davanstrien/model_cards_with_readmes · Datasets at Hugging Face

reichenbach/wav2vec2-large-xls-r-300m-hi

reichenbach

wav2vec2

30

9

transformers

1

automatic-speech-recognition

true

false

apache-2.0

['hi']

['common_voice']

null

0

['generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event']

true

1,906

false

# wav2vec2-large-xls-r-300m-hi This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 2.4749 - Wer: 0.9420 ## 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: 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: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.8626 | 4.76 | 400 | 3.6151 | 1.0 | | 3.5463 | 9.52 | 800 | 3.5778 | 1.0 | | 3.4415 | 14.28 | 1200 | 3.4525 | 1.0 | | 3.0927 | 19.05 | 1600 | 2.6220 | 0.9860 | | 2.0573 | 23.8 | 2000 | 2.3974 | 0.9610 | | 1.5905 | 28.57 | 2400 | 2.4427 | 0.9558 | | 1.426 | 33.33 | 2800 | 2.4736 | 0.9475 | | 1.3147 | 38.09 | 3200 | 2.4494 | 0.9417 | | 1.2642 | 42.85 | 3600 | 2.4665 | 0.9450 | | 1.2289 | 47.62 | 4000 | 2.4749 | 0.9420 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.10.3

a983f532738e6c9ec04454fd0d2b3bb7

microsoft/git-large

microsoft

git

10

208

transformers

1

image-to-text

true

false

mit

['en']

null

0

['vision', 'image-captioning']

false

true

2,941

false

# GIT (GenerativeImage2Text), large-sized GIT (short for GenerativeImage2Text) model, large-sized version. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/microsoft/GenerativeImage2Text). Disclaimer: The team releasing GIT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description GIT is a Transformer decoder conditioned on both CLIP image tokens and text tokens. The model is trained using "teacher forcing" on a lot of (image, text) pairs. The goal for the model is simply to predict the next text token, giving the image tokens and previous text tokens. The model has full access to (i.e. a bidirectional attention mask is used for) the image patch tokens, but only has access to the previous text tokens (i.e. a causal attention mask is used for the text tokens) when predicting the next text token. ![GIT architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/git_architecture.jpg) This allows the model to be used for tasks like: - image and video captioning - visual question answering (VQA) on images and videos - even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text). ## Intended uses & limitations You can use the raw model for image captioning. See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for fine-tuned versions on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/git.html). ## Training data From the paper: > We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions (CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016), Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al., 2021a), and an extra 0.6B data following a similar collection procedure in Hu et al. (2021a). => however this is for the model referred to as "GIT" in the paper, which is not open-sourced. This checkpoint is "GIT-large", which is a smaller variant of GIT trained on 20 million image-text pairs. See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details. ### Preprocessing We refer to the original repo regarding details for preprocessing during training. During validation, one resizes the shorter edge of each image, after which center cropping is performed to a fixed-size resolution. Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation. ## Evaluation results For evaluation results, we refer readers to the [paper](https://arxiv.org/abs/2205.14100).

3b96d9f9b3696d8c0794d6344e701575

redevaaa/fin1

redevaaa

bert

14

3

transformers

0

token-classification

true

false

apache-2.0

null

['fin']

null

0

['generated_from_trainer']

true

1,682

false

# fin1 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the fin dataset. It achieves the following results on the evaluation set: - Loss: 0.0778 - Precision: 0.8315 - Recall: 0.9243 - F1: 0.8755 - Accuracy: 0.9852 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 129 | 0.0860 | 0.8535 | 0.9283 | 0.8893 | 0.9904 | | No log | 2.0 | 258 | 0.1513 | 0.7993 | 0.9203 | 0.8556 | 0.9799 | | No log | 3.0 | 387 | 0.0977 | 0.8221 | 0.9203 | 0.8684 | 0.9831 | | 0.0017 | 4.0 | 516 | 0.0783 | 0.8286 | 0.9243 | 0.8738 | 0.9848 | | 0.0017 | 5.0 | 645 | 0.0778 | 0.8315 | 0.9243 | 0.8755 | 0.9852 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

1ab446c17d7b1d1ab5f5f25472d74cb9

bertin-project/bertin-base-gaussian

bertin-project

roberta

71

14

transformers

0

fill-mask

true

false

true

cc-by-4.0

['es']

null

0

['spanish', 'roberta']

false

true

1,206

false

This is a **RoBERTa-base** model trained from scratch in Spanish. The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding more often documents with very large values (poor quality) of very small values (short, repetitive texts). This model has been trained for 250.000 steps. Please see our main [card](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for more information. This is part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google. ## Team members - Eduardo González ([edugp](https://huggingface.co/edugp)) - Javier de la Rosa ([versae](https://huggingface.co/versae)) - Manu Romero ([mrm8488](https://huggingface.co/)) - María Grandury ([mariagrandury](https://huggingface.co/)) - Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps)) - Paulo Villegas ([paulo](https://huggingface.co/paulo))

84910a0becd8acf97fb05a138a805418

sameearif88/wav2vec2-base-timit-demo-colab1

sameearif88

wav2vec2

14

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,342

false

# wav2vec2-base-timit-demo-colab1 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7411 - Wer: 0.5600 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.0773 | 13.89 | 500 | 3.1073 | 1.0 | | 1.2444 | 27.78 | 1000 | 0.7411 | 0.5600 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

7fd7bde366715d6ec8b1b93a2d568db5

Kuaaangwen/bert-base-cased-finetuned-revision-booklet-chemistry

Kuaaangwen

bert

9

3

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,377

false

# bert-base-cased-finetuned-revision-booklet-chemistry This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4864 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 105 | 1.8484 | | No log | 2.0 | 210 | 1.6418 | | No log | 3.0 | 315 | 1.5820 | | No log | 4.0 | 420 | 1.4826 | | 1.8696 | 5.0 | 525 | 1.4521 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

845229438346dc8989b243fd6a17da75

bnsh/ddpm-butterflies-128

bnsh

null

13

0

diffusers

0

null

false

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

null

0

[]

false

true

1,226

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/bnsh/ddpm-butterflies-128/tensorboard?#scalars)

0c16b6282239aed707c26869c67b229f

fathyshalab/massive_alarm-roberta-large-v1-5-50

fathyshalab

roberta

14

2

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

['setfit', 'sentence-transformers', 'text-classification']

false

true

1,460

false

# fathyshalab/massive_alarm-roberta-large-v1-5-50 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/massive_alarm-roberta-large-v1-5-50") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

30144d9fa0f5322d92ecf8afcd2e714d

Jellevdl/checkpoint-24453

Jellevdl

bert

10

19

transformers

0

question-answering

true

false

apache-2.0

null

0

['generated_from_trainer']

true

946

false

# checkpoint-24453 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-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: 12 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 48 - optimizer: Adafactor - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 6000 - num_epochs: 30 ### Framework versions - Transformers 4.18.0 - Pytorch 1.5.0 - Datasets 2.4.0 - Tokenizers 0.12.1

2c0902c01dc6d1a635c1c9735f8fb092

EMBO/sd-geneprod-roles

EMBO

roberta

10

9

transformers

0

token-classification

true

false

agpl-3.0

['english']

['EMBO/sd-nlp']

null

1

0

['token classification']

false

true

3,139

false

# sd-geneprod-roles ## Model description This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of English scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It was then fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `GENEPROD_ROLES` configuration to perform pure context-dependent semantic role classification of bioentities. ## Intended uses & limitations #### How to use The intended use of this model is to infer the semantic role of gene products (genes and proteins) with regard to the causal hypotheses tested in experiments reported in scientific papers. To have a quick check of the model: ```python from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification example = """<s>The <mask> overexpression in cells caused an increase in <mask> expression.</s>""" tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512) model = RobertaForTokenClassification.from_pretrained('EMBO/sd-geneprod-roles') ner = pipeline('ner', model, tokenizer=tokenizer) res = ner(example) for r in res: print(r['word'], r['entity']) ``` #### Limitations and bias The model must be used with the `roberta-base` tokenizer. ## Training data The model was trained for token classification using the [EMBO/sd-nlp dataset](https://huggingface.co/datasets/EMBO/sd-nlp) which includes manually annotated examples. ## Training procedure The training was run on an NVIDIA DGX Station with 4XTesla V100 GPUs. Training code is available at https://github.com/source-data/soda-roberta - Model fine-tuned: EMBL/bio-lm - Tokenizer vocab size: 50265 - Training data: EMBO/sd-nlp - Dataset configuration: GENEPROD_ROLES - Training with 48771 examples. - Evaluating on 13801 examples. - Training on 15 features: O, I-CONTROLLED_VAR, B-CONTROLLED_VAR, I-MEASURED_VAR, B-MEASURED_VAR - Epochs: 0.9 - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 0.0001 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 ## Eval results On 7178 example of test set with `sklearn.metrics`: ``` precision recall f1-score support CONTROLLED_VAR 0.81 0.86 0.83 7835 MEASURED_VAR 0.82 0.85 0.84 9330 micro avg 0.82 0.85 0.83 17165 macro avg 0.82 0.85 0.83 17165 weighted avg 0.82 0.85 0.83 17165 {'test_loss': 0.03846803680062294, 'test_accuracy_score': 0.9854472664459946, 'test_precision': 0.8156312625250501, 'test_recall': 0.8535974366443344, 'test_f1': 0.8341825841897008, 'test_runtime': 58.7369, 'test_samples_per_second': 122.206, 'test_steps_per_second': 1.924} ```

52fa5a191b21623256806eddcbc2ca21

Minxuan/distilbert-base-uncased-finetuned-emotion

Minxuan

distilbert

14

6

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,907

false

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2055 - Accuracy: 0.9355 - F1: 0.9354 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.1775 | 1.0 | 250 | 0.1765 | 0.929 | 0.9287 | | 0.1205 | 2.0 | 500 | 0.1516 | 0.9395 | 0.9393 | | 0.0981 | 3.0 | 750 | 0.1530 | 0.9345 | 0.9351 | | 0.0799 | 4.0 | 1000 | 0.1654 | 0.935 | 0.9348 | | 0.0641 | 5.0 | 1250 | 0.1638 | 0.937 | 0.9364 | | 0.0495 | 6.0 | 1500 | 0.1695 | 0.937 | 0.9369 | | 0.0417 | 7.0 | 1750 | 0.1873 | 0.935 | 0.9350 | | 0.0332 | 8.0 | 2000 | 0.1941 | 0.935 | 0.9351 | | 0.0275 | 9.0 | 2250 | 0.1977 | 0.9385 | 0.9385 | | 0.0224 | 10.0 | 2500 | 0.2055 | 0.9355 | 0.9354 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3

bb9c88944c2fa2fec3e5ec2fe1e5c05e

Shobhank-iiitdwd/Distiled-bert-medium-squad2-QA

Shobhank-iiitdwd

bert

9

11

transformers

0

question-answering

true

false

mit

['en']

['squad_v2']

null

0

['exbert']

true

1,757

false

## Overview **Language model:** deepset/roberta-base-squad2-distilled **Language:** English **Training data:** SQuAD 2.0 training set **Eval data:** SQuAD 2.0 dev set **Infrastructure**: 1x V100 GPU **Published**: Apr 21st, 2021 ## Details - haystack's distillation feature was used for training. deepset/bert-large-uncased-whole-word-masking-squad2 was used as the teacher model. ## Hyperparameters ``` batch_size = 6 n_epochs = 2 max_seq_len = 384 learning_rate = 3e-5 lr_schedule = LinearWarmup embeds_dropout_prob = 0.1 temperature = 5 distillation_loss_weight = 1 ``` ## Performance ``` "exact": 68.6431398972458 "f1": 72.7637083790805 ``` ## Authors - Timo Möller: `timo.moeller [at] deepset.ai` - Julian Risch: `julian.risch [at] deepset.ai` - Malte Pietsch: `malte.pietsch [at] deepset.ai` - Michel Bartels: `michel.bartels [at] deepset.ai` ## About us ![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo) We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems. Some of our work: - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) - [FARM](https://github.com/deepset-ai/FARM) - [Haystack](https://github.com/deepset-ai/haystack/) Get in touch: [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)

14f5618bc875d3281bfbb1ce1bae4fd2

bthomas/article2KW_test1.3_barthez-orangesum-title_finetuned_for_summerization

bthomas

mbart

10

3

transformers

0

summarization

true

false

apache-2.0

null

0

['summarization', 'generated_from_trainer']

true

1,591

false

# article2KW_test1.3_barthez-orangesum-title_finetuned_for_summerization This model is a fine-tuned version of [moussaKam/barthez-orangesum-title](https://huggingface.co/moussaKam/barthez-orangesum-title) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1217 - Rouge1: 0.2933 - Rouge2: 0.0810 - Rougel: 0.2937 - Rougelsum: 0.2933 ## 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: 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 1.6383 | 1.0 | 1497 | 1.3027 | 0.2623 | 0.0709 | 0.2627 | 0.2625 | | 1.201 | 2.0 | 2994 | 1.1714 | 0.2879 | 0.0748 | 0.2888 | 0.2884 | | 1.036 | 3.0 | 4491 | 1.1217 | 0.2933 | 0.0810 | 0.2937 | 0.2933 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.11.0

f55377e4ae9240de6505ea259f600b4a

dxiao/bert-finetuned-ner-60percent

dxiao

bert

12

5

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,525

false

# bert-finetuned-ner-60percent This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5110 - Precision: 0.7917 - Recall: 0.8333 - F1: 0.8120 - Accuracy: 0.9154 ## 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: 2022 - 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 | 45 | 0.5094 | 0.7638 | 0.8303 | 0.7957 | 0.9078 | | No log | 2.0 | 90 | 0.4844 | 0.8137 | 0.8393 | 0.8263 | 0.9207 | | No log | 3.0 | 135 | 0.5110 | 0.7917 | 0.8333 | 0.8120 | 0.9154 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

3a00abbe557b3c50f0bf480aa5f88ecf

lewtun/ccorgi-dog

lewtun

null

17

diffusers

2

text-to-image

true

false

creativeml-openrail-m

null

1

0

['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal']

false

true

719

false

# DreamBooth model for ccorgi trained by lewtun on the lewtun/corgi dataset. This is a Stable Diffusion model fine-tuned the ccorgi concept taught to Stable Diffusion with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of ccorgi dog** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! ## Description This is a Stable Diffusion model fine-tuned on `dog` images for the animal theme. ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('lewtun/ccorgi-dog') image = pipeline().images[0] image ```

85053384314a2e5c433a30fd3000c74c

facebook/mask2former-swin-tiny-cityscapes-panoptic

facebook

mask2former

5

10

transformers

0

image-segmentation

true

false

other

null

['coco']

null

1

0

1

0

['vision', 'image-segmentation']

false

true

2,960

false

# Mask2Former Mask2Former model trained on Cityscapes panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, [MaskFormer](https://arxiv.org/abs/2107.06278) both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png) ## Intended uses & limitations You can use this particular checkpoint for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python import requests import torch from PIL import Image from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation # load Mask2Former fine-tuned on Cityscapes panoptic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-tiny-cityscapes-panoptic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-cityscapes-panoptic") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) # model predicts class_queries_logits of shape `(batch_size, num_queries)` # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` class_queries_logits = outputs.class_queries_logits masks_queries_logits = outputs.masks_queries_logits # you can pass them to processor for postprocessing result = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] # we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs) predicted_panoptic_map = result["segmentation"] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former).

09454ae0f3ff3ee79c094fa4a1782efe

climatebert/distilroberta-base-climate-f

climatebert

roberta

10

697

transformers

9

fill-mask

true

false

apache-2.0

['en']

null

0

[]

false

true

2,946

false

# Model Card for distilroberta-base-climate-f ## Model Description This is the ClimateBERT language model based on the FULL-SELECT sample selection strategy. *Note: We generally recommend choosing this language model over those based on the other sample selection strategies (unless you have good reasons not to). This is also the only language model we will update from time to time.* Using the [DistilRoBERTa](https://huggingface.co/distilroberta-base) model as starting point, the ClimateBERT Language Model is additionally pre-trained on a text corpus comprising climate-related research paper abstracts, corporate and general news and reports from companies. The underlying methodology can be found in our [language model research paper](https://arxiv.org/abs/2110.12010). *Update September 2, 2022: Now additionally pre-trained on an even larger text corpus, comprising >2M paragraphs. If you are looking for the language model before the update (i.e. for reproducibility), just use an older commit like [6be4fbd](https://huggingface.co/climatebert/distilroberta-base-climate-f/tree/6be4fbd3fedfd78ccb3c730c1f166947fbc940ba).* ## Climate performance model card | distilroberta-base-climate-f | | |--------------------------------------------------------------------------|----------------| | 1. Is the resulting model publicly available? | Yes | | 2. How much time does the training of the final model take? | 48 hours | | 3. How much time did all experiments take (incl. hyperparameter search)? | 350 hours | | 4. What was the power of GPU and CPU? | 0.7 kW | | 5. At which geo location were the computations performed? | Germany | | 6. What was the energy mix at the geo location? | 470 gCO2eq/kWh | | 7. How much CO2eq was emitted to train the final model? | 15.79 kg | | 8. How much CO2eq was emitted for all experiments? | 115.15 kg | | 9. What is the average CO2eq emission for the inference of one sample? | 0.62 mg | | 10. Which positive environmental impact can be expected from this work? | This work can be categorized as a building block tools following Jin et al (2021). It supports the training of NLP models in the field of climate change and, thereby, have a positive environmental impact in the future. | | 11. Comments | Block pruning could decrease CO2eq emissions | ## Citation Information ```bibtex @article{wkbl2021, title={ClimateBERT: A Pretrained Language Model for Climate-Related Text}, author={Webersinke, Nicolas and Kraus, Mathias and Bingler, Julia and Leippold, Markus}, journal={arXiv preprint arXiv:2110.12010}, year={2021} } ```

847cf012ade8d026a1aba2b0d7fc4ec1

Maltehb/aelaectra-danish-electra-small-uncased-ner-dane

Maltehb

electra

8

12

transformers

0

token-classification

true

false

mit

['da']

['DAGW']

null

0

['ælæctra', 'pytorch', 'danish', 'ELECTRA-Small', 'replaced token detection']

false

true

6,448

false

# Ælæctra - Finetuned for Named Entity Recognition on the [DaNE dataset](https://danlp.alexandra.dk/304bd159d5de/datasets/ddt.zip) (Hvingelby et al., 2020) by Malte Højmark-Bertelsen. **Ælæctra** is a Danish Transformer-based language model created to enhance the variety of Danish NLP resources with a more efficient model compared to previous state-of-the-art (SOTA) models. Ælæctra was pretrained with the ELECTRA-Small (Clark et al., 2020) pretraining approach by using the Danish Gigaword Corpus (Strømberg-Derczynski et al., 2020) and evaluated on Named Entity Recognition (NER) tasks. Since NER only presents a limited picture of Ælæctra's capabilities I am very interested in further evaluations. Therefore, if you employ it for any task, feel free to hit me up your findings! Ælæctra was, as mentioned, created to enhance the Danish NLP capabilties and please do note how this GitHub still does not support the Danish characters "*Æ, Ø and Å*" as the title of this repository becomes "*-l-ctra*". How ironic.🙂 Here is an example on how to load the finetuned Ælæctra-uncased model for Named Entity Recognition in [PyTorch](https://pytorch.org/) using the [🤗Transformers](https://github.com/huggingface/transformers) library: ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Maltehb/-l-ctra-danish-electra-small-uncased-ner-dane") model = AutoModelForTokenClassification.from_pretrained("Maltehb/-l-ctra-danish-electra-small-uncased-ner-dane") ``` ### Evaluation of current Danish Language Models Ælæctra, Danish BERT (DaBERT) and multilingual BERT (mBERT) were evaluated: | Model | Layers | Hidden Size | Params | AVG NER micro-f1 (DaNE-testset) | Average Inference Time (Sec/Epoch) | Download | | --- | --- | --- | --- | --- | --- | --- | | Ælæctra Uncased | 12 | 256 | 13.7M | 78.03 (SD = 1.28) | 10.91 | [Link for model](https://www.dropbox.com/s/cag7prs1nvdchqs/%C3%86l%C3%A6ctra.zip?dl=0) | | Ælæctra Cased | 12 | 256 | 14.7M | 80.08 (SD = 0.26) | 10.92 | [Link for model](https://www.dropbox.com/s/cag7prs1nvdchqs/%C3%86l%C3%A6ctra.zip?dl=0) | | DaBERT | 12 | 768 | 110M | 84.89 (SD = 0.64) | 43.03 | [Link for model](https://www.dropbox.com/s/19cjaoqvv2jicq9/danish_bert_uncased_v2.zip?dl=1) | | mBERT Uncased | 12 | 768 | 167M | 80.44 (SD = 0.82) | 72.10 | [Link for model](https://storage.googleapis.com/bert_models/2018_11_03/multilingual_L-12_H-768_A-12.zip) | | mBERT Cased | 12 | 768 | 177M | 83.79 (SD = 0.91) | 70.56 | [Link for model](https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip) | On [DaNE](https://danlp.alexandra.dk/304bd159d5de/datasets/ddt.zip) (Hvingelby et al., 2020) without the *MISC-tag*, Ælæctra scores slightly worse than both cased and uncased Multilingual BERT (Devlin et al., 2019) and Danish BERT (Danish BERT, 2019/2020), however, Ælæctra is less than one third the size, and uses significantly fewer computational resources to pretrain and instantiate. ### Pretraining To pretrain Ælæctra it is recommended to build a Docker Container from the [Dockerfile](https://github.com/MalteHB/Ælæctra/tree/master/notebooks/fine-tuning/). Next, simply follow the [pretraining notebooks](https://github.com/MalteHB/Ælæctra/tree/master/infrastructure/Dockerfile/) The pretraining was done by utilizing a single NVIDIA Tesla V100 GPU with 16 GiB, endowed by the Danish data company [KMD](https://www.kmd.dk/). The pretraining took approximately 4 days and 9.5 hours for both the cased and uncased model ### Fine-tuning To fine-tune any Ælæctra model follow the [fine-tuning notebooks](https://github.com/MalteHB/Ælæctra/tree/master/notebooks/fine-tuning/) ### References Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020). ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. ArXiv:2003.10555 [Cs]. http://arxiv.org/abs/2003.10555 Danish BERT. (2020). BotXO. https://github.com/botxo/nordic_bert (Original work published 2019) Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. ArXiv:1810.04805 [Cs]. http://arxiv.org/abs/1810.04805 Hvingelby, R., Pauli, A. B., Barrett, M., Rosted, C., Lidegaard, L. M., & Søgaard, A. (2020). DaNE: A Named Entity Resource for Danish. Proceedings of the 12th Language Resources and Evaluation Conference, 4597–4604. https://www.aclweb.org/anthology/2020.lrec-1.565 Strømberg-Derczynski, L., Baglini, R., Christiansen, M. H., Ciosici, M. R., Dalsgaard, J. A., Fusaroli, R., Henrichsen, P. J., Hvingelby, R., Kirkedal, A., Kjeldsen, A. S., Ladefoged, C., Nielsen, F. Å., Petersen, M. L., Rystrøm, J. H., & Varab, D. (2020). The Danish Gigaword Project. ArXiv:2005.03521 [Cs]. http://arxiv.org/abs/2005.03521 #### Acknowledgements As the majority of this repository is build upon [the works](https://github.com/google-research/electra) by the team at Google who created ELECTRA, a HUGE thanks to them is in order. A Giga thanks also goes out to the incredible people who collected The Danish Gigaword Corpus (Strømberg-Derczynski et al., 2020). Furthermore, I would like to thank my supervisor [Riccardo Fusaroli](https://github.com/fusaroli) for the support with the thesis, and a special thanks goes out to [Kenneth Enevoldsen](https://github.com/KennethEnevoldsen) for his continuous feedback. Lastly, i would like to thank KMD, my colleagues from KMD, and my peers and co-students from Cognitive Science for encouriging me to keep on working hard and holding my head up high! #### Contact For help or further information feel free to connect with the author Malte Højmark-Bertelsen on [hjb@kmd.dk](mailto:hjb@kmd.dk?subject=[GitHub]%20ÆlæctraUncasedNER) or any of the following platforms: [<img align="left" alt="MalteHB | Twitter" width="22px" src="https://cdn.jsdelivr.net/npm/simple-icons@v3/icons/twitter.svg" />][twitter] [<img align="left" alt="MalteHB | LinkedIn" width="22px" src="https://cdn.jsdelivr.net/npm/simple-icons@v3/icons/linkedin.svg" />][linkedin] [<img align="left" alt="MalteHB | Instagram" width="22px" src="https://cdn.jsdelivr.net/npm/simple-icons@v3/icons/instagram.svg" />][instagram] </details> [twitter]: https://twitter.com/malteH_B [instagram]: https://www.instagram.com/maltemusen/ [linkedin]: https://www.linkedin.com/in/malte-h%C3%B8jmark-bertelsen-9a618017b/

c153de9760238320183f4201dd441e2f

eduardopds/bert-finetuned-ner

eduardopds

bert

8

9

transformers

0

token-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,428

false

# eduardopds/bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0269 - Validation Loss: 0.0545 - 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': 2631, '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: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1719 | 0.0627 | 0 | | 0.0457 | 0.0576 | 1 | | 0.0269 | 0.0545 | 2 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.2.1 - Tokenizers 0.12.1

