lint - phrase - cub - smash - conv - ding - dongs - guathier - eliminates - briberies - sidedness - lengthy - judo - hoc - deltaing - disagreement - wapner - judean - vibrant - undoable - semitic - predetermined - wandered - defeated - astaire - sto - plank - poultry - empenadas - eu - scallions - sesa - slivers - overcook - dashes - ketchup - bishu - meats - empanadas - bun - niokes - requi - bah - humbug - fives - phony - interdisciplinary - dispelled - grating - reputations - impaired - institutional - quiche - growls - overrun - hussy - settlements - poll - tiddlywinks - volumes - ignorant - ironsides - affixing - chart - commingle - confusion - issuer - conven - shucks - profitability - shifted - itemized - alpha - beta - accusation - linemen - rotation - thereafter - proves - encouragement - chemists - overinflate - southward - nonconventional - warheads - parallel - resolves - negotiations - inhabiting - lith - neutral - crazier - libya - treaties - overthrow - survives - inhabitants - dancers - outweigh - wayward - attained - sharpness - acuity - disorient - decimeter - superpowers - toddler - indoctrinate - understa - skipping - lows - chillier - handicappers - mosey - twosome - mellowed - doubles - rationalizing - purged - goofed - nastier - cashed - burgeoning - metropolis - carey - thes - intern - sanger - harris - lifelong - thunderbird - citation - mazaratti - conceive - degray - stutters - antennas - roadside - cords - heaters - hookups - sopping - dialect - hums - nuns - trin - shun - hospitalized - pumps - stimul - flipper - retraining - stagnant - sores - golan - kishkes - matzi - goyim - pocketful - heston - commandments - grips - muslim - religions - sects - protestants - lennon - zionist - nosed - tampa - scariest - coincidently - lox - generic - predates - jihads - toge - secretly - unity - revert - baltics - forcibly - impossibility - insightful - prays - dissimilar - forefathers - esc - disseminated - giv - postpones - juniors - disgust - centeredness - inability - multicultural - multiracial - psychologist - refers - preoccupied - infor - cults - motorbike - maureen - solomon - eastland - farmed - millennium - hopeless - ideology - eden - distributorship - supplier - dirkson - extansion - dirk - pearson - embarked - isometric - chlorination - firsthand - detectives - hunky - dory - gi - barbados - colleagues - covert - suburbia - roasted - goat - hating - stunts - bending - alleviates - indicative - handcuffed - elem - escalated - bett - reemphasis - rote - spitted - memorizer - wiping - mennonites - electronically - determines - sherwin - molding - bled - spackle - lighting - nerdy - garfunkel - fascination - innate - supp - manilow - badness - behinds - pajamas - yardage - enclose - fanatically - subcontract - ducts - materialistic - dwelling - necess - branched - dishwasher - inventions - trashing - diskette - ordeal - configured - prestigious - innova - innovation - audits - pry - peripherals - lance - restraints - thermal - razzle - dazzle - flats - clairon - rath - educa - feast - waking - tentatively - receptacle - raisers - distribute - disposables - incremental - fiery - luther - galvanized - bashing - environmentalist - respons - glow - wartime - overlook - affirmative - junkyards - testimonies - defendants - legalistic - achieving - likelihood - tilted - sleaze - protects - choreographed - patents - antic - repeater - vendetta - observing - proceedings - weightless - effortless - sweatless - surveys - adjusters - expressed - meningitis - fetal - terminated - termination - codependents - goddess - observations - firemen - overtones - astonished - phys - cokes - sternness - forbi - expressways - patricia - handlebars - rewarded - dubbed - booger - diamonds - numbered - redeem - attache - suitcases - lamps - wheelbarrows - mixer - toaster - waffle - clocks - candlesticks - aloud - fussy - babbly - druthers - rockville - ballady - abortions - pregnancies - handing - landscapers - replant - alleys - cultivate - replenished - subside - prune - hosted - correspondents - translating - masks - typeface - piddley - braunsfel - unread - skimming - imperialism - reasserting - hangings - needlepointed - outlined - intricate - geometric - upholster - stiffened - streamers - stiffener - quilted - stamp - foresaw - refrain - expedite - franc - francs - diem - consternation - godfrey - goodies - prin - perforated - metrics - typos - retyping - retypes - encyclopedia - prints - limi - clone - bleep - lionheart - singular - superstar - norris - deserts - bates - floats - animation - retitled - reshot - rout - cosmic - enlightenment - dichotomy - educatable - prodigies - precocious - harks - schoolwork - construct - convey - verbally - stressing - penalizing - eternity - bradley - activists - demonstrating - agreeable - gerrymandered - lipscomb - disservice - pauken - politicking - upmanship - fooled - nationally - applicants - dissolved - shutdown - mathematics - outgo - kidney - positives - spe - sadder - anxieties - detected - dismissal - pard - certainty - handcraft - wreaths - eucalyptus - dowels - goofs - bulch - straying - koala - shapes - wintered - transplanting - leafed - pasture - jungles - rubs - validity - disagrees - guessed - lux - accom - transcontinental - throats - coalition - armaments - congressional - fuss - shiites - fiddling - shaped - topsoil - herb - rollback - spurts - loppers - rotor - dethatch - heave - ingredient - shrip - fettucini - straightens - disconnect - sucking - depended - peeled - chestnuts - burgundy - browned - bruises - retires - swivels - collisions - automation - iaccoca - airbags - sc - spine - harness - nifty - chryslers - aerodynamic - conveyor - magnet - pennsylvanians - brownie - pamphlet - slicks - slot - poundage - instant - wisely - shboom - befriended - ironically - resumed - gymnasium - flooring - chrome - height - pounding - engineered - curbs - gravity - singles - assorted - immobilized - screamed - climbers - limp - matches - ammn - amm - initi - initiation - mishandle - guiding - deregister - tumbling - themself - banding - pis - julie - tense - bundles - childish - kazoo - numb - suffices - rela - weakness - weaknesses - experi - temporaries - retest - retested - rx7 - whatso - seater - narrowed - assessment - thirsty - stint - wanderlust - poker - admiration - miners - roadsides - harvey - uneducated - flaunting - relinquished - strikers - speeded - aerobically - calmed - postnatal - cise - birthing - axle - windstorm - overlooking - embankment - arkan - sweeping - tows - beavers - flee - attitu - flaunt - americanism - slums - coops - inoculation - hungary - requesting - rotely - panamanian - quieted - anticommunist - excesses - playtex - flowery - jaded - comforts - thorn - bureaucratics - dyed - pollen - gah - blowy - rebellions - massacred - protested - diminishing - renegade - launching - strifes - defect - obtaining - globally - demise - glasnost - escalate - reins - intentioned - conveniences - nonfeeling - uphold - unpopularity - geez - honorable - massad - madman - straddle - personalties - rethinking - gesture - miscalculated - liberate - underestimated - miscalculation - huss - assassinate - staking - precedent - bullies - powdered - bombing - khomeini - normalized - sanc - juggle - friction - bookkeeping - earner - kite - idling - spooky - lat - tracing - hitter - shorten - saberhagen - crain - craning - reds - stri - fouls - steinbrenner - bogus - workable - peripheral - notebook - modems - revise - furnishes - deadline - courier - magee - peretti - piercing - fic - soun - illu - illusions - quintupled - flied - nailed - gibbons - exempts - planters - shedding - proj - beau - insi - sunlight - sulked - overmilitarization - disparity - civilization - bigge - trickle - hemisphere - kingsport - masala - sweeter - amaretta - dijon - basil - turgeon - laroute - gastro - lamink - restructured - hardships - subcultures - debates - patronizing - demeaning - midwife - pater - paternity - troit - misunderstood - ranks - aines - peak - olajuwon - dunk - businessman - murchison - bottomless - leanings - assholes - reaganomics - nonexempt - visitations - shuts - hunts - wan - degreed - jenny - outdoorsie - twix - braniff - gossip - hound - host - pause - mic - '''clo' - participators - primal - kicks - tabloids - journalistic - fondly - steeped - repu - unnecessarily - glancing - nod - tonic - unhooking - uncoupling - rotating - rotated - dieting - ourself - wrapping - kip - centrally - sickness - folder - emphasize - miniskirt - evoke - overdo - laces - flounces - adornment - unprofessional - sexist - tailored - vulgar - redford - lewisburg - emblems - grotesque - imag - shoo - padlock - pawn - someway - neatness - psychiatric - hinkleys - accidently - distinguishable - barbed - curi - prayed - reestablish - lengthways - mounds - clumps - southw - slapping - formidable - adcose - exaggeration - harmful - structural - hankering - tick - excalibur - newmarket - edmunds - barnyard - treacherous - journey - climbs - creation - touristing - asbestos - repaint - roughed - energized - bids - bleed - caulk - masonite - bid - varnished - intervene - toppling - descend - latinos - mee - meek - europeans - vocalism - comparably - bitch - moan - compromise - dependence - cartels - mistreating - slovak - catacombs - persecution - idi - amin - oopsy - pood - greets - recouped - evi - burial - countenance - uncanny - litterbox - anointed - buzzer - cheerleaders - courage - cheerleader - precincts - precinct - harmfulness - heroin - forefront - estimation - demolish - cur - tract - scaredy - straits - quieter - comfy - husb - prance - paw - lovable - lapdogs - cockatoos - squawking - som - cower - akita - aq - padding - chewed - wiper - blades - tinkering - rightly - punctured - patched - restores - feminist - amer - undoing - stains - altar - spooked - butterflies - dee - nicaraguan - housed - spiders - repent - evangelical - surpassing - override - rejoice - borrower - bondage - squatters - witchcraft - mayans - incas - worshipped - pyramids - sacrifices - gods - oppressed - warehouses - cumulative - itemizing - scrimp - walkabout - boonies - attribute - eric - dickerson - smi - linebacker - bickering - wen - appropriately - arcade - drafts - archie - manning - nobodies - showi - furious - veg - padded - opposing - satin - bridesmaids - maids - accessibility - harsher - aerostar - stealth - slipping - celicas - perfor - racing - surreal - fulfilled - blair - reformed - gambler - microbiologist - competitions - minnea - dowling - ren - entrances - periphery - paired - deacons - blesses - fugate - proverb - macy - lowe - purebreds - studs - sweetest - sweetheart - breeders - bree - inbreeding - inquisitive - hindquarters - predominate - rex - rexes - rodents - groundhogs - mesh - remains - teetering - refusal - presc - pharmacy - mens - absoluteness - foiled - mere - outlawing - conspicuous - inconspicuous - inappropriately - hunted - squirted - novelty - outdo - raciness - calculators - euphonium - mellow - deejays - grafting - cough - graphs - sponsoring - enhanced - bytes - '128' - callously - deterr - blooded - midsized - porting - attendant - vessels - overbuilding - phe - phenomenally - galant - serviced - 49ers - harbor - niners - kim - redskin - cartoonist - ellicott - basicall - importantly - devaluated - goats - schoolyard - motherhood - overcompensate - destabilize - vying - regroup - standpoints - easterners - couched - proclaim - weaving - dike - plug - unveiling - takers - roomie - slaughtered - sudan - occurrence - shredding - bedding - wrappers - reviving - yosemite - objectors - assigning - examined - idealistic - pakistan - algeria - blinking - manipulations - insofar - clowns - partition - dividers - baloney - daylilies - orchid - closes - velvety - multiplied - weeded - lilies - azalea - glories - ned - skeldon - ojeda - hubie - offerman - prediction - cecil - orel - hershiser - darrell - interleague - introduce - anoth - homey - randi - dawdle - steamy - lawrence - mae - rambo - hogan - associates - realist - garments - vogues - knits - garment - loopers - piping - cording - twe - sewn - exceptional - bev - reap - sow - establishes - pardons - lust - incest - swiftly - integral - reeks - expediting - compunction - appropr - sins - stoning - clog - streamlining - extremism - bubble - habitat - humanity - inefficient - preconceived - notions - delivering - spiraling - conservatism - hampers - patchwork - unflattering - autobiographies - randolph - descriptive - affluents - tale - binge - bookl - francis - momentarily - connecting - sigh - chowperd - snowbirds - spawned - contend - melts - kitty - apso - panic - preserve - campsites - twang - pfeiffer - rim - glenrose - latrines - gemini - genocide - hmong - unsure - slash - intercultural - dissimilated - conceptualize - slavery - linguist - withholding - worthless - cambodians - graft - falk - drugstore - coils - mosquito - crickets - foamy - pristine - froth - bobber - reeling - saturated - soggy - damp - claustrophobia - terrify - spanking - revamping - lev - plaques - stenciling - cushions - impeme - interface - janitor - reams - dalmarva - deinking - contaminate - wastebaskets - publicly - yucky - interven - occupying - schwartz - iranians - egyptians - kane - matinees - burton - batman - glover - kline - dennehe - goldblum - clease - arquett - untouchables - graffiti - broderick - marlon - parody - tinman - humphrey - bogart - maltese - falcon - quinn - rainman - okie - homeboys - optimism - reconstruction - redefining - trait - longhorns - randal - streaky - touted - sentimental - instability - indoctrination - marines - ak - 47s - cubans - capturing - nicaraguans - crate - patrice - lamumba - teachings - extremist - gen - irregardless - albania - revolts - psychos - chiefs - staffs - uprisings - squadrons - afghanistan - boils - cen - berlin - wat - steppers - soles - reword - indi - environmentalism - ruther - environmentally - blasphemy - acutely - bureaucracies - relegated - heartache - grudge - succeeding - parish - policed - comforting - reminders - pyrex - teaspoon - blackened - skewers - basin - chefs - clams - instinctual - demographically - democratically - proposition - proposals - revolted - obligatory - considers - australians - looses - leas - denies - hamilt - passionate - democ - candi - antigovernment - misspending - bastards - inte - hundredths - sixteenths - mismatch - clamps - meters - drams - perfume - machinist - indic - indicators - micrometer - finders - nondecimal - halves - listing - beverages - whiskey - ploy - conversant - milling - measu - calipers - pliers - milliliter - drilling - hundre - lawy - strangle - neiman - marcus - outgrowing - necked - embellished - dre - presentable - outrageously - busters - campinas - oursel - asses - orient - optimist - jungle - resonates - profound - bullying - dreamed - wildest - semantics - transcribes - onl - guzzlers - fours - threes - transverse - mounted - shoved - serpentine - stickers - reinstalled - nozzle - stroking - groves - surinam - natio - internationally - amaco - mobil - rectified - inward - hateful - kilom - thumbnail - kilogram - britain - adopting - precisely - grams - sync - orchestrate - unfamiliar - toting - stroganoff - allendale - waldwick - adirondacks - pancakes - outgrew - beth - knowl - roanoke - randall - duplicated - gamble - ditka - nate - newton - branded - outlaws - webster - cocky - lambert - bloopers - receivers - tackled - necks - fav - entities - overburdened - fairness - pondsy - invu - invulnerable - belongs - electing - politic - floored - maryl - nurture - credits - ukrainian - scallop - buns - batter - bourguignonne - grudgingly - pinch - reversal - beck - subsidize - bennington - liber - refinement - etiquette - advises - renaissance - bowdoin - bucknell - lectures - confirm - guitarist - yale - minoring - irrevocable - irrespective - clinical - pathologist - kayla - bachelors - profess - traced - rung - maladjusted - compelling - distaste - resp - beret - uzis - disorderly - unc - unconcealed - matched - vibes - clearest - confi - junkins - mandated - prompted - tobacco - bandwagon - cour - tricked - syst - maintenances - scoop - fetch - pooper - scooper - colombia - reek - kindhearted - nixed - asthma - outgrown - misclass - stately - sunk - furnished - swoop - situational - punches - momentum - lockheed - arose - courageous - accredita - accreditation - keying - adjacent - refine - classified - chemicalwise - refining - strean - stillwater - stephenville - toxins - bacterial - bleaching - sinked - australian - dominique - neek - wimp - feline - unconditionally - feisty - snuggle - investigate - beaner - wadded - fixture - decor - panty - garb - polyesters - wools - neatly - layerings - eyesore - mended - ironed - compose - upgrading - plummeted - acro - daltons - wholly - understands - disadvantaged - winnowed - structures - casing - connectors - workmanship - hal - fluke - highlands - patronage - cranberry - pou - lobsters - billboard - steams - culinary - adventurer - franchised - shacks - shoney - reliably - communercation - compe - renditions - organizer - defeat - registration - dragginess - headache - draggy - locker - sauna - motiv - agony - dictatorship - uganda - mils - distances - centigrade - celsius - metropolitans - heeley - wentworth - differential - microns - whatev - responded - favorably - bagged - ecological - prod - additives - pickups - hangers - cupboards - fountain - faucet - exceeding - decomposed - shocker - bizmart - upseted - taxwise - toilets - smashing - soaker - sheltered - disapp - rankled - cheerfully - outermost - inland - curving - ventura - buildi - overflows - anaheim - simi - meanings - rhymed - balti - strayed - kabob - breakfasts - galunkies - marsh - pierogies - grandparent - newarth - cholest - margarine - margarines - kebabs - utensils - goulashes - juices - sealed - galore - finer - drains - shakers - journalist - crux - remo - appease - pob - patr - paro - paroles - partake - traumatizing - viaducts - ceremonies - dozens - pageants - riveted - confuses - thrilling - producers - tony - dorsett - hershel - rationalized - cinemax - correspondence - '30' - cod - reso - repossessed - 635's - looper - ramblers - brook - dealie - diversion - chevys - nex - v8 - carburetors - gingerly - yanked - tinkerer - evaporator - rubbing - testers - diagnostic - tester - diagnostics - carriage - chilton - multiplying - lincolns - tremend - leaking - condenser - busted - haas - ovolacto - lard - nutrient - lactose - synthesize - slough - utilizing - rids - utili - paperback - novelization - lucas - freder - brink - feinstein - fairfax - deaf - insulate - scrubby - pecan - paralegals - clears - interference - surplus - tariffs - mon - apprentices - advisable - journeyman - exporting - imminent - oodles - salutatorian - prided - welcom - welcoming - tol - resentful - zales - spiegel - hurried - circulating - walrus - porpoises - mainland - sanctuary - whooping - cranes - pelicans - antone - alamo - brewery - caverns - uncourteous - actua - irritant - hullabaloo - stockholders - inebriated - unsafe - surgeries - subsidizing - quack - waiveable - refresh - somewh - willy - horton - consolation - microscopic - kneecap - curtailed - forming - bison - weakening - strengthening - '401' - continuation - telephones - handbook - badger - showering - physiological - advan - fledgling - bikers - bicyclist - knocks - coronary - artery - decreases - embark - motivating - disevered - knobby - vaulted - woodhollow - villa - secluded - joking - sellers - coworker - doorstep - housebroken - playful - gastrointestinal - beagle - romping - waters - retrieve - paddled - unrequir - degenerating - rosebud - sociable - smu - synopsis - furrier - judgement - distribution - wrongfully - penitentiary - sitt - caravans - lending - simulation - resemble - adroit - oddity - moonlighting - strengthwise - divulging - tarnished - faye - socialist - undone - inefficiency - platform - lieu - mamma - disruptive - brow - browbeat - wist - mugging - faceless - persuadable - thunderbirds - topaz - camaro - reim - dominated - wrenches - eas - champ - premeditate - premeditatively - stiffening - lessening - retarded - pleaded - phrased - dayers - correctness - promoting - niceness - vouch - waterfall - busch - blacksburg - portsmith - williamsburg - epcot - temp - buccaneers - assessing - opp - benef - wadley - milestone - tainted - snickered - examine - aircraft - astound - pusher - circularly - chairman - judy - perturbed - promotions - programmed - brightens - hallmark - servi - seizures - brighten - tonya - sneaks - rainstorm - breezes - temperate - promises - westernize - intact - extensly - vely - woodward - projected - commanders - colin - powell - embargo - misread - earliest - disarray - hopeful - prosecute - stature - statesman - foreseeable - selves - volatile - retile - bathtubs - scouter - drippy - panes - putty - gazoo - pes - pesticides - bulging - chlorinating - coronarys - diets - quadrupled - ingestion - clogging - primates - regimen - kenneth - innovator - inactivity - neurosurgeon - strictest - idiots - stan - destruction - symbolism - evokes - lynched - modified - possess - condone - adamantly - symbolizes - circum - satisfactory - budg - spartan - frugally - jordache - nonessential - victory - cliche - enactment - adjourned - mot - expending - reasoning - allege - myriad - departure - restocked - guided - unconstitutional - reforms - gard - arranging - orig - florist - slowdown - runners - geraniums - coleus - vinca - thuringiansis - caterpillars - expands - unlicensed - brittle - excelled - wei - denotes - tension - bicep - tricep - instructing - grindstone - hovering - configuration - blended - muscular - dystrophy - documentaries - paroe - planner - uruguay - concepts - yuppies - legislated - dynamics - auditing - rev - revenues - millspec - operates - elevens - hammers - federalized - ci - emphas - identi - americard - adios - commu - demeanor - announcement - calcutta - foreigner - worldliness - attributed - chuckle - pogo - mourn - tolerated - drumming - scrunch - glamor - sprigs - ricksun - tender - lamp - ashes - overcame - nondescript - damned - hierarchy - restructuring - feminism - boomer - creep - rapidity - electroni - luncheon - existent - consulted - alters - stamina - goi - denying - revolve - entrusting - omniscious - omniscipotent - alec - precedes - daders - shrinking - worthy - whate - responses - spoils - flashbacks - flashback - fidgety - discriminate - pertaining - distraction - males - ital - entree - sagar - presby - kimonos - grishman - bavarian - constricted - putrid - folley - tableclo - crayons - disintegration - flickers - prevalence - excusing - signals - mechanized - requiring - antipasta - stuffing - poached - kernel - spinach - wilson - beeping - bakes - frosting - frostings - chatting - mentor - adversaries - manuscript - harried - interruptions - feedback - videotaping - adopts - twelfth - tangible - overseen - alternately - ilk - phonic - pistons - snooty - telev - leno - carvey - deduce - cros - wheeled - porked - termites - chess - rearrange - hisself - bathtub - prettier - rewired - shorting - surges - famili - rearranging - shuffle - pane - breakers - valve - drips - walkway - splash - vein - downfall - yuppiedom - restructure - biologically - physiologically - wonderment - swooshed - viva - talents - mongst - jealousy - computerizing - pecking - punched - slightest - epidemiological - guesswork - transmitted - semen - illegitimate - exploded - stepchildren - socio - radios - faxes - sensors - stalk - jurisdiction - outnumber - solicitation - prostitution - unlocked - fallout - probability - indentured - servitude - vigilantes - victimless - ridicul - auctioning - bidding - patios - insecticide - diazinon - carefu - deb - wallpa - stagger - renovator - sheeting - resilient - stairway - sworn - rud - veto - bout - yea - dams - droughts - reservoirs - poole - reflected - counteract - learners - genius - perspiration - diagnose - predisposition - flashing - drowsy - facilitators - manipulated - burdening - toot - weekdays - racket - drawer - dennison - derby - siphon - cu - uba - tailgate - deterrents - publishers - poisons - ergotisms - fungus - gender - confidential - tide - vatted - archeology - shoelace - promising - upcoming - reprinting - thurber - hundredth - riveting - viorst - sci - revol - revolves - shoelaces - binds - melody - workbooks - workbook - geometry - cypress - greece - irrelevant - tortola - gorda - infusion - ethnicity - familial - acclimate - retaining - latino - continentals - roberto - unprepared - vociferous - attain - imported - territorialism - horns - encompass - handcrafts - wreath - phillips - ranching - contemplating - stabilize - occupies - baseline - flextime - grading - scribble - sensitivities - akin - minimized - prematurely - dumper - geria - empathize - tandem - providers - prohibitive - fantastically - moslem - surro - surrogate - regretful - arou - swims - nationals - quarries - tumbled - avail - denmark - appliqued - eraser - maturing - rite - unmarried - aquariums - zoos - paternal - traditions - disintegrated - trinket - sociologist - multigeneration - eightch - scorer - rebounders - assists - thown - laker - marriott - spittering - sputtering - swimsuit - mavs - favored - endorsements - prospects - stanley - underclassmen - myrna - curfew - fiscally - jockey - catton - dives - cayman - itinerary - viet - doves - abnormal - puppet - heartbeats - reviewing - bocket - hannibal - lector - fascin - luster - attractiveness - originality - pinpoint - lavon - upstream - sever - benders - grea - musky - perches - salami - sonar - maneuver - charter - suntan - hobbyist - styled - convertibles - sevi - welded - welding - sunroof - soured - contention - jags - contractors - bends - enthused - enthusi - ap - vending - cartilage - glanced - fenced - econ - repeatable - bundy - exe - strauss - punish - electrocute - problematic - candid - fraud - intangible - reinstate - mario - cuomo - legislatures - molested - incarcerate - sylvan - reenacted - paltry - polishing - lotions - meniar - cringes - thrifty - flier - psycholinguistics - ivory - godsend - pathe - willow - cana - bacally - obese - reimburses - collared - widget - bramalea - 401k - weeny - nonex - censored - bombarding - dramatize - statues - weld - epoxy - resin - shattered - statue - cricket - thatches - thatched - vapors - stained - lacquered - tung - fanatical - pills - hem - sweating - bulge - wrinkles - vices - sha - germ - ecru - undercoat - peachy - steamers - mottled - grey - maroon - vivid - turquoise - coral - renovating - hallucinations - cloths - slop - soluble - tricks - skimp - tediously - rewallpaper - racks - metlife - worki - workm - inconsistencies - amateurs - footballs - fencing - earl - princeton - pacers - subminimum - administered - reluctant - poured - chiropractor - cautious - janitorial - rafael - septien - applicant - eduardo - mana - sai - mafia - newcomers - ellis - redoing - comm - elitist - concise - rathers - yous - segregate - wretched - horrid - shortchanged - brokaw - demi - ringwald - sixteenth - doogie - howser - freckly - ferris - moustache - reeve - dreaming - ooze - bride - pretended - occupational - exemption - judiciously - incidental - figuratively - westport - bradford - indirectly - clair - dayt - baldwin - bebble - foreclosed - rider - homestead - creeping - livable - retrial - retry - wond - seeded - raping - choking - shotcross - televised - vendettas - trialed - revoted - annihilated - enterprises - misgivings - quiz - sprint - capture - extending - endowment - joes - alumni - splits - governme - faired - undertaken - deficiency - dilly - sangre - cristos - wichitas - lakefront - pinon - naturalist - stools - binding - component - carol - playroom - realtors - dominantly - alleyways - shifting - popping - bangla - hugo - bedroo - barometric - borger - funnel - pillowy - radar - veer - swirl - junes - budding - crimp - scorch - distracting - heats - therapeutic - northe - mayer - denison - purify - purifying - philodendron - acc - divert - blurred - fluoro - fluorocarbons - provoking - brandeis - fift - readings - iliad - mythology - choo - scientifically - grumbled - unpleasant - imparting - cluster - vicarious - compromised - profiles - telemarketeers - outcry - cited - crashes - eroded - erosion - lockers - latitudes - motorists - liens - representing - landlo - dakotas - alarmed - exclusion - parameters - interpreted - adoptive - carting - arresting - interval - orwell - tay - unusually - leathery - venture - wea - pebbles - drainage - deceptive - fiend - wrinkled - oils - fishermen - tricycles - kiddie - wilds - calves - heifer - jea - flared - hep - themsel - continuum - astute - propagate - raccoon - filleted - livestock - whiskers - growling - widen - weaker - ticker - pentagon - whomever - nutrisweet - bitterness - ancient - vets - complicate - preregister - registrations - eligibility - preceded - theodore - upward - rascals - stinks - precluded - gullibility - democracies - redistricting - subsidizes - lineman - spilled - camouflage - booby - traps - apocalypse - influx - surge - buckle - overcome - castaways - depicting - dudley - bloody - olden - realism - pioneer - worship - chri - videotapes - shrunk - eastwood - showy - westerns - cursed - pointy - melissa - gilbert - idol - verse - shep - immemorial - misdemeanor - waving - prevail - appoint - bailiffs - clerk - verbalize - tripled - cameras - reporters - prosecutors - outweighs - prosecuted - sump - sewage - towed - aut - trad - marina - hears - acclaim - sequels - earle - recluse - essays - qu - conclusions - photographers - arro - gorillas - sloth - fascinates - bottoming - landers - tycoon - bloomed - fade - spiky - bl - hya - colossians - thistles - landscaper - junipers - puny - foliage - iris - fuzzies - wildflower - insists - camcorder - pastime - muggings - grates - claustrophobic - tendencies - deviant - anguished - cleaners - meridian - inlaws - sneakers - jordans - brains - caps - videoed - repeated - repetition - termed - allowable - purs - discretion - freely - altering - preparations - namely - minuses - factored - competitor - trevino - influencing - wholesome - exclamations - sportsman - phooey - applicator - nurseryman - elm - circumference - stubs - propelled - pest - sawed - rot - rotter - autobiography - liquidating - emulating - compu - ause - accomplishing - spacings - formattings - insert - reset - rewrite - typesetting - typeset - spaces - compatibles - adhere - brochco - hillstreet - finale - nudity - delight - shudder - flabby - telemarketing - classification - lotteries - kalamazoo - sinus - carton - stakes - mounts - hub - airports - altitudes - intermediate - simp - fluorides - guerrilla - marched - lied - expire - xerox - modify - soo - terminals - insur - breakable - hangouts - haunts - southerners - rudest - bartenders - wee - ferrings - taiwanese - jambalaya - wowed - univerisity - arias - casks - hospitalization - hos - crowns - fluctuate - celebr - inordinate - axe - newscast - js - recap - sensationalize - sensationalized - asinine - puzzle - precede - preclu - preclude - stretches - wakes - depreciate - tru - unibody - granddaughters - gol - wagging - trainers - airheaded - yappy - dignified - culling - tamper - innately - tractable - selectively - culled - belgian - distinct - breeds - kennel - translates - shit - unreliable - handlers - indiscriminate - breeder - handler - bab - doorbell - stipulation - laundromat - grasslands - surrounds - betty - parades - palestine - id - peg - catalyst - palestinian - kindest - abounding - kindness - godly - compassion - humanness - mandarin - oranges - grape - fridge - gelatin - carrot - eggo - waffles - adolph - breakfa - craftsmanship - opt - stanza - glitters - oasis - warp - clearinghouse - consolidating - salespers - tel - compan - announcing - telepho - discard - episodes - cramp - vela - someb - thirtysomething - mclaughlin - yogi - loner - comedian - cantankerous - echoed - withdrawal - grumpy - stooges - mouthiest - kiddos - mouthy - touristy - besieged - defini - badgering - galapagos - sidney - adelaide - chengdu - quingdao - retreat - flights - rita - oah - destitute - ree - snorkeling - prawns - milli - arsenal - traffi - bennett - gangsters - corp - arr - pris - crowding - statutory - verbalizing - stints - citing - intensity - limbaugh - lamenting - microwaved - healthiest - teases - accuses - deprivation - nourishing - evaporated - broil - marinara - grapefruit - starch - pleasurable - kalli - cater - rodolfo - royal - maitre - pilgrim - unnatural - lookout - arby - wastes - reduces - speedup - healthily - sup - quoting - disputes - commas - reevaluated - inma - blinded - restitution - willfully - contradictory - caveman - coleslaw - tablecloths - bakeries - regretted - purch - pastrami - '''oeuvre' - complicat - sustain - addressing - fellowship - prefers - troublesome - camels - beatle - orchestration - okeydoke - statler - stated - debut - investigating - bootstraps - baptisms - clergy - imprisoned - confiscated - bourgeoisie - commonality - recanting - courtyard - motions - commandant - escaped - perseverance - bureauc - persecuted - dab - chorus - mothering - rerate - precluding - analogy - spade - marketeer - warring - peacefully - trampling - fantas - crabby - coated - willis - sarandon - gena - vatican - paradeso - befriends - friendship - califor - drying - nippy - mucky - thunderstormed - shoveling - michelle - lan - footnoting - retype - appetizer - criterion - alumnae - heavyset - poignant - subtleties - gore - warlock - omelet - characterizing - conceited - portay - goer - prosecu - cutor - struggles - flowing - ir - slicing - locust - omar - swallowed - redwood - brownstone - caulking - myneer - spacious - inhaled - revived - airway - revive - sol - dignity - luxurious - blossoming - brazos - sleeps - purdis - sandlin - quake - mak - caramelized - customary - orchard - accor - ply - crier - waistline - jewels - earhart - thurow - perceptive - pinpointing - flimflam - hughes - assis - plod - rereading - ditched - findings - bonfire - vanities - temporally - burdened - cafeterias - linen - napkins - duplexes - hodgkin - undergoing - interim - constancy - sufficiently - farfetched - wheeler - cock - slowing - pals - unjudgmental - homy - reprimand - secrets - brooksville - campuses - eyesight - enrichment - schooled - rejection - proceed - herman - foreigners - polluter - rigs - busses - incinerate - pollutant - untold - cockroach - accelerated - nutrients - sponges - tending - newark - vividly - entrance - biggies - consumable - calculation - physiology - snowball - dieters - robbers - trendsetters - correspond - circulates - centralize - descendancy - closeness - caliber - differentiate - stevens - shippensburg - specializes - novelist - intricately - johann - sebastian - copyright - compile - poems - baudelaire - jennie - abridged - reunited - rituals - equated - communion - repetitively - vernon - salmonella - silverware - caterer - biographer - obituaries - succeeded - vigor - bulletins - chorals - beginner - violinist - percussion - accompany - choruses - audition - verdi - hermit - vacationed - anonymous - whirlwinded - effortlessly - elicited - unwound - guadalupe - penetrates - alda - burt - reynolds - vignettes - dinosaurs - robots - satur - sniping - howling - gleason - snippets - idle - workshop - gra - dividing - moses - hab - scavenge - conserve - indulgent - exceptions - contemplate - permitting - calming - aboard - docks - cozumel - ocho - rios - jurisdictions - tapping - lynda - slandered - landslide - thornburg - landslided - characteristically - savory - petition - resisted - dirtier - muddier - sensibilities - transpired - nixon - edible - accumulating - elbow - cho - grandes - refried - katy - avocados - avocado - coolwhip - horseshoes - auctions - sidelines - loosely - socioeconomic - tracked - pressured - vandalism - outward - custodial - skyline - irritable - unattended - environments - dunked - compaq - honk - prodigy - mush - shareware - paradox - shooter - crawford - andrew - webber - paranoid - unlucky - anonymously - competency - wholesale - lon - exa - beginnings - kuenzer - rebelled - debtor - angela - eyeglasses - indiv - staffing - examines - optometrist - ophthalmologist - extractions - publication - unfeasible - bettle - orthodontal - outsor - roo - suite - scattering - leniency - underhanded - perpetrator - injustices - wherein - dist - unsavory - elimi - rarity - chairmen - ministers - congregations - catholicism - forthright - disorders - soothe - exertion - characteristic - cram - guarded - sacrificing - mediators - interpersonal - mediator - doable - devised - stimulations - goof - whipping - nickie - snail - hards - futuristically - subjective - harmony - impregnated - challenges - motherly - competent - militaristic - colonel - infantry - embrey - reynold - riddle - aeronautical - pratt - whitney - daphne - dictated - qualifying - rhodes - scholars - homogeneous - realities - socialization - insular - sheriffs - evict - continuances - abundantly - appealing - retried - lowers - percep - gypped - slicker - bruno - kirby - chauvinistic - punching - correlations - opium - dens - weakened - duress - drunken - induced - legalized - quantify - deg - safeguards - fraction - oath - sensings - sentencings - pertains - introduction - accordance - clark - parachute - presiding - reorganizing - sweeper - univerty - versity - lakeway - expose - jun - bethany - unfocused - midst - instigated - marrie - remained - tomorr - whitmore - arbor - slushy - sled - icy - lingering - exodus - eternally - snowfall - grassy - sachse - goddard - stickler - mulcher - seni - antisocial - adapting - deteriorates - glimpse - unwilling - appalachia - stopgap - rougher - strategic - fails - worded - peoria - dropouts - insecure - scaring - stylish - interpretive - fathom - expanding - wean - referrals - advisory - myrtle - barricaded - blackberry - defeats - enchila - boiled - toasted - calorie - hereditary - headstart - preschooler - tacos - tamales - romanian - backfires - waiters - batty - momo - colter - pas - campari - adventured - souper - prey - backlogged - patrolled - frus - imme - dialogue - aisles - cornball - overacted - applauding - waterskiing - ashley - jamie - warner - deanna - cheeks - backdraft - berry - raspberries - shaved - entrees - accompaniments - gershwin - puree - antipollution - gases - accumulates - groundwater - fusion - optimistic - pessimistic - reconvicted - sicko - merciful - cannibalism - hunch - coordinate - communicable - memos - orchestral - fiddler - oboe - classy - corresponds - christening - elijah - marches - poinsettias - bouncy - haunting - conventional - disposal - odors - throwaway - ditches - drinkers - churn - shipwrecked - explodes - maims - sylvester - mermaid - outfitted - crushing - hobnail - phobia - bifocers - trifocals - mccalls - byte - afflicted - exceeded - antibody - realm - telethons - doling - receives - ociety - aesthetic - enhancing - frightens - dahmer - burglary - enquirer - cranks - fuzz - repala - sil - shiny - heartbeat - spins - rainbow - packaged - trespass - tidbit - refrozen - cheesecakes - refreeze - liabilities - wrecks - tattoos - speedboats - chambers - afloat - maneuvers - stormy - nibble - rope - entice - sneaking - paged - favo - flyer - shaky - iffy - sentra - subdued - urinalysis - bums - overdress - overkill - businesslike - nylons - nutrisystem - dreaded - toppers - ceramics - seamstress - cramped - negligent - initiates - squeegees - newscasters - postponed - a1 - alfredo - clowning - circuits - sfuzzi - copeland - transported - thirteenth - wobbly - bookends - jug - viscosity - saver - brushed - tooken - turpentine - towels - shi - jul - shindig - boulevard - maizeland - skier - minnie - canaveral - reschedule - hilton - eighteenth - raton - '287' - '70' - broadmoor - breckenridge - trinidad - '25' - hexpired - disheartening - elders - albertson - limbs - sodas - arranged - brookshires - pickle - piles - emporium - cinch - consolidate - alluring - cupcake - henpecked - instilled - gatherings - subtracts - debits - incidentals - scotch - igloos - strateg - strategically - incurred - cashes - reunio - entryway - roaming - ris - risen - appraisal - disoriented - blissful - unexpectedly - cockroaches - complacent - bitterly - polling - campaigning - napping - structuring - digested - perfumes - geese - peaked - balloon - canyons - weatherwise - sleet - maps - sy - pearls - loafers - distinguishes - '1200' - whereby - extract - generates - bursts - navc - blazey - obscure - promotes - goe - refrigerate - tartness - raspberry - connoisseur - tastings - mesina - exorbitant - kaiser - mccullum - catastrophic - implants - transplants - howe - dislikes - chopin - expresses - discussions - chords - panicking - kielbasa - bak - ravioli - reggae - twangy - agr - cackle - atteck - scholar - adolf - imaginative - sty - antiques - winnie - pooh - grimm - fairy - tales - gentlest - jewel - restroom - spitz - extravagant - overpass - littering - timers - tans - mauve - distantly - swap - bichons - barks - hind - origina - bernards - lega - belittling - liberals - suppos - tcat - examination - clicker - screens - carpooled - bolivia - sundresses - polyester - overheat - sweltering - newborn - pleats - absent - strep - bookkeeper - partitions - duality - extenuating - newsworthy - leafing - mccall - subscribing - gott - newsy - putterer - caladiums - hardened - semitropical - carrollton - architecture - hairless - coon - manx - tame - ships - folklore - faint - chincoteague - burgers - teriyaki - shakes - grandy - fend - snowballed - inconveniences - woozy - sys - squirt - flicking - whales - showtime - adder - dragon - rosa - sorrento - dine - mah - jongg - yearbook - imprinted - depreciated - cribs - bestes - giver - enables - ly - confining - bronco - moder - cowb - cheer - schnauzers - dachshund - starved - curled - skittish - spaying - belon - severing - sr - suicidal - craziness - mistrust - lacks - poland - weeding - mankind - uninsurable - medcenter - hearings - overstaffed - mortgages - outlaid - intergovernmental - plugging - indepth - capsize - sensationalism - blase - sel - sadist - oleo - oregano - ight - semolina - absorbs - vulnerable - align - bombings - aligned - tensions - forceful - cr - expedited - deserving - mandate - grassroots - introspective - schoo - visitation - advantaged - energies - tiananmen - custodians - immigrated - brightest - burst - lanes - winterized - yourselfer - representatives - homemaking - accessed - uzi - flyswatter - utilized - acquiring - illicit - gatlinburg - cosa - hiked - ardmore - cloud - ledges - hyatt - gully - trench - tenkiller - enlisting - seductive - pinion - totality - revealed - legislat - abrupt - ruder - arrives - '1' - microcomputers - gateway - apollo - faulkner - emblem - candice - bergen - ghosts - haunted - dianetics - gibberish - broudigan - journeys - mailman - karl - malone - hacking - fillmont - generically - cyclist - techy - hackers - davy - crockett - sailor - sailed - mck - equalize - semiretired - dementia - insisted - rejuvenating - coldest - cus - celltrex - jeri - maceo - rampages - cocoons - occa - uniqueness - winfrey - prebuilt - workbench - subcontracted - subbed - scramble - championships - peacefulness - birdie - quadruple - whizzing - spectators - scrambles - kerr - mcgee - infrared - suffice - notifies - supplying - angles - anticrime - outings - sec - arlene - lister - poked - togethers - dearly - swoosh - skate - begonias - destruct - concessions - drizzly - huddled - cages - fanatics - straightforward - piston - oiling - altog - reelection - provisional - locate - incomewise - ifs - ands - buts - '4' - hel - discontinue - narrowing - nitty - gritty - faithful - shoppers - yourselves - straighten - stems - relating - supporters - antisupporters - contras - dictator - fascist - siesta - mouths - reflecting - dabble - chalk - chesapeake - suspended - ath - tutored - goofing - piney - diameter - calmness - outwitting - shiners - infla - inflatable - raft - cottonmouth - coves - walkie - talkies - handcrafted - semifixed - automated - crafted - stateside - adage - advising - embarrassment - jessie - helms - intelligently - mistreated - papa - doc - tyrant - puberty - tibby - perfumed - legendary - brookies - rainbows - accommodated - specialists - replanted - rods - norfolk - portsmouth - hikes - pests - chaperon - calloway - variegated - beetles - borderline - zaps - ligustrum - apron - gourds - bolton - symphonies - caller - sax - houseful - crabs - sensation - tingling - oddball - waitressing - crunches - relevance - federally - hogs - barns - revealing - horticultural - groundskeepers - dormant - centipede - crops - behold - cuttings - mit - diamante - boozier - passengers - shining - becca - nina - palmer - remarrying - griffins - crackers - burritos - debone - notoriety - jurisprudence - thoroughfare - sleeper - herd - cima - savages - plywood - beams - migrate - undercover - barbiturates - codeine - drixoral - unsolved - mcgillis - weeknights - physicist - facet - hurst - greensboro - celebrities - repeaters - zealand - statistically - outbound - astronomy - gallagher - pictured - betters - hubble - telescope - planets - habitable - backers - zippers - snaps - dull - pretechnology - shelled - duplicates - regulat - regulators - regulator - lever - pulley - chev - oi - resur - ourse - hesitating - russ - noons - flaw - gasket - fury - exceptionally - surfaced - repeatedly - escapes - pragmatic - consti - opponents - laural - squeaked - andrews - clou - crept - firewood - maples - dogwoods - lowell - unu - periodicals - historic - interes - lawful - scanners - attempted - thoroughness - mag - announcers - tele - ivan - rodriguez - ballplayers - routing - enthusiast - ducted - gettin - brussels - sprouts - kale - pony - grazing - pears - extinguishers - depleter - extinguisher - timed - contaminants - probe - ionization - miller - temptation - squareness - buckles - fea - lettering - vin - vinyl - balloons - recy - commented - nudge - decomposable - flips - emptying - regressive - defen - kate - curves - raphael - atchafalaya - sausa - alvarez - applebee - nonstructured - torture - nur - fai - glorious - esoteric - producer - hairspray - batch - partic - preteen - unlikely - dynamic - raunchy - horrifyingly - poppins - differed - eclipses - belie - lebaron - peeling - gears - oklahoman - beatings - proy - condoms - stupidity - truthful - faded - marker - reflective - adheres - sealing - dings - variance - prop - pressuring - primed - bragging - sickening - shitty - drags - burners - putts - teeing - lodging - dialers - provision - specify - dialing - prised - weir - overloads - hoosiers - crossing - delancey - thrillers - backless - ani - nick - nite - dragnet - bald - marlo - collier - brigham - estonia - agriculture - foodwise - rioting - secede - proportionately - hinders - tubs - brougham - trunks - shy - gadgetry - '6' - interiors - veered - revolving - reverting - envy - exhausts - hairy - gettingest - daught - bertinelli - dysfunctional - childfaring - miracles - bette - midler - redbook - previewing - postage - unauthorized - mayors - discredit - ps - productions - chariots - gladiator - fluent - batches - subtitle - subtitled - gems - supernatural - accusing - migh - mondays - thrust - lifters - drills - rocking - referee - abrasive - maintaining - posed - refusing - coins - conversions - dormitory - unused - ramp - hydraulic - disposer - escapement - incorporating - leonard - nimoy - trekkie - luke - spock - mccoy - admiral - hobbled - vulcans - doohan - scotty - addams - averaging - decrease - munich - snows - chattanooga - lori - coldness - membered - unemp - fetus - complications - slobs - equation - nameless - malformed - sincere - deliberations - dismissed - indicted - revenge - subsequent - provoked - provocation - qualifies - mitigating - contender - linguini - hawaiian - luau - angie - shellfish - clam - cheeses - nachos - resurrection - lutheran - scanned - cooperating - toss - inmate - interpretation - blanks - executioner - bamorghini - skyhawk - dominican - nantes - castles - vineyard - consignment - goodwill - crushes - sewer - res - unoccupied - assassinated - menace - perspec - relativity - vantage - weighted - reflect - subservient - integration - ith - frien - drudgery - montpe - mont - monteplier - montpelier - everett - yack - tromping - unlimited - wedge - fairway - flus - startling - '286' - turret - scien - simulators - plugged - upgrades - custer - '386' - trenches - trencher - stunt - cul - sac - rearranged - clancy - novell - netware - ark - ladonna - peck - bourne - ultimatum - enveloped - amsterdam - holland - harpsichordist - forte - warrington - cheating - harry - heroic - mayfield - corrupts - lig - hatteras - imaging - legalese - himsnelf - koop - scarcity - highland - jogs - gyms - inequities - stimulate - deductor - bentsen - drunks - lafferty - infringe - snuffed - snuff - compares - gilmore - accomplishes - william - thrice - mating - sows - suckling - hernia - carcass - cloves - pineapples - cranberries - hominy - barb - automatics - avis - crashed - lens - porsche - turbo - carrera - mys - mushrooming - percentagewise - folderol - lifeguard - jarring - flui - watchers - pokes - blamed - ceases - intravenous - cell - quests - subsidies - slashed - entitlement - trades - beauticians - unending - spiral - consumers - unf - ailments - magerick - celtic - transplanted - rolando - harper - plaint - straighter - dayer - plumbed - bolted - logan - accredited - professorship - distressing - fiel - treasury - refunds - halt - spying - scaled - loading - challenger - stat - mirv - roomy - cargo - recommends - volvos - wagons - conscientiously - emiss - hypothesize - muncie - terre - haute - triggering - verify - drivable - emerges - overgrazed - reclaimed - prettiest - palm - paintbrush - septic - hummingbirds - hummingbird - pooped - annuals - countrified - supermarket - coaster - afterburners - gliding - oomph - subs - gambled - insulating - spec - verandas - genes - drapes - guppies - platies - fishies - glacier - playgrounds - wilderness - scaries - rayburn - curling - nominal - fulfill - synagogue - geriatrics - app - degenerative - communiky - enhance - assist - text - biogra - daniels - prince - phillip - criticizing - miniseries - scarlett - spectacular - torrents - ligh - horizontally - arid - crisp - sleigh - brighton - springtime - skie - hammered - subtly - brianna - lib - submerged - loosening - leaks - tar - gravel - plastered - drywalled - plastering - terri - exasperating - swelling - squirming - swells - shrinks - retains - highlight - captive - legos - technic - lego - stare - engagements - sousa - refreshments - rehearsal - donations - municipal - conduct - nitny - altoona - lockhaven - nighttimes - ama - emerson - maceboast - circuitry - vacationer - wausau - unduly - sunglasses - grip - durable - faulty - recliner - pinto - sequoias - redwoods - bryce - tetons - sequoia - driveways - snowmen - snowballs - marketed - acceleration - suspension - lumbar - sma - bur - skyrocketing - govern - exclude - ballgame - warrant - rounds - brats - eff - nativity - facings - casings - relieve - strase - reliever - relieving - sander - cabinet - equipments - dado - rotary - sicknesses - bryan - mamas - packards - solburns - frown - niggardly - chintzy - megs - mirroring - epidemic - immunizations - rays - mumps - rubella - inaccuracy - defined - issued - hypocritical - stings - laundering - contr - governed - discomfort - stea - holster - spontaneous - headquarters - bitterest - fluctuations - texts - doen - rosie - '''neil' - thomases - trimmer - clump - tithing - homeowner - computerization - stale - subroutine - libra - clara - beastie - triggered - pledged - fren - ally - organi - trombone - weathers - facetious - directors - spells - compulsive - childr - fluffs - toppings - brea - torque - underdrive - sportier - beetle - coolers - bonneville - secondaries - quadrajet - compulsion - elevation - variations - hilltops - mines - hamster - cruelty - parakeet - parakreet - burmese - deactivated - infatuated - jobbies - visualize - boggling - slid - clamped - kisses - everywh - brag - gramm - overturning - renegotiate - kickbacks - valdez - defi - batted - hangs - threats - emit - che - churning - remembrance - networking - conformance - wyatt - extremey - bennigan - vincent - chefalia - whataburger - zillion - mercado - juarez - tallest - ewaldes - cont - stoneleigh - chews - yapping - collies - roughest - hollered - battling - obedience - squats - vaca - pilgrims - medieval - relics - bemerton - newness - turin - muffins - requests - helman - tart - zing - cele - layering - fluffier - joins - jennifer - unselfish - tutoring - affiliated - aimlessly - perky - shins - hyper - burdensome - earphones - timbuktu - onna - lieutenant - biologist - sliding - tremors - variedly - bakers - aprons - sweatshirt - wigs - lamb - bunnies - symbols - milky - polytechnochloride - mought - trashmore - lifts - riverview - tranged - strongest - recessionary - stagnate - unteachable - prominent - chide - remaining - backbone - newborns - fullest - firewh - daffodil - jung - aquinas - libretto - rossini - mahler - dutchen - trumpets - elixir - floated - swapped - tyme - tempco - trooper - gisland - carribean - unpacking - lotto - alcatraz - hairdresser - crui - janice - furry - eaves - rafter - cactuses - furrows - wrung - plink - construe - thinkings - bue - buechele - grieves - gullible - manufactures - borden - bib - overalls - oshman - evaluated - unfor - linguistic - austria - niagara - coasts - carolinas - leisurely - modesto - cheeseburgers - incapable - hygienic - inoperable - oxygen - banish - relocated - realtor - listings - precautions - integrate - cooperatives - reallocate - reorganize - accelerate - transient - commish - tenderhearted - galaxies - crud - mutations - feazure - ballooned - reclamation - merits - axiom - fiends - sensitivity - aboveboard - evaluating - veggies - unarmed - resembling - tallow - scalloped - weighing - strap - squeaker - closing - mullin - squeakers - marquee - bluish - hydrogen - sulfide - h2s - ramps - vaccine - preventable - syringes - needles - feared - ruf - riffraff - haves - nots - earhout - bulletproof - vest - hedge - tollbooth - hatcher - taverns - sailboats - ancle - lounge - cocktail - sailer - cruiser - hull - spars - rigging - gusts - wearisome - flaky - markups - arming - stra - quail - swedish - munch - intermission - doughy - frosts - iceberg - schoolteacher - altrusa - upholstery - garl - jupiter - musically - auditions - repertory - outlet - auditory - lear - educationally - verified - chording - pianist - min - ec - subbranch - emigrated - beware - entrepreneurial - ventures - banked - stored - footsteps - postcards - notify - notifying - steals - hides - subsequently - corrective - leers - downright - outright - shu - newest - apathetic - absol - prolong - roofing - retool - zigzag - kan - untalented - washed - salvageable - gluing - feds - interrupting - faults - caucasian - educ - thei - officed - deputy - pruned - gladiolas - amaryllis - conf - plantings - sprout - narcissus - psychic - rerun - activate - rusted - rusts - fenders - repainted - acco - dreary - expen - salting - weinstocks - wad - hilt - dolphene - feelt - throwed - wheelchairs - emjoy - anheimer - tela - kindly - innovated - endeavors - adam - particulars - abusive - evolutionary - duplication - imagers - allocate - optimally - squawk - evolution - insurers - entity - burnable - ticketed - charities - braved - suede - cardigan - appointments - unlined - toasty - lightweight - fireplaces - dense - ethanol - smokestacks - mowers - wedded - organism - nutritionally - bamba - szechuan - pancho - binders - assignments - developments - cashew - avoiding - suey - disburse - squeeze - sq - faculties - pauper - brokerage - anticipation - cherished - commodity - famuel - slopes - biness - furlough - promoted - nec - shasta - salmon - sk - walleye - fighters - fillet - foil - seekers - scrutiny - tarrant - bobsy - accu - smiled - growled - mistrials - railroaded - convalescent - unsettling - senile - graying - exercisings - unaffordable - restricts - casse - gabrielli - bankrupted - cello - viola - composers - boutiques - darling - chanting - canseco - ramming - vinny - utility - outweighing - sundance - smithsonian - crosswords - planners - artists - bazo - faron - spiro - gyro - dulcimer - jarreau - contorted - bonnie - rait - grammy - unedu - sprayer - routers - cookie - varnish - smoother - hayloft - franklin - gradual - increasement - torpedoed - downside - blythe - tonkin - macintoshes - graphical - multitasking - gestures - vocabulary - compilers - consultation - interactive - discriminating - correlate - funnest - gentler - panicked - sassy - westmin - westminster - infra - mondale - situa - circuses - disrepair - dashboard - ce - beefing - patrols - visibility - lifted - cumberland - cobb - thefts - superficial - cracked - electrically - manufactured - bordering - elects - aerodyne - aerob - brace - publicize - killings - duri - commentators - blurbs - bog - dur - countdown - newscasts - unreasonable - moderator - unorganized - moderated - assumingly - importers - dahlmer - ohi - nightmarish - withheld - sovereign - martial - puritanical - permissible - acquitting - acquit - impaneling - dismissing - foreman - deliberating - una - restate - unannounced - sweep - definitive - bodily - behaviors - enters - privacies - melanie - spry - announcements - anson - fayetteville - waynesboro - delinquency - fre - gainfully - tremen - thriving - towar - grit - pail - latent - compression - ovens - armor - fierce - finagle - nationalizing - cutoff - operat - unionized - distinction - institutionally - expedient - innovativeness - expedi - unequal - plaintiff - novices - bets - leaky - luby - taping - promo - blurb - mutt - hooper - veterin - spay - neuter - frie - shorties - decreased - unrestricted - glut - magnum - rushes - oper - preset - styro - frank - shocks - allot - frowned - chronicle - analytical - abnormality - overwhelmingly - academia - descriptions - addictive - reevaluate - divvy - allocated - psy - psychedelic - crosby - stills - performers - secular - druggie - shipping - maximize - actuall - revelation - polymers - roadways - hoop - funn - heavenly - retailers - induce - inducement - recycler - saskatoon - welfor - employing - deposits - arithmetic - sums - colleague - internet - infusions - incurring - surveying - assesses - footloose - smattering - greetings - snobby - paled - refrained - acute - indivigal - thrives - categorized - receptionist - lar - curve - critter - incumbent - entrenched - standardizing - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: null model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false extract_feats_in_collect_stats: false use_preprocessor: true token_type: word bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: s3prl frontend_conf: frontend_conf: upstream: wav2vec2_large_ll60k download_dir: ./hub multilayer_feature: true fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: utterance_mvn normalize_conf: {} preencoder: linear preencoder_conf: input_size: 1024 output_size: 80 encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 8 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 required: - output_dir - token_list version: 0.10.7a1 distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

