modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
anas-awadalla/bert-base-uncased-few-shot-k-128-finetuned-squad-seed-8
|
anas-awadalla
| 2022-02-25T01:56:24Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-128-finetuned-squad-seed-8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-8
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/bert-base-uncased-few-shot-k-128-finetuned-squad-seed-6
|
anas-awadalla
| 2022-02-25T01:41:01Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-128-finetuned-squad-seed-6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-6
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/bert-base-uncased-few-shot-k-128-finetuned-squad-seed-4
|
anas-awadalla
| 2022-02-25T01:28:25Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-128-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
arjundd/skm-tea-models
|
arjundd
| 2022-02-25T00:00:26Z | 0 | 1 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
|
anas-awadalla/bert-base-uncased-few-shot-k-64-finetuned-squad-seed-2
|
anas-awadalla
| 2022-02-24T23:40:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-64-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-64-finetuned-squad-seed-2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-10
|
anas-awadalla
| 2022-02-24T23:09:57Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-32-finetuned-squad-seed-10
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-10
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-8
|
anas-awadalla
| 2022-02-24T22:54:48Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-32-finetuned-squad-seed-8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-8
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-6
|
anas-awadalla
| 2022-02-24T22:39:42Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-32-finetuned-squad-seed-6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-6
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-4
|
anas-awadalla
| 2022-02-24T22:24:38Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-32-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-2
|
anas-awadalla
| 2022-02-24T22:09:35Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-32-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-0
|
anas-awadalla
| 2022-02-24T21:54:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-32-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-0
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-6
|
anas-awadalla
| 2022-02-24T21:09:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-16-finetuned-squad-seed-6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-6
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-4
|
anas-awadalla
| 2022-02-24T20:53:59Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-16-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-2
|
anas-awadalla
| 2022-02-24T20:38:56Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-16-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
kapilchauhan/distilbert-base-uncased-CoLA-finetuned-cola
|
kapilchauhan
| 2022-02-24T19:54:55Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-CoLA-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5689051637185746
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-CoLA-finetuned-cola
This model is a fine-tuned version of [textattack/distilbert-base-uncased-CoLA](https://huggingface.co/textattack/distilbert-base-uncased-CoLA) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6996
- Matthews Correlation: 0.5689
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 1.0 | 134 | 0.6061 | 0.5074 |
| No log | 2.0 | 268 | 0.5808 | 0.5652 |
| No log | 3.0 | 402 | 0.6996 | 0.5689 |
| 0.0952 | 4.0 | 536 | 0.8249 | 0.5385 |
| 0.0952 | 5.0 | 670 | 0.8714 | 0.5567 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
damlab/HIV_V3_bodysite
|
damlab
| 2022-02-24T19:18:26Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"dataset:damlab/HIV_V3_bodysite",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
licence: mit
widget:
- text: "T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
example_title: "V3 Macrophage"
- text: 'C T R P N N N T R K S I H I G P G R A F Y T T G Q I I G D I R Q A Y C'
example_title: "V3 T-cell"
datasets:
- damlab/HIV_V3_bodysite
metrics:
- accuracy
---
# Model Card for [HIV_V3_bodysite]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Summary](#model-summary)
- [Model Description](#model-description)
- [Intended Uses & Limitations](#intended-uses-&-limitations)
- [How to Use](#how-to-use)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Training](#training)
- [Evaluation Results](#evaluation-results)
- [BibTeX Entry and Citation Info](#bibtex-entry-and-citation-info)
## Summary
The HIV-BERT-Bodysite-Identification model was trained as a refinement of the HIV-BERT model (insert link) and serves to better predict the location that an HIV V3 loop sample was derived from. HIV-BERT is a model refined from the ProtBert-BFD model (https://huggingface.co/Rostlab/prot_bert_bfd) to better fulfill HIV-centric tasks. This model was then trained using HIV V3 sequences from the Los Alamos HIV Sequence Database (https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html), allowing even more precise prediction of body site location than the HIV-BERT model can provide.
## Model Description
The HIV-BERT-Bodysite-Identification model is intended to predict the location as to where an HIV sequence was most likely derived from. Because HIV infects immune cells, it uses these as a means of rapidly spreading throughout the body. Thus, body site identification can help determine where exactly these HIV particles ultimately end up. This would be helpful when attempting to study HIV treatment strategies. When provided with an HIV genomic sequence, the HIV-BERT-Bodysite-Identification model can predict which tissue it was derived from.
## Intended Uses & Limitations
This tool can be used as a predictor of which body site an HIV sample was derived from based on its genomic sequence. It should not be considered a clinical diagnostic tool.
This tool was trained using the Los Alamos HIV sequence dataset (https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html). Due to the sampling nature of this database, it is predominantly composed of subtype B sequences from North America and Europe with only minor contributions of Subtype C, A, and D. Currently, there was no effort made to balance the performance across these classes. As such, one should consider refinement with additional sequences to perform well on non-B sequences.
## How to use
This model is able to predict the likely bodysite from a V3 sequence.
This may be use for surveillance of cells that are emerging from latent reservoirs.
Remember, a sequence can come from multiple sites, they are not mutually exclusive.
```python
from transformers import pipeline
predictor = pipeline("text-classification", model="damlab/HIV_V3_bodysite")
predictor(f"C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C")
[
[
{
"label": "periphery-tcell",
"score": 0.29097115993499756
},
{
"label": "periphery-monocyte",
"score": 0.014322502538561821
},
{
"label": "CNS",
"score": 0.06870711594820023
},
{
"label": "breast-milk",
"score": 0.002785981632769108
},
{
"label": "female-genitals",
"score": 0.024997007101774216
},
{
"label": "male-genitals",
"score": 0.01040483545511961
},
{
"label": "gastric",
"score": 0.06872137635946274
},
{
"label": "lung",
"score": 0.04432062804698944
},
{
"label": "organ",
"score": 0.47476938366889954
}
]
]
```
## Training Data
This model was trained using the damlab/HIV_V3_bodysite dataset using the 0th fold. The dataset consists of 5510 sequences (approximately 35 tokens each) extracted from the Los Alamos HIV Sequence database.
## Training Procedure
### Preprocessing
As with the rostlab/Prot-bert-bfd model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.
### Training
The damlab/HIV-BERT model was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can be found in multiple sites) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance.
## Evaluation Results
*Need to add*
## BibTeX Entry and Citation Info
[More Information Needed]
|
nateraw/autoencoder-keras-mnist-demo-with-card
|
nateraw
| 2022-02-24T19:05:13Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
library_name: keras
---
## 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': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
## Training Metrics
| Epochs | Train Loss | Validation Loss |
|--- |--- |--- |
| 1| 0.184| 0.105|
| 2| 0.101| 0.097|
| 3| 0.096| 0.094|
| 4| 0.094| 0.092|
| 5| 0.092| 0.091|
| 6| 0.091| 0.09|
| 7| 0.09| 0.089|
| 8| 0.09| 0.089|
| 9| 0.089| 0.089|
| 10| 0.089| 0.088|
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
damlab/HIV_BERT
|
damlab
| 2022-02-24T18:59:51Z | 21 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"dataset:damlab/HIV_FLT",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: mit
datasets:
- damlab/HIV_FLT
metrics:
- accuracy
widget:
- text: 'C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C'
example_title: 'V3'
- text: 'M E P V D P R L E P W K H P G S Q P K T A C T N C Y C K K C C F H C Q V C F I T K A L G I S Y G R K K R R Q R R R A H Q N S Q T H Q A S L S K Q P T S Q P R G D P T G P K E S K K K V E R E T E T D P F D'
example_title: 'Tat'
- text: 'P Q I T L W Q R P L V T I K I G G Q L K E A L L D T G A D D T V L E E M N L P G R W K P K M I G G I G G F I K V R Q Y D Q I L I E I C G H K A I G T V L V G P T P V N I I G R N L L T Q I G C T L N F'
example_title: 'PR'
---
# HIV_BERT model
## Table of Contents
- [Summary](#model-summary)
- [Model Description](#model-description)
- [Intended Uses & Limitations](#intended-uses-&-limitations)
- [How to Use](#how-to-use)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Training](#training)
- [Evaluation Results](#evaluation-results)
- [BibTeX Entry and Citation Info](#bibtex-entry-and-citation-info)
## Summary
The HIV-BERT model was trained as a refinement of the [ProtBert-BFD model](https://huggingface.co/Rostlab/prot_bert_bfd) for HIV centric tasks. It was refined with whole viral genomes from the [Los Alamos HIV Sequence Database](https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html). This pretraining is important for HIV related tasks as the original BFD database contains few viral proteins making it sub-optimal when used as the basis for transfer learning tasks. This model and other related HIV prediction tasks have been published (link).
## Model Description
Like the original [ProtBert-BFD model](https://huggingface.co/Rostlab/prot_bert_bfd), this model encodes each amino acid as an individual token. This model was trained using Masked Language Modeling: a process in which a random set of tokens are masked with the model trained on their prediction. This model was trained using the damlab/hiv-flt dataset with 256 amino acid chunks and a 15% mask rate.
## Intended Uses & Limitations
As a masked language model this tool can be used to predict expected mutations using a masking approach. This could be used to identify highly mutated sequences, sequencing artifacts, or other contexts. As a BERT model, this tool can also be used as the base for transfer learning. This pretrained model could be used as the base when developing HIV-specific classification tasks.
## How to use
As this is a BERT-style Masked Language learner, it can be used to determine the most likely amino acid at a masked position.
```python
from transformers import pipeline
unmasker = pipeline("fill-mask", model="damlab/HIV_FLT")
unmasker(f"C T R P N [MASK] N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C")
[
{
"score": 0.9581968188285828,
"token": 17,
"token_str": "N",
"sequence": "C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
},
{
"score": 0.022986575961112976,
"token": 12,
"token_str": "K",
"sequence": "C T R P N K N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
},
{
"score": 0.003997281193733215,
"token": 14,
"token_str": "D",
"sequence": "C T R P N D N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
},
{
"score": 0.003636382520198822,
"token": 15,
"token_str": "T",
"sequence": "C T R P N T N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
},
{
"score": 0.002701344434171915,
"token": 10,
"token_str": "S",
"sequence": "C T R P N S N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
}
]
```
## Training Data
The dataset [damlab/HIV_FLT](https://huggingface.co/datasets/damlab/HIV_FLT) was used to refine the original [rostlab/Prot-bert-bfd](https://huggingface.co/Rostlab/prot_bert_bfd). This dataset contains 1790 full HIV genomes from across the globe. When translated, these genomes contain approximately 3.9 million amino-acid tokens.
## Training Procedure
### Preprocessing
As with the [rostlab/Prot-bert-bfd](https://huggingface.co/Rostlab/prot_bert_bfd) model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.
### Training
Training was performed with the HuggingFace training module using the MaskedLM data loader with a 15% masking rate. The learning rate was set at E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset.
## BibTeX Entry and Citation Info
[More Information Needed]
|
lilitket/wav2vec2-large-xls-r-300m-turkish-colab
|
lilitket
| 2022-02-24T18:57:13Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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: 1.7126
- Wer: 0.8198
## 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 120
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 6.7419 | 2.38 | 200 | 3.1913 | 1.0 |
| 3.0446 | 4.76 | 400 | 2.3247 | 1.0 |
| 1.3163 | 7.14 | 600 | 1.2629 | 0.9656 |
| 0.6058 | 9.52 | 800 | 1.2203 | 0.9343 |
| 0.3687 | 11.9 | 1000 | 1.2157 | 0.8849 |
| 0.2644 | 14.29 | 1200 | 1.3693 | 0.8992 |
| 0.2147 | 16.67 | 1400 | 1.3321 | 0.8623 |
| 0.1962 | 19.05 | 1600 | 1.3476 | 0.8886 |
| 0.1631 | 21.43 | 1800 | 1.3984 | 0.8755 |
| 0.15 | 23.81 | 2000 | 1.4602 | 0.8798 |
| 0.1311 | 26.19 | 2200 | 1.4727 | 0.8836 |
| 0.1174 | 28.57 | 2400 | 1.5257 | 0.8805 |
| 0.1155 | 30.95 | 2600 | 1.4697 | 0.9337 |
| 0.1046 | 33.33 | 2800 | 1.6076 | 0.8667 |
| 0.1063 | 35.71 | 3000 | 1.5012 | 0.8861 |
| 0.0996 | 38.1 | 3200 | 1.6204 | 0.8605 |
| 0.088 | 40.48 | 3400 | 1.4788 | 0.8586 |
| 0.089 | 42.86 | 3600 | 1.5983 | 0.8648 |
| 0.0805 | 45.24 | 3800 | 1.5045 | 0.8298 |
| 0.0718 | 47.62 | 4000 | 1.6361 | 0.8611 |
| 0.0718 | 50.0 | 4200 | 1.5088 | 0.8548 |
| 0.0649 | 52.38 | 4400 | 1.5491 | 0.8554 |
| 0.0685 | 54.76 | 4600 | 1.5939 | 0.8442 |
| 0.0588 | 57.14 | 4800 | 1.6321 | 0.8536 |
| 0.0591 | 59.52 | 5000 | 1.6468 | 0.8442 |
| 0.0529 | 61.9 | 5200 | 1.6086 | 0.8661 |
| 0.0482 | 64.29 | 5400 | 1.6622 | 0.8517 |
| 0.0396 | 66.67 | 5600 | 1.6191 | 0.8436 |
| 0.0463 | 69.05 | 5800 | 1.6231 | 0.8661 |
| 0.0415 | 71.43 | 6000 | 1.6874 | 0.8511 |
| 0.0383 | 73.81 | 6200 | 1.7054 | 0.8411 |
| 0.0411 | 76.19 | 6400 | 1.7073 | 0.8486 |
| 0.0346 | 78.57 | 6600 | 1.7137 | 0.8342 |
| 0.0318 | 80.95 | 6800 | 1.6523 | 0.8329 |
| 0.0299 | 83.33 | 7000 | 1.6893 | 0.8579 |
| 0.029 | 85.71 | 7200 | 1.7162 | 0.8429 |
| 0.025 | 88.1 | 7400 | 1.7589 | 0.8529 |
| 0.025 | 90.48 | 7600 | 1.7581 | 0.8398 |
| 0.0232 | 92.86 | 7800 | 1.8459 | 0.8442 |
| 0.0215 | 95.24 | 8000 | 1.7942 | 0.8448 |
| 0.0222 | 97.62 | 8200 | 1.6848 | 0.8442 |
| 0.0179 | 100.0 | 8400 | 1.7223 | 0.8298 |
| 0.0176 | 102.38 | 8600 | 1.7426 | 0.8404 |
| 0.016 | 104.76 | 8800 | 1.7501 | 0.8411 |
| 0.0153 | 107.14 | 9000 | 1.7185 | 0.8235 |
| 0.0136 | 109.52 | 9200 | 1.7250 | 0.8292 |
| 0.0117 | 111.9 | 9400 | 1.7159 | 0.8185 |
| 0.0123 | 114.29 | 9600 | 1.7135 | 0.8248 |
| 0.0121 | 116.67 | 9800 | 1.7189 | 0.8210 |
| 0.0116 | 119.05 | 10000 | 1.7126 | 0.8198 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
nateraw/keras-dummy-functional-demo-w-card
|
nateraw
| 2022-02-24T18:23:41Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
library_name: keras
---
## 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': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
nateraw/keras-dummy-sequential-demo-with-card
|
nateraw
| 2022-02-24T18:18:08Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
library_name: keras
---
## 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': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
jj-co/sbert-feature_extraction
|
jj-co
| 2022-02-24T15:53:14Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
|
Ayham/bert_roberta_summarization_cnn_dailymail
|
Ayham
| 2022-02-24T15:40:51Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: bert_roberta_summarization_cnn_dailymail
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_roberta_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
izzy-lazerson/wav2vec2-base-timit-demo-colab
|
izzy-lazerson
| 2022-02-24T13:44:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4545
- Wer: 0.3450
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.3801 | 4.0 | 500 | 1.1501 | 0.8820 |
| 0.561 | 8.0 | 1000 | 0.4583 | 0.4211 |
| 0.2198 | 12.0 | 1500 | 0.4467 | 0.3997 |
| 0.1255 | 16.0 | 2000 | 0.4390 | 0.3677 |
| 0.0862 | 20.0 | 2500 | 0.4934 | 0.3603 |
| 0.0617 | 24.0 | 3000 | 0.4641 | 0.3549 |
| 0.0465 | 28.0 | 3500 | 0.4545 | 0.3450 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
debjyoti007/new_doc_classifier
|
debjyoti007
| 2022-02-24T13:22:54Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
This model has been trained for the purpose of classifying text from different domains. Currently it is trained with much lesser data and it has been trained to identify text from 3 domains, "sports", "healthcare" and "financial". Label_0 represents "financial", Label_1 represents "Healthcare" and Label_2 represents "Sports". Currently I have trained it with these 3 domains only, I am pretty soon planning to train it on more domains and more data, hence its accuracy will improve further too.
|
juanhebert/wav2vec2-indonesia
|
juanhebert
| 2022-02-24T12:34:31Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-indonesia
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-indonesia
This model is a fine-tuned version of [juanhebert/wav2vec2-indonesia](https://huggingface.co/juanhebert/wav2vec2-indonesia) on the commonvoice "id" dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0727
- Wer: 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: 0.0001
- train_batch_size: 5
- 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: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 2.8744 | 0.68 | 200 | 3.0301 | 1.0 |
| 2.868 | 1.36 | 400 | 3.0727 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
moshew/mpnet-base-sst2-distilled
|
moshew
| 2022-02-24T11:43:00Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mpnet",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
{'test_accuracy': 0.9426605504587156,
'test_loss': 0.1693699210882187,
'test_runtime': 1.7713,
'test_samples_per_second': 492.29,
'test_steps_per_second': 3.952}
|
cammy/pegasus-multi_news-finetuned-weaksup-1000-pegasus
|
cammy
| 2022-02-24T11:21:53Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-multi_news-finetuned-weaksup-1000-pegasus
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-multi_news-finetuned-weaksup-1000-pegasus
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1309
- Rouge1: 23.342
- Rouge2: 8.67
- Rougel: 17.2865
- Rougelsum: 19.8228
- Gen Len: 69.79
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:|
| 2.4526 | 1.0 | 1000 | 2.1309 | 23.342 | 8.67 | 17.2865 | 19.8228 | 69.79 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
lhbit20010120/distilgpt2-finetuned-wikitext2
|
lhbit20010120
| 2022-02-24T10:45:51Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6423
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7602 | 1.0 | 2334 | 3.6669 |
| 3.633 | 2.0 | 4668 | 3.6455 |
| 3.6078 | 3.0 | 7002 | 3.6423 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
phongdtd/fb-vindata-vi-large
|
phongdtd
| 2022-02-24T10:24:38Z | 26 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"phongdtd/VinDataVLSP",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- phongdtd/VinDataVLSP
- generated_from_trainer
model-index:
- name: fb-vindata-vi-large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fb-vindata-vi-large
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the PHONGDTD/VINDATAVLSP - NA 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: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 8
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 40.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
DTAI-KULeuven/robbertje-1-gb-merged
|
DTAI-KULeuven
| 2022-02-24T09:56:43Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"nl",
"arxiv:2101.05716",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: "nl"
thumbnail: "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png"
tags:
- Dutch
- Flemish
- RoBERTa
- RobBERT
- RobBERTje
license: mit
datasets:
- oscar
- oscar (NL)
- dbrd
- lassy-ud
- europarl-mono
- conll2002
widget:
- text: "Hallo, ik ben RobBERTje, een gedistilleerd <mask> taalmodel van de KU Leuven."
---
<p align="center">
<img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%">
</p>
# About RobBERTje
RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robbert). There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch [the repository](http://github.com/iPieter/robbertje) for updates.
# News
- **February 21, 2022**: Our paper about RobBERTje has been published in [volume 11 of CLIN journal](https://www.clinjournal.org/clinj/article/view/131)!
- **July 2, 2021**: Publicly released 4 RobBERTje models.
- **May 12, 2021**: RobBERTje was accepted at [CLIN31](https://www.clin31.ugent.be) for an oral presentation!
# The models
| Model | Description | Parameters | Training size | Huggingface id |
|--------------|-------------|------------------|-------------------|------------------------------------------------------------------------------------|
| Non-shuffled | Trained on the non-shuffled variant of the oscar corpus, without any operations to preserve this order during training and distillation. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-non-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-non-shuffled) |
| Shuffled | Trained on the publicly available and shuffled OSCAR corpus. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-shuffled) |
| Merged (p=0.5) | Same as the non-shuffled variant, but sequential sentences of the same document are merged with a probability of 50%. | 74 M | 1 GB | this model |
| BORT | A smaller version with 8 attention heads instead of 12 and 4 layers instead of 6 (and 12 for RobBERT). | 46 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-bort](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-bort) |
# Results
## Intrinsic results
We calculated the _pseudo perplexity_ (PPPL) from [cite](), which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution.
| Model | PPPL |
|-------------------|-----------|
| RobBERT (teacher) | 7.76 |
| Non-shuffled | 12.95 |
| Shuffled | 18.74 |
| Merged (p=0.5) | 17.10 |
| BORT | 26.44 |
## Extrinsic results
We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a [Dutch NLI task named SICK-NL](https://arxiv.org/abs/2101.05716) was also released and we evaluated our models with it as well.
| Model | DBRD | DIE-DAT | NER | POS |SICK-NL |
|------------------|-----------|-----------|-----------|-----------|----------|
| RobBERT (teacher)|94.4 | 99.2 |89.1 |96.4 | 84.2 |
| Non-shuffled |90.2 | 98.4 |82.9 |95.5 | 83.4 |
| Shuffled |92.5 | 98.2 |82.7 |95.6 | 83.4 |
| Merged (p=0.5) |92.9 | 96.5 |81.8 |95.2 | 82.8 |
| BORT |89.6 | 92.2 |79.7 |94.3 | 81.0 |
|
moshew/minylm-L3-aug-sst2-distilled
|
moshew
| 2022-02-24T09:50:53Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
{'test_accuracy': 0.911697247706422,
'test_loss': 0.24090610444545746,
'test_runtime': 0.4372,
'test_samples_per_second': 1994.475,
'test_steps_per_second': 16.011}
|
iarfmoose/t5-base-question-generator
|
iarfmoose
| 2022-02-24T08:41:19Z | 119,683 | 55 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# Model name
## Model description
This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output. It is based on a pretrained `t5-base` model.
## Intended uses & limitations
The model is trained to generate reading comprehension-style questions with answers extracted from a text. The model performs best with full sentence answers, but can also be used with single word or short phrase answers.
#### How to use
The model takes concatenated answers and context as an input sequence, and will generate a full question sentence as an output sequence. The max sequence length is 512 tokens. Inputs should be organised into the following format:
```
<answer> answer text here <context> context text here
```
The input sequence can then be encoded and passed as the `input_ids` argument in the model's `generate()` method.
For best results, a large number of questions can be generated, and then filtered using [iarfmoose/bert-base-cased-qa-evaluator](https://huggingface.co/iarfmoose/bert-base-cased-qa-evaluator).
For examples, please see https://github.com/iarfmoose/question_generator.
#### Limitations and bias
The model is limited to generating questions in the same style as those found in [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/), [CoQA](https://stanfordnlp.github.io/coqa/), and [MSMARCO](https://microsoft.github.io/msmarco/). The generated questions can potentially be leading or reflect biases that are present in the context. If the context is too short or completely absent, or if the context and answer do not match, the generated question is likely to be incoherent.
## Training data
The model was fine-tuned on a dataset made up of several well-known QA datasets ([SQuAD](https://rajpurkar.github.io/SQuAD-explorer/), [CoQA](https://stanfordnlp.github.io/coqa/), and [MSMARCO](https://microsoft.github.io/msmarco/)). The datasets were restructured by concatenating the answer and context fields into the previously-mentioned format. The question field from the datasets was used as the target during training. The full training set was roughly 200,000 examples.
## Training procedure
The model was trained for 20 epochs over the training set with a learning rate of 1e-3. The batch size was only 4 due to GPU memory limitations when training on Google Colab.
|
aypan17/roberta-base-imdb
|
aypan17
| 2022-02-24T07:33:44Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
---
TrainingArgs:
lr=2e-5,
train-batch-size=16,
eval-batch-size=16,
num-train-epochs=5,
weight-decay=0.01,
|
cammy/bart-large-cnn-finetuned-weaksup-10000-pad-early
|
cammy
| 2022-02-24T04:48:02Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bart-large-cnn-finetuned-weaksup-10000-pad-early
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-weaksup-10000-pad-early
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3541
- eval_rouge1: 27.8229
- eval_rouge2: 12.9484
- eval_rougeL: 21.4909
- eval_rougeLsum: 24.7737
- eval_gen_len: 67.365
- eval_runtime: 1162.9446
- eval_samples_per_second: 0.86
- eval_steps_per_second: 0.86
- epoch: 2.0
- step: 20000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
hfl/english-pert-large
|
hfl
| 2022-02-24T02:58:41Z | 31 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"en",
"license:cc-by-nc-sa-4.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- en
license: "cc-by-nc-sa-4.0"
---
# Please use 'Bert' related functions to load this model!
# ALL English models are UNCASED (lowercase=True)
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT
|
hfl/chinese-pert-base
|
hfl
| 2022-02-24T02:57:09Z | 66 | 12 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"zh",
"license:cc-by-nc-sa-4.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- zh
license: "cc-by-nc-sa-4.0"
---
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT
|
PhilSad/gpt-scp-neo-125M
|
PhilSad
| 2022-02-23T22:41:55Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: output_gptneo125-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output_gptneo125-2
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Ayham/roberta_bert_summarization_cnn_dailymail
|
Ayham
| 2022-02-23T22:17:54Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: roberta_bert_summarization_cnn_dailymail
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_bert_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
MarcBrun/ixambert-finetuned-squad-eu-en
|
MarcBrun
| 2022-02-23T20:25:49Z | 44 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"en",
"es",
"eu",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:04Z |
---
language:
- en
- es
- eu
datasets:
- squad
widget:
- text: "When was Florence Nightingale born?"
context: "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820."
example_title: "English"
- text: "¿Por qué provincias pasa el Tajo?"
context: "El Tajo es el río más largo de la península ibérica, a la que atraviesa en su parte central, siguiendo un rumbo este-oeste, con una leve inclinación hacia el suroeste, que se acentúa cuando llega a Portugal, donde recibe el nombre de Tejo.
Nace en los montes Universales, en la sierra de Albarracín, sobre la rama occidental del sistema Ibérico y, después de recorrer 1007 km, llega al océano Atlántico en la ciudad de Lisboa. En su desembocadura forma el estuario del mar de la Paja, en el que vierte un caudal medio de 456 m³/s. En sus primeros 816 km atraviesa España, donde discurre por cuatro comunidades autónomas (Aragón, Castilla-La Mancha, Madrid y Extremadura) y un total de seis provincias (Teruel, Guadalajara, Cuenca, Madrid, Toledo y Cáceres)."
example_title: "Español"
- text: "Zer beste izenak ditu Tartalo?"
context: "Tartalo euskal mitologiako izaki begibakar artzain erraldoia da. Tartalo izena zenbait euskal hizkeratan herskari-bustidurarekin ahoskatu ohi denez, horrelaxe ere idazten da batzuetan: Ttarttalo. Euskal Herriko zenbait tokitan, Torto edo Anxo ere esaten diote."
example_title: "Euskara"
---
# ixambert-base-cased finetuned for QA
This is a basic implementation of the multilingual model ["ixambert-base-cased"](https://huggingface.co/ixa-ehu/ixambert-base-cased), fine-tuned on SQuAD v1.1 and an experimental version of SQuAD1.1 in Basque (1/3 size of original SQuAD1.1), that is able to answer basic factual questions in English, Spanish and Basque.
## Overview
* **Language model:** ixambert-base-cased
* **Languages:** English, Spanish and Basque
* **Downstream task:** Extractive QA
* **Training data:** SQuAD v1.1 + experimental SQuAD1.1 in Basque
* **Eval data:** SQuAD v1.1 + experimental SQuAD1.1 in Basque
* **Infrastructure:** 1x GeForce RTX 2080
## Outputs
The model outputs the answer to the question, the start and end positions of the answer in the original context, and a score for the probability for that span of text to be the correct answer. For example:
```python
{'score': 0.9667195081710815, 'start': 101, 'end': 105, 'answer': '1820'}
```
## How to use
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "MarcBrun/ixambert-finetuned-squad-eu-en"
# To get predictions
context = "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820"
question = "When was Florence Nightingale born?"
qa = pipeline("question-answering", model=model_name, tokenizer=model_name)
pred = qa(question=question,context=context)
# To load the model and tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Hyperparameters
```
batch_size = 8
n_epochs = 3
learning_rate = 2e-5
optimizer = AdamW
lr_schedule = linear
max_seq_len = 384
doc_stride = 128
```
|
sw005320/Shinji_Watanabe_laborotv_asr_train_blstm
|
sw005320
| 2022-02-23T20:25:19Z | 4 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"jp",
"dataset:laborotv",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: jp
datasets:
- laborotv
license: cc-by-4.0
---
## ESPnet2 ASR model
### `sw005320/Shinji_Watanabe_laborotv_asr_train_blstm`
This model was trained by Shinji Watanabe using laborotv recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 9963fc53747c26417023546d3449e92884f13be0
pip install -e .
cd egs2/laborotv/asr1
./run.sh --skip_data_prep false --skip_train true --download_model sw005320/Shinji_Watanabe_laborotv_asr_train_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Fri May 14 08:32:17 EDT 2021`
- python version: `3.8.5 (default, Sep 4 2020, 07:30:14) [GCC 7.3.0]`
- espnet version: `espnet 0.9.9`
- pytorch version: `pytorch 1.7.1`
- Git hash: `8c580e3da5d8a308ccdab104fdc29de114e56c60`
- Commit date: `Wed May 5 13:26:08 2021 -0400`
## asr_train_asr_rnn_raw_jp_char_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_jp_char_valid.loss.ave_asr_model_valid.acc.ave/dev|12000|12000|36.1|63.9|0.0|0.0|63.9|63.9|
|decode_asr_lm_lm_train_lm_jp_char_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|3971|3971|41.7|58.3|0.0|0.0|58.3|58.3|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_jp_char_valid.loss.ave_asr_model_valid.acc.ave/dev|12000|273004|89.3|6.3|4.4|3.0|13.7|63.9|
|decode_asr_lm_lm_train_lm_jp_char_valid.loss.ave_asr_model_valid.acc.ave/dev_4k|3971|98424|91.9|4.7|3.3|2.4|10.5|58.3|
|decode_asr_lm_lm_train_lm_jp_char_valid.loss.ave_asr_model_valid.acc.ave/tedx-jp-10k|10000|190568|0.0|0.0|100.0|0.0|100.0|100.0|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_rnn.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_rnn_raw_jp_char_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 40852
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: 8
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 1
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
detect_anomaly: false
pretrain_path: null
init_param: []
freeze_param: []
num_iters_per_epoch: null
batch_size: 128
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_jp_char_sp/train/speech_shape
- exp/asr_stats_raw_jp_char_sp/train/text_shape.char
valid_shape_file:
- exp/asr_stats_raw_jp_char_sp/valid/speech_shape
- exp/asr_stats_raw_jp_char_sp/valid/text_shape.char
batch_type: folded
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_nodev_sp/wav.scp
- speech
- sound
- - dump/raw/train_nodev_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_4k/wav.scp
- speech
- sound
- - dump/raw/dev_4k/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 1.0
rho: 0.95
eps: 1.0e-08
weight_decay: 0
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- い
- の
- で
- て
- と
- し
- た
- す
- に
- な
- が
- っ
- ま
- か
- う
- は
- る
- ん
- を
- こ
- れ
- も
- ら
- り
- さ
- ー
- あ
- そ
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- だ
- き
- け
- よ
- ど
- ン
- ね
- ち
- お
- え
- や
- ス
- 人
- 一
- わ
- 日
- つ
- 十
- め
- イ
- ト
- ゃ
- 大
- ル
- じ
- 今
- み
- 二
- ょ
- ッ
- せ
- ラ
- 中
- ろ
- リ
- ク
- 見
- 思
- 事
- 出
- 分
- 感
- 時
- ア
- ご
- 三
- 本
- 方
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- ば
- 行
- 者
- タ
- ロ
- 生
- 気
- 間
- コ
- 年
- 前
- 言
- ほ
- シ
- カ
- ず
- 自
- 入
- マ
- レ
- 国
- 手
- 子
- 会
- 五
- 染
- メ
- 新
- プ
- ナ
- 何
- ド
- 四
- 場
- ジ
- チ
- ウ
- フ
- 後
- 合
- 月
- 対
- 地
- 東
- 当
- バ
- 体
- 全
- べ
- 回
- 目
- テ
- 性
- 最
- 発
- 部
- 百
- 先
- 動
- げ
- 私
- 続
- 高
- ャ
- 作
- 来
- パ
- 取
- キ
- グ
- 家
- 的
- 業
- 所
- オ
- 度
- 長
- 雨
- ム
- 食
- 実
- 内
- 話
- 開
- ぐ
- 下
- 京
- 九
- 六
- 多
- 持
- ニ
- 関
- サ
- 県
- 代
- 学
- 状
- 明
- 七
- 八
- デ
- 市
- 意
- 理
- ュ
- 千
- 田
- へ
- ハ
- ぱ
- 水
- 数
- 物
- 現
- び
- ビ
- 使
- ブ
- 外
- 通
- 心
- 知
- 要
- 番
- 変
- 用
- 以
- 店
- 山
- ミ
- 立
- 力
- 女
- 金
- 確
- 定
- ィ
- 々
- 都
- 員
- ざ
- 型
- 小
- 考
- ピ
- 選
- 強
- 野
- 近
- 結
- ぶ
- 安
- 検
- 表
- 川
- 初
- ダ
- エ
- 受
- 世
- 同
- ツ
- ズ
- 味
- ふ
- 不
- 込
- 認
- ホ
- 報
- 予
- 風
- 査
- 警
- ポ
- 向
- 海
- む
- ョ
- 道
- 社
- 北
- 活
- 名
- 切
- 少
- 男
- ワ
- ひ
- 決
- 連
- 様
- 聞
- 重
- 万
- 解
- ぎ
- 始
- 調
- 症
- 戦
- 化
- ボ
- 面
- 相
- 広
- 付
- 問
- セ
- ベ
- 増
- 政
- 島
- 僕
- モ
- 期
- 車
- 経
- 特
- ネ
- 策
- ケ
- 楽
- 違
- 議
- 能
- 害
- 必
- 止
- 界
- 屋
- 伝
- 組
- 常
- 次
- 民
- ガ
- 加
- 再
- 元
- 態
- 題
- 降
- 機
- 週
- 指
- 仕
- 円
- 勝
- 影
- 校
- 正
- 点
- 集
- 流
- 書
- 引
- 情
- 院
- 主
- 皆
- 法
- 急
- 客
- 台
- 難
- 木
- 料
- 身
- 起
- 疑
- 進
- 成
- 空
- 応
- 真
- 口
- 品
- 防
- 在
- 況
- 教
- 保
- ェ
- 原
- 好
- 病
- 着
- 色
- 画
- 運
- 半
- 務
- 果
- ぜ
- 夜
- 件
- 朝
- 然
- 直
- 過
- ペ
- 医
- 別
- 置
- 俺
- ぞ
- 可
- 制
- 葉
- 無
- 設
- 温
- ゆ
- 死
- 療
- 線
- 住
- 早
- ソ
- 夫
- 注
- 判
- 呼
- 公
- 信
- 治
- 容
- 電
- 待
- 響
- 例
- 午
- 察
- 想
- 支
- 落
- 府
- 和
- 配
- 歳
- 打
- 休
- 売
- 村
- 親
- 残
- ギ
- 段
- 乗
- 去
- 平
- 転
- 際
- 終
- 天
- 足
- 形
- 張
- 白
- 記
- 位
- 利
- 側
- 非
- 観
- 井
- 土
- 美
- 被
- 送
- 球
- 総
- 第
- 声
- 映
- 宅
- ノ
- 係
- ゴ
- 熱
- 願
- 断
- 神
- 火
- Ｔ
- 営
- 材
- 更
- 西
- 藤
- 文
- 構
- 光
- 消
- 母
- 産
- 投
- 帰
- 州
- 飲
- 殺
- 象
- 拡
- 町
- 接
- 離
- 割
- 党
- 示
- 君
- 有
- 頂
- 辺
- 減
- Ｎ
- 悪
- 優
- 職
- 局
- 除
- 緒
- Ｓ
- 超
- 歌
- 得
- 南
- 備
- ヒ
- ぼ
- 返
- 戻
- 歩
- 済
- 父
- づ
- 演
- 避
- 由
- 達
- Ｒ
- ァ
- 介
- 試
- 昨
- 音
- 収
- 彼
- 交
- 亡
- 限
- 反
- 街
- 像
- 参
- 園
- 役
- 門
- 統
- 育
- 岡
- ザ
- 命
- 族
- 夏
- ヤ
- 工
- 路
- 量
- 買
- 速
- 飛
- 誰
- 肉
- 験
- 太
- 働
- 区
- 頭
- 士
- 字
- 顔
- 官
- 域
- 若
- 追
- 施
- 姿
- 花
- 危
- Ａ
- 石
- 災
- 説
- Ｃ
- 師
- 愛
- 絶
- 守
- ｏ
- 計
- 覚
- 移
- 横
- 突
- 各
- 告
- 焼
- 団
- 激
- 曜
- 種
- 階
- 香
- 専
- 負
- 領
- 基
- 求
- 黒
- 周
- 渡
- 谷
- 放
- 緊
- 供
- 援
- ％
- 曲
- 護
- Ｐ
- 任
- 語
- 改
- 犯
- 覧
- 省
- 共
- 差
- 船
- 両
- 暑
- 赤
- 倍
- 雲
- 視
- 失
- 個
- 患
- 挙
- ゲ
- 勢
- 寄
- 古
- 毎
- 念
- 旅
- 触
- ｍ
- 深
- 細
- 捕
- 戒
- 習
- 式
- 助
- ・
- 録
- 撮
- 崎
- 波
- 証
- 補
- 冷
- 普
- 登
- 撃
- 規
- 識
- 室
- 比
- 効
- 低
- 提
- 請
- 玉
- 館
- 談
- 素
- 福
- 格
- 臣
- 越
- 写
- 振
- 苦
- 存
- Ｏ
- 質
- 首
- 権
- 戸
- 険
- 技
- 紹
- 申
- 厳
- 密
- Ｉ
- 完
- ヨ
- 頼
- 宣
- 捜
- 案
- 囲
- 準
- 器
- 担
- 単
- 座
- 号
- 根
- 復
- 押
- ぁ
- 建
- 具
- 氏
- 約
- ぬ
- 抜
- 恐
- 走
- 松
- 術
- 我
- 宮
- 帯
- 訪
- 研
- 企
- 答
- ォ
- 軍
- 届
- 馬
- 港
- 鮮
- 協
- 青
- 厚
- 商
- 阪
- 吉
- 森
- 資
- 宿
- 庁
- 婚
- 程
- 菜
- 景
- 率
- 席
- 含
- 良
- 健
- 梅
- 薬
- 究
- 類
- 満
- 舞
- 丈
- 論
- 末
- 給
- 軽
- 奥
- 王
- 裁
- 科
- 砂
- 駅
- 佐
- 倒
- 遺
- 陽
- 城
- ゅ
- 晴
- 友
- 逮
- 息
- 練
- 整
- 迎
- 笑
- 郎
- 労
- 江
- 逃
- 米
- 境
- ！
- 極
- 逆
- 河
- 暮
- 粛
- Ｅ
- 模
- 破
- 児
- 右
- 与
- 頑
- ゼ
- 費
- 久
- 抗
- 圧
- 韓
- 橋
- 武
- 罪
- 針
- 酒
- Ｇ
- 継
- 血
- 居
- 並
- 読
- 齢
- 価
- 授
- 芸
- 値
- 派
- 洗
- 史
- 池
- 沖
- 義
- 独
- 弁
- 頃
- 筋
- 奈
- 描
- 左
- 余
- 混
- 縄
- 盛
- Ｋ
- 銀
- 故
- 望
- 他
- 延
- 億
- 庭
- ｅ
- 紙
- 管
- 妻
- 未
- 丸
- 背
- 散
- 絵
- ユ
- 底
- 造
- 幕
- 従
- 訴
- 製
- 爆
- 探
- 導
- 茶
- 裏
- 伸
- 委
- 簡
- 油
- 布
- 退
- 仲
- 毒
- 歴
- 怖
- 詳
- 因
- ヘ
- 救
- 遅
- 富
- 沢
- 額
- 絡
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- 嬪
- 涜
- 譽
- 踵
- 駱
- 奸
- 攘
- 榑
- 黌
- 聚
- 甕
- 偈
- 尭
- 拿
- 柯
- 隋
- 魑
- 芻
- 岱
- 烙
- 竺
- 鼈
- 簗
- 頒
- 馗
- 閏
- 羞
- 褥
- 馨
- 邂
- 逅
- 脩
- 鉈
- 姥
- 檮
- 蜃
- 糀
- 臂
- 麥
- 漿
- 憺
- 渤
- 匝
- 瑳
- 聲
- 滿
- 澱
- 塘
- 礫
- 對
- 蚤
- 鬆
- 諧
- 拈
- 盃
- 陝
- 掩
- 闍
- 餉
- 皓
- 諷
- 晏
- 赳
- 薛
- 暈
- 猜
- 驕
- 偸
- 瑶
- 皺
- 湫
- 鮪
- 縋
- 筵
- 鞋
- 愼
- 蠅
- 〆
- 訥
- 蜀
- 瞥
- 窈
- 吃
- 箏
- 鷽
- 逞
- 嘲
- 旛
- 琺
- 瑯
- 蕾
- 蕃
- 尤
- 將
- 臍
- 游
- 盥
- 楳
- 礬
- 婢
- 魏
- 滲
- 撚
- 鑼
- 飄
- 悛
- 駝
- 魄
- 賤
- 檸
- 檬
- 蟠
- 鉤
- 寶
- 蜻
- 蛉
- 蕗
- 銑
- 跨
- 壬
- 撻
- 茗
- 椹
- 寓
- 矧
- 篁
- 簪
- 叢
- 趨
- 羂
- 偃
- 橿
- 栂
- 亢
- 蕁
- μ
- 囂
- 臑
- 皷
- 歇
- 姨
- 悍
- 孜
- 篇
- 樸
- 矮
- 夛
- 矩
- 瑣
- 齧
- 畷
- 衾
- 瞞
- 刎
- 舷
- 莞
- 綵
- 翳
- 瘻
- 跋
- 耘
- 旁
- 刮
- 燭
- 灣
- 磊
- 嘶
- 禽
- 脾
- 靜
- 壟
- 栩
- 虱
- 乍
- 疸
- 禮
- 霍
- 囮
- 沽
- 詢
- 耆
- 籐
- 詛
- 、
- 絃
- 兒
- 譬
- 壷
- 禿
- 庖
- 闢
- 妍
- 按
- 坤
- 趾
- 屠
- 籏
- 轍
- 瑤
- 勸
- 寳
- 梳
- 梃
- 棠
- 艱
- 劔
- 蚯
- 蚓
- 僻
- 啄
- 赭
- 緡
- 痙
- 攣
- 瑩
- 勗
- 瀋
- 袱
- 摸
- 掠
- 颪
- 錘
- 痣
- 眸
- ゝ
- 耽
- 饌
- 鮒
- 碾
- 浚
- 渫
- 且
- 衲
- 筅
- 蹊
- 誂
- 疔
- 詔
- 嗚
- 椨
- 馥
- 滸
- 賎
- 殆
- 灌
- 暹
- 躯
- 喃
- 蔟
- 媼
- 蟇
- 泙
- 癬
- 繚
- 聊
- 藹
- 盒
- 嘴
- 尹
- Σ
- 柩
- 蔚
- 蓉
- 渠
- 掣
- 寥
- 狄
- 傀
- 儡
- 悌
- 恬
- 樒
- 涅
- 槃
- 徨
- 滌
- 藺
- 睥
- 孺
- 蠕
- 筥
- 呻
- 緬
- 睾
- 堯
- 瓔
- 珞
- 燼
- 鶸
- 筐
- 碓
- 岨
- 襷
- 蝕
- 璋
- 襞
- 鵯
- 俑
- 癰
- 彪
- 籬
- 辨
- 粂
- 迸
- 僥
- 簸
- 閂
- 娶
- 牆
- 筰
- 綽
- 菫
- 蛸
- 銕
- 鞨
- 狢
- 啖
- 忸
- 怩
- 耄
- 碌
- 滂
- 沱
- 芒
- 韜
- 曝
- 痂
- 襤
- 褸
- 釋
- 艤
- 搏
- 璽
- 慟
- 哭
- 衞
- 聾
- 憬
- 愈
- 讓
- 綯
- 繹
- 瑾
- 鐡
- 夥
- 柢
- 笊
- 已
- 抛
- 舳
- 鎚
- 逡
- 鬨
- 滾
- 戍
- 卉
- 篷
- 狆
- 浣
- 闖
- 鑞
- 箙
- 瞋
- 癡
- 邏
- ★
- 訝
- 駈
- 戈
- 錺
- 諚
- 縒
- 斂
- 褶
- 鑚
- 侭
- 淨
- 悵
- 虔
- 棹
- 泯
- 蛆
- 孀
- 瓏
- 楯
- 舁
- 榕
- 覺
- 蒋
- 筺
- 蝗
- 坩
- 堝
- 楔
- 證
- 胛
- 怙
- 丙
- 窺
- 罔
- 儘
- 掖
- 棊
- 仗
- 炯
- 專
- 扈
- 鞆
- 咸
- 鰆
- 凭
- 芍
- 牒
- 幟
- 狒
- 絲
- 聟
- 唖
- 燧
- 頚
- Ⅶ
- 啼
- 鯱
- 觜
- 縊
- 瑜
- 旱
- 咬
- 籃
- 袍
- 敲
- 恍
- 慾
- 愾
- 杲
- 繻
- 搗
- 褻
- 栢
- 矍
- 鑠
- 觸
- 獏
- 弼
- 疚
- 斟
- 鮃
- <sos/eos>
init: chainer
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: false
model_conf:
ctc_weight: 0.5
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'
frontend: default
frontend_conf:
fs: 16k
specaug: null
specaug_conf: {}
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_jp_char_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
decoder: rnn
decoder_conf:
rnn_type: lstm
num_layers: 1
hidden_size: 1024
sampling_probability: 0.0
att_conf:
atype: location
adim: 1024
awin: 5
aheads: 4
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.9.9
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}
}
```
|
MarcBrun/ixambert-finetuned-squad-eu
|
MarcBrun
| 2022-02-23T20:21:21Z | 29 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"en",
"es",
"eu",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:04Z |
---
language:
- en
- es
- eu
widget:
- text: "When was Florence Nightingale born?"
context: "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820."
example_title: "English"
- text: "¿Por qué provincias pasa el Tajo?"
context: "El Tajo es el río más largo de la península ibérica, a la que atraviesa en su parte central, siguiendo un rumbo este-oeste, con una leve inclinación hacia el suroeste, que se acentúa cuando llega a Portugal, donde recibe el nombre de Tejo.