814553436379cb7f246fa1ddd0b64e2f

Salvatore/bert-finetuned-ner

Salvatore

bert

30

3

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,789

false

# bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0997 - Proteinmutation F1: 0.1309 - Snp F1: 0.1953 - Dnamutation F1: 0.3778 - Precision: 0.2380 - Recall: 0.2416 - F1: 0.2398 - Accuracy: 0.9703 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Proteinmutation F1 | Snp F1 | Dnamutation F1 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------------:|:------:|:--------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 324 | 0.0533 | 0.0396 | 0.2830 | 0.4667 | 0.2334 | 0.3221 | 0.2707 | 0.9788 | | 0.1072 | 2.0 | 648 | 0.0437 | 0.6065 | 0.4906 | 0.5009 | 0.4802 | 0.6348 | 0.5468 | 0.9868 | | 0.1072 | 3.0 | 972 | 0.0592 | 0.1379 | 0.2485 | 0.2005 | 0.1639 | 0.2228 | 0.1889 | 0.9731 | | 0.0573 | 4.0 | 1296 | 0.0722 | 0.0749 | 0.2530 | 0.4692 | 0.2705 | 0.2959 | 0.2826 | 0.9749 | | 0.0431 | 5.0 | 1620 | 0.0766 | 0.1574 | 0.1847 | 0.2540 | 0.1766 | 0.2285 | 0.1992 | 0.9723 | | 0.0431 | 6.0 | 1944 | 0.0805 | 0.1099 | 0.2202 | 0.2383 | 0.1657 | 0.2097 | 0.1851 | 0.9715 | | 0.0396 | 7.0 | 2268 | 0.0886 | 0.1337 | 0.2138 | 0.4318 | 0.2683 | 0.2678 | 0.2680 | 0.9724 | | 0.0354 | 8.0 | 2592 | 0.0927 | 0.1535 | 0.2113 | 0.3769 | 0.2505 | 0.2528 | 0.2516 | 0.9714 | | 0.0354 | 9.0 | 2916 | 0.0978 | 0.1011 | 0.2540 | 0.3812 | 0.2495 | 0.2528 | 0.2512 | 0.9705 | | 0.0312 | 10.0 | 3240 | 0.0997 | 0.1309 | 0.1953 | 0.3778 | 0.2380 | 0.2416 | 0.2398 | 0.9703 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2 - Datasets 2.0.0 - Tokenizers 0.12.1

0764f9d4d4b7c34a8dc4ac5b437869ec

espnet/jiyangtang_magicdata_asr_conformer_lm_transformer

espnet

null

32

3

espnet

0

automatic-speech-recognition

false

cc-by-4.0

['zh']

['magicdata']