776f7890b1f0f2615e3234e25cfbc211

jaykmr/ESMCrystal_t12_35M_v2

jaykmr

esm

28

3

transformers

0

text-classification

true

false

mit

null

0

[]

false

true

8,802

false

## Label Semantics: Label 0: Non-crystallizable (Negative) Label 1: Crystallizable (Positive) ## Dataset 1. [DeepCrystal Train](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/blob/main/Datasets/train.csv) 2. [DeepCrystal Test](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/blob/main/Datasets/test.csv) 3. [BCrystal Test](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/tree/main/Datasets/BCrystal_Balanced_Test_set) 4. [SP Test](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/tree/main/Datasets/SP_Final_set) 5. [TR Test](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/tree/main/Datasets/TR_Final_set) ## Model #### ESMCrystal_t12_35M_v2 ESMCrystal_t12_35M_v2 is a state-of-the-art protein crystallization prediction model finetuned on [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D), having 12 layers and 35M parameters with size of [approx. 136MB](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/blob/main/pytorch_model.bin) using transfer learning to predict whether an input protein sequence will crystallize or not. ## Accuracy : | Dataset | Accuracy | |------------------|--------------------| | DeepCrystal Test | 0.8161222339304531 | | BCrystal test | 0.8052602126468943 | | SP test | 0.7637130801687764 | | TR test | 0.8389328063241107 | ## Comparision Table: | Dataset | Count | Positives | Negatives | TP | FP | FN | TN | Precision | Recall | F1 | Accuracy | ROC | Mathew's Coefficient | PPV | NPV | |-------------------|-------------|---------------|---------------|--------|------------|-----------|------------|-------------------|-------------------|-------------------|-------------------|---------------|--------------------------|-------------------|-------------------| | | | | | | | | | | | | | | | | | | DeepCrystalTest | 1898 | 898 | 1000 | 579 | 319 | 30 | 970 | 0.64476615 | 0.95073892 | 0.76841407 | 0.81612223 | 0.9403 | 0.657526117 | 0.64476615 | 0.97 | | | | | | | | | | | | | | | | | | | BCrystal Test | 1787 | 891 | 896 | 573 | 318 | 30 | 866 | 0.64309764 | 0.95024876 | 0.76706827 | 0.80526021 | 0.9396 | 0.644635696 | 0.64309764 | 0.96651786 | | | | | | | | | | | | | | | | | | | SP Test | 237 | 148 | 89 | 97 | 51 | 5 | 84 | 0.65540541 | 0.95098039 | 0.776 | 0.76371308 | 0.9293 | 0.586069704 | 0.65540541 | 0.94382022 | | | | | | | | | | | | | | | | | | | TR Test | 1012 | 374 | 638 | 225 | 149 | 14 | 624 | 0.60160428 | 0.94142259 | 0.73409462 | 0.83893281 | 0.9562 | 0.658766192 | 0.60160428 | 0.97805643 | | | | | | | | | | | | | | | | | | ## Graphs ### ROC-AUC Curve * DeepCrystal Test ![Test ROC-AUC Curve](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/blob/main/Graphs/ROC-final-test.png?raw=true) * BCrystal Test ![BCrystal Test ROC-AUC Curve](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/blob/main/Graphs/ROC-final-BCtest.png?raw=true) * SP Test ![SP Test ROC-AUC Curve](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/blob/main/Graphs/ROC-final-SPtest.png?raw=true) * TR Test ![TR Test ROC-AUC Curve](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/blob/main/Graphs/ROC-final-TRtest.png?raw=true) ### PR-AUC Curve * DeepCrystal Test ![Test PR-AUC Curve](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/blob/main/Graphs/PR-final-test.png?raw=true) * BCrystal Test ![BCrystal Test PR-AUC Curve](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/blob/main/Graphs/PR-final-BCtest.png?raw=true) * SP Test ![SP Test PR-AUC Curve](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/blob/main/Graphs/PR-final-SPtest.png?raw=true) * TR Test ![TR Test PR-AUC Curve](https://huggingface.co/jaykmr/ESMCrystal_t12_35M_v2/blob/main/Graphs/PR-final-TRtest.png?raw=true) ## Final scores : * on DeepCrystal test: | | precision | recall | f1-score | support | |--------------------|-----------|--------|----------|---------| | non-crystallizable | 0.75 | 0.97 | 0.85 | 1000 | | crystallizable | 0.95 | 0.64 | 0.77 | 898 | | accuracy | | | 0.82 | 1898 | | macro avg | 0.85 | 0.81 | 0.81 | 1898 | | weighted avg | 0.85 | 0.82 | 0.81 | 1898 | * on BCrystal test: | | precision | recall | f1-score | support | |--------------------|-----------|--------|----------|---------| | non-crystallizable | 0.73 | 0.97 | 0.83 | 896 | | crystallizable | 0.95 | 0.64 | 0.77 | 891 | | accuracy | | | 0.81 | 1787 | | macro avg | 0.84 | 0.80 | 0.80 | 1787 | | weighted avg | 0.84 | 0.81 | 0.80 | 1787 | * on SP test: | | precision | recall | f1-score | support | |--------------------|-----------|--------|----------|---------| | non-crystallizable | 0.62 | 0.94 | 0.75 | 89 | | crystallizable | 0.95 | 0.66 | 0.78 | 148 | | accuracy | | | 0.76 | 237 | | macro avg | 0.79 | 0.80 | 0.76 | 237 | | weighted avg | 0.83 | 0.76 | 0.77 | 237 | * on TR test: | | precision | recall | f1-score | support | |--------------------|-----------|--------|----------|---------| | non-crystallizable | 0.81 | 0.98 | 0.88 | 638 | | crystallizable | 0.94 | 0.60 | 0.73 | 374 | | accuracy | | | 0.84 | 1012 | | macro avg | 0.87 | 0.79 | 0.81 | 1012 | | weighted avg | 0.86 | 0.84 | 0.83 | 1012 | ## Confusion matrix: * on DeepCrystal test: ``` | 579 | 319 | | 30 | 970 | ``` * on BCrystal test: ``` | 573 | 318 | | 30 | 866 | ``` * on SP test: ``` | 97 | 51 | | 5 | 84 | ``` * on TR test: ``` | 225 | 149 | | 14 | 624 | ``` ## Metrics roc score: * on DeepCrystal test: 0.9403474387527841 * on BCrystal test: 0.9395705567580568 * on SP test: 0.9293197692074097 * on TR test: 0.9561924798417515 Mathews Coefficient: * on DeepCrystal test: 0.6575261170551334 * on BCrystal test: 0.6446356961702661 * on SP test: 0.586069703866632 * on TR test: 0.6587661924247377 NPV: * on DeepCrystal test: 0.97 * on BCrystal test: 0.9665178571428571 * on SP test: 0.9438202247191011 * on TR test: 0.9780564263322884 PPV: * on DeepCrystal test: 0.6447661469933185 * on BCrystal test: 0.6430976430976431 * on SP test: 0.6554054054054054 * on TR test: 0.6016042780748663 Researchers: * [Jayanth Kumar](https://jaykmr.com) * [Kavya Jaykumar](https://www.linkedin.com/in/kavya-jayakumar-6390271b5/) Credits: * [Meta ESMFold2](https://github.com/facebookresearch/esm) * [Huggingface](https://huggingface.co/jaykmr) * [Paperspace Compute Cloud](https://www.paperspace.com/)

bc418ff5036b5269226f145ddde70916

Padomin/t5-base-TEDxJP-0front-1body-1rear

Padomin

t5

20

1

transformers

0

text2text-generation

true

false

cc-by-sa-4.0

null

['te_dx_jp']

null

0

['generated_from_trainer']

true

2,953

false

# t5-base-TEDxJP-0front-1body-1rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4869 - Wer: 0.1801 - Mer: 0.1739 - Wil: 0.2635 - Wip: 0.7365 - Hits: 55253 - Substitutions: 6626 - Deletions: 2708 - Insertions: 2296 - Cer: 0.1411 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6609 | 1.0 | 1457 | 0.5121 | 0.2181 | 0.2049 | 0.2958 | 0.7042 | 54651 | 6867 | 3069 | 4151 | 0.1880 | | 0.5633 | 2.0 | 2914 | 0.4719 | 0.1891 | 0.1817 | 0.2714 | 0.7286 | 55015 | 6654 | 2918 | 2644 | 0.1558 | | 0.5212 | 3.0 | 4371 | 0.4626 | 0.1838 | 0.1771 | 0.2666 | 0.7334 | 55168 | 6635 | 2784 | 2452 | 0.1462 | | 0.4498 | 4.0 | 5828 | 0.4616 | 0.1807 | 0.1747 | 0.2643 | 0.7357 | 55148 | 6630 | 2809 | 2231 | 0.1420 | | 0.4058 | 5.0 | 7285 | 0.4633 | 0.1799 | 0.1739 | 0.2631 | 0.7369 | 55200 | 6592 | 2795 | 2231 | 0.1419 | | 0.3802 | 6.0 | 8742 | 0.4675 | 0.1796 | 0.1733 | 0.2630 | 0.7370 | 55311 | 6636 | 2640 | 2321 | 0.1412 | | 0.4126 | 7.0 | 10199 | 0.4737 | 0.1781 | 0.1724 | 0.2617 | 0.7383 | 55245 | 6595 | 2747 | 2163 | 0.1394 | | 0.3436 | 8.0 | 11656 | 0.4772 | 0.1788 | 0.1729 | 0.2624 | 0.7376 | 55247 | 6616 | 2724 | 2208 | 0.1401 | | 0.3249 | 9.0 | 13113 | 0.4827 | 0.1796 | 0.1735 | 0.2632 | 0.7368 | 55265 | 6635 | 2687 | 2281 | 0.1407 | | 0.3347 | 10.0 | 14570 | 0.4869 | 0.1801 | 0.1739 | 0.2635 | 0.7365 | 55253 | 6626 | 2708 | 2296 | 0.1411 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu116 - Datasets 2.4.0 - Tokenizers 0.12.1

d5cdf8f5f4efdc084393944f92b7af41

timm/coatnet_2_rw_224.sw_in12k

timm

null

4

52

timm

0

image-classification

true

false

apache-2.0

null

['imagenet-12k']

null

0

['image-classification', 'timm']

false

true

21,960

false

# Model card for coatnet_2_rw_224.in12k A timm specific CoAtNet image classification model. Trained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) by Ross Wightman. ### Model Variants in [maxxvit.py](https://github.com/rwightman/pytorch-image-models/blob/main/timm/models/maxxvit.py) MaxxViT covers a number of related model architectures that share a common structure including: - CoAtNet - Combining MBConv (depthwise-separable) convolutional blocks in early stages with self-attention transformer blocks in later stages. - MaxViT - Uniform blocks across all stages, each containing a MBConv (depthwise-separable) convolution block followed by two self-attention blocks with different partitioning schemes (window followed by grid). - CoAtNeXt - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in CoAtNet. All normalization layers are LayerNorm (no BatchNorm). - MaxxViT - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in MaxViT. All normalization layers are LayerNorm (no BatchNorm). - MaxxViT-V2 - A MaxxViT variation that removes the window block attention leaving only ConvNeXt blocks and grid attention w/ more width to compensate. Aside from the major variants listed above, there are more subtle changes from model to model. Any model name with the string `rw` are `timm` specific configs w/ modelling adjustments made to favour PyTorch eager use. These were created while training initial reproductions of the models so there are variations. All models with the string `tf` are models exactly matching Tensorflow based models by the original paper authors with weights ported to PyTorch. This covers a number of MaxViT models. The official CoAtNet models were never released. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 85.0 - GMACs: 15.1 - Activations (M): 49.2 - Image size: 224 x 224 - **Papers:** - CoAtNet: Marrying Convolution and Attention for All Data Sizes: https://arxiv.org/abs/2201.03545 - **Dataset:** ImageNet-12k ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('coatnet_2_rw_224.in12k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'coatnet_2_rw_224.in12k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 128, 192, 192]) # torch.Size([1, 128, 96, 96]) # torch.Size([1, 256, 48, 48]) # torch.Size([1, 512, 24, 24]) # torch.Size([1, 1024, 12, 12]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'coatnet_2_rw_224.in12k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled (ie.e a (batch_size, num_features, H, W) tensor output = model.forward_head(output, pre_logits=True) # output is (batch_size, num_features) tensor ``` ## Model Comparison ### By Top-1 |model |top1 |top5 |samples / sec |Params (M) |GMAC |Act (M)| |------------------------------------------------------------------------------------------------------------------------|----:|----:|--------------:|--------------:|-----:|------:| |[maxvit_xlarge_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) |88.53|98.64| 21.76| 475.77|534.14|1413.22| |[maxvit_xlarge_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) |88.32|98.54| 42.53| 475.32|292.78| 668.76| |[maxvit_base_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) |88.20|98.53| 50.87| 119.88|138.02| 703.99| |[maxvit_large_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) |88.04|98.40| 36.42| 212.33|244.75| 942.15| |[maxvit_large_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) |87.98|98.56| 71.75| 212.03|132.55| 445.84| |[maxvit_base_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) |87.92|98.54| 104.71| 119.65| 73.80| 332.90| |[maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.81|98.37| 106.55| 116.14| 70.97| 318.95| |[maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.47|98.37| 149.49| 116.09| 72.98| 213.74| |[coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k) |87.39|98.31| 160.80| 73.88| 47.69| 209.43| |[maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.89|98.02| 375.86| 116.14| 23.15| 92.64| |[maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.64|98.02| 501.03| 116.09| 24.20| 62.77| |[maxvit_base_tf_512.in1k](https://huggingface.co/timm/maxvit_base_tf_512.in1k) |86.60|97.92| 50.75| 119.88|138.02| 703.99| |[coatnet_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_2_rw_224.sw_in12k_ft_in1k) |86.57|97.89| 631.88| 73.87| 15.09| 49.22| |[maxvit_large_tf_512.in1k](https://huggingface.co/timm/maxvit_large_tf_512.in1k) |86.52|97.88| 36.04| 212.33|244.75| 942.15| |[coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k) |86.49|97.90| 620.58| 73.88| 15.18| 54.78| |[maxvit_base_tf_384.in1k](https://huggingface.co/timm/maxvit_base_tf_384.in1k) |86.29|97.80| 101.09| 119.65| 73.80| 332.90| |[maxvit_large_tf_384.in1k](https://huggingface.co/timm/maxvit_large_tf_384.in1k) |86.23|97.69| 70.56| 212.03|132.55| 445.84| |[maxvit_small_tf_512.in1k](https://huggingface.co/timm/maxvit_small_tf_512.in1k) |86.10|97.76| 88.63| 69.13| 67.26| 383.77| |[maxvit_tiny_tf_512.in1k](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) |85.67|97.58| 144.25| 31.05| 33.49| 257.59| |[maxvit_small_tf_384.in1k](https://huggingface.co/timm/maxvit_small_tf_384.in1k) |85.54|97.46| 188.35| 69.02| 35.87| 183.65| |[maxvit_tiny_tf_384.in1k](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) |85.11|97.38| 293.46| 30.98| 17.53| 123.42| |[maxvit_large_tf_224.in1k](https://huggingface.co/timm/maxvit_large_tf_224.in1k) |84.93|96.97| 247.71| 211.79| 43.68| 127.35| |[coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k) |84.90|96.96| 1025.45| 41.72| 8.11| 40.13| |[maxvit_base_tf_224.in1k](https://huggingface.co/timm/maxvit_base_tf_224.in1k) |84.85|96.99| 358.25| 119.47| 24.04| 95.01| |[maxxvit_rmlp_small_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_small_rw_256.sw_in1k) |84.63|97.06| 575.53| 66.01| 14.67| 58.38| |[coatnet_rmlp_2_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in1k) |84.61|96.74| 625.81| 73.88| 15.18| 54.78| |[maxvit_rmlp_small_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_small_rw_224.sw_in1k) |84.49|96.76| 693.82| 64.90| 10.75| 49.30| |[maxvit_small_tf_224.in1k](https://huggingface.co/timm/maxvit_small_tf_224.in1k) |84.43|96.83| 647.96| 68.93| 11.66| 53.17| |[maxvit_rmlp_tiny_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_tiny_rw_256.sw_in1k) |84.23|96.78| 807.21| 29.15| 6.77| 46.92| |[coatnet_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_1_rw_224.sw_in1k) |83.62|96.38| 989.59| 41.72| 8.04| 34.60| |[maxvit_tiny_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_tiny_rw_224.sw_in1k) |83.50|96.50| 1100.53| 29.06| 5.11| 33.11| |[maxvit_tiny_tf_224.in1k](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) |83.41|96.59| 1004.94| 30.92| 5.60| 35.78| |[coatnet_rmlp_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw_224.sw_in1k) |83.36|96.45| 1093.03| 41.69| 7.85| 35.47| |[maxxvitv2_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvitv2_nano_rw_256.sw_in1k) |83.11|96.33| 1276.88| 23.70| 6.26| 23.05| |[maxxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_nano_rw_256.sw_in1k) |83.03|96.34| 1341.24| 16.78| 4.37| 26.05| |[maxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_nano_rw_256.sw_in1k) |82.96|96.26| 1283.24| 15.50| 4.47| 31.92| |[maxvit_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_nano_rw_256.sw_in1k) |82.93|96.23| 1218.17| 15.45| 4.46| 30.28| |[coatnet_bn_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_bn_0_rw_224.sw_in1k) |82.39|96.19| 1600.14| 27.44| 4.67| 22.04| |[coatnet_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_0_rw_224.sw_in1k) |82.39|95.84| 1831.21| 27.44| 4.43| 18.73| |[coatnet_rmlp_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_nano_rw_224.sw_in1k) |82.05|95.87| 2109.09| 15.15| 2.62| 20.34| |[coatnext_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnext_nano_rw_224.sw_in1k) |81.95|95.92| 2525.52| 14.70| 2.47| 12.80| |[coatnet_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_nano_rw_224.sw_in1k) |81.70|95.64| 2344.52| 15.14| 2.41| 15.41| |[maxvit_rmlp_pico_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_pico_rw_256.sw_in1k) |80.53|95.21| 1594.71| 7.52| 1.85| 24.86| ### By Throughput (samples / sec) |model |top1 |top5 |samples / sec |Params (M) |GMAC |Act (M)| |------------------------------------------------------------------------------------------------------------------------|----:|----:|--------------:|--------------:|-----:|------:| |[coatnext_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnext_nano_rw_224.sw_in1k) |81.95|95.92| 2525.52| 14.70| 2.47| 12.80| |[coatnet_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_nano_rw_224.sw_in1k) |81.70|95.64| 2344.52| 15.14| 2.41| 15.41| |[coatnet_rmlp_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_nano_rw_224.sw_in1k) |82.05|95.87| 2109.09| 15.15| 2.62| 20.34| |[coatnet_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_0_rw_224.sw_in1k) |82.39|95.84| 1831.21| 27.44| 4.43| 18.73| |[coatnet_bn_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_bn_0_rw_224.sw_in1k) |82.39|96.19| 1600.14| 27.44| 4.67| 22.04| |[maxvit_rmlp_pico_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_pico_rw_256.sw_in1k) |80.53|95.21| 1594.71| 7.52| 1.85| 24.86| |[maxxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_nano_rw_256.sw_in1k) |83.03|96.34| 1341.24| 16.78| 4.37| 26.05| |[maxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_nano_rw_256.sw_in1k) |82.96|96.26| 1283.24| 15.50| 4.47| 31.92| |[maxxvitv2_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvitv2_nano_rw_256.sw_in1k) |83.11|96.33| 1276.88| 23.70| 6.26| 23.05| |[maxvit_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_nano_rw_256.sw_in1k) |82.93|96.23| 1218.17| 15.45| 4.46| 30.28| |[maxvit_tiny_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_tiny_rw_224.sw_in1k) |83.50|96.50| 1100.53| 29.06| 5.11| 33.11| |[coatnet_rmlp_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw_224.sw_in1k) |83.36|96.45| 1093.03| 41.69| 7.85| 35.47| |[coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k) |84.90|96.96| 1025.45| 41.72| 8.11| 40.13| |[maxvit_tiny_tf_224.in1k](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) |83.41|96.59| 1004.94| 30.92| 5.60| 35.78| |[coatnet_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_1_rw_224.sw_in1k) |83.62|96.38| 989.59| 41.72| 8.04| 34.60| |[maxvit_rmlp_tiny_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_tiny_rw_256.sw_in1k) |84.23|96.78| 807.21| 29.15| 6.77| 46.92| |[maxvit_rmlp_small_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_small_rw_224.sw_in1k) |84.49|96.76| 693.82| 64.90| 10.75| 49.30| |[maxvit_small_tf_224.in1k](https://huggingface.co/timm/maxvit_small_tf_224.in1k) |84.43|96.83| 647.96| 68.93| 11.66| 53.17| |[coatnet_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_2_rw_224.sw_in12k_ft_in1k) |86.57|97.89| 631.88| 73.87| 15.09| 49.22| |[coatnet_rmlp_2_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in1k) |84.61|96.74| 625.81| 73.88| 15.18| 54.78| |[coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k) |86.49|97.90| 620.58| 73.88| 15.18| 54.78| |[maxxvit_rmlp_small_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_small_rw_256.sw_in1k) |84.63|97.06| 575.53| 66.01| 14.67| 58.38| |[maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.64|98.02| 501.03| 116.09| 24.20| 62.77| |[maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.89|98.02| 375.86| 116.14| 23.15| 92.64| |[maxvit_base_tf_224.in1k](https://huggingface.co/timm/maxvit_base_tf_224.in1k) |84.85|96.99| 358.25| 119.47| 24.04| 95.01| |[maxvit_tiny_tf_384.in1k](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) |85.11|97.38| 293.46| 30.98| 17.53| 123.42| |[maxvit_large_tf_224.in1k](https://huggingface.co/timm/maxvit_large_tf_224.in1k) |84.93|96.97| 247.71| 211.79| 43.68| 127.35| |[maxvit_small_tf_384.in1k](https://huggingface.co/timm/maxvit_small_tf_384.in1k) |85.54|97.46| 188.35| 69.02| 35.87| 183.65| |[coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k) |87.39|98.31| 160.80| 73.88| 47.69| 209.43| |[maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.47|98.37| 149.49| 116.09| 72.98| 213.74| |[maxvit_tiny_tf_512.in1k](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) |85.67|97.58| 144.25| 31.05| 33.49| 257.59| |[maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.81|98.37| 106.55| 116.14| 70.97| 318.95| |[maxvit_base_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) |87.92|98.54| 104.71| 119.65| 73.80| 332.90| |[maxvit_base_tf_384.in1k](https://huggingface.co/timm/maxvit_base_tf_384.in1k) |86.29|97.80| 101.09| 119.65| 73.80| 332.90| |[maxvit_small_tf_512.in1k](https://huggingface.co/timm/maxvit_small_tf_512.in1k) |86.10|97.76| 88.63| 69.13| 67.26| 383.77| |[maxvit_large_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) |87.98|98.56| 71.75| 212.03|132.55| 445.84| |[maxvit_large_tf_384.in1k](https://huggingface.co/timm/maxvit_large_tf_384.in1k) |86.23|97.69| 70.56| 212.03|132.55| 445.84| |[maxvit_base_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) |88.20|98.53| 50.87| 119.88|138.02| 703.99| |[maxvit_base_tf_512.in1k](https://huggingface.co/timm/maxvit_base_tf_512.in1k) |86.60|97.92| 50.75| 119.88|138.02| 703.99| |[maxvit_xlarge_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) |88.32|98.54| 42.53| 475.32|292.78| 668.76| |[maxvit_large_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) |88.04|98.40| 36.42| 212.33|244.75| 942.15| |[maxvit_large_tf_512.in1k](https://huggingface.co/timm/maxvit_large_tf_512.in1k) |86.52|97.88| 36.04| 212.33|244.75| 942.15| |[maxvit_xlarge_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) |88.53|98.64| 21.76| 475.77|534.14|1413.22| ## Citation ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} } ``` ```bibtex @article{tu2022maxvit, title={MaxViT: Multi-Axis Vision Transformer}, author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao}, journal={ECCV}, year={2022}, } ``` ```bibtex @article{dai2021coatnet, title={CoAtNet: Marrying Convolution and Attention for All Data Sizes}, author={Dai, Zihang and Liu, Hanxiao and Le, Quoc V and Tan, Mingxing}, journal={arXiv preprint arXiv:2106.04803}, year={2021} } ```