Nace en los montes Universales, en la sierra de Albarracín, sobre la rama occidental del sistema Ibérico y, después de recorrer 1007 km, llega al océano Atlántico en la ciudad de Lisboa. En su desembocadura forma el estuario del mar de la Paja, en el que vierte un caudal medio de 456 m³/s. En sus primeros 816 km atraviesa España, donde discurre por cuatro comunidades autónomas (Aragón, Castilla-La Mancha, Madrid y Extremadura) y un total de seis provincias (Teruel, Guadalajara, Cuenca, Madrid, Toledo y Cáceres)."
example_title: "Español"
- text: "Zer beste izenak ditu Tartalo?"
context: "Tartalo euskal mitologiako izaki begibakar artzain erraldoia da. Tartalo izena zenbait euskal hizkeratan herskari-bustidurarekin ahoskatu ohi denez, horrelaxe ere idazten da batzuetan: Ttarttalo. Euskal Herriko zenbait tokitan, Torto edo Anxo ere esaten diote."
example_title: "Euskara"
---
# ixambert-base-cased finetuned for QA
This is a basic implementation of the multilingual model ["ixambert-base-cased"](https://huggingface.co/ixa-ehu/ixambert-base-cased), fine-tuned on an experimental version of SQuAD1.1 in Basque (1/3 size of original SQuAD1.1), that is able to answer basic factual questions.
## Overview
* **Language model:** ixambert-base-cased
* **Languages:** English, Spanish and Basque
* **Downstream task:** Extractive QA
* **Training data:** Experimental SQuAD1.1 in Basque
* **Eval data:** Experimental SQuAD1.1 in Basque
* **Infrastructure:** 1x GeForce RTX 2080
## Outputs
The model outputs the answer to the question, the start and end positions of the answer in the original context, and a score for the probability for that span of text to be the correct answer. For example:
```python
{'score': 0.9667195081710815, 'start': 101, 'end': 105, 'answer': '1820'}
```
## How to use
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "MarcBrun/ixambert-finetuned-squad-eu"
# To get predictions
context = "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820"
question = "When was Florence Nightingale born?"
qa = pipeline("question-answering", model=model_name, tokenizer=model_name)
pred = qa(question=question,context=context)
# To load the model and tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Hyperparameters
```
batch_size = 8
n_epochs = 3
learning_rate = 2e-5
optimizer = AdamW
lr_schedule = linear
max_seq_len = 384
doc_stride = 128
```
|
Yanjie/message-intent-onnx
|
Yanjie
| 2022-02-23T20:08:05Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2022-03-02T23:29:05Z |
ONNX version of message-intent model. Will be used on GPU machine.
|
andresestevez/bert-base-cased-finetuned-squad
|
andresestevez
| 2022-02-23T19:12:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-cased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2
- Datasets 1.13.3
- Tokenizers 0.10.3
|
vyang/plc2proc
|
vyang
| 2022-02-23T15:43:40Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
|
mwesner/bert-base-uncased
|
mwesner
| 2022-02-23T15:18:51Z | 17 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased
results: []
---
# bert-base-uncased
This model was trained on a dataset of issues from github.
It achieves the following results on the evaluation set:
- Loss: 1.2437
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Masked language model trained on github issue data with token length of 128.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- 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 |
|:-------------:|:-----:|:------:|:---------------:|
| 2.205 | 1.0 | 9303 | 1.7893 |
| 1.8417 | 2.0 | 18606 | 1.7270 |
| 1.7103 | 3.0 | 27909 | 1.6650 |
| 1.6014 | 4.0 | 37212 | 1.6052 |
| 1.523 | 5.0 | 46515 | 1.5782 |
| 1.4588 | 6.0 | 55818 | 1.4836 |
| 1.3922 | 7.0 | 65121 | 1.4289 |
| 1.317 | 8.0 | 74424 | 1.4414 |
| 1.2622 | 9.0 | 83727 | 1.4322 |
| 1.2123 | 10.0 | 93030 | 1.3651 |
| 1.1753 | 11.0 | 102333 | 1.3636 |
| 1.1164 | 12.0 | 111636 | 1.2872 |
| 1.0636 | 13.0 | 120939 | 1.3705 |
| 1.021 | 14.0 | 130242 | 1.3013 |
| 0.996 | 15.0 | 139545 | 1.2756 |
| 0.9625 | 16.0 | 148848 | 1.2437 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.3
|
phongdtd/fb-youtube-vi-large
|
phongdtd
| 2022-02-23T13:56:55Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"phongdtd/youtube_casual_audio",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- phongdtd/youtube_casual_audio
- generated_from_trainer
model-index:
- name: fb-youtube-vi-large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fb-youtube-vi-large
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the PHONGDTD/YOUTUBE_CASUAL_AUDIO - NA 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: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 8
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 25.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
EleutherAI/enformer-corr_coef_obj
|
EleutherAI
| 2022-02-23T12:18:12Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"enformer",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
license: apache-2.0
inference: false
---
# Enformer
Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://www.nature.com/articles/s41592-021-01252-x) by Avsec et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/enformer).
This particular model was trained on sequences of 131,072 basepairs, target length 896 on v3-64 TPUs for 3 days with sequence augmentations and pearson correlation objective.
This repo contains the weights of the PyTorch implementation by Phil Wang as seen in the [enformer-pytorch repository](https://github.com/lucidrains/enformer-pytorch).
Disclaimer: The team releasing Enformer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
Enformer is a neural network architecture based on the Transformer that led to greatly increased accuracy in predicting gene expression from DNA sequence.
We refer to the [paper](https://www.nature.com/articles/s41592-021-01252-x) published in Nature for details.
### How to use
Refer to the README of [enformer-pytorch](https://github.com/lucidrains/enformer-pytorch) regarding usage.
### Citation info
```
Avsec, Ž., Agarwal, V., Visentin, D. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat Methods 18, 1196–1203 (2021). https://doi.org/10.1038/s41592-021-01252-x
```
|
panashe/autonlp-eo-590516680
|
panashe
| 2022-02-23T11:29:10Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:panashe/autonlp-data-eo",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- panashe/autonlp-data-eo
co2_eq_emissions: 2.3709499644854883
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 590516680
- CO2 Emissions (in grams): 2.3709499644854883
## Validation Metrics
- Loss: 0.6466107964515686
- Accuracy: 0.6608695652173913
- Precision: 0.6515151515151515
- Recall: 0.7288135593220338
- AUC: 0.6334745762711864
- F1: 0.688
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/panashe/autonlp-eo-590516680
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("panashe/autonlp-eo-590516680", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("panashe/autonlp-eo-590516680", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
chrisknowles/en_stylecheck
|
chrisknowles
| 2022-02-23T11:05:56Z | 6 | 1 |
spacy
|
[
"spacy",
"token-classification",
"en",
"license:mit",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- en
license: mit
model-index:
- name: en_stylecheck
results: []
---
Check style on English text (currently passive text).
| Feature | Description |
| --- | --- |
| **Name** | `en_stylecheck` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.1,<3.2.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner`, `stylecheck` |
| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner`, `stylecheck` |
| **Vectors** | 684830 keys, 20000 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | `MIT` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (115 labels for 5 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
| **`senter`** | `I`, `S` |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
| **`entity_ruler`** | `PASSIVE` |
</details>
|
Ninja5000/DialoGPT-medium-TWEWYJoshua
|
Ninja5000
| 2022-02-23T10:47:52Z | 23 | 3 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- conversational
---
# DialoGPT-medium-TWEWYJoshua
Another not-so-good AI chatbot. Joshua from the game TWEWY(The World Ends With You).
* Credits to Lynn's Devlab who made the amazing tutorial.
|
Aron/distilbert-base-uncased-finetuned-emotion
|
Aron
| 2022-02-23T10:34:14Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.92
- name: F1
type: f1
value: 0.9201604193183255
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2295
- Accuracy: 0.92
- F1: 0.9202
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8187 | 1.0 | 250 | 0.3137 | 0.902 | 0.8983 |
| 0.2514 | 2.0 | 500 | 0.2295 | 0.92 | 0.9202 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
bettertextapp/bart_large_teaser_de_v2
|
bettertextapp
| 2022-02-23T10:17:34Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
model-index:
- name: bart_large_teaser_de_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart_large_teaser_de_v2
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
{'eval_loss': 0.2028738558292389, 'eval_score': 80.750962016922, 'eval_counts': [342359, 316072, 304925, 294258], 'eval_totals': [376475, 371475, 366475, 361475], 'eval_precisions': [90.93804369480046, 85.08567198330978, 83.20485708438503, 81.40479977868456], 'eval_bp': 0.9490684186878129, 'eval_sys_len': 376475, 'eval_ref_len': 396155, 'eval_runtime': 431.9447, 'eval_samples_per_second': 11.576, 'eval_steps_per_second': 0.363}
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.16.2
- Pytorch 1.11.0a0+bfe5ad2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Holako/NER_model_holako
|
Holako
| 2022-02-23T09:07:06Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Holako/NER_model_holako")
model = AutoModelForTokenClassification.from_pretrained("Holako/NER_model_holako")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "اسمي احمد"
ner_results = nlp(example)
print(ner_results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
=======
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
Language|Dataset
-|-
Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/)
|
junzai/demotest
|
junzai
| 2022-02-23T07:51:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert_finetuning_test
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8284313725490197
- name: F1
type: f1
value: 0.8817567567567567
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_finetuning_test
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4023
- Accuracy: 0.8284
- F1: 0.8818
- Combined Score: 0.8551
## 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: 1.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.11.0
|
jkang/espnet2_librispeech_100_conformer_word
|
jkang
| 2022-02-23T00:23:45Z | 1 | 1 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:librispeech_100",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: noinfo
datasets:
- librispeech_100
license: cc-by-4.0
---
## ESPnet2 ASR model
### `jkang/espnet2_librispeech_100_conformer_word`
This model was trained by jaekookang using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 82a0a0fa97b8a4a578f0a2c031ec49b3afec1504
pip install -e .
cd egs2/librispeech_100/asr1
./run.sh --skip_data_prep false --skip_train true --download_model jkang/espnet2_librispeech_100_conformer_word
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Tue Feb 22 17:38:22 KST 2022`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.10.1`
- Git hash: `e79e7185780b90e56618859855a038b4369b002c`
- Commit date: `Tue Feb 22 15:34:12 2022 +0900`
## asr_conformer_lr2e-3_warmup15k_amp_nondeterministic
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|91.0|8.4|0.6|1.0|10.0|70.1|
|decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|82.9|15.6|1.5|2.5|19.6|83.3|
|decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|90.7|8.7|0.6|1.0|10.3|71.4|
|decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|82.1|16.1|1.7|2.3|20.2|85.9|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|95.7|2.6|1.7|1.3|5.6|70.1|
|decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|91.0|5.6|3.4|2.5|11.5|83.3|
|decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|95.7|2.7|1.7|1.2|5.5|71.4|
|decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|90.9|5.6|3.6|2.5|11.6|85.9|
### 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_conformer_lr2e-3_warmup15k_amp_nondeterministic
ngpu: 1
seed: 2022
num_workers: 4
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
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: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: 400
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: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 16000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_word_sp/train/speech_shape
- exp/asr_stats_raw_en_word_sp/train/text_shape.word
valid_shape_file:
- exp/asr_stats_raw_en_word_sp/valid/speech_shape
- exp/asr_stats_raw_en_word_sp/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_clean_100_sp/wav.scp
- speech
- kaldi_ark
- - dump/raw/train_clean_100_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- kaldi_ark
- - 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-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 15000
token_list:
- <blank>
- <unk>
- THE
- AND
- OF
- TO
- A
- IN
- I
- WAS
- HE
- THAT
- IT
- HIS
- HAD
- AS
- WITH
- YOU
- FOR
- HER
- BUT
- IS
- NOT
- SHE
- AT
- 'ON'
- BE
- HIM
- THEY
- BY
- HAVE
- THIS
- MY
- WERE
- WHICH
- ALL
- FROM
- SO
- SAID
- ONE
- ME
- WE
- THERE
- THEIR
- 'NO'
- WHEN
- AN
- OR
- THEM
- WOULD
- IF
- WHO
- ARE
- BEEN
- WHAT
- UP
- THEN
- OUT
- COULD
- WILL
- INTO
- MORE
- SOME
- VERY
- MAN
- DO
- NOW
- LITTLE
- ABOUT
- YOUR
- DID
- THAN
- TIME
- LIKE
- UPON
- WELL
- HAS
- ONLY
- TWO
- OTHER
- ANY
- OUR
- MADE
- AFTER
- BEFORE
- ITS
- DOWN
- OVER
- SUCH
- OLD
- SEE
- THESE
- KNOW
- CAME
- DAY
- GREAT
- US
- MISTER
- GOOD
- SHOULD
- MUCH
- CAN
- HOW
- WAY
- NEVER
- MUST
- COME
- AGAIN
- BACK
- FIRST
- WHERE
- GO
- HIMSELF
- OWN
- LONG
- MAY
- MEN
- EVEN
- WENT
- SAY
- JUST
- MIGHT
- HERE
- THROUGH
- EYES
- MAKE
- TOO
- WITHOUT
- HOUSE
- THINK
- THOSE
- THOUGHT
- MANY
- MOST
- EVERY
- LIFE
- AWAY
- BEING
- STILL
- AM
- WHILE
- NOTHING
- DON'T
- LAST
- THOUGH
- YOUNG
- YET
- FOUND
- PEOPLE
- THREE
- 'OFF'
- HAND
- GET
- TAKE
- ASKED
- SAW
- SAME
- NIGHT
- MISSUS
- HEAD
- RIGHT
- LEFT
- ANOTHER
- TELL
- ONCE
- SHALL
- PLACE
- EVER
- TOOK
- FACE
- SEEMED
- ALWAYS
- ROOM
- NEW
- UNDER
- WHY
- TOLD
- LOOKED
- HEARD
- PUT
- BECAUSE
- THINGS
- SOMETHING
- LET
- GOING
- GIVE
- LOOK
- SOON
- THING
- MIND
- FATHER
- LOVE
- KNEW
- EACH
- FAR
- AGAINST
- HAVING
- HEART
- MOTHER
- WORLD
- FEW
- BEGAN
- 'YES'
- MISS
- DOOR
- BETTER
- WORK
- HOME
- MOMENT
- YEARS
- ENOUGH
- SIR
- DONE
- GOT
- SIDE
- SEEN
- WOMAN
- CALLED
- IT'S
- WHOLE
- BETWEEN
- FELT
- KING
- MORNING
- HERSELF
- FIND
- TURNED
- HOWEVER
- WHITE
- ALSO
- HALF
- PERHAPS
- GIRL
- REPLIED
- HUNDRED
- QUITE
- OH
- MYSELF
- PART
- WATER
- COURSE
- VOICE
- POOR
- BOTH
- NAME
- GAVE
- HANDS
- WHOM
- DAYS
- ALMOST
- AMONG
- SET
- TOGETHER
- WORDS
- UNTIL
- ANYTHING
- FEET
- NEXT
- WANT
- STOOD
- FOUR
- I'M
- BROUGHT
- BEST
- LIGHT
- OTHERS
- FIVE
- LOOKING
- SMALL
- ALONG
- NOR
- NEAR
- RATHER
- SINCE
- BELIEVE
- PASSED
- DOES
- MONEY
- OPEN
- LAY
- END
- INDEED
- ROUND
- KIND
- FULL
- TWENTY
- CRIED
- TAKEN
- SURE
- MATTER
- WORD
- DEAR
- GONE
- COUNTRY
- WHOSE
- ANSWERED
- LESS
- HIGH
- THEMSELVES
- SAT
- AIR
- BLACK
- BEHIND
- POWER
- 'TRUE'
- UNCLE
- AROUND
- NATURE
- CHILD
- DEATH
- DURING
- CERTAIN
- REST
- KEEP
- JOHN
- OFTEN
- TILL
- WOMEN
- ALREADY
- CHILDREN
- THUS
- PRESENT
- HOPE
- LARGE
- LADY
- BECAME
- RETURNED
- WIFE
- CANNOT
- WISH
- DIDN'T
- GOD
- BOY
- SENT
- GIVEN
- LEAVE
- ALONE
- CASE
- SHORT
- BODY
- LAND
- EVERYTHING
- COMING
- GENERAL
- SAYS
- REALLY
- HELD
- DOCTOR
- ABOVE
- GROUND
- FELL
- FIRE
- HELP
- THOUSAND
- SPEAK
- EVENING
- FACT
- CITY
- SOMETIMES
- HEAR
- ORDER
- STATE
- FRIEND
- KEPT
- WITHIN
- POINT
- FRIENDS
- LEAST
- MASTER
- HOUR
- THAT'S
- USE
- FAMILY
- CARE
- MAKING
- WHETHER
- BEAUTIFUL
- SIGHT
- TIMES
- SUDDENLY
- BED
- SIX
- I'LL
- DEAD
- EITHER
- CALL
- ITSELF
- USED
- ABLE
- TOWARDS
- DARK
- MANNER
- MEAN
- SEVERAL
- CAPTAIN
- LOST
- APPEARED
- STORY
- TOWN
- KNOWN
- BIG
- POSSIBLE
- THOU
- FINE
- MEANS
- SEA
- SECOND
- CONTINUED
- STRANGE
- SON
- RED
- HUMAN
- LORD
- HARD
- PERSON
- STREET
- REACHED
- FEEL
- CLOSE
- HAIR
- QUESTION
- ARMS
- ROSE
- THEREFORE
- BECOME
- LONGER
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- ASKING
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- PLENTY
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- TINY
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- COMPLETELY
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- MARCH
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- INTENTION
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- SWEPT
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- EFFECTS
- EAGERLY
- BOLD
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- SPEND
- SUFFICIENTLY
- ENEMIES
- REGARDED
- SAILED
- MILITARY
- RESOLUTION
- COOK
- SHAKING
- MELANCHOLY
- DYING
- BEHELD
- FLUNG
- FIGHTING
- BIRTH
- STARED
- KINDLY
- THOUSANDS
- CHRISTIAN
- TEMPLE
- WIDOW
- BRANCHES
- LOVER
- SPANISH
- CIVIL
- ALICE
- COMMUNITY
- DIRECTED
- LET'S
- TRAMP
- BEN
- PHILIP
- POOL
- HELPED
- STORE
- ELEVEN
- EXTREME
- WISHING
- EXTREMELY
- ASHAMED
- KINDNESS
- CRIME
- WITNESS
- IMPRESSION
- BECOMING
- CREDIT
- SCATTERED
- STRUGGLE
- SIGNS
- GHOST
- PEASANT
- MANAGED
- SINGING
- EVERYONE
- RETURNING
- TEACHER
- MILLION
- GOVERNOR
- MAGNIFICENT
- PORTION
- SPIRITUAL
- CAP
- BEARING
- COUNTESS
- KATE
- SYLVIA
- HATED
- WHENCE
- BELONGED
- WICKED
- CRUEL
- SLIM
- OURS
- FAULT
- EDUCATION
- GLOOMY
- TREATMENT
- DANCING
- AGREEABLE
- FIRM
- SIMILAR
- ACTIVE
- UNIVERSAL
- CLEVER
- SEPARATED
- USEFUL
- ENDS
- STANDS
- NINETEEN
- CITIES
- FEATURES
- AMOUNT
- MONSIEUR
- NEWSPAPER
- HOLE
- SHONE
- OCEAN
- SANK
- MARGUERITE
- MARIUS
- ERIC
- MATTHEW
- THEY'RE
- OFFICIAL
- FOREHEAD
- MANNERS
- SOLEMN
- DOUBTLESS
- THEORY
- SUGGESTION
- INTERESTS
- DESTROYED
- NATIONS
- NECESSITY
- BREAKING
- INCREASE
- ASTONISHMENT
- AFTERWARD
- CAREFUL
- BAR
- METHOD
- HAST
- ESCAPED
- SOLDIER
- COMPOSED
- HANGING
- EXAMINED
- FAVOR
- WANDERED
- WAVES
- PATTY
- COSETTE
- ACCUSTOMED
- BRIDGE
- FALLS
- JEFF
- ATTEND
- ACCORDINGLY
- NATIVE
- TRIP
- NAY
- LOVES
- ASSURED
- CRYING
- TENT
- HOUSEHOLD
- SENTIMENT
- MURDER
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- APPOINTED
- SAIL
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- OBTAINED
- SKILL
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- REQUEST
- STOCK
- THRUST
- ASSISTANCE
- BEG
- EXHAUSTED
- CHOOSE
- SUFFER
- RESUMED
- MOUNTED
- RANK
- SOUTHERN
- SPECIES
- PARTED
- TROOPS
- SCIENCE
- CLIFF
- SURROUNDED
- CHAIN
- SHED
- VOYAGE
- BASKET
- SHOUTING
- RANGE
- JIMMIE
- UDO
- JUNE
- DRIVING
- UNUSUAL
- SLIGHTLY
- MAYBE
- ASTONISHED
- STUPID
- PICK
- BRINGING
- DEMAND
- VEIL
- YARDS
- IMAGINED
- MERCY
- FUNNY
- TYPE
- COVER
- CHEEKS
- STRIKE
- STORIES
- SIGHED
- CARED
- LITERATURE
- APART
- RARE
- COMMANDER
- ENERGY
- FRIENDLY
- ACCOMPLISHED
- WOODEN
- OWNER
- SOUNDED
- INVITED
- ACCIDENT
- DISCOVER
- DISTINGUISHED
- CONNECTION
- CHARM
- TREMBLING
- FAMILIES
- MILLIONS
- RUSH
- BARON
- ATMOSPHERE
- SENSIBLE
- EDITH
- NEEDS
- DAVID
- OLDER
- WASH
- NED
- ANNIE
- SEVERE
- PURPLE
- MARBLE
- WORST
- BRANCH
- LEANING
- MERCHANT
- WEIGHT
- MOTHER'S
- HASTE
- SUSPECTED
- PRISONERS
- ABROAD
- TRIAL
- CONSENT
- CONVINCED
- ARGUMENT
- WESTERN
- BADE
- EVENT
- LIBRARY
- ABSOLUTE
- DISCOVERY
- FAILURE
- SUPERIOR
- FIFTH
- BELOVED
- DESTRUCTION
- GAIN
- VAGUE
- DAWN
- LILY
- HASTENED
- MACHINE
- FOREVER
- WOMAN'S
- CHANGES
- LIKEWISE
- DISTRICT
- DEPTHS
- STOUT
- PICTURES
- HIDE
- SUCCESSFUL
- GOODNESS
- IMMENSE
- BOBO
- APARTMENT
- SHOES
- SANG
- RETIRED
- NEIGHBORS
- REGRET
- MINISTER
- PRACTICE
- CROWDED
- DESCENDED
- MISERABLE
- HATE
- OBSERVATION
- FAMILIAR
- MEASURES
- DISPOSED
- REFUSE
- DESCRIBE
- SHOCK
- MILK
- REPUTATION
- DEVELOPMENT
- FEARED
- LIGHTNING
- BROWNIE
- BEGGED
- STIFF
- LEVEL
- ADAM
- SETTING
- NEWSPAPERS
- HUNT
- CLASSES
- ROOTS
- MINGLED
- CONSEQUENCE
- APPROACH
- DIANA
- COLORED
- STAYED
- GRATITUDE
- BOW
- SPEED
- HOST
- PASSENGERS
- WONDERING
- PSYCHE
- NATASHA
- PERCEVAL
- CIRCLE
- GLIMPSE
- DEEPLY
- VILLEFORT
- PREVIOUS
- ADVENTURE
- LEST
- USELESS
- KNIFE
- PULL
- CONTINUE
- CAUSES
- JULY
- COUNTRIES
- TITLE
- DELIVERED
- UNCONSCIOUS
- SOMEONE
- POUNDS
- SLIPPED
- MOTIVE
- LANDSCAPE
- DEPARTURE
- EXPRESS
- FINAL
- MOVEMENTS
- ARRANGED
- NERVOUS
- RUIN
- KISSED
- DRAW
- LEANED
- CONCERNED
- HUNGER
- ELDER
- PIPE
- TIS
- GANEM
- JENNY
- THENARDIER
- ANXIETY
- JAPANESE
- DESERTED
- DELIGHTFUL
- VIEWS
- MATCH
- SUSPICION
- GUILTY
- LEADER
- KISS
- CHIEFLY
- JUDGMENT
- WASTE
- EXERCISE
- HITHERTO
- EXTENT
- DELICATE
- PROPOSED
- THANKS
- SALT
- BUTTER
- RELATION
- SEES
- PROCEED
- DISTURBED
- BAND
- COW
- FISHES
- WIN
- UTTER
- SPARE
- CLAIM
- PEN
- CHEEK
- INSTRUMENT
- BEATING
- AGES
- EASTERN
- ATTENDED
- PAINTED
- ENTHUSIASM
- FERKO
- EARL
- HELEN
- GUNS
- COMMITTEE
- EARLIER
- HE'D
- TODAY
- LACK
- STEADILY
- PAINFUL
- BONES
- ENORMOUS
- CONFUSION
- MAGISTRATE
- PLAGUE
- BLAME
- SACRED
- TREAT
- APPLIED
- COOL
- PIANO
- STRIKING
- DISTINCT
- ATTACKED
- PORT
- BITTERLY
- MIDNIGHT
- POSSESS
- RAPID
- PRODUCE
- SAVAGE
- WET
- SMALLER
- APPEARS
- AUDIENCE
- JOB
- HEADED
- EXPERIENCES
- CROWN
- FAITHFUL
- EXPEDITION
- REGION
- DEGREES
- MISERY
- FED
- LEAPED
- PEEP
- OFFICER
- HUNDREDS
- NAUTILUS
- MABEL
- HYACINTH
- ORCHARD
- BUSHES
- CHEERFUL
- EARNEST
- GRANDFATHER
- SOMEHOW
- UNFORTUNATE
- FLASH
- VENTURED
- DANGLARS
- RESTED
- OBTAIN
- CONTEMPT
- EXTENDED
- GENIUS
- HESITATED
- SWIFT
- AMONGST
- AUGUST
- WHOLLY
- NUMBERS
- ARTICLE
- NOON
- FILL
- GODS
- VARIETY
- WEARY
- KINDS
- JUMPED
- COMMITTED
- BACHELOR
- BOTTLE
- SOLE
- DESERT
- GOD'S
- HIGHLY
- INTRODUCED
- CITIZENS
- POVERTY
- EQUALITY
- BEINGS
- RAYS
- JOLLY
- QUALITIES
- TALE
- LIMBS
- AMBITION
- CREW
- KNOCKED
- JOE
- BELONG
- CONFESS
- BRIDE
- BOOTS
- NINETY
- CAPITAL
- LIGHTLY
- PROPORTION
- GAZED
- AFFORD
- DESCRIPTION
- TREMBLED
- FITTED
- BYE
- RANG
- DISAPPOINTED
- CONSTANTLY
- CONTAINED
- THREATENED
- SEAS
- VALUABLE
- HERO
- INSISTED
- WANDERING
- LOVING
- VISION
- EXAMINATION
- THOROUGHLY
- RID
- FORTUNATE
- SHORTLY
- NEST
- HORRIBLE
- POURED
- OCCASIONALLY
- FEMALE
- MISTAKEN
- PURPOSES
- ANYWHERE
- CHEESE
- PERCEIVE
- HATH
- ACTUAL
- NOTES
- BURNED
- PROBLEM
- HABITS
- CHRIST
- HIDING
- BECOMES
- CONCLUSION
- INTELLECTUAL
- MIRROR
- VANISHED
- DAUGHTERS
- PRESERVED
- TRIBE
- GROUPS
- NORTHERN
- NOTWITHSTANDING
- NEAREST
- CHILDHOOD
- DISTRESS
- EMPIRE
- CONNECTED
- SNAKE
- SHAKE
- GREGG
- PARISH
- TILNEY
- PORTHOS
- REPRESENTATIVE
- FORT
- GOOSE
- FLORINA
- FRIENDSHIP
- BEARD
- AIN'T
- UNION
- CONTINUALLY
- DISCUSSION
- SHARPLY
- SURROUNDING
- REWARD
- PURSUED
- VISITOR
- SHADOWS
- LEARNING
- FEVER
- INTENTIONS
- GENEROUS
- INTELLIGENT
- HOLLAND
- HATRED
- VESSELS
- FIRED
- AVOID
- SUPREME
- DATE
- FAVOUR
- USING
- STUFF
- INFINITE
- PAGE
- HUMANITY
- T
- EYED
- ADDRESS
- HOUSEKEEPER
- LONELY
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- MURMURED
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- GATHER
- MUTTERED
- MONSTER
- AGREE
- PROFOUND
- STAR
- GATES
- FOX
- CUP
- RE
- HAPPENS
- YONDER
- KINGS
- WARRIORS
- DEPARTED
- FREELY
- SOAP
- MEAT
- TRAVELLING
- DRUNK
- CAROLINE
- AGONY
- CRAFT
- CORDIAL
- QUOTH
- MERCER
- UNIVERSITY
- FRANCIS
- COMMONS
- POYSER
- CRAWLEY