null

0

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

false

true

24,651

false

## ESPnet2 ASR model ### `espnet/jiyangtang_magicdata_asr_conformer_lm_transformer` This model was trained by Jiyang Tang using magicdata 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 9d0f3b3e1be6650d38cc5008518f445308fe06d9 pip install -e . cd egs2/magicdata/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/jiyangtang_magicdata_asr_conformer_lm_transformer ```  # RESULTS ## Environments - date: `Wed Sep 21 01:11:58 EDT 2022` - python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]` - espnet version: `espnet 202207` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `9d0f3b3e1be6650d38cc5008518f445308fe06d9` - Commit date: `Mon Sep 19 20:27:41 2022 -0400` ## asr_train_asr_raw_zh_char_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test|24279|24286|84.4|15.6|0.0|0.0|15.6|15.6| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test|24279|243325|96.4|1.7|2.0|0.1|3.7|15.6| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| ## ASR config <details><summary>expand</summary> ``` config: conf/train_asr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_raw_zh_char_sp ngpu: 0 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: null 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: 20 patience: null val_scheduler_criterion: - valid - acc early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false 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: null batch_size: 20 valid_batch_size: null batch_bins: 20000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_zh_char_sp/train/speech_shape - exp/asr_stats_raw_zh_char_sp/train/text_shape.char valid_shape_file: - exp/asr_stats_raw_zh_char_sp/valid/speech_shape - exp/asr_stats_raw_zh_char_sp/valid/text_shape.char 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_noeng_sp/wav.scp - speech - sound - - dump/raw/train_noeng_sp/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: 0.0005 scheduler: warmuplr scheduler_conf: warmup_steps: 30000 token_list: - <blank> - <unk> - 的 - 我 - 一 - 歌 - 你 - 天 - 不 - 了 - 放 - 来 - 播 - 下 - 个 - 是 - 有 - 给 - 首 - 好 - 请 - 在 - 听 - 么 - 气 - 要 - 想 - 曲 - 上 - 吗 - 去 - 到 - 这 - 啊 - 点 - 那 - 没 - 就 - 说 - 大 - 唱 - 人 - 最 - 第 - 看 - 会 - 明 - 集 - 吧 - 音 - 还 - 乐 - 今 - 电 - 开 - 能 - 度 - 哪 - 里 - 多 - 打 - 十 - 可 - 怎 - 道 - 什 - 新 - 雨 - 以 - 家 - 回 - 话 - 儿 - 他 - 时 - 小 - 温 - 样 - 爱 - 都 - 吃 - 呢 - 知 - 谁 - 为 - 子 - 们 - 也 - 过 - 老 - 很 - 出 - 中 - 现 - 冷 - 和 - 情 - 行 - 心 - 发 - 专 - 几 - 视 - 张 - 事 - 二 - 辑 - 五 - 三 - 后 - 找 - 些 - 早 - 学 - 晚 - 车 - 别 - 演 - 手 - 呀 - 调 - 感 - 问 - 九 - 饭 - 快 - 风 - 得 - 如 - 自 - 生 - 少 - 地 - 用 - 叫 - 帮 - 机 - 台 - 班 - 欢 - 候 - 起 - 等 - 把 - 年 - 干 - 高 - 太 - 啦 - 方 - 提 - 面 - 八 - 四 - 信 - 意 - 王 - 真 - 求 - 热 - 喜 - 觉 - 周 - 近 - 名 - 做 - 公 - 告 - 关 - 六 - 字 - 安 - 再 - 变 - 间 - 国 - 分 - 着 - 哈 - 水 - 节 - 只 - 动 - 北 - 刚 - 空 - 月 - 玩 - 让 - 伤 - 东 - 谢 - 网 - 七 - 见 - 之 - 比 - 杰 - 又 - 买 - 对 - 始 - 无 - 查 - 声 - 文 - 经 - 醒 - 美 - 西 - 哦 - 走 - 两 - 海 - 妈 - 李 - 报 - 诉 - 接 - 定 - 午 - 外 - 才 - 流 - 长 - 宝 - 门 - 收 - 己 - 室 - 林 - 种 - 南 - 日 - 目 - 陈 - 许 - 词 - 服 - 设 - 记 - 频 - 琴 - 主 - 完 - 友 - 花 - 跟 - 钱 - 睡 - 像 - 嗯 - 何 - 京 - 所 - 预 - 边 - 带 - 作 - 零 - 头 - 号 - 果 - 嘛 - 路 - 办 - 吉 - 语 - 本 - 合 - 卫 - 影 - 市 - 摄 - 通 - 加 - 女 - 成 - 因 - 前 - 衣 - 然 - 档 - 位 - 聊 - 哥 - 载 - 原 - <space> - 思 - 氏 - 同 - 题 - 但 - 红 - 火 - 她 - 亲 - 传 - 江 - 清 - 息 - 注 - 死 - 啥 - 州 - 片 - 朋 - 相 - 星 - 华 - 已 - 负 - 白 - 色 - 姐 - 春 - 转 - 半 - 换 - 黄 - 游 - 工 - 法 - 理 - 山 - 该 - 英 - 较 - 先 - 穿 - 推 - 直 - 力 - 当 - 冻 - 费 - 刘 - 男 - 写 - 场 - 呵 - 克 - 正 - 单 - 身 - 系 - 苏 - 婆 - 难 - 阳 - 光 - 重 - 荐 - 越 - 马 - 城 - 错 - 次 - 期 - 口 - 金 - 线 - 准 - 爸 - 忙 - 体 - 于 - 句 - 广 - 福 - 活 - 应 - 亮 - 黑 - 特 - 司 - 喝 - 式 - 飞 - 介 - 者 - 慢 - 静 - 百 - 平 - 绍 - 差 - 照 - 团 - 烦 - 便 - 师 - 站 - 德 - 短 - 远 - 需 - 谱 - 郑 - 化 - 或 - 器 - 急 - 钢 - 您 - 忘 - 店 - 妹 - 梦 - 青 - 适 - 总 - 每 - 业 - 夜 - 神 - 版 - 健 - 区 - 实 - 从 - 孩 - 奏 - 韩 - 伦 - 志 - 算 - 雪 - 世 - 认 - 眼 - 模 - 全 - 与 - 书 - 拿 - 送 - 结 - 其 - 解 - 格 - 洗 - 幸 - 舞 - 望 - 速 - 试 - 钟 - 内 - 联 - 停 - 丽 - 课 - 河 - 沙 - 笑 - 久 - 永 - 贝 - 民 - 址 - 超 - 教 - 代 - 件 - 降 - 脑 - 恋 - 常 - 交 - 低 - 伙 - 而 - 毛 - 阿 - 齐 - 习 - 量 - 段 - 选 - 欣 - 昨 - 进 - 闻 - 住 - 受 - 类 - 酒 - 背 - 藏 - 暴 - 摇 - 云 - 怕 - 考 - 咋 - 武 - 赶 - 孙 - 识 - 嵩 - 景 - 某 - 省 - 界 - 罗 - 任 - 坐 - 级 - 遇 - 麻 - 县 - 被 - 龙 - 品 - 蛋 - 湖 - 离 - 希 - 卖 - 轻 - 岁 - 香 - 赏 - 忆 - 答 - 滚 - 保 - 运 - 深 - 央 - 更 - 况 - 部 - ， - 猪 - 休 - 校 - 留 - 嘿 - 弹 - 挺 - 院 - 泪 - 拉 - 懂 - 暖 - 讲 - 顺 - 底 - 卡 - 使 - 表 - 剧 - 包 - 故 - 导 - 凉 - 连 - 咱 - 制 - 蔡 - 容 - 向 - 物 - 微 - 步 - 切 - 搜 - 婚 - 童 - 约 - 芳 - 凯 - 复 - 未 - 陪 - 防 - 典 - 夏 - 万 - 备 - 指 - 冰 - 管 - 基 - 琪 - 宇 - 晓 - 房 - 良 - 戏 - 悲 - 牛 - 千 - 达 - 汉 - 拜 - 奇 - 梅 - 菜 - 满 - 徐 - 楼 - 询 - 图 - 改 - 练 - 敬 - 票 - 吴 - 络 - 码 - 整 - 简 - 队 - 购 - 普 - 附 - 响 - 胡 - 装 - 暑 - 非 - 喂 - 消 - 浪 - 凤 - 愿 - 累 - 球 - 聚 - 启 - 假 - 潮 - 弟 - 玉 - 绿 - 康 - 拍 - 失 - 哭 - 易 - 木 - 斯 - 跳 - 军 - 处 - 搞 - 升 - 除 - 傻 - 骗 - 证 - 杨 - 园 - 茹 - 赵 - 标 - 窗 - 庆 - 惠 - 够 - 烟 - 俊 - 掉 - 建 - 呗 - 插 - 座 - 害 - 智 - 贵 - 左 - 落 - 计 - 客 - 宁 - 梁 - 舒 - 取 - 往 - 漫 - 兰 - 战 - 随 - 晴 - 条 - 入 - 叶 - 强 - 伟 - 雅 - 尔 - 树 - 余 - 弄 - 季 - 排 - 伍 - 吹 - 宏 - 商 - 柔 - 郊 - 铁 - 遍 - 确 - 闭 - 雄 - 似 - 冒 - 待 - 尘 - 群 - 病 - 退 - 务 - 育 - 坏 - 娘 - 莫 - 资 - 楚 - 辛 - 索 - 利 - 数 - 秦 - 燕 - 且 - 录 - 姑 - 念 - 痛 - 冬 - 尾 - 共 - 初 - 粤 - 哎 - 印 - 示 - 抱 - 终 - 泉 - 货 - 肯 - 它 - 伞 - 性 - 古 - 跑 - 腾 - 鱼 - 曾 - 源 - 银 - 读 - 油 - 川 - 言 - 倩 - 峰 - 激 - 置 - 灯 - 独 - 命 - 谈 - 苦 - 限 - 乡 - 菲 - 伴 - 将 - 震 - 炎 - 散 - 依 - 米 - 及 - 贞 - 兴 - 湿 - 寒 - 敏 - 否 - 俩 - 祝 - 慧 - 精 - 律 - 功 - 托 - 洋 - 敢 - 街 - 铃 - 必 - 弦 - 寻 - 涵 - 突 - 皮 - 反 - 烧 - 秋 - 刮 - 末 - 双 - 细 - 范 - 由 - 君 - 款 - 邮 - 醉 - 紧 - 哲 - 缘 - 岛 - 疼 - 阴 - 旋 - 怪 - 草 - 持 - 狼 - 具 - 至 - 汪 - 鸡 - 医 - 邓 - 份 - 右 - 密 - 士 - 修 - 亚 - 画 - 灵 - 妇 - 甜 - 靠 - 荣 - 程 - 莲 - 魂 - 此 - 户 - 属 - 贤 - 充 - 萧 - 血 - 逼 - 闹 - 吸 - 娜 - 肉 - 抒 - 价 - 桥 - 剑 - 巴 - 暗 - 豆 - 迪 - 戴 - 迅 - 朝 - 艺 - 谭 - 治 - 祥 - 尽 - 闷 - 宫 - 艳 - 父 - 存 - 媳 - 跪 - 雾 - 杜 - 味 - 奕 - 兵 - 脸 - 炫 - 兄 - 妮 - 优 - 熊 - 床 - 般 - 净 - 航 - 帝 - 刻 - 孤 - 轩 - 村 - 支 - 玮 - 狗 - 纯 - 楠 - 呐 - 冠 - 元 - 盛 - 决 - 诗 - 爷 - 堵 - 陶 - 乖 - 迷 - 羽 - 忧 - 倒 - 蜜 - 晒 - 仔 - 却 - 姜 - 哟 - 餐 - 雷 - 鸟 - 馆 - 韶 - 箱 - 操 - 乌 - 借 - 恒 - 舍 - 药 - 块 - 澡 - 石 - 软 - 奶 - 笨 - 夫 - 朴 - 义 - 派 - 晨 - 佳 - 科 - 姿 - 显 - 咏 - 饿 - 付 - 宗 - 键 - 止 - 员 - 磊 - 勤 - 崔 - 偏 - 额 - 免 - 乱 - 怀 - 侠 - 岳 - 斌 - 助 - 征 - 概 - 吕 - 彩 - 板 - 松 - 各 - 组 - 历 - 济 - 象 - 茶 - 领 - 按 - 创 - 镇 - 翻 - 配 - 宿 - 咯 - 帅 - 型 - 估 - 佩 - 惜 - 详 - 续 - 蓝 - 麟 - 珠 - 颜 - 彦 - 农 - 盘 - 母 - 鞋 - 账 - 博 - 礼 - 环 - 套 - 效 - 郭 - 居 - 佑 - 根 - 惊 - 圳 - 叔 - 若 - 逆 - 鸿 - 锁 - 食 - 芸 - 裤 - 娱 - 漂 - 野 - 麦 - 豫 - 顾 - 爽 - 族 - 仙 - 围 - 观 - 链 - 嗨 - 厅 - 巍 - 劲 - 极 - 呼 - 咖 - 淑 - 丝 - 昌 - 嘉 - 绝 - 史 - 击 - 承 - 蔚 - 堂 - 沉 - 笔 - 朵 - 凰 - 琥 - 匆 - 炜 - 输 - 须 - 娴 - 嘻 - 牌 - 田 - 杀 - 滴 - 鬼 - 桦 - 赛 - 玟 - 抽 - 案 - 轮 - 立 - 摆 - 屋 - 诺 - 丁 - 佰 - 蒙 - 澄 - 羊 - 添 - 质 - 波 - 萨 - 狂 - 丹 - 屁 - 角 - 章 - 产 - 宜 - 笛 - 严 - 维 - 测 - 娃 - 料 - 宋 - 洲 - 卦 - 猜 - 港 - 挂 - 淘 - 郁 - 统 - 断 - 锅 - 稍 - 绮 - 汗 - 辉 - 乎 - 破 - 钧 - 芹 - 择 - 胖 - 即 - 呜 - 旅 - 拨 - 紫 - 哇 - 默 - 论 - 朱 - 登 - 脚 - 订 - 秀 - ？ - 社 - 飘 - 尚 - 另 - 骂 - 并 - 恶 - 扫 - 裸 - 姨 - 苹 - 压 - 厌 - 汇 - 爆 - 局 - 睛 - 庄 - 唐 - 嘞 - 偶 - 乔 - 染 - 熟 - 喆 - 愉 - 虎 - 技 - 威 - 布 - 嘴 - 湾 - 术 - 讨 - 尼 - 诶 - 坊 - 删 - 桑 - 庾 - 斗 - 呃 - 仁 - 训 - 汤 - 脱 - 凡 - 例 - 唉 - 畅 - 参 - 晕 - 肥 - 营 - 鲁 - 减 - 琳 - 瑞 - 透 - 素 - 厉 - 追 - 扰 - 控 - 谣 - 足 - 检 - 扬 - 娇 - 耳 - 津 - 倾 - 淡 - 露 - 妞 - 熙 - 值 - 罪 - 浩 - 探 - 盐 - 列 - 券 - 潘 - 官 - 篇 - 纪 - 签 - 棒 - 丑 - 陆 - 养 - 佛 - 唯 - 芮 - 哒 - 榜 - 培 - 疯 - 财 - 卷 - 痴 - 凌 - 瓜 - 猫 - 泡 - 据 - 厦 - 辣 - 恩 - 土 - 补 - 递 - 伏 - 灰 - 糖 - 玛 - 黎 - 湘 - 遥 - 谅 - 桃 - 曼 - 招 - 勇 - 泰 - 杭 - 缓 - 朗 - 替 - 刷 - 封 - 骨 - 盖 - 眠 - 担 - 忽 - 蛮 - 蜗 - 肚 - 喽 - 懒 - 继 - 辈 - 魔 - 哼 - 顶 - 冲 - 番 - 释 - 形 - 页 - 渡 - 触 - 裂 - 逛 - 圆 - 迎 - 态 - 弃 - 洛 - 丰 - 困 - 展 - 束 - 巧 - 临 - 际 - 涛 - 酷 - 洁 - 毕 - 呆 - 励 - 臭 - 暂 - 评 - 沧 - 磨 - 洞 - 厂 - 吵 - 煮 - 旧 - 幽 - 寄 - 政 - 丫 - 闯 - 举 - 误 - 护 - 状 - 寂 - 牙 - 杯 - 议 - 眉 - 享 - 剩 - 秘 - 噢 - 耿 - 致 - 偷 - 丢 - 刀 - 销 - 盒 - 编 - 珍 - 葛 - 译 - 颗 - 括 - 奥 - 鲜 - 沈 - 婷 - 摩 - 炒 - 惯 - 啡 - 混 - 燥 - 扣 - 晶 - 柏 - 拥 - 旭 - 拾 - 验 - 嫁 - 铺 - 棉 - 划 - 虾 - 浙 - 寓 - 剪 - 贴 - 圣 - 颖 - 申 - 枝 - 艾 - 旁 - 溪 - '?' - 厚 - 驶 - 燃 - 虽 - 途 - 祖 - 职 - 泽 - 腿 - 薇 - 阵 - 移 - 淋 - 灭 - 寞 - 森 - 延 - 孝 - 沥 - 迟 - 伪 - 催 - 投 - 伯 - 谓 - 诚 - 架 - 耶 - 项 - 撒 - 邦 - 善 - 鼻 - 芬 - 闲 - 增 - 卓 - 层 - 鹏 - 敲 - 镖 - 粉 - 欧 - 纸 - 甘 - 昆 - 哩 - 坚 - 苍 - 积 - 筝 - 擦 - 董 - 吻 - 折 - 欺 - 疆 - 勒 - 售 - 船 - 胜 - 甄 - 杂 - 骑 - 贱 - 饼 - 称 - 隆 - 竟 - 逃 - 啷 - 引 - 宾 - 莉 - 境 - 奖 - 救 - 讯 - 恰 - 垃 - 圾 - 宅 - 潜 - 皇 - 符 - 徽 - 造 - 翔 - 粥 - 桌 - 租 - 险 - 驾 - 祭 - 昂 - 牧 - 宣 - 综 - 谷 - 私 - 瓷 - 避 - 肖 - 闪 - 圈 - 喱 - 耀 - 悟 - 秒 - 篮 - 逗 - 蝶 - 趣 - 恨 - 恐 - 饺 - 碎 - 奔 - 幼 - 股 - 锦 - 锡 - 椅 - 玲 - 刑 - 嗓 - 喊 - 虑 - 俺 - 镜 - 耐 - 鹿 - 狄 - 兮 - 返 - 恭 - 含 - 傅 - 沟 - 莹 - 妃 - 忠 - 赤 - 喔 - 抓 - 迈 - 众 - 豪 - 祈 - 馨 - 嬛 - 庭 - 异 - 辰 - 琅 - 荷 - 匪 - 吐 - 警 - 虹 - 吓 - 聪 - 悔 - 归 - 富 - 陕 - 魏 - 欲 - 菊 - 雹 - 隐 - 涯 - 忍 - 芦 - 琊 - 酸 - 逊 - 亦 - 咪 - 瞎 - 滨 - 胸 - 采 - 穹 - 究 - 炊 - 痒 - 莎 - 柳 - 井 - 洪 - 胎 - 鼓 - 润 - 迁 - 玫 - 滩 - 傲 - 袁 - 赚 - 研 - 躺 - 烤 - 莱 - 搬 - 蒋 - 曹 - 孟 - 嫂 - 甲 - 瑰 - 窝 - 令 - 堆 - 废 - 掌 - 巡 - 妙 - 袋 - 争 - 萌 - 挑 - 册 - 饮 - 勋 - 珊 - 戒 - 绵 - 亡 - 劳 - 搭 - 甩 - 匙 - 彭 - 锋 - 钥 - 率 - 吟 - 鼠 - 纱 - 坡 - 潇 - 挣 - 逝 - 针 - 弱 - 妍 - 稳 - 怒 - 塘 - 卢 - 宵 - 悠 - 饱 - 披 - 瘦 - 浮 - 烂 - 壶 - 截 - 勿 - 序 - 委 - 兔 - 塔 - 执 - 墨 - 府 - 宙 - 欠 - 巨 - 帽 - 占 - 顿 - 权 - 坠 - 碰 - 著 - 硬 - 炮 - 骚 - 肃 - 规 - 厕 - 贾 - 葫 - 徒 - 瓶 - 辽 - 耍 - 赢 - 桂 - 浦 - 趟 - 柯 - 悉 - 恼 - 禁 - 殊 - 卧 - 赞 - 益 - 责 - 虚 - 姓 - 愁 - 舅 - 残 - 既 - 拖 - 棍 - 幻 - 库 - 骄 - 烈 - 尊 - 伊 - 缺 - 迹 - 疑 - 汽 - 郎 - 鸭 - 仪 - 盗 - 幺 - 萱 - 胃 - 脏 - 努 - 勉 - 池 - 咳 - 奋 - 批 - 蝴 - 监 - 犯 - 滑 - 牵 - 冯 - 败 - 毒 - 怖 - 绪 - 帐 - 协 - 韵 - 怜 - 薛 - 姚 - 副 - 塞 - 蕉 - 夹 - 萝 - 爹 - 貌 - 奈 - 乞 - 隔 - 澳 - 姥 - 妖 - 腰 - 纳 - 龄 - 材 - 旗 - 萤 - 俗 - 昼 - 坛 - 霍 - 怡 - 丐 - 咒 - 础 - 嘎 - 虫 - 枪 - 遗 - 献 - 陌 - 侣 - 。 - 昧 - 筒 - 袭 - 厨 - 爬 - 茂 - 媛 - 慰 - 填 - 霞 - 娟 - 摸 - 逍 - 赫 - 霾 - 泥 - 暧 - 翅 - 谦 - 夕 - 瑶 - 鑫 - 刺 - 袖 - 拒 - 玄 - 涂 - 溜 - 旬 - 鸣 - 泷 - 距 - 阻 - 绩 - 狠 - 宽 - 狐 - 赖 - 握 - 循 - 靓 - 述 - 糕 - 踏 - 侯 - 劵 - 壮 - 抄 - 苟 - 岗 - 供 - 湛 - 炼 - 烫 - 棋 - 糊 - 饶 - 悄 - 霸 - 竹 - 哀 - 拔 - 蓉 - 旦 - 晰 - 振 - 漠 - 苗 - 帘 - 糟 - 崇 - 踩 - 汕 - 寝 - 刹 - 蔬 - 旺 - 躁 - 守 - 液 - 疗 - 晋 - 坤 - 洒 - 串 - 屏 - 翠 - 鹅 - 腻 - 毅 - 蹈 - 党 - 咩 - 灿 - 哄 - 核 - 横 - 谎 - 忏 - 映 - 倔 - 则 - 肤 - 贺 - 潍 - 焦 - 渐 - 坑 - 瞄 - 融 - 琼 - 尤 - 逸 - 碧 - 葡 - 卜 - 察 - 邢 - 薄 - 亏 - 绒 - 萄 - 婉 - 闺 - 势 - 描 - 均 - 梨 - 椒 - 慕 - 污 - 弯 - 繁 - 炸 - 肿 - 阅 - 肺 - 席 - 呦 - 碟 - 耻 - 端 - 叹 - 庸 - 危 - 痘 - 峡 - 腐 - 霜 - 拳 - 昴 - 荡 - 屎 - 纠 - 夸 - 尿 - 钰 - 撼 - 嗽 - 雯 - 症 - 衡 - 互 - 孔 - 钻 - 萍 - 娄 - 斤 - 悦 - 谊 - 扯 - 驴 - 歉 - 扎 - 庐 - 蒲 - 吼 - 熬 - 鸳 - 蒸 - 驹 - 允 - 射 - 酱 - 鸯 - 企 - 馒 - 乘 - 葱 - 泳 - 莞 - 脆 - 寨 - 损 - 陀 - 膀 - 淮 - 侃 - 霉 - 施 - 橙 - 煲 - 妆 - 审 - 宠 - 穷 - 敌 - 堡 - 樱 - 诞 - 胆 - 彤 - 祷 - 渭 - 霆 - 亭 - 璐 - 邵 - 壁 - 禺 - 墙 - 葬 - 垫 - 吾 - 粒 - 爵 - 弘 - 妻 - 蕾 - 咨 - 固 - 幕 - 粗 - 抢 - 访 - 贸 - 挥 - 饰 - 硕 - 域 - 岸 - 咬 - 晗 - 姆 - 骤 - 抖 - 判 - 鄂 - 获 - 锻 - 郝 - 柜 - 醋 - 桐 - 泣 - 粘 - 革 - 脾 - 尸 - 侧 - 辆 - 埋 - 稻 - 肠 - 嫌 - 彬 - 庚 - 彼 - 龟 - 弥 - 籍 - 纽 - 喷 - 氛 - 币 - 蠢 - 磁 - 袜 - 柴 - 寸 - 韦 - 忐 - 忑 - 恢 - 缩 - 捷 - 绕 - 翼 - 琦 - 玻 - 驻 - 屈 - 岩 - 颂 - 仓 - 茜 - 璃 - 裙 - 僵 - 柿 - 稿 - 巾 - 撑 - 尹 - 嘟 - 牡 - 昏 - 歇 - 诵 - 丸 - 梯 - 挡 - 袄 - 逢 - 徙 - 渴 - 仰 - 跨 - 碗 - 阔 - 税 - 拼 - 宥 - 丞 - 凶 - 析 - 炖 - 舌 - 抗 - 脖 - 甚 - 豚 - 敷 - 瓦 - 织 - 邀 - 浏 - 猛 - 歪 - 阶 - 兽 - 俄 - 鹤 - 禹 - 纹 - 闽 - 惹 - 煤 - 患 - 岭 - 瑜 - 稀 - 拆 - 凄 - 崎 - 芝 - 摊 - 尺 - 彻 - 览 - 贷 - 珂 - 憋 - 径 - 抚 - 魅 - 悬 - 胶 - 倍 - 贯 - 籁 - 乃 - 哑 - 惑 - 撞 - 箫 - 绣 - 扁 - 苑 - 靖 - 漏 - 挤 - 轶 - 叮 - 烨 - 菇 - 砸 - 趁 - 媚 - 仅 - 藤 - 邱 - 陵 - 躲 - 滋 - 叛 - 捉 - 孕 - 铜 - 衫 - 寿 - 寺 - 枫 - 豹 - 伽 - 翡 - 蜂 - 丙 - 姗 - 羡 - 凑 - 鄙 - 庙 - 铭 - 宰 - 廖 - 肩 - 臣 - 抑 - 辅 - 誓 - 扇 - 啪 - 羞 - 诊 - 敦 - 跃 - 俞 - 肝 - 坦 - 贡 - 踢 - 齿 - 尧 - 淀 - 叉 - 浴 - 狮 - 昊 - 蟹 - 捏 - 略 - 禾 - 纲 - 赔 - 憾 - 赋 - 丘 - 尝 - 钓 - 涕 - 猴 - 鸽 - 纵 - 奉 - 涨 - 揍 - 怨 - 挨 - 兜 - 冈 - 凭 - 策 - 裴 - 摔 - 喵 - 佐 - 喉 - 膏 - 瑟 - 抬 - 纷 - 廊 - 贼 - 煎 - 熄 - 渝 - 缠 - 纶 - 岚 - 衬 - 遮 - 翰 - 誉 - 摘 - 勾 - 赣 - 姬 - 娅 - 撤 - 霖 - 泊 - 膝 - 耽 - 犹 - 仍 - 辞 - 溃 - 骏 - 弓 - 膜 - 诱 - 慌 - 惨 - 噪 - 涩 - 潭 - 幂 - 梓 - 植 - 罚 - 扮 - 涮 - 雁 - 兆 - 舟 - 咸 - 犀 - 炉 - 筋 - 陇 - 狸 - 帕 - 噶 - 茄 - 嗒 - 纬 - 障 - 聘 - 盼 - 盟 - 咧 - 灏 - 菠 - 巷 - 帖 - 慈 - 枕 - 唤 - 慨 - 呛 - 叽 - 砖 - 窍 - 瞒 - 龚 - 促 - 尖 - 螺 - 捞 - 盆 - 茫 - 屌 - 械 - 乳 - 啤 - 玺 - 廷 - 谐 - 吖 - 帆 - 蛇 - 琵 - 琶 - 扑 - 跌 - 崩 - 扭 - 扔 - 咿 - 菩 - 茉 - 攻 - 虐 - 甸 - 璇 - 驰 - 瞬 - 鸦 - 厢 - 囊 - 闫 - 届 - 墓 - 芒 - 栗 - 沫 - 违 - 缝 - 棵 - 杏 - 赌 - 灾 - 颤 - 沂 - 肇 - 桶 - 霄 - ！ - 咙 - 绥 - 仲 - 愈 - 竖 - 菌 - 捕 - 烘 - 阮 - 皆 - 咚 - 劫 - 揭 - 郸 - 庞 - 喇 - 拐 - 奴 - 咔 - 幅 - 偿 - 咦 - 召 - 薪 - 盯 - 黛 - 杉 - 辨 - 邯 - 枯 - 沃 - 吊 - 筷 - 陷 - 鹰 - 嗦 - 噻 - 屯 - 殇 - 抵 - 雕 - 辩 - 枣 - 捂 - 瘾 - 粮 - 巢 - 耗 - 储 - 殷 - 糯 - 轨 - 沾 - 淇 - 毁 - 沐 - 蚊 - 鉴 - 灌 - 玖 - 唔 - 芙 - 淳 - 昕 - 裹 - 茧 - 浑 - 睿 - 踪 - 邪 - 瘩 - 恺 - 斜 - 汰 - 逐 - 铮 - 毫 - 胞 - 昭 - 妥 - 筑 - 贪 - 蘑 - 皓 - 颐 - 疙 - 捡 - 泛 - 债 - 栎 - 棚 - 腹 - 构 - 蓬 - 宪 - 叭 - 愚 - 押 - 蜀 - 夷 - 娶 - 盾 - 倪 - 牟 - 抛 - 壳 - 衍 - 杆 - 撕 - 亿 - 纤 - 淹 - 翘 - 蔷 - 芊 - 罩 - 拯 - 嗷 - 浇 - 宴 - 遵 - 冥 - 祸 - 塑 - 沛 - 猎 - 携 - 噜 - 喘 - 缴 - 砍 - 唢 - 曦 - 遛 - 罢 - 峨 - 戚 - 稚 - 揉 - 堰 - 螃 - 薯 - 乙 - 矿 - 挽 - 弛 - 埃 - 淅 - 疲 - 窦 - 烛 - 媒 - 尬 - 汀 - 谨 - 罐 - 劣 - 伶 - 煜 - 栏 - 榆 - 矛 - 琐 - 槽 - 驼 - 渤 - 沒 - 泄 - 粑 - 匀 - 囧 - 茵 - 霹 - 澈 - 岑 - 乏 - 栋 - 拌 - 框 - 祁 - 叨 - 斋 - 玥 - 僧 - 疏 - 绳 - 晃 - 抹 - 授 - 蓄 - 檬 - 仇 - 毯 - 啵 - 泼 - 阁 - ',' - 邹 - 阎 - 渠 - 函 - 腊 - 割 - 绑 - 扶 - 肌 - 卑 - 匠 - 雳 - 绯 - 婧 - 煌 - 蒂 - 腔 - 仿 - 遭 - 阜 - 峻 - 劝 - 绎 - 黔 - 贫 - 剁 - 荆 - 樊 - 卸 - 锄 - 阕 - 狱 - 冉 - 鲍 - 荒 - 侄 - 唇 - 忌 - 掖 - 竞 - 匹 - 仗 - 锤 - 穆 - 践 - 冶 - 柱 - 聂 - 捧 - 唠 - 翁 - 掏 - 塌 - 沁 - 巩 - 沸 - 蜡 - 痕 - 削 - 晟 - 眯 - 灶 - 婴 - 啸 - 釜 - 兼 - 剂 - 氧 - 赐 - 铠 - 攀 - 扩 - 朦 - 胧 - 孽 - 挖 - 钞 - 碍 - 凝 - 鼎 - 屉 - 斑 - 抠 - 哗 - 哨 - 婶 - 劈 - 冕 - 霏 - 汾 - 雀 - 浚 - 屠 - 唰 - 疚 - 芽 - 惦 - 裕 - 仑 - 厘 - 烁 - 瞧 - 蚂 - 涿 - 尴 - 埔 - 橘 - 磕 - 苇 - 脂 - 臂 - 蛙 - 镁 - 绽 - 卿 - 荃 - 莺 - 迫 - 敖 - 呈 - 勃 - 碌 - 讶 - 赠 - 巫 - 篱 - 浓 - 攒 - 裁 - 嫣 - 彪 - 娣 - 坟 - 廉 - 聆 - 铉 - 瞌 - 葵 - 鞍 - 坎 - 畜 - 爪 - 锯 - 潼 - 矣 - 闸 - 俱 - 蹭 - 戈 - 扒 - 滤 - 撇 - 浅 - 唧 - 觅 - 婕 - 牢 - 堕 - 丈 - 滕 - 御 - 溢 - 阑 - 楞 - 伺 - 馋 - 禄 - 胳 - 措 - 伐 - 滔 - 沦 - 澎 - 谙 - 桢 - 肾 - 熏 - 炅 - 邻 - 吞 - 噔 - 哔 - 沿 - 竺 - 闵 - 妨 - 啰 - 儒 - 锈 - 虞 - 颠 - 脊 - 膊 - 搓 - 岐 - 浸 - 兹 - 吨 - 垂 - 晏 - 痹 - 哆 - 漆 - 叠 - 莓 - 嘀 - 挫 - 馈 - 愧 - 佟 - 疾 - 蒜 - 盈 - 侬 - 烊 - 炙 - 蜢 - 诡 - 莆 - 蛾 - 轴 - 妒 - 洱 - 擎 - 脉 - 飓 - 泫 - 浆 - 岔 - 蹦 - 愤 - 琛 - 趴 - 绘 - 忻 - 拽 - 牲 - 馅 - 鲨 - 靴 - 鳅 - 俐 - 罕 - 呕 - 凋 - 绫 - 蕊 - 圃 - 猥 - 氓 - 歧 - 秧 - 栈 - 梧 - 衷 - 巅 - 彝 - 嚎 - 菁 - 渔 - 茬 - 汐 - 拓 - 昔 - 囚 - 舜 - 搁 - 泸 - 涟 - 蚁 - 裳 - 鞭 - 辟 - 蝎 - 簧 - 予 - 倦 - 傍 - 荔 - 瞳 - 碑 - 桨 - 疫 - 骁 - 驿 - 柠 - 妾 - 隶 - 菏 - 煽 - 麒 - 奎 - 驯 - 飙 - 姻 - 沅 - 扉 - 斩 - 奢 - 蚌 - 掩 - 蹲 - 丧 - 辱 - 焉 - 佘 - 襄 - 芯 - 枉 - 谋 - 渊 - 哮 - 喀 - 朔 - 侏 - 姝 - 戎 - 磅 - 督 - 诛 - 奸 - 苞 - 庵 - 馄 - 聋 - 滁 - 垚 - 柬 - 猩 - 夺 - 啼 - 坝 - 竭 - 黏 - 衰 - 遂 - 潞 - 谜 - 蜻 - 蜓 - 瓣 - 秉 - 檐 - 楂 - 嗑 - 搅 - 嘚 - 倚 - 乒 - 宛 - 崽 - 恕 - 轰 - 淄 - 晞 - 酬 - 砂 - 筠 - 薰 - 蒿 - 瞅 - 勺 - 阙 - 伸 - 嚏 - 湄 - 咆 - 坂 - 役 - 掰 - 渣 - 魁 - 诅 - 浒 - 妓 - 珑 - 捎 - 焊 - 饲 - 脍 - 荫 - 堤 - 轿 - 乓 - 筹 - 撸 - 饨 - 渺 - 桓 - 旷 - 笙 - 晖 - 慎 - 埠 - 挪 - 汝 - 浊 - 仨 - 鳄 - 濮 - 汶 - 邰 - 钉 - 蔽 - 亨 - 屑 - 铅 - 喃 - 葩 - 哉 - 睁 - 骆 - 涉 - 汁 - 拦 - 痞 - 芜 - 俪 - 兑 - 梵 - 刊 - 缅 - 彰 - 俑 - 桔 - 堪 - 鸥 - 契 - 覆 - 拷 - 珞 - 诸 - 棱 - 忒 - 嫩 - 梶 - 贻 - 藕 - 愣 - 湃 - 趋 - 甭 - 嗖 - 怯 - 憧 - 珀 - 缸 - 蔓 - 稣 - 筱 - 杠 - 崖 - 凳 - 裆 - 隧 - 锣 - 嘣 - 瀑 - 漪 - 柄 - 凸 - 颁 - 迦 - 烙 - 岱 - 瑄 - 吭 - 肆 - 鳞 - 晾 - 憬 - 邑 - 甥 - 掀 - 褂 - 淫 - 瓢 - 暮 - 喧 - 祛 - 恙 - 禅 - 柚 - 樟 - 疮 - 嗡 - 懈 - 茨 - 矮 - 诠 - 侮 - 眨 - 羲 - 掐 - 琉 - 雍 - 晔 - 凹 - 怂 - 禧 - 蹬 - 绅 - 榄 - 箍 - 詹 - 溶 - 黯 - 啃 - 驸 - 朕 - 婺 - 援 - 铲 - 呻 - 犬 - 捣 - 眷 - 剃 - 惧 - 芷 - 叱 - 娥 - 钦 - 矫 - 憨 - 骊 - 坪 - 俏 - 炳 - 妲 - 冀 - 刁 - 馍 - 琢 - 扛 - 瞿 - 辙 - 茅 - 寡 - 絮 - 呷 - 哺 - 咕 - 驱 - 搂 - 圭 - 嫉 - 涓 - 茱 - '"' - 笼 - 讽 - 涡 - 泓 - 弊 - 诀 - 璧 - 舔 - 嬅 - 亢 - 沪 - 绢 - 钙 - 喏 - 馥 - 怅 - 簿 - 薜 - 捶 - 冤 - 脐 - 岂 - 溺 - 蕙 - 铿 - 锵 - 锐 - 呸 - 砰 - 亩 - 漳 - 阪 - 栀 - 坞 - 跤 - 蓓 - 舰 - 缕 - 羁 - 芋 - 畔 - 衔 - 铝 - 盲 - 株 - 搏 - 曙 - 惩 - 逻 - 蹄 - 涤 - 宕 - 咤 - 尉 - 嘘 - 瀚 - 仃 - 稽 - 霑 - 飕 - 垮 - 酿 - 畏 - 鲸 - 梗 - 署 - 砒 - 雏 - 茗 - 恬 - 螂 - 拂 - 憔 - 悴 - 钗 - 棕 - 劭 - 歹 - 笠 - 厄 - 焖 - 拣 - 逮 - 蕴 - 淌 - 枸 - 杞 - 雇 - 漯 - 邂 - 逅 - · - 荟 - 塾 - 涌 - 挚 - 舱 - 惬 - 剖 - 榴 - 侦 - 摁 - 烹 - 烽 - 俘 - 麓 - 犊 - 酌 - 匿 - 梭 - 覃 - 隽 - 惆 - 掠 - 舵 - 艰 - 蟑 - 瘤 - 仆 - 穴 - 涅 - 衿 - 嚷 - 峪 - 榕 - 吒 - 酪 - 曝 - 帧 - 靶 - 嚣 - 踝 - 翊 - 陂 - 髓 - 瑚 - 裘 - 芍 - 炬 - 鲅 - 蚕 - 肢 - 颊 - 陛 - 籽 - 粟 - 滞 - 煞 - 乾 - 媞 - 刨 - 碾 - 瘫 - 盔 - 侈 - 徘 - 徊 - 熔 - 吆 - 褪 - 拟 - 廓 - 翟 - 俾 - 沽 - 垒 - 萎 - 僻 - 豌 - 卵 - 狡 - 篓 - 栽 - 崴 - 拧 - 颈 - 咐 - 胭 - 阱 - 鄱 - 漓 - 厥 - 烬 - 糙 - 褥 - 炕 - 恍 - 襟 - 韧 - 眸 - 毙 - 垢 - 叙 - 辜 - 酝 - 璋 - 荧 - 魇 - 皈 - 觞 - 喻 - 孺 - 匈 - 铛 - 诈 - 盏 - 淼 - 佣 - 苓 - 缚 - 洼 - 疡 - 猬 - 腑 - 阡 - 鲫 - 鹭 - 鹂 - 笆 - 埙 - 癌 - 璀 - 璨 - 疹 - 蓑 - 芭 - 嘶 - 桀 - 吩 - 泾 - 铂 - 倘 - 囗 - 璜 - 窃 - 癫 - 璞 - 墟 - 钩 - 粹 - 镐 - 韬 - 牺 - 寮 - 喳 - 鄞 - 笋 - 臧 - 疤 - 捐 - 腥 - 嬷 - 燮 - 濠 - 棠 - 夙 - 弑 - 乍 - 剔 - 嘈 - 钇 - 衅 - 挝 - 橡 - 矜 - 圩 - 恳 - 瑛 - 蔺 - 兖 - 焕 - 懿 - 钏 - 栾 - 筐 - 苒 - 碳 - 韭 - 箭 - 婵 - 迭 - 枷 - 孜 - 咽 - 悯 - 漉 - 噬 - 侍 - 蝉 - 涧 - 鹦 - 鹉 - 冼 - 竿 - … - 袈 - 诏 - 锢 - 泠 - 匡 - 枚 - 坷 - 邝 - 癖 - 绷 - 皖 - 滦 - 滥 - 荨 - 虏 - 拈 - 浜 - 颓 - “ - ” - 戳 - 钮 - 梳 - 溅 - 徨 - 旨 - 罂 - 蹉 - 腌 - 隙 - 侨 - 槟 - 泌 - 珈 - 芵 - 腮 - 晤 - 墩 - 鲤 - 扳 - 栓 - 窑 - 荏 - 饪 - 泵 - 猿 - 眀 - 嗝 - 禽 - 朽 - 偕 - 胀 - 谍 - 捅 - 蜉 - 蝣 - 蹋 - 拱 - 氯 - 噼 - 蚩 - 芥 - 蛟 - 貂 - 荚 - 痰 - 殿 - 遣 - 丛 - 碱 - 殖 - 炽 - 嚓 - 彗 - 窟 - 鳌 - 矶 - 镯 - 乜 - 髙 - 蛤 - 荤 - 坨 - 漱 - 惰 - 跎 - 萸 - 曰 - 亘 - 窘 - 厮 - 绐 - 黝 - 鞠 - 漩 - 蚱 - 垣 - 翩 - 嬴 - 彷 - 椰 - 砚 - 褐 - 黍 - 噗 - 耕 - 挠 - 妩 - 掂 - 峯 - 灸 - 晌 - 溧 - 鹃 - 屿 - 昙 - 廾 - 冢 - 龌 - 龊 - 瞪 - 刽 - 脓 - 壹 - 羱 - 奠 - 贰 - 佬 - 拙 - 颢 - 嘱 - 糗 - 昀 - 巳 - 辕 - 惫 - 黒 - 辐 - 窈 - 窕 - 拢 - 缪 - 逞 - 吝 - 裟 - 钝 - 寇 - 耙 - 隋 - 蝇 - 仟 - 铨 - 赊 - 皑 - 衢 - 胚 - 腺 - 啧 - 淤 - 妄 - 氢 - 寅 - 叻 - 嘲 - 叼 - 沮 - 磐 - 芈 - 饥 - 槿 - 卤 - 懵 - 惴 - 毋 - 箩 - 苔 - 峥 - 斥 - 矬 - 佚 - 肮 - 皎 - 憎 - 樨 - 讴 - 鳖 - 煦 - 焚 - 泗 - 皂 - 礁 - 睬 - 梢 - 妤 - 佗 - 蝌 - 蚪 - 渗 - 暇 - 卟 - 悼 - 瑨 - 伎 - 纺 - 耆 - 舶 - 礴 - 豺 - 涪 - 谬 - 赴 - 婪 - 吱 - 麽 - 犁 - 潸 - 鸪 - 鸢 - 鄯 - 讷 - 弶 - 橄 - 撬 - 赦 - 岷 - 垓 - 绞 - 虔 - 剥 - 澜 - 酗 - 谛 - 骥 - 撅 - 鱿 - 犷 - 讪 - 秃 - 卞 - 缆 - 蓦 - 庶 - 勐 - 笫 - 敛 - 弗 - 痱 - 啬 - 硚 - 昱 - 忿 - 撩 - 椿 - 侵 - 窄 - 邛 - 崃 - 涸 - 赈 - 狭 - 嵌 - 淖 - 瑙 - 踹 - 傈 - 僳 - 缭 - 睦 - 窜 - 嘅 - 樵 - 爰 - 侗 - 逑 - 弧 - 侑 - ： - 娉 - 蝙 - 蝠 - 骅 - 饴 - 揣 - / - 鲈 - 綦 - 拴 - 硝 - 梆 - 馗 - 夭 - 扼 - 鳃 - 惚 - 扈 - 矢 - 藁 - 飚 - 妊 - 踮 - 惟 - 痊 - 艇 - 偎 - 魄 - 篝 - 簸 - 擞 - 粽 - 缥 - 缈 - 跷 - 咁 - 悍 - 菀 - 陡 - 橱 - 遐 - 榨 - 渎 - 蹂 - 躏 - 舂 - 轼 - 枰 - 焰 - 幌 - 邸 - 捜 - 灼 - 茯 - 芎 - 穗 - 棘 - 碜 - 颉 - 鹧 - 啄 - 趾 - 茎 - 揽 - 靳 - 黜 - 惋 - 亥 - 铡 - 栅 - 挞 - 眈 - 膘 - 犍 - 珉 - 镪 - 昵 - 霓 - 圪 - 汲 - 惺 - 瑕 - 桩 - 洽 - 唏 - 耒 - 唻 - 豁 - 郓 - 纣 - 亊 - 鳝 - 蟆 - 癣 - 碚 - 踌 - 殁 - 缉 - 痔 - 頔 - 蔫 - ； - 掺 - 愫 - 祟 - 拘 - 蜘 - 蛛 - 涎 - 耸 - 揪 - 芪 - 腕 - 袍 - 慵 - 绻 - 绛 - 螨 - 捌 - 墅 - 篷 - 啾 - 孪 - 唬 - 褛 - 跶 - 壤 - 慷 - 痧 - 懦 - 郯 - 莴 - 茴 - 嘬 - 铎 - 辫 - 绚 - 簇 - 墘 - 婿 - 咻 - 斡 - 沱 - 譬 - 羔 - 藓 - 肋 - 棂 - 赎 - 炭 - 徵 - 簌 - 艘 - 苪 - 眶 - 嘭 - 霎 - 馊 - 秽 - 仕 - 镶 - 纨 - 摧 - 蒨 - 闰 - 迩 - 篙 - 嚯 - 郫 - 陋 - 殒 - 邃 - 浔 - 瑾 - 鳟 - 祯 - 泻 - 氟 - 猾 - 酥 - 萦 - 郴 - 祀 - 涼 - 屡 - 摹 - 毡 - 妪 - 郡 - 柘 - 裱 - 囔 - 楷 - 鄄 - 蕲 - 偲 - 菘 - 姣 - 瞥 - 肪 - 饽 - 惭 - 胁 - 垄 - 榻 - 讼 - 旱 - 鬓 - 凇 - 钊 - 掣 - 浣 - 凃 - 蓥 - 臊 - 夔 - 脯 - 苛 - 阀 - 睫 - 腋 - 姊 - 躬 - 瘁 - 奄 - 靡 - 盂 - 柑 - 渑 - 恻 - 缱 - 拎 - 恤 - 缶 - 嵬 - 簋 - 囤 - 褴 - 蔼 - 沌 - 薏 - 鸵 - 跋 - 篪 - 罡 - 颇 - 嗄 - 胺 - 烯 - 酚 - 祠 - 迢 - 硖 - 眺 - 珏 - 怆 - 斧 - 痪 - 祺 - 嘤 - 谑 - 婊 - 滂 - 骇 - 帔 - 荼 - 硅 - 猖 - 皱 - 顽 - 榔 - 锌 - 蔻 - 滢 - 茸 - 捋 - 壥 - 孰 - 娩 - 锥 - 逾 - 诬 - 娠 - 厝 - 噎 - 秤 - 祢 - 嗳 - 嗜 - 滘 - 尅 - 悚 - 履 - 馕 - 簪 - 俭 - 摞 - 妗 - 蛎 - 暹 - 钾 - 膨 - 孚 - 驷 - 卯 - 猇 - 褚 - 町 - 骞 - － - 芩 - 赁 - 粱 - 隼 - 掘 - 莽 - 郾 - 擒 - 叁 - 敕 - 镊 - 惘 - 蚤 - 邳 - 嗫 - 扪 - 瀛 - 凿 - 雎 - 啲 - 鲲 - 帼 - 枭 - 羹 - 驳 - 铆 - 肴 - 嫦 - 媲 - 鹳 - 秩 - 銮 - 饯 - 毽 - 珩 - 眩 - 仄 - 葳 - 撮 - 睇 - 塄 - 肘 - 钠 - 诓 - 呱 - 垅 - 菱 - 亍 - 戍 - 酯 - 袱 - 隘 - 蓟 - 暨 - 痣 - 辗 - 埵 - 殉 - 郏 - 孢 - 悳 - 讫 - 诲 - 髋 - 孑 - 睹 - 擅 - 嗮 - 慒 - 琰 - 濛 - 雌 - 恁 - 擀 - 娼 - 谕 - 撵 - 苯 - 聴 - 唛 - 撂 - 栖 - 拗 - 孬 - 怏 - 掇 - 肽 - 胰 - 沣 - 卅 - 箅 - 氨 - 浠 - 蠡 - 募 - 肛 - 岀 - 瞑 - 蛆 - 舀 - 蚝 - 歙 - 涔 - 诘 - 、 - 垡 - 涠 - 嘢 - 糸 - 胤 - 绊 - 柒 - 沓 - 粼 - 菖 - 犒 - 呒 - 唑 - 莘 - 莪 - 宸 - 睨 - \ - 鲶 - 蛐 - 溏 - 菈 - 蹩 - 焙 - 釆 - 瑗 - 睾 - 槐 - 榉 - 杷 - 鄢 - 僕 - 诽 - 嗲 - 蜃 - 戆 - 蘼 - 糜 - 霁 - 坻 - 硼 - 槛 - 枞 - 麸 - 谒 - 荀 - 邋 - 遢 - 锴 - 啶 - 粪 - 驭 - 筵 - 砌 - 莩 - 蹼 - 吔 - 缳 - 埭 - 隗 - 厶 - 丶 - "\x14" - "\x17" - 稼 - 铖 - 涣 - 亳 - 幢 - 沭 - 驮 - 奚 - 藐 - 颅 - 埤 - 愘 - 镲 - 窒 - 暄 - 诃 - 噘 - 歼 - 隅 - 爻 - 蘅 - 锹 - 锇 - 椎 - 琨 - 烩 - 枢 - 觧 - 萁 - 镂 - 龈 - 怠 - 阐 - 藉 - 凛 - 冽 - 珣 - 泘 - 抉 - 锭 - 蕃 - 蠃 - 毓 - 啐 - 栩 - 骷 - 髅 - 耷 - 寥 - 杵 - 蚬 - 窖 - 孛 - 舆 - 皿 - 柸 - 粳 - 钣 - 趸 - 叄 - 腚 - 杖 - 鸸 - 犲 - 浗 - 缮 - 哓 - 箧 - 攘 - 冇 - 钛 - 郗 - 囡 - 酆 - 姌 - 雉 - 胯 - 椭 - 埏 - 钵 - 绌 - 蝾 - 坼 - 濂 - ｗ - ｏ - ｒ - ｄ - 袒 - 峦 - 鹫 - 炯 - 悱 - 漕 - 莦 - 蔑 - 樽 - 牒 - 濡 - 嫯 - 陖 - 疸 - 桅 - 辖 - 僢 - 《 - 》 - 酣 - 遨 - 邬 - ':' - 嫲 - 哌 - 锚 - 淙 - Ｑ - 濑 - 熨 - 谴 - 筛 - 薹 - 磬 - 熠 - 腓 - 阉 - 钴 - 恂 - 溉 - 陨 - 螳 - 孵 - 瘠 - 嫡 - 哝 - 狙 - 怼 - 斟 - 甫 - 渌 - 卒 - 翕 - 沏 - 旮 - 旯 - 菡 - 變 - 狈 - 鳜 - 嵋 - 仞 - 鳕 - 噩 - 踟 - 躇 - 蛀 - 瘸 - 篡 - 锊 - 団 - 斐 - 蹍 - 冗 - "\uFEFF" - 歆 - 圴 - 泯 - 伥 - 愎 - 坌 - 碘 - 赉 - 骧 - 矩 - 綽 - 秭 - 怵 - 麝 - 贩 - 溥 - 捆 - 腩 - 溴 - 卉 - 痦 - 荻 - 缇 - 秸 - 秆 - 捍 - 炀 - 阆 - 泞 - 懊 - 啕 - 蚶 - 衩 - 桜 - 旖 - 贬 - 酵 - 滟 - 纥 - 倭 - 赝 - 呶 - 哧 - 煸 - 劢 - 炝 - 僚 - 豇 - 阂 - 涝 - 骡 - 霭 - 窨 - 殴 - 竣 - 醇 - 擂 - 怦 - 怩 - 臾 - 搔 - 伱 - 啉 - 嫖 - 囝 - 糠 - 胥 - 酰 - 镫 - 蟒 - 荞 - 醪 - 颦 - 吏 - 颛 - 赳 - 贿 - 赂 - 痩 - 仂 - 颍 - 罔 - 猕 - 嚒 - 蘸 - 熹 - 捺 - 坜 - 郜 - 鉄 - 蒌 - 荑 - 藻 - 谌 - 钳 - 屮 - 疵 - 哞 - 琮 - 潴 - 讹 - 镭 - '3' - 尕 - 倬 - 庇 - 侩 - 瘆 - 傀 - 儡 - 诧 - 葆 - 唾 - 皋 - 逄 - 诌 - 氦 - 彳 - 盅 - 曳 - 槲 - 挟 - 怿 - 顷 - 臃 - 衙 - 踵 - 霈 - 嗪 - 闩 - 锟 - 恿 - 抻 - 茁 - 惢 - 菅 - 迂 - 瞟 - 痉 - 挛 - 绦 - 晁 - 挢 - 蠕 - 洙 - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: char 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' short_noise_thres: 0.5 frontend: default frontend_conf: 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: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_zh_char_sp/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false preencoder: null preencoder_conf: {} 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: 0.0 input_layer: conv2d normalize_before: true pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish macaron_style: true use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.0 src_attention_dropout_rate: 0.0 required: - output_dir - token_list version: '202207' 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} } ```