bb7f9d5099533a0c3078e5c0235dd7af

derhuli/vit-base-beans

derhuli

vit

9

11

transformers

0

image-classification

true

false

apache-2.0

null

['beans']

null

0

['generated_from_trainer']

true

1,319

false

# vit-base-beans This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0410 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0751 | 1.54 | 100 | 0.0768 | 0.9850 | | 0.0121 | 3.08 | 200 | 0.0410 | 0.9925 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.10.0 - Datasets 2.7.1 - Tokenizers 0.13.2

1d6867ca66c92241aa6bb1434e508622

pietrotrope/hate_trained

pietrotrope

distilbert

10

3

transformers

0

text-classification

true

false

apache-2.0

null

['tweet_eval']

null

0

['generated_from_trainer']

true

1,389

false

# hate_trained This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9661 - F1: 0.7730 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.303025140957233e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4767 | 1.0 | 2250 | 0.5334 | 0.7717 | | 0.4342 | 2.0 | 4500 | 0.7633 | 0.7627 | | 0.3813 | 3.0 | 6750 | 0.9452 | 0.7614 | | 0.3118 | 4.0 | 9000 | 0.9661 | 0.7730 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

2f7569a203b153cc38fb96550ef34cae

Leizhang/xlm-roberta-base-finetuned-panx-de-fr

Leizhang

xlm-roberta

10

5

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,321

false

# xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1631 - F1: 0.8579 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2878 | 1.0 | 715 | 0.1840 | 0.8247 | | 0.1456 | 2.0 | 1430 | 0.1596 | 0.8473 | | 0.0925 | 3.0 | 2145 | 0.1631 | 0.8579 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

b71d74fdebaceee3888a3c5d2f04da19

janeel/muppet-roberta-base-finetuned-squad

janeel

roberta

13

5

transformers

2

question-answering

true

false

mit

null

['squad_v2']

null

1

0

['generated_from_trainer']

true

1,241

false

# muppet-roberta-base-finetuned-squad This model is a fine-tuned version of [facebook/muppet-roberta-base](https://huggingface.co/facebook/muppet-roberta-base) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9017 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.7007 | 1.0 | 8239 | 0.7905 | | 0.4719 | 2.0 | 16478 | 0.9017 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

e80d1f325b7f1d103591d88a85f117a8

Helsinki-NLP/opus-mt-fr-ilo

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-fr-ilo * source languages: fr * target languages: ilo * OPUS readme: [fr-ilo](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-ilo/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-ilo/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-ilo/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-ilo/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.fr.ilo | 30.6 | 0.528 |

05b8c0270f25c48e81bcafb61806b872

Salesforce/codegen-350M-mono

Salesforce

codegen

10

12,253

transformers

20

text-generation

true

false

bsd-3-clause

null

0

1

0

[]

false

true

2,949

false

# CodeGen (CodeGen-Mono 350M) ## Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in [this repository](https://github.com/salesforce/CodeGen), under 3 pre-training data variants (`NL`, `Multi`, `Mono`) and 4 model size variants (`350M`, `2B`, `6B`, `16B`). The checkpoint included in this repository is denoted as **CodeGen-Mono 350M** in the paper, where "Mono" means the model is initialized with *CodeGen-Multi 350M* and further pre-trained on a Python programming language dataset, and "350M" refers to the number of trainable parameters. ## Training data This checkpoint (CodeGen-Mono 350M) was firstly initialized with *CodeGen-Multi 350M*, and then pre-trained on BigPython dataset. The data consists of 71.7B tokens of Python programming language. See Section 2.1 of the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Training procedure CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs. The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism. See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Evaluation results We evaluate our models on two code generation benchmark: HumanEval and MTPB. Please refer to the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Intended Use and Limitations As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well. ## How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-mono") text = "def hello_world():" input_ids = tokenizer(text, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=128) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) ``` ## BibTeX entry and citation info ```bibtex @article{Nijkamp2022ACP, title={A Conversational Paradigm for Program Synthesis}, author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming}, journal={arXiv preprint}, year={2022} } ```

b82ae34b496234c879ede059390662bd

PhilSad/gpt-scp-neo-125M

PhilSad

gpt_neo

15

6

transformers

0

text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,036

false

# output_gptneo125-2 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: tpu - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0

d56f52a44fb8beafd7ff51bd78463bb5

aipicasso/cool-japan-diffusion-2-1-1

aipicasso

null

21

553

diffusers

18

text-to-image

false

other

null

1

0

1

0

['stable-diffusion', 'text-to-image']