- SLENDER
- CANADIAN
- FEARS
- GRAVELY
- SOIL
- ROADS
- INSTINCT
- FLUSHED
- GAY
- WENDY
- RAISE
- NEGRO
- CONVICTION
- TRAVEL
- TROUBLED
- DEPEND
- OCCASIONS
- INCREASING
- INDIGNATION
- POWDER
- DIFFICULTIES
- SING
- LOCKED
- ALOUD
- CANDLE
- IMPULSE
- PEARLS
- STRAW
- FIERCE
- QUARTERS
- STEADY
- RESTORED
- OBEYED
- UNEXPECTED
- MEDICINE
- DRESSING
- PRECISELY
- TRACKS
- CLIMBED
- THIRTEEN
- KNEE
- CONCERNING
- CREEK
- LATELY
- PEASANTS
- OBSERVE
- ORIGIN
- COMMANDED
- BUILD
- FETNAH
- MADAM
- WHILST
- SHOUT
- FOURTEEN
- THOMPSON
- UTMOST
- RICHMOND
- CONDUCTED
- DEVELOPED
- DESPERATE
- TIED
- ANYHOW
- UTTERLY
- REMARK
- FIRMLY
- ASPECT
- LOSING
- TRIUMPH
- INSTRUCTIONS
- MISSED
- INTENSE
- MOTIONLESS
- MERIT
- HOSPITAL
- REFLECTED
- RECORD
- MORTAL
- PUBLISHED
- RUINED
- ATTEMPTED
- ESSENTIAL
- SLIGHTEST
- OPPOSITION
- SEASON
- SCORE
- ASSURE
- KEEPS
- CONSTITUTION
- DREAD
- PRIVILEGE
- PRAISE
- MAGIC
- CAPACITY
- SATURDAY
- LOCAL
- INHABITANTS
- CALLS
- PER
- RENDERED
- THROWING
- FATAL
- WEPT
- FEAST
- COFFEE
- IGNORANT
- VISITED
- BADLY
- GIANT
- FRAME
- VIOLENCE
- PRUDENCE
- STERN
- FANCIED
- REMAINS
- BURNING
- LANDED
- SONS
- HID
- CIVILIZATION
- HANDKERCHIEF
- PONY
- HIT
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- MARCHED
- SHEPHERD
- LEIF
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- SAZEN
- PENCROFT
- LANE
- FEARFUL
- IDEAL
- SUPPORTED
- REFLECTION
- SURGEON
- ACTED
- CIRCUMSTANCE
- TORN
- PIRATE
- CONTACT
- IMAGE
- HE'LL
- FEELS
- DIVIDED
- COLLECTION
- DAMP
- ABRUPTLY
- INCLUDING
- ACQUIRED
- BREATHING
- SENSES
- WRAPPED
- NOTED
- LEATHER
- CHEST
- SERVICES
- BURDEN
- DAY'S
- CONCERN
- PUNISHMENT
- DETAILS
- GRATEFUL
- REMOVE
- EXTERNAL
- WHEAT
- LONGED
- ENGINEER
- MEANTIME
- MULTITUDE
- UNC
- CONFUSED
- OPINIONS
- REVOLUTION
- PINE
- SENTENCE
- SLAVERY
- ET
- TRIBES
- DIAMOND
- WARNING
- MOUNT
- RONICKY
- CENTRE
- TRAP
- ROMANS
- ELZEVIR
- BEAVER
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- ROLLIN
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- OLENIN
- QUARLES
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- BLOOM
- STRANGERS
- ENJOYMENT
- AREN'T
- CHRISTMAS
- DISPOSITION
- SENSATION
- PLATFORM
- CONCEALED
- PRONOUNCED
- RESTING
- DUTIES
- ACTIVITY
- RUE
- RAISING
- REQUIRE
- TOPS
- SHEET
- RALPH
- DISAPPOINTMENT
- OLIVER
- CRIES
- ACKNOWLEDGE
- RETREAT
- DIVINE
- ARTICLES
- EXCHANGE
- FISHER
- STARING
- SNAPPED
- LABOUR
- POT
- READILY
- REAR
- LAWYER
- ARRIVE
- RELIEVED
- BOSTON
- CENTS
- CUSTOM
- GRANT
- RESIST
- MASTER'S
- EXPERIENCED
- REPRESENTED
- RAILROAD
- SEEKING
- PRACTICAL
- GARMENTS
- HEAVILY
- ADVANCING
- PROCESS
- CREPT
- ASSUMED
- SILENTLY
- ROLL
- SWORDS
- RESPECTABLE
- SMITH
- ANGEL
- SUMMIT
- ROC
- EATEN
- PEARL
- SILK
- DIM
- TEACH
- SHOWS
- ABSORBED
- HEARTED
- LONGING
- CAREER
- INDUSTRY
- PRACTICALLY
- FLAG
- WITHDREW
- AROUSED
- PROFESSIONAL
- ISSUE
- LEAF
- EMOTION
- POINTING
- MESSENGER
- HEAP
- CHOSE
- READER
- WHEREVER
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- SHELLS
- OWING
- PRESENTS
- SEATS
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- SUCCESSION
- CONSIDERING
- FENCE
- CLOSER
- INDIFFERENCE
- PERFORM
- FILLING
- RESULTS
- RELATED
- ADDITION
- SATISFY
- RIDING
- GLORIOUS
- GUESTS
- TREASURE
- BEARS
- FASTENED
- VENTURE
- RECOGNIZE
- LESSON
- IMPATIENCE
- ROLLING
- FORESTS
- SOULS
- ACCUSED
- ENGAGEMENT
- VENGEANCE
- REGIMENT
- BARBARA
- JENKS
- TROUTINA
- STEEP
- CLEARED
- TWISTED
- STARTING
- DREAMING
- EXPECTATION
- ANDREA
- SCARED
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- VOLUME
- EXCEPTION
- DARLING
- WAKE
- DOUBTFUL
- PRETENDED
- GALLANT
- PERMITTED
- VOTE
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- OTHER'S
- SIGH
- SINGULAR
- QUALITY
- GIFT
- GLOOM
- HAPPILY
- PERSUADED
- GUESSED
- ABILITY
- PACE
- HENCE
- BALANCE
- NEIGHBORHOOD
- SQUIRE
- DRIVER
- ENDURE
- MARKET
- PERMIT
- BENEFIT
- CONSEQUENTLY
- VICTIM
- THITHER
- MISCHIEF
- NECESSARILY
- BASE
- BARBICANE
- BEASTS
- LANDING
- REMAINING
- DRAGGED
- AMID
- WAVED
- BELLE
- CONCEPTION
- NAKED
- LOFTY
- ASSEMBLED
- SUPPLY
- BROW
- SOLID
- THINKS
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- DECLARE
- SILLY
- SECURED
- MODE
- CURATE
- RUSSIAN
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- LEOPOLD
- CONWAY
- THOMAS
- NEAT
- STUCK
- DENY
- SAFELY
- SECRETLY
- HANDLE
- RESPONDED
- SECRETARY
- INDEPENDENT
- PREVIOUSLY
- MISFORTUNE
- MISFORTUNES
- MANKIND
- LA
- RENEWED
- GRACEFUL
- ESTABLISHMENT
- CHEER
- CONSTANT
- ENDLESS
- RECALLED
- APRIL
- INDEPENDENCE
- CREATION
- STRONGER
- CAPTURED
- WINDS
- SUSPECT
- SHELTER
- HUMBLE
- PREPARE
- PARTIES
- SOLITARY
- DINE
- APPARENT
- STAFF
- HEELS
- SOVEREIGN
- JOKE
- OARS
- ARRANGE
- HOLES
- SADDLE
- BARK
- COVERING
- POSSIBILITY
- QUARREL
- GETS
- GROWTH
- FURNITURE
- ALARMED
- FOLLOWS
- CENT
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- SAM
- BIBLE
- FOG
- JACK
- LOUDLY
- THEATRE
- ANYWAY
- OVERHEAD
- LOG
- SWUNG
- AGENTS
- POLITE
- PLAINS
- MOONLIGHT
- PRINCIPLES
- ISLANDS
- VIRTUES
- CALMLY
- CAKES
- SPEEDILY
- AGITATION
- WING
- RIDGE
- ELDEST
- MUSICAL
- MAIDEN
- SUNK
- ISABELLA
- ARTIST
- TIMBER
- BINGLEY
- CHARACTERS
- AUTHORITIES
- FANNY
- THUMB
- HISTORIANS
- BERYL
- ALI
- GWYNPLAINE
- GRAMMONT
- BERNARD
- PUZZLED
- APPLE
- TIGHT
- SAILOR
- NURSE
- INTIMATE
- REPEAT
- CRIMINAL
- COUNTED
- DEAREST
- LUCKY
- PROFESSION
- ORANGE
- LIST
- ADVANTAGES
- METAL
- THUNDER
- DECISION
- FLOWING
- VIVID
- APPEAL
- STOPPING
- REACHING
- HUMOUR
- ADMIRED
- CURRENT
- TEAR
- RECEIVING
- ENTERPRISE
- MATE
- BEACH
- FURNISHED
- TRUNK
- DECIDE
- CLOTHING
- FROZEN
- BEAST
- DEFINITE
- STATEMENT
- OBVIOUS
- PRAYERS
- RUBBED
- PRAIRIE
- WHOEVER
- HA
- GARDENS
- GLASSES
- EXISTS
- RABBIT
- ATTACHED
- ROUSED
- PARK
- MICHEL
- GATHERING
- SIXTH
- DEADLY
- OUTER
- REASONABLE
- YO
- MEMORIES
- SCENES
- COLOURED
- CHAIRS
- TOUCHING
- BETH
- SIGNOR
- MERRICK
- AWOKE
- LODGE
- CUNNING
- ENCOUNTER
- CHASE
- LOADED
- SCARLET
- TREMENDOUS
- CAPE
- TOWER
- SUFFERINGS
- WREN
- SEPARATE
- WORSHIP
- FRANZ
- PAUL
- SHOOT
- NATURED
- PURSUIT
- INNER
- IGNORANCE
- TROOP
- MA'AM
- GUARDS
- IRELAND
- REPORTER
- ICELAND
- JULIA
- JULIUS
- CROPPER
- POLLY
- ESTHER
- JULIET
- HOOPDRIVER
- MONTGOMERY
- COLLAR
- CONTENTED
- SUNLIGHT
- ADOPTED
- MEADOWS
- PREVENTED
- REVEALED
- REPORTED
- STRONGLY
- BRINGS
- HIDEOUS
- PREFER
- SLAVES
- IRISH
- SHOULDN'T
- DENIED
- EMOTIONS
- RECKON
- ABSURD
- JANUARY
- BRITISH
- JEALOUS
- SERIES
- EIGHTH
- KNOCK
- DECEIVED
- SENDING
- FREDERICK
- POETRY
- FEED
- FAVOURITE
- PAYING
- STEEL
- CONTENTS
- PLATE
- SEX
- GROUNDS
- REJOINED
- FEEBLE
- LOUDER
- GUIDE
- JEWELS
- WORRY
- AMAZEMENT
- LIVELY
- UNPLEASANT
- DOLLAR
- SECURITY
- URGED
- MOOD
- WAGON
- CONTAINING
- PROVISIONS
- DIRECTIONS
- ROBE
- GUEST
- SHORES
- MODEST
- BREEZE
- FOLLY
- DOORWAY
- INDIVIDUALS
- ALIKE
- HARE
- HEAVENS
- CIRCULAR
- UNEASY
- SUGGEST
- GRAIN
- CATCHING
- INSTANCES
- EXCEEDINGLY
- PACKED
- DRIED
- FATHERS
- YOUNGEST
- PROPERLY
- BOXES
- LAP
- DUSK
- DINING
- WEEPING
- FLAME
- BLESS
- PLANTS
- SHELL
- ROSES
- FETCH
- COUNSEL
- WILLIAMS
- MARIPOSA
- GROVE
- BO
- LAUNCELOT
- CABINET
- DAMON
- FIDDLER
- WILMINGTON
- SOURCE
- STAYING
- EXISTED
- SECONDS
- TROUBLES
- INDICATED
- PURELY
- UNCOMFORTABLE
- CARELESSLY
- FASHIONED
- WISDOM
- POSITIVELY
- RECENT
- BLEW
- ISSUED
- ERROR
- INTERIOR
- CURIOUSLY
- PRIZE
- MISSING
- GROWS
- DRANK
- INTELLECT
- FORMERLY
- LAWN
- GRANTED
- BELIEF
- PROTECTION
- PROSPECT
- RIGHTS
- DESTROY
- VEINS
- CLOSING
- PURSE
- SWIM
- TABLES
- HEARTILY
- DESIRES
- GESTURE
- BILLS
- CLAY
- DREAMED
- GENUINE
- WARNED
- SLIP
- HARMONY
- REMEDY
- DISEASE
- MC
- CLOTH
- OIL
- SETTLE
- INQUIRE
- POCKETS
- POPULATION
- SENATOR
- CULTURE
- TEAM
- CHARITY
- SUBSTANCE
- PITCH
- CONCEAL
- RECOVER
- GLADLY
- ACTING
- MASSES
- ITALIAN
- CHANCES
- SHIRT
- CENTURIES
- STREAMS
- DISCOURSE
- IDLE
- EXECUTION
- IMPATIENT
- INSTRUMENTS
- PAINT
- BOSOM
- AUTUMN
- EXPENSE
- ACCOMPANY
- FAVORITE
- NONSENSE
- PUPILS
- GOWN
- TURNS
- FLOW
- SAILORS
- PROBABLE
- TOSSED
- IMPRESSED
- HOMES
- BUILDINGS
- PERFORMED
- BULLET
- TALES
- LORDS
- MAYOR
- FLEECE
- FROGS
- FAREWELL
- ANDREW
- LARK
- HARDING
- BARN
- CAKE
- PILE
- LION
- GLOWING
- EXACT
- ENJOYING
- DEBT
- PERSUADE
- SADNESS
- TELEGRAPH
- SEARCHING
- OBSERVING
- FINEST
- ITALY
- PRESERVE
- FIRING
- CENTRAL
- NOVEMBER
- STORES
- DEMANDS
- HOPING
- OFFICES
- HEIR
- OPERATION
- SIGNED
- CLERK
- FLOUR
- DOMESTIC
- RUDE
- THRONG
- PILLOW
- WHIP
- OBEY
- DIRTY
- SMILES
- NEIGHBOURHOOD
- SADLY
- IMPRESSIONS
- MOTHERS
- DROWNED
- WHISPER
- INVISIBLE
- HAY
- TRUSTED
- DISTINCTION
- LETTING
- FATIGUE
- PUSHING
- TEMPORARY
- BRUSH
- INTERVIEW
- AWAKENED
- SUMMONED
- TIP
- HEADQUARTERS
- CHICAGO
- COAL
- WASHED
- FRIGHTFUL
- PERMISSION
- LOAD
- DESIGN
- CAMPAIGN
- NEGLECTED
- LESSONS
- FASTER
- EXPOSED
- GLOW
- REIGN
- RESCUE
- HYPNOTIC
- STUDIED
- STRANGELY
- BACKS
- WHIRLWIND
- FURY
- GLOBE
- EXIST
- SUNSET
- JEWS
- SORTS
- RENDER
- ACTS
- HORN
- EXECUTIVE
- CONFESSION
- TOTAL
- BORNE
- RUSSIA
- MIST
- ERE
- TORE
- PRAYER
- BOATS
- RUSHING
- POET
- VENUS
- PRIME
- SPORT
- CANVAS
- WILSON
- FLOCK
- CONGRESS
- BULL
- JIMMY
- JASPER
- BAB
- GREGGORY
- LECOQ
- AMEER
- CARLINI
- MANAGE
- FLOOD
- HORIZON
- HARDER
- DECIDEDLY
- DWELLING
- CRUSHED
- ASSOCIATION
- OATH
- WEAKNESS
- JANE'S
- PIRATES
- TELLS
- RETORTED
- COMPLIMENT
- DECLARATION
- GIRL'S
- BEAUTIFULLY
- HANG
- FOLDED
- ESTATES
- STIRRED
- REDUCED
- MARTIN
- CHANNEL
- MAJORITY
- DEFEND
- SEVENTH
- MOTIVES
- KEEN
- WALKS
- AWE
- NORMAL
- LUNCH
- WIFE'S
- EAGERNESS
- INVOLVED
- RENT
- THANKED
- ELSEWHERE
- PERMANENT
- COLUMN
- FINDS
- DAYLIGHT
- BELONGING
- BUSH
- EXHIBITED
- WARMTH
- RESERVE
- PREPARATIONS
- IMPOSED
- PSYCHIC
- CAROL
- SELLING
- LIT
- ABUNDANCE
- ACKNOWLEDGED
- SERIOUSLY
- BACKGROUND
- SUGAR
- INCH
- STIR
- UNIVERSE
- METHODS
- STEAM
- COMPARATIVELY
- NAILS
- WILLINGLY
- OPPOSED
- PRINCES
- ALTERED
- DISPLAYED
- WAVE
- STATED
- EARNESTLY
- ACTIONS
- ELEMENTS
- PERIL
- CATTLE
- COMMISSION
- DEPTH
- OBEDIENCE
- DIAMONDS
- FRO
- SKINS
- DEEDS
- TOIL
- FLOATED
- SOLITUDE
- HASN'T
- POD
- SMOKING
- THENCE
- REFUGE
- THINE
- STEAMER
- CALIFORNIA
- MINK
- HELL
- MORLAND
- SOFA
- JERUSALEM
- EMILY
- BENNET
- GAZING
- CHINESE
- ADAMS
- TIE
- MONICA
- CETERA
- RULES
- CLIFFS
- SNAP
- HALTED
- CARLING
- MARTIAN
- WEAPONS
- ISRAEL
- WRITER
- CATERPILLAR
- TAYLOR
- BRENDA
- CHOKICHI
- GNOME
- CHAUVELIN
- SEED
- SMART
- PEOPLE'S
- THEIRS
- WITNESSED
- CAUTION
- SHAPED
- REASONING
- ARREST
- RECOLLECTION
- WEARING
- FAINTLY
- MARGARET
- APPLICATION
- ENCOURAGED
- HOLDS
- BARRIER
- SHE'D
- LIMITED
- MOSS
- AMUSEMENT
- REGARDING
- FANCIES
- APT
- GRANITE
- BOHEMIA
- PROTECT
- ANGRILY
- WHEREAS
- COMPARED
- VIGOROUS
- CLAIMED
- DELIVER
- BEATEN
- ROOT
- HEROIC
- PLEASURES
- WAVING
- BEDROOM
- CHECK
- ASSIST
- AMUSED
- ROAR
- REPROACH
- INDIFFERENT
- PERPETUAL
- ENABLED
- DEEPER
- INCIDENT
- GAMES
- LOTS
- PINK
- PATIENTLY
- BEGINS
- TRAINING
- HEALTHY
- CORRECT
- BARS
- TRACE
- CORONER
- PLANNED
- GLANCING
- OBJECTION
- ANSWERS
- CUTTING
- HIND
- CALF
- SCALE
- UNIFORM
- CAPTURE
- INQUIRY
- CENTER
- GOSSIP
- CORPSE
- FUNERAL
- OWE
- SCIENTIFIC
- B
- DISGUISE
- CROOK
- FLASHED
- COMMENCED
- SENSATIONS
- HESITATE
- TRICK
- GRIN
- TONES
- SAILING
- TREMBLE
- PREPARING
- GLEAM
- LE
- ALLIES
- PRINT
- PORCH
- COMPOSITION
- SATISFACTORY
- CONCEIVE
- REPOSE
- TIDE
- RESIDENCE
- SEIZE
- PROMPTLY
- COMRADES
- DOONE
- SHAKEN
- YOURSELVES
- GRANDMOTHER
- ANXIOUSLY
- LEISURE
- BOUGHS
- CLOCK
- COUNTY
- MILTON
- HEROES
- MACHINERY
- ENGLISHMAN
- MARS
- HALE
- HOPKINS
- PARKER
- ROBARTS
- COTTON
- RARELY
- EXPECTING
- WE'D
- TRAINED
- BEDS
- PREFERRED
- CARPET
- QUESTIONED
- TUMULT
- ANGUISH
- CLASPED
- OFFENCE
- DANCED
- REMINDED
- CARELESS
- DARING
- LIFT
- FLORENCE
- SAN
- FORTUNATELY
- GIFTS
- RECOGNISED
- COLLECT
- SHEER
- INFANT
- HOPELESS
- PHILOSOPHY
- FLAMES
- COARSE
- DEED
- KARA
- PASSES
- VALET
- DESCEND
- COMPLETED
- AGED
- BREATHED
- ADDRESSING
- HUSBAND'S
- LUNGS
- SUCCEED
- RESISTANCE
- INCLINATION
- GROOM
- COUSINS
- LAZY
- SCARCE
- RISEN
- CROWDS
- VIOLENTLY
- STRUGGLED
- HOLIDAY
- FURIOUS
- DESIRABLE
- REALIZE
- SIGHTED
- ROMANTIC
- RESPONSE
- SYMPTOMS
- FARMERS
- UNCONSCIOUSLY
- ADVISED
- REMOTE
- EMERGED
- SUBMIT
- CLAD
- GERMANY
- RAY
- RECENTLY
- PRINTED
- FAME
- CONFINED
- JOHNNY
- GAS
- EMBRACE
- SUPPLIED
- RYNCH
- LEAN
- ORGANS
- FAVORABLE
- ELEGANT
- GUIDED
- INFORM
- SINISTER
- PASSIONS
- MEDICAL
- NAMELY
- HESITATION
- PAGES
- SWORE
- BREATHE
- CAVE
- NATIVES
- CONSISTED
- MANIFEST
- EMBARRASSMENT
- HEAPS
- HURRYING
- STRING
- LOCK
- ETERNAL
- DETAIL
- ABSENT
- HOARSE
- SPECTATORS
- DISTINGUISH
- FROST
- SNOWY
- THEY'VE
- BACKWARD
- FIERY
- ILLNESS
- PRIESTS
- BALLOON
- QUIXOTE
- JAWS
- MISSION
- REFERENCE
- SHAW
- BARREL
- TERM
- BIBBS
- THEO
- FALK
- CRISTEL
- GENZABURO
- RAWDON
- LYNDE
- SLOPE
- GABLES
- SHY
- ENCOUNTERED
- EARTHLY
- BRED
- MAINTAIN
- APARTMENTS
- DAUGHTER'S
- APPLY
- RINGING
- COMMANDS
- ARRESTED
- ADVENTURES
- AMAZED
- GASPED
- STOOPED
- COUNTER
- JUDGED
- MINDED
- PROTEST
- DISAGREEABLE
- FAITHFULLY
- RESPONSIBILITY
- PEACEFUL
- PHRASE
- DESERVE
- CONSENTED
- OCTOBER
- PRESSURE
- RESPECTS
- LASTED
- INEVITABLE
- RESPONSIBLE
- BID
- YIELD
- EXCLUSION
- MAINTAINED
- SAUCE
- FORMIDABLE
- OLDEST
- WEAPON
- QUEST
- PARLOUR
- AFRICA
- DRAWER
- PANIC
- PLEASING
- DAMAGE
- WIT
- UNDERTAKE
- ENTERTAINMENT
- WINDING
- DWELT
- CEREMONY
- NET
- SUITS
- PRODUCT
- TENDENCY
- CEASE
- AVOIDED
- IMPROVEMENT
- BONE
- STOMACH
- ARRANGEMENT
- SEARCHED
- INQUIRIES
- FIX
- TRACES
- GRASP
- SPEAKER
- FACING
- CONVENIENT
- PRAYED
- TENDERNESS
- SUSPENDED
- LEARNT
- RESERVED
- SHOPS
- RULED
- UNCERTAIN
- SINK
- MARKS
- RELATIVES
- SENSITIVE
- SPAIN
- SINCERE
- DIGNIFIED
- SIGNIFICANT
- VEHICLE
- AVERAGE
- FIRES
- SUPPLIES
- ARRANGEMENTS
- TRIFLE
- REPEATING
- ADDING
- PHENOMENA
- AIM
- LIMITS
- LIP
- BOY'S
- MURMUR
- PILLARS
- BRIGHTLY
- SWIFTLY
- JOYOUS
- JEALOUSY
- WARRIOR
- CONTRAST
- EXTRA
- AWFULLY
- DEFEAT
- ENTHUSIASTIC
- INCHES
- DROPPING
- REDCOAT
- NERVES
- BITE
- CRACK
- SERGEANT
- DOCTRINE
- C
- MIXTURE
- INTERVALS
- FEATHERS
- BUFFALO
- FOLK
- OFFERING
- COMRADE
- BELLS
- STOLE
- SIGNAL
- SWINGING
- AUTHOR
- DISMISSED
- THORPE
- RELATE
- WILDERNESS
- TREASURES
- PROPHET
- FELIX
- COMPREHEND
- DARCY
- ASSUME
- FRANCES
- WEEP
- JACKET
- HERD
- ACCENT
- OPERATOR
- KNIGHTS
- LANTERN
- SIN
- METERS
- GREENLAND
- THRESHOLD
- TWAS
- GLACIER
- MACHINES
- KWAIRYO
- ASSISTANT
- BULLS
- REX
- ELK
- SHERIFF
- SPILETT
- CRAGGS
- STRONGEST
- WONT
- WIRE
- BRAND
- CHIN
- UNFORTUNATELY
- CONFESSED
- MUTUAL
- CARD
- FIRMNESS
- BLUSH
- CORNERS
- BABIES
- HELPLESS
- FRANKLY
- SURROUNDINGS
- HARSH
- INTERFERE
- RESTLESS
- BENCH
- PROPOSAL
- ORGAN
- AGITATED
- SUBLIME
- GREETED
- FEBRUARY
- PROCEEDING
- VAN
- ANGLE
- FAIRER
- PASSAGES
- PARCEL
- WASTED
- CORRIDOR
- ARTIFICIAL
- THOUGHTFULLY
- DEPARTMENT
- SPECTACLE
- AGENT
- BEHALF
- STAMPED
- OCCUPATION
- ELEMENT
- ROMANCE
- TEST
- PIG
- DEER
- FROG
- COMPLEXION
- LINEN
- RADIANCE
- CONTEST
- PARTNER
- LIABLE
- CALCULATED
- LATIN
- BALLS
- ADMIRABLE
- FOOTSTEPS
- REGULARLY
- INCLUDED
- UPWARD
- DISLIKE
- TEACHING
- COLLECTED
- SWALLOWED
- WONDERS
- FINISH
- GENIE
- EXPRESSIONS
- DESTINY
- RICHES
- CIGAR
- AMIABLE
- TRIBUTE
- BONDS
- FORMING
- HOSTILE
- BELT
- WARS
- QUIT
- FREQUENT
- IMPULSES
- INFLUENCES
- DISCUSSED
- CONSEQUENCES
- THEREBY
- BELIEVING
- SCHEME
- COMPLEX
- OUTWARD
- CLOAK
- TERRIFIC
- AMBITIOUS
- VANITY
- IMPROVED
- STROKE
- WHITHER
- LOCKS
- STRICTLY
- CHILD'S
- FRIDAY
- CHARGED
- MONDAY
- SHINE
- SONGS
- ENDURED
- EMBRACED
- BOWING
- POLE
- CART
- POPULACE
- VISITORS
- HERE'S
- CIRCUS
- SISTER'S
- STOVE
- SWOLLEN
- JAPAN
- ABOARD
- LADDER
- MILD
- BOILING
- ATTEMPTS
- AFFECT
- MURDERED
- SNAKES
- LACE
- APPETITE
- GENERATIONS
- GALLERY
- JOSEPH
- HURSTWOOD
- DANDY
- WHEREUPON
- ENTERTAINED
- PULLING
- MOSCOW
- POLITICS
- TOOLS
- MONSTROUS
- WOUNDS
- DOTH
- ANTS
- NICHOLAS
- DORA
- ACADEMY
- AIRSHIP
- CYRUS
- SEXUAL
- JOSIANA
- AVONLEA
- BARELY
- SITUATED
- PARLOR
- RIGID
- HUMOR
- HIRED
- BURNS
- STOLEN
- HORRID
- GLOVES
- REGRETTED
- SEEMING
- BETRAYED
- MOURNING
- SWEAR
- FEVERISH
- MURDERER
- LIKES
- INVENTION
- RECOMMEND
- PROTESTED
- TUNE
- DESTINED
- REMEMBERING
- NINTH
- OVERWHELMED
- CONSIDERABLY
- TENTH
- INDUCED
- INSIST
- ASSENT
- BUNCH
- DELICIOUS
- UNNECESSARY
- GROAN
- VERSES
- COWARD
- RECOGNITION
- ADJOINING
- ENCOURAGEMENT
- RIDICULOUS
- INTEND
- GREEK
- ATTRACTED
- OBVIOUSLY
- VOLUMES
- GRASPED
- NEIGHBOURS
- CARDS
- ADMIRE
- EXCHANGED
- ROWS
- REMARKS
- STRINGS
- LADEN
- DETERMINATION
- OCCUR
- LIVER
- WHALE
- BLOCK
- COMPLICATED
- DISTINCTLY
- UPRIGHT
- OPENLY
- PROMINENT
- GUARDED
- UPSTAIRS
- P
- VICTIMS
- PURCHASE
- CHERISHED
- COMPASSION
- MORALITY
- MERCHANTS
- WARMLY
- WELCOMED
- AMUSING
- FLOWED
- AVENUE
- ORGANIZATION
- LEAGUES
- UNEASINESS
- SNAPPING
- ROARING
- SMELL
- RIVERS
- ROUNDED
- EXAMINE
- AMERICANS
- COUNTING
- PLANTED
- REPORTS
- GRAVITY
- CITIZEN
- PANTING
- STRETCHING
- PROMISES
- ARMIES
- OBTAINING
- SUGGESTIVE
- SUGGESTIONS
- CRITICISM
- STRIVING
- WINNING
- STUDENTS
- GIGANTIC
- SILVERY
- BENDING
- FORGETTING
- HAIRED
- EXQUISITE
- EXCESS
- TORRENT
- POLICY
- NIECES
- THOUGHTFUL
- STABLE
- FLOATING
- HIGHNESS
- PROVIDENCE
- HASTY
- CANADA
- ROCKY
- SEEMINGLY
- MASSIVE
- RUBBING
- MIRANDA
- BRONZE
- UNDERNEATH
- PACK
- BURN
- ONLOOKER
- HORSEBACK
- KEEPER
- EUROPEAN
- CHAINS
- HAIL
- PLAYS
- STORMS
- DASHED
- MINES
- DRAG
- DARTED
- STICKS
- SIMON
- SLOPES
- DESCENT
- LILIES
- TEACHERS
- LAYING
- DETECTIVE
- LADY'S
- TRACK
- PRECEDING
- JEW
- BEWILDERED
- BUNDLE
- ALBERT
- BRIEFLY
- HYPNOSIS
- NOVEL
- BOLDLY
- CHARACTERISTIC
- PRIMITIVE
- ABANDON
- H
- MUSCLES
- PROVIDE
- NAPOLEON
- LAIN
- BORODINO
- SUPPOSING
- DURHAM
- DEMOCRACY
- HEROD
- BATES
- PEER
- STEPHEN
- ANTHONY
- PYE
- CHARLEY
- KOYO
- CONSTANCE
- CONNISTON
- BARGAIN
- PRESSING
- VISITS
- PRECISE
- DOCTOR'S
- ORPHAN
- DREADED
- SHE'LL
- FADED
- SPARED
- PHANTOM
- BLESSING
- CONDEMNED
- TWIN
- GAILY
- PRETEND
- HULLO
- QUICKER
- MOSTLY
- TRAGEDY
- OPPRESSED
- WANTING
- DECENT
- NEIGHBOUR
- INFERIOR
- EXISTING
- STROLLED
- PUNISH
- COMMERCE
- PROVINCES
- TROMP
- TERRIBLY
- MONK
- FIERCELY
- CONSULTED
- THREATENING
- STRAIN
- STIRRING
- MELTED
- INWARD
- DWELL
- RUNS
- ATTRACTION
- ESTEEM
- REPLACED
- ANSWERING
- YIELDED
- LIFTING
- CONFIRMED
- ELBOW
- SORE
- WHO'S
- SNEER
- STAINED
- STRUGGLING
- SWEEP
- COLUMBIA
- BANNER
- DOCTORS
- FINER
- NEEDN'T
- SWALLOW
- SUITED
- BIDDING
- PROBLEMS
- RESTORATION
- PROFIT
- WIVES
- PRODUCING
- ASSISTED
- INJURED
- HARVEST
- BEHAVIOUR
- OBSCURE
- JAIL
- SUITABLE
- ROOFS
- FORBIDDEN
- SALVATION
- WITS
- GHOSTS
- DOWNWARD
- DUG
- W
- AFFECTIONS
- RESTORE
- CONTAIN
- PIERCED
- EXCITE
- ENDEAVOURED
- SIRE
- TOBACCO
- GENERATION
- INSTITUTIONS
- SOUP
- SCHOOLS
- COURTEOUS
- WHEELS
- GRACIOUS
- ASSERTED
- DIFFERENTLY
- COLORS
- LUXURY
- RECEPTION
- MONTE
- CONSOLATION
- PAVEMENT
- ROTTEN
- HAILED
- ARDAN
- TOMB
- TRAVELING
- FOLLOWERS
- DRIFTED
- HEATHERSTONE
- FORTUNES
- HUMPHREY
- ATTENDANT
- SURRENDER
- LOVERS
- PARTICULARS
- CONFLICT
- DANGERS
- CLIMBING
- CRUELTY
- INJUSTICE
- BLANK
- INCAPABLE
- CONTINUAL
- AWKWARD
- TIMID
- TRADITION
- SWIMMING
- SWAM
- CONSTANTINOPLE
- TURKEY
- APPLES
- ACRES
- CAESAR
- PRACTISED
- CREEP
- PIPES
- SLAIN
- MEETINGS
- SEAL
- IRRESISTIBLE
- CROP
- ACCORD
- KILLING
- SYNDIC
- RESEMBLANCE
- DEAF
- BLOSSOMS
- DRINKING
- EDUCATED
- DETERMINE
- REVENGE
- MASK
- TWILIGHT
- AMIDST
- BLOWN
- DRAKE
- CHARLOTTE
- UNDOUBTEDLY
- LOGS
- OWL
- EXERTION
- DERIVED
- CIGARETTE
- LEADS
- ENABLE
- THIRST
- PERFORMANCE
- INTERVAL
- CONFIDENT
- DAT
- PROCURED
- APOLOGY
- ADMISSION
- ATLANTIC
- PERSONALLY
- FOUL
- THREAD
- MUSKETEERS
- DISTURBANCE
- RUINS
- HUNTER
- MOTOR
- PULSE
- V
- ROUTE
- EARLIEST
- BLOT
- GRANDCOURT
- GLEAMING
- COACHMAN
- ONWARD
- REVIEW
- WAGES
- CUPID
- GREATNESS
- BRIG
- FRERE
- WIRELESS
- MERRIWIG
- WHISTLER
- FERRIS
- CUTHBERT
- KNITTING
- MARE
- NOTION
- MAIL
- THEY'LL
- HALLS
- GLANCES
- PERFECTION
- CONTRACT
- WRETCH
- HONORABLE
- RECALL
- REMEMBRANCE
- SUSPICIOUS
- APPRECIATION
- VEIN
- DISCUSSING
- REGARDS
- SMALLEST
- REVIVED
- BASED
- ADMIRAL
- DESPITE
- SUBMITTED
- LARGELY
- BLOWS
- ENEMY'S
- YOUTHFUL
- COMPLAINED
- DEFENCE
- TEMPTED
- RADIANT
- DISTURB
- COLDLY
- SLEEVE
- SERVING
- EXAMINING
- PATRIOTISM
- FOLDS
- PASSIONATE
- OFFERS
- NIECE
- VEXED
- LEAP
- CROSSING
- POUND
- DRESSES
- PUSH
- TAP
- UNIQUE
- CONTINUING
- REQUIRES
- HAUNTED
- ECHOED
- REFLECTIONS
- MANAGER
- ACCOMPLISH
- STUMP
- MINISTERS
- POLISHED
- PERCEIVING
- COMMUNICATE
- BANQUET
- FACTORY
- STUDIO
- CHUCKLED
- DIGGING
- TUNNEL
- INSIGNIFICANT
- ALTER
- CRISTO
- ENTERS
- PROPOSITION
- MAGPIE
- MARCHING
- NICHOLL
- OCCUPY
- MATERIALS
- BET
- NEEDLE
- PERIODS
- RELATIVE
- WORLDS
- INTENT
- RECOLLECT
- STANDARD
- ACCEPTING
- HYPNOTISM
- HYPNOTIZED
- MYSTERIES
- DISPLAY
- CREATE
- POISON
- STUDIES
- NON
- NEGATIVE
- UNEXPECTEDLY
- GLITTERING
- ANALYSIS
- DISMAY
- ZEAL
- PROPRIETOR
- STOCKINGS
- CRACKED
- ENVELOPED
- GRANDEUR
- PLENTIFUL
- SUSTAINED
- MAGUA
- EXTREMITY
- PACIFIC
- ERECT
- CRIMSON
- HARBOR
- PORTER
- PROCEEDINGS
- DISGRACE
- CLOSET
- ROBIN
- RESEMBLED
- EIGHTEENTH
- TALENT
- SHOOTING
- DEVOTION
- SINS
- CANOE
- CABLE
- TRAVELLED
- TEMPTATION
- PIT
- CORRAL
- JEST
- TRIGGER
- BASIN
- QUEEN'S
- MARRYING
- SEPTEMBER
- PATTERN
- ERRAND
- QUANTITY
- CREAM
- ALLOWING
- SPARKLED
- BOAST
- EQUIPPED
- ELECTION
- ARTS
- MOUTHS
- WHARTON
- INTERRUPTION
- HORSEMEN
- INDIA
- REACTION
- DRUNKEN
- DROUET
- CAUTIOUSLY
- UNREASONABLE
- WOLF
- SCREAM
- ENDEAVORED
- BEATS
- CHAP
- SOURCES
- GULF
- LIONS
- FISHERMAN
- SALOON
- SLEDGE
- MARTIANS
- CHEERING
- PISTOL
- RAIL
- MANAGEMENT
- COPY
- WRITES
- GUINEAS
- SWELL
- SANCHO
- TARS
- TUESDAY
- SCOUT
- AGNES
- RIFLE
- DANTES
- MORTON
- BARRY
- PINES
- BORG
- NATHAN
- CARSON
- DEASEY
- LYRA
- CUCUMETTO
- ABUNDANT
- KNOT
- SAVING
- SPENCER
- EASIER
- RICHARD
- DOUBTS
- DEARLY
- PLUMS
- NOD
- ATTENDING
- AWAITED
- VICE
- ROUGHLY
- FEROCIOUS
- ABANDONED
- GRATIFIED
- TWENTIETH
- PAINS
- RESOLVE
- BEHAVED
- FRIEND'S
- DELICACY
- BEECH
- ANTICIPATED
- HUSH
- REPUBLIC
- ORDERLY
- AFFORDED
- RESENTMENT
- UNDERTAKING
- THIRTIETH
- DEPENDED
- NAVY
- SCOTLAND
- PROTECTED
- ANCESTORS
- OWED
- DEBATE
- LIQUID
- POUR
- STRAINED
- INTRODUCTION
- CARRIES
- ASSOCIATED
- SIGHTS
- APPREHENSION
- VULGAR
- GROTESQUE
- PRIVILEGES
- REVERENCE
- DISMAL
- CHIMNEY
- GRIM
- SPECIMENS
- EMINENT
- MIRTH
- REFLECT
- TRANSFERRED
- WANDER
- WAIST
- ENVY
- COWS
- INTIMACY
- PERSONALITY
- BASIS
- SELFISH
- SPOIL
- FOUNDATION
- PEAKS
- SPOTS
- VEXATION
- CLOTHED
- BARBER
- MALE
- HONEY
- BRIDLE
- DELIBERATELY
- PATCH
- WEARINESS
- THICKET
- OHIO
- TOTALLY
- PILES
- RELIEVE
- WAKING
- CURE
- SURPRISING
- FOUNTAIN
- CELEBRATED
- INJURY
- RETIRE
- MIRACLE
- FIST
- COMMERCIAL
- GOPHER
- LANDS
- PATHS
- SUFFRAGE
- CLIMB
- COMPARISON
- PENCIL
- UNWILLING
- PROCESSION
- INSULT
- TRAVELERS
- STRETCH
- CLUNG
- RETREATED
- HARNESS
- SCENT
- COUNTLESS
- BELONGS
- PERPLEXITY
- GENEROSITY
- CHARMS
- READERS
- ARGUMENTS
- TESTIMONY
- EXPERIMENTS
- VITAL
- OCCASIONAL
- CLINGING
- BROWN'S
- RESISTED
- KNOCKING
- CASTING
- SWEEPING
- SUBDUED
- SUBTLE
- APPLAUSE
- MARVELOUS
- ESTABLISH
- BLOWING
- BRUTAL
- SPARKLING
- CONFOUNDED
- RACES
- OFFENDED
- BITS
- EGYPT
- MICE
- SAVAGES
- MOOSE
- AREA
- BOTHER
- CAPITALIST
- MISSISSIPPI
- JAR
- NEWLY
- PERISH
- ANGELS
- PICKING
- HAWK
- HONESTLY
- USES
- HEED
- REGIONS
- SHOTS
- HOMEWARD
- PILOT
- BORROWED
- TASTED
- FURNISH
- EXHAUSTION
- KEYS
- ALLEN
- WEALTHY
- FORTNIGHT
- MEMORABLE
- MEN'S
- S
- ORLEANS
- RESEMBLING
- DECAY
- BLAZE