9e7fb4dae0150348d8bc64f2c66ba138

Gxg/OpNum_Bert_Merge

Gxg

bert

22

2

transformers

0

feature-extraction

true

false

true

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

['exbert']

false

true

10,425

false

# BERT base model (uncased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BERT 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. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. 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 BERT model as inputs. ## Model variations BERT has originally been released in base and large variations, for cased and uncased input text. The uncased models also strips out an accent markers. Chinese and multilingual uncased and cased versions followed shortly after. Modified preprocessing with whole word masking has replaced subpiece masking in a following work, with the release of two models. Other 24 smaller models are released afterwards. The detailed release history can be found on the [google-research/bert readme](https://github.com/google-research/bert/blob/master/README.md) on github. | Model | #params | Language | |------------------------|--------------------------------|-------| | [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) | 110M | English | | [`bert-large-uncased`](https://huggingface.co/bert-large-uncased) | 340M | English | sub | [`bert-base-cased`](https://huggingface.co/bert-base-cased) | 110M | English | | [`bert-large-cased`](https://huggingface.co/bert-large-cased) | 340M | English | | [`bert-base-chinese`](https://huggingface.co/bert-base-chinese) | 110M | Chinese | | [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) | 110M | Multiple | | [`bert-large-uncased-whole-word-masking`](https://huggingface.co/bert-large-uncased-whole-word-masking) | 340M | English | | [`bert-large-cased-whole-word-masking`](https://huggingface.co/bert-large-cased-whole-word-masking) | 340M | English | ## 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=bert) 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='bert-base-uncased') >>> unmasker("Hello I'm a [MASK] model.") [{'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.1073106899857521, 'token': 4827, 'token_str': 'fashion'}, {'sequence': "[CLS] hello i'm a role model. [SEP]", 'score': 0.08774490654468536, 'token': 2535, 'token_str': 'role'}, {'sequence': "[CLS] hello i'm a new model. [SEP]", 'score': 0.05338378623127937, 'token': 2047, 'token_str': 'new'}, {'sequence': "[CLS] hello i'm a super model. [SEP]", 'score': 0.04667217284440994, 'token': 3565, 'token_str': 'super'}, {'sequence': "[CLS] hello i'm a fine model. [SEP]", 'score': 0.027095865458250046, 'token': 2986, 'token_str': 'fine'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained("bert-base-uncased") 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 BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = TFBertModel.from_pretrained("bert-base-uncased") 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='bert-base-uncased') >>> unmasker("The man worked as a [MASK].") [{'sequence': '[CLS] the man worked as a carpenter. [SEP]', 'score': 0.09747550636529922, 'token': 10533, 'token_str': 'carpenter'}, {'sequence': '[CLS] the man worked as a waiter. [SEP]', 'score': 0.0523831807076931, 'token': 15610, 'token_str': 'waiter'}, {'sequence': '[CLS] the man worked as a barber. [SEP]', 'score': 0.04962705448269844, 'token': 13362, 'token_str': 'barber'}, {'sequence': '[CLS] the man worked as a mechanic. [SEP]', 'score': 0.03788609802722931, 'token': 15893, 'token_str': 'mechanic'}, {'sequence': '[CLS] the man worked as a salesman. [SEP]', 'score': 0.037680890411138535, 'token': 18968, 'token_str': 'salesman'}] >>> unmasker("The woman worked as a [MASK].") [{'sequence': '[CLS] the woman worked as a nurse. [SEP]', 'score': 0.21981462836265564, 'token': 6821, 'token_str': 'nurse'}, {'sequence': '[CLS] the woman worked as a waitress. [SEP]', 'score': 0.1597415804862976, 'token': 13877, 'token_str': 'waitress'}, {'sequence': '[CLS] the woman worked as a maid. [SEP]', 'score': 0.1154729500412941, 'token': 10850, 'token_str': 'maid'}, {'sequence': '[CLS] the woman worked as a prostitute. [SEP]', 'score': 0.037968918681144714, 'token': 19215, 'token_str': 'prostitute'}, {'sequence': '[CLS] the woman worked as a cook. [SEP]', 'score': 0.03042375110089779, 'token': 5660, 'token_str': 'cook'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The BERT 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 WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. 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. ### Pretraining The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Glue test results: | Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average | |:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:| | | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 | ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=bert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

80dc2e7436919367c55300f942c4c32f

AshishBalhara/distilbert-base-uncased-finetuned-clinc

AshishBalhara

distilbert

12

2

transformers

0

text-classification

true

false

apache-2.0

null

['clinc_oos']

null

0

['generated_from_trainer']

true

1,482

false

# distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7721 - Accuracy: 0.9184 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2896 | 1.0 | 318 | 3.2890 | 0.7432 | | 2.6284 | 2.0 | 636 | 1.8756 | 0.8377 | | 1.5483 | 3.0 | 954 | 1.1572 | 0.8961 | | 1.015 | 4.0 | 1272 | 0.8573 | 0.9132 | | 0.7953 | 5.0 | 1590 | 0.7721 | 0.9184 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.0+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3

5d16edcba87f749b6ca34b3ce7d86bbe

trojblue/yys

trojblue

null

7

0

null

3

null

false

openrail

null

1

0

1

0

[]

false

true

3,990

false

## yy model 768ARB, fp32, EMA, finetuned on animefull **epoch48**: probably overfitted, good results overall **epoch28**: not overfitted: less stylized but mostly good anatomy tested on: - 768x1024 DPM++ SDE Karras, no hires fix - 512x768 Latent hires → 1024x1536 DPM++ SDE Karras token word: - `yingyi, best quality, explicit` → nsfw - `yingyi, concept art` → concepts - `yingyi, best quality` → less nsfw tagging: - wd1.4, booru, blip lr & augments: - 2e-6 sqrt bs2, weight decay 5e-2 - rotate, brightness, contrast, flip nsfw sample 1 (e48): ``` yingyi, best quality, explicit, multiple girls, 2girls, breasts, long hair, twintails, rabbit ears, pink hair, sex toy, thighhighs, animal ears, vibrator, very long hair, blue eyes, holding, high heels, full body, blush, outdoors, vaginal object insertion, pink eyes, blue hair, standing, gloves, looking at viewer, bow, medium breasts, bangs, fake animal ears, heart, bodysuit, vibrator cord, object insertion, small breasts, public indecency, navel, elbow gloves, heart-shaped pupils, pussy juice, revealing clothes, hair ornament, piercing, remote control vibrator, body writing, red footwear, flower, dildo, red eyes, vaginal, symbol-shaped pupils, black footwear, aqua hair Negative prompt: error, signature, watermark, username, multiple people, animals, lowres, cropped, worth quality ,low quality, normal quality, jpeg artifacts, blurry, bad anatomy, bad hands, bad arms, bad feet, bad anatomy, missing fingers, extra digits, fewer digits, long neck, missing legs, huge person, optical_illusion Steps: 25, Sampler: DPM++ SDE Karras, CFG scale: 6, Seed: 1438393047, Size: 512x768, Model hash: f32626be, Model: yye48, Denoising strength: 0.7, Clip skip: 2, ENSD: 31338, Hires upscale: 2, Hires upscaler: Latent (bicubic) ``` nsfw sample 2 (e48): ``` yingyi, best quality, explicit, restrained, 1girl, thighhighs, sex toy, bound, bdsm, blindfold, object insertion, gloves, dildo, gag, solo, breasts, anal, body writing, gagged, vaginal, bondage, white gloves, elbow gloves, short hair, vibrator, vaginal object insertion, small breasts, ball gag, anal object insertion, white thighhighs, white hair, stationary restraints, nipples, sex machine, soles, pussy, black blindfold, motion blur, navel, feet, tally, spread legs Negative prompt: error, signature, watermark, username, multiple people, animals, lowres, cropped, worth quality ,low quality, normal quality, jpeg artifacts, blurry, bad anatomy, bad hands, bad arms, bad feet, bad anatomy, missing fingers, extra digits, fewer digits, long neck, missing legs, huge person, optical_illusion Steps: 25, Sampler: DPM++ SDE Karras, CFG scale: 6, Seed: 1438393014, Size: 512x768, Model hash: f32626be, Model: yye48, Denoising strength: 0.7, Clip skip: 2, ENSD: 31338, Hires upscale: 2, Hires upscaler: Latent (bicubic) ``` sfw sample (e48): [file here](https://huggingface.co/trojblue/yys/blob/main/%5Blatnet%20nearest%20exact%5D31338-DPM%2B%2B%202M%20Karras-step27-cfg6.5-8a648075-20230108_133253_026580.png) ``` yingyi, 1girl, waves, water, long hair, splashing, liquid hair, very long hair, a painting of a blue wave with a white background and a person standing on the wave of water in the middle, ying yi, hatsune miku, vocaloid, absurdres, highres, aqua eyes, aqua hair, bird, boots, bridal gauntlets, cape, fish, nail polish, thigh boots, tropical fish, twintails, wading, white bird Negative prompt: error, signature, watermark, username, realistic,3d, multiple people, extra legs, animals, lowres, cropped, worth quality, low quality, normal quality, jpeg artifacts, bad anatomy, bad hands, bad arms, bad feet, bad anatomy, missing fingers, extra digits,explicit, fewer digits, long neck, missing legs, huge person, optical_illusion Steps: 27, Sampler: DPM++ 2M Karras, CFG scale: 6.5, Seed: 31338, Size: 384x576, Model hash: 8a648075, Model: yy_e28, Denoising strength: 0.7, Clip skip: 2, ENSD: 31338, Hires upscale: 2, Hires upscaler: Latent (nearest-exact) ```

0483e4731f87a800a6a555fa1aa7ff1b

mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization

mrm8488

encoder-decoder

8

2,568

transformers

4

summarization

true

false

apache-2.0

['en']

['cnn_dailymail']

null

0

['summarization']

false

true

1,702

false

# Bert-small2Bert-small Summarization with 🤗EncoderDecoder Framework This model is a warm-started *BERT2BERT* ([small](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8)) model fine-tuned on the *CNN/Dailymail* summarization dataset. The model achieves a **17.37** ROUGE-2 score on *CNN/Dailymail*'s test dataset. For more details on how the model was fine-tuned, please refer to [this](https://colab.research.google.com/drive/1Ekd5pUeCX7VOrMx94_czTkwNtLN32Uyu?usp=sharing) notebook. ## Results on test set 📝 | Metric | # Value | | ------ | --------- | | **ROUGE-2** | **17.37** | ## Model in Action 🚀 ```python from transformers import BertTokenizerFast, EncoderDecoderModel import torch device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization') model = EncoderDecoderModel.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization').to(device) def generate_summary(text): # cut off at BERT max length 512 inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt") input_ids = inputs.input_ids.to(device) attention_mask = inputs.attention_mask.to(device) output = model.generate(input_ids, attention_mask=attention_mask) return tokenizer.decode(output[0], skip_special_tokens=True) text = "your text to be summarized here..." generate_summary(text) ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) > Made with &hearts; in Spain

47d3a87c8e9e19c7df3b03069fe18863

TencentGameMate/chinese-wav2vec2-large

TencentGameMate

wav2vec2

6

658

transformers

4

null

true

false

mit

null

0

[]

false

true

1,902

false

Pretrained on 10k hours WenetSpeech L subset. More details in [TencentGameMate/chinese_speech_pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain) This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. python package: transformers==4.16.2 ```python import torch import torch.nn.functional as F import soundfile as sf from fairseq import checkpoint_utils from transformers import ( Wav2Vec2FeatureExtractor, Wav2Vec2ForPreTraining, Wav2Vec2Model, ) from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices model_path="" wav_path="" mask_prob=0.0 mask_length=10 feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_path) model = Wav2Vec2Model.from_pretrained(model_path) # for pretrain: Wav2Vec2ForPreTraining # model = Wav2Vec2ForPreTraining.from_pretrained(model_path) model = model.to(device) model = model.half() model.eval() wav, sr = sf.read(wav_path) input_values = feature_extractor(wav, return_tensors="pt").input_values input_values = input_values.half() input_values = input_values.to(device) # for Wav2Vec2ForPreTraining # batch_size, raw_sequence_length = input_values.shape # sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length) # mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.0, mask_length=2) # mask_time_indices = torch.tensor(mask_time_indices, device=input_values.device, dtype=torch.long) with torch.no_grad(): outputs = model(input_values) last_hidden_state = outputs.last_hidden_state # for Wav2Vec2ForPreTraining # outputs = model(input_values, mask_time_indices=mask_time_indices, output_hidden_states=True) # last_hidden_state = outputs.hidden_states[-1] ```

d5b463713cb4cd17d9c5281d02c0bc10

hesw23168/SD_Elysium_Kuro_Model

hesw23168

null

4

0

null

12

null

false

openrail

null

0

[]

false

true

856

false

Also on https://civitai.com/models/5301/elysium-kuro-anime Anime model is custom mix + finetune on dataset of high quality images (mix including Anything 4.0, WD 1.4 Booru, Seek Art Mega V1) and contains the contains the kl-f8-anime2 VAE from Waifu Diffusion. Example settings: Negative prompt: (lowres:1.1), (worst quality:1.2), (low quality:1.1), bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, normal quality, jpeg artifacts, signature, watermark, username, blurry (General model): Clip skip 1, VAE: 'vae-ft-mse-840000' from StabilityAI (included) (Anime model): Clip skip 2, VAE: 'kl-f8-anime2.ckpt' from Waifu Diffusion (included) Example images from anime model: ![awiog.jpg](https://s3.amazonaws.com/moonup/production/uploads/1674681388521-6351b7e2ea4e5b421fb0d42d.jpeg) General model coming soon.

4e5acad4b15c14b71707cc70be1af34a

Geotrend/distilbert-base-lt-cased

Geotrend

distilbert

6

2

transformers

0

fill-mask

true

false

apache-2.0

['lt']

['wikipedia']

null

0

[]

false

true

1,215

false

# distilbert-base-lt-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-lt-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-lt-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermdistilbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact amine@geotrend.fr for any question, feedback or request.

b14bf8c7dfcf5d6d7cb31c8319b5bac2

inovex/multi2convai-corona-de-bert

inovex

bert

8

3

transformers

1

text-classification

true

false

mit

['de']

null

0

['text-classification', 'pytorch', 'transformers']

false

true

860

false

# Multi2ConvAI-Corona: finetuned Bert for German This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project: - domain: Corona (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases))) - language: German (de) - model type: finetuned Bert ## How to run Requires: - Huggingface transformers ### Run with Huggingface Transformers ````python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-logistics-de-bert") model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-logistics-de-bert") ```` ## Further information on Multi2ConvAI: - https://multi2conv.ai - https://github.com/inovex/multi2convai - mailto: info@multi2conv.ai

a6bdac6aa5e94a38b6dbe261b8da5bda

TeamFnord/manga-ocr

TeamFnord

vision-encoder-decoder

8

1

transformers

2

image-to-text

true

false

apache-2.0

['ja']

['manga109s']

null

0

1

0

1

['image-to-text']

false

true

618

false

# Manga OCR Optical character recognition for Japanese text, with the main focus being Japanese manga. It uses [Vision Encoder Decoder](https://huggingface.co/docs/transformers/model_doc/visionencoderdecoder) framework. Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality text recognition, robust against various scenarios specific to manga: - both vertical and horizontal text - text with furigana - text overlaid on images - wide variety of fonts and font styles - low quality images Code is available [here](https://github.com/kha-white/manga_ocr).

e29c225b38a608b65973a67fb7401c23

CalamitousVisibility/enron-spam-checker-10000

CalamitousVisibility

distilbert

13

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,057

false

# enron-spam-checker-10000 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0512 - Accuracy: 0.9915 - F1: [0.99143577 0.99156328] ## 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 ### Training results ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cpu - Datasets 2.5.1 - Tokenizers 0.12.1

9852e110fbae9ca77d76e6b0f3495d0a

annahaz/xlm-roberta-base-finetuned-misogyny-en-it-hi-beng

annahaz

xlm-roberta

10

1

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

2,323

false

# xlm-roberta-base-finetuned-misogyny-en-it-hi-beng 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: 0.0140 - Accuracy: 0.9970 - F1: 0.9969 - Precision: 0.9937 - Recall: 1.0 - Mae: 0.0030 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| | 0.3131 | 1.0 | 1759 | 0.4655 | 0.7820 | 0.7682 | 0.7855 | 0.7516 | 0.2180 | | 0.2644 | 2.0 | 3518 | 0.3231 | 0.8619 | 0.8665 | 0.8091 | 0.9326 | 0.1381 | | 0.2408 | 3.0 | 5277 | 0.3515 | 0.8801 | 0.8877 | 0.8071 | 0.9863 | 0.1199 | | 0.1927 | 4.0 | 7036 | 0.1428 | 0.9514 | 0.9512 | 0.9194 | 0.9853 | 0.0486 | | 0.1333 | 5.0 | 8795 | 0.1186 | 0.9712 | 0.9707 | 0.9478 | 0.9947 | 0.0288 | | 0.1163 | 6.0 | 10554 | 0.0546 | 0.9879 | 0.9875 | 0.9803 | 0.9947 | 0.0121 | | 0.0854 | 7.0 | 12313 | 0.0412 | 0.9899 | 0.9896 | 0.9804 | 0.9989 | 0.0101 | | 0.086 | 8.0 | 14072 | 0.0252 | 0.9949 | 0.9948 | 0.9896 | 1.0 | 0.0051 | | 0.0395 | 9.0 | 15831 | 0.0179 | 0.9965 | 0.9963 | 0.9927 | 1.0 | 0.0035 | | 0.0343 | 10.0 | 17590 | 0.0140 | 0.9970 | 0.9969 | 0.9937 | 1.0 | 0.0030 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.9.0+cu111 - Datasets 2.3.2 - Tokenizers 0.12.1

ecf6c4bc2e80e8526f8309fa3f98c8bb

kadirnar/yolov6s-v3.0

kadirnar

null

3

0

null

0

object-detection

false

gpl-3.0

null

['detection-datasets/coco']

null

0

['object-detection', 'computer-vision', 'yolov6', 'pypi']

false

true

1,168

false

### Model Description [YOLOv6:](https://arxiv.org/abs/2209.02976) A single-stage object detection framework dedicated to industrial applications. [YOLOv6 v3.0](https://arxiv.org/abs/2301.05586): A Full-Scale Reloading [YOLOv6-Pip: Packaged version of the Yolov6 repository](https://github.com/kadirnar/yolov6-pip/) [Paper Repo: Implementation of paper - YOLOv6](https://github.com/meituan/YOLOv6/) ### Installation ``` pip install yolov6detect ``` ### Yolov6 Inference ```python from yolov6 import YOLOV6 model = YOLOV6(weights='kadirnar/yolov6s-v2.0', device='cuda:0', hf_model=True) model.classes = None model.conf = 0.25 model.iou = 0.45 model.show = False model.save = True pred = model.predict(source='data/images',yaml='data/coco.yaml', img_size=640) ``` ### BibTeX Entry and Citation Info ``` @article{li2022yolov6, title={YOLOv6: A single-stage object detection framework for industrial applications}, author={Li, Chuyi and Li, Lulu and Jiang, Hongliang and Weng, Kaiheng and Geng, Yifei and Li, Liang and Ke, Zaidan and Li, Qingyuan and Cheng, Meng and Nie, Weiqiang and others}, journal={arXiv preprint arXiv:2209.02976}, year={2022} } ```

75098da5951924099efd3c31176f4b33

Helsinki-NLP/opus-mt-hr-fr

Helsinki-NLP

marian

10

75

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

768

false

### opus-mt-hr-fr * source languages: hr * target languages: fr * OPUS readme: [hr-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/hr-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/hr-fr/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/hr-fr/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/hr-fr/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.hr.fr | 26.1 | 0.482 |

2ea55f6463cdc0b251bd0456bfd431c7

rikkar/dsd_futurism

rikkar

null

3

0

null

0

null

false

cc0-1.0

null

0

[]

false

true

1,196

false

Stable Diffusion model trained for 5k steps on the art style "futurism". Invoke the style with "in the style of futtt". Play with weights, it's a strong style so prompt accordingly. Sample images: ![20221101103331_00000_78550141.png](https://s3.amazonaws.com/moonup/production/uploads/1667339716454-62ad05306bc70f4a0ce27214.png) ![20221031150530_00000_4006546038.png](https://s3.amazonaws.com/moonup/production/uploads/1667339752250-62ad05306bc70f4a0ce27214.png) ![20221031151001_00007_222715945.png](https://s3.amazonaws.com/moonup/production/uploads/1667339780164-62ad05306bc70f4a0ce27214.png) Sample training images: ![futtt_(4).jpg](https://s3.amazonaws.com/moonup/production/uploads/1667315842086-62ad05306bc70f4a0ce27214.jpeg) ![futtt_(8).jpg](https://s3.amazonaws.com/moonup/production/uploads/1667315860265-62ad05306bc70f4a0ce27214.jpeg) ![futtt_(16).jpg](https://s3.amazonaws.com/moonup/production/uploads/1667315873761-62ad05306bc70f4a0ce27214.jpeg) ![futtt_(36).jpg](https://s3.amazonaws.com/moonup/production/uploads/1667315883690-62ad05306bc70f4a0ce27214.jpeg) ![futtt_(50).jpg](https://s3.amazonaws.com/moonup/production/uploads/1667315890969-62ad05306bc70f4a0ce27214.jpeg)

74a2d1fc3b1b15d66fca985e281ce1af

lkm2835/distilbert-imdb

lkm2835

distilbert

10

5

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

1

0

1

0

['generated_from_trainer']

true

1,113

false

# distilbert-imdb 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: 5e-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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 391 | 0.1849 | 0.9281 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

48f608b576343b1207926fa44674957f

infinitejoy/wav2vec2-large-xls-r-300m-indonesian

infinitejoy

wav2vec2

17

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['id']

['common_voice']

null

0

['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer']

true

2,571

false

# wav2vec2-large-xls-r-300m-indonesian 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_7_0 - ID dataset. It achieves the following results on the evaluation set: - Loss: 0.2759 - Wer: 0.3256 ## 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: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 4000 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.0387 | 4.72 | 1000 | 3.0892 | 1.0 | | 1.7911 | 9.43 | 2000 | 0.8451 | 0.6702 | | 1.2826 | 14.15 | 3000 | 0.4211 | 0.4166 | | 1.1802 | 18.87 | 4000 | 0.3508 | 0.4690 | | 1.1065 | 23.58 | 5000 | 0.3319 | 0.4662 | | 1.0921 | 28.3 | 6000 | 0.3056 | 0.3880 | | 1.0366 | 33.02 | 7000 | 0.2997 | 0.3665 | | 0.9988 | 37.74 | 8000 | 0.2972 | 0.3653 | | 0.9864 | 42.45 | 9000 | 0.2697 | 0.3371 | | 0.9558 | 47.17 | 10000 | 0.2739 | 0.3141 | | 0.9094 | 51.89 | 11000 | 0.2657 | 0.3533 | | 0.9034 | 56.6 | 12000 | 0.2699 | 0.3397 | | 0.8907 | 61.32 | 13000 | 0.2765 | 0.3470 | | 0.8631 | 66.04 | 14000 | 0.2774 | 0.3346 | | 0.8389 | 70.75 | 15000 | 0.2743 | 0.3365 | | 0.8214 | 75.47 | 16000 | 0.2778 | 0.3201 | | 0.8195 | 80.19 | 17000 | 0.2725 | 0.3286 | | 0.7994 | 84.91 | 18000 | 0.2782 | 0.3315 | | 0.7816 | 89.62 | 19000 | 0.2775 | 0.3363 | | 0.7816 | 94.34 | 20000 | 0.2731 | 0.3278 | | 0.7635 | 99.06 | 21000 | 0.2767 | 0.3259 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0

ecb0cbb8b084f6dd89700182f3194d73

BestJammer/HASDX

BestJammer

null

3

0

null

26

null

false

creativeml-openrail-m

null

3

0

3

2

0

2

[]

false

true

2,712

false

# About this bad ass beast of a checkpoint: I merged a few checkpoints and got something buttery and amazing. Does great with things other then people too. It can do anything really. It doesn't need crazy prompts either. Keep it simple. No need for all the artist names and trending on whatever. # PRUNED AND SMALLER ckpt FILES! AS WELL AS DIFFUSERS! [Link here for diffusers and pruned](https://huggingface.co/johnslegers/hasdx) There are the download links below as well ![grid-0010.jpg](https://s3.amazonaws.com/moonup/production/uploads/1672801449030-6344cba8762379fc63032a74.jpeg) Example Prompts: * female, pale purple hair, frills, detailed skin, perfect face, fashion photography, photo realistic, 20 megapixel, canon eos r3, detailed skin, detailed, detailed face, (full body intricate, vibrant, photo realistic, realistic, dramatic, sharp focus, 8k) * (extremely detailed photo 8k), full body shot photo of the most beautiful artwork, beautiful woman soldier, green hair, cleavage, wearing intricate advanced futuristic blue power armor, propped up on one elbow, cinematic lighting, very detailed face and eyes, park in background, high quality photo Negative prompt examples: * Asian, cartoon, 3d, (disfigured), (bad art), (deformed), (poorly drawn), (extra limbs), (close up), strange colors, blurry, boring, sketch, lackluster, big breast, large breast, huge breasts, face portrait, self-portrait, signature, letters, watermark * Asian, large boobs, muscular, out of frame , worst quality , text , blurred , monstrous , hideous , ugly , duplicate , cropped , mutilated , horrifying ### CKPT Here and diffuers [Download ckptSXDHAS.ckpt (7.7GB)](https://huggingface.co/BestJammer/HASDX/resolve/main/ckptSXDHAS.ckpt) [Download hasdx_emaonly.ckpt (4.27GB)](https://huggingface.co/johnslegers/hasdx/resolve/main/hasdx_emaonly.ckpt) [Download hasdx.ckpt (2.13GB)](https://huggingface.co/johnslegers/hasdx/resolve/main/hasdx.ckpt) # What I merged: https://civitai.com/models/1349/sxd-berrymix-merge https://civitai.com/models/2504/handas-3dkx10b https://civitai.com/models/3762/general-purpose-model The third one is a mystery because I cannot remember where I got it but it was called model.ckpt and I uploaded it myself because it was lost somewhere I cannot find. I did some .4 but most merges were .5 weight. I have tried so many merges and this one just clicked great. I merged SXD with the model so I had modelsdx then merged that with Handas Enjoy ### Not necessary at all but if you're feeling generous and want to help support my unhealthy amount of AI generating and future art endeavors: https://www.buymeacoffee.com/OnlyJams https://www.Only-Jams.redbubble.com

1a51b82337fec752d16a509a4ccf5cac

espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2_2

espnet

null

20

3

espnet

0

automatic-speech-recognition

false

cc-by-4.0

['en']

['swbd_sentiment']

null

0

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

false

true

179,437

false

## ESPnet2 ASR model ### `espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2_2` 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 17089cb2cf5f1275132163f6327defbcc1b1bc1b 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_2 ``` ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_wav2vec2_2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_wav2vec2_2_raw_en_word ngpu: 1 seed: 2022 num_workers: 2 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 43183 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: 70 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: 10 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 2 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: 100 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: 6000000 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: 0.002 weight_decay: 1.0e-05 scheduler: warmuplr scheduler_conf: warmup_steps: 5000 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 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 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: 256 attention_heads: 4 linear_units: 1024 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d2 normalize_before: true macaron_style: true rel_pos_type: latest 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: 4 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} } ```

8b1dd5e7feb1362a44c13a6b7027e781

BSC-LT/roberta-base-bne-capitel-pos

BSC-LT

roberta

9

1

transformers

3

token-classification

true

false

apache-2.0

['es']

['bne', 'capitel']

null

0

['national library of spain', 'spanish', 'bne', 'capitel', 'pos']

false

true

1,662

false

**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-pos # Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne ## Dataset The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 2). ## Evaluation and results F1 Score: 0.9846 (average of 5 runs). For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish). ## Citing Check out our paper for all the details: https://arxiv.org/abs/2107.07253 ``` @misc{gutierrezfandino2021spanish, title={Spanish Language Models}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas}, year={2021}, eprint={2107.07253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

899034236ad2d1c4f87c3a183d512cb0

aytugkaya/python-gpt2-large-issues-128

aytugkaya

bert

11

0

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,936

false

# python-gpt2-large-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2286 ## 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: 16 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.9843 | 1.0 | 1163 | 1.6715 | | 1.5713 | 2.0 | 2326 | 1.4301 | | 1.4226 | 3.0 | 3489 | 1.3808 | | 1.332 | 4.0 | 4652 | 1.3806 | | 1.2708 | 5.0 | 5815 | 1.2737 | | 1.2089 | 6.0 | 6978 | 1.2354 | | 1.167 | 7.0 | 8141 | 1.2250 | | 1.126 | 8.0 | 9304 | 1.2262 | | 1.0846 | 9.0 | 10467 | 1.1891 | | 1.0647 | 10.0 | 11630 | 1.2263 | | 1.0301 | 11.0 | 12793 | 1.1383 | | 1.0054 | 12.0 | 13956 | 1.0922 | | 0.9714 | 13.0 | 15119 | 1.1141 | | 0.9713 | 14.0 | 16282 | 1.1614 | | 0.9362 | 15.0 | 17445 | 1.0753 | | 0.9382 | 16.0 | 18608 | 1.2286 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.6

44890dfc321fade478a7291843912478

l3cube-pune/hing-gpt-devanagari

l3cube-pune

gpt2

7

3

transformers

0

text-generation

true

false

cc-by-4.0

['hi', 'en', 'multilingual']

['L3Cube-HingCorpus']

null

1

0

1

0

['hi', 'en', 'codemix']

false

true

898

false

## HingGPT-Devanagari HingGPT-Devanagari is a Hindi-English code-mixed GPT model trained on Devanagari text. It is a GPT2 model trained on L3Cube-HingCorpus. [dataset link] (https://github.com/l3cube-pune/code-mixed-nlp) More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2204.08398) ``` @inproceedings{nayak-joshi-2022-l3cube, title = "{L}3{C}ube-{H}ing{C}orpus and {H}ing{BERT}: A Code Mixed {H}indi-{E}nglish Dataset and {BERT} Language Models", author = "Nayak, Ravindra and Joshi, Raviraj", booktitle = "Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.wildre-1.2", pages = "7--12", } ```

801b47798dfeba184df0a1fed5540754

padmajabfrl/distilbert-base-uncased-finetuned_gender_classification

padmajabfrl

distilbert

13

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,498

false

# distilbert-base-uncased-finetuned_gender_classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 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: 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 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.003 | 1.0 | 4390 | 0.0000 | 1.0 | | 0.0069 | 2.0 | 8780 | 0.0000 | 1.0 | | 0.0014 | 3.0 | 13170 | 0.0000 | 1.0 | | 0.0014 | 4.0 | 17560 | 0.0000 | 1.0 | | 0.0008 | 5.0 | 21950 | 0.0000 | 1.0 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

c00e7cb78565f780e3f8a3c0936de1d8

DOOGLAK/Article_100v1_NER_Model_3Epochs_AUGMENTED

DOOGLAK

bert

13

5

transformers

0

token-classification

true

false

apache-2.0

null

['article100v1_wikigold_split']

null

0

['generated_from_trainer']

true

1,559

false

# Article_100v1_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v1_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3140 - Precision: 0.4708 - Recall: 0.4550 - F1: 0.4628 - Accuracy: 0.8932 ## 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 | 40 | 0.4092 | 0.1933 | 0.1445 | 0.1654 | 0.8398 | | No log | 2.0 | 80 | 0.3279 | 0.4254 | 0.3924 | 0.4082 | 0.8851 | | No log | 3.0 | 120 | 0.3140 | 0.4708 | 0.4550 | 0.4628 | 0.8932 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.11.6

9ba66f560745ddaaa5afb231aee6e306

krishnateja/wav2vec2-telugu_150

krishnateja

wav2vec2

17

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['openslr']

null

0

['generated_from_trainer']

true

3,665

false

# wav2vec2-telugu_150 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the [openslr](https://openslr.org/66) dataset. It achieves the following results on the evaluation set: - Loss: 0.3312 - Wer: 0.2213 ### 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: 150 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 6.096 | 3.84 | 400 | 0.5762 | 0.7029 | | 0.427 | 7.69 | 800 | 0.3124 | 0.5148 | | 0.208 | 11.54 | 1200 | 0.2994 | 0.4201 | | 0.1506 | 15.38 | 1600 | 0.3106 | 0.3844 | | 0.1223 | 19.23 | 2000 | 0.3080 | 0.3608 | | 0.1094 | 23.08 | 2400 | 0.3206 | 0.3332 | | 0.0949 | 26.92 | 2800 | 0.3085 | 0.3253 | | 0.0802 | 30.77 | 3200 | 0.3076 | 0.3425 | | 0.0713 | 34.61 | 3600 | 0.3280 | 0.3398 | | 0.0687 | 38.46 | 4000 | 0.3042 | 0.3081 | | 0.0613 | 42.31 | 4400 | 0.3227 | 0.3073 | | 0.0548 | 46.15 | 4800 | 0.3152 | 0.3213 | | 0.0508 | 50.0 | 5200 | 0.3259 | 0.3107 | | 0.0455 | 53.84 | 5600 | 0.3046 | 0.2881 | | 0.0427 | 57.69 | 6000 | 0.2779 | 0.3007 | | 0.0391 | 61.54 | 6400 | 0.2996 | 0.2693 | | 0.0388 | 65.38 | 6800 | 0.3016 | 0.2695 | | 0.0339 | 69.23 | 7200 | 0.3225 | 0.2935 | | 0.0312 | 73.08 | 7600 | 0.2907 | 0.2942 | | 0.029 | 76.92 | 8000 | 0.3148 | 0.3029 | | 0.0254 | 80.77 | 8400 | 0.3118 | 0.2996 | | 0.0229 | 84.61 | 8800 | 0.3022 | 0.2993 | | 0.0231 | 88.46 | 9200 | 0.3203 | 0.2465 | | 0.019 | 92.31 | 9600 | 0.3223 | 0.2460 | | 0.0173 | 96.15 | 10000 | 0.3178 | 0.2501 | | 0.0168 | 100.0 | 10400 | 0.2937 | 0.2415 | | 0.015 | 103.84 | 10800 | 0.3062 | 0.2415 | | 0.014 | 107.69 | 11200 | 0.3104 | 0.2383 | | 0.012 | 111.54 | 11600 | 0.3308 | 0.2408 | | 0.0111 | 115.38 | 12000 | 0.3228 | 0.2335 | | 0.01 | 119.23 | 12400 | 0.3228 | 0.2374 | | 0.0096 | 123.08 | 12800 | 0.3241 | 0.2304 | | 0.009 | 126.92 | 13200 | 0.3237 | 0.2295 | | 0.0075 | 130.77 | 13600 | 0.3221 | 0.2261 | | 0.0065 | 134.61 | 14000 | 0.3310 | 0.2277 | | 0.0064 | 138.46 | 14400 | 0.3348 | 0.2266 | | 0.0064 | 142.31 | 14800 | 0.3330 | 0.2229 | | 0.0056 | 146.15 | 15200 | 0.3310 | 0.2229 | | 0.0053 | 150.0 | 15600 | 0.3312 | 0.2213 | ### Test results WER(without LM): 42.8\% WER(with LM): 42\% ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.2 ps: 150 in repo name denotes number of epochs

aff10b3f38210c09ef54d6dccbc3d3d1

KenP/codeparrot-ds

KenP

gpt2

9

2

transformers

0

text-generation

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,490

false

# KenP/codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 10.3900 - Validation Loss: 9.6171 - 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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -922, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, '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: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.3900 | 9.6171 | 0 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.2.0 - Tokenizers 0.12.1

f66082a3cca7864f98b61e52911c59a8

gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-v1

gary109

wav2vec2

14

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['automatic-speech-recognition', 'gary109/AI_Light_Dance', 'generated_from_trainer']

true

3,821

false

# ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-v1 This model is a fine-tuned version of [gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53](https://huggingface.co/gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5760 - Wer: 0.2905 ## 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: 4e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 40.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.656 | 1.0 | 112 | 1.7625 | 0.9265 | | 1.3693 | 2.0 | 224 | 1.5135 | 0.9243 | | 1.2172 | 3.0 | 336 | 1.2657 | 0.8533 | | 1.0456 | 4.0 | 448 | 1.0893 | 0.7691 | | 0.9385 | 5.0 | 560 | 1.0110 | 0.7097 | | 0.8165 | 6.0 | 672 | 0.9243 | 0.6682 | | 0.7491 | 7.0 | 784 | 0.8948 | 0.6583 | | 0.6772 | 8.0 | 896 | 0.7894 | 0.6007 | | 0.6096 | 9.0 | 1008 | 0.7684 | 0.5663 | | 0.5714 | 10.0 | 1120 | 0.6978 | 0.4826 | | 0.5213 | 11.0 | 1232 | 0.8433 | 0.4927 | | 0.4624 | 12.0 | 1344 | 0.6695 | 0.4469 | | 0.4298 | 13.0 | 1456 | 0.6569 | 0.3868 | | 0.3939 | 14.0 | 1568 | 0.6633 | 0.3694 | | 0.3803 | 15.0 | 1680 | 0.6376 | 0.3920 | | 0.3415 | 16.0 | 1792 | 0.6463 | 0.3414 | | 0.3239 | 17.0 | 1904 | 0.5841 | 0.3197 | | 0.2946 | 18.0 | 2016 | 0.5948 | 0.3112 | | 0.2751 | 19.0 | 2128 | 0.5760 | 0.2905 | | 0.2834 | 20.0 | 2240 | 0.5884 | 0.2975 | | 0.2383 | 21.0 | 2352 | 0.5989 | 0.2775 | | 0.2265 | 22.0 | 2464 | 0.6151 | 0.2853 | | 0.2158 | 23.0 | 2576 | 0.5843 | 0.2670 | | 0.2015 | 24.0 | 2688 | 0.6621 | 0.2738 | | 0.215 | 25.0 | 2800 | 0.6068 | 0.2652 | | 0.1859 | 26.0 | 2912 | 0.6136 | 0.2570 | | 0.1745 | 27.0 | 3024 | 0.6191 | 0.2624 | | 0.1611 | 28.0 | 3136 | 0.6364 | 0.2578 | | 0.1513 | 29.0 | 3248 | 0.6402 | 0.2535 | | 0.172 | 30.0 | 3360 | 0.6330 | 0.2500 | | 0.1488 | 31.0 | 3472 | 0.6275 | 0.2521 | | 0.1371 | 32.0 | 3584 | 0.6539 | 0.2540 | | 0.1356 | 33.0 | 3696 | 0.6544 | 0.2491 | | 0.1319 | 34.0 | 3808 | 0.6545 | 0.2491 | | 0.1465 | 35.0 | 3920 | 0.6573 | 0.2495 | | 0.13 | 36.0 | 4032 | 0.6594 | 0.2494 | | 0.1244 | 37.0 | 4144 | 0.6651 | 0.2476 | | 0.1228 | 38.0 | 4256 | 0.6754 | 0.2497 | | 0.1181 | 39.0 | 4368 | 0.6684 | 0.2468 | | 0.1338 | 40.0 | 4480 | 0.6713 | 0.2471 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1

e1eedf024e089c6d045975ab62d0328c

CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth

CAMeL-Lab

bert

9

56

transformers

2

fill-mask

true

apache-2.0

['ar']

null

0

[]

false

true

10,765

false

# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks ## Model description **CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth). The details are described in the paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* This model card describes **CAMeLBERT-MSA-sixteenth** (`bert-base-arabic-camelbert-msa-sixteenth`), a model pre-trained on a sixteenth of the full MSA dataset. ||Model|Variant|Size|#Word| |-|-|:-:|-:|-:| ||`bert-base-arabic-camelbert-mix`|CA,DA,MSA|167GB|17.3B| ||`bert-base-arabic-camelbert-ca`|CA|6GB|847M| ||`bert-base-arabic-camelbert-da`|DA|54GB|5.8B| ||`bert-base-arabic-camelbert-msa`|MSA|107GB|12.6B| ||`bert-base-arabic-camelbert-msa-half`|MSA|53GB|6.3B| ||`bert-base-arabic-camelbert-msa-quarter`|MSA|27GB|3.1B| ||`bert-base-arabic-camelbert-msa-eighth`|MSA|14GB|1.6B| |✔|`bert-base-arabic-camelbert-msa-sixteenth`|MSA|6GB|746M| ## Intended uses You can use the released model for either masked language modeling or next sentence prediction. However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. We release our fine-tuninig code [here](https://github.com/CAMeL-Lab/CAMeLBERT). #### 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='CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth') >>> unmasker("الهدف من الحياة هو [MASK] .") [{'sequence': '[CLS] الهدف من الحياة هو التغيير. [SEP]', 'score': 0.08320745080709457, 'token': 7946, 'token_str': 'التغيير'}, {'sequence': '[CLS] الهدف من الحياة هو التعلم. [SEP]', 'score': 0.04305094853043556, 'token': 12554, 'token_str': 'التعلم'}, {'sequence': '[CLS] الهدف من الحياة هو العمل. [SEP]', 'score': 0.0417640283703804, 'token': 2854, 'token_str': 'العمل'}, {'sequence': '[CLS] الهدف من الحياة هو الحياة. [SEP]', 'score': 0.041371218860149384, 'token': 3696, 'token_str': 'الحياة'}, {'sequence': '[CLS] الهدف من الحياة هو المعرفة. [SEP]', 'score': 0.039794355630874634, 'token': 7344, 'token_str': 'المعرفة'}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth') model = AutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth') text = "مرحبا يا عالم." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import AutoTokenizer, TFAutoModel tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth') model = TFAutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth') text = "مرحبا يا عالم." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data - MSA (Modern Standard Arabic) - [The Arabic Gigaword Fifth Edition](https://catalog.ldc.upenn.edu/LDC2011T11) - [Abu El-Khair Corpus](http://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus) - [OSIAN corpus](https://vlo.clarin.eu/search;jsessionid=31066390B2C9E8C6304845BA79869AC1?1&q=osian) - [Arabic Wikipedia](https://archive.org/details/arwiki-20190201) - The unshuffled version of the Arabic [OSCAR corpus](https://oscar-corpus.com/) ## Training procedure We use [the original implementation](https://github.com/google-research/bert) released by Google for pre-training. We follow the original English BERT model's hyperparameters for pre-training, unless otherwise specified. ### Preprocessing - After extracting the raw text from each corpus, we apply the following pre-processing. - We first remove invalid characters and normalize white spaces using the utilities provided by [the original BERT implementation](https://github.com/google-research/bert/blob/eedf5716ce1268e56f0a50264a88cafad334ac61/tokenization.py#L286-L297). - We also remove lines without any Arabic characters. - We then remove diacritics and kashida using [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools). - Finally, we split each line into sentences with a heuristics-based sentence segmenter. - We train a WordPiece tokenizer on the entire dataset (167 GB text) with a vocabulary size of 30,000 using [HuggingFace's tokenizers](https://github.com/huggingface/tokenizers). - We do not lowercase letters nor strip accents. ### Pre-training - The model was trained on a single cloud TPU (`v3-8`) for one million steps in total. - The first 90,000 steps were trained with a batch size of 1,024 and the rest was trained with a batch size of 256. - The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. - We use whole word masking and a duplicate factor of 10. - We set max predictions per sequence to 20 for the dataset with max sequence length of 128 tokens and 80 for the dataset with max sequence length of 512 tokens. - We use a random seed of 12345, masked language model probability of 0.15, and short sequence probability of 0.1. - The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Evaluation results - We evaluate our pre-trained language models on five NLP tasks: NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. - We fine-tune and evaluate the models using 12 dataset. - We used Hugging Face's transformers to fine-tune our CAMeLBERT models. - We used transformers `v3.1.0` along with PyTorch `v1.5.1`. - The fine-tuning was done by adding a fully connected linear layer to the last hidden state. - We use \\(F_{1}\\) score as a metric for all tasks. - Code used for fine-tuning is available [here](https://github.com/CAMeL-Lab/CAMeLBERT). ### Results | Task | Dataset | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 | | -------------------- | --------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- | | NER | ANERcorp | MSA | 80.8% | 67.9% | 74.1% | 82.4% | 82.0% | 82.1% | 82.6% | 80.8% | | POS | PATB (MSA) | MSA | 98.1% | 97.8% | 97.7% | 98.3% | 98.2% | 98.3% | 98.2% | 98.2% | | | ARZTB (EGY) | DA | 93.6% | 92.3% | 92.7% | 93.6% | 93.6% | 93.7% | 93.6% | 93.6% | | | Gumar (GLF) | DA | 97.3% | 97.7% | 97.9% | 97.9% | 97.9% | 97.9% | 97.9% | 97.9% | | SA | ASTD | MSA | 76.3% | 69.4% | 74.6% | 76.9% | 76.0% | 76.8% | 76.7% | 75.3% | | | ArSAS | MSA | 92.7% | 89.4% | 91.8% | 93.0% | 92.6% | 92.5% | 92.5% | 92.3% | | | SemEval | MSA | 69.0% | 58.5% | 68.4% | 72.1% | 70.7% | 72.8% | 71.6% | 71.2% | | DID | MADAR-26 | DA | 62.9% | 61.9% | 61.8% | 62.6% | 62.0% | 62.8% | 62.0% | 62.2% | | | MADAR-6 | DA | 92.5% | 91.5% | 92.2% | 91.9% | 91.8% | 92.2% | 92.1% | 92.0% | | | MADAR-Twitter-5 | MSA | 75.7% | 71.4% | 74.2% | 77.6% | 78.5% | 77.3% | 77.7% | 76.2% | | | NADI | DA | 24.7% | 17.3% | 20.1% | 24.9% | 24.6% | 24.6% | 24.9% | 23.8% | | Poetry | APCD | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% | ### Results (Average) | | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 | | -------------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- | | Variant-wise-average[[1]](#footnote-1) | MSA | 82.1% | 75.7% | 80.1% | 83.4% | 83.0% | 83.3% | 83.2% | 82.3% | | | DA | 74.4% | 72.1% | 72.9% | 74.2% | 74.0% | 74.3% | 74.1% | 73.9% | | | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% | | Macro-Average | ALL | 78.7% | 74.7% | 77.1% | 79.2% | 79.0% | 79.2% | 79.1% | 78.6% | <a name="footnote-1">[1]</a>: Variant-wise-average refers to average over a group of tasks in the same language variant. ## Acknowledgements This research was supported with Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC). ## Citation ```bibtex @inproceedings{inoue-etal-2021-interplay, title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models", author = "Inoue, Go and Alhafni, Bashar and Baimukan, Nurpeiis and Bouamor, Houda and Habash, Nizar", booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", month = apr, year = "2021", address = "Kyiv, Ukraine (Online)", publisher = "Association for Computational Linguistics", abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.", } ```

40b30436cfce977b174cee14f623f530

sd-concepts-library/Aflac-duck

sd-concepts-library

null

10

0

null

1

null

false

mit

null

0

[]

false

true

1,153

false

### Aflac duck on Stable Diffusion This is the `<aflac duck>` 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 an `object`: ![<aflac duck> 0](https://huggingface.co/sd-concepts-library/Aflac-duck/resolve/main/concept_images/duck1.jpg) ![<aflac duck> 1](https://huggingface.co/sd-concepts-library/Aflac-duck/resolve/main/concept_images/duck9.jpg) ![<aflac duck> 2](https://huggingface.co/sd-concepts-library/Aflac-duck/resolve/main/concept_images/duck2.jpg) ![<aflac duck> 3](https://huggingface.co/sd-concepts-library/Aflac-duck/resolve/main/concept_images/duck8.jpg) ![<aflac duck> 4](https://huggingface.co/sd-concepts-library/Aflac-duck/resolve/main/concept_images/duck3.jpg)

7f55c57c6adfa952420972b54fb780ab

SpectaclesLLC/distilbert-legal-chunk

SpectaclesLLC

distilbert

10

218

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,845

false

# distilbert-legal-chunk This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0699 - Precision: 0.8994 - Recall: 0.8721 - Macro F1: 0.8855 - Micro F1: 0.8855 - Accuracy: 0.9789 - Marker F1: 0.9804 - Marker Precision: 0.9687 - Marker Recall: 0.9925 - Reference F1: 0.9791 - Reference Precision: 0.9804 - Reference Recall: 0.9778 - Term F1: 0.8670 - Term Precision: 0.8844 - Term Recall: 0.8502 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Macro F1 | Micro F1 | Accuracy | Marker F1 | Marker Precision | Marker Recall | Reference F1 | Reference Precision | Reference Recall | Term F1 | Term Precision | Term Recall | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|:--------:|:---------:|:----------------:|:-------------:|:------------:|:-------------------:|:----------------:|:-------:|:--------------:|:-----------:| | 0.0857 | 1.0 | 3125 | 0.0966 | 0.8374 | 0.7889 | 0.8124 | 0.8124 | 0.9676 | 0.6143 | 0.5874 | 0.6437 | 0.9628 | 0.9423 | 0.9842 | 0.8291 | 0.8656 | 0.7955 | | 0.058 | 2.0 | 6250 | 0.0606 | 0.8869 | 0.9146 | 0.9006 | 0.9006 | 0.9814 | 0.9405 | 0.9126 | 0.9702 | 0.9689 | 0.9511 | 0.9873 | 0.8923 | 0.8805 | 0.9045 | | 0.0415 | 3.0 | 9375 | 0.0642 | 0.9077 | 0.9131 | 0.9104 | 0.9104 | 0.9823 | 0.9524 | 0.9262 | 0.9801 | 0.9742 | 0.9614 | 0.9873 | 0.9021 | 0.9026 | 0.9016 | | 0.0283 | 4.0 | 12500 | 0.0646 | 0.9066 | 0.9089 | 0.9077 | 0.9077 | 0.9819 | 0.9564 | 0.9326 | 0.9815 | 0.9712 | 0.9555 | 0.9873 | 0.8986 | 0.9008 | 0.8965 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

a73a0f96c9291af6d76b0c4005ef59b7

Yanjie24/bart-samsung-5

Yanjie24

bart

13

1

transformers

0

text2text-generation

true

false

apache-2.0

null

['samsum']

null

0

['generated_from_trainer']

true

1,901

false

# bart-samsung-5 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4959 - Rouge1: 48.4734 - Rouge2: 25.3475 - Rougel: 40.9144 - Rougelsum: 44.7797 - Gen Len: 18.22 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.6107 | 1.0 | 1841 | 1.5390 | 47.1407 | 24.384 | 40.4826 | 43.4437 | 17.5513 | | 1.5528 | 2.0 | 3682 | 1.4971 | 48.5483 | 25.1562 | 41.1806 | 44.7254 | 18.3521 | | 1.4225 | 3.0 | 5523 | 1.5013 | 48.2461 | 25.2181 | 40.9022 | 44.4942 | 18.0844 | | 1.3266 | 4.0 | 7364 | 1.4976 | 48.8949 | 25.4367 | 41.2355 | 45.0961 | 18.2359 | | 1.2635 | 5.0 | 9205 | 1.4959 | 48.4734 | 25.3475 | 40.9144 | 44.7797 | 18.22 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

66384a18546a62146c1fd84b215e2f68

Helsinki-NLP/opus-mt-sv-guw

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-sv-guw * source languages: sv * target languages: guw * OPUS readme: [sv-guw](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-guw/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/sv-guw/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-guw/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-guw/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sv.guw | 33.5 | 0.531 |

0d783ae86430d2af16d9a85569a82c12

twieland/MIX1_ja-en_helsinki

twieland

marian

11

2

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

9,727

false

# MIX1_ja-en_helsinki This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on a combination of Visual Novel, Light Novel, and Subtitle data. A total of ~10MM lines of training data were used. It achieves the following results on the evaluation set: - Loss: 1.7947 - Otaku Benchmark VN BLEU: 17.78 - Otaku Benchmark LN BLEU: 11.80 - Otaku Benchmark MANGA BLEU: 13.66 ## 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: 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.7495 | 0.01 | 2000 | 2.5989 | | 2.5415 | 0.03 | 4000 | 2.4746 | | 2.4409 | 0.04 | 6000 | 2.4731 | | 2.3743 | 0.05 | 8000 | 2.4012 | | 2.3254 | 0.06 | 10000 | 2.3904 | | 2.2857 | 0.08 | 12000 | 2.3649 | | 2.2448 | 0.09 | 14000 | 2.3188 | | 2.2158 | 0.1 | 16000 | 2.2975 | | 2.193 | 0.11 | 18000 | 2.2756 | | 2.1669 | 0.13 | 20000 | 2.2852 | | 2.144 | 0.14 | 22000 | 2.2689 | | 2.1222 | 0.15 | 24000 | 2.2721 | | 2.1045 | 0.16 | 26000 | 2.2489 | | 2.0885 | 0.18 | 28000 | 2.2359 | | 2.0732 | 0.19 | 30000 | 2.2771 | | 2.0584 | 0.2 | 32000 | 2.2582 | | 2.0471 | 0.21 | 34000 | 2.2093 | | 2.0369 | 0.23 | 36000 | 2.1768 | | 2.0241 | 0.24 | 38000 | 2.1884 | | 2.0196 | 0.25 | 40000 | 2.2025 | | 2.004 | 0.27 | 42000 | 2.1507 | | 1.9936 | 0.28 | 44000 | 2.1668 | | 1.9869 | 0.29 | 46000 | 2.1432 | | 1.9735 | 0.3 | 48000 | 2.1662 | | 1.9651 | 0.32 | 50000 | 2.1824 | | 1.9551 | 0.33 | 52000 | 2.1608 | | 1.9485 | 0.34 | 54000 | 2.1322 | | 1.9421 | 0.35 | 56000 | 2.1476 | | 1.9303 | 0.37 | 58000 | 2.0994 | | 1.9236 | 0.38 | 60000 | 2.1182 | | 1.9183 | 0.39 | 62000 | 2.1305 | | 1.9108 | 0.4 | 64000 | 2.1469 | | 1.9051 | 0.42 | 66000 | 2.1414 | | 1.9018 | 0.43 | 68000 | 2.1089 | | 1.8959 | 0.44 | 70000 | 2.0908 | | 1.886 | 0.46 | 72000 | 2.0968 | | 1.8802 | 0.47 | 74000 | 2.0503 | | 1.8713 | 0.48 | 76000 | 2.0542 | | 1.8648 | 0.49 | 78000 | 2.0990 | | 1.8599 | 0.51 | 80000 | 2.1112 | | 1.8563 | 0.52 | 82000 | 2.1007 | | 1.8541 | 0.53 | 84000 | 2.0849 | | 1.845 | 0.54 | 86000 | 2.0831 | | 1.8448 | 0.56 | 88000 | 2.0560 | | 1.8342 | 0.57 | 90000 | 2.0349 | | 1.8344 | 0.58 | 92000 | 2.0301 | | 1.8291 | 0.59 | 94000 | 2.0300 | | 1.819 | 0.61 | 96000 | 2.0378 | | 1.8154 | 0.62 | 98000 | 2.0197 | | 1.82 | 0.63 | 100000 | 2.0463 | | 1.8081 | 0.64 | 102000 | 2.0077 | | 1.8046 | 0.66 | 104000 | 2.0101 | | 1.7978 | 0.67 | 106000 | 2.0150 | | 1.7934 | 0.68 | 108000 | 2.0215 | | 1.7904 | 0.7 | 110000 | 2.0278 | | 1.7871 | 0.71 | 112000 | 2.0588 | | 1.779 | 0.72 | 114000 | 2.0062 | | 1.7784 | 0.73 | 116000 | 2.0300 | | 1.7749 | 0.75 | 118000 | 1.9664 | | 1.7691 | 0.76 | 120000 | 2.0033 | | 1.7622 | 0.77 | 122000 | 1.9983 | | 1.7587 | 0.78 | 124000 | 2.0030 | | 1.755 | 0.8 | 126000 | 1.9955 | | 1.7531 | 0.81 | 128000 | 1.9764 | | 1.7439 | 0.82 | 130000 | 1.9942 | | 1.7406 | 0.83 | 132000 | 2.0221 | | 1.7385 | 0.85 | 134000 | 1.9835 | | 1.7332 | 0.86 | 136000 | 1.9967 | | 1.7332 | 0.87 | 138000 | 2.0247 | | 1.7309 | 0.88 | 140000 | 1.9817 | | 1.7248 | 0.9 | 142000 | 2.0063 | | 1.7209 | 0.91 | 144000 | 1.9583 | | 1.7154 | 0.92 | 146000 | 1.9779 | | 1.7153 | 0.94 | 148000 | 1.9478 | | 1.7094 | 0.95 | 150000 | 1.9706 | | 1.7061 | 0.96 | 152000 | 1.9605 | | 1.7017 | 0.97 | 154000 | 1.9447 | | 1.6965 | 0.99 | 156000 | 1.9419 | | 1.6929 | 1.0 | 158000 | 1.9589 | | 1.6628 | 1.01 | 160000 | 1.9383 | | 1.6535 | 1.02 | 162000 | 1.9487 | | 1.6495 | 1.04 | 164000 | 1.9400 | | 1.6516 | 1.05 | 166000 | 1.9353 | | 1.6513 | 1.06 | 168000 | 1.9253 | | 1.6518 | 1.07 | 170000 | 1.9132 | | 1.6491 | 1.09 | 172000 | 1.9076 | | 1.6453 | 1.1 | 174000 | 1.9192 | | 1.6426 | 1.11 | 176000 | 1.9191 | | 1.6353 | 1.13 | 178000 | 1.9367 | | 1.6352 | 1.14 | 180000 | 1.9218 | | 1.6304 | 1.15 | 182000 | 1.9305 | | 1.6299 | 1.16 | 184000 | 1.9072 | | 1.6263 | 1.18 | 186000 | 1.9211 | | 1.6284 | 1.19 | 188000 | 1.9037 | | 1.6237 | 1.2 | 190000 | 1.8951 | | 1.6231 | 1.21 | 192000 | 1.8998 | | 1.6184 | 1.23 | 194000 | 1.8960 | | 1.6153 | 1.24 | 196000 | 1.8776 | | 1.6122 | 1.25 | 198000 | 1.8747 | | 1.6109 | 1.26 | 200000 | 1.8951 | | 1.6072 | 1.28 | 202000 | 1.8705 | | 1.6094 | 1.29 | 204000 | 1.8903 | | 1.6063 | 1.3 | 206000 | 1.8660 | | 1.599 | 1.31 | 208000 | 1.8696 | | 1.5931 | 1.33 | 210000 | 1.8598 | | 1.5943 | 1.34 | 212000 | 1.8760 | | 1.5906 | 1.35 | 214000 | 1.8833 | | 1.5858 | 1.37 | 216000 | 1.8645 | | 1.5873 | 1.38 | 218000 | 1.8620 | | 1.5842 | 1.39 | 220000 | 1.8632 | | 1.5808 | 1.4 | 222000 | 1.8782 | | 1.5756 | 1.42 | 224000 | 1.8627 | | 1.5728 | 1.43 | 226000 | 1.8649 | | 1.5709 | 1.44 | 228000 | 1.8735 | | 1.5704 | 1.45 | 230000 | 1.8630 | | 1.5659 | 1.47 | 232000 | 1.8598 | | 1.5637 | 1.48 | 234000 | 1.8519 | | 1.5628 | 1.49 | 236000 | 1.8569 | | 1.5559 | 1.5 | 238000 | 1.8401 | | 1.5532 | 1.52 | 240000 | 1.8528 | | 1.557 | 1.53 | 242000 | 1.8637 | | 1.5499 | 1.54 | 244000 | 1.8701 | | 1.5476 | 1.55 | 246000 | 1.8423 | | 1.5502 | 1.57 | 248000 | 1.8320 | | 1.5469 | 1.58 | 250000 | 1.8542 | | 1.5382 | 1.59 | 252000 | 1.8526 | | 1.5396 | 1.61 | 254000 | 1.8537 | | 1.528 | 1.62 | 256000 | 1.8248 | | 1.532 | 1.63 | 258000 | 1.8322 | | 1.5269 | 1.64 | 260000 | 1.8381 | | 1.5269 | 1.66 | 262000 | 1.8389 | | 1.5269 | 1.67 | 264000 | 1.8445 | | 1.525 | 1.68 | 266000 | 1.8232 | | 1.5175 | 1.69 | 268000 | 1.8561 | | 1.5172 | 1.71 | 270000 | 1.8342 | | 1.5174 | 1.72 | 272000 | 1.8167 | | 1.5114 | 1.73 | 274000 | 1.8281 | | 1.5094 | 1.74 | 276000 | 1.8164 | | 1.5083 | 1.76 | 278000 | 1.8317 | | 1.5047 | 1.77 | 280000 | 1.8207 | | 1.5045 | 1.78 | 282000 | 1.8155 | | 1.497 | 1.8 | 284000 | 1.8275 | | 1.4996 | 1.81 | 286000 | 1.8152 | | 1.497 | 1.82 | 288000 | 1.8137 | | 1.4967 | 1.83 | 290000 | 1.8109 | | 1.4936 | 1.85 | 292000 | 1.8037 | | 1.4867 | 1.86 | 294000 | 1.7955 | | 1.4859 | 1.87 | 296000 | 1.8181 | | 1.4869 | 1.88 | 298000 | 1.7999 | | 1.4811 | 1.9 | 300000 | 1.8062 | | 1.4831 | 1.91 | 302000 | 1.8042 | | 1.4791 | 1.92 | 304000 | 1.8020 | | 1.4797 | 1.93 | 306000 | 1.7972 | | 1.483 | 1.95 | 308000 | 1.8044 | | 1.4748 | 1.96 | 310000 | 1.8036 | | 1.4772 | 1.97 | 312000 | 1.7958 | | 1.4708 | 1.98 | 314000 | 1.7967 | | 1.4743 | 2.0 | 316000 | 1.7947 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

1b243268a2d32ed7a2fc60038e5910e1

Helsinki-NLP/opus-mt-de-el

Helsinki-NLP

marian

10

184

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

770

false

### opus-mt-de-el * source languages: de * target languages: el * OPUS readme: [de-el](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-el/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-el/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-el/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-el/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.de.el | 45.7 | 0.649 |

6dfb46970acb3dd72e777eade115258e

sd-concepts-library/doener-red-line-art

sd-concepts-library

null

11

0

null

0

null

false

mit

null

0

[]

false

true

1,258

false

### doener_red_line_art on Stable Diffusion This is the `<dnr>` 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`: ![<dnr> 0](https://huggingface.co/sd-concepts-library/doener-red-line-art/resolve/main/concept_images/5.jpeg) ![<dnr> 1](https://huggingface.co/sd-concepts-library/doener-red-line-art/resolve/main/concept_images/3.jpeg) ![<dnr> 2](https://huggingface.co/sd-concepts-library/doener-red-line-art/resolve/main/concept_images/0.jpeg) ![<dnr> 3](https://huggingface.co/sd-concepts-library/doener-red-line-art/resolve/main/concept_images/2.jpeg) ![<dnr> 4](https://huggingface.co/sd-concepts-library/doener-red-line-art/resolve/main/concept_images/1.jpeg) ![<dnr> 5](https://huggingface.co/sd-concepts-library/doener-red-line-art/resolve/main/concept_images/4.jpeg)

5ab2f36327de736151e388b4ed0dc46c

netsvetaev/netsvetaev-free

netsvetaev

null

16

0

null

2

text-to-image

false

mit

['en']

null

1

0

1

0

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

false

true

1,961

false

Hello! This is the model based on my paintings and SD 1.5. I did it as an experiment. The token is «in style of netsvetaev abstract paintings». Best suited for: abstract seamless patterns, simple prompts like «orange, fruit», and large objects like «cat face» or «girl face». It works well with landscape orientation. It has MIT license, you can use it for free. Best used with Invoke AI: https://github.com/invoke-ai/InvokeAI (The examples below contain metadata for it) ![Pattern by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-free/resolve/main/000131.cf3e6f6e.164440057.png) ![Pattern by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-free/resolve/main/000130.f75dbfc3.2213579322.png) ![Cat Face by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-free/resolve/main/000106.069be65a.1503437039.png) ![Kitchen interior by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-free/resolve/main/000119.bb62abc0.3887203566.png) ![Abstract painting by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-free/resolve/main/000155.0cbae2e0.3926227309.png) ![Abstract painting by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-free/resolve/main/000127.27ae4dfb.2213579322.png) ![Abstract painting by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-free/resolve/main/000157.0d9fdfd1.404674009.png) ![Abstract painting by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-free/resolve/main/000153.4584aca0.3124844272.png) ![Abstract painting by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-free/resolve/main/000123.44e4c27a.3515775029.png) ________________________ Artur Netsvetaev, 2022 https://netsvetaev.com

e5eb5508ea422f025bcacadeb03e926d

wietsedv/xlm-roberta-base-ft-udpos28-is

wietsedv

xlm-roberta

8

7

transformers

0

token-classification

true

false

apache-2.0

['is']

['universal_dependencies']

null

0

['part-of-speech', 'token-classification']

true

569

false

# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Icelandic This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-is") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-is") ```

7d127bd7484b6b3fce8548f572bad2ee

joebobby/finetuning-sentiment-model-5000-samples3

joebobby

bert

22

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

918

false

# finetuning-sentiment-model-5000-samples3 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 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 ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

c7b98279ec4be9bb013f1b749e375dc3

ptro/model1_test

ptro

bert

13

3

transformers

1

text-classification

true

false

cc-by-sa-4.0

null

0

['generated_from_trainer']

true

1,407

false

# model1_test This model is a fine-tuned version of [DaNLP/da-bert-hatespeech-detection](https://huggingface.co/DaNLP/da-bert-hatespeech-detection) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1816 - Accuracy: 0.9667 - F1: 0.3548 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 150 | 0.1128 | 0.9667 | 0.2 | | No log | 2.0 | 300 | 0.1666 | 0.9684 | 0.2963 | | No log | 3.0 | 450 | 0.1816 | 0.9667 | 0.3548 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

82ea95ad99670aef6b67451f9579184e

Helsinki-NLP/opus-mt-sv-yap

Helsinki-NLP

marian

10

25

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-sv-yap * source languages: sv * target languages: yap * OPUS readme: [sv-yap](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-yap/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/sv-yap/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-yap/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-yap/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sv.yap | 27.3 | 0.461 |

871874f04f86fb5a9116b122711458ce

joe5campbell/BERT_Tweet_Sentiment_50k_2eps

joe5campbell

bert

4

transformers

0

text-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,395

false

# BERT_Tweet_Sentiment_50k_2eps 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: 0.1131 - Train Accuracy: 0.9596 - Validation Loss: 0.6972 - Validation Accuracy: 0.8229 - 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', 'clipnorm': 1.0, 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.3420 | 0.8511 | 0.4293 | 0.8299 | 0 | | 0.1131 | 0.9596 | 0.6972 | 0.8229 | 1 | ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Tokenizers 0.11.0

8bee5ab0c40511ac2aafda8596abd915

vesteinn/IceBERT

vesteinn

roberta

8

166

transformers

3

fill-mask

true

false

agpl-3.0

['is']

null

0

['roberta', 'icelandic', 'masked-lm', 'pytorch']

false

true

895

false

# IceBERT IceBERT was trained with fairseq using the RoBERTa-base architecture. The training data used is shown in the table below. | Dataset | Size | Tokens | |------------------------------------------------------|---------|--------| | Icelandic Gigaword Corpus v20.05 (IGC) | 8.2 GB | 1,388M | | Icelandic Common Crawl Corpus (IC3) | 4.9 GB | 824M | | Greynir News articles | 456 MB | 76M | | Icelandic Sagas | 9 MB | 1.7M | | Open Icelandic e-books (Rafbókavefurinn) | 14 MB | 2.6M | | Data from the medical library of Landspitali | 33 MB | 5.2M | | Student theses from Icelandic universities (Skemman) | 2.2 GB | 367M | | Total | 15.8 GB | 2,664M |

d3bb385bc3abcf87748cd721d808e090

gokuls/bert-tiny-sst2-KD-BERT_and_distilBERT

gokuls

bert

13

3

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

0

['generated_from_trainer']

true

1,541

false

# bert-tiny-sst2-KD-BERT_and_distilBERT 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 glue dataset. It achieves the following results on the evaluation set: - Loss: 1.5530 - Accuracy: 0.8326 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.7317 | 1.0 | 4210 | 1.5887 | 0.8222 | | 1.0068 | 2.0 | 8420 | 1.5530 | 0.8326 | | 0.7961 | 3.0 | 12630 | 1.7072 | 0.8245 | | 0.6852 | 4.0 | 16840 | 1.8794 | 0.8177 | | 0.6039 | 5.0 | 21050 | 1.8691 | 0.8142 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

d2abdc091dea2c0fa5dc82b0b2a31c6a

strickvl/nlp-redaction-classifier

strickvl

deberta-v2

11

1

transformers

2

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,688

false

# Redaction Classifier: NLP Edition This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on a custom dataset. It achieves the following results on the evaluation set: - Loss: 0.0893 - Pearson: 0.8273 ## Model description Read more about the process and the code used to train this model on my blog [here](https://mlops.systems). ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2054 | 1.0 | 729 | 0.1382 | 0.6771 | | 0.1386 | 2.0 | 1458 | 0.1099 | 0.7721 | | 0.0782 | 3.0 | 2187 | 0.0950 | 0.8083 | | 0.054 | 4.0 | 2916 | 0.0945 | 0.8185 | | 0.0319 | 5.0 | 3645 | 0.0880 | 0.8251 | | 0.0254 | 6.0 | 4374 | 0.0893 | 0.8273 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0a0+17540c5 - Datasets 2.2.2 - Tokenizers 0.12.1

242efc088f677a1b7d785707eec3166a

cansen88/PromptGenerator_5_topic_finetuned

cansen88

gpt2

9

2

transformers

0

text-generation

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,965

false

# PromptGenerator_5_topic_finetuned This model is a fine-tuned version of [kmkarakaya/turkishReviews-ds](https://huggingface.co/kmkarakaya/turkishReviews-ds) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.6861 - Train Sparse Categorical Accuracy: 0.8150 - Validation Loss: 1.9777 - Validation Sparse Categorical Accuracy: 0.7250 - Epoch: 4 ## 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': 5e-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 Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | 3.0394 | 0.5171 | 2.7152 | 0.5841 | 0 | | 2.5336 | 0.6247 | 2.4440 | 0.6318 | 1 | | 2.2002 | 0.6958 | 2.2557 | 0.6659 | 2 | | 1.9241 | 0.7608 | 2.1059 | 0.6932 | 3 | | 1.6861 | 0.8150 | 1.9777 | 0.7250 | 4 | ### Framework versions - Transformers 4.21.1 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1

dcf764223f19e7f1f0b56bae288668d3

jmassot/xlm-roberta-base-jm-finetuned-panx-en_hub

jmassot

xlm-roberta

10

11

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,327

false

# xlm-roberta-base-jm-finetuned-panx-en_hub 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: 0.4209 - F1: 0.6542 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.2098 | 1.0 | 50 | 0.6167 | 0.4802 | | 0.5605 | 2.0 | 100 | 0.4436 | 0.6184 | | 0.4035 | 3.0 | 150 | 0.4209 | 0.6542 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.1

8c16b10210af560f4ce7e26cae2fef0c

autoevaluate/natural-language-inference

autoevaluate

distilbert

13

2

transformers

1

text-classification

true

false

apache-2.0

null

['glue']

null

8

7

1

0

['generated_from_trainer']

true

1,326

false

# natural-language-inference This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4120 - Accuracy: 0.8284 - F1: 0.8822 ## 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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 230 | 0.4288 | 0.8039 | 0.8644 | | No log | 2.0 | 460 | 0.4120 | 0.8284 | 0.8822 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

c22c5592b371ab685562db290d86f50e

kz/mt5base-finetuned-ECC-japanese-small

kz

mt5

7

28

transformers

1

text2text-generation

true

false

mit

['ja']

null

0

[]

false

true

2,087

false

Google's mt5-base fine-tuned in Japanese to solve error detection and correction task. # 日本語誤り訂正 - "吾輩をは猫である。名前えはまだない。"→"吾輩は猫である。名前はまだない。" - "-small" has been trained on 20,000 text pairs only. - dataset: [link](http://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9EWikipedia%E5%85%A5%E5%8A%9B%E8%AA%A4%E3%82%8A%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88)　*used only first 20,000 text pairs. - prefix: "correction: "　（notice: single task trained.) - text-to-textのお気持ち体験版ぐらいの感覚でどうぞ． ## 参考 - "東北大学でMASKが研究をしています。"→"東北大学でMASKの研究をしています。"　ジム・キャリーを主語とした唯一のガ格が消され、ジム・キャリーは研究対象となった。易読化のために用いられる主語と動詞を近づける記法は誤り扱い？ - "東北大学でマスクが研究をしています。"→"東北大学でマスクの研究をしています。" - "東北大学でイーロン・マスクが研究をしています。"→"東北大学でイーロン・マスクが研究をしています。" - "東北大学で「イーロン・マスク」が研究をしています。"→"東北大学で「イーロン・マスク」の研究をしています。"　単語の意味も考慮されている？ - "東北大学でイマスクが研究をしています。"→"東北大学でイマスクの研究をしています。" - "東北大学でクが研究をしています。"→"東北大学でコンピューターが研究をしています。" それはちょっと待って。 ## 参考　extra_idを用い探索＜＞は半角に変更してください - "東北大学で＜extra_id_0＞の研究をしています。"→"東北大学で化学の研究をしています。" - "東北大学で＜extra_id_0＞が研究をしています。"→"東北大学で工学が研究をしています。"　工学さん。 - "吾輩は＜extra_id_0＞である。"→"吾輩は吾輩である。" - "答えは猫です。吾輩は＜extra_id_0＞である。"→"答えは猫です。吾輩は猫である。" - "答えは猫です。吾輩の＜extra_id_0＞である。"→"答えは猫です。吾輩の心は猫である。" - "私は猫です。私は＜extra_id_0＞"→"私は猫です。私は猫です。" - "私は猫です。N/A ＜extra_id_0＞"→"猫です。" - "あなたは女性で猫です。彼は犬です。彼女は＜extra_id_0＞"→"あなたは女性で猫です。彼は犬です。彼女は猫です。" - "あなたは女性で猫です。彼は犬です。彼は＜extra_id_0＞"→"あなたは女性で猫です。彼は犬です。" - "あなたは女性で猫です。彼は犬です。彼は男性で＜extra_id_0＞"→"あなたは女性で猫です。彼は犬です。彼は男性で猫です。" - "あなたは女性で猫です。彼は犬です。ライオンは＜extra_id_0＞"→"あなたは女性で猫です。彼は犬です。ライオンは猫です。" - "あなたがは女性で猫です。彼はが犬です。ライオンが＜extra_id_0＞"→"あなたが女性で猫です。彼は犬です。ライオンが犬です。" - "Aは11、Bは9。Aは＜extra_id_0＞。Bは＜extra_id_1＞。"→"Aは11、Bは9。Aは11。Bは9。" - "彼の名前はallenです。彼のnameは＜extra_id_0＞"→"彼の名前はallenです。彼の名前は英語です。" - "translate japanease to english: 赤い花． => red flower. 青い花． => <extra_id_0>"→"赤い花. => red flower. 青い花. => blue flower"　タスク比依存翻訳可能性の片鱗．japaneseをjapaneaseと間違えたことは秘密だ・・・と言うか間違えても動くのか ## Prompting参考 Chain of Thought Prompting Elicits Reasoning in Large Language Models https://arxiv.org/abs/2201.11903 **check in progress** ## Licenese - The MIT license

3476c03a67efa22bf8e871b4b898e946

Evelyn18/distilbert-base-uncased-becasv2-6

Evelyn18

distilbert

13

7

transformers

0

question-answering

true

false

apache-2.0

null

['becasv2']

null

0

['generated_from_trainer']

true

1,530

false

# distilbert-base-uncased-becasv2-6 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 3.8936 ## 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: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 9 | 4.0542 | | No log | 2.0 | 18 | 3.0865 | | No log | 3.0 | 27 | 2.8069 | | No log | 4.0 | 36 | 3.3330 | | No log | 5.0 | 45 | 3.4108 | | No log | 6.0 | 54 | 3.5562 | | No log | 7.0 | 63 | 3.8846 | | No log | 8.0 | 72 | 3.8936 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

ff5b1978c99e7e6f6910e3ef21b43922

morenolq/distilbert-base-cased-hate-speech

morenolq

distilbert

13

2

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,067

false

# distilbert-base-cased-hate-speech **Training:** The model has been trained using the script provided in the following repository https://github.com/MorenoLaQuatra/transformers-tasks-templates This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on [hate speech](https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech) dataset. It achieves the following results on the evaluation set: - Loss: 0.6837 - Mae: 1.9686 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.6857 | 1.0 | 3389 | 0.6471 | 1.9725 | | 0.3645 | 2.0 | 6778 | 0.4359 | 1.9725 | | 0.2266 | 3.0 | 10167 | 0.3664 | 1.9725 | | 0.1476 | 4.0 | 13556 | 0.3253 | 1.9725 | | 0.0992 | 5.0 | 16945 | 0.3047 | 1.9725 | | 0.0737 | 6.0 | 20334 | 0.2869 | 1.9725 | | 0.0537 | 7.0 | 23723 | 0.2709 | 1.9725 | | 0.0458 | 8.0 | 27112 | 0.2667 | 1.9725 | | 0.0313 | 9.0 | 30501 | 0.2589 | 1.9725 | | 0.027 | 10.0 | 33890 | 0.2540 | 1.9725 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

810bd429241b3685c11e15026f8e415b

shabohin/ddpm-butterflies-128

shabohin

null

11

0

diffusers

0

null

false

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

null

0

[]

false

true

1,230

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/shabohin/ddpm-butterflies-128/tensorboard?#scalars)

bd760e0f3e848aebc41d00081c9eda57

jonatasgrosman/exp_w2v2t_fr_unispeech-ml_s51

jonatasgrosman

unispeech

10

5

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

499

false

# exp_w2v2t_fr_unispeech-ml_s51 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) 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.

f3a7e4fa7c2bd511039659f11857b392

firqaaa/indo-dpr-ctx_encoder-single-squad-base

firqaaa

dpr

8

6

transformers

0

feature-extraction

true

false

apache-2.0

['id']

['squad_v2']

null

0

['feature-extraction', 'transformers']

false

true

1,821

false

### indo-dpr-question_encoder-single-squad-base Indonesian Dense Passage Retrieval trained on translated SQuADv2.0 dataset in DPR format. ### Evaluation | Class | Precision | Recall | F1-Score | Support | |-------|-----------|--------|----------|---------| | hard_negative | 0.9963 | 0.9963 | 0.9963 | 183090 | | positive | 0.8849 | 0.8849 | 0.8849 | 5910 | | Metric | Value | |--------|-------| | Accuracy | 0.9928 | | Macro Average | 0.9406 | | Weighted Average | 0.9928 | Note: This report is for evaluation on the dev set, after 12000 batches. ### Usage ```python from transformers import DPRContextEncoder, DPRContextEncoderTokenizer tokenizer = DPRContextEncoderTokenizer.from_pretrained('firqaaa/indo-dpr-ctx_encoder-single-squad-base') model = DPRContextEncoder.from_pretrained('firqaaa/indo-dpr-ctx_encoder-single-squad-base') input_ids = tokenizer("Ibukota Indonesia terletak dimana?", return_tensors='pt')["input_ids"] embeddings = model(input_ids).pooler_output ``` You can use it using `haystack` as follows: ``` from haystack.nodes import DensePassageRetriever from haystack.document_stores import InMemoryDocumentStore retriever = DensePassageRetriever(document_store=InMemoryDocumentStore(), query_embedding_model="firqaaa/indo-dpr-ctx_encoder-single-squad-base", passage_embedding_model="firqaaa/indo-dpr-ctx_encoder-single-squad-base", max_seq_len_query=64, max_seq_len_passage=256, batch_size=16, use_gpu=True, embed_title=True, use_fast_tokenizers=True) ```

4c6a0c0f03424e8425ade1787371760e

Rakib/roberta-base-on-cuad

Rakib

roberta

11

31,600

transformers

1

question-answering

true

false

mit

['en']

['cuad']

null

3

0

2

1

0

['legal-contract-review', 'roberta', 'cuad']

false

true

4,841

false

# Model Card for roberta-base-on-cuad # Model Details ## Model Description - **Developed by:** Mohammed Rakib - **Shared by [Optional]:** More information needed - **Model type:** Question Answering - **Language(s) (NLP):** en - **License:** MIT - **Related Models:** - **Parent Model:** RoBERTa - **Resources for more information:** - GitHub Repo: [defactolaw](https://github.com/afra-tech/defactolaw) - Associated Paper: [An Open Source Contractual Language Understanding Application Using Machine Learning](https://aclanthology.org/2022.lateraisse-1.6/) # Uses ## Direct Use This model can be used for the task of Question Answering on Legal Documents. # Training Details Read: [An Open Source Contractual Language Understanding Application Using Machine Learning](https://aclanthology.org/2022.lateraisse-1.6/) for detailed information on training procedure, dataset preprocessing and evaluation. ## Training Data See [CUAD dataset card](https://huggingface.co/datasets/cuad) for more information. ## Training Procedure ### Preprocessing More information needed ### Speeds, Sizes, Times More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data See [CUAD dataset card](https://huggingface.co/datasets/cuad) for more information. ### Factors ### Metrics More information needed ## Results More information needed # Model Examination More information needed - **Hardware Type:** More information needed - **Hours used:** More information needed - **Cloud Provider:** More information needed - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware Used V100/P100 from Google Colab Pro ### Software Python, Transformers # Citation **BibTeX:** ``` @inproceedings{nawar-etal-2022-open, title = "An Open Source Contractual Language Understanding Application Using Machine Learning", author = "Nawar, Afra and Rakib, Mohammed and Hai, Salma Abdul and Haq, Sanaulla", booktitle = "Proceedings of the First Workshop on Language Technology and Resources for a Fair, Inclusive, and Safe Society within the 13th Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lateraisse-1.6", pages = "42--50", abstract = "Legal field is characterized by its exclusivity and non-transparency. Despite the frequency and relevance of legal dealings, legal documents like contracts remains elusive to non-legal professionals for the copious usage of legal jargon. There has been little advancement in making legal contracts more comprehensible. This paper presents how Machine Learning and NLP can be applied to solve this problem, further considering the challenges of applying ML to the high length of contract documents and training in a low resource environment. The largest open-source contract dataset so far, the Contract Understanding Atticus Dataset (CUAD) is utilized. Various pre-processing experiments and hyperparameter tuning have been carried out and we successfully managed to eclipse SOTA results presented for models in the CUAD dataset trained on RoBERTa-base. Our model, A-type-RoBERTa-base achieved an AUPR score of 46.6{\%} compared to 42.6{\%} on the original RoBERT-base. This model is utilized in our end to end contract understanding application which is able to take a contract and highlight the clauses a user is looking to find along with it{'}s descriptions to aid due diligence before signing. Alongside digital, i.e. searchable, contracts the system is capable of processing scanned, i.e. non-searchable, contracts using tesseract OCR. This application is aimed to not only make contract review a comprehensible process to non-legal professionals, but also to help lawyers and attorneys more efficiently review contracts.", } ``` # Glossary [optional] More information needed # More Information [optional] More information needed # Model Card Authors [optional] Mohammed Rakib in collaboration with Ezi Ozoani and the Hugging Face team # Model Card Contact More information needed # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Rakib/roberta-base-on-cuad") model = AutoModelForQuestionAnswering.from_pretrained("Rakib/roberta-base-on-cuad") ``` </details>

dcf90e27ad5247d9a5c3e9c2981b8771

krirk/wav2vec2-large-xls-r-300m-turkish-colab

krirk

wav2vec2

13

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

1,791

false

# wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3942 - Wer: 0.3149 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.9921 | 3.67 | 400 | 0.7820 | 0.7857 | | 0.4496 | 7.34 | 800 | 0.4630 | 0.4977 | | 0.2057 | 11.01 | 1200 | 0.4293 | 0.4627 | | 0.1328 | 14.68 | 1600 | 0.4464 | 0.4068 | | 0.1009 | 18.35 | 2000 | 0.4461 | 0.3742 | | 0.0794 | 22.02 | 2400 | 0.4328 | 0.3467 | | 0.0628 | 25.69 | 2800 | 0.4036 | 0.3263 | | 0.0497 | 29.36 | 3200 | 0.3942 | 0.3149 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

9bacefd09c02f854acf7a897768332a4

Tristan/distilbert_summarization_reward_model

Tristan

distilbert

10

5

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,292

false

# distilbert_summarization_reward_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6972 - Accuracy: 0.5271 ## 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: 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 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6922 | 1.0 | 11608 | 0.6918 | 0.5237 | | 0.6762 | 2.0 | 23216 | 0.6972 | 0.5271 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2

3238a94ec7d857acd5da386d056f7f48

jonatasgrosman/exp_w2v2t_fa_wavlm_s545

jonatasgrosman

wavlm

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['fa']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

439

false

# exp_w2v2t_fa_wavlm_s545 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (fa)](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.

ec889aa61b03343879096791ea2f87c5

EricJai/ddpm-butterflies-128

EricJai

null

11

2

diffusers

0

null

false

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

null

0

[]

false

true

1,229

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/EricJai/ddpm-butterflies-128/tensorboard?#scalars)

578a8a2e535fe506dc16b964f4d2248b

Helsinki-NLP/opus-mt-swc-fr

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-swc-fr * source languages: swc * target languages: fr * OPUS readme: [swc-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/swc-fr/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/swc-fr/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/swc-fr/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/swc-fr/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.swc.fr | 28.6 | 0.470 |

18e44ca6fbd6eecea5916cddbb4b5ccd

doc2query/stackexchange-t5-base-v1

doc2query

t5

10

1

transformers

0

text2text-generation

true

false

apache-2.0

['en']

['flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl']

null

0

[]

false

true

2,824

false

# doc2query/stackexchange-t5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini. - **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'doc2query/stackexchange-t5-base-v1' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." input_ids = tokenizer.encode(text, max_length=320, truncation=True, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=5) print("Text:") print(text) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ``` **Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 449k training steps. For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (title, best_answer_pairs) from StackExchange.

4bcf824e2f8c42f39ae179c029bb4a18

Lvxue/distilled-mt5-small-0.05-0.25

Lvxue

mt5

14

1

transformers

0

text2text-generation

true

false

apache-2.0

['en', 'ro']

['wmt16']

null

1

0

['generated_from_trainer']

true

1,040

false

# distilled-mt5-small-0.05-0.25 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8318 - Bleu: 7.1808 - Gen Len: 44.1986 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

d1d2a9fb135b3740eac77ee77fc4d79a

Helsinki-NLP/opus-mt-ts-fi

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

768

false

### opus-mt-ts-fi * source languages: ts * target languages: fi * OPUS readme: [ts-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ts-fi/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/ts-fi/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ts-fi/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ts-fi/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.ts.fi | 27.7 | 0.509 |

0c57edf4b48e7e2ab2f5466f230df92c

fathyshalab/massive_datetime-roberta-large-v1-2-0.82

fathyshalab

roberta

14

2

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

['setfit', 'sentence-transformers', 'text-classification']

false

true

1,470

false

# fathyshalab/massive_datetime-roberta-large-v1-2-0.82 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/massive_datetime-roberta-large-v1-2-0.82") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

81540dc9e0e49a883f3ca7f0fa9a17f5

Helsinki-NLP/opus-mt-tc-big-en-lv

Helsinki-NLP

marian

12

44

transformers

0

translation

true

false

cc-by-4.0

['en', 'lv']

null

1

0

1

0

['translation', 'opus-mt-tc']

true

5,394

false

# opus-mt-tc-big-en-lv Neural machine translation model for translating from English (en) to Latvian (lv). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2022-03-13 * source language(s): eng * target language(s): lav * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-lav/opusTCv20210807+bt_transformer-big_2022-03-13.zip) * more information released models: [OPUS-MT eng-lav README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-lav/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>lav<< A day has twenty-four hours.", ">>ltg<< He's a good lawyer." ] model_name = "pytorch-models/opus-mt-tc-big-en-lv" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Dienā ir divdesmit četras stundas. # Vyss ir labs advokats. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-lv") print(pipe(">>lav<< A day has twenty-four hours.")) # expected output: Dienā ir divdesmit četras stundas. ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-lav/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-lav/opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | eng-lav | tatoeba-test-v2021-08-07 | 0.66411 | 44.0 | 1631 | 9932 | | eng-lav | flores101-devtest | 0.59397 | 30.1 | 1012 | 22092 | | eng-lav | newsdev2017 | 0.58082 | 28.9 | 2003 | 41503 | | eng-lav | newstest2017 | 0.53202 | 22.1 | 2001 | 39392 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 3405783 * port time: Wed Apr 13 17:36:04 EEST 2022 * port machine: LM0-400-22516.local

0f27dc51a8b2aaf94a2d9819f4018ad3

nateraw/modelcard-creator-demo

nateraw

null

2

0

pytorch

0

null

true

false

mit

['en']

['beans']

null

0

['modelcards', 'autogenerated-modelcard']

false

true

3,511

false

# modelcard-creator-demo ## Table of Contents - [Model Details](#model-details) - [How To Get Started With the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Direct Use](#direct-use) - [Downstream Use](#downstream-use) - [Misuse and Out of Scope Use](#misuse-and-out-of-scope-use) - [Limitations and Biases](#limitations-and-biases) - [Training](#training) - [Training Data](#training-data) - [Training Procedure](#training-procedure) - [Evaluation Results](#evaluation-results) - [Environmental Impact](#environmental-impact) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Model Details  This isn't really a model, it's just a test repo to see if the [model card creator](https://huggingface.co/spaces/nateraw/modelcard-creator) works! - Developed by: Nathan Raw - Language(s): - License: modelcard-creator-demo is licensed under the mit license - Resources for more information: - [Research Paper](https://arxiv.org/pdf/1810.03993.pdf) - [GitHub Repo](https://github.com/nateraw/modelcards) ## How to Get Started with the Model Use the code below to get started with the model. ```python # A nice code snippet here that describes how to use the model... ``` ## Uses #### Direct Use  [More Information Needed] #### Downstream Use  [More Information Needed] #### Misuse and Out-of-scope Use  [More Information Needed] ## Limitations and Biases  **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes.** [More Information Needed] ## Training #### Training Data   See the data card for additional information. #### Training Procedure  [More Information Needed] ## Evaluation Results  [More Information Needed] ## Environmental Impact  You can estimate carbon emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700) - **Hardware Type:** - **Hours used:** - **Cloud Provider:** - **Compute Region:** - **Carbon Emitted:** ## Citation Information ```bibtex @inproceedings{Mitchell_2019, doi = {10.1145/3287560.3287596}, url = {https://doi.org/10.1145%2F3287560.3287596}, year = 2019, month = {jan}, publisher = {{ACM} }, author = {Margaret Mitchell and Simone Wu and Andrew Zaldivar and Parker Barnes and Lucy Vasserman and Ben Hutchinson and Elena Spitzer and Inioluwa Deborah Raji and Timnit Gebru}, title = {Model Cards for Model Reporting}, booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency} } ```

424d6e6df924560e4a45d7fccaa1091b

Geotrend/distilbert-base-en-el-ru-cased

Geotrend

distilbert

6

5

transformers

0

fill-mask

true

false

apache-2.0

['multilingual']

['wikipedia']

null

1

0

[]

false

true

1,233

false

# distilbert-base-en-el-ru-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-el-ru-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-el-ru-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermdistilbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact amine@geotrend.fr for any question, feedback or request.

27ad9106bb8d2121fc85075d406f35e7

arbml/whisper-medium-ar

arbml

whisper

42

90

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['arbml/mgb2']

null

0

['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard']

true

1,851

false

# openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8488 - Wer: 16.5882 ## 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: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.2963 | 0.1 | 1000 | 0.9115 | 27.3641 | | 0.2676 | 0.2 | 2000 | 0.8796 | 24.1024 | | 0.3166 | 0.3 | 3000 | 0.8467 | 20.1700 | | 0.2797 | 0.4 | 4000 | 0.8756 | 29.4889 | | 0.2302 | 0.5 | 5000 | 0.8523 | 19.6414 | | 0.2803 | 0.6 | 6000 | 0.8715 | 19.7413 | | 0.2794 | 0.7 | 7000 | 0.8548 | 18.6840 | | 0.2173 | 0.8 | 8000 | 0.8543 | 17.9019 | | 0.217 | 0.9 | 9000 | 0.8518 | 16.3840 | | 0.1718 | 1.0 | 10000 | 0.8488 | 16.5882 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

975acd3c5847fe5f9d9e4d58238f3d47

jonatasgrosman/exp_w2v2t_it_vp-nl_s335

jonatasgrosman

wav2vec2

10

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['it']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

469

false

# exp_w2v2t_it_vp-nl_s335 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (it)](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.

40509d93d7a7f74df9fc87be85c4cdb7

keithanpai/swin-tiny-patch4-window7-224-finetuned-eurosat

keithanpai

swin

20

3

transformers

0

image-classification

true

false

apache-2.0

null

['imagefolder']

null

0

['generated_from_trainer']

true

1,492

false

# swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5603 - Accuracy: 0.7914 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.67 | 0.99 | 70 | 0.7920 | 0.7265 | | 0.5856 | 1.99 | 140 | 0.6192 | 0.7804 | | 0.5612 | 2.99 | 210 | 0.5603 | 0.7914 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

5f1d343aafd771705ef4bb520784ab0e

marioarteaga/distilbert-base-uncased-finetuned-squad

marioarteaga

distilbert

12

5

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,178

false

# distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.2052 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2493 | 1.0 | 5533 | 1.2052 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

6b9029f45401732d19d6339885dbfc3b

Deep98/Human_Development_Index-clustered

Deep98

distilbert

8

0

transformers

0

question-answering

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,870

false

# Deep98/Human_Development_Index-clustered This model is a fine-tuned version of [nandysoham16/4-clustered_aug](https://huggingface.co/nandysoham16/4-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1876 - Train End Logits Accuracy: 0.9757 - Train Start Logits Accuracy: 0.9271 - Validation Loss: 0.6587 - Validation End Logits Accuracy: 0.6667 - Validation Start Logits Accuracy: 1.0 - 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': '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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.1876 | 0.9757 | 0.9271 | 0.6587 | 0.6667 | 1.0 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

5657e8bc42029f6ab6e9edab10ad7ad9

cleandata/distilbert-base-uncased-finetuned-imdb

cleandata

distilbert

13

18

transformers

0

fill-mask

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,318

false

# distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4721 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7086 | 1.0 | 157 | 2.4898 | | 2.5796 | 2.0 | 314 | 2.4230 | | 2.5269 | 3.0 | 471 | 2.4354 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

82dae3038c814377c396a537ad49579e

qmeeus/whisper-small-ner-combined

qmeeus

whisper_for_slu

22

1

transformers

0

token-classification

true

false

apache-2.0

null

['qmeeus/slue-voxpopuli']

null

0

['whisper-event', 'generated_from_trainer']

true

2,284

false

# WhisperForNamedEntityRecognition This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the qmeeus/slue-voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 8.1514 - Wer: 10.4828 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - 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: 200 - training_steps: 1600 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 31.3741 | 0.06 | 100 | 25.8582 | 10.4828 | | 13.0078 | 1.03 | 200 | 13.4173 | 10.4828 | | 10.3619 | 1.09 | 300 | 10.8540 | 10.4828 | | 8.7869 | 2.06 | 400 | 9.6249 | 10.4828 | | 7.3964 | 3.02 | 500 | 9.1812 | 10.4828 | | 6.6321 | 3.08 | 600 | 8.6536 | 10.4828 | | 6.4612 | 4.05 | 700 | 8.6046 | 10.4828 | | 4.8358 | 5.02 | 800 | 8.0890 | 10.4828 | | 4.4918 | 5.08 | 900 | 8.3141 | 10.4828 | | 4.7548 | 6.04 | 1000 | 8.1660 | 10.4828 | | 3.7881 | 7.01 | 1100 | 8.2471 | 10.4828 | | 3.1916 | 7.07 | 1200 | 8.0779 | 10.4828 | | 3.2039 | 8.04 | 1300 | 8.1106 | 10.4828 | | 3.038 | 9.0 | 1400 | 8.0875 | 10.4828 | | 2.3249 | 9.07 | 1500 | 8.1025 | 10.4828 | | 2.6124 | 10.03 | 1600 | 8.1514 | 10.4828 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.10.0 - Datasets 2.7.1.dev0 - Tokenizers 0.11.0

86fefed50ff26c3fa783479bdda05b6b

Devarshi/Brain_Tumor_Classification

Devarshi

swin

11

7

transformers

0

image-classification

true

false

apache-2.0

null

['imagefolder']

null

0

['generated_from_trainer']

true

1,850

false

# Brain_Tumor_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 imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1012 - Accuracy: 0.9647 - F1: 0.9647 - Recall: 0.9647 - Precision: 0.9647 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.4856 | 0.99 | 83 | 0.3771 | 0.8444 | 0.8444 | 0.8444 | 0.8444 | | 0.3495 | 1.99 | 166 | 0.2608 | 0.8949 | 0.8949 | 0.8949 | 0.8949 | | 0.252 | 2.99 | 249 | 0.1445 | 0.9487 | 0.9487 | 0.9487 | 0.9487 | | 0.2364 | 3.99 | 332 | 0.1029 | 0.9588 | 0.9588 | 0.9588 | 0.9588 | | 0.2178 | 4.99 | 415 | 0.1012 | 0.9647 | 0.9647 | 0.9647 | 0.9647 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1

a7adf5f556216f91fd32e6f7be66d83c

jonatasgrosman/exp_w2v2t_fr_no-pretraining_s929

jonatasgrosman

wav2vec2

10

5

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

414

false

# exp_w2v2t_fr_no-pretraining_s929 Fine-tuned randomly initialized wav2vec2 model 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.

b55c07f9ca7c2b00c6124fa7b7a411b9

jonatasgrosman/exp_w2v2t_ar_wavlm_s3

jonatasgrosman

wavlm

10

5

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

437

false

# exp_w2v2t_ar_wavlm_s3 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) 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.

fa62ac6b0a62d92d0d48598db4e9db06

krinal214/bert-all-squad_ben_tel_context

krinal214

bert

12

5

transformers

0

question-answering

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,174

false

# bert-all-squad_ben_tel_context This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5393 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.996 | 1.0 | 12676 | 0.5393 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.1 - Datasets 1.18.4 - Tokenizers 0.11.6

1f68b3cf28915dbd8f53daed1116cb1a

ju-bezdek/slovakbert-conll2003-sk-ner

ju-bezdek

null

14

2

null

1

null

true

false

mit

null

['ju-bezdek/conll2003-SK-NER']

null

0

['generated_from_trainer']

true

6,326

false

# outputs This model is a fine-tuned version of [gerulata/slovakbert](https://huggingface.co/gerulata/slovakbert) on the [ju-bezdek/conll2003-SK-NER](https://huggingface.co/datasets/ju-bezdek/conll2003-SK-NER) dataset. It achieves the following results on the evaluation (validation) set: - Loss: 0.1752 - Precision: 0.8190 - Recall: 0.8390 - F1: 0.8288 - Accuracy: 0.9526 ## Model description More information needed ## Code example ```python: from transformers import pipeline, AutoModel, AutoTokenizer from spacy import displacy import os model_path="ju-bezdek/slovakbert-conll2003-sk-ner" aggregation_strategy="max" ner_pipeline = pipeline(task='ner', model=model_path, aggregation_strategy=aggregation_strategy) input_sentence= "Ruský premiér Viktor Černomyrdin v piatok povedal, že prezident Boris Jeľcin , ktorý je na dovolenke mimo Moskvy , podporil mierový plán šéfa bezpečnosti Alexandra Lebedu pre Čečensko, uviedla tlačová agentúra Interfax" ner_ents = ner_pipeline(input_sentence) print(ner_ents) ent_group_labels = [ner_pipeline.model.config.id2label[i][2:] for i in ner_pipeline.model.config.id2label if i>0] options = {"ents":ent_group_labels} dicplacy_ents = [{"start":ent["start"], "end":ent["end"], "label":ent["entity_group"]} for ent in ner_ents] displacy.render({"text":input_sentence, "ents":dicplacy_ents}, style="ent", options=options, jupyter=True, manual=True) ``` ### Result: <div> <div class="entities" style="line-height: 2.5; direction: ltr"> Ruský MISC premiér Viktor Černomyrdin PER v piatok povedal, že prezident Boris Jeľcin, PER , ktorý je na dovolenke mimo Moskvy LOC , podporil mierový plán šéfa bezpečnosti Alexandra Lebedu PER pre Čečensko, LOC uviedla tlačová agentúra Interfax ORG </div> </div> ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3237 | 1.0 | 878 | 0.2541 | 0.7125 | 0.8059 | 0.7563 | 0.9283 | | 0.1663 | 2.0 | 1756 | 0.2370 | 0.7775 | 0.8090 | 0.7929 | 0.9394 | | 0.1251 | 3.0 | 2634 | 0.2289 | 0.7732 | 0.8029 | 0.7878 | 0.9385 | | 0.0984 | 4.0 | 3512 | 0.2818 | 0.7294 | 0.8189 | 0.7715 | 0.9294 | | 0.0808 | 5.0 | 4390 | 0.3138 | 0.7615 | 0.7900 | 0.7755 | 0.9326 | | 0.0578 | 6.0 | 5268 | 0.3072 | 0.7548 | 0.8222 | 0.7871 | 0.9370 | | 0.0481 | 7.0 | 6146 | 0.2778 | 0.7897 | 0.8156 | 0.8025 | 0.9408 | | 0.0414 | 8.0 | 7024 | 0.3336 | 0.7695 | 0.8201 | 0.7940 | 0.9389 | | 0.0268 | 9.0 | 7902 | 0.3294 | 0.7868 | 0.8140 | 0.8002 | 0.9409 | | 0.0204 | 10.0 | 8780 | 0.3693 | 0.7657 | 0.8239 | 0.7938 | 0.9376 | | 0.016 | 11.0 | 9658 | 0.3816 | 0.7932 | 0.8242 | 0.8084 | 0.9425 | | 0.0108 | 12.0 | 10536 | 0.3607 | 0.7929 | 0.8256 | 0.8089 | 0.9431 | | 0.0078 | 13.0 | 11414 | 0.3980 | 0.7915 | 0.8240 | 0.8074 | 0.9423 | | 0.0062 | 14.0 | 12292 | 0.4096 | 0.7995 | 0.8247 | 0.8119 | 0.9436 | | 0.0035 | 15.0 | 13170 | 0.4177 | 0.8006 | 0.8251 | 0.8127 | 0.9438 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3

0c018c4d6391ea846568b2b682fc19af

ramrajput/bert-finetuned-squad

ramrajput

bert

16

3

transformers

0

question-answering

true

false

apache-2.0

null

0

['generated_from_trainer']

true

875

false

# bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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: 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 ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Tokenizers 0.12.1

bf8ce5c7a28f71f120950be82ff173a8

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

fathyshalab

roberta

11

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,528

false

# all-roberta-large-v1-kitchen_and_dining-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: 2.3560 - Accuracy: 0.2692 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7421 | 1.0 | 1 | 2.5878 | 0.2012 | | 2.1065 | 2.0 | 2 | 2.4975 | 0.2012 | | 1.5994 | 3.0 | 3 | 2.4274 | 0.2249 | | 1.1739 | 4.0 | 4 | 2.3808 | 0.2456 | | 1.083 | 5.0 | 5 | 2.3560 | 0.2692 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

4a223fb321a9324e85f13883ad9664fe