false

true

6,948

false

# Cool Japan Diffusion 2.1.1 Model Card ![アイキャッチ](eyecatch.jpg) [注意事项。中国将对图像生成的人工智能实施法律限制。 ](http://www.cac.gov.cn/2022-12/11/c_1672221949318230.htm) （中国国内にいる人への警告） English version is [here](README_en.md). # はじめに Cool Japan Diffusion はStable Diffsionをファインチューニングして、アニメやマンガ、ゲームなどのクールジャパンを表現することに特化したモデルです。なお、内閣府のクールジャパン戦略とは特に関係はありません。 # ライセンスについてライセンスについては、もとのライセンス CreativeML Open RAIL++-M License に例外を除き商用利用禁止を追加しただけです。例外を除き商用利用禁止を追加した理由は創作業界に悪影響を及ぼしかねないという懸念からです。この懸念が払拭されれば、次のバージョンから元のライセンスに戻し、商用利用可能とします。ちなみに、元のライセンスの日本語訳は[こちら](https://qiita.com/robitan/items/887d9f3153963114823d)になります。営利企業にいる方は法務部にいる人と相談してください。趣味で利用する方はあまり気にしなくても一般常識を守れば大丈夫なはずです。なお、ライセンスにある通り、このモデルを改造しても、このライセンスを引き継ぐ必要があります。 # 法律や倫理について本モデルは日本にて作成されました。したがって、日本の法律が適用されます。本モデルの学習は、著作権法第30条の4に基づき、合法であると主張します。また、本モデルの配布については、著作権法や刑法175条に照らしてみても、正犯や幇助犯にも該当しないと主張します。詳しくは柿沼弁護士の[見解](https://twitter.com/tka0120/status/1601483633436393473?s=20&t=yvM9EX0Em-_7lh8NJln3IQ)を御覧ください。ただし、ライセンスにもある通り、本モデルの生成物は各種法令に従って取り扱って下さい。しかし、本モデルを配布する行為が倫理的に良くないとは作者は思っています。これは学習する著作物に対して著作者の許可を得ていないためです。ただし、学習するには著作者の許可は法律上必要もなく、検索エンジンと同様法律上は問題はありません。したがって、法的な側面ではなく、倫理的な側面を調査する目的も本配布は兼ねていると考えてください。 # 使い方手軽に楽しみたい方は、こちらの[Space](https://huggingface.co/spaces/aipicasso/cool-japan-diffusion-latest-demo)をお使いください。詳しい本モデルの取り扱い方は[こちらの取扱説明書](https://alfredplpl.hatenablog.com/entry/2023/01/11/182146)にかかれています。モデルは[ここ](https://huggingface.co/aipicasso/cool-japan-diffusion-2-1-1/resolve/main/v2-1-1.ckpt)からダウンロードできます。以下、一般的なモデルカードの日本語訳です。 ## モデル詳細 - **開発者:** Robin Rombach, Patrick Esser, Alfred Increment - **モデルタイプ:** 拡散モデルベースの text-to-image 生成モデル - **言語:** 日本語 - **ライセンス:** CreativeML Open RAIL++-M-NC License - **モデルの説明:** このモデルはプロンプトに応じて適切な画像を生成することができます。アルゴリズムは [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) と [OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip) です。 - **補足:** - **参考文献:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## モデルの使用例 Stable Diffusion v2と同じ使い方です。たくさんの方法がありますが、２つのパターンを提供します。 - Web UI - Diffusers ### Web UIの場合こちらの[取扱説明書](https://alfredplpl.hatenablog.com/entry/2023/01/11/182146)に従って作成してください。 ### Diffusersの場合 [🤗's Diffusers library](https://github.com/huggingface/diffusers) を使ってください。まずは、以下のスクリプトを実行し、ライブラリをいれてください。 ```bash pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy ``` 次のスクリプトを実行し、画像を生成してください。 ```python from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler import torch model_id = "aipicasso/cool-japan-diffusion-2-1-1" scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "anime, masterpiece, a portrait of a girl, good pupil, 4k, detailed" negative_prompt="deformed, blurry, bad anatomy, bad pupil, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, bad hands, fused fingers, messy drawing, broken legs censor, low quality, mutated hands and fingers, long body, mutation, poorly drawn, bad eyes, ui, error, missing fingers, fused fingers, one hand with more than 5 fingers, one hand with less than 5 fingers, one hand with more than 5 digit, one hand with less than 5 digit, extra digit, fewer digits, fused digit, missing digit, bad digit, liquid digit, long body, uncoordinated body, unnatural body, lowres, jpeg artifacts, 3d, cg, text, japanese kanji" images = pipe(prompt,negative_prompt=negative_prompt, num_inference_steps=20).images images[0].save("girl.png") ``` **注意**: - [xformers](https://github.com/facebookresearch/xformers) を使うと早くなるらしいです。 - GPUを使う際にGPUのメモリが少ない人は `pipe.enable_attention_slicing()` を使ってください。 #### 想定される用途 - コンテスト - [AIアートグランプリ](https://www.aiartgrandprix.com/)への投稿 - ファインチューニングに用いた全データを開示し、審査基準を満たしていることを判断してもらうようにします。 - コンテストに向けて、要望があれば、Hugging Face の Community などで私に伝えてください。 - 画像生成AIに関する報道 - 公共放送だけでなく、営利企業でも可能 - 画像合成AIに関する情報を「知る権利」は創作業界に悪影響を及ぼさないと判断したためです。また、報道の自由などを尊重しました。 - クールジャパンの紹介 - 他国の人にクールジャパンとはなにかを説明すること。 - 他国の留学生はクールジャパンに惹かれて日本に来ることがおおくあります。そこで、クールジャパンが日本では「クールでない」とされていることにがっかりされることがとても多いとAlfred Incrementは感じております。他国の人が憧れる自国の文化をもっと誇りに思ってください。 - 研究開発 - Discord上でのモデルの利用 - プロンプトエンジニアリング - ファインチューニング（追加学習とも） - DreamBooth など - 他のモデルとのマージ - Latent Diffusion Modelとクールジャパンとの相性 - 本モデルの性能をFIDなどで調べること - 本モデルがStable Diffusion以外のモデルとは独立であることをチェックサムやハッシュ関数などで調べること - 教育 - 美大生や専門学校生の卒業制作 - 大学生の卒業論文や課題制作 - 先生が画像生成AIの現状を伝えること - 自己表現 - SNS上で自分の感情や思考を表現すること - Hugging Face の Community にかいてある用途 - 日本語か英語で質問してください #### 想定されない用途 - 物事を事実として表現するようなこと - 収益化されているYouTubeなどのコンテンツへの使用 - 商用のサービスとして直接提供すること - 先生を困らせるようなこと - その他、創作業界に悪影響を及ぼすこと # 使用してはいけない用途や悪意のある用途 - デジタル贋作 ([Digital Forgery](https://arxiv.org/abs/2212.03860)) は公開しないでください（著作権法に違反するおそれ） - 特に既存のキャラクターは公開しないでください（著作権法に違反するおそれ） - なお、学習していない[キャラクターも生成できる](https://twitter.com/ThePioneerJPnew/status/1609074173892235264?s=20&t=-rY1ufzNeIDT3Fm5YdME6g)そうです。（このツイート自体は研究目的として許可しています。） - 他人の作品を無断でImage-to-Imageしないでください（著作権法に違反するおそれ） - わいせつ物を頒布しないでください (刑法175条に違反するおそれ） - いわゆる業界のマナーを守らないようなこと - 事実に基づかないことを事実のように語らないようにしてください（威力業務妨害罪が適用されるおそれ） - フェイクニュース ## モデルの限界やバイアス ### モデルの限界 - よくわかっていない ### バイアス Stable Diffusionと同じバイアスが掛かっています。気をつけてください。 ## 学習 **学習データ** 次のデータを主に使ってStable Diffusionをファインチューニングしています。 - VAEについて - Danbooruなどの無断転載サイトを除いた日本の国内法を遵守したデータ: 60万種類（データ拡張により無限枚作成） - U-Netについて - Danbooruなどの無断転載サイトを除いた日本の国内法を遵守したデータ: 100万ペア **学習プロセス** Stable DiffusionのVAEとU-Netをファインチューニングしました。 - **ハードウェア:** RTX 3090, A6000 - **オプティマイザー:** AdamW - **Gradient Accumulations**: 1 - **バッチサイズ:** 1 ## 評価結果 ## 環境への影響ほとんどありません。 - **ハードウェアタイプ:** RTX 3090, A6000 - **使用時間（単位は時間）:** 600 - **クラウド事業者:** なし - **学習した場所:** 日本 - **カーボン排出量:** そんなにない ## 参考文献 @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } *このモデルカードは [Stable Diffusion v2](https://huggingface.co/stabilityai/stable-diffusion-2/raw/main/README.md) に基づいて、Alfred Incrementがかきました。

8d68b2b2b518394033bf1b8ac6269daa

it5/mt5-small-informal-to-formal

it5

mt5

11

4

transformers

0

text2text-generation

true

apache-2.0

['it']

['yahoo/xformal_it']

{'emissions': '17g', 'source': 'Google Cloud Platform Carbon Footprint', 'training_type': 'fine-tuning', 'geographical_location': 'Eemshaven, Netherlands, Europe', 'hardware_used': '1 TPU v3-8 VM'}

0

['italian', 'sequence-to-sequence', 'style-transfer', 'formality-style-transfer']

true

1,798

false

# mT5 Small for Informal-to-formal Style Transfer 🧐 This repository contains the checkpoint for the [mT5 Small](https://huggingface.co/google/mt5-small) model fine-tuned on Informal-to-formal style transfer on the Italian subset of the XFORMAL dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io). A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach. ## Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines i2f = pipeline("text2text-generation", model='it5/mt5-small-informal-to-formal') i2f("nn capisco xke tt i ragazzi lo fanno") >>> [{"generated_text": "non comprendo perché tutti i ragazzi agiscono così"}] ``` or loaded using autoclasses: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("it5/mt5-small-informal-to-formal") model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-small-informal-to-formal") ``` If you use this model in your research, please cite our work as: ```bibtex @article{sarti-nissim-2022-it5, title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, author={Sarti, Gabriele and Nissim, Malvina}, journal={ArXiv preprint 2203.03759}, url={https://arxiv.org/abs/2203.03759}, year={2022}, month={mar} } ```

f3ed241957699cbe2b72788d0ed8b823

autoevaluate/image-multi-class-classification

autoevaluate

swin

20

60

transformers

1

image-classification

true

false

apache-2.0

null

['mnist', 'autoevaluate/mnist-sample']

null

3

0

['generated_from_trainer']

true

1,336

false

# image-classification This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the mnist dataset. It achieves the following results on the evaluation set: - Loss: 0.0556 - Accuracy: 0.9833 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - 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_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3743 | 1.0 | 422 | 0.0556 | 0.9833 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

f151ab75be8f1f92ad88af604bfb3d00

itsGanni/Canadian_Armed_Forces-clustered

itsGanni

distilbert

8

0

transformers

0

question-answering

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,858

false

# itsGanni/Canadian_Armed_Forces-clustered This model is a fine-tuned version of [nandysoham/0-clustered](https://huggingface.co/nandysoham/0-clustered) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7260 - Train End Logits Accuracy: 0.8160 - Train Start Logits Accuracy: 0.7292 - Validation Loss: 0.5889 - Validation End Logits Accuracy: 1.0 - Validation Start Logits Accuracy: 0.6000 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.7260 | 0.8160 | 0.7292 | 0.5889 | 1.0 | 0.6000 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

c7bd01e05282e90f88dceaa99153aa6c

JeanneRbs/ddpm-butterflies-128

JeanneRbs

null

14

0

diffusers

0

null

false

apache-2.0

['en']

['imagefolder']

null

0

[]

false

true

1,205

false

# ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/JeanneRbs/ddpm-butterflies-128/tensorboard?#scalars)

242eb5b31866c370fec67ec59e2d1f42

rmihaylov/roberta-base-use-qa-theseus-bg

rmihaylov

xlm-roberta

9

4

transformers

0

sentence-similarity

true

false

mit

['bg']

['oscar', 'chitanka', 'wikipedia']

null

0

['torch']

false

true

6,100

false

# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data This is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences. Using the ideas from [Sentence-BERT](https://arxiv.org/abs/2004.09813), the training is based on the idea that a translated sentence should be mapped to the same location in the vector space as the original sentence. The teacher model is the [USE model by Google](https://aclanthology.org/D18-2029/). This model is cased: it does make a difference between bulgarian and Bulgarian. It was trained on private Bulgarian-English parallel data. Then, it was compressed via [progressive module replacing](https://arxiv.org/abs/2002.02925). ### How to use Here is how to use this model in PyTorch: ```python >>> import scipy >>> import torch >>> from transformers import AutoModel, AutoTokenizer >>> >>> model = AutoModel.from_pretrained('rmihaylov/roberta-base-use-qa-theseus-bg') >>> tokenizer = AutoTokenizer.from_pretrained('rmihaylov/roberta-base-use-qa-theseus-bg') >>> >>> query = "Какви са съставките на бисквитките?" >>> >>> answers = [ >>> "Бисквитката е печена или варена храна, която обикновено е малка, плоска и сладка.", >>> "Бисквитките обикновено съдържат брашно, захар и някакъв вид масло или мазнини. Те могат да включват други съставки като стафиди, овес, шоколадов чипс, ядки и др.", >>> "В повечето англоговорящи страни, с изключение на САЩ и Канада, хрупкавите бисквитки се наричат бисквити.", >>> "Бисквитите Chewier понякога се наричат бисквитки дори в Обединеното кралство. Някои бисквитки могат също да бъдат назовавани според формата им, като квадратчета с дата или барове.", >>> "Бисквитките или бисквитите могат да се произвеждат масово във фабрики, направени в малки пекарни или домашно приготвени.", >>> "Вариантите за бисквити или бисквити включват сандвич бисквити, като крем крем, Jammie Dodgers, Bourbons и Oreos, с пълнеж от ружа или конфитюр и понякога потопени в шоколад или друго сладко покритие.", >>> "Бисквитките често се сервират с напитки като мляко, кафе или чай.", >>> "Фабричните бисквитки се продават в магазини за хранителни стоки, магазини за удобство и автомати.", >>> "Американската употреба произлиза от холандското koekje „малка торта“, което е умалително от „koek“ („торта“), което произлиза от средно холандската дума „koke“.", >>> "Cookie Monster е Muppet в дългогодишното детско телевизионно шоу Sesame Street, който е най-известен с ненаситния си апетит към бисквитките и известните си фрази за ядене, като „Me want cookie!“, „Me eat cookie!“ (или просто „COOKIE!“) и „Om nom nom nom“ (казано през уста, пълна с храна).", >>> "Домашните бисквитки обикновено се правят от тесто, оформено на малки топчета и пуснато върху лист с бисквитки. След това се пекат във фурна за 5 до 15 минути, в зависимост от рецептата. Температурата на фурната варира от 250 до 350 градуса.", >>> "Повечето бисквитки със среден размер, ако са направени със захар, брашно и скъсяване, ще съдържат между 100 и 200 калории.", >>> ] >>> >>> query_embedding = model.question(**tokenizer.encode_plus(query, return_tensors='pt')).detach().numpy()[0] >>> >>> corpus, corpus_embeddings = [], [] >>> for answer in answers: >>> value_inputs = tokenizer.encode_plus(answer, answer, return_tensors='pt') >>> embedding = model.answer(**value_inputs).detach().numpy()[0] >>> corpus.append(answer) >>> corpus_embeddings.append(embedding) >>> >>> distances = scipy.spatial.distance.cdist([query_embedding], corpus_embeddings, "cosine")[0] >>> >>> results = zip(range(len(distances)), distances) >>> results = sorted(results, key=lambda x: x[1]) >>> >>> print([[corpus[idx].strip(), (1.0 - distance)] for idx, distance in results]) [['Бисквитките обикновено съдържат брашно, захар и някакъв вид масло или мазнини. Те могат да включват други съставки като стафиди, овес, шоколадов чипс, ядки и др.', 0.5449754306536151], ['Фабричните бисквитки се продават в магазини за хранителни стоки, магазини за удобство и автомати.', 0.5049509545814316], ['В повечето англоговорящи страни, с изключение на САЩ и Канада, хрупкавите бисквитки се наричат \u200b\u200bбисквити.', 0.5029661338050297], ['Бисквитките или бисквитите могат да се произвеждат масово във фабрики, направени в малки пекарни или домашно приготвени.', 0.4991678233218718], ['Вариантите за бисквити или бисквити включват сандвич бисквити, като крем крем, Jammie Dodgers, Bourbons и Oreos, с пълнеж от ружа или конфитюр и понякога потопени в шоколад или друго сладко покритие.', 0.49050297326146386], ['Повечето бисквитки със среден размер, ако са направени със захар, брашно и скъсяване, ще съдържат между 100 и 200 калории.', 0.48950875441294106], ['Бисквитката е печена или варена храна, която обикновено е малка, плоска и сладка.', 0.48646309549536737], ['Бисквитите Chewier понякога се наричат \u200b\u200bбисквитки дори в Обединеното кралство. Някои бисквитки могат също да бъдат назовавани според формата им, като квадратчета с дата или барове.', 0.4840599482604815], ['Cookie Monster е Muppet в дългогодишното детско телевизионно шоу Sesame Street, който е най-известен с ненаситния си апетит към бисквитките и известните си фрази за ядене, като „Me want cookie!“, „Me eat cookie!“ (или просто „COOKIE!“) и „Om nom nom nom“ (казано през уста, пълна с храна).', 0.45209677893728206], ['Домашните бисквитки обикновено се правят от тесто, оформено на малки топчета и пуснато върху лист с бисквитки. След това се пекат във фурна за 5 до 15 минути, в зависимост от рецептата. Температурата на фурната варира от 250 до 350 градуса.', 0.4511516464302119], ['Бисквитките често се сервират с напитки като мляко, кафе или чай.', 0.42364528401677803], ['Американската употреба произлиза от холандското koekje „малка торта“, което е умалително от „koek“ („торта“), което произлиза от средно холандската дума „koke“.', 0.3267314582662877]] ```

f8e4df03a46fa7d840bf5bd8dc049097

inhee/m2m100_418M-finetuned-ko-to-en3

inhee

m2m_100

12

1

transformers

0

text2text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,499

false

# m2m100_418M-finetuned-ko-to-en3 This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5175 - Bleu: 75.215 - Gen Len: 9.726 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 256 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 0.99 | 103 | 2.7756 | 8.9955 | 9.425 | | No log | 1.99 | 206 | 0.7248 | 63.7645 | 9.6421 | | No log | 2.99 | 309 | 0.5175 | 75.215 | 9.726 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

bd35792f34bb8807abb641e7c78ac236

platzi/platzi-vit-model-orlando-murcia

platzi

vit

9

4

transformers

0

image-classification

true

false

apache-2.0

null

['beans']

null

0

['generated_from_trainer']

true

1,240

false

# platzi-vit-model-orlando-murcia This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0532 - Accuracy: 0.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0776 | 3.85 | 500 | 0.0532 | 0.9850 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