- UNUSUALLY
- PACES
- ROGER
- PICTURESQUE
- CHECKED
- HUNTED
- THEREUPON
- EXTENSIVE
- BROTHER'S
- PREVAILED
- ARISE
- COMMONLY
- COMMENT
- SOBER
- STATIONED
- THEREAFTER
- WALLACE
- FRAGMENTS
- ACCOUNTS
- PLACING
- LEADERS
- STRUCTURE
- SUBSEQUENT
- MYLES
- SUBSTITUTE
- RAFT
- FORMATION
- DEFEATED
- NEIGHBORING
- PUDDING
- AMPLE
- APPOINTMENT
- LOCATED
- SICKNESS
- TIGER
- SHALT
- JUDITH
- HULL
- RIVAL
- UPROAR
- WI
- EVERLASTING
- BUTTERFLY
- PARRY
- SONYA
- SPEAR
- TOBY'S
- CONVICTS
- MACKINSON
- GERMANS
- LEGAL
- CHEE
- OGLETHORPE
- PHRONSIE
- GIMBLET
- CAVELL
- PASTRINI
- BADGER
- TURTLES
- TRAVERSED
- THEREOF
- FLUSH
- J
- FOUNDED
- ASYLUM
- STRICKEN
- ALEXANDER
- MISTS
- DEN
- EXTENDING
- OBSERVER
- BARONESS
- PRODUCES
- CAVALCANTI
- GUILT
- INVOLUNTARILY
- WHISTLE
- MOURNFUL
- PURSUE
- CRIMES
- HANDFUL
- GRIP
- CLEANING
- BERRIES
- HEROINE
- ASSERTION
- ENCOURAGE
- VELVET
- LIKING
- FOLIAGE
- OBSTINATE
- ADVISE
- SUMMON
- LORDSHIP
- BIND
- RIPE
- BOARDS
- PROVINCE
- DECEMBER
- PORTIONS
- OFFICIALS
- RECESS
- MOMENT'S
- MARVELLOUS
- OYSTERS
- FELICITY
- VARIED
- IMAGES
- VIOLET
- STANDPOINT
- COVE
- JUNIOR
- IMPATIENTLY
- EH
- TRIUMPHANT
- SUSPICIONS
- REMARKABLY
- EMBARRASSED
- JUDGING
- HOSPITALITY
- MIXED
- INCIDENTS
- HINT
- REMIND
- HOUARN
- HASTEN
- TEMPEST
- PAWS
- SHELF
- MOMENTARY
- SLIPPING
- HELPING
- COMBINATION
- STRIP
- MAP
- TROUSERS
- SARAH
- BRASS
- COUCH
- INEVITABLY
- DEPOSITED
- JURY
- CLEARING
- PERSISTED
- WHERE'S
- GREETING
- TELEPHONE
- SMOKED
- LIMIT
- SLEEVES
- STARTLING
- RESOURCES
- REVOLT
- SPEAKS
- PHYSICIANS
- CURED
- MEDICINES
- COMPLIMENTS
- BISCUITS
- PROCURE
- AFFECTING
- LIBERAL
- DEPART
- RECOMMENDED
- DESERVES
- HARRY
- EFFICIENT
- ELECTRIC
- COOKING
- COLUMNS
- EVENINGS
- IMAGINARY
- COURTESY
- MILLIONAIRE
- G
- MINING
- CLAWS
- EXECUTED
- ASCERTAIN
- PREPARATION
- EXPENSIVE
- PROJECTILE
- ACHIEVEMENT
- CONCEIVED
- INTENTLY
- PUPIL
- TENTS
- OUTLINE
- BRINK
- SUPPRESSED
- ADVERTISEMENT
- PSYCHOLOGICAL
- DOCTRINES
- TWINKLING
- STEAL
- HEN
- EXAMPLES
- HESITATING
- BARBAROUS
- FERRALTI
- DECEIVE
- OBJECTED
- ELIZA
- REPRESENTATIVES
- OBSERVATIONS
- ORIGINALLY
- CIVILIZED
- CONCLUDE
- SALE
- ATTENTIVE
- DEPENDENT
- BESTOWED
- VILLAGES
- RETURNS
- STOOL
- PRAYING
- RUBY
- HEAVENLY
- LUMBER
- PITCHED
- PARADISE
- CHANGING
- NOSES
- REPAIR
- UNWORTHY
- TOMORROW
- PUBLICLY
- SOBBED
- CARTER
- LANDLORD
- EX
- GLACIERS
- CHALK
- FAMINE
- RISES
- PROPRIETY
- ALONGSIDE
- CHOKED
- INGENIOUS
- REVELATION
- REPRESENT
- CARVED
- FEATURE
- ASSOCIATIONS
- CERTAINTY
- DRAGON'S
- SIEGE
- CRICKET
- COMMUNICATION
- TERRIFIED
- MONKEY
- BATHING
- CRAZY
- RULERS
- TUMBLED
- ROBBED
- GWENDOLEN
- PORTRAIT
- TEMPERANCE
- MONKEYS
- ERECTED
- COMBAT
- RANKS
- HAUGHTY
- CHAMPION
- MOB
- GROSS
- BANNERS
- FAILING
- RAVEN
- MAGICIAN
- WOLVES
- ROBBERY
- JEWEL
- FORE
- PIN
- RECORDS
- ROPE
- KIN
- SOB
- SEPARATION
- MOHAMMED
- CHURCHES
- ULTIMATELY
- SPECIALLY
- HISTORIAN
- BACKWARDS
- LUSH
- SECTION
- DENSHER
- HONORS
- MOTIONED
- BIGGEST
- ICY
- LEVISON
- LEAPING
- KEATS
- AWAITING
- TARKAS
- SKELETON
- OAR
- MANUSCRIPT
- PITI
- GAMBLING
- ARISEN
- RUSSIANS
- REDOUBT
- COLLINS
- STEAMERS
- WEIGHED
- PAINTING
- GERARD
- SOCIALIST
- THEODORA
- ZVERKOV
- JEWISH
- ETHEL
- LUFTON
- KEMP
- KAVIN
- HARDQUANONNE
- WINGFOLD
- O'SHAUGHNESSY
- TEMPLETON
- AUGUSTINE
- CONCERNS
- HANGED
- DUMB
- PUTS
- PERSONAGE
- LACKING
- GROANED
- PECULIARLY
- WORLDLY
- MODEL
- ASCENDING
- ROBBER
- DESOLATE
- MANSION
- COMPLAINTS
- MINOR
- TALKS
- HOOK
- WIG
- NURSERY
- FLIES
- ASKS
- STRICT
- DEFINED
- THRILL
- UNDERTAKEN
- COMMUNICATED
- UNCLE'S
- SEVERELY
- DEEMED
- OPPORTUNITIES
- TERRITORY
- CONSIDERATIONS
- COMFORTED
- SWEETEST
- ENCLOSED
- BROODING
- ASSEMBLY
- ATTACKS
- PREY
- CROMWELL
- GALE
- STORMY
- FAVOURABLE
- CONQUEST
- DISCOURAGED
- CO
- BETRAY
- EGG
- PARTIAL
- SPED
- INTERCOURSE
- BROWS
- WHEREIN
- CONTRIVED
- INVITE
- PITIFUL
- JUSTIFIED
- VIEWED
- SHIVERED
- TRAVELLERS
- LATEST
- STAMMERED
- CROOKED
- PLEADED
- EMPLOY
- HATEFUL
- INFERNAL
- NIGHT'S
- RAGGED
- TRAVELLER
- FLOAT
- REFRESHED
- CATHEDRAL
- COTTAGES
- THATCHED
- SPENDING
- LODGING
- BLUSHING
- CRADLE
- JUMP
- SPELL
- PROUDLY
- AMUSE
- HEDGE
- APRON
- DECLINED
- SCREAMING
- DEVELOP
- UNITY
- INTENSITY
- HOTELS
- VICINITY
- BATHED
- PLEASANTLY
- TRIFLING
- APPROPRIATE
- THICKLY
- CARES
- LADS
- DRUG
- HEEL
- DAINTY
- DISPATCHED
- REMAINDER
- MULE
- ENRAGED
- JOYFULLY
- ENGAGE
- MONARCH
- RESPECTFUL
- FACTORIES
- ASHES
- BLOCKS
- LAMPS
- ACQUAINTANCES
- DIVISION
- WAVERING
- SQUIRREL
- CEILING
- EXPERIMENT
- INDESCRIBABLE
- FORMAL
- EMPTIED
- INVARIABLY
- DISGUST
- CRANE
- CAGE
- APPARATUS
- INCREDIBLE
- ADVERTISING
- IRREGULAR
- BLUNT
- VINE
- GOAL
- SALUTED
- DEPENDS
- REPAY
- CIRCLES
- HARVARD
- DISCIPLINE
- PSYCHOLOGY
- STICKING
- NAUGHTY
- CONTINUOUS
- WONDERFULLY
- STAGGERED
- REALM
- THEORIES
- COMMANDING
- TERRACE
- NOBLEMAN
- NOBILITY
- JESSE
- WINESBURG
- HISTORICAL
- EXTINGUISHED
- HEARTY
- ESTIMATE
- SHARED
- NOSTRILS
- CONVINCE
- STATUE
- ENTITLED
- WARMED
- AY
- BABE
- MUSTN'T
- INTRODUCE
- ROSY
- REFINED
- R
- FAILS
- BREATHLESS
- CHICKEN
- CONCERT
- RAGS
- DISORDER
- FLUTTERING
- BLEEDING
- FLUTTERED
- BEGGAR
- WRATH
- RESPECTFULLY
- COMBINED
- FULFIL
- DESPISE
- NOWADAYS
- TYPES
- NINETEENTH
- DEMOCRATIC
- RIDER
- FUEGIANS
- STRAIT
- ADMIRING
- CANOES
- HURLED
- SPEECHES
- COMPARE
- LOWEST
- BRUTE
- SHELTERED
- MARTHA
- TIDINGS
- MAST
- CANNON
- DRAMA
- ARMOUR
- BIGGER
- HURRIEDLY
- WAISTCOAT
- BACKED
- CONTINENT
- ARROW
- DESPERATELY
- ATTAINED
- FELLER
- ONTO
- JUMPING
- WRIT
- CHANCED
- ANTI
- N'T
- SPRINGING
- HISSING
- SERENE
- ENGINE
- CROWNED
- DINAH
- EELS
- RASPBERRY
- DEVICE
- BOUNDS
- INDICATE
- HARVEY
- HOWL
- FLASK
- BATTLES
- PURCHASED
- CLUBS
- JOHN'S
- SETTLING
- TRACED
- ENERGETIC
- FEARING
- OBJECTIVE
- ARTILLERY
- MESS
- CASTLES
- GRATIFY
- HOBBS
- ELECTED
- LIFELESS
- LAWRENCE
- MAJESTIC
- CARTHAGE
- ANTIQUITY
- BEER
- SUPERINTENDENT
- DRIFTING
- HITHER
- EXILE
- STRINGHAM
- BEND
- GRADUATE
- FORTRESS
- SHOE
- BLESSED
- WORKERS
- ATTRACTIVE
- BRISTOL
- COSSACKS
- STEPPING
- VOTES
- VOTED
- TROUT
- DATA
- DURATION
- PICKETS
- WORKHOUSE
- DUDLEY
- WHITTAKER
- NORHALA
- CATHOLIC
- LAURA
- BARTON
- ARMAND
- MUNGER
- WESTON
- RECTANGLE
- NEWBERRY
- LEGISLATURE
- DRAMATIC
- MEDEA
- BRAZEN
- ROBY
- BARTHOLEMY
- REHNHJELM
- FALANDER
- SELLEN
- JEAN
- VON
- GLOODY
- NORWAY
- ALLAH
- TEAPOT
- RUGGLES
- WIGAN
- CLAVIER
- CITOYEN
- LOKI
- CHANNING
- SPOILED
- NODDING
- PLATES
- EXCLAMATION
- LOBSTER
- WARN
- COMFORTABLY
- GRASPING
- CHEERFULLY
- PLUM
- MISSIONARY
- DEBRAY
- BOND
- WITHDRAW
- REJECTED
- EXCITING
- CLEARER
- FASHIONABLE
- CONTRACTED
- PURSUING
- EXPRESSING
- REFER
- CODE
- FAULTS
- JOYFUL
- HATS
- TWINS
- SHOCKED
- DOUBLED
- FAIRIES
- ARCH
- SHIVER
- PETER'S
- OBSTACLE
- IMMENSELY
- SCORN
- DREARY
- SYMPATHETIC
- DIFFER
- FRIGHTEN
- DENSE
- READINESS
- ENVOYS
- NEIGHBOURING
- WALTER
- ALLIANCE
- STEWART
- SQUADRON
- INTERFERENCE
- SOLUTION
- WELFARE
- SIXTEENTH
- EFFECTED
- ADVERSARY
- PROSPERITY
- UNEQUAL
- PERPLEXED
- PROFESSED
- OPPONENT
- INDIGNANTLY
- ACHIEVED
- OBSTACLES
- BOILED
- OYSTER
- BOIL
- INSTRUCTION
- MOTIONS
- PEEPING
- STAKE
- EMPLOYMENT
- CASH
- ROARED
- CELLAR
- POLICEMAN
- WRIST
- GRINNED
- CRITICAL
- GRIMLY
- WALKER
- ALE
- PATCHES
- ANNOYED
- HINDER
- WINES
- BOWL
- TASTES
- DISPLEASURE
- CHAOS
- FACTOR
- DASH
- BEHAVE
- FARE
- CONVENTION
- SHADY
- CEMETERY
- ILLUSION
- HAPPIER
- CRUSH
- SHRANK
- STUDYING
- RECKONING
- CATASTROPHE
- PROMPT
- EFFECTIVE
- BOTTLES
- COMPOUND
- WIPED
- BETWIXT
- INHABITED
- PROMISING
- SON'S
- ENCHANTED
- MACE
- COURTIERS
- PURITY
- VIGOR
- SORROWFUL
- STRETCHES
- FURIOUSLY
- MAUD
- DISCIPLES
- CHUCK
- WHISKERS
- VEGETABLES
- SORROWS
- DUCHESS
- INVOLUNTARY
- CALAMITY
- RESTRAIN
- AWAKENING
- WORRIED
- STUPIDITY
- BOOT
- WOOL
- CARS
- L
- ALERT
- GESTURES
- MID
- GRAVEL
- STEWARD
- IMITATION
- ROB
- EXTEND
- POSSIBILITIES
- URGE
- BITING
- BRAINS
- GOTTEN
- SUNNY
- SCENERY
- YIELDING
- ANIMATED
- SHOUTS
- SHRILL
- FITS
- UNLUCKY
- INSPIRED
- DEEPEST
- VOID
- DROWSY
- SOBBING
- SHRIEK
- DISTRACTED
- HOSTS
- ACCOUNTED
- SIMULTANEOUSLY
- REIGNED
- SIMPSONS
- CRISIS
- RIGHTLY
- MODESTLY
- OPERATIONS
- MAPLE
- GOVERNED
- PACKING
- POLITELY
- EXHIBITION
- DREADFULLY
- BUTTON
- AL
- RESPECTED
- SYRIA
- CAUSING
- POURING
- ABBE
- EPOCH
- LEGITIMATE
- WOE
- FOOLS
- SPECTATOR
- WIDELY
- BORDER
- SOUTHWARD
- SHIFTED
- DIVE
- SLAUGHTER
- ENSUED
- MUTE
- CAPTAIN'S
- HUMMING
- TEDDY
- DAN
- CELL
- SCRAPPER
- WORKER
- WORM
- CHARACTERISTICS
- FERTILE
- RESULTED
- MUSKRATS
- BLAZING
- EDITION
- TORTURE
- CARRIAGES
- TRICKS
- URGENT
- CRYSTAL
- FOXES
- COPPER
- DOWNSTAIRS
- DEVELOPING
- SINKING
- TRAVELED
- SLIPPERY
- ABYSS
- INDULGED
- BUCCANEERS
- HAZARD
- MUFFLED
- FASCINATED
- DOUBTED
- CLAIMS
- LAUNCHED
- HAMLET
- CRAYFISH
- THORNTON
- DEW
- MARIANNE
- DISGUSTED
- ZADIG
- ATTENDANTS
- REQUESTED
- GENTEEL
- AXE
- ADAPTED
- MONTONI
- HOOD
- ASH
- FLOCKS
- FERNANDO
- FALSEHOOD
- ATTACHMENT
- LOAF
- DOOMED
- HOUNDS
- UTTERING
- NARRATIVE
- REJOICING
- INSTINCTIVELY
- ROPES
- ACTIVITIES
- ARTISTIC
- CUSTOMARY
- EMPHASIS
- VANDALS
- EMPEROR'S
- NEMO
- TIGHTLY
- SLEDGES
- CHOCOLATE
- PARSONAGE
- PERISHED
- FORWARDS
- LEGGED
- WHEEL
- LARRY
- MATCHES
- JOHNSON
- OXFORD
- PREMISES
- IVORY
- PARSON
- RECKONED
- MADNESS
- MILLER
- PRESERVATION
- MAGISTRATES
- STRAYED
- CHEERS
- TREASON
- MESOPOTAMIA
- THEREIN
- FRIGATE
- BEGGING
- ARCHIBALD
- ORNAMENTS
- HORNS
- ARROWS
- TRAFFIC
- LODGED
- REBELLION
- FLANK
- GIANTS
- VENERABLE
- SIMPLETON
- SANDY
- PICKET
- LOGIC
- ARMOR
- CHIU
- VENTNOR
- SAVONAROLA
- LORENZO
- SOLEMNLY
- EURALIA
- ER
- DENIS
- KENNETH
- FORBES
- LEVIN
- SIMONOV
- GRAPES
- BAXTER
- GAVROCHE
- REGINALD
- TEBBS
- BEECHES
- CHAPEL
- KIHACHI
- MARTINEAU
- VAMPA
- CHOPIN
- ELLISON
- AMABEL
- TAD
- CROXLEY
- SECRETS
- PRIVATELY
- PECK
- CHERRY
- VINES
- WEREN'T
- TONIGHT
- FEMININE
- WISER
- STOOPING
- HOMELY
- MEDIUM
- INNOCENCE
- AFFLICTED
- LABYRINTH
- CORRUPTION
- LENT
- PEEPED
- AFFECTIONATE
- PARALLEL
- RASCAL
- ENDEAVOR
- ATTORNEY
- FASCINATING
- NOTICING
- SOBS
- ECSTASY
- APPRECIATED
- TOUCHETT
- SELECTED
- GUESSING
- HENRIETTA
- HEALING
- SPREADING
- TURF
- FACULTY
- APPRECIATE
- PERPETUALLY
- RECONCILED
- ATTRACT
- CULTIVATE
- ADDITIONAL
- CONFERENCE
- COMMANDERS
- VICTOR
- DISCONTENT
- ESCORT
- SUCCESSFULLY
- REPRESENTING
- INDUCE
- PROTECTOR
- RULER
- SHATTERED
- ANNUAL
- INTERNAL
- SUMMONS
- ASSIGNED
- CORRESPONDENCE
- PROMPTED
- PEPPER
- INNUMERABLE
- OPENS
- HARDNESS
- ATTAIN
- IMMORTAL
- PHILOSOPHER
- INSPIRATION
- HORRORS
- FROWNED
- TIPPED
- WHIM
- GLARING
- GENIAL
- DEFENDED
- ABUSE
- CLIMATE
- HANDLING
- APPROVED
- CONFIDENTLY
- INASMUCH
- PROLONGED
- COLOURS
- DWARF
- SHAPES
- NEATLY
- MOUNTING
- ALTAR
- VOW
- COURSES
- SUBMISSION
- ACCEPTABLE
- FUNCTION
- FRANKNESS
- BRAVELY
- INVENTED
- COMPLAINT
- CHILL
- MUSCULAR
- BREAKS
- SWAMP
- DITCH
- DESCRIBING
- RELEASE
- STAIRCASE
- JERKED
- RHYTHM
- COLOUR
- LAWYERS
- HARMLESS
- WALLET
- DEBTS
- ALMS
- STREAMING
- FORBEAR
- FAINTED
- RIBS
- CHAIRMAN
- AMATEUR
- MILLS
- MONOTONOUS
- PEERING
- IDEALS
- POTATOES
- HOLIDAYS
- FOLDING
- NERVOUSLY
- CLARA
- ACCESS
- PARTITION
- SPHERE
- PLANET
- EXCEPTIONAL
- LONELINESS
- CRAWLED
- VEGETATION
- DRIFT
- PANEL
- EQUIPMENT
- WITHDRAWN
- CATS
- SOUNDING
- RELEASED
- SPANIARDS
- WEARIED
- PROCLAIMED
- BEAUTIES
- ATTENTIONS
- TOAST
- REFERRED
- REWARDED
- ELDERLY
- ABNORMAL
- PERVERSE
- SMOOTHLY
- MISTAKES
- BEFOREHAND
- WITNESSES
- BODILY
- ENERGIES
- POSSUM
- SCARE
- RECOGNISE
- SCRAMBLED
- MAGNIFICENCE
- PARTIALLY
- LOVELINESS
- IMPELLED
- NOISY
- SEASONS
- INSOLENT
- SIMPLICITY
- DU
- TEARING
- HAPPENING
- BOYHOOD
- FLAMING
- HABITABLE
- INSUFFICIENT
- NOWHERE
- POLES
- TEMPERATURE
- LAPSE
- MISTOOK
- ALOFT
- ELEVATION
- PARTING
- DISAPPEAR
- EVILS
- DARKENED
- UTTERANCE
- DIES
- ABODE
- DELAWARES
- LANGUAGES
- SUBJECTED
- MUSING
- WRINKLED
- IMPOSING
- HUM
- SPLENDOR
- MAC
- CURLED
- EARN
- MUSED
- LITERARY
- SWEETNESS
- PERCHED
- EYEBROWS
- EXAGGERATED
- THURSDAY
- UNLOCKED
- BAGGAGE
- RAILING
- GANEM'S
- DAMASCUS
- USAGE
- DECLARING
- WROUGHT
- CRUELLY
- GRACEFULLY
- BUDS
- TUT
- INSECTS
- SCAMPERED
- CARDINAL
- HARDEST
- HOPPED
- GRAPE
- STEALING
- ACCUSE
- PEOPLES
- TRANQUIL
- RANDOM
- APPEARANCES
- TOLERABLY
- ECHO
- HALT
- EYELIDS
- EXCEPTING
- SULLEN
- UPWARDS
- BLINDLY
- CHANNELS
- WIGWAM
- DETAINED
- CONSTITUTE
- VACANT
- BUD
- ATTEMPTING
- SUNG
- ATTACKING
- WHISTLING
- STATELY
- SEEDS
- RESULTANT
- HATCH
- PA
- GAUNT
- PHOTOGRAPH
- TOOTH
- BANISHED
- UPSET
- EAGLE
- ABBEY
- PUBLICATION
- PETITION
- DETECTED
- REFRAIN
- TERRORS
- PROMOTE
- GARDENER
- PLANTATION
- SAMSON
- SKULL
- CUTTER
- AUDIBLE
- COATS
- BREADTH
- PREACH
- BLADE
- SHIELD
- TARLING
- LINED
- RIDERS
- CARING
- BABYLON
- SUBSTANTIAL
- JONES
- REMOVAL
- LUCAS
- TORCH
- CONTINUES
- CUB
- GEORGIA
- ANNETTE
- HEIGHTENED
- FEDERAL
- OWNERS
- WEDNESDAY
- CHATTERING
- BOAR
- OXEN
- BREECHES
- ENTREATIES
- REJOICED
- KNELT
- TREVILLE
- CHILDISH
- STEALTHILY
- CONVEY
- RESOLUTIONS
- FLINT
- MECHANICAL
- SWING
- OUTFIT
- LEWIS
- PRODUCTION
- YOKE
- DAMNED
- GRAMMAR
- SPY
- GENSERIC
- SENATE
- IMPERIAL
- UNDERWATER
- NAUTILUS'S
- PROCEEDS
- VIRGIN
- ESSENCE
- CHEAP
- GRATIFICATION
- SKI
- TROUBLESOME
- ONESELF
- MEASURED
- CULTIVATION
- VENZA
- CURLS
- MARQUIS
- DERONDA
- SUMMER'S
- CAB
- GLARE
- CREVICE
- CANYON
- FRENCHMEN
- LAMB
- STUDENT
- BLINDED
- TRANQUILLITY
- KINGDOMS
- SUPPOSITION
- KNEELING
- EXPEDIENT
- PENNSYLVANIA
- CHAMBERS
- INSOLENCE
- SELECT
- ARTERY
- ROSTOV
- MARY'S
- PROJECT
- RESIGNATION
- SPEEDY
- DECKS
- PRODUCTS
- DISTRIBUTION
- TANGLED
- COMMISSIONER
- LAMENTED
- FULFILLED
- MANHOOD
- VILLONA
- DOYLE
- BRIGHAM
- FUEL
- INVESTIGATION
- MAIDENS
- MAXWELL
- PACKET
- GUB
- FIRS
- CHANCELLOR
- SHASTA
- PHILIP'S
- FUNDEVOGEL
- JEFF'S
- INSURRECTION
- CRANES
- COULSON
- CARAVAN
- POSTMAN
- LOCH
- INVENTOR
- HENSHAW
- VERONICA
- DIETRICH
- SHALMANESER
- ASSYRIAN
- BECHAMEL
- SOUSSIO
- MINKS
- HEADLONG
- AWED
- RACHEL'S
- BEES
- GRASSY
- WILLOWS
- DIRT
- DISHES
- PRESERVES
- BRISKLY
- SETS
- TICKET
- SHABBY
- BRUSHED
- EXCUSED
- EXECUTIONER
- ASSURANCE
- OCCUPYING
- ELOQUENCE
- POLITENESS
- WOVEN
- INQUIRING
- HUDDLED
- STERNLY
- BUTLER
- FALTERED
- DISLIKED
- ORNAMENTED
- ARBITRARY
- FOOTING
- INVALID
- WARRANT
- VISIONS
- SHILLING
- WARBURTON
- CORRESPONDING
- PROPOSALS
- REPARATION
- AMSTERDAM
- ECONOMY
- GENERALS
- JOINT
- PUNISHED
- PATRIOT
- INSPIRING
- ALLY
- TWELFTH
- FANTASTIC
- TREATY
- FEAT
- SECRECY
- SECURING
- REMONSTRANCE
- ACCEPTANCE
- GUARANTEE
- ATTRIBUTES
- COMPOSE
- MOAN
- TOPIC
- DISTANCES
- RICHER
- CREED
- DISCUSS
- DRAWERS
- COPIES
- ECCENTRIC
- CLUMSY
- CULTIVATED
- TOUGH
- PRAISES
- SOMBRE
- REINS
- UNLIKE
- CONFIDED
- INDICATION
- DIVIDE
- FLOORS
- HANGS
- REEDS
- TOES
- AWHILE
- INABILITY
- IMPRESS
- LOUNGE
- PHYSICALLY
- REFRESHMENT
- COMIC
- ARTISTS
- POETIC
- MATURITY
- ADJUSTMENT
- IMPOSSIBILITY
- COURTS
- EVE
- NORTHWARD
- BLANKETS
- GRAHAM'S
- CONVENIENCE
- CHALLENGE
- RAW
- YEAR'S
- INTERPOSED
- PENSIVE
- TWIGS
- ACCUSATION
- IMPRISONMENT
- EDGES
- RHEUMATISM
- JELLY
- TIPS
- D
- SHEETS
- MERITS
- PLANT
- LUSTRE
- ALIGHTED
- SIGHS
- F
- N
- GRIEVED
- ABOMINABLE
- FESTIVAL
- MALICE
- ALMIGHTY
- PERSIAN
- PENETRATE
- SWEAT
- DESERVED
- VIRTUOUS
- UNJUST
- PENSION
- COMMIT
- CREEPING
- SITE
- BLAUSSER
- LL
- SEMI
- MASSACHUSETTS
- WISELY
- LAVA
- NATURE'S
- GRUMBLED
- DIG
- DESIGNED
- TRIALS
- RECEIPT
- PERSPIRATION
- RECEIVER
- PREFERENCE
- CORRUPT
- IMPRISONED
- LIGHTER
- COMPASS
- EXPENSES
- ANKLE
- ECHOES
- QUIETED
- CROUCHED
- TUBE
- WHIRLING
- PENETRATING
- NOBLES
- CEREMONIES
- PROPORTIONS
- ARDENT
- MESSAGES
- CORDIALLY
- LOYALTY
- PSYCHOTHERAPEUTIC
- DEPRIVE
- CRITICS
- STRUGGLES
- TYPICAL
- SUPPRESS
- PROBABILITY
- REFORM
- OL
- LANGUID
- INTENSELY
- QUIVERING
- RICHLY
- GARMENT
- INDISTINCT
- RESOLUTE
- HABITUAL
- CONJECTURE
- GREEDY
- APPROVAL
- INTOLERABLE
- LEND
- OMINOUS
- DANCERS
- CLUTCHED
- NIGH
- SHUTTING
- PLUNDER
- TENDERLY
- CURVE
- SCREEN
- TEMPERED
- INDEFINITE
- CRUST
- HINTED
- FRUITS
- HUMBLY
- HURON
- CAPTIVE
- BLANKET
- PRIVACY
- DELAWARE
- DEVOURED
- INHERITED
- MARGIN
- PATENT
- CORRECTED
- OAKS
- SLIPPERS
- ASCRIBED
- ROCKING
- WASHING
- PROFITS
- CUSTOMERS
- TUCKED
- MORTALS
- TOM'S
- IMPROVE
- LADD
- SHIRTS
- GLOWED
- CONVEYED
- GLEE
- LID
- SATIN
- BELIEVERS
- COMPLAIN
- CORDS
- INSENSIBLE
- ALLIED
- COLOSSAL
- NUT
- PRETENSIONS
- CORPSES
- SPIED
- ERRORS
- FURNACE
- SHAVE
- DEVILS
- WEB
- ACCORDANCE
- DISCOVERING
- WORLD'S
- ACCOMPANYING
- TENSION
- E
- RAPIDITY
- FEEDING
- JEMMY
- WHITENESS
- SCRAP
- BURY
- WARFARE
- BATTERY
- SWAYED
- RAPTURE
- HEART'S
- LOVELIEST
- CRESTY
- WEE
- SHRIEKED
- KICKED
- TOMMY
- LONGBILL
- SPOTTED
- FRANCISCO
- SALMON
- ASHORE
- CONSTRUCTED
- SIGNIFICANCE
- ASIA
- SOLOMON
- DISPLEASED
- SAFER
- CROWNS
- CREST
- HOSS
- SHAPELESS
- ASCENT
- FIEND
- BENEVOLENT
- NORTHANGER
- MODERATE
- VOLUNTARY
- CONTRADICT
- DIRECTING
- MELODY
- SHAWL
- FRIGHT
- BRUTALITY
- DESPAIRING
- ARAB
- DESCENDING
- CARGO
- MOANING
- STARE
- SOOTHING
- RESENTED
- KEENLY
- JACKSON
- WHISPERING
- HENCEFORTH
- DARKER
- ILLUSTRIOUS
- COMBATANTS
- TAX
- TROOPER
- DESTROYING
- REBEL
- DOST
- INFECTION
- GROVES
- STARVATION
- COMMUNITIES
- JEFFERSON
- SHREWD
- HIGHWAY
- PRETENCE
- TREACHERY
- DAGGER
- STROVE
- CORPS
- EXCELLENCY
- BUCKINGHAM
- SEALED
- PLANK
- MECHANICALLY
- RUSK
- WILDLY
- SHADED
- LOWERED
- PHENOMENON
- WHIRL
- RAILWAY
- POSITIONS
- MIRRORS
- BAGS
- INVASION
- WAGONS
- POPE
- FLEE
- TIGERS
- TANKS
- MOCKERY
- SACK
- CIRCULATION
- SPECTACLES
- CONTRIBUTED
- STEVENTON
- APPROPRIATED
- CONVICT
- SHOVED
- CURSED
- HARDSHIPS
- JORDAN
- FUGITIVES
- REFRESHING
- REPUBLICAN
- APPROBATION
- DESPISED
- SAINTS
- DISASTER
- SERPENT
- IRWINE
- THEY'D
- DIVORCE
- RHYMES
- PRINTING
- EDITOR
- LUNCHEON
- CAVITY
- DECREE
- SITUATIONS
- BANDS
- RUBBISH
- SPIDER
- AVAIL
- CONTAINS
- APE
- BLOODY
- DEJAH
- THORIS
- HARROW
- WINCHESTER
- SERMON
- DAM
- NOME
- JOURNAL
- CUBA
- BUSHY
- MALEAGANS
- WILT
- SLAY
- HORACE
- MING
- DARKENING
- WADED
- SWITCH
- FARLEY
- HO
- COMSTOCK
- INLAND
- FINN
- CHURCHILL
- OVEREND
- CAREY
- ORPHEUS
- ENGAGEMENTS
- SANCH
- BULLFROG
- LINA
- COLONY
- JUNIORS
- DISTORTED
- ABOLITION
- USHANT
- TWENTYMAN
- REMSEN
- HOMO
- LYRE
- GIDEON
- ASHUR
- PITT
- BOOKSTALL
- GROCER
- YORKE
- PATRICIUS
- GETTYSBURG
- DIZZY
- TWITCHED
- THERE'LL
- ADJUSTED
- AMAZING
- RISKS
- AGONIES
- FIR
- COPE
- MOODS
- EXPRESSIVE
- ELBOWS
- OVERBOARD
- PRETTIEST
- TIMIDLY
- BANKER
- TACIT
- RECOLLECTED
- BASKETS
- PROSPEROUS
- ASSASSIN
- PROCUREUR
- PRECAUTION
- SINCERELY
- CRIMINALS
- BENEDETTO
- HEAVEN'S
- CURLY
- MICHAEL
- IMITATE
- TRAGIC
- STACKPOLE
- TACT
- SERENELY
- FLICKERING
- ALBANY
- TREAD
- PATIENTS
- HUMILIATION
- HUMILITY
- DEFINITELY
- INSIGHT
- VETERAN
- BINDING
- FISHERIES
- BOTTOMS
- PENSIONARY
- GOVERNMENTS
- INDIES
- BLAKE
- ADMIRALTY
- PORTLAND
- STATEMENTS
- INSISTING
- DOCUMENT
- EXPECTATIONS
- DEALING
- CRUMBS
- PINT
- MULTITUDES
- INSTINCTS
- CLOVER
- RELATIONSHIP
- TYRANT
- FRED
- GROWLED
- BRICK
- HILLSIDE
- MUTTERING
- SNEERED
- FOUNTAINS
- NEGLECT
- IRRITATED
- QUICKENED
- PHAETON
- WHEELED
- SPECIMEN
- LOWERING
- TOUCHES
- BELLAH
- IMPLORED
- NECKS
- BEGGARS
- DAZZLED
- BECKONED
- DAIRY
- FEATHER
- JEGU
- BEAMING
- GAINING
- PICTURED
- SOLICITUDE
- EXERT
- FUNDAMENTAL
- HANDLED
- GAIT
- RECEIVES
- BAT
- DOINGS
- LITERALLY
- DISAPPEARANCE
- FUNDS
- COURTYARD
- WEARILY
- AUTOMOBILE
- FOREIGNER
- CHART
- EVENTUALLY
- VISITING
- UNNATURAL
- LEISURELY
- RETAINED
- CLERGYMAN
- GLEAMED
- GOVERN
- COUGH
- HANDY
- POPULARITY
- LAMENT
- QUITTING
- CONVERSING
- JUSTLY
- AVERSION
- PRETENDING
- DEFERENCE
- OPPOSE
- GREECE
- PRESCRIBED
- VIZIER
- KENNICOTT
- NAIL
- LAKES
- LUXURIES
- DICTATED
- LEAFY
- RECITAL
- CURTAINS
- DRIPPING
- THANKFUL
- DAWNED
- CHATEAU
- AFFLICTION
- SCRAPS
- BAH
- ACUTE
- OUTLINED
- GHASTLY
- MENACE
- DELIGHTS
- TACKLE
- STORED
- SUFFICED
- ADVANCES
- CLASPING
- HEARS
- RUGGED
- SILKS
- CONCEPTIONS
- ENGINES
- ELECTRICITY
- CONTRIBUTE
- CONCEALMENT
- SENTRY
- SQUEEZED
- SPLIT
- SURVEY
- CLUMP
- SPARKS
- ANCHOR
- TOSS
- RUSTY
- JOINING
- PLEDGE
- ENFORCED
- RESIGNED
- ROYALTY
- QUITTED
- MYSTIC
- FLUID
- OBJECTIONS
- INCLUDE
- CRAWLING
- REASONABLY
- AWAKEN
- PILGRIMAGE
- DAZZLING
- ARCHWAY
- FUTILE
- HARDENED
- EXPOSING
- SPECULATION
- OPPONENTS
- EXALTATION
- FAIN
- INGENUITY
- PRODIGIOUS
- NICELY
- MISUNDERSTOOD
- GUARDIAN
- APPEARING
- OVERHEARD
- DISSATISFIED
- BEDSIDE
- EARNESTNESS
- ATTRIBUTED
- HYPOTHESIS
- TERRESTRIAL
- PROVES
- GRAVITATION
- COOLLY
- SLEEPY
- BISCUIT
- PROVOKED
- PRESERVING
- ENTERTAIN
- SEVERITY
- FOE
- TERMED
- SUBSIDED
- RIVERBORO
- SIMPSON
- SHAN'T
- UNBOUNDED
- CONSCIENTIOUS
- CUSTOMER
- PRICES
- ARGUE
- IT'LL
- BEGINNINGS
- SLATE
- RASH
- INSTRUCTED
- FURS
- ADAM'S
- DISGRACEFUL
- FLUTTER
- ERRANT
- STREWN
- EMBRACING
- INTERRUPTING
- REVIVE
- AFRESH
- DESIRING
- ROSEBREAST
- PROJECTED
- BONY
- PORTRAITS
- FRENCHMAN
- BALANCED
- MARINE
- PLUCK
- CONCURRENCE
- TRUNKS
- SHALLOW
- NATURES
- DENIAL
- VOLUNTARILY
- UTILITY
- LUDICROUS
- TEMPT
- ALTERNATELY
- VALLEYS
- DISASTROUS
- VEILED
- RULING
- ROBBERS
- TALENTS
- O'ER
- ELMS
- DISCERN
- SEAMED
- SEVENTEENTH
- SCANTY
- VARYING
- WRECK
- BEE
- PRAISED
- FERNS
- SEPARATING
- STUFFED
- CHASED
- SLAP
- MOVES
- COOLNESS
- SWELLING
- ISOLATED
- STIFFLY
- SPLENDOUR
- TEND
- SNATCHED
- HOPELESSLY
- TUMBLE
- RAINBOW
- RUSTLING
- SHIFT
- CATHERINE'S
- DESIROUS
- ALACRITY
- ADVANTAGEOUS
- PRONOUNCE
- DECLINE
- ACTORS
- INCESSANTLY
- ANTONIA
- BATTERED
- STRIPED
- SHADES
- FASCINATION
- MOUSTACHE
- STREAMED
- POPULOUS
- BOWS
- SLID
- PALM
- BOYISH
- GLAMOUR
- CARTRIDGE
- COINS
- STRODE
- DINNERS
- PAT
- INDIGNANT
- FEARFULLY
- ELEGANCE
- ODDS
- CONQUER
- PROPOSE
- LANCE
- BRACELETS
- COWARDLY
- EXCESSIVE
- NEPHEW
- SUPERIORITY
- AZURE
- INHERITANCE
- SCHEMES
- BRETHREN
- REVERSE
- MATCHED
- ARISTOCRACY
- RENTS
- RECORDED
- MA'AMSELLE
- RESTAURANT
- POLITICIANS
- OPERA
- FINANCIAL
- FUNCTIONS
- SPORTING
- COMMENTED
- DECORATED
- MORN
- SMOTE
- VILE
- PLEASES
- OATHS
- ESCAPING
- BENEFACTOR
- DROPS
- GUARDSMEN
- INTOXICATION
- PISTOLS
- INTERPRETED
- CONFIDENTIAL
- FIDDLE
- REGAINED
- GASPING
- LIKENESS
- COACH
- DAME
- LEE
- BARLEY
- QUAINT
- CRASH
- DISCOVERIES
- BEHAVIOR
- JEROME
- ASSOCIATE
- EFFICIENCY
- FILM
- PENNY
- GILBERT
- IMPROVEMENTS
- EXCEEDING
- EMPRESS
- TEMPLES
- LARGEST
- WRITERS
- COFFIN
- FISHERMEN
- ARRIVING
- SHARKS
- WORTHLESS
- BELLY
- QUANTITIES
- WILLARD
- INTERNATIONAL
- SECTIONS
- INTERFERED
- ADDER
- BOUNDARY
- ARCHITECTURE
- REQUISITE
- RIVALRY
- IGNORED
- ANITA
- STRIPPED
- INTERPRETATION
- FLORA
- SHAW'S
- GLADNESS
- COTTAGERS
- MURDERERS
- RATTLE
- SNUFF
- INK
- DISPOSE
- DUCK
- AMMUNITION
- IDENTICAL
- MUSEUM
- SPUR
- FORTS
- ZONE
- STUNNED
- BLACKNESS
- ARTHUR'S
- WORRYING
- EMANCIPATION
- MISCHIEVOUS
- HEIGHTS
- HUNTERS
- ARTERIES
- MUSO
- LAWTON
- DUNWOODIE
- INTERVENTION
- GAYETY
- GRACES
- TERESA
- OX
- SENTIMENTS
- APPARITION
- EXCEED
- SHEVARDINO
- SINGER
- SHOWER
- MINNOW
- DISCHARGE
- DUCKS
- PICKETING
- PHILADELPHIA
- CELLS
- SUPERB
- COLONISTS
- SLEW
- TEXT
- STEPMOTHER
- MOUNTAINEER
- LATENT
- DILWORTHY
- BUFFALOES
- REGIME
- SYBIL
- FLIGHTS
- MORLEY
- ANNA
- BOURGEOIS
- BELVANE
- GOWER
- THROCKMORTON
- HORNBY
- SPRUCE
- SCIENTIST
- CONSUMPTION
- LOS
- ETERNITY
- HOVEL
- CUBAN
- BOULEVARD
- WEYMOUTH
- BOB
- ADELA
- KINRAID
- FAUCHELEVENT
- JESSICA
- CHUMS
- GARTER
- MENSTRUATION
- MENSTRUAL
- MENSTRUATING
- PHYSIC
- EAMES
- O'TOOLE
- MORGESON
- JOST
- GERTRUDE
- CLANCHARLIE
- WITHAN
- AGATHA
- BACH
- HAYNES
- FOX'S
- PEERS
- ADDERS
- AUGUSTA
- BROWNRIGG
- INTRICATE
- CARMODY
- STRAY
- PAINFULLY
- MELLOW
- ANGLES
- TELEGRAM
- SANDS
- COMFORTING
- OFFEND
- ACCOMPLISHMENT
- SHRINK
- REFLECTING
- SNUG
- PREPARATORY
- BRISK
- WHISTLED
- INFANCY
- BLISS
- CHEERED
- IMAGINATIONS
- MOMENTARILY
- IMPLY
- MERCILESS
- DISGRACED
- INDULGENCE
- INFECTED
- FOOTMAN
- PRECAUTIONS
- ASCENDED
- STRIKES
- DRAGOON
- STAIR
- FROCK
- THRILLING
- BEARDED
- SWEETHEART
- WOKE
- BAFFLED
- GLITTER
- SOWN
- PASSIVE
- DRUM
- HARBOUR
- DEALT
- CASUAL
- FOREMOST
- OFFENSIVE
- GRIEVANCES
- MISUNDERSTANDING
- NIGHTFALL
- UNIVERSALLY
- JUNCTION
- EXPERT
- MEDITERRANEAN
- CHAPTERS
- TEMPERAMENT
- BOLT
- CLERKS
- PERCH
- SIMMER
- PINCH
- BAKED
- SALAD
- SENIORS
- BEAMS
- DISFIGURED
- NOURISHMENT
- MEETS
- ABJECT
- RENDERING
- ENVELOPE
- JUSTIFY
- SHRUGGED
- KARA'S
- SURVEYED
- SECURELY
- OVERCOAT
- DOUBTFULLY
- DELIBERATION
- OCCUPANT
- DEL
- DAMN
- PROFOUNDLY
- PINS
- INFLICTED
- TOLERABLE
- HABITUALLY
- BORED
- GLIMPSES
- PIGS
- HEARTH
- CABBAGE
- BARBAIK
- HORSE'S
- TENDS
- FLEETING
- IMPLIED
- DISADVANTAGES
- RESTS
- PETTY
- MENTALLY
- BREED
- WIDER
- PENETRATED
- RESPONSIBILITIES
- CONCENTRATED
- ENDURING
- BUCK
- ADMINISTERED
- SENIOR
- BICYCLE
- BIRTHDAY
- WHEREBY
- BOWER
- HEREAFTER
- ULTIMATE
- SLUMBER
- ASCERTAINED
- CONVERSED
- RETAIN
- PEERED
- CONSPICUOUS
- BATHE
- ROBINSON
- DARESAY
- CONQUERED
- WEAVING
- DAZED
- SCRATCHED
- COLONIAL
- RAG
- FOREFINGER
- TOKEN
- RUB
- SAMPLE
- EXCELLENCE
- STOPS
- RETIRING
- TOILET
- RECKLESS
- RELATING
- CARESSES
- APPREHENDED
- SURPASSED
- SOLELY
- CONVERSE
- SACRIFICED
- SIGNIFY
- PLUNGE
- STALL
- MAJESTY'S
- VILLAIN
- BOLDNESS
- WIPE
- WINDY
- NURSING
- REARED
- ELEPHANT
- GOAT
- REFINEMENT
- INTEGRITY
- GINGERBREAD
- CAUTIOUS
- INSTITUTION
- ESTEEMED
- JERSEY
- NEGROES
- SETTLEMENT
- SLANTING
- RIDICULE
- UNLIMITED
- STUDDED
- BANG
- GRAB
- DECIDING
- IMMORTALITY
- DOME
- STARTS
- CHICKENS
- RELATIVELY
- GRADUATED
- IDENTITY
- TRUTHS
- MENACING
- LUMINOUS
- PLAYERS
- OBLIGE
- EXPLAINING
- AUNTS
- COSTUME
- ILLUSTRATION
- WHISKEY
- CURES
- GAINS
- FREED
- WARMER
- VAGUELY
- REALITIES
- PERCEPTION
- RIDDLE
- PANE
- PRICKED
- HAZE
- MERRIMENT
- ARDOR
- ETERNALLY
- FINELY
- FRESHNESS
- PATHETIC
- CLEARNESS
- PLAUSIBLE
- CONSISTENT
- RIGHTEOUSNESS
- BUSILY
- STROLL
- HARDY
- STROKED
- POSSESSIONS
- SHUDDER
- STROKING
- BRILLIANCY
- UNANIMOUSLY
- MANTLE
- WANDERINGS
- RADICAL
- PRINCESS'S
- ROWED
- PANGLOSS
- PRUDENT
- CAPTIVITY
- PLOT
- HOVERED
- ENDEAVORING
- BREEDING
- CHIEFS
- ANNOUNCE
- FORTIFIED
- WHITES
- KNIVES
- SCENTED
- FLATTERING
- FORESEE
- POWERFULLY
- LABORS
- CONSULTING
- PURSUITS
- CEDAR
- THANKSGIVING
- MEALS
- BARNS
- SUSAN
- TROTTED
- FITTING
- ASTONISHING
- BOUT
- DRIVES
- MORTIFICATION
- TURNIPS
- FLASHES
- YALE
- GASP
- INVADED
- JOURNEYS
- DEVOTE
- TYRANNY
- THIRDS
- ATTENTIVELY
- RECOVERING
- PROVISION
- WINKED
- MEEKLY
- CRAWL
- CIRCUIT
- TAVERN
- BLAMED
- SWITZERLAND
- EXPOSITION
- MECHANISM
- MORALLY
- ENDOWED
- MORALS
- HAPPIEST
- EXPRESSLY
- REPULSIVE
- COLDNESS
- MODES
- HONESTY
- GILDED
- ANCHORED
- CONSISTS
- WESTWARD
- NOTIONS
- DISTRESSED
- SINGULARLY
- FREIGHT
- RUSHES
- SLICES
- BELIEVES
- ONWARDS
- SENSELESS
- ASSAULT
- SPACES
- JO
- BULLY
- ROAMING
- OFTENER
- BARRIERS
- ALASKA
- DIAMETER
- BEAK
- GALLERIES
- OVERTOOK
- MUSKRAT
- WINGED
- TROT
- JAW
- THREATS
- AIRS
- POMP
- ANIMATION
- WARD
- IDLENESS
- ENDEAVOUR
- PITIED
- ALLEN'S
- BLUSHED
- EXPOSE
- COMMUNION
- DESIGNATED
- SUNKEN
- WHIPPED
- FISTS
- APPALLING
- AFT
- RIM
- JUNGLE
- STARBOARD
- DELIBERATE
- ILLUSIONS
- EXALTED
- ANOTHER'S
- EEL
- GAPING
- INTERRUPT
- SCRIPTURES
- CARLISLE
- DISGUISED
- KISSING
- POETS
- AUDACITY
- LODGINGS
- STROKES
- RECOURSE
- CONCEALING
- AMENDMENT
- KATY
- CANST
- FLOURISH
- HABITATION
- SINGLY
- LOSSES
- POSSESSOR
- BUTCHER
- OVERCAME
- OCCURRENCE
- CONTEMPLATION
- SIMILARLY
- INSECT
- TANNER
- GRIPPED
- KNAVE
- SAUCY
- THYSELF
- UNAWARE
- SIGHING
- TRAILING
- ENCOURAGING
- FIREPLACE
- RESUME
- LOUIS
- GLITTERED
- CONQUEROR
- STAG
- DISPUTE
- NOBLEST
- WONDROUS
- SHADOWY
- BRANDY
- VAULT
- DEJECTION
- VIGILANCE
- COCKE
- VIGOROUSLY
- ALFRED
- CONTEMPLATE
- SECONDLY
- HUSKY
- MASTS
- WHARF
- LORD'S
- URGING
- COLUMBUS
- PACED
- STAGES
- NERVE
- FOUNDATIONS
- BELISARIUS
- CHRISTIANITY
- ENCLOSURE
- DIVERS
- EXTRACT
- SCALES
- WOMEN'S
- BUYING
- ACHED
- STRIFE
- IMPROPER
- HANS
- PAIRS
- INSPECTION
- MAKERS
- SPECK
- VIVIDLY
- TOUR
- SLIDING
- IRONY
- FARTHEST
- CARLING'S
- OB
- DEAN
- CONTEMPORARIES
- APPROVE
- RATIONAL
- RESCUED
- DISTURBING
- ENDING
- PROSPECTS
- MURDEROUS
- BONNET
- SUNBEAMS
- EXCUSES
- OMITTED
- YOSEMITE
- TENAYA
- WESTMINSTER
- BUSIED
- BRIDE'S
- REFORMATION
- SUBURBS
- CELEBRATE
- CAMPS
- COIN
- HANDING
- RANCH
- PLAZA
- FOSSIL
- SHIP'S
- HEADING
- RACING
- BACON
- REPAIRS
- TOADS
- REMORSE
- HANDWRITING
- BUTCHER'S
- ABRUPT
- SUMMITS
- IMPENETRABLE
- PLUNGING
- SHEPHERDESS
- DIMLY
- YELLED
- MODESTY
- HUNGARIAN
- ROOSEVELT
- DESOLATION
- CONVERTED
- YELL
- RITES
- JAGGED
- ARUJI
- SITGREAVES
- SENOR
- HENS
- MUTINEERS
- MUSKET
- CAMBRIDGE
- POORER
- SHAVING
- BLAST
- KITTY
- APPROACHES
- PECULIARITIES
- PREJUDICE
- MANLY
- OPERATE
- PLASMOID
- OFFENSE
- VACATION
- GARRISON
- CAPRICE
- KAY
- MARVEL
- MAPS
- RUTH'S
- TONGUES
- SEGOUIN
- COLLECTOR
- DOCUMENTS
- SHAMEFUL
- PREACHER
- ACTOR
- WADMAN
- AVAILABLE
- PHINEAS
- RAYMOND
- CHURCHWARDEN
- TRENCHES
- RAWLE
- ALEXEY
- ALEXANDROVITCH
- COLCHIS
- BEAM
- PATSY'S
- HUMOROUS
- DADDY
- CLAIR
- CONGREGATION
- GANGWAY
- FUSS
- GODMOTHER
- BARRICADES
- INSURGENTS
- MOLIERE
- ROYLAKE
- THOR
- SYLVIE
- MASKERS
- CHELTENHAM
- TILBURY
- AYRTON
- NATSIR
- BANDITS
- COONSKIN
- LYNDE'S
- NOTABLE
- KNITTED
- TURNIP
- BUGGY
- IRISHMAN
- UNUSED
- SCOTIA
- EXPLANATIONS
- MOUTHFUL
- AMOROUS
- SCANDAL
- EUGENIE
- INSURE
- ACHING
- MUSE
- SHUDDERED
- PIERCING
- WICKEDNESS
- PASSIONATELY
- NESTS
- TOYS
- PAN
- OBLIGATION
- BOARDING
- FAITHLESS
- GOODWOOD
- RIBBONS
- HARMONIOUS
- MAINLY
- ENTERTAINING
- DYED
- INQUISITIVE
- FASTEN
- LONGEST
- ODIOUS
- DIET
- MATTERED
- SAILS
- MAZE
- OBLIGATIONS
- LIVID
- PRONE
- FLATTERED
- LESSER
- CONSTERNATION
- DEMANDING
- SALUTE
- OUTBREAK
- DOVER
- HUMILIATING
- FORCING
- SUCCESSES
- CONDUCTING
- NUMBERED
- AMBIGUOUS
- AFFIRM
- SESSION
- PRINCESSES
- DULY
- ASSENTED
- CHOPPED
- JUICE
- JOYS
- MATURE
- BETRAYING
- LOVE'S
- LIFE'S
- TRIVIAL
- AMBER
- RELISH
- CONSUMED
- REMNANTS
- INSERTED
- PRESUME
- PRETEXT
- BLEAK
- DILIGENCE
- SALARY
- APPEALING
- BUREAU
- LATCH
- FRAMEWORK
- ACCIDENTALLY
- RELUCTANTLY
- ADVISABLE
- DISAPPEARING
- ANNIVERSARY
- GALLOWS
- DANGLING
- GREEKS
- CONFERRED
- SCORCHED
- PEAR
- SURVIVE
- REMNANT
- EDIFICE
- HONOURS
- LANES
- NEEDLES
- TENDED
- HOSPITABLE
- DELAYED
- INDICATING
- RINGS
- BESOUGHT
- OBSTINACY
- ENVIED
- SPOILT
- LO
- COALS
- LASTING
- CENTERED
- WILLINGNESS
- SATISFYING
- STITCH
- EXPOSURE
- CUTS
- POSSESSING
- SMELLS
- BULK
- SYSTEMATIC
- TRACT
- EXPLORED
- MIRACLES
- VANISHING
- ENMITY
- DILEMMA
- SHARPER
- ALARMING
- UNSCRUPULOUS
- CONTROLLED
- FETCHED
- LESSENED
- DRAWS
- PEBBLE
- BANKERS
- BOWELS
- DISEASES
- TOE
- DOSE
- NOISES
- TISSUE
- ANNOYANCE
- PROMOTED
- STARVE
- DIDST
- SULTAN
- SCHEHERAZADE
- RELY
- BEFALLEN
- CREATOR
- CONFIDE
- REVEAL
- TRAITOR
- DOMINIONS
- REPENT
- CONTRADICTION
- FRONTIER
- MOUND
- PAW
- ALIEN
- RICHEST
- EXPANSE
- DES
- POSTS
- WOODED
- BASS
- FAVORABLY
- NECESSITIES
- LOGICAL
- ROUTINE
- SPACIOUS
- CONVERSATIONS
- BOASTED
- STOCKS
- DEPRIVED
- PIOUS
- RELIGIONS
- GENEROUSLY
- SLEEPS
- PAVED
- FESTIVITIES
- PHILOSOPHERS
- CREDITED
- CONVENT
- EDGED
- SHRIEKS
- TRANSFORMED
- SUICIDE
- MATRON
- DIALOGUE
- ROSALIND
- TEASING
- COMPETITION
- OCCURS
- SHAFT
- ARGUING
- FROZE
- BRIGHTER
- LURKING
- DOTTED
- PRINCE'S
- ENLISTED
- THANKING
- FIFTEENTH
- FAVORED
- ARNWOOD
- PRINCELY
- DISCRETION
- ELOQUENT
- OPIUM
- ELDERS
- CREATING
- PSYCHOLOGIST
- FACTORS
- SUPPRESSING
- DISPOSAL
- WHITISH
- POPPED
- LIPPERTY
- RAT
- MUSCLE
- TRANSPARENT
- ORNAMENT
- BALCONY
- CONTROVERSY
- CURRENTS
- RESOURCE
- VENT
- RESTRAINT
- FROWNING
- ACCENTS
- COMMONPLACE
- PARALYZED
- IMPORT
- POSTED
- BENTLEY
- FARMHOUSE
- STABLES
- JOVE
- SOLVE
- CEASING
- CLING
- DIVING
- PARROT
- AFLOAT
- FEEBLY
- FRANTIC
- HORRIBLY
- PIASTRES
- PRE
- STABBED
- UNNOTICED
- WATCHFUL
- INWARDLY
- NEIGHBOR
- GIRDLE
- HEREDITARY
- PETTICOATS
- WIGWAMS
- SHIFTING
- OFFERINGS
- SPIES
- SIGNIFIED
- EXCEEDED
- SPORTS
- PRECEDED
- ISSUING
- ALTERATION
- TURKEYS
- SUNRISE
- PARENT
- BUFF
- GORGEOUS
- PETTICOAT
- TRIMMED
- ALADDIN
- THEME
- SMASH
- SITS
- PICKS
- GRUDGE
- SPLASH
- LOOSENED
- RECREATION
- SWARMED
- IRENE
- TENNIS
- CHORUS
- JOKES
- TRUDGED
- SENSIBLY
- DISTRIBUTED
- GRIEVOUS
- ENGAGING
- HASTENING
- PROCLAMATION
- REPAIRED
- VIEWING
- DISDAIN
- CLASSIC
- SCAMP
- CLAW
- ANCIENTS
- ARISES
- MINGLE
- BITTERNESS
- PURITAN
- STOREROOM
- CARNIVAL
- IMPERFECT
- ACQUISITION
- SHAKESPEARE
- SUPER
- BRAVERY
- ROASTED
- STALK
- STALKED
- WATER'S
- DEMONSTRATION
- UNCOMMON
- NOTORIOUS
- ROY
- DETERMINING
- DEGRADED
- ADORNED
- TINGE
- EXCURSION
- COMPACT
- TREACHEROUS
- SUCCEEDING
- FAVOURED
- DIMENSIONS
- SPRAY
- DEVOUR
- RAGED
- CHOOSING
- CONVENTIONAL
- INCESSANT
- HAROLD
- SCORNED
- WHIRLED
- HARP
- LEAPT
- AIRY
- TRIUMPHANTLY
- SIDEWAYS
- CHUCKLING
- PET
- DEVICES
- THORNY
- MATES
- WORMS
- REDTAIL
- DARTING
- HOOKS
- PEWEE
- MUDDY
- TEETER
- SETTLERS
- SCREECHER
- JOHNNIE
- LICK
- EGOTISM
- FRAGRANCE
- EMBODIED
- GODDESS
- STRAIGHTWAY
- NASH
- TENDERFOOT
- MIDDAY
- FAMILIARITY
- AUTOMATIC
- SIDEWALK
- CRUSHING
- CONGRATULATE
- ASS
- WIRES
- ENTRUSTED
- CONFINEMENT
- VAULTED
- EAGLES
- ADVERTISED
- PROOFS
- MONUMENT
- SKETCH
- FULLERTON
- IRRITATION
- BRIGHTENED
- PERSUASION
- GENERAL'S
- CONTINUANCE
- PERFORMANCES
- HENRY'S
- COSTLY
- LILY'S
- ACHIEVEMENTS
- OBLIGING
- EQUILIBRIUM
- SLEIGH
- KANSAS
- BOOM
- CHOKING
- RECESSES
- FEARLESS
- GLIDED
- TRUTHFUL
- CONFOUND
- HERBS
- MIX
- LIQUOR
- CHILLED
- MILBURGH
- DOCK
- MINIATURE
- FORGIVENESS
- UNIT
- WAIL
- THIEVES
- CHIVALRY
- MOONLIT
- DROOPING
- FLATTERY
- DREAMILY
- SKIRTS
- MAGICAL
- FLOURISHING
- CONCLUSIONS
- CONTRIVANCE
- SPRUNG
- FONDNESS
- DEPENDENCE
- GALLANTRY
- FORTIFY
- VIGOUR
- DISAPPOINTMENTS
- COMPLY
- PEYTON
- PROFUSION
- BIRCH
- BALMY
- FORSAKE
- COMMUNICATING
- LIMB
- DEGRADATION
- OCCUPATIONS
- REMEDIES
- SUPPRESSION
- OBSCURITY
- DIMINISHED
- RESORT
- AGRICULTURE
- VALANCOURT
- DREAMT
- ATTENDANCE
- IMPRUDENT
- ASSUMING
- INJURE
- BLAND
- THIEF
- LUSCINDA
- TRESSES
- DAMSEL
- SINCERITY
- SWOON
- AIDED
- DESIGNS
- HOWLING
- PEACEFULLY
- CAULDRON
- SCREAMED
- EMINENCE
- MUSKETEER
- GUARDSMAN
- COMPANIES
- HOSTESS
- GOVERNESS
- MELTING
- FINISHING
- SUPPORTING
- MANIFESTATIONS
- DESCRIPTIONS
- EARL'S
- BARRELS
- STILLED
- CEDRIC
- LUMP
- SPANIARD
- DICK'S
- LATITUDE
- CAPTAINS
- POSTURE
- PLUS
- DESTINATION
- SPECIALIST
- ALIGHT
- SPAN
- MARKHAM
- SOLEMNITY
- ATTILA
- VICTORIES
- COMPEL
- TRADER
- SUMS
- MECCA
- CLUSTER
- CEYLON
- WIDTH
- FEARSOME
- SNAILS
- VALVES
- YEARLY
- TIES
- MUNICIPAL
- ARDENTLY
- UNDO
- GEAR
- UNDERGO
- MANOR
- CASSANDRA
- MONTHLY
- SCRAMBLING
- INDICATES
- AISLE
- EXECUTE
- RELAXED
- DALE'S
- COMPOSURE
- UNEASILY
- CUR
- SALON
- WARLIKE
- PERFUME
- STATEROOM
- COMMOTION
- MURMURING
- INERT
- MATRIMONY
- NOVELS
- PERCEPTIBLE
- KICK
- MIDWAY
- ARISING
- PLACID
- ADVENT
- BRIGHTNESS
- SKIES
- HUE
- SHAFTS
- SHROUDED
- DART
- FROSTY
- CHOIR
- CANDY
- PARISHES
- ANDREW'S
- CARTS
- APPREHENSIONS
- VICES
- OVERWHELMING
- MURPHY
- TWIST
- AUSPICIOUS
- SHIELDS
- HEAPED
- STRATA
- NARWHALE
- FARRAGUT
- OVERFLOWING
- DECISIVE
- DENYING
- BALD
- SARCASTIC
- CHIMNEYS
- PROTESTANT
- LYNNE
- DILL
- FISSURE
- MORNING'S
- MYRIADS
- PRINTER
- SWEETLY
- CLAPPING
- SWAYING
- SALLOW
- SHRIEKING
- NOVELTY
- PLUTO
- K
- CHESHIRE
- TIN
- REVEREND
- ASSOCIATES
- JUDICIOUS
- SPECIFIC
- LIVERY
- DISPERSED
- FORBID
- HISTORIES
- PIGEON
- PILLAR
- SCIENCES
- TOWERING
- BUTTONS
- LEAGUE
- JARS
- JEDDAK
- COMAS
- BLOCKED
- LOAN
- SLICE
- CRUISE
- BLACKENED
- RESPECTING
- MEMOIR
- TITLES
- TUTOR
- SCHOOLFELLOWS
- RAZOR
- STUPOR
- INFLAMMATION
- REMEMBERS
- CONSTRUCTION
- CABINS
- PETERSBURG
- NAPOLEON'S
- PLAYFUL
- ACCENTED
- KISSES
- HURRICANE
- MUTILATED
- ASSYRIA
- LOCALITY
- DECEASED
- MANTELISH
- PAL
- OKLAHOMA
- FURTHERMORE
- BUCCANEER
- ASSERT
- DOUGLAS
- SWEEPS
- ACQUIRE
- RUFFIANS
- GOBLET
- DINSMORE
- DAD
- TOW
- DUBLIN
- FLASHING
- MASKED
- VICKERS
- SCOUNDREL
- SIMIAN
- POLITICIAN
- ACTUATED
- WOODHOUSE
- HIGHBURY
- CORNY
- PUTNEY
- HOSKINS
- ANTENNAE
- METER
- PEAK
- POKING
- BLOUNT
- TRUMPETS
- PHILLIPS
- PREJUDICES
- ANNE'S
- JOSHUA
- PLAYMATES
- PULPIT
- PUGWASH
- BEARERS
- MINISTRY
- SURVEYING
- BRAG
- MARSH
- L'OLONNOIS
- LICKED
- PROPOSITIONS
- STURDY
- CHILLY
- CLUCK
- STICKEEN
- TOLLER
- COSETTE'S
- MIRIAM
- CONSTITUTED
- EBONY
- LOWESTOFT
- HARMON
- SOU
- SURREY
- BAILEY
- BINNY
- YORITOMO
- ZEPPELIN
- PUBLICAN
- MACMURDO
- SEYFFERT
- WHITLOCK
- SIDNEY
- STRUBLE
- MON
- TED
- DIPPED
- SEWING
- UNSEEN
- BRIDAL
- HUMMED
- MYRIAD
- MEEK
- RETREATING
- BIDDEN
- EVERYDAY
- NOVA
- BURNT
- CRISP
- ROBERT
- EJACULATED
- JOGGED
- NOTING
- ORPHANS
- ORDEAL
- PLUMP
- WITCH
- PROCESSES
- CONTEMPLATING
- OCCURRENCES
- LOYAL
- SHUTTERS
- INSULTING
- CALMNESS
- IMPOSTOR
- READS
- DEPRESSED
- REPULSED
- PLAINTIVE
- UNTRUE
- UMBRELLA
- EMBARKED
- EASIEST
- LIBERTIES
- CORRESPONDENT
- BREACH
- MIDDLING
- STROLLING
- AUTHORS
- GENTLEMAN'S
- EXCEPTIONS
- KINDLED
- CONTEMPTIBLE
- IMPERFECTLY
- PRELIMINARY
- MERLE
- ENLIGHTENED
- PANG
- COMMISSIONERS
- BOUNDARIES
- ADHERENTS
- AGREEMENT
- MAINTENANCE
- SOVEREIGNTY
- AYSCUE
- FLEETS
- PROTESTS
- WITT
- COLONIES
- CONVOY
- NORTHERLY
- SUFFERER
- INTRIGUE
- SWORN
- UNAVAILING
- INFORMING
- ALTERNATIVE
- PATRIOTIC
- DIP
- VINEGAR
- CORNS
- ENTRY
- INFINITELY
- ANEW
- CLOWN
- X
- RACK
- BALANCING
- FAVOURS
- BEASTLY
- CHEQUE
- CHARITABLE
- INVESTIGATIONS
- SCREWED
- FROWN
- PILLOWS
- MATERIALLY
- HAIRS
- BOOMING
- WARRANTED
- MASTERED
- PARCHMENT
- OUTLOOK
- GRATIFYING
- REGRETS
- MIDSUMMER
- REGISTERED
- ILLUSTRATED
- ROWING
- ACCOMPLISHMENTS
- VIGIL
- ABOUNDED
- CORAL
- ENTREAT
- HATCHED
- OVERJOYED
- CAVES
- REGARDLESS
- OVERNIGHT
- BESET
- ISSUES
- LIFETIME
- ESSENTIALLY
- SELFISHNESS
- SKIRMISH
- HEADSTRONG
- WHINING
- TABOO
- RELIEVING
- MARKING
- DISSATISFACTION
- INSISTS
- DISHONEST
- STEER
- SAVAREEN
- UNACCOUNTABLE
- SHORTEST
- ADJACENT
- DIGGERS
- SILAS
- DIVERTED
- EXPLORE
- BURIAL
- CONGENIAL
- INFLUENCED
- MISUNDERSTAND
- REDDISH
- CIRCLING
- BECKONING
- AUTOMATICALLY
- ENTANGLED
- CANDLES
- POEMS
- PAIL
- DISCOMFORT
- NEEDLESS
- WAXED
- DATES
- GROANS
- DEMONSTRATIONS
- EXPIRED
- FORTITUDE
- RESISTING
- CORD
- HEAL
- ACRE
- CUPS
- THREATEN
- ACHIEVE
- FAIREST
- INSTALLED
- MODELS
- RENOWNED
- ENDURANCE
- FLITTED
- EXPERTS
- SCORES
- EXCITEDLY
- FARMING
- SYSTEMS
- BRIAR
- SIGNATURE
- CONSOLED
- IMMEASURABLE
- BANKER'S
- GILT
- FUND
- SIGNATURES
- FREEZING
- INCREDULITY
- MA
- RESTRAINED
- TOWERS
- PINCHING
- COOLING
- STEAMBOAT
- BEATRICE
- WILDER
- HURTS
- SPARK
- INVESTIGATE
- IMAGINABLE
- FABRIC
- FEMALES
- TOLERATE
- SLIDE
- PERFECTED
- STATIONARY
- ELABORATE
- PRINCIPALLY
- CURVED
- PITS
- TERRIFYING
- RUSTLE
- THICKER
- BEWILDERMENT
- CONDE
- CALCULATE
- COUNTRYMEN
- CHALONER
- GRENVILLE
- HANDKERCHIEFS
- INDEBTED
- TIMIDITY
- TALLER
- RESIGN
- MOURNFULLY
- ARGUED
- HYPNOTIZATION
- HYPNOTIZER
- PAMPHLETS
- INSANE
- SUPERFICIAL
- SERVES
- STATUS
- HYPNOTIZE
- REMOVING
- SUCCESSIVE
- LABORATORY
- QUOTE
- DAYTIME
- ETHICAL
- STRENGTHEN
- OVERTHROW
- PERSISTENT
- SUPERSTITION
- THOROUGH
- ABSURDITY
- VARIETIES
- WINK
- SUBSEQUENTLY
- DROWNING
- GIDDY
- MATTRESS
- PILED
- GESTICULATING
- INCOMPLETE
- JOYOUSLY
- ENGROSSED
- FRENZY
- IMPRESSIVE
- ORDINARILY
- INDULGE
- UNCERTAINTY
- VICIOUS
- ELEVATED
- MULTIPLY
- CUSTOMS
- WEARS
- LINKS
- SUBJECTIVE
- STRESS
- ADOPT
- HESITATINGLY
- INLAID
- CLAPPED
- NETWORK
- INEXPLICABLE
- ORGANIZED
- EXTINCT
- SPECULATIONS
- AERIAL
- ZERO
- EARTH'S
- WITHERED
- TRANSPORTED
- RESENT
- DROWN
- PEEVISH
- UNDERGONE
- SENDS
- ENRICHED
- REAPPEARED
- MEDITATION
- LINGERED
- STAGGER
- TRUSTING
- FORLORN
- DEFECTS
- COMFORTS
- PLUNDERED
- SELECTION
- TRANSLATED
- APATHY
- MESSENGERS
- EXCLAMATIONS
- RENOWN
- CONSULTATION
- ELASTIC
- ANKLES
- PRESUMED
- BURSTING
- CHORD
- MAPLES
- SASH
- GATEWAY
- TOSSING
- SUPERHUMAN
- VENICE
- MONUMENTS
- HARRIET
- APPEALED
- CHIPS
- MILKING
- PANTED
- WICK
- HANDSOMELY
- APIECE
- GLIMMER
- CONNY
- RATTLED
- GYMNASIUM
- GREET
- BREATHLESSLY
- MASCULINE
- LUGGAGE
- EDNA
- STRAINING
- SHEW
- MULES
- SINGERS
- ROBIN'S
- POTATO
- TWIG
- PAVING
- SPLENDIDLY
- FARTHING
- BRUSSELS
- PROWESS
- CROSSES
- WRONGS
- COINCIDENCE
- EUROPEANS
- PRIVILEGED
- NOTICES
- CLOTHE
- DOMAIN
- SECONDARY
- CLOAKS
- ATTITUDES
- MOCK
- LASTLY
- SHORTER
- FLOODS
- RAVINE
- INTERVENING
- RESEMBLES
- FAMISHED
- NEUTRAL
- ERRONEOUS
- CANNONS
- SUNDAYS
- BISHOP
- DEMONSTRATED
- ABIDING
- CONCESSIONS
- HEROISM
- DISCREET
- BOOTY
- PITEOUS
- ENACTED
- PITILESS
- WRECKED
- DOLLY
- NIBBLING
- RESOLUTELY
- ASSURING
- ADAPT
- GRINDING
- TRAPS
- SUPERNATURAL
- SPRINKLED
- CHAT
- COMBINATIONS
- ROBES
- LUXURIANT
- APOLLO
- IVY
- D'YOU
- FOREMAN
- TAME
- STRAP
- GALLOP
- MINER
- SPRAWLING
- LIAR
- GRINNING
- BIN
- CONTEMPTUOUS
- ENCAMPED
- ROAST
- SPOON
- UNDERGROUND
- TORMENT
- LAGREE
- REASSURED
- STRICTEST
- LUCKILY
- SILL
- REJOIN
- CIRCLED
- LOVER'S
- CHEERFULNESS
- MORLAND'S
- UNAFFECTED
- TETE
- HEIRESS
- UNFRIENDLY
- OPPOSING
- STILLNESS
- FRIVOLOUS
- WORSHIPPED
- DIFFERING
- D'ARNAULT
- OMAHA
- DRINKS
- OBEDIENT
- KEYBOARD
- LENA
- TAPPED
- HAIRY
- OUTRAGE
- HYSTERICAL
- THICKETS
- OVERFLOWED
- GRAVES
- DERISION
- CLOUDED
- CANE
- FELLOWSHIP
- GREED
- DISCOURAGEMENT
- FULLER
- PAINTER
- HYMN
- YEARNING
- BUCKET
- EXTRACTED
- ODETTE
- UPTURNED
- UNHAPPINESS
- COMPREHENSIVE
- HOARSELY
- GWENDOLEN'S
- ROUSE
- BEAD
- TORTURES
- THIRTEENTH
- WEIRD
- MESSIAH
- EXCHANGING
- TAXES
- MYTH
- NECKLACE
- SARA
- PIE
- GAP
- CLOCKS
- AFFECTATION
- DISCRIMINATION
- THEFT
- INVITING
- CURTAIN
- COMPETITORS
- REDOUBLED
- VILLAINS
- ESTIMATION
- LONGBOURN
- AYE
- AFFECTIONATELY
- MANIFESTED
- PESTILENCE
- REFUSING
- SHINES
- NURSED
- DELUGE
- EMIGRANTS
- EARTHQUAKE
- MENACED
- EMPLOYING
- ROLLS
- DWELLINGS
- VEILS
- PERSEVERANCE
- COMPROMISING
- CANS
- VERILY
- UNARMED
- RAP
- CUDGEL
- SPUN
- SENORA
- LOWLY
- BOUNDED
- DAYBREAK
- ASSAILED
- SIERRA
- DISMALLY
- BEAMED
- INCLINE
- ARAMIS
- APPRENTICE
- HEEDED
- SALUTATION
- COMPLAISANCE
- LULL
- UNEARTHLY
- ACCOSTED
- TENDING
- SEW
- INCONVENIENCE
- MAKER
- PREDECESSOR
- INCREDULOUS
- SEIZING
- DESCRIBES
- FIXING
- INCIDENTALLY
- FACULTIES
- CHILDREN'S
- PROPHETS
- RECITED
- FOWL
- PRIVY
- RODS
- CLUTCH
- DIVERSION
- GAYLY
- GANG
- BENJAMIN
- FRUITLESS
- ILLUMINATED
- STATISTICS
- ORGANISM
- REGRESSION
- CONTROLS
- ACCURACY
- RABBLE
- NIAGARA
- SUSTAIN
- PREVAILING
- TELLER
- TRADING
- CONQUERING
- INSULTS
- PREACHING
- REENTERED
- LAVISHED
- MANNED
- AGGRESSIVE
- MEASURING
- SERIOUSNESS
- RIPPING
- UNCLEAN
- CARPENTER
- PLANTING
- PREVENTS
- VALUED
- PLANKS
- STOWED
- SEPARATELY
- BINDINGS
- EXCLUSIVELY
- MONSTERS
- RECOMMENDATION
- ALTITUDE
- VIOLETS
- PATRON
- COMBINE
- CLERGY
- PECULIARITY
- QUALIFIED
- WISTFUL
- CLENCHED
- SEALS
- DISCLOSED
- ORE
- PLUCKED
- RANKIN
- THEATER
- TECHNICAL
- NIMBLE
- SMOTHERED
- RESPECTIVE
- CROUCHING
- ADVANCEMENT
- FORK
- MUSICIANS
- KICKING
- SCHOLAR
- STINGS
- OUTLINES
- REPETITION
- LOWDER
- IMPART
- VISIBLY
- BRAVEST
- GULLS
- HEDGES
- HOPEFUL
- REFRESH
- DEFIANT
- RESERVATION
- COMPETITOR
- SURVIVED
- CUPBOARD
- VANKA
- SOUR
- WEEKLY
- JUSTICES
- OVERTAKE
- SOOTHINGLY
- MOTHERLY
- OFFICIALLY
- GRANDMA
- PADDLE
- LOCKE
- VAINLY
- MILITIA
- ASSYRIANS
- ARCHERS
- DIVERSE
- SIZED
- ADMIRINGLY
- INTENDING
- HIPS
- THREAT
- DECEIVING
- ANNOUNCEMENT
- DECKED
- TRAY
- WISEST
- WISTING
- CREVASSES
- CAMPED
- UNDULATING
- SORROWFULLY
- OSTROG
- LINCOLN'S
- FOAM
- ASANO
- COMMENTS
- RIVALS
- REAP
- BATHS
- ODE
- FITNESS
- ERIE
- EASTWARD
- CONFRONTED
- STAIN
- SUFFICE
- WAX
- FOOTPRINTS
- BRISTLING
- SINGLETON
- COMPREHENDED
- PETITIONS
- AMISS
- DRUMS
- FEROCITY
- LIMP
- EXPLODED
- CHIEFTAIN
- HISPANIOLA
- MORGAN
- GUNN
- SPIT
- SACKS
- INTELLIGIBLE
- THEOLOGIANS
- ARTERIAL
- DISPERSE
- EXPAND
- EXTREMITIES
- WEAKER
- ECCLESIASTICAL
- FRATERNITY
- SOARING
- BRIGANDS
- DRON
- KARP
- DELIVERANCE
- DEVISED
- FORCIBLY
- GUARDING
- IDENTIFIED
- INDEFINITELY
- PORK
- WINNER
- FAYLE
- MARYLAND
- LEGALLY
- DEFIANCE
- OVERTURNED
- RELIED
- SNARES
- HONOURABLE
- ESQUEMELING
- EARNED
- KNIGHTHOOD
- SPEARS
- HOARY
- PURSUERS
- ARMORED
- BULLETS
- PALACES
- FLAGS
- DETACHED
- SHERIDAN
- REFEREE
- TEMPTING
- CONVINCING
- RATIONS
- PROPORTIONED
- MONTERO
- POLISH
- INCOMES
- HEROD'S
- ANTONY
- CORDIALS
- COVERS
- FORSAKEN
- BONDAGE
- PILOT'S
- FOAMING
- LABORERS
- TESTS
- APES
- EVOLUTION
- PENALTY
- TREASURY
- DAUBENY
- KNIGHTLEY
- WHOLESOME
- SMITH'S
- REPROACHED
- LICENSE
- ALDERMEN
- HINGES
- SEXES
- CARBON
- OXYGEN
- GUIDANCE
- COMPELLING
- BARODIA
- ELIZABETH'S
- MUFF
- THORNDYKE
- HOP
- ELMHURST
- WRIGHT
- STABILITY
- ARRAY
- CRAGS
- STUPENDOUS
- CARDBOARD
- ABUSES
- GORGE
- SURGERY
- DESKS
- ADMIRERS
- AFAR
- PROFESSORS
- CARTRIDGES
- LANTERNS
- PRESTIGE
- FERFITCHKIN
- DANIEL
- COMPANIONSHIP
- BYRNE
- TYRKER
- ISAAC
- FOSTERS
- VALJEAN
- MEDITATED
- HOGGLESTOCK
- UTENSILS
- PHI
- SIGMA
- TAU
- MARIAN
- ARDOUR
- BAKERS
- BABYLONIA
- INVERASHIEL
- FOOTBALL
- WARE
- CROSBIE
- SOUTHARD
- IDOL
- COD
- JURISPRUDENCE
- MICKY
- GEORGIE
- ADVERTISER
- JUDAH
- MISER
- ADVERTISERS
- COLLINGWOOD
- JACKAL
- WANDS
- PHOTOPLAY
- ZENA
- GEMMEN
- ECONOMIC
- POLDIE
- CHILDS
- LUIGI
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- BERENGARIA
- OEDIPUS
- LEGISLATIVE
- BROOKS
- BORDERED
- TANGLE
- CURVES
- ADOPTING
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- REVIVING
- ORCHARDS
- SUNDRY
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- HELPLESSLY
- DUNNO
- IMAGINING
- PROWLING
- SLOWER
- ADVISER
- PECUNIARY
- CATALOGUE
- FIDELITY
- RECOLLECTIONS
- AWAIT
- EXAGGERATE
- DEATHS
- TRAMPLING
- DISHONOR
- FLUSHING
- LAUGHS
- JENKINS
- HEARTLESS
- APOLOGETICALLY
- BETHOUGHT
- GARDENCOURT
- LIGHTEST
- ISABEL'S
- RECOMMENDING
- CONTRIBUTION
- ENQUIRED
- MOULD
- SYMPATHIES
- TRANSFER
- BOUGH
- AUNT'S
- AMBITIONS
- REVIVAL
- ENGLISHMEN
- STATESMAN
- REFUSAL
- AVOWED
- NAVIGATION
- PROHIBITION
- AFFRONT
- DEPUTY
- ATHWART
- AVERSE
- SUCCESSOR
- CONFERENCES
- COMPENSATION
- REJECT
- UNPRECEDENTED
- CLEVERNESS
- ILLEGAL
- PROVING
- WITHHOLD
- LEMON
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- ONION
- TABLESPOONFUL
- SEASONED
- BENEVOLENCE
- ENCHANTMENT
- LINGERING
- AUGHT
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- ACCIDENTAL
- ENCHANTMENTS
- PUREST
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- VERSE
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- DOWNCAST
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- PLANE
- DETAIN
- RUG
- SMILINGLY
- SOCKET
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- DISCERNMENT
- SYMPATHETICALLY
- CREEPS
- SOILED
- GUNPOWDER
- SHINY
- TIERRA
- FUEGO
- SLAMMED
- OBLONG
- ALLUSIONS
- DEFENDING
- SENTIMENTAL
- EXTRAVAGANCE
- TREATING
- EXERCISED
- INDIFFERENTLY
- HEATED
- HUMBUG
- INDISPENSABLE
- ABILITIES
- UNGRATEFUL
- RURAL
- PARKS
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- GALLOPED
- CROAKED
- PARTNERS
- MANE
- WONDERINGLY
- COURTSHIP
- COVETED
- HAZY
- TRAITS
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- COMPROMISE
- ROLE
- WEIGH
- DELICATELY
- SPOUSE
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- RESENTFUL
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- PREVENTING
- HUSBANDS
- UNCHANGED
- BERTH
- MOSQUITO
- ALDERMAN
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- SKIRT
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- EASTER
- PERRY
- RIDDEN
- SCOTT
- SUBSCRIBED
- SHIVERING
- UNDERBRUSH
- CROWDING
- LESSEN
- RAWLINS
- BOBBY'S
- CEDARS
- LOOMED
- STUMBLED
- DISPLAYING
- REALIZATION
- MANTEL
- TRANSIENT
- ECONOMICAL
- RESTORATIVE
- OPERATED
- GREASE
- ODOR
- REPAST
- LAMENTATIONS
- PREVAIL
- INTERRUPTIONS
- DISTRESSING
- PITEOUSLY
- STEAD
- FORTHWITH
- RELATES
- COMMITTING
- AVENGE
- ACKNOWLEDGMENT
- GRECIAN
- APPLICATIONS
- POSTERITY
- COMMENDATION
- AVARICIOUS
- DECAYING
- BEWARE
- HUGH
- VIDA
- HOBBLED
- CHAMP
- ASSISTANTS
- WATCHMAN
- DISCHARGED
- BLOUSE
- ESTABLISHING
- INSULTED
- HINTS
- HUDSON
- PITCHER
- BROADWAY
- LOCATION
- WHOLESALE
- LINKED
- LACKED
- STREAK
- CASUALLY
- PROVINCIAL
- SPRAWLED
- SOOT
- MANUFACTURERS
- TASKS
- BROWNISH
- DOORWAYS
- CORNERED
- USHERED
- RENAUD
- EFFICACY
- FOCUS
- REMINDS
- GRACIOUSLY
- DISPUTED
- WAGER
- FATHOM
- GLISTENING
- UNWELCOME
- RATTLING
- POLICEMEN
- THRUSTING
- EXTERIOR
- LIGHTING
- PADDED
- CUSHIONS
- CROPS
- TELEGRAPHED
- PAYS
- LUSTER
- HIP
- PRACTICES
- SOLVED
- PREVALENT
- FINS
- MUZZLE
- GREENISH
- PRINTS
- BUCKLE
- STRAND
- RETINUE
- YON
- GOWNS
- RESIDED
- TREATS
- COOPER
- MATURED
- EXPENDED
- DISINTERESTED
- MARRIAGES
- PERSONAGES
- PSYCHOTHERAPY
- HYPNOTIST
- MAGNETIC
- AGENCY
- OUTCOME
- ORGANIC
- PURPOSIVE
- CAUSAL
- TENDENCIES
- VICTORIOUS
- MORPHINE
- COURAGEOUS
- GRAINS
- UNFAIR
- MAXIMS
- UNTOUCHED
- SKILLFUL
- INJURIES
- IGNORING
- INVOLVES
- INTRUSION
- LABEL
- EATERS
- HOPS
- HELLO
- POKED
- EATS
- RUBBER
- CUNNINGLY
- THERMOMETER
- REALISED
- BLOTTED
- PROJECTING
- SIDED
- MERITED
- GENTLENESS
- SENTENCES
- EXACTING
- IMMINENT
- SCRUPLE
- STAGGERING
- LAMENTABLE
- DISREGARDED
- PROVOCATION
- DRILY
- ORIGINATED
- WANING
- PEERAGE
- FARMS
- ACQUIRING
- WEAKENED
- LEDGE
- GRANDSON
- CAVITIES
- ASTONISH
- COMPANION'S
- DELIVERING
- FOOTPATH
- SCOLDING
- ASCEND
- STAMP
- ROVING
- CANOPY
- RUDENESS
- FARED
- DISASTERS
- MISERIES
- DETESTABLE
- CONSULT
- STRANGLED
- MISERABLY
- PREFERABLE
- EDEN
- INCURRED
- VOLITION
- UNCONTROLLABLE
- BURDENS
- ASSEMBLE
- CONVERT
- ENCAMPMENT
- PAUSING
- AUSTERE
- ALLUSION
- REGAIN
- TIRE
- PROFITABLE
- ASSEMBLAGE
- VALIANT
- INFLAMED
- TRIPS
- STATING
- MILLTOWN
- SUBMERGED
- BEWILDERING
- WHIMSICAL
- QUERIED
- ALLUDING
- ARABIAN
- TRAVELER
- FLANNEL
- GOIN
- PHILANTHROPY
- FLOWN
- LOYALLY
- SQUEEZE
- HEARSE
- PEAS
- MYSTERIOUSLY
- OWNS
- LANGUIDLY
- QUOTED
- CHIRPED
- EUPHRATES
- BAGDAD
- DIMINISHING
- TRIBUNAL
- INVESTED
- PROSTRATED
- SANCTUARY
- AMENDS
- JEWELLERS
- SYNDIC'S
- OCCASIONED
- CONSORT
- CAMEL
- MANAGING
- DECEITFUL
- INDISPOSED
- PERSECUTED
- ACCIDENTS
- CHARMED
- SCOLD
- GROSBEAK
- EQUIVALENT
- ANGULAR
- MARAUDING
- BARRACKS
- PONDERED
- INTELLECTUALLY
- UNSELFISH
- PREACHED
- SPECIFIED
- BARBARITY
- FLOWS
- HOMER
- CORDIALITY
- DEFECTIVE
- DIVINITY
- BRUISED
- INAUDIBLE
- CHARCOAL
- BREASTS
- IMITATED
- FITZ
- STOUTLY
- SOAKED
- DECEIT
- STONY
- PROMONTORY
- ALLUDED
- TONS
- DISCORDANT
- COILED
- CARCASS
- GALES
- WILFUL
- FAILURES
- INDOORS
- DELUSION
- SHRUNK
- POOLS
- REVOLVER
- ACORNS
- BLENDED
- FLICKER
- FELLOW'S
- TADPOLES
- ELEVENTH
- SACRIFICING
- WHISKED
- PREFERMENT
- PINCHED
- PROPHECY
- TEMPTATIONS
- GOSSIPING
- PEE
- DOWNY
- DESERTS
- NORTHWEST
- BUNDLES
- OPENINGS
- SILENCED
- GNAWED
- THRILLS
- TANGIBLE
- VARIOUSLY
- PAUSES
- CURL
- PAGAN
- BARD
- HAM
- UN
- RECOGNIZING
- POKER
- SIPPED
- DURABLE
- PIEBALD
- BRUISES
- SPYING
- PROFILE
- CYNICISM
- BLUSHES
- BRAGGING
- PROTESTATIONS
- VOWED
- SMELLING
- SLUNG
- FLOWERY
- UNDERTOOK
- CONTEMPLATED
- ENTREATED
- CONJECTURED
- FABLE
- MENDING
- SOFTENED
- FINERY
- INDUSTRIOUS
- LANGUOR
- ELAPSED
- UNBROKEN
- EXCELLENCIES
- SANCTIONED
- PARENTAL
- REJOICE
- DECEPTION
- TILNEY'S
- AVARICE
- THORPE'S
- CONSTRAINED
- CONNECTIONS
- RELUCTANCE
- CLIMAX
- ANDERSON
- MAMMY
- UPLIFTED
- MELODIES
- TRAINS
- BARREN
- DISABLED
- YAWNING
- ENTERPRISES
- SEAMEN
- HUTS
- RIBBON
- STEM
- STEADFAST
- BENCHES
- BLAZED
- SHAVED
- DRUMMED
- PRECISION
- GLIDING
- FRAGMENT
- PLANES
- SQUARELY
- DRAUGHT
- ANCHOVIES
- BLADES
- WILLED
- CATCHES
- FLAP
- OCUMPAUGH
- CORRECTLY
- HAGGARD
- GOLF
- GRUNT
- DONKEY
- THIRSTY
- RIOT
- BRIDEGROOM
- COOKIES
- EMERALD
- FAIRYLAND
- YARN
- ELINOR
- AFFECTS
- POSSESSES
- PUMP
- ENLIGHTEN
- ARGYLE
- BILLIARD
- THRO
- CHAMPIONS
- LISTS
- DOUBTING
- HELMETS
- CONFORMITY
- UNDAUNTED
- SORELY
- POORLY
- SENTINELS
- DISMOUNTED
- ANNIHILATED
- LASHING
- TOTTERING
- STREAKED
- ALPS
- FRAIL
- DARES
- SCRATCH
- WAILING
- PERCHANCE
- THORN
- FILLS
- WINDSOR
- STARVING
- RUMOUR
- EXERTIONS
- MORTALITY
- CAVALIERS
- DUNGEON
- IMPOSE
- DINED
- DIXIE
- PERFORMER
- GIFTED
- ARRAYED
- DEWY
- NE'ER
- GARB
- NOBLY
- CHRISTIANS
- WANTON
- LAWFUL
- CURSES
- SCOLDED
- COOLER
- MERRILY
- SOUNDLY
- COCK
- FOREFATHERS
- DUEL
- BERNAJOUX
- SHARPENED
- QUARRELS
- ASSAULTED
- TENANT
- DEUCE
- HEAVED
- GUINEA
- PERNICIOUS
- INSTINCTIVE
- SLY
- SUGGESTING
- DOLL
- BROOM
- CHASTE
- ROARS
- AKIN
- VIENNA
- EXTENDS
- DEAFENING
- CRACKLED
- DIN
- NOSED
- LITTER
- VENTURING
- LOOKOUT
- BRACING
- PERSON'S
- GRANDPAPA
- WILDEST
- NURSES
- ACTRESS
- FLAW
- U
- AINT
- GEOGRAPHY
- HYSTERICS
- HARNESSED
- SUSPENSION
- CHEERY
- MULTIPLIED
- MASTERY
- DEALINGS
- COMPREHENSION
- DILAPIDATED
- MELT
- HERMIT
- COLLECTING
- ARABIA
- TRADERS
- DREAMER
- BEARINGS
- PENINSULA
- ARONNAX
- NETS
- DEPENDING
- ISOLATION
- ANNUALLY
- UNBEARABLE
- SANE
- RIGHTEOUS
- INVOLVE
- WHIT
- CHRISTIANIA
- RUNNERS
- FEWER
- WEAKEN
- SLEDGING
- REINDEER
- ESKIMO
- MITS
- STAMPING
- ALLOWS
- DEPOT
- SUSCEPTIBLE
- BORDERS
- NOOK
- AUSTEN
- PASTURE
- CHAPLAIN
- GENTRY
- OBLIVION
- INTERMINABLE
- TAILOR
- CARELESSNESS
- GRADUAL
- EJECTED
- NUNS
- GUIDING
- AIMED
- SLEEK
- ELUDED
- UNOBSERVED
- CAFE
- COURTEOUSLY
- DEVIL'S
- PACKAGES
- OVAL
- STARRY
- HAHN
- RANCE
- ANNOY
- GAG
- PROMOTION
- LEADERSHIP
- INVITATIONS
- WAITERS
- INCONVENIENCES
- BESEECHING
- CALICO
- OWES
- RESULTING
- HISTORIC
- SYMBOLS
- SHOWERED
- DOZENS
- RAINS
- SHELVES
- HIRE
- HARDSHIP
- SHILLINGS
- MERCIFUL
- MILLY
- INTERPRET
- STRINGHAM'S
- INDIRECTLY
- PROMINENCE
- CAPRICES
- NUMBERLESS
- ZIGZAG
- WHEELING
- JOSEPH'S
- STRAGGLING
- RASCALS
- STRONGHOLD
- CAPTIVES
- CONSISTING
- PALL
- COOKS
- FLOGGED
- CONVULSIVELY
- JAMES'S
- DISTEMPER
- DISAPPOINT
- MONARCHY
- ALLEGED
- DIS
- SHAGGY
- DESTITUTE
- REGIMENTS
- AUSTRIA
- JURISDICTION
- SCOTCH
- HOLOFERNES
- PROSTRATE
- INHABIT
- VELOCITY
- VIA
- ARDUOUS
- CLASSIFICATION
- ADMITTING
- TORRENTS
- NARROWS
- HATES
- POUNDING
- STUPIDLY
- FRINGES
- CONTEMPTUOUSLY
- PROFFERED
- BARTLE
- TWELVEMONTH
- EXQUISITELY
- UNDERTONE
- CONGRATULATING
- VANE
- OUTWARDLY
- MEND
- BOASTING
- HANSSEN
- CONTRIBUTIONS
- CRUDE
- BLEED
- PATTED
- TYRANTS
- INSTRUMENTAL
- PLATFORMS
- DUSTY
- DEFIANTLY
- MURMURS
- DOVE
- ERA
- LEGENDS
- HIERARCHY
- CHEESES
- RICHNESS
- IROQUOIS
- KINDRED
- ANTAGONIST
- KINDER
- MIRACULOUS
- VILLAGERS
- PRECIPICES
- BOUNDING
- FLITTING
- MEANEST
- MAXIM
- HONORED
- TREMULOUS
- CHAISE
- GUIDES
- PERCEIVES
- CAGES
- WHEREABOUTS
- DRAPERY
- RELIC
- CONCLUSIVE
- THARKS
- DAK
- KOSIS
- STATIONS
- SAB
- PLEA
- CRIPPLE
- PLATEAU
- ISLE
- SURF
- PRIMARY
- ACCURATELY
- PLANETS
- DENOMINATED
- VENOUS
- MODERATELY
- ADEQUATE
- TREATISE
- FANTASY
- PUBLISH
- PRACTISING
- SCHOLARS
- FEE
- MONKS
- SUBSCRIPTION
- WARDROBE
- UNCEASING
- TIMOKHIN
- AMBULANCE
- TI
- HALO
- ROSTOVS
- JEREMIAH
- CELEBRATION
- NAILED
- KUTUZOV
- COSSACK
- FORTHCOMING
- HEARERS
- BEDDING
- LAUGHINGLY
- SWEDISH
- NEARING
- SIZES
- GNARLED
- FULFILL
- ROBERTS
- ALTERNATING
- TANK
- VICKSBURG
- COPIED
- INDICATIONS
- ESPECIAL
- LEARNS
- HUMORIST
- CRECHE
- QUILLAN
- ELECTRICAL
- INDIANA
- NOLAN
- FOURTEENTH
- INHUMAN
- DISCLOSE
- APPRECIATIVE
- BESTOW
- PROGRESSIVE
- TRANSFIGURED
- CONSECRATED
- UNOCCUPIED
- ENCOUNTERING
- OWAIN
- ELSIE'S
- ADELAIDE
- CRUMBLING
- ATHLETE
- SPURRED
- PARCHED
- DECREED
- REASONED
- ETIQUETTE
- GIT
- RIVIERE
- STEERED
- INCONSISTENT
- WADMAN'S
- SAUSAGES
- MILBY
- ANTIGONUS
- SOSIUS
- EXCURSIONS
- LABORED
- MARGUERITE'S
- STUNG
- BALLAST
- MAURICE
- MUSKETS
- STAPLES
- D'YE
- VERITABLE
- DRIFTS
- PIONEER
- IMMIGRANTS
- FERRY
- GRADUATES
- MEXICAN
- LINK
- STRUTTED
- THEREWITH
- WHICHEVER
- LAUDONNIERE
- ESCORTED
- ASTOUNDED
- RANSOM
- TANKERVILLE
- BUNCE
- BAKER
- ELECTORS
- HARTFIELD
- CONNEXIONS
- EXTRAVAGANT
- SIBYL
- TREASURER
- CORNELIA
- CARLYLE'S
- QUAKING
- VARY
- ELEANOR'S
- COUNTIES
- CLUE
- GRIZZLED
- MARION
- MOWBRAY
- IMPUDENT
- HATTON
- TURBULENT
- MANETTE
- MATHEMATICS
- FLOODED
- ARGO
- JASON'S
- STRATEGY
- TEXAS
- NEBRASKA
- INCOMPREHENSIBLE
- GRASSHOPPER
- GODFATHER
- FISCHER
- PANTALOON
- CRYSTALS
- ARABY
- CONTEMPORARY
- SIGNORE
- MAJOR'S
- DISREGARD
- DEALER
- SMOOTHED
- MARVIN
- JUG
- CHESTER
- MOURNED
- CURRANT
- PYES
- COMPOSITIONS
- GATHERS
- SLOANE
- COPLEY
- SUBORDINATE
- PRESENTING
- CANYONS
- TINTED
- MOTORS
- SCRIPTURE
- SABBATH
- SENTINEL
- HAVANA
- BENEFITS
- WAKEN
- PRECARIOUS
- CHAPERONE
- KETTLE
- CHANDELIER
- STRUCTURES
- EQUIVOCAL
- TER
- FAINTEST
- TRUDOLYUBOV
- ROUBLES
- MONTH'S
- ARISTOCRATIC
- ANTIQUE
- RUSKIN
- HINGHAM
- OBSERVANCE
- STRUT
- FOWLS
- BYSTANDERS
- HEAVING
- DRAINED
- FIGHTER
- CAPRON
- MARKEN
- EMBROIDERED
- DISAPPROVE
- PHEASANTS
- MOSQUITOES
- JACKALS
- CHECHEN
- SKIFF
- IMPETUS
- CONSTITUTIONAL
- NIVER
- EF
- VERBAL
- CONFINE
- PLANTATIONS
- COUNSELS
- BASKETBALL
- FRICTION
- PLUMBER
- AMBIGUITY
- BRAGTON
- GORE
- EXIT
- MORGUE
- LABORER
- CONFEDERACY
- CONFEDERATE
- HEPSEY
- MATERIALISTS
- PATIO
- COLYUMIST
- LYRIC
- BLASI
- MEEKS
- PHIL
- ABIGAIL
- RIYOS
- GENZABURO'S
- SAZEN'S
- KIYOMORI
- ARGUS
- ARCHIVES
- STEYNE
- GERALD
- GUNTER
- ANGLO
- NIGHTINGALE
- SHOREDITCH
- WAND
- RATIBOR
- AMEN
- REVENUES
- PROPAGANDA
- DAEMON
- HERACLIUS
- POFFENBURGH
- MERCER'S
- COLLIE
- ODIN
- RITZNER
- JUNG
- HERMANN
- ABOLITIONISTS
- ORIOLE
- HAMISH
- WEBB
- RANDOLPH
- AXEL
- LIEDENBROCK
- FRINGED
- REPUTED
- DECORUM
- NEIGHBOR'S
- DINT
- NEGLECTING
- HOUSEWIFE
- SOWING
- PLACIDLY
- SCANT
- THERE'D
- BIRCHES
- CRAB
- UNHEARD
- UPSIDE
- PERFORCE
- UNCANNY
- SLOPED
- PASSENGER
- FRECKLED
- MOONSHINE
- BLOOMING
- MISTY
- SIDEWISE
- ASCENDANCY
- RELATIONSHIPS
- CLASSED
- CONTIGUOUS
- BOLTS
- MADEMOISELLE
- SATIRE
- CONCIERGE
- STUPEFIED
- FORMALITIES
- CRUCIFIX
- GALLEYS
- ACCOMPLICE
- HARSHNESS
- SINNED
- CONDEMN
- DESPATCH
- ADIEU
- PAINED
- HOMOGENEOUS
- DETEST
- FREEMEN
- DOMESTICS
- HILARITY
- IMPLACABLE
- INTIMATION
- FICKLE
- NOTIFIED
- CAPRICIOUS
- GENTLEWOMAN
- DARNED
- BLOSSOM
- CONSISTENCY
- PRESCRIPTION
- POWERLESS
- EMINENTLY
- SARCASM
- SCAR
- PARLIAMENTARY
- NEGOTIATIONS
- RESTRICTIONS
- FORBIDDING
- INJURIOUS
- PARTISANS
- CESSATION
- DIPLOMACY
- CONTINUATION
- OBSTINATELY
- DIRE
- DRAGGING
- DISPUTES
- MASSACRE
- DIGNITIES
- WITT'S
- HOPELESSNESS
- NAVAL
- PUBLICITY
- WHITEHALL
- RENEWAL
- DEXTERITY
- FRY
- SALTED
- COOKED
- BEEF
- CIVIC
- ADDS
- PERMANENCE
- THROBBING
- SUFFERS
- BUDDING
- DISPOSING
- THROB
- DEFY
- FOREBODING
- FORGETFULNESS
- BLANDLY
- PROVIDING
- SORDID
- ADMIRABLY
- RUFFLED
- KILLS
- RUM
- MEDITATIVE
- UNKEMPT
- INFIRMITY
- BANGED
- TWITCHING
- WREATHED
- ANTAGONISM
- CHALLENGED
- LIMPLY
- HONOURED
- CHUCKLE
- REPROACHFULLY
- TESTING
- GRATUITOUS
- CRITICISE
- ARROGANCE
- TACITLY
- GOTHIC
- GRUMBLE
- JUDICIAL
- AFTERNOONS
- FOREGROUND
- COMPLACENCY
- TERMINATING
- PERILS
- SKIMMING
- SWIFTER
- CONTRIVE
- CHARIOT
- BUSTLING
- POTS
- MASKS
- HIDES
- INDIVIDUALITY
- APPEALS
- PATHWAY
- UNAVOIDABLE
- DECISIONS
- BUILDS
- BENDS
- ENVIRONMENT
- NERVOUSNESS
- CONCENTRATION
- CONTENTMENT
- LEVELS
- ADULT
- NOCTURNAL
- BANDIT
- FENCES
- PRISONS
- SHOVING
- SKIRTED
- BROKER
- ORATION
- HARRINGTON
- IMPROBABLE
- MARSHY
- LANDLADY
- MINISTER'S
- TELEPHONED
- KATHERINE
- OBSCURED
- MARIA
- TURMOIL
- REVEALING
- SMITHTOWN
- BASTILLE
- REPRESENTS
- CARLOS
- OMEN
- STIMULATION
- SHROUD
- UNCOUTH
- FLEEING
- IRONICAL
- NOISILY
- NASTY
- BULGING
- PHASE
- ODDLY
- FORMULA
- MECHANICS
- DELAYS
- FAINTING
- GERM
- DRUGS
- VARNISHED
- DISARMED
- ENQUIRE
- TRANSPORT
- SHEWED
- UNJUSTLY
- DIVERT
- ALLEYS
- VETERANS
- RAYMIE
- SPECULATE
- SLABS
- DRENCHED
- REFERENCES
- CLAIMING
- CLUSTERS
- CIGARETTES
- CLARK
- AVENUES
- ELM
- ALLEY
- SCOTTISH
- LECTURES
- CANAL
- JEALOUSIES
- INCONTINENTLY
- PLANNING
- CINDER
- GREASY
- HATING
- WATERY
- VAGUENESS
- BLOATED
- DOORSTEP
- GROWLING
- REDDY
- COACHES
- TRANSACT
- ROBBING
- SCRUPLES
- TAXI
- MATINEE
- SCATTERING
- STALE
- CRUTCHES
- PALLID
- AMORY
- ASPECTS
- STINKING
- PATTERNS
- REITERATED
- PATHOS
- QUESTIONER
- WARMING
- COLDER
- HONEYMOON
- BLENDING
- ABSORBING
- HAULED
- HERMETICALLY
- CHRONOMETER
- SOLIDLY
- SATELLITE
- HEIRS
- MARVELS
- TRANSMITTED
- COSTS
- DASHES
- VEHICLES
- MANUFACTURE
- UNANSWERABLE
- TEMPORAL
- TESTED
- PERSECUTION
- DETOUR
- SLING
- PARTICLES
- MAGNET
- CLUMPS
- HALFWAY
- FIRMER
- AU
- REVOIR
- CHAMPAGNE
- ADVERSE
- ADVENTURERS
- ACCLAMATIONS
- PAYMENT
- RESPLENDENT
- IMPETUOUS
- OSWALD
- REPEATEDLY
- HYPNOTIZING
- REQUESTS
- TECHNIQUE
- CONVICTED
- CENSURE
- MEANINGLESS
- DISTURBANCES
- OVERSHADOWED
- SYSTEMATICALLY
- SUPPLEMENT
- DISORDERS
- EMOTIONAL
- LOSES
- PSYCHICAL
- REFORMERS
- EMPHASIZE
- TRAIT
- NATUREDLY
- DAWNING
- TRAVERSE
- WATERFALL
- PEBBLES
- TINT
- TRIPPED
- VISTA
- ARCHITECTURAL
- NEGLIGIBLE
- MALICIOUS
- ADROIT
- FASTIDIOUS
- CORRECTNESS
- FICTITIOUS
- GNAWING
- DESPOTIC
- IMPROMPTU
- FUSSY
- SUPREMACY
- UNANIMOUS
- INCONCEIVABLE
- INDULGING
- STUBBORN
- MALIGNITY
- SUPERFLUOUS
- UNFLINCHING
- LASH
- LEADEN
- DISTRUST
- MINUTELY
- PREGNANT
- GOODLY
- INTRODUCING
- DANCES
- LOBBY
- LIMITLESS
- DAVID'S
- SCHEMING
- MAGAZINES
- REPLACE
- PARALYSIS
- ACHE
- GLEAMS
- CONFIRM
- INEQUALITY
- COOLED
- AFFIRMATIVE
- OUTSKIRTS
- HEATH
- CONCEDED
- IMPUDENCE
- EXILES
- EXILED
- PRESENTATION
- QUARRELLED
- DERVISH
- DUSKY
- DUNCAN
- LODGES
- ACCORDED
- EXCLUDED
- REPRESSED
- RESUMING
- SICKENING
- EMULATE
- SQUAWS
- ADO
- HATCHETS
- SOFTEN
- STERNNESS
- SCOUTS
- DESPATCHED
- FEARLESSLY
- PROJECTS
- ADVENTUROUS
- PREMIUM
- SEESAW
- ATTIC
- REBUKED
- MANUFACTURED
- PUNCTUALLY
- SAMPLES
- MUSTACHE
- VEGETABLE
- BLACKSMITH
- DELIGHTEDLY
- GINGHAM
- COBB
- SOMETHIN
- RECITE
- SATISFACTORILY
- SWEARING
- PROFANE
- BLOODHOUNDS
- UNRULY
- LYDIA
- ADORED
- REVERENT
- ACCOMMODATE
- COATED
- PROPPED
- CONDUCTOR
- CHAUFFEUR
- TRIFLES
- LINING
- HORRIFIED
- RICE
- FANCYING
- BESEECH
- DISCONSOLATE
- REQUITE
- WRONGED
- AWAITS
- QUESTIONING
- COCKED
- CROSSLY
- BLOND
- PANES
- WATERLOO
- FLANDERS
- BELGIAN
- TATTERED
- TEMPERATE
- SCREW
- BURDENED
- PASSERS
- OPPRESSION
- PHYSICS
- DISGUISING
- PANTOMIME
- SERMONS
- INMOST
- SPIRITUALITY
- PREACHERS
- GROANING
- OPERATING
- MINGLING
- IMPLIES
- YEA
- SAXON
- PROXIMITY
- SHUDDERING
- APPLIES
- MOCKING
- OVERLOOK
- METAPHOR
- FALKLAND
- OVERHANGING
- VEHEMENTLY
- WRETCHES
- MAGELLAN
- DER
- COUNTENANCES
- COUGHED
- YIELDS
- WATERFALLS
- SOLITUDES
- WINTER'S
- INCONSIDERABLE
- EASTERLY
- ISLES
- CONSIST
- JERK
- MERCILESSLY
- BEHOLDING
- INVENT
- OLYMPIANS
- EXPLORING
- RESPONSIVE
- HAUNTS
- UNDENIABLE
- DISPLACED
- CIVILISATION
- PILGRIM
- DISCUSSIONS
- PRETENCES
- ODOUR
- BELATED
- TRUANT
- FROLIC
- VESTRY
- FOOTHOLD
- SNEAK
- AIDS
- MENDED
- CHAFED
- SCARRED
- LULLED
- CONGRATULATIONS
- FLYCATCHER
- OLIVE
- APOLOGIZED
- BUG
- DISTRICTS
- NEWER
- MOUNDS
- CHINOOK
- WASTING
- NETTING
- PADDY
- MODIFIED
- LENGTHENED
- OCCUPIES
- SHAKES
- SOMEBODY'S
- TIRELESS
- CRACKERS
- LAME
- BUNK
- APPARITIONS
- BROOKLYN
- ARC
- ORIENTAL
- RETORT
- TUSH
- EXPLOSION
- PERSUADING
- FAVORS
- CLAMPED
- FIERCENESS
- DISADVANTAGE
- JOURNEYED
- DIMINUTIVE
- HANDSOMER
- BARRED
- GALLOPING
- OBSOLETE
- DOLLS
- WATERING
- SALLY
- SONNETS
- PRELUDE
- REPROOF
- DEJECTED
- MALADY
- SYLLABLE
- PARTIALITY
- ECSTASIES
- BESTOWING
- TRANSACTIONS
- AIMING
- SUSPECTING
- AVOWAL
- DICTATE
- STRIDES
- MARSHALL
- DOCILE
- LONESOME
- APPREHENSIVE
- SIMPLER
- VOYAGES
- FURIES
- TORMENTED
- UNINTELLIGENT
- RECOVERY
- TROPICAL
- SOFTNESS
- PALMS
- WHITEWASHED
- PILGRIMS
- TREADING
- UPHELD
- POUNDED
- LIGHTHOUSE
- HISS
- COLLISION
- BOLTED
- MOANED
- THICKNESS
- VICIOUSLY
- REACHES
- MEAGRE
- NARROWER
- ENLARGED
- HETTY
- CLOUDLESS
- VERGE
- SEASONING
- MORTAR
- CRUMB
- LOBSTERS
- ONIONS
- REALISE
- TINGLING
- ENDEAVOURING
- CAREW
- CONTENTEDLY
- DENUNCIATION
- WEIGHING
- BEADS
- WORKMANSHIP
- WEEDS
- DELIRIUM
- HELEN'S
- SUH
- MOTIONING
- SHEEPISH
- PSALM
- GALILEE
- THRONGED
- RANGED
- PIPING
- SILKEN
- CECILY
- MAGAZINE
- PLEDGED
- COMERS
- EVERMORE
- BRAVER
- GRASSES
- TRIES
- PROFESS
- INSTANTANEOUSLY
- PENETRATION
- MARGARET'S
- WILLOUGHBY
- CONDEMNATION
- SEEM'D
- BABYLONIANS
- FACTIONS
- ELECT
- ENIGMAS
- CANDIDATES
- NARROWLY
- COMMENCE
- SWAY
- COMBATANT
- TILTED
- THO
- DISTINGUISHING
- HURST
- DISH
- WRETCHEDNESS
- NETHERFIELD
- MENTIONING
- TAPPING
- EMERGENCY
- SHARING
- ARRESTS
- QUAKE
- DUN
- VAPOURS
- DESTRUCTIVE
- BORDERING
- CONTAGION
- DISTRESSES
- COMMENCEMENT
- FORBADE
- ASIATIC
- REVENUE
- EXPENDITURE
- LUXURIOUS
- SUMPTUOUS
- SCREENS
- ENTERTAINMENTS
- FAVOURABLY
- ANOMALY
- DECORATIONS
- INNATE
- FARCE
- COMETH
- WOULDST
- TRUSTY
- CARDENIO
- HEEDLESS
- LASTS
- OPPRESSIVE
- INCLINATIONS
- ATTIRE
- UNAWARES
- FERKO'S
- GLUTTON
- TORMENTS
- FOURS
- HEALED
- PACING
- LIME
- PARTISAN
- LACKEY
- COMPRESSED
- SWELLED
- INSPIRE
- CROWNING
- MAIDS
- STATUES
- CELESTIAL
- VISIONARY
- PAPA'S
- PROCEDURE
- KNOLLYS
- HELPLESSNESS
- COMICAL
- EMBODIMENT
- NOTICEABLE
- ENORMOUSLY
- INVARIABLE
- OUTRAGEOUS
- CRUSADE
- ORIENT
- SQUIRES
- JESTS
- SEMBLANCE
- LOOSED
- SLOPING
- HAVISHAM
- FAUNTLEROY
- SWELLS
- CREAKING
- YER
- LONGITUDE
- BROADER
- METAMORPHOSIS
- LIMITATION
- PRIMARILY
- APPLICABLE
- QUICKEN
- REBUILD
- WHATSOEVER
- TRADITIONS
- LIMITATIONS
- SANITY
- RUGS
- SHOULDERED
- COMPARTMENT
- POCKETED
- ADDRESSES
- FORMALITY
- VICAR
- TRACING
- PONDEROUS
- HUNS
- SHEPHERDS
- GAUL
- LEO
- BONIFACE
- CAPTURING
- INTRUSTED
- MOSQUE
- CHANTING
- ASSUREDLY
- CLAMS
- PRICED
- INDIVIDUALLY
- SORTING
- REDUCE
- ESTIMATED
- BELLIES
- PRONOUNCING
- BLINKING
- SHAPELY
- SPAT
- BLONDE
- VIRGINAL
- FLOURISHED
- TREATIES
- LOFT
- TRIPLE
- CRACKS
- CLOSEST
- FASTENING
- BLINDNESS
- FLANKED
- RECTOR
- UNDISTURBED
- ACCOMMODATION
- TOPOGRAPHY
- FATED
- ABOLISHED
- FACILITIES
- MISTRESSES
- TEDIOUS
- PATTEN
- DAMSELS
- EFFECTUAL
- COWPER
- HEATHEN
- ORCHESTRA
- DISREPUTABLE
- RISKED
- AGILITY
- INSPECTOR
- MERCANTILE
- ICILY
- MOYNE
- CLEW
- CLUTCHING
- PURSER
- LECTURER
- HALJAN
- EXIGENCIES
- BLACKSTONE
- EXOTIC
- FRANK'S
- DASHING
- TORTURED
- METALLIC
- LUNGED
- FRISKY
- MEANLY
- DELICACIES
- PUFFS
- SIXPENCE
- QUIVER
- AVERTED
- IMPLORINGLY
- ABHORRENCE
- MANCHESTER
- GLADE
- HARLEY
- DEPRESSION
- GRANDLY
- ANTICIPATIONS
- OVERPOWERED
- CICERO
- CONCERTS
- POSTPONED
- SHYNESS
- FOOTLIGHTS
- YOUTHS
- WAKED
- BROTHERHOOD
- QUIVERED
- COMPLEXITY
- SUPPLYING
- TREMENDOUSLY
- MILDLY
- UNEARTHED
- SHEPHERD'S
- ENTRENCHMENTS
- CHEMICAL
- SWARD
- EXULTATION
- BREEZES
- PLIGHT
- LOVINGLY
- MOLE
- SWAN
- RIDGES
- MOSSES
- FETCHING
- INTERPRETER
- FRESHMAN
- REPRESENTATION
- MASTERPIECE
- EXERCISES
- THRASHING
- STIRRUP
- VESTIBULE
- WALNUT
- RUMOURS
- COUNCILS
- DRURY
- HOLBORN
- CERTIFICATES
- SURROUND
- EXCLUSIVE
- TRADES
- KESEBERG
- GRANDPA
- STARVED
- CHARGES
- SHUNNED
- CALENDAR
- WILLOW
- ASTOUNDING
- COLOMA
- ANNOUNCING
- FOOTED
- BUNCHES
- HOOKER
- FRAGRANT
- TIDY
- RESORTED
- GOVERNORS
- TORPEDO
- PINNED
- WAG
- PIERCE
- TUSKS
- ENJOYS
- SURPRISES
- TIRESOME
- DEPARTING
- CLASSIFIED
- ANNEX
- ABREAST
- LAND'S
- CRAMPED
- UNEXPLORED
- MUTINY
- SQUEAKING
- UD
- SQUIRE'S
- RATS
- AFORE
- CHRISTENED
- WAGE
- INFLICT
- FORFEIT
- AMOUNTING
- DEEM
- PERSIST
- CHASM
- TWISTING
- TRAMPLED
- GROUNDLESS
- RANGES
- GRANDCHILDREN
- RESEARCH
- LORE
- RESEARCHES
- GOSPEL
- GOOCH
- GEMS
- WEAVE
- DRILLED
- MASSED
- FLUCTUATING
- GESTICULATED
- NOISELESSLY
- REVOLUTIONARY
- LABOURS
- DIALECT
- ILLUMINATION
- WARREN
- TRUCK
- SERFS
- THUNDERSTORM
- UNSPEAKABLE
- ORACLE
- FEASTED
- ANT
- WHEREFORE
- COWARDICE
- PINING
- SHRINE
- FLAVOR
- LACKS
- FRANKLIN
- SAGE
- TENANTS
- HORDES
- CONVERSION
- TAMED
- STUMBLING
- MADMEN
- CAUSELESS
- BELIEFS
- BUDDHIST
- UMEGAE
- BRIM
- RUINOUS
- MEDITATING
- ROKURO
- KUBI
- CONFISCATED
- QUICKNESS
- TESTAMENT
- CONTEND
- CONSIDERATE
- FRAGILE
- STEED
- DISCOMFITURE
- RELICS
- DISMAYED
- DESCENDANTS
- SANTA
- HELMET
- PLAYER
- HALTING
- LICENTIATE
- GEESE
- PINIONED
- INFURIATED
- SOLA
- KOVA
- GRATING
- ZODANGA
- CUSHION
- KANTOS
- EMBERS
- CAP'N
- KNOWED
- WIPING
- SCRAMBLE
- BLUNDERED
- ENSURE
- SNATCH
- BAFFLING
- SUNDOWN
- PAINTERS
- CONFUSEDLY
- STRAIGHTENED
- BRUTES
- PREFACE
- FRAMLEY
- ACCUMULATED
- UNWILLINGNESS
- ARCHBISHOP
- SALUTARY
- UNDRESSED
- MOANS
- HOW'S
- COLLAPSE
- RHYTHMIC
- GNAW
- ENJOYABLE
- GRANDADDY
- DEY
- MEMPHIS
- BOGUCHAROVO
- QUARTERED
- KOLOCHA
- OBJECTIONABLE
- COLLINS'S
- RESERVES
- DIFFIDENCE
- PULSES
- UNCOMFORTABLY
- UNALASKA
- MISSIONARIES
- ZEALOUS
- POP
- COMPARING
- MARSHAL
- SKINNED
- ATTRIBUTE
- GEOGRAPHICAL
- INERTNESS
- VARIATION
- ROSS
- GRACE'S
- EMERGE
- GUST
- ELEPHANTS
- ACCUMULATIONS
- ENGINEERS
- AD
- LOUISIANA
- GAMBLE
- UNHAPPILY
- PICKETED
- BRANNAN
- UTAH
- EVA
- OBSTRUCTING
- O'BRIEN
- GANNON
- BRAZILIAN
- NAP
- CUTLASS
- SETTLEMENTS
- CAMPEACHY
- IMPERTINENT
- RECKLESSNESS
- STING
- DREAMY
- UTTERANCES
- PETALS
- ATOM
- OMNIPOTENCE
- IMPARTED
- DIFFUSED
- UNINTERRUPTED
- GUENEVER
- MAYING
- COVENANT
- CONVEYING
- FIREWOOD
- DISCOURSED
- BABES
- AMOUNTED
- SUFFUSED
- UNKIND
- FUSILLADE
- FOOTMEN
- RIFLES
- LUSTROUS
- WRITHING
- CHARGING
- CARPETED
- THUMPING
- SUAVE
- CHUBBY
- UNMISTAKABLE
- RESONANT
- STEPHEN'S
- JOVIAL
- DI
- EXPLOITS
- COLONEL'S
- VOLUNTEERED
- OFT
- BOWLING
- TONGS
- LADYSHIP
- CORPORAL'S
- CZERLASKI
- NOUGHT
- SYRIAN
- ENDANGERED
- OFTENTIMES
- ANOINTED
- DEVELOPMENTS
- DEFECT
- SLEEPLESS
- CONSOLE
- BRAVADO
- TYING
- STEERING
- EXULTANT
- PERPENDICULAR
- BURGESS
- STOOP
- EMITTED
- STEILACOOM
- MT
- JUAN
- TRINITY
- YEAST
- BEQUEATHED
- ROUNDS
- PLASTERED
- BARRINGTON
- ELTON
- BODY'S
- NICER
- DISSIPATION
- PALLOR
- SCRAWL
- ARMCHAIR
- HURRAH
- TORTOISE
- DUCKED
- JERRY'S
- BAPTISM
- STOMACHS
- INFANTRY
- TAUBE
- SPINE
- DECLINING
- MICK
- MUTTON
- DARNAY
- SCAFFOLD
- DISTASTEFUL
- PANELS
- WORKMEN
- PUMPS
- STRIPES
- SEVERED
- BOON
- TALLEST
- FILE
- MOSES
- JUDICIARY
- FOLD
- LIBERATION
- INSINCERITY
- WRONGLY
- CAPITOL
- STUYVESANT
- BOXING
- EVERGREENS
- HARLEQUIN
- LEANT
- THEO'S
- OCTAGON
- NECKED
- BELGIUM
- GIRLISH
- PAMELA
- POLTON
- FATIGUED
- AFFORDS
- REUBEN
- MEMORANDUM
- MONASTERY
- PATRICIA
- DANNY
- PLEADING
- SUSPICIOUSLY
- PANTRY
- STRAWBERRY
- UNACCUSTOMED
- HARRIS
- FADE
- PAUL'S
- CLAP
- SKETCHES
- CHRISTOPHER
- SLICK
- UNDERSTANDS
- JIST
- LOPEZ
- RATTLER
- IGNORE
- JACKETS
- COLOSSEUM
- FLAKES
- STIFLED
- FEEBLENESS
- SCULPTURE
- ETHEREAL
- SEATTLE
- VICTORIA
- RUNG
- JEHU
- LAYS
- SPAKE
- PADDLING
- PRECEDE
- CLEMENT
- EASEL
- PROJECTILES
- TRAVERSING
- MANIFESTATION
- SICILY
- EPITHET
- FORESTER
- MILLER'S
- GILL
- ABSORPTION
- CARNEGIE
- ROCKEFELLER
- FACILITY
- REIGNS
- PREDECESSORS
- CONCRETE
- CHRONIC
- RELIANCE
- MAINE
- OILED
- PEWS
- PURITANS
- FRINGE
- EDUCATIONAL
- PATRIARCHS
- SPECKLE
- COCKLETOP
- ARRANGING
- DISPATCH
- SENTIMENTALITY
- PETTED
- GARDENING
- ENLIGHTENMENT
- WRANGELL
- GLENORA
- MORAINE
- YOUNG'S
- BUSTLED
- VOLLEY
- THORBIORN
- KODAK
- MAITLAND
- CROPPER'S
- ELUSIVE
- ROMISH
- IMAGINES
- TEREK
- PRO
- TARTAR
- ROBSON
- FOSTER
- MILLVILLE
- WEGG
- HUCKS
- THOMPSON'S
- COMEDY
- AFFIRMED
- HOSE
- WID
- WOODYARD
- SOWERBY
- CRAWLEY'S
- ARABIN
- VERBS
- FANNICOT
- REPRESSION
- NESBIT
- LEGACY
- VARIATIONS
- STIMULI
- CHORDS
- OSTLER
- REGULATIONS
- CRIANAN
- HERALDED
- ACADEMICAL
- PREUSS
- GLASGOW
- MANDERSON
- DIRHAMS
- STERLING
- TICKETS
- GREGGORY'S
- FAMILIARLY
- MAXIMILIAN
- CARTERET
- INDOLENCE
- HEREDITY
- REGIS
- WAN
- VERRY
- ROSVILLE
- HOISTED
- TEACHES
- CENTIPEDES
- CONWAY'S
- RODGERS
- GRIMSHAW
- ARAMAEANS
- REVOLTS
- AHAB
- BECKY
- PAYNE
- DENOUNCE
- MAUMEE
- ANKLET
- ANGELES
- ATTIRED
- KEATS'S
- BRAWNE
- ATTAINMENT
- BRYAN
- FERMAIN
- COUNTERS
- GOVERNING
- CENSORSHIP
- OSSOLI
- RITA'S
- RITA
- ZENZA
- BOBO'S
- HAUNTING
- PRODUCTIVE
- SEDUCED
- BAGPIPES
- FALCON
- WEBBED
- GLUTEN
- FONTAINEBLEAU
- BEAN
- SPOOKS
- CHICHESTER
- TRUMPETED
- ERADICATE
- SWEETWATER
- ROMANIANUS
- PLANTAGENET
- OLDACRE
- GRANNIE
- AUXILIARY
- SORREL
- DEFTLY
- MINUTE'S
- RAMBLING
- FURTHEST
- RAPPED
- HAIRPINS
- MARILLA'S
- HEADACHE
- SUCK
- IMPORTED
- BELL'S
- FULNESS
- TENSE
- LOCKING
- YELLOWISH
- VIVACITY
- SCOPE
- DIMPLES
- PITYING
- PLUMES
- KID
- TALKATIVE
- PATERNAL
- ENDEAVORS
- APPELLATION
- CHATTING
- GRADE
- INTRIGUES
- IMPERTINENCE
- TRADESMAN
- PREOCCUPIED
- VAGABOND
- REFLECTS
- INACTION
- CYPHER
- HOOP
- TINK
- LONGINGLY
- TIGHTENED
- MUMMY
- JEERED
- NEVERLAND
- TINKER
- ARTFUL
- SQUEEZING
- FALTERING
- GLORIOUSLY
- SHAMELESS
- LISTENS
- ENQUIRY
- EXPERIMENTAL
- EXTREMES
- ESTABLISHMENTS
- LIVERPOOL
- CLEVERLY
- LISTENER
- INFALLIBLE
- CASPAR
- SULTRY
- FADING
- MEDITATIONS
- DISMISSAL
- DECAYED
- COMPASSES
- PICTURING
- PREDICTED
- SUITOR
- CHERISH
- TINGED
- SORES
- REFUGEES
- AMBASSADOR
- UNTHINKABLE
- COUPLED
- REDRESS
- PORTS
- STATESMEN
- DOWNS
- MERCHANTMEN
- CRUISING
- ADRIAN
- STIFFENED
- HOSTILITIES
- ABUSED
- THAMES
- NORTHWARDS
- RUYTER
- PLYMOUTH
- STRAITS
- EMISSARY
- SUPPLICATION
- FORCIBLE
- MARITIME
- EXTINCTION
- INSTIGATION
- STRANGEST
- DIPLOMATIC
- MOMENTOUS
- BRIBE
- AVOIDING
- CLAMOUR
- DEFECTION
- LABORIOUSLY
- TRIUMPHED
- THICKEN
- CLEANED
- CARROTS
- THICKENED
- LEMONS
- GRATED
- STEWPAN
- STANLEY
- TINTS
- INCUR
- CIVILITY
- RUSTIC
- RUDELY
- WREATH
- SWEETER
- MADMAN
- MUTUALLY
- INFERIORS
- IDLY
- AMPLY
- HACK
- REPLENISHED
- GLARED
- FOREARM
- STORMED
- SNARLED
- COMPRISED
- DODGE
- RAFTERS
- APOLOGETIC
- WHITENED
- GOINGS
- CHAFF
- RENDERS
- COUSIN'S
- ORIGINALITY
- HEDGEROWS
- APPRECIABLE
- VACANCY
- FRAY
- ADORN
- EXCELLED
- BEWAILING
- FATTEN
- SPELLS
- ASUNDER
- LOAVES
- CHERRIES
- UNTIDY
- FARMER'S
- CHARACTERIZED
- HELPS
- STUMBLE
- CONFLICTING
- CONTESTS
- TRADITIONAL
- OUTLET
- HYPOCRISY
- IRRITABLE
- COUNTS
- UPS
- IMPERFECTIONS
- REVERSED
- RECIPIENT
- PROGRAM
- SAVES
- EMBARRASSING
- BENEDICT
- PIETY
- BLUBBER
- BREVITY
- BUMP
- EFFECTUALLY
- SUBSTANCES
- INTACT
- DEEPENING
- INTERMISSION
- CORONER'S
- ADVERTISEMENTS
- PAREDES'S
- PUZZLING
- TRAMPING
- SPADE
- GRANDFATHER'S
- BREAKFASTED
- MORBID
- JUSTIFIABLE
- YESTERDAY'S
- INCONGRUOUS
- SUBTLER
- CLEANSING
- EXCAVATION
- JET
- SCARF
- HANDLES
- UNHEEDED
- EXCLAIMING
- CANCER
- DRUGGIST
- GUARANTEES
- ADULTS
- WRIGHT'S
- BALM
- ASTHMA
- STRAINS
- HOLDER
- WITHER
- RESTORES
- ITCHING
- BENEFICIAL
- SPOONFUL
- PURIFYING
- INNOCENTLY
- AGREEABLY
- OMIT
- ENDEAVOURS
- EMBRACES
- TURKISH
- HEBREW
- LEPROSY
- APPLYING
- INTIMATED
- RAISES
- PREJUDICED
- SINBAD
- CONDESCENSION
- INVADE
- BINS
- EMBLEMS
- MINNEAPOLIS
- LAYER
- TICKLED
- LOCATE
- ENTERPRISING
- BRIGHTEST
- UNIFORMS
- PIKE
- LAWNS
- COMMODIOUS
- SKILLED
- SPHERES
- HAMPSHIRE
- AVIATORS
- PASTOR
- CYNICAL
- ARTIST'S
- FICTION
- HEMMED
- DEVOTING
- SPURIOUS
- DEMURE
- DEVOID
- COILS
- SENSITIVENESS
- JOINS
- DISOBEY
- WADDLING
- GREETINGS
- SQUIRRELS
- PRAIRIES
- UNMOVED
- PROVERB
- MORCERF
- GRIEFS
- THUNDERBOLT
- WIDENED
- COUNT'S
- HOSPITALS
- CLAMOR
- GLIMMERED
- CABS
- COLLARS
- MIRACULOUSLY
- RESTAURANTS
- COARSENESS
- REACTIONS
- GRANDER
- INSINCERE
- MERGED
- BAYONET
- CONICAL
- FLOATS
- LAUNCH
- HUMPH
- DISC
- DECREASED
- REVOLUTIONS
- ARITHMETIC
- LEASE
- SENSATIONAL
- ADVERTISE
- PROPHESIED
- MOVABLE
- HIGHWAYS
- MOISTURE
- THREADED
- HERETOFORE
- CRASHED
- LOOP
- BUBBLE
- RELUCTANT
- NEEDLER
- FLAPPED
- SWIRLED
- WARY
- AWAKING
- SNARLING
- SPITTING
- JERKING
- TURENNE
- TACTICS
- SUBURB
- FRONTIERS
- REVERSES
- STIRRUPS
- OBEISANCE
- REIGNING
- RESIDE
- REBUILT
- EFFACED
- REBUILDING
- CORRESPONDED
- REVERE
- FETE
- INFIRM
- CURATIVE
- DISEASED
- LAYERS
- OVERWHELM
- DESTROYS
- DIRECTS
- HOURLY
- OVERPOWERING
- ADDICTED
- IMMORAL
- INCREASES
- METAPHYSICAL
- THERAPEUTIC
- AGENCIES
- IMITATING
- MISGIVINGS
- PERSUASIONS
- OVERCOMING
- TRANSITION
- POSSUMS
- LONE
- WHITER
- YOUNGSTERS
- POOH
- CLEVEREST
- RECUR
- WHISPERS
- RECEDING
- TROUGH
- APPLIANCES
- REELED
- INTERVENED
- TRANSVERSE
- RAILED
- SLEEPER
- THEOLOGICAL
- FRUITFUL
- EXTRAORDINARILY
- FANATICAL
- CONVICTIONS
- SOULED
- IMPARTIAL
- CONCEIVABLE
- LUST
- PERSPECTIVE
- INVINCIBLE
- EXTORTED
- ACKNOWLEDGING
- NOTORIETY
- SCANDALOUS
- CONTINUITY
- FORESEEN
- SORRENTO
- TOURISTS
- VOLCANO
- REGISTER
- ARISTOCRAT
- POSE
- ENJOINED
- WOMANHOOD
- QUARRELSOME
- DISMISSING
- UNIMPORTANT
- EXPANDED
- EXPECTANCY
- WRIGGLED
- GRIPPING
- NORTHEAST
- JETS
- MERIDIAN
- LUNAR
- SAVANT
- RESTRICTED
- ABUNDANTLY
- DIFFERENCES
- VAPOR
- PERSISTENTLY
- RAREFIED
- EMBARRASS
- GALLANTLY
- ABIDE
- GUSH
- SHALLOWS
- CHAMBERLAIN
- NIGHTCAP
- CACAMBO
- GALLEY
- DECENTLY
- IMPALED
- PORTE
- RAVISHED
- DISSIPATED
- MEDDLE
- JEHOIADA
- POMPEY
- NERO
- PASTRY
- EL
- POPE'S
- UNCAS
- HURONS
- STALKING
- RETRACED
- ADVENTURER
- RIVETED
- CHEATED
- CONTRADICTED
- CHILDLESS
- IMPENDING
- BLACKER
- SCALP
- POLITIC
- IMPLEMENTS
- ORATOR
- BEANS
- ACQUIESCENCE
- YENGEESE
- HUNTS
- DISCARDED
- SHORN
- PATRIARCH
- AFFINITY
- MEDALS
- REVERENTLY
- PERILOUS
- SAGACITY
- DISTRIBUTE
- INFANTILE
- AWARDED
- RECLINING
- PRACTICED
- TUB
- ACQUIESCED
- CIDER
- COLT
- MITE
- REPLIES
- REBECCA'S
- SHAVEN
- DIMPLED
- ACID
- DEARBORN
- GRAPPLE
- CLASH
- HASH
- BURNHAM
- RAISINS
- ELLEN
- S'POSE
- RIB
- MINCE
- CURVING
- JOLTING
- BESIEGED
- CAVALCADE
- DOWAGER
- ACTIVELY
- TRIO
- LUGGING
- JERMYN
- UNPACK
- POPPING
- WATCHES
- PENCILS
- THRILLED
- SUSPENDERS
- FLOCKED
- HEIGHTEN
- ABOU
- BANISH
- UNITING
- WITHSTAND
- APPAREL
- SYRIANS
- SURPASS
- SQUEAKY
- SPARROW
- AGILE
- WENCH
- FISHWIFE
- VOLTAIRE
- DANGEROUSLY
- COMPOSITE
- RICKETY
- ATTACHING
- FURROWS
- TALKER
- ASTUTE
- COMER
- DISAGREEMENT
- PENNILESS
- BAREFOOTED
- VENOMOUS
- TILLAGE
- UNFORESEEN
- CULTIVATOR
- CONCURRED
- FIRMAMENT
- MOTLEY
- BLUNDERS
- FLATTER
- GRADATIONS
- RELINQUISHED
- OMISSION
- PRESUMPTION
- PRACTISE
- IGNOBLE
- NAPLES
- BARBARIANS
- STRIVE
- SCULPTOR
- INCLINES
- THINKER
- INHOSPITABLE
- STUNTED
- BARED
- ARTICULATE
- EDUCATE
- MINSTER
- BEARDS
- HANDSOMEST
- REPAID
- CONFINES
- BOISTEROUS
- RAINING
- INTIMATELY
- BROILED
- OTTERS
- SUBSISTENCE
- BRAZIL
- UNCLES
- HERDS
- QUALIFICATIONS
- AIMLESS
- SUBMITTING
- ANON
- EGO
- GIANT'S
- OUTDOOR
- FRISKING
- BRIMMING
- MUFFINS
- UNICORN
- RACED
- SHOWERS
- HYPOCRITE
- EVADE
- RALLY
- IRRESPONSIBLE
- FULLEST
- CALVES
- ANTICS
- BUBBLING
- VALOROUS
- FRETTED
- WRISTS
- BHAER
- SURVIVORS
- HARASSED
- SHIPPING
- BON
- JOSIE
- HEROINES
- STRENGTHENED
- MATRONS
- TOILING
- REAPED
- DINGY
- SHORTCOMINGS
- CHEBEC
- FEATHERED
- LATTER'S
- CLICK
- SITES
- STRUNG
- MOUSING
- UNPLEASANTLY
- FARMYARD
- OUTSET
- PEACH
- RIPPLE
- IMMOVABLE
- CARVINGS
- BURSTS
- HEADLESS
- RESEMBLE
- SPIRITED
- FAULTLESS
- LOGAN
- OVERLOOKED
- METHODICALLY
- THAT'LL
- YOUNGSTER
- RUNNIN
- FIGHTIN
- RELIGIOUSLY
- OUTSTRETCHED
- LOATHING
- HUSKILY
- GENT
- STEALTH
- ROT
- CAVALIER
- CONTRACTING
- GRATE
- DISOBEDIENCE
- SUN'S
- REVENGED
- DEPRIVING
- MYRTLE
- EXPIRE
- INHABITANT
- DRAGONS
- SERVICEABLE
- PIANOFORTE
- SENSIBILITY
- BARONET
- FRETTING
- COMPREHENDING
- PROFESSIONS
- INADEQUATE
- RUINING
- ACQUIESCE
- CHAGRIN
- PROBABILITIES
- TROPHIES
- STALLS
- ALLUDE
- CAIRO
- VIRTUALLY
- KIRKPATRICK
- SHRIVELED
- DETECT
- SPASM
- TRICKLING
- WRENCHED
- MOLLY
- CUTTERS
- BRUSHING
- SNOUT
- PLASTER
- AROUSE
- ZEST
- BEWITCHING
- PAGEANT
- SERENITY
- BILLET
- ALLOWANCE
- WALES
- CURSE
- SHUFFLE
- SKIPPER
- WRINKLE
- NAUTICAL
- REQUIRING
- TON
- SPECKLED
- SUBJUGATION
- CONVULSIVE
- PROPAGATED
- PERCEPTIBLY
- NEEDFUL
- DISCOURAGING
- SNOWFIELD
- MOURN
- REMINISCENCES
- ACCOMPANIMENT
- ALEXANDRIA
- NETTLE
- NECESSITATES
- SEASONABLE
- HARVEY'S
- MOISTEN
- BROTH
- LIDS
- THRUSTS
- SPOILS
- PRAWNS
- MOISTENED
- COMPROMISED
- OVERLOOKS
- COMPATIBLE
- NEARED
- AEROPLANE
- ALIBI
- FO
- PHARISEES
- NEEDING
- JUDAS
- OVERHEAR
- SNEAKED
- DEMORALIZED
- PEG
- BOWEN
- SPECTRAL
- SIMULTANEOUS
- LEAPS
- SPRUCES
- RUMBLE
- PRETENDS
- CASTS
- NAMELESS
- COMPLIED
- FORGIVEN
- TRIPPING
- IMPEDIMENT
- AFFRONTED
- EASED
- HOTTAM
- LANCES
- CONFER
- IMPERCEPTIBLY
- SUBSTITUTED
- IGNOMINY
- REVOLVING
- MALIGNANT
- EXCESSIVELY
- PEMBERLEY
- PROTESTING
- CONDESCEND
- REBELLIOUS
- DRAGOONS
- LOCUSTS
- STATURE
- SCRUTINY
- TREPIDATION
- DANK
- AVALANCHE
- VERDANT
- UNSETTLED
- GENERATED
- UNPRODUCTIVE
- LEGISLATORS
- ASSIGN
- SUBDUE
- BANKRUPT
- MEXICO
- BOSOMS
- WILDS
- HOLDERS
- LEVIED
- METROPOLIS
- MARSEILLES
- ITALIANS
- PERISHING
- SHARES
- CONDESCENDED
- TRIFLED
- REPENTANCE
- RECTOR'S
- MONROE
- CRAVED
- BUZZ
- TILES
- PROFESSIONALLY
- THEATRES
- LUXURIOUSLY
- MOTHS
- CHATTER
- ELATED
- FEATS
- WELCOMING
- DONNED
- DOUBLET
- YEOMAN
- CRACKING
- CHIDE
- AFOOT
- UNDONE
- KNOWEST
- ROUGHNESS
- DISHEVELLED
- FALSEHOODS
- SIRS
- PRESSES
- DISPARITY
- FOREGO
- FERVOUR
- BETROTHED
- DISTRACTION
- TRAVELS
- DISCONSOLATELY
- TROUBLING
- BLOOMED
- YELLING
- BELONGINGS
- JUSSAC
- CARDINAL'S
- RECOIL
- HURLING
- AJAR
- RENDEZVOUS
- DESIGNATE
- MYSTIFICATION
- THRICE
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- DEMON
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- MOONBEAMS
- INTIMATIONS
- BRILLIANTLY
- SEPULCHRE
- HORIZONTALLY
- SWEDENBORG
- COLOURING
- RAMBLE
- MEDICINAL
- DERISIVE
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- MISGIVING
- DIVINED
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- NEWSBOY
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- EXHIBIT
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- DARKEST
- CONCENTRATE
- TRANSFERENCE
- THIRDLY
- INCOMPATIBLE
- DISCORD
- QUARRELING
- TEACHINGS
- DERYCK'S
- PEARLY
- DERYCK
- COMPLICATIONS
- COPPERS
- THANKFULNESS
- STEAMING
- DEFINITION
- REGULATION
- GREEDILY
- PERUSAL
- RINGLETS
- ECSTATIC
- PALENESS
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- VISIGOTHS
- COASTS
- IMPROVING
- REFORMED
- MOSLEMS
- CAMELS
- SURRENDERED
- CIRCUMFERENCE
- GAUGE
- PANAMA
- REWARDS
- ARRIVES
- GENUS
- ATLAS
- SCHOLARLY
- MUSSELS
- SHIMMERING
- EMBROIDERY
- DIVIDING
- AUTHENTIC
- UGLINESS
- SMELLED
- HOMAGE
- FERVOR
- HEM
- IDEALISM
- PERMITTING
- RUDIMENTARY
- FOREIGNERS
- ADVOCATE
- UPHOLD
- JANGLING
- MAHDI
- RAIDS
- UNWISE
- GABLE
- SHOD
- STRAPS
- POLAR
- GLOVE
- PRIZED
- FREEZE
- ANIMAL'S
- SCISSORS
- DAMAGED
- COMMODITY
- SINKS
- ENTAILED
- AUSTEN'S
- THEATRICALS
- OBSERVES
- INVENTIONS
- HARPSICHORD
- FAN
- FANS
- PREMATURELY
- SPIN
- GARDENERS
- MANUAL
- CONTRASTED
- GROOMS
- DEFAULT
- STIMULATED
- UNDERWORLD
- CRITICALLY
- THINNED
- FEVERISHLY
- SPECULATIVE
- TOPPED
- PLEAD
- OMINOUSLY
- REPOSING
- GENUINELY
- FRACTION
- HOUND
- PACKAGE
- GRANTLINE
- FERROK
- SHAHN
- CORDON
- EAVESDROPPER
- PROWLER
- CRESCENT
- IRONICALLY
- ENCASED
- INADVERTENTLY
- MATHEMATICAL
- FANCIFUL
- ALLURING
- SATAN
- MUSTER
- SNEAKING
- SHAC
- PATCHED
- GLIBLY
- INTRUDER
- WOMANLY
- UNSATISFACTORY
- CARESSING
- OBSERVANT
- FLING
- INCONVENIENTLY
- SNEERING
- SCHOLARSHIP
- HINDERED
- COLORING
- COMPETENT
- THORNS
- ROSEBUD
- SHADOWED
- STAID
- ANCESTRAL
- WAREHOUSES
- FEASIBLE
- COMPARATIVE
- BENEFICENT
- PLASTIC
- DISCIPLINED
- ROUT
- RARITY
- JOURNALIST
- ACCESSIBLE
- WAISTCOATS
- DEFICIENT
- MEANINGS
- GAIETY
- FOOLISHNESS
- DISCOVERS
- IRRESISTIBLY
- INSCRUTABLE
- VANISH
- CHRISTENDOM
- BULWARK
- QUARRY
- WRAP
- POSSE
- HADST
- STAKES
- NARROWED
- HOUR'S
- SEAWARD
- BITTEN
- QUARRIES
- RUDDY
- SCREENED
- INSTRUCTING
- VINTAGE
- NEEDLESSLY
- FURTIVELY
- CHRIST'S
- GILES
- MAYOR'S
- ADVISING
- BEFALL
- SOUTHWARK
- GENERALITY
- INTERVIEWS
- ADOBE
- INQUIRINGLY
- BEREAVED
- CHUNKS
- LISTENERS
- SHOPKEEPERS
- WADDLED
- COWARDS
- UNCOVERED
- BIER
- VALLEJO
- CHEROKEE
- MODELLING
- SKILFULLY
- MUSLIN
- UNFEELING
- ELECTIONS
- REGAL
- CHIVALROUS
- RETAINERS
- EXPEDITIONS
- ABSTAINED
- FACTION
- SATE
- CHARIOTS
- HYPOTHESES
- HOTLY
- UNTOLD
- TITANIC
- RATES
- PLOTS
- COLLECTIONS
- VERSED
- SPERM
- BUCKLING
- FRIGATE'S
- PIER
- HORIZONTAL
- PROPELLER
- SEEKERS
- PASSAGEWAY
- FORECASTLE
- RAILINGS
- CORRESPONDS
- WATERWAYS
- MONSTER'S
- PLOWED
- TRAILED
- UNDULATED
- SEEKER
- BAILIFF
- POYSER'S
- CURTSIED
- REDDER
- PASTURES
- ABSTRACT
- EXPLANATORY
- EVERYBODY'S
- HILARIOUS
- MASSEY
- GRIEVE
- REVENGEFUL
- MORRIS
- SETH
- PROSPECTIVE
- FORFEITED
- REMONSTRATED
- SOLICITORS
- DATED
- MILADI
- INTERPOSE
- CRACKLING
- COMPLIMENTARY
- OVERTAKEN
- REPROACHFUL
- SARCASTICALLY
- DESCRY
- HOWLED
- JOKING
- BLIZZARD
- SASTRUGI
- DOWNHILL
- UNEVEN
- ERECTION
- MOUNTS
- OSCAR
- NILSEN
- CHASMS
- DRIVERS
- EVOKED
- PHOTOGRAPHER
- UNDERLYING
- BASES
- ELIOT
- WHEELER
- SOOTHED
- WAGGING
- REPRESSING
- PONDERING
- THUNDERING
- EMBOLDENED
- RECEDED
- TRIBUTARY
- DISLOCATION
- DEXTEROUS
- PARAPET
- DISOBEYED
- SWATHED
- THREADS
- SUBTERRANEAN
- VISTAS
- TROTTING
- ALTARS
- ECLIPSED
- JUNO
- UNLAWFUL
- PAINTINGS
- PRODUCTIONS
- ADORE
- UNDUTIFUL
- PIGEONS
- ODORS
- OBEDIENTLY
- PRECIPITATE
- JUPITER
- NUPTIALS
- DWELLS
- TEEMING
- MOUTHED
- LIQUORS
- GIN
- MONTREAL
- ALGONQUIN
- SUBSIST
- FORTIFICATIONS
- ATHLETIC
- CHIRP
- UNPERCEIVED
- REPOSED
- CHEERILY
- CHATTED
- FOOLISHLY
- ADEQUATELY
- ILLUSTRATE
- SIGNIFICATION
- NARRATION
- RESIDENT
- DELUDED
- WOODCUTTER
- FUGITIVE
- MONSTROUSLY
- JUDGES
- COMPLIANCE
- TENEMENT
- DESPISABLE
- INTERFERES
- STAIRWAY
- PIAZZA
- ROANOKE
- DOMAINS
- UNGOVERNABLE
- COMBATS
- SCOUNDRELS
- TOILS
- BLESSINGS
- TOOTHACHE
- INACTIVITY
- VIRGINS
- MOURNERS
- DROLL
- CAMACHO
- QUITERIA
- MOODY
- BLITHE
- SINNER
- PROTRUDING
- ANNIHILATE
- JED
- HORDE
- DISORDERED
- RAID
- IDIOT
- GROPING
- SOLDIERY
- DISCERNIBLE
- KAN
- DISCLOSING
- PATROL
- CITY'S
- CONSPIRATORS
- BUNGLED
- GIBBET
- SHIPMATES
- STRANDED
- FLICKERED
- HUMANE
- AVAILED
- TRANSMIT
- ESSAYED
- INDEPENDENTLY
- ORIFICES
- WOMB
- RETAINS
- INVENTING
- DUKE'S
- UNFINISHED
- APTITUDE
- NAUGHT
- ACCURATE
- CONSERVATIVE
- THRASHED
- ICONS
- ADJUTANT
- UNDRESS
- UNDRESSING
- CREAKED
- KITTEN
- FLOPPED
- BUZZING
- SPHINX
- BODIED
- GAL
- RUTHLESS
- ARRIVALS
- CAPS
- TRAITORS
- PIERRE'S
- IDIOTIC
- HOSTEL
- ENTRENCHED
- CONGRATULATED
- STRANGENESS
- RECURRENCE
- GROUPING
- INHERENT
- COMBINING
- RESPECTIVELY
- SOCIABLE
- SEQUEL
- SLANG
- MOLLIE
- CHINIK
- RADISHES
- PRICKS
- PURER
- OBSERVERS
- ANNALS
- SUCKED
- MINNOWS
- ADAPTATION
- PONDS
- HEAVIEST
- ALABASTER
- ITEM
- REPULSION
- REPUTE
- INTERMEDIATE
- INVESTIGATED
- SWARMING
- NANCY
- LOADING
- CLYTIE
- TONED
- SPOOK
- LAB
- MANON
- REASSURINGLY
- IMPRISONING
- MOREY
- SPRINGS
- FLORIDA
- OCCOQUAN
- EXTORT
- DISCREETLY
- BRITAIN
- RECRUITS
- JAMAICA
- PIRATICAL
- BIOGRAPHER
- PERSECUTIONS
- MODERATION
- CAPTORS
- REVERED
- WRITINGS
- TAPESTRY
- VERDURE
- HAPHAZARD
- INDIVISIBLE
- CONSUMING
- ANTICIPATION
- SYMBOL
- HORSED
- SEEST
- AMBUSH
- SPORTIVE
- TAUNTS
- LEOPARD
- TRAPPINGS
- VALOR
- UNWILLINGLY
- WAST
- PRANCING
- AVENGED
- HEAVINESS
- OBSERVANCES
- REMARKING
- HOWARD
- CARRINGTON
- LUCY'S
- LETS
- PORTICO
- LABELLED
- ROADWAY
- PONIES
- PIKES
- NEWCASTLE
- NICETY
- SWIFTNESS
- SURMOUNTED
- SHIN
- MOCKED
- ENCIRCLED
- GOODWIN
- REVELATIONS
- BUTT
- PORCELAIN
- FRAMES
- EVENTFUL
- POPLAR
- ROD
- DRAWLED
- MOUNTAINEER'S
- HEERD
- FER
- GUT
- GRATEFULLY
- NODS
- INVESTMENT
- ROUTH
- CONGRESSIONAL
- FRAUD
- BUCKSTONE
- COUNTRY'S
- CONFIDING
- ABLEST
- VOICED
- VOWS
- SECLUSION
- SLAB
- PROPHETIC
- ILLEGITIMATE
- BAREHEADED
- DEI
- REASSURE
- PRELIMINARIES
- PLUG
- CRITTER
- OFFICER'S
- BENNETT
- PREVALENCE
- DOWNWARDS
- VENERATION
- BESPEAK
- SLOP
- COMPLETION
- PREDICTION
- GRIEVOUSLY
- DISCRIMINATE
- ATTORNEY'S
- UNSUCCESSFUL
- BRIDMAIN
- RESPECTABILITY
- DEFENSE
- DOMINION
- DEDICATED
- ARABIANS
- VENOM
- SOONEST
- DISCOMFORTS
- DEPRECATION
- LIAISON
- MISTRESS'S
- BORROWING
- CHEAT
- RETREATS
- RIPENING
- NAVIGATE
- LESLY
- TAUNT
- STIFLING
- RUSSEN
- LYON
- RUDDER
- BOAT'S
- CRAG
- TUMULTUOUS
- OUTLYING
- EVERGREEN
- PUGET
- DEEPS
- DREAMLAND
- SLACKENED
- OUTBURSTS
- MISHAP
- SHOVEL
- RECRUITING
- POTTER
- RIOTS
- BENIGN
- IMPOSITION
- ADROITNESS
- DESPOTISM
- BISHOPS
- REMINDING
- DECLARES
- ISABELLA'S
- HANNAH
- CONNEXION
- DECEASE
- INSTITUTE
- SAVIOUR
- PROVOKE
- DENS
- UNSTEADY
- SODA
- HUSHED
- TECHNICALLY
- ACCUMULATION
- PROCLAIM
- IMPERCEPTIBLE
- SEVERN
- BARGAINED
- DECISIVELY
- RENOUNCED
- UNACQUAINTED
- NOIRTIER
- PALAIS
- AIDE
- SUFFOCATION
- SAILOR'S
- HISSED
- WAKEFULNESS
- BARBED
- LAD'S
- TICK
- TRENCH
- ELIGIBLE
- PAVEMENTS
- PRECIPITATED
- DELEGATES
- LORRY
- ENTREATY
- ASPHYXIATION
- TENACITY
- ABSORB
- COUNTERACT
- AGONIZING
- DEPICT
- THROES
- TIPSY
- SHARK
- DEGRADING
- LAUREL
- MOUTHFULS
- FORKED
- WRIGGLE
- BAYONETS
- SENATORS
- REPUBLICANS
- DEMOCRATS
- CAROLINA
- WARDEN
- DISAPPROVAL
- LANDSCAPES
- LAURELS
- WRAPS
- COLUMBINE
- OUTSTANDING
- TRANSFORMATION
- PALER
- BROOME
- BENJAMIN'S
- MULTIPLICITY
- ASSISTING
- SOLACE
- CONSTRUCT
- CORROBORATION
- TOUCHSTONE
- SYLLOGISM
- AMALFI
- RIG
- DEARS
- AMBLED
- RAINED
- VERANDA
- SCOWLED
- OUNCE
- SKIPPED
- HARRISON
- LASS
- WORKMAN
- STUMPS
- ANNETTA
- UNPROFITABLE
- TRANSFIXED
- POINTER
- PLOUGHING
- MUNSON
- FITFUL
- BLUISH
- CELIA
- WADE
- PEAKED
- CARESSED
- SULLENNESS
- WAGED
- BLOODSHED
- GLOOMILY
- HIPPOPOTAMUS
- CY
- BILLY'S
- LICKING
- SISSON
- CUMULI
- HEAVIER
- PILING
- BENUMBED
- MOUNTAINEERS
- FILMS
- SPUTTERING
- PROFUSELY
- WAHSATCH
- OQUIRRH
- LILIACEOUS
- CRUMBLED
- BUTTERFLIES
- FRITILLARIA
- SHOOTS
- DOLEFUL
- HEARKENED
- BUSHELS
- FRIED
- MOORED
- INDEFATIGABLE
- CAYOS
- OBLITERATED
- TRUDGING
- CURTLY
- WAREHOUSE
- NUDE
- SWEDEN
- WOOD'S
- EVIDENCES
- CONCLUDING
- SCATTER
- EXCEPTIONALLY
- SOUL'S
- BUCKETS
- TILED
- ECONOMICS
- BANANA
- MULLINS
- SMELTERS
- CONSPIRACY
- MYRA
- CUBANS
- STEPHANUS
- COMBEFERRE
- MONDETOUR
- PHASES
- CONTRADICTIONS
- DOGMA
- JAVERT
- FORKS
- MONTPARNASSE
- SOUS
- THENCEFORTH
- UPRISING
- FATHOMS
- COWBOY
- NEWCOMB
- WAGGED
- MILL'S
- INDUCTIVE
- OBNOXIOUS
- PRESCRIBE
- PREFERS
- AGGREGATE
- HOSTILITY
- MONARCHICAL
- CERTIFICATE
- SWAGGERING
- CONCEALS
- UNPARALLELED
- TIMBERS
- CEREMONIAL
- ARCHED
- COURTED
- CHICKS
- CHANTY
- BOLDEST
- SCRATCHING
- HOWLS
- SCOWLING
- LARBOARD
- HAMPER
- CHOKE
- RUBIES
- CRISTEL'S
- COMPONENT
- COMPONENTS
- CHATEAUBRIAND
- STODDARD
- CRISTY
- INFIRMITIES
- WEEK'S
- SERVANT'S
- SIBONEY
- CHAPPARAL
- CONNECT
- LLEWELLYN
- KANE
- CAVALRY
- FORETOLD
- COWBOYS
- CARBINE
- CRITICISMS
- OCCUPANTS
- FIGHTS
- THORHILD
- THORSTEIN
- ERIC'S
- FISHED
- WINELAND
- ASGARD
- UNQUESTIONABLY
- EXACTED
- SADDLED
- CROPPED
- BARKING
- HEPBURN
- MONKSHAVEN
- HESTER
- CHANTED
- MANUFACTURER
- FOOTSTEP
- TIBERIUS
- CRAZED
- CHASTITY
- GILLENORMAND
- WIGS
- REBUKE
- SOOTH
- SANCY
- SCEPTRE
- COO
- PLUME
- PHRASEOLOGY
- MODIFICATIONS
- PRICELESS
- ACES
- HALLO
- PUCK
- PURCHASING
- BARCHESTER
- INTREPID
- COLLISIONS
- COURFEYRAC
- BLINDING
- ALLISON
- CULPRITS
- SAVELL
- SOPHOMORE
- ANNABEL
- CROSBY
- SCORED
- WHOOPING
- VERSAILLES
- PRUDE
- PIPED
- OCCULT
- HAMS
- DEMEANOUR
- HERB
- MONTEZUMA
- FLESCHE
- WEBSTER
- PRINCETON
- NEILL
- CRITIC
- ABU
- HASSAN
- ZIYADI
- GUESTWICK
- HANDIWORK
- JULIA'S
- THEATRICAL
- KEMP'S
- HARLOW
- IMPULSIVE
- MAGISTRACY
- CHIMERA
- SOOTHE
- FIDDLER'S
- LACHENEUR
- THROWS
- SOMERS
- POTHIER
- HUSBANDMAN
- LEGISLATOR
- MELCOMBE
- GROWL
- PORRINGER
- URSUS
- DOUGHERTY
- CHEEKED
- RIGOROUS
- PALELY
- FRESHMEN
- COUNSELLED
- GROTTO
- TOPICS
- BALDWIN
- ETA
- PYROXYLE
- HERBERT'S
- ADAD
- HITTITES
- ARAMAEAN
- INVADING
- KALKHI
- TEUTON
- MEMORIALS
- COMBED
- ROGERS
- VOTERS
- CYNTHIA
- FAIRFAX
- INTRUDE
- VERDICT
- POINDEXTER
- ERRED
- PALANQUIN
- ANKLETS
- LIED
- OWNING
- HATBORO
- KILBURN
- CALIFORNIAN
- RICHMOND'S
- RUBEZAHL
- COLVIN
- CURFEW
- WAPENTAKE
- QUORUM
- GENEVA
- CONFRONTATION
- MAYNARD
- BURGLARS
- TUNES
- MILAN
- CULTIVATING
- CAPITALISM
- ANTIPATHY
- LEDGER
- SAFIE
- EMPIRES
- SUPERINTENDENCE
- PALESTRINA
- FROSINONE
- CAVERN
- MONOCHORD
- LEGATO
- RUBATO
- PRELUDES
- CURRANTS
- ASP
- GRAHAME
- SHRIMPS
- IMPLICITLY
- CHAUVELIN'S
- LUTHER
- ENIGMA
- PROPERTIES
- LASHER
- NACKERSON
- MAHOMET
- SARACENS
- CHERSON
- PHILIPPICUS
- PORSENNA
- CHASKEY
- PRINTZ
- PLANCHET'S
- TRUCHEN
- ROCHELLE
- INDISCRETION
- UNWITTINGLY
- STEAK
- PETRIFIED
- INTUITIVELY
- BUTTERFLY'S
- ORDINANCE
- BRIGGS
- CRAYFISHES
- NEWFOUNDLAND
- BELOSTOMA
- OUCH
- FEELERS
- TAPPAN
- STOWAWAY
- SUTHERLAND
- AUGUSTINE'S
- PEARS
- MALLESON
- BERNARD'S
- KOREAN
- ANTUNG
- PURVIS
- BREAKER
- THOU'LL
- QUATERNARY
- GEOLOGISTS
- ARCHAIC
- EGOISM
- DECENCY
- SOW
- PATRIARCHAL
- PRIM
- STREAKS
- DISAPPROVED
- SPENCER'S
- SCHOOLING
- JOB'S
- BRUNSWICK
- SHINGLES
- BLANKLY
- WINCEY
- BRAIDS
- AWKWARDLY
- SHYLY
- PULLS
- SEASICK
- PROWL
- DETESTED
- MAGNITUDE
- GUARDIANSHIP
- REPELLING
- RECURRED
- INCURABLE
- EXTRICATE
- CORE
- IGNOMINIOUS
- INFLEXIBILITY
- SUPPLICATE
- MERCHANDISE
- MAGNIFY
- IMPERTURBABLE
- IRREPARABLE
- RUMORS
- UNPUNISHED
- RUNAWAY
- SIREN
- ASSIZES
- BEHAVING
- UNWORTHILY
- ABSURDLY
- KENNEL
- CROWING
- DARN
- SHRINKING
- GENIALITY
- MENIAL
- CONCESSION
- LISTLESS
- IMPARTIALLY
- PAINTS
- SOFTENING
- RECTITUDE
- PALAZZO
- CRESCENTINI
- HOVERING
- RECOGNISABLE
- JUDGEMENT
- THOROUGHNESS
- REPUBLICS
- STRICKLAND
- ABUSIVE
- DEFENSIVE
- STRENUOUSLY
- EMBASSY
- PURSUANCE
- NETHERLANDS
- STRENUOUS
- CREWS
- INTERCEPT
- BALTIC
- SOUTHERLY
- SHETLANDS
- INFERIORITY
- SEAMAN
- WARSHIPS
- PRIZES
- DENMARK
- ASSUMPTION
- INSISTENCE
- CONTESTED
- BLOCKADE
- NEGOTIATION
- OBSESSED
- ACCESSION
- INGRATITUDE
- GUARANTEED
- ENVOY
- CIPHER
- FORWARDED
- VALIDITY
- INSURMOUNTABLE
- DRAIN
- SAUCEPAN
- SPRIG
- CELERY
- NUTMEG
- MALT
- TABLESPOONFULS
- UNITES
- PORTRAY
- SAVOR
- CHILLING
- MUSES
- CANKER
- PRECINCT
- RECOUNT
- SOOTHES
- EXPANDS
- EMPLOYER
- MIGHTILY
- GINGERLY
- KNOB
- STUDS
- NEWCOMER
- KNUCKLES
- STRANGER'S
- GLOVED
- AGONIZED
- SEALING
- THUD
- SHOCKING
- HALLWAY
- DETAINING
- PATAGONIA
- CLANG
- ARCHER
- DULLEST
- GOUTY
- MORSELS
- INCIDENTAL
- DRYNESS
- IMMODESTY
- LICENCE
- CENTRED
- CARICATURE
- FLAGGED
- STEEPED
- MANOEUVRE
- TRIUMPHS
- SPONTANEOUS
- BAS
- HARMONIOUSLY
- LUGUBRIOUS
- PREVISION
- CAPACIOUS
- TIMBERED
- PROFUNDITY
- ENDURABLE
- HURL
- RIVERSIDE
- LAZINESS
- SONOROUS
- BLEST
- MYSTIFIED
- MISTAKING
- INTERTWINED
- SHIPWRECKED
- CRICKETS
- ROSETTE
- ELSE'S
- OVEN
- DWARFS
- CLOTHS
- PANS
- LONGINGS
- INTENSIFIED
- CRIPPLED
- GLORIFICATION
- REPUGNANCE
- EXPLOIT
- RELAXATION
- DEGENERATE
- ACHIEVING
- FAIRNESS
- DEVELOPS
- DOSES
- PARALYZING
- RETALIATION
- INCREASINGLY
- KEEPERS
- SPELLING
- SMACKS
- BENEDICTION
- PULLMAN
- TREMORS
- JAY
- EFFULGENCE
- ELASTICITY
- IMPLEMENT
- CANNED
- UNTROUBLED
- PAWN
- BRIMSTONE
- FIREWORKS
- SPECULATING
- LAPIERRE
- ASTIR
- JONATHAN
- HERBAGE
- PUDDLE
- UNRAVEL
- WOODLAND
- PROCLAIMING
- WESTCHESTER
- TORONTO
- DETENTION
- BURLY
- FUNEREAL
- OUGHTN'T
- AFTERMATH
- TINKLING
- MANNERED
- TEMPORARILY
- FUTILITY
- LOPPED
- GRUNTING
- IGNORES
- SETTLES
- SURER
- ABOMINABLY
- IMAGINATIVE
- LOUNGED
- UNCTUOUS
- MUTELY
- WEIGHTS
- MORSEL
- DOUBLY
- NAMING
- PALATE
- LABORING
- SCALD
- ARNICA
- TUBES
- RHEUMATIC
- NOSTRIL
- USEFULNESS
- PROMOTING
- WRAPPER
- UNSURPASSED
- TONIC
- DUSTS
- VARNISH
- PERFUMES
- CIMETER
- DISCONTINUED
- DINARZADE
- RESOLVING
- MISTRUST
- CONJURING
- ACQUAINT
- WIDOWER
- ARABIC
- POTION
- RAVED
- PROVEN
- ELEVATOR
- LOADS
- RUMORED
- UNANNOUNCED
- WUTHERSPOON
- CROSSROADS
- REGRETFUL
- MINNESOTA
- MORTGAGED
- CIGARS
- ORATORICAL
- PUNCH
- BOUNTIFUL
- HEARTHSTONE
- CAMEL'S
- CRABS
- CHEWING
- WESTERNER
- FOOTSTOOL
- BOOST
- FAMED
- EQUALLED
- PROMOTER
- FILED
- ANXIETIES
- STUDIOS
- EXPLORERS
- METHODIST
- SUPPERS
- AMBASSADORS
- INFIDEL
- SCIENTISTS
- ENLIST
- MILITANT
- SUFFRAGIST
- GRUNTED
- BANTER
- FLATS
- FRENZIED
- SARDONIC
- GROCERY
- PROTECTING
- TIMOROUS
- POISE
- INVOLVING
- IMPERSONAL
- STRAIGHTENING
- ALLEVIATE
- BEAUCHAMP
- AFFABLE
- PURITANICAL
- DISHONORED
- FINANCE
- DEMOLISH
- DEPOSIT
- RALLIED
- PORTFOLIO
- CHARITIES
- WIDOWS
- NABOB
- DEFICIENCY
- MONOTONY
- SKYLIGHT
- CLATTER
- COMPOUNDED
- BORES
- SQUALID
- SUNFLOWERS
- SUMMERS
- STICKY
- DEPOSITS
- MAGNIFIED
- DISTASTE
- DODGING
- UMBRELLAS
- ADOLESCENCE
- CALORIES
- MIND'S
- SUE
- JILL
- INSTRUCTOR
- THOUGHTLESS
- HYDROGEN
- MURCHISON
- COLUMBIAD
- HUMORED
- DEADEN
- PULSATION
- BALTIMORE
- RAMMED
- INTERPLANETARY
- CLASP
- STANDARDS
- AXIS
- CEREALS
- WEIRDNESS
- VITALITY
- TRASH
- ANNOUNCEMENTS
- BRIEFITES
- PARTAKE
- TROLLEY
- WIZARD
- CONSTRUCTING
- WHEREON
- TRANSLATION
- KERM
- CHER
- GLORIFIED
- RYNCH'S
- MOLD
- JUTTING
- WATCHERS
- SPACER
- GROUPED
- WEBBING
- ANGLED
- CLAWED
- THIGHS
- SHIED
- FRANTICALLY
- WORCESTER
- BEVERLEY
- EDWARD'S
- EXPELLED
- DISBANDED
- MERITORIOUS
- RUSSET
- DESERVEDLY
- RIDDLES
- HAMPTON
- BRIDES
- DETAILED
- TELEPATHIC
- SYMPTOM
- ELABORATION
- LEGISLATURES
- RECOGNIZES
- SUGGESTIBILITY
- REENFORCED
- NATURALISTIC
- THERAPY
- ARGUES
- THERAPEUTICS
- INALIENABLE
- URGES
- POSTULATES
- INJECTIONS
- PERVERTED
- OVERLOOKING
- UNINTENTIONALLY
- ABSTAIN
- UNDESIRABLE
- CLINIC
- FULLNESS
- UNDESERVED
- VIEWPOINT
- MASTERING
- UNSTABLE
- PO'LY
- UNS
- BABYHOOD
- OVERHUNG
- FLUX
- GULFS
- EPOCHS
- PERPLEXING
- PANORAMA
- UNFAMILIAR
- WAKES
- CONFUSING
- MAXIMUM
- TRANSLUCENT
- COLOURLESS
- SURGING
- REMOTELY
- DEEPENED
- ENORMITIES
- ESPOUSED
- ALLEGIANCE
- EXPLOSIVE
- EXASPERATING
- UNRESTRAINED
- LUCIDITY
- FEIGNING
- UNSUSPECTED
- EXPEDIENCY
- GROOVES
- DIVERSITY
- ABSTRACTION
- UNDISPUTED
- SICKLY
- INSPIRITED
- INSUFFERABLE
- IRRITABILITY
- IRRELEVANT
- CONSTRAINT
- BLUNDER
- CONSOLING
- INSUPPORTABLE
- REQUIREMENTS
- MARVELOUSLY
- RASHNESS
- UNMISTAKABLY
- PARAMOUNT
- PREDICT
- PROTRACTED
- VEXATIOUS
- LOUISE'S
- IMPOSTURE
- WELDON
- ASSORTED
- MOROSE
- ECSTATICALLY
- BARNYARD
- WETTING
- INSISTENT
- VOLCANIC
- SATURATED
- INCANDESCENT
- HABITABILITY
- SOLAR
- DIFFUSE
- CALCULATIONS
- DIMINUTION
- LINEAMENTS
- SIEVE
- TUNIC
- SNAPPISHLY
- DEVOTEDLY
- ROGUISH
- STEEPER
- BLACKED
- WETTED
- BUMPED
- SIPS
- BUBBLED
- PUNISHING
- UGLIER
- SIGNIFIES
- MUFTI
- PEEL
- ORANGES
- SUPPING
- ASSASSINATED
- DARTS
- LAUDABLE
- ILIAD
- PROFITING
- DEPORTMENT
- ENTERTAINERS
- EMPIRICISM
- INHALE
- WEED
- INHALED
- EDDIES
- SQUAW
- PRECEDES
- TERRITORIES
- LOUNGING
- IMPRESSIVELY
- ASSURANCES
- SALUTATIONS
- FRUGAL
- TOMAHAWKS
- REPULSE
- CORA
- SWALLOWS
- AUDITORS
- EVASIVELY
- SLAUGHTERED
- INSTANTANEOUS
- DUR
- INSINUATION
- DISAPPROBATION
- TEMERITY
- UNCONCERNED
- VIGILANT
- FASHIONS
- WORKINGS
- ADOPTION
- APPETITES
- FAVORITES
- HOUSEWIVES
- JUNCTURE
- WHOLEHEARTEDLY
- PERKINS
- PLUSH
- CEILINGS
- HUES
- SYNDICATE
- TRUSTWORTHY
- INGREDIENTS
- REEL
- MARKETS
- INTERVIEWED
- IMPERIOUS
- BLINDS
- UNFOLDED
- PRONOUN
- INEXPERIENCED
- CONFIDENTIALLY
- CORROBORATE
- UNBELIEVABLE
- NICKNAME
- WRESTLED
- BEREFT
- ALLERS
- SAG
- APPROVINGLY
- BAREFOOT
- BELLE'S
- FESTAL
- GLISTENED
- ENCHANTING
- INFER
- INUNDATED
- DRIVER'S
- PATTY'S
- HILLIARD
- INITIALS
- BRIDESMAIDS
- FERVENTLY
- CHRONICLE
- PADS
- SNIFFING
- DAINTILY
- MAE
- BRISTLED
- JALIB
- MOSQUES
- MAT
- SUSTENANCE
- TESTIFY
- HAROON
- FRAILTY
- ENLARGING
- EXTOLLED
- ALIGHTING
- WAITS
- INCAPACITY
- DURST
- OVERCHARGED
- JAAFFIER
- EGYPTIANS
- CONTRACTS
- WREN'S
- GRAYISH
- REDWING
- BLACKBIRD
- COAXED
- BLOTCHES
- FLARING
- FLEMING
- ASTRIDE
- EBB
- DISPENSE
- GRIEVANCE
- CALAMITIES
- LEAVEN
- DWARFED
- PURSES
- LIGHTEN
- RUSE
- CREDITORS
- HOSTELRY
- DEFINE
- DISPOSES
- SUNS
- ATTACH
- BEHAVES
- LOFTIER
- CULTURED
- UNINTERESTING
- MYSTICAL
- PEDANT
- PLEBEIAN
- COSTUMES
- PREFERENCES
- DESPERATION
- ANDREWS
- VALUATIONS
- UNINTELLIGIBLE
- SUFFICIENCY
- EVOKE
- BOUNDLESS
- CREATIVE
- UNDERGOING
- SHRED
- MIRE
- HAMMER
- GRANTING
- SANCTIFIED
- SANCTITY
- EXPRESSES
- QUESTIONABLE
- FUEGIAN
- PATTING
- SLAPS
- GUTTURAL
- GRIMACES
- INSTRUCT
- INDUCEMENT
- POX
- TACITURN
- SIMPLEST
- HARANGUE
- MOUNTAINOUS
- PEAT
- DESCENDS
- SWAMPY
- PREDOMINANT
- ENLIVENED
- DWINDLED
- SUCCEEDS
- HABITATIONS
- LACED
- SCREAMS
- SUPERSTITIOUS
- PERSONIFIED
- LABORIOUS
- PERPETRATED
- BYRON
- DECREASE
- EMERGING
- KEN
- INANITY
- CHASING
- POPLARS
- PUDDLES
- MUFFIN
- THRONGING
- GRIZZLY
- VALIANTLY
- LION'S
- PANT
- STILE
- RAMBLED
- BILL'S
- LAWLESS
- HEDGEROW
- AIMS
- ALTERNATE
- CHANT
- WHIPPING
- CLIPPED
- HOMEWARDS
- DAINTIEST
- BROTHERLY
- REBELLED
- DESPAIRED
- PLAYMATE
- ECLIPSE
- SUFFERERS
- CONGRATULATION
- POEM
- OATS
- PLEASANTER
- WINTRY
- VIOLINIST
- ORDERING
- PENITENCE
- SOAR
- KINGBIRD
- RACKET
- CRESTED
- SHADING
- RESTFUL
- UGH
- WOODPECKER
- NUTHATCH
- NESTING
- SCREECH
- BITES
- BRUSHY
- SWAMPS
- PEBBLY
- MARKINGS
- BOBBING
- DIFFERED
- SURFACES
- RAPIDS
- BALES
- DISAPPEARS
- FRONTS
- HEWN
- CLAN
- JARGON
- GREEN'S
- DAWDLED
- SKIP
- TWIXT
- BREATHES
- THROBS
- INEXHAUSTIBLE
- WARPED
- ALBUM
- DECREES
- SCULPTURED
- JOG
- SNAPPER
- STEVE
- BULLDOG
- UNSADDLED
- FRYING
- THINKIN
- GLIMMERING
- MADDEN
- PRETTINESS
- FLESHLESS
- DEATH'S
- TREMOR
- REALIZING
- DESERTING
- WINEGLASS
- SPITEFUL
- FAIRY'S
- LOCRINOS
- BIRD'S
- FAREWELLS
- PRISONER'S
- JEWELLED
- FONDLY
- APPEASED
- REJOICINGS
- RAVENS
- MORLANDS
- LANK
- ENJOYMENTS
- PROPENSITIES
- QUOTATIONS
- HUMOURED
- NEEDLEWORK
- FRET
- ESSAY
- AVOCATIONS
- APOLOGIZE
- SILENCING
- WOODSTON
- AFFRIGHTED
- LATTERLY
- SHREWDNESS
- NECESSITOUS
- MURDERING
- PITIABLE
- SANCTION
- REPLETE
- RETROSPECT
- LUDLOW
- EXHILARATING
- EXCLAIM
- PYRAMIDS
- SHRUNKEN
- ADMITTEDLY
- SOLICITOUS
- GARDENER'S
- DAPPER
- SALESMAN
- DAINTIES
- SACKING
- NELLIE
- CRASHING
- HUSSY
- HIDEOUSLY
- DISCOLORED
- DENVER
- FIBRE
- UNBRIDLED
- SPAR
- UNREST
- LUCID
- OCEANS
- RAILS
- CLEARINGS
- UNROLLED
- WISP
- PUNKAHS
- POIGNANT
- PAD
- SPLASHING
- BULKHEAD
- VIVIDNESS
- REPRODUCED
- PERDITION
- ASSESSOR
- SUNBURNT
- DRAPERIES
- BUTTONED
- INCOHERENT
- AUDIBLY
- ELONGATED
- HARBOURED
- EXPANDING
- BINDS
- MEDITATE
- DINAH'S
- TREADS
- WANED
- NAG
- BLURRED
- MUSICIAN
- MILDER
- SHELTERING
- SOFTER
- STARTLE
- QUARTS
- RECIPE
- EIGHTHS
- BATTER
- KETCHUP
- PRICKLY
- RIFLED
- MUSHROOM
- FRAMED
- UNSTEADILY
- CARESS
- CYCLING
- DANGLED
- LENGTHEN
- CAREWORN
- OCUMPAUGH'S
- EXAGGERATION
- BURDOCK
- DISHONESTY
- FELDERSON
- DINNAH
- DONKEY'S
- HOSANNA
- PALESTINE
- PASSOVER
- NAZARETH
- CLINKING
- BAPTIST
- ANECDOTES
- BEAU
- INTOLERABLY
- CONCEITED
- PURR
- THRIFTY
- BLAIR
- SPICE
- PICNIC
- PRECINCTS
- WAVERED
- SOUNDLESS
- MARIANNE'S
- HACKNEYED
- BLASTED
- PLAIT
- EYEING
- JENNINGS
- RAILLERY
- NEWNESS
- ARCHNESS
- HUMBLED
- PERFECTIONS
- HABITUATED
- VICTORS
- VALOUR
- QUERIES
- AMUSEMENTS
- CANDIDATE
- ILLUMINATE
- RETURN'D
- MAGUS
- IMPERIOUSLY
- HOUSINGS
- FEINT
- ANTAGONISTS
- SADDLES
- COMMENCING
- BINGLEY'S
- LOUISA
- AGREEING
- MEANNESS
- APOTHECARY
- LIZZY
- UNVARYING
- WITTICISMS
- WILY
- ASSEMBLING
- FRATERNAL
- UNAVOIDABLY
- DRYLY
- SUBDUING
- QUENCHED
- POURS
- ELEMENTARY
- SUBSERVIENT
- PINNACLES
- THUNDERS
- WRECKS
- DISORGANIZED
- INSIGNIFICANCE
- EPIDEMIC
- LAMENTATION
- TAMPERED
- PARAGRAPH
- GRAVEN
- RESIDENTS
- NECESSARIES
- PLOUGH
- WORTHIER
- MANUFACTORIES
- DIMINISH
- LIKELIHOOD
- CHEVALIER
- GRUFF
- TURRET
- ASSERTIONS
- EXERTED
- LAVISHLY
- STRAIGHTEN
- BARTLETT
- CARYOE
- CHARLIE
- THUMBS
- DISTINCTNESS
- EXPENSIVELY
- TREND
- TWINKLE
- TILTING
- QUARTERSTAFF
- VENISON
- NOTTINGHAM
- TANNED
- WILES
- TROD
- DRUBBING
- SMITE
- THUMPED
- EVENLY
- INNS
- CARDED
- GAITERS
- VOUCH
- DOROTHEA
- VASSALS
- INDUCING
- BETROTHAL
- IDLERS
- STAB
- PRECIPICE
- PANGS
- SCORCH
- WOLF'S
- PLOUGHED
- PERFUMED
- FOAMED
- LAMBS
- EDICT
- GASCON
- APPRENTICESHIP
- MELEE
- CALMING
- LOUVRE
- BLOCKHEAD
- DILATED
- LIBERALITY
- BONACIEUX
- CONJUGAL
- MAJESTIES
- GOLDSMITH'S
- GEM
- CADET
- SHUN
- FLIRTATION
- KNOWL
- SITUATE
- REVERIE
- DEVOURING
- COMMISSIONED
- FLUENT
- MADAME'S
- COUGHING
- HAUNT
- ACCOMPANIES
- KINDEST
- INMATE
- CRACKY
- FUMBLING
- OPERATES
- EXHIBITS
- PHRASES
- UNLIKELY
- OBEYING
- SUBJECT'S
- GLASSY
- SIMILARITY
- DILATE
- COOPER'S
- MAGNETISM
- RESPOND
- PARADOXICAL
- PROGRESSED
- TAM
- MUSKETRY
- PRESUMABLY
- UNEDUCATED
- SINGLED
- TOUGHEST
- WALLED
- SODDEN
- CROWS
- VICTUALS
- INFIRMARY
- LATTICE
- BRUSHES
- BUSTLE
- HORSESHOES
- LONGS
- PARTNERSHIP
- NEWPORT
- BEN'S
- AX
- STRANDS
- TRANSMUTE
- ALEMBIC
- EXPLORER
- DRAUGHTS
- NOBLER
- ARROGANT
- WONDROUSLY
- SMOOTHNESS
- INCORPORATED
- NOTABLY
- SHRINES
- UNCRITICAL
- EXTENSION
- GODDARD
- WEAKENS
- ADJUST
- SIMPLIFY
- INFECTIOUS
- LUSTY
- NEATNESS
- STEAMER'S
- WIMPOLE
- SCANNED
- PRACTITIONER
- KNOWETH
- BLANDISHMENTS
- PORTERS
- CIRCULATED
- WILDFELL
- NIGHTLY
- FROLICSOME
- CEMENT
- PERUSE
- PROSPERED
- BRILLIANCE
- BLANCHED
- HOOFS
- SALUTING
- MILLWARD
- VICARAGE
- DEPRAVITY
- BEVERAGE
- REPREHENSIBLE
- INVADERS
- BARBARIAN
- MAXIMUS
- TIBER
- SACKED
- SEAPORT
- RAVAGES
- TRUCE
- NARSES
- CARAVANS
- KORAN
- MEDINA
- ATROCIOUS
- MULTIPLE
- HAZARDS
- CALCIUM
- NATURALISTS
- SECRETE
- INSIDES
- SOLIDIFYING
- OYSTER'S
- STERILE
- DUBIOUS
- EXTRACTING
- TWOFOLD
- BASTARD
- OPAQUE
- STRAINERS
- CLEOPATRA
- COLLECTS
- SAGGING
- HAIRLESS
- LINGER
- ED
- SUPERVISION
- DISCHARGING
- DAYTON
- COMELY
- PITFALLS
- STRIVEN
- FRAUGHT
- THREESCORE
- MASSACRES
- ARMENIANS
- VIOLATED
- EDITORIALS
- SUDAN
- RAPE
- FULFILMENT
- NORWEGIAN
- TARRED
- ANTARCTIC
- STAYS
- PROPORTIONATELY
- BRIDGES
- WORKSHOPS
- SLACK
- TEMPERATURES
- DAMPNESS
- SEAMS
- DISINCLINED
- SMARTING
- TALLOW
- LENGTHS
- ALTERATIONS
- FISSURES
- PHOTOGRAPHIC
- RELIABLE
- PEMMICAN
- WISCONSIN
- BEACON
- ATTAINING
- NOOKS
- PRETTILY
- PROPRIETORS
- UNDISTINGUISHED
- REPRESENTATIONS
- QUARTERLY
- EDMUND
- RESTLESSNESS
- SAVOURY
- PRACTICABLE
- MINORITY
- ENTRUST
- UNEQUIVOCALLY
- DERIVES
- SUPPORTS
- CLOG
- FINED
- SPINDLE
- LOOSELY
- JADES
- PRESIDED
- FATES
- SKULKED
- TEXTURE
- HUNGRILY
- ATTRACTING
- POCKETBOOK
- GROOMED
- WOWZER
- POKE
- GETTER
- LURE
- IRRITABLY
- POMPOUS
- MAHOGANY
- MELODRAMATIC
- GRUFFLY
- ASHEN
- HELLISH
- KNOBS
- BANK'S
- STEAMSHIP
- DENIALS
- BABBLING
- REELING
- TWEEZERS
- GRANTLINE'S
- SUITE
- SOMBER
- PLOTTING
- JERKIN
- PANTS
- SWAGGER
- RASP
- HERITAGE
- TRAJECTORY
- SNAKY
- JOCULAR
- LASHES
- INTOXICATED
- RADIO
- ASSAILANT
- SIZZLING
- STARLIGHT
- DRAMAS
- LEVER
- STARCH
- UNEXPLAINED
- HOTTEST
- RHETORIC
- UNSHAKEN
- CONSTANCY
- PATS
- REPRESS
- QUETCHAM
- ALLOWANCES
- OFFSPRING
- FIXEDLY
- QUICKEST
- RASHLY
- REVISED
- HOLINESS
- INSTITUTIONAL
- CHIP
- HELSTONE
- FERN
- REVELLING
- URGENCY
- UPLAND
- AUTUMNAL
- GORMANS
- LABOURERS
- BACKGAMMON
- DIXON
- ALIAS
- HALES
- MORTIFIED
- ALTERCATION
- ASPERITY
- MITIGATE
- OVATION
- BARRELLED
- SAGACIOUS
- INCENSE
- KEYED
- SOARED
- MILLY'S
- ODDEST
- ENDLESSLY
- IMPERTURBABLY
- STOCKING
- MASKEW
- EXCISE
- OFFING
- GENTLEST
- SMUGGLERS
- PURBECK
- TWINGE
- SNAIL
- TWOULD
- BRAMBLES
- STRIDE
- RUBBLE
- SUFFOCATING
- HATCHWAY
- WOES
- TUMULTS
- SINEWS
- COMMOTIONS
- SCANDALIZED
- CARNAL
- GOATS
- BOLING
- JEALOUSLY
- ALMA
- MATER
- COLLEGES
- SAKI
- SAKI'S
- BLEAR
- DIPS
- TEASE
- VANKA'S
- RUBLES
- OLGA
- IGNATYEVNA
- WRAPT
- FRIGHTFULLY
- DOG'S
- TOKENS
- VARIABLE
- ABATEMENT
- DISTEMPERS
- SADDLER
- OVERSEER
- PORTUGAL
- APPOINT
- IRRESOLUTE
- NOISOME
- WHEREOF
- INEXPRESSIBLE
- FALLON
- FRESHLY
- DRYING
- HAPPENINGS
- SORROWING
- CUPBOARDS
- PUNISHMENTS
- PANTHER
- LIEUTENANTS
- DESERVING
- HUGGED
- TRIMMING
- FRISBIE
- TUFT
- NEUCHATEL
- ENTRANCED
- HERALDS
- FRAU
- MARIE
- SLIPS
- SENORITA
- CONTRIBUTORS
- TOWERED
- SAP
- WILDMAN
- FLETCHER
- AUGURED
- FEUDAL
- SHAFTESBURY
- VULNERABLE
- CANONS
- PUNCTILIOUSLY
- CONGREGATED
- TRANSACTION
- EDINBURGH
- REVOLTED
- SUPPORTERS
- RAM
- SURVEILLANCE
- PRUSSIA
- SOUNDINGS
- TENFOLD
- RAMS
- DEBATED
- CRYSTALLIZED
- PURGE
- ENERGETICALLY
- FURNACES
- STOIC
- DEFIED
- FANATIC
- KIT
- AFTERDECK
- MAJESTICALLY
- FERRIES
- TENDERS
- WHARVES
- THROATS
- HAILING
- NEGOTIATE
- ISLET
- AMAZINGLY
- BLACKISH
- DENSELY
- CHESTS
- TACK
- STRIPPING
- FICKLENESS
- BUNKER
- DONNITHORNE
- HEV
- HEARER
- CITED
- DISAGREE
- PITCHING
- TONGUED
- MARTYR
- MICHAELMAS
- CALLOUS
- RETRIBUTION
- RUMINATING
- WOUNDING
- LEVISON'S
- WOOLEN
- UNDECEIVED
- CALMER
- AMICABLY
- CAMPING
- UNABATED
- BRAKES
- GUSTS
- SNOWFALL
- TOILED
- FORENOON
- IRREGULARITIES
- BJAALAND
- THORVALD
- ABYSSES
- THOUGHTFULNESS
- CREVASSE
- BALLADS
- JINGLES
- RHYME
- PUSSY
- PUSS
- DESCENDANT
- FLEET'S
- DISMISS
- CRADLES
- RESIDING
- EDITIONS
- DECADE
- ILLINOIS
- NESTLED
- PROFICIENT
- RUFFLES
- HAMMERING
- DEPUTED
- THAWING
- INDISTINGUISHABLE
- BAWLING
- ALCOVE
- OBLIQUELY
- BALCONIES
- ROBED
- ARCHES
- DIPPING
- BARGES
- LONGITUDINAL
- UNDULY
- TESTIFIED
- NOTICEABLY
- VICTORIAN
- LABOURER
- CHINKS
- AISLES
- MASONRY
- VAULTS
- ILLIMITABLE
- YELPING
- DETERMINATE
- USURP
- NUPTIAL
- ZEPHYR
- PERFORMERS
- LUTE
- PREYED
- SICKLES
- FROWNS
- INEXTRICABLE
- WOOLLY
- PROSERPINE
- REALMS
- ALLEGORY
- ALLUDES
- WOVE
- SAPPHIRE
- HOLIEST
- NIBBLE
- ROQUEFORT
- CUBE
- BUNG
- ANDRE
- PALATABLE
- UNCHANGING
- HUDSON'S
- BOUNTY
- INVETERATE
- LIGHTENED
- ONTARIO
- ROAMED
- WINDINGS
- JESUITS
- UNMOLESTED
- RICHELIEU
- DESTINIES
- DOWNFALL
- HINDRANCE
- DULLED
- VULTURE
- FORESIGHT
- ACUTENESS
- REFRAINED
- FLUENTLY
- SECT
- BAMBOO
- NAZORAERU
- GOBLIN
- HERMITAGE
- CAPACITIES
- DEWS
- HAUNTERS
- WICKEDLY
- SUTRAS
- RECITING
- PLUCKING
- BRIGHTENING
- GOBLINS
- SUWA
- SAMURAI
- TRANSITORY
- EXCLUDE
- CROWED
- SPINSTER
- DENOTED
- DEIGN
- ENGENDERED
- MOIST
- EXHIBITING
- CONSTRUED
- DEARER
- REBELS
- COMPLETING
- CASUALTIES
- COSTING
- CHASTISEMENT
- LINEAGES
- PYRAMID
- MAINTAINS
- MONARCHS
- MEDES
- WILLS
- ERRANTRY
- PROVERBS
- CODICIL
- EMERGENCIES
- JINGLE
- BASILIO
- MATRIMONIAL
- NOONDAY
- NOTARY
- AFFIDAVIT
- EXCELLENCES
- SLOTH
- SNORING
- ENVYING
- THRESHING
- SUCKING
- INTERMEDIARY
- PRESERVER
- BACKING
- ACCOMPLISHING
- EMITTING
- FOREBODINGS
- PINNACLE
- CHAINED
- WARHOON
- THARK
- VANQUISHED
- INCUBATOR
- MANIACAL
- FIENDISH
- ABASHED
- STOCKADE
- LOCKER
- DAVY
- JONES'S
- SURVIVING
- SNORED
- SPOUT
- STRANGLING
- NICK
- MAROON
- GUNN'S
- SUPERSTITIONS
- LOWLANDS
- INGRATIATE
- GUNSHOT
- EXPOUND
- COMETS
- NECESSITATED
- FEIGNED
- DEMONSTRATE
- TRAVERSES
- OBSERVABLE
- INANIMATE
- DEDUCING
- KINDLING
- FERMENTATION
- DISSECTED
- VIZ
- CONVENIENTLY
- POUCHES
- ENTRANCES
- AMOUNTS
- SURGEONS
- ESCAPES
- THINNER
- HUMORS
- PUBLISHING
- SPONTANEOUSLY
- ANALOGOUS
- ASTRAY
- FIFTHS
- MESSRS
- BIOGRAPHY
- PLAYGROUND
- SLOWNESS
- EXHIBITIONS
- TRADESMEN
- BATTELS
- DUNG
- SCHOOLFELLOW
- SCHOSS
- DISTRACT
- NATASHA'S
- MYTISHCHI
- QUILT
- SUPPLE
- EARTHEN
- GOSPELS
- WHIFF
- RESTRAINING
- SOMNAMBULIST
- IMPUNITY
- NIGGER
- MARSER
- LARD
- SALOONS
- NATCHEZ
- FORGETFUL
- REPLYING
- ALPATYCH
- OBDURATE
- SHOVE
- BROADSHEET
- ARSENAL
- SHUFFLING
- RIOTING
- BANTERING
- BURGHERS
- MOZHAYSK
- SMOLENSK
- HIGHROAD
- UTITSA
- STIFFNESS
- EXPECTS
- SUSPECTS
- CHARLOTTE'S
- PLEASANTEST
- EXERCISING
- UNACCENTED
- DUPLE
- VARIES
- GOLOVIN
- MINERS
- ARCTIC
- SEAWEED
- IMPEDED
- MAILS
- COT
- LEGION
- LACES
- BOARDED
- JENNIE
- MIGRATION
- DATUM
- SEGREGATION
- QUASI
- SCOOP
- WHIRLWINDS
- INDIGENOUS
- ZOOLOGIST
- ORTHODOXY
- DISCHARGES
- STICKLEBACK
- HERESY
- CRUCIFIXION
- RIGOROUSLY
- ORIGINS
- PRECIPITATION
- ARCHAEOLOGISTS
- ORTHODOX
- CONCEIVING
- CORNWALL
- UNINTERESTED
- LIZARDS
- DAMNATION
- TENNESSEE
- HOGS
- HOG
- NIGGERS
- PARSONS
- DURHAM'S
- WHO'LL
- SPORTY
- CLIMATES
- FOREFEET
- INFAMOUS
- SOMEWAYS
- CARPENTERS
- WIDOW'S
- BUD'S
- TATE
- ENGINEERING
- HANDICAP
- GESS
- HOLATI
- FEDERATION
- PLASMOIDS
- INDUSTRIAL
- RAIDER
- TARGET
- PROFESSOR'S
- HUH
- WINTERS
- OREGON
- BETTY
- COSU
- LOGICALLY
- DEXTEROUSLY
- HERNDON
- MATS
- CLIENTS
- ADMINISTRATION'S
- STRAITJACKET
- CLOSETS
- BAYING
- HABEAS
- CORPUS
- NORFOLK
- LEGALITY
- VIOLATION
- CONTENDED
- MILFORD
- FOLLIES
- EMBLEMATIC
- RESORTS
- WHACK
- EXTENUATING
- ROC'S
- OUTLAWED
- SHIPPED
- PIRACY
- DOUGHTY
- MERIDA
- IGNOMINIOUSLY
- UNFOLDING
- STRAIGHTFORWARD
- AWKWARDNESS
- ALOOF
- BETHINK
- PERMISSIBLE
- GLEANED
- FATHERLAND
- ENTITY
- BEFELL
- SENESCHAL
- FORSOOTH
- DOLEFULLY
- SPURS
- COMMANDMENT
- ASKEST
- KNIGHT'S
- HEREUPON
- APOSTLE
- JACOB
- PORING
- COAXINGLY
- AILS
- ADDITIONS
- DUET
- LESLIE
- ENNA
- SMOOTHING
- VACATED
- BONBONS
- INTERSPERSED
- UNWHOLESOME
- VENTNOR'S
- TIMED
- SUMMONING
- JAVELINS
- WEIRDLY
- DISQUIETING
- GLOBES
- RECTANGULAR
- CLICKED
- PARODY
- SMITING
- ROCKED
- ANNIHILATION
- NORHALA'S
- WRAITHS
- ENIGMATIC
- BILLOWS
- COUNTERPART
- SHRUBS
- SNUGLY
- QUEERLY
- THAR
- FISHIN
- WAYWARD
- PUFFING
- WHAR
- MOTTLED
- GEE
- PEAL
- BUMPING
- TRANSLATE
- ROWBOAT
- YACHT
- BOHEMIAN
- FLINGING
- DISSOLVED
- DOMINICAN
- CLOISTER
- SUPERIORS
- DEATHBED
- MEDICI
- RIGHTFUL
- CALIBRE
- LUCRETIA
- REFEREES
- RAGING
- SCOUTING
- WYANDOTTE
- WILCOX
- ROCHESTER
- BUNTLINE
- LIZZIE
- SHANDY
- CIVILIAN
- CHATTELS
- CITADEL
- TURN'D
- ALL'S
- TRIM'S
- HUMOURS
- FLIMSY
- DISTINCTIONS
- WIDOWHOOD
- RECALCITRANT
- MAGNATE
- STOCKED
- PAPPUS
- JUDEA
- MACHAERUS
- JERICHO
- DEMOLISHED
- AUXILIARIES
- ADVERSARIES
- CUSTODY
- EMPTYING
- SLAYING
- FAINTNESS
- WITHHELD
- FREEING
- INTERPRETING
- MODERATED
- PREPOSTEROUS
- COURTLY
- INDIGNITY
- CHIRPING
- FIREARMS
- RILEY
- GALLED
- DRAFTED
- PENAL
- PRECEDENT
- MUZZLES
- KNELL
- SURVILLE
- VETCH
- BILLOW
- OUTSPREAD
- SPRAINED
- COX
- DESCRIED
- HELM
- SPURTS
- WEARISOME
- THEODORE
- TACOMA
- CONSEQUENT
- IMPROVISED
- CONTENTION
- COWLITZ
- OLYMPIA
- DRENCHING
- SELLS
- BUYS
- AGRICULTURAL
- PATRONS
- SHIRLEY
- GRAZING
- CAUTIONED
- UNCOMPLAINING
- NARRATED
- RECOUNTED
- BREVET
- CANAAN
- PLUMB
- MACKENZIE
- DEDICATION
- CITIZENSHIP
- DISTINCTIVELY
- EQUALS
- FROWSY
- KINGSHIP
- ATROCITIES
- ANT'S
- ATOMS
- ABOLISH
- WORSHIPPING
- FOODS
- PUNY
- PARASITES
- ADAPTABILITY
- RHINOCEROS
- QUIVERS
- ARQUEBUSIERS
- ROBUST
- SOUTHWESTERN
- HOGSHEAD
- JOURNEYING
- EXTORTION
- BRETON
- EXASPERATED
- OUTCRIES
- BROWBOROUGH
- COUNTESSES
- BROUGHTON
- DAUBENY'S
- KENNEDY
- INEFFABLE
- TAYLOR'S
- TUMBLER
- VIRGINIA'S
- NORMAN
- ORGANIZE
- PARASOL
- WINSLOW
- TREASURED
- CAPTIVATING
- PROPOSING
- GRAFT
- SOLICITOR
- REFLECTIVE
- ROTTENNESS
- CROOKEDNESS
- BUTCHERS
- ATTRIBUTING
- UNDERTAKER
- LEAKED
- BULKY
- CLIENT
- DEVISE
- DISSENTING
- BUCK'S
- SCRAPE
- REMONSTRANCES
- SLIMY
- DAMAGES
- YELLOWS
- LOFTILY
- OUTCAST
- ABBE'S
- CHISEL
- FARIA
- PUZZLES
- ENABLES
- OVERFLOW
- REPROACHES
- IMPERFECTION
- NOBLENESS
- INFATUATION
- FREDERICK'S
- CANDID
- SUFFOCATED
- PORTABLE
- CIRCUMSTANTIAL
- BOILER
- BELLIGERENT
- BOMBS
- CANNONADING
- BITTEREST
- HOAX
- SIGNALS
- SOCIALLY
- ENFORCE
- NATION'S
- SOMME
- HAYDEN
- CIGARETS
- CELLARS
- LITHE
- JACKASS
- CARRION
- PETS
- EVREMONDE
- DOGGED
- EMIGRANT
- ENTHUSIASTICALLY
- REDDENED
- REGRETTING
- PRIOR
- CREWMEN
- CUBIC
- DIOXIDE
- TRACTS
- BATTERIES
- YAWNS
- MUTTER
- ALLOTTED
- HEADACHES
- TOWED
- POUNCE
- BAROMETER
- MOLLUSKS
- OPULENT
- JOINTS
- SNORE
- ARGONAUTS
- COMPOSEDLY
- PLOW
- HARROWED
- SPROUTED
- RECOMPENSE
- WREATHS
- POTENTATE
- ENTANGLEMENTS
- ASPIRATIONS
- TUMBLING
- VERNON
- FLURRY
- TELEGRAMS
- LEGISLATION
- WIDESPREAD
- PROPHECIES
- PRESIDENTIAL
- PRESIDENT'S
- SAVORED
- CZAR
- NA
- DERIVE
- MOLESTED
- ADMONITION
- MONTANA
- CALIBER
- SWINDLED
- DICKENS
- REDDENING
- BURGLAR
- CLAUS
- NUISANCE
- CONVENTIONS
- THING'S
- PASTE
- SCHOOLBOY
- UDO'S
- CASUALTY
- GINGER
- BARODIA'S
- IMPULSIVELY
- INTERCHANGED
- AHA
- REPAIRING
- ANASTASIA
- MARRIES
- BERLIN
- FLUTE
- IMPERATIVE
- GHOSTLY
- SMASHED
- CONCUSSION
- DISAPPOINTING
- COLLEAGUE
- REUBEN'S
- IL
- SIRENS
- HORSEMAN
- WATSON
- SCORNFULLY
- MERRICK'S
- STUBBY
- COZY
- MISCHIEVOUSLY
- PAUPER
- COMPLACENTLY
- BUNS
- ISHAM
- AMALGAMATED
- NAPKIN
- AESTHETIC
- BLYTHE
- NICEST
- SMALLPOX
- GRAVEYARD
- WAILED
- DISLIKES
- WRATHFULLY
- IMPROVERS
- LUCKLESS
- MUDDLE
- TABLECLOTH
- EMERSON
- YANKEE
- JONAH
- WAKEFUL
- ANGELIC
- SCHOOLROOM
- SMOKY
- PROVOCATIVE
- SQUEAKED
- HUMILIATED
- QUIETER
- WHISKY
- SECULAR
- CALMED
- FACILITATE
- CLOCKMAKER
- SAWDER
- MARRED
- LOVABLE
- ENTITLE
- ORLANDO
- PATRONAGE
- EDITORS
- GRESHAM
- EXCHEQUER
- MENAGERIE
- CLOWNS
- NORTHUMBERLAND
- FAY
- WINKING
- WAYSIDE
- JINGLING
- SPLENDORS
- PRANCED
- BOTHERING
- TIMBERLINE
- SNOWS
- CHAPARRAL
- BUTTE
- BOULDERS
- PACKS
- ICEBERGS
- HOLLOWS
- SACRAMENTO
- SPRINGTIME
- BOXED
- BOOMED
- BRUISE
- MITIGATED
- OUTPOURING
- DENSER
- GLACIAL
- RAVISHING
- DORMANT
- DAISIES
- INACCESSIBLE
- TERMINUS
- CHARMINGLY
- RAILROADS
- RIDES
- CONES
- FIRELIGHT
- RAVENOUS
- BAIT
- PAPOOSE
- REDEMPTION
- DIGNITARY
- UNWIELDY
- TORCHES
- CRAFTY
- RETRACT
- YOUTH'S
- EMPHASIZED
- DISHEARTENED
- REBS
- BRIERS
- TALKIN
- VEXATIONS
- STATUTES
- TAILORS
- UNRIVALLED
- GNATS
- KNOCKS
- LECTURE
- HAWKS
- WHATE'ER
- INHERIT
- STEWARDS
- VENGEFUL
- DUELS
- SAK
- PUPPY
- DOYLES
- CUSHIONED
- SNORT
- MULTI
- UNMARRIED
- CAT'S
- FEASTING
- TALERS
- COBALT
- ENDOWMENT
- ASBESTOS
- FIZZLECHIP
- HARDWARE
- CASKET
- OUTLANDISH
- TRICKERY
- BANANAS
- DRAFT
- CALCULATION
- AUCTION
- MATTRESSES
- OMNIBUS
- GUTTER
- BRUTUS
- CAESAR'S
- FREDERIC
- WEARER
- MINUS
- ADORABLE
- INTERSECTION
- USURPATION
- PURCHASES
- EPONINE
- UNDERWENT
- FARTHINGS
- CORKS
- JACQUES
- SOCIETIES
- RENDING
- CHANVRERIE
- INFAMY
- PROTESTATION
- INESTIMABLE
- WHEWELL
- DEMOLISHING
- INTRINSICALLY
- INSUBORDINATION
- STUPIDER
- ANTON
- ANTONITCH
- SERVITUDE
- PURPOSELY
- SHAM
- REBUFF
- SIMONOV'S
- APOLLON
- PALTRY
- FUNKED
- HOMESTEADS
- EPHEMERAL
- TRAILS
- HINGHAM'S
- BELFRY
- DERBY
- RIPPLES
- SNOWBALL
- BROOD
- CHICK
- DIGEST
- MOW
- IMPLORING
- SCUFFLING
- GLIDE
- BISON
- PLUMAGE
- SNARL
- TIGER'S
- CAPERS
- BOA
- KEEPER'S
- BUOYANCY
- RECALLING
- SLUGGISH
- ADRIFT
- VASTNESS
- REVOLVED
- VAPOUR
- POISED
- UNRECOGNIZABLE
- REMINDER
- BLAMING
- NATIONALITIES
- ALEXANDER'S
- INTERACTION
- MISDIRECTED
- BOURBONS
- STAEL
- TALLEYRAND
- MURDERS
- INTRINSIC
- LADDERS
- NATHAN'S
- TRIBUTARIES
- TERMINAL
- STEADYING
- ROYLAKE'S
- UPPERCLIFF
- DRAINS
- FALSELY
- GLOODY'S
- STEPMOTHER'S
- DIMMED
- EARTHY
- NOSEGAY
- WARNINGS
- SANTIAGO
- UNDERGROWTH
- SCRIBBLED
- ROUTED
- SOLDIER'S
- THORGEST
- GUNNBIORN
- ODIN'S
- TIGHTS
- ERICSSON
- WHIZ
- CLATTERED
- PRANKS
- PRESS'D
- PITIES
- DUNKIRK
- PRITHEE
- ACQUITTED
- FRIAR
- DISSERTATION
- PATHETICALLY
- PREDESTINED
- HALTER
- OLENIN'S
- ABREK
- LUKASHKA'S
- KUNAK
- LUKE
- MARYANKA
- DRABANT
- VANYUSHA
- UNDREAMT
- WILLIAM'S
- SYLVIA'S
- YO'R
- IDENTIFY
- EXPEND
- LUNNON
- INTERROGATOR
- LOITERING
- LIGHTNESS
- IVER
- DEMONS
- RANGING
- FOUNDING
- SCENTS
- RUMOR
- INDISPUTABLE
- EVOLVED
- TRODDEN
- MADELEINE
- SHROVE
- METAPHORS
- PROUVAIRE
- COQUETTE
- ERR
- BROOCHES
- PRELATES
- WUZ
- KASE
- DAR
- LEER
- BRAGGER
- PADDLES
- LUFTON'S
- SCOWL
- DAMASK
- GRANTLY
- CROUP
- ARTISANS
- PRECOCIOUS
- MARVELLOUSLY
- INTRACTABLE
- O'MALLEY
- IMPLICATED
- HARLOWE
- THEMES
- HUSTLED
- SCION
- BOUDOIR
- KNOTS
- PARBLEU
- BOUQUET
- CATASTROPHES
- DIAGNOSIS
- DEALS
- HYSTERIC
- WIDEST
- PRESCRIPTIONS
- REDUCTION
- STIMULATE
- BRAITHWAITE
- WORTHILY
- TREMULOUSLY
- PANEGYRIC
- BANDAGED
- WIRED
- VOLOR
- UNREASONABLY
- DRAPERS
- HALLIDAY
- NOBBLER