53231400fbe0d656d5cd584188e0ffd7

SerdarHelli/SDF-StyleGAN-3D

SerdarHelli

null

3

0

null

3

null

false

other

null

['shapenet']

null

1

0

1

0

['Shape modeling', 'Volumetric models']

false

true

1,419

false

### Model Description - SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation - Zheng, Xin-Yang and Liu, Yang and Wang, Peng-Shuai and Tong, Xin, 2022 The proposed deeplearning model for 3D shape generation called signed distance field (SDF) - SDF-StyleGAN, whicH is based on StyleGAN2. The goal of this approach is to minimize the visual and geometric differences between the generated shapes and a collection of existing shapes. ### Documents - [GitHub Repo](https://github.com/Zhengxinyang/SDF-StyleGAN) - [Paper - SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation](https://arxiv.org/pdf/2206.12055.pdf) ### Datasets ShapeNet is a comprehensive 3D shape dataset created for research in computer graphics, computer vision, robotics and related diciplines. - [Offical Dataset of ShapeNet](https://shapenet.org/) - [author's data preparation script](https://github.com/Zhengxinyang/SDF-StyleGAN) - [author's training data](https://pan.baidu.com/s/1nVS7wlcOz62nYBgjp_M8Yg?pwd=oj1b) ### How to use Training snippets are published under the official GitHub repository above. ### BibTeX Entry and Citation Info ``` @inproceedings{zheng2022sdfstylegan, title = {SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation}, author = {Zheng, Xin-Yang and Liu, Yang and Wang, Peng-Shuai and Tong, Xin}, booktitle = {Comput. Graph. Forum (SGP)}, year = {2022}, } ```

09538bce1a6cacf2a88e719473e546cd

jonatasgrosman/exp_w2v2r_en_xls-r_age_teens-10_sixties-0_s807

jonatasgrosman

wav2vec2

10

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'en']

false

true

476

false

# exp_w2v2r_en_xls-r_age_teens-10_sixties-0_s807 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

6274cc995a405a6db75584fc91160cfd

nikhil6041/wav2vec2-commonvoice-hindi

nikhil6041

wav2vec2

26

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

1,372

false

# wav2vec2-commonvoice-hindi This model is a fine-tuned version of [theainerd/Wav2Vec2-large-xlsr-hindi](https://huggingface.co/theainerd/Wav2Vec2-large-xlsr-hindi) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9825 - Wer: 0.6763 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 20.0 | 100 | 0.8801 | 0.6754 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

690eaea6d040d9e6f782ce2e4c1b4baf

morahil/wav2vec2-hindi-new-3

morahil

wav2vec2

12

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,281

false

# wav2vec2-hindi-new-3 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 2.1206 - eval_wer: 0.8949 - eval_runtime: 20.2358 - eval_samples_per_second: 19.767 - eval_steps_per_second: 2.471 - epoch: 25.8 - step: 1600 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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_scheduler_warmup_steps: 500 - num_epochs: 40 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.2.3.dev0 - Tokenizers 0.12.1

b809926a3e7c7791df3e6f4b642c39b5

research-backup/t5-small-subjqa-vanilla-electronics-qg

research-backup

t5

34

2

transformers

0

text2text-generation

true

false

cc-by-4.0

['en']

['lmqg/qg_subjqa']

null

0

['question generation']

true

4,056

false

# Model Card of `research-backup/t5-small-subjqa-vanilla-electronics-qg` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [t5-small](https://huggingface.co/t5-small) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (electronics) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="en", model="research-backup/t5-small-subjqa-vanilla-electronics-qg") # model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-small-subjqa-vanilla-electronics-qg") output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-subjqa-vanilla-electronics-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) | | Score | Type | Dataset | |:-----------|--------:|:------------|:-----------------------------------------------------------------| | BERTScore | 52.85 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_1 | 2.89 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_2 | 0.7 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_3 | 0 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_4 | 0 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | METEOR | 2.64 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | MoverScore | 49.57 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | ROUGE_L | 2.38 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: electronics - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 512 - max_length_output: 32 - epoch: 1 - batch: 32 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/t5-small-subjqa-vanilla-electronics-qg/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

04d8a2ef1dc8a45b5acf88bb9484cbd7

david-whittaker-td/finetuning-sentiment-model-3000-samples

david-whittaker-td

distilbert

19

12

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,053

false

# finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2973 - Accuracy: 0.88 - F1: 0.8808 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

1cb6f84ab73fa51c56bf8d78ec8f72d1

espnet/kan-bayashi_csmsc_fastspeech

espnet

null

6

espnet

0

text-to-speech

false

cc-by-4.0

['zh']

['csmsc']

null

0

['espnet', 'audio', 'text-to-speech']

false

true

1,797

false

## Example ESPnet2 TTS model ### `kan-bayashi/csmsc_fastspeech` ♻️ Imported from https://zenodo.org/record/3986227/ This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```python # coming soon ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

2d9b5bf2314e2863f4ab6c3a3f0c55b7

Helsinki-NLP/opus-mt-pis-es

Helsinki-NLP

marian

10

8

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-pis-es * source languages: pis * target languages: es * OPUS readme: [pis-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pis-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/pis-es/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pis-es/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pis-es/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.pis.es | 24.1 | 0.421 |

868b07f87077d8c4780e29775f7d27f6

Matthijs/mobilenet_v1_0.75_192

Matthijs

mobilenet_v1

5

9

transformers

0

image-classification

true

false

other

null

['imagenet-1k']

null

0

['vision', 'image-classification']

false

true

2,397

false

# MobileNet V1 MobileNet V1 model pre-trained on ImageNet-1k at resolution 192x192. It was introduced in [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Howard et al, and first released in [this repository](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md). Disclaimer: The team releasing MobileNet V1 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description From the [original README](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md): > MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=mobilenet_v1) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import MobileNetV1FeatureExtractor, MobileNetV1ForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = MobileNetV1FeatureExtractor.from_pretrained("Matthijs/mobilenet_v1_1.0_224") model = MobileNetV1ForImageClassification.from_pretrained("Matthijs/mobilenet_v1_1.0_224") inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Note: This model actually predicts 1001 classes, the 1000 classes from ImageNet plus an extra “background” class (index 0). Currently, both the feature extractor and model support PyTorch.

497e40f4075ef92906d9ba306e470feb

kadirnar/osnet_x1_0_imagenet

kadirnar

null

3

0

null

0

object-detection

false

gpl-3.0

null

0

['object-detection', 'computer-vision', 'sort', 'tracker', 'osnet']

false

true

2,685

false

<div align="center"> <h1> Torchreid-Pip: Packaged version of Torchreid </h1> <h4> <img width="700" alt="teaser" src="https://raw.githubusercontent.com/goksenin-uav/torchreid-pip/main/doc/logo.png"> </h4> </div> This repo is a packaged version of the [Torchreid](https://github.com/KaiyangZhou/deep-person-reid) algorithm. ### Installation ``` pip install torchreid ``` ### Model Description [Learning Generalisable Omni-Scale Representations for Person Re-Identification](https://arxiv.org/abs/1905.00953): [Omni-Scale Feature Learning for Person Re-Identification](https://arxiv.org/abs/1910.06827) [Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch](https://arxiv.org/abs/1910.10093) ### Overview ##### 1. Import ``torchreid`` ```python import torchreid ``` ##### 2. Load data manager ```python datamanager = torchreid.data.ImageDataManager( root="reid-data", sources="market1501", targets="market1501", height=256, width=128, batch_size_train=32, batch_size_test=100, transforms=["random_flip", "random_crop"] ) ``` ##### 3 Build model, optimizer and lr_scheduler ```python model = torchreid.models.build_model( name="resnet50", num_classes=datamanager.num_train_pids, loss="softmax", pretrained=True ) model = model.cuda() optimizer = torchreid.optim.build_optimizer( model, optim="adam", lr=0.0003 ) scheduler = torchreid.optim.build_lr_scheduler( optimizer, lr_scheduler="single_step", stepsize=20 ) ``` ##### 4. Build engine ```python engine = torchreid.engine.ImageSoftmaxEngine( datamanager, model, optimizer=optimizer, scheduler=scheduler, label_smooth=True ) ``` ##### 5. Run training and test ```python engine.run( save_dir="log/resnet50", max_epoch=60, eval_freq=10, print_freq=10, test_only=False ) ``` Citation --------- If you use this code or the models in your research, please give credit to the following papers: ```bibtex @article{torchreid, title={Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch}, author={Zhou, Kaiyang and Xiang, Tao}, journal={arXiv preprint arXiv:1910.10093}, year={2019} } @inproceedings{zhou2019osnet, title={Omni-Scale Feature Learning for Person Re-Identification}, author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao}, booktitle={ICCV}, year={2019} } @article{zhou2021osnet, title={Learning Generalisable Omni-Scale Representations for Person Re-Identification}, author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao}, journal={TPAMI}, year={2021} } ```

dcdfe865eedbfc8baf0ac64edc57a533

tokeron/TRBLLmaker

tokeron

gpt2

14

6

transformers

2

text-generation

true

false

afl-3.0

['eng']

['TRBLLmaker']

null

0

['generative-model']

false

true

1,867

false

[Link to the arxiv](https://arxiv.org/pdf/2212.04917.pdf) [Datasets](https://huggingface.co/datasets/MorVentura/TRBLLmaker) ### About Us Created by [Mor Ventura](https://www.linkedin.com/in/mor-ventura/) and [Michael Toker](https://www.linkedin.com/in/mnlp/). --- tags: - generated_from_trainer metrics: - accuracy model-index: - name: checkpoint_gpt2-medium_lyrics_meaning_2022-03-10-16-16-32 results: [] ---  # checkpoint_gpt2-medium_lyrics_meaning_2022-03-10-16-16-32 This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5896 - Accuracy: 0.4923 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7781 | 1.01 | 128 | 2.6284 | 0.4875 | | 2.6217 | 2.02 | 256 | 2.6022 | 0.4908 | | 2.569 | 3.02 | 384 | 2.5928 | 0.4917 | ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.1 - Datasets 1.18.3 - Tokenizers 0.11.6

3a547f9a898920b243ec3f8bf102dcc3

gokuls/mobilebert_sa_GLUE_Experiment_data_aug_mrpc_128

gokuls

mobilebert

17

0

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

3,614

false

# mobilebert_sa_GLUE_Experiment_data_aug_mrpc_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.0 - Accuracy: 1.0 - F1: 1.0 - Combined Score: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.2019 | 1.0 | 1959 | 0.0211 | 0.9926 | 0.9947 | 0.9936 | | 0.0464 | 2.0 | 3918 | 0.0122 | 0.9951 | 0.9964 | 0.9958 | | 0.0307 | 3.0 | 5877 | 0.0049 | 0.9975 | 0.9982 | 0.9979 | | 0.0223 | 4.0 | 7836 | 0.0041 | 0.9975 | 0.9982 | 0.9979 | | 0.0179 | 5.0 | 9795 | 0.0006 | 1.0 | 1.0 | 1.0 | | 0.0147 | 6.0 | 11754 | 0.0005 | 1.0 | 1.0 | 1.0 | | 0.012 | 7.0 | 13713 | 0.0001 | 1.0 | 1.0 | 1.0 | | 0.0086 | 8.0 | 15672 | 0.0001 | 1.0 | 1.0 | 1.0 | | 0.0064 | 9.0 | 17631 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0058 | 10.0 | 19590 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0043 | 11.0 | 21549 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0035 | 12.0 | 23508 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.003 | 13.0 | 25467 | 0.0001 | 1.0 | 1.0 | 1.0 | | 0.0024 | 14.0 | 27426 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0018 | 15.0 | 29385 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0017 | 16.0 | 31344 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0014 | 17.0 | 33303 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0014 | 18.0 | 35262 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.001 | 19.0 | 37221 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0008 | 20.0 | 39180 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0009 | 21.0 | 41139 | 0.0 | 1.0 | 1.0 | 1.0 | | 0.0006 | 22.0 | 43098 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0007 | 23.0 | 45057 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0004 | 24.0 | 47016 | 0.0000 | 1.0 | 1.0 | 1.0 | | 0.0007 | 25.0 | 48975 | 0.0 | 1.0 | 1.0 | 1.0 | | 0.0002 | 26.0 | 50934 | 0.0 | 1.0 | 1.0 | 1.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

593c74557ccd91e058e98bdbf48b2613

Intel/roberta-base-mrpc-int8-dynamic

Intel

roberta

5

22

transformers

0

text-classification

false

mit

['en']

['mrpc']

null

0

['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx']

false

true

663

false

# INT8 roberta base finetuned MRPC ## Post-training dynamic quantization ### ONNX This is an INT8 ONNX model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [Intel/roberta-base-mrpc](https://huggingface.co/Intel/roberta-base-mrpc). #### Test result | |INT8|FP32| |---|:---:|:---:| | **Accuracy (eval-f1)** |0.9085|0.9138| | **Model size (MB)** |122|476| #### Load ONNX model: ```python from optimum.onnxruntime import ORTModelForSequenceClassification model = ORTModelForSequenceClassification.from_pretrained('Intel/roberta-base-mrpc-int8-dynamic') ```

e4f42c15c3f714bbaa37e3ee5a76f3f9

GioReg/bertMULTINEGsentiment

GioReg

bert

12

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

947

false

# bertMULTINEGsentiment This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

e8b9485723188c047a527e6a41936d77

choeunsoo/bert-base-uncased-finetuned-cola

choeunsoo

bert

13

14

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

0

['generated_from_trainer']

true

1,553

false

# bert-base-uncased-finetuned-cola This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8347 - Matthews Correlation: 0.5914 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4921 | 1.0 | 535 | 0.5622 | 0.4713 | | 0.301 | 2.0 | 1070 | 0.4454 | 0.5611 | | 0.1999 | 3.0 | 1605 | 0.6690 | 0.5521 | | 0.1437 | 4.0 | 2140 | 0.7627 | 0.5851 | | 0.0915 | 5.0 | 2675 | 0.8347 | 0.5914 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

3d1d35de5f259dcceba0ca4d7fd62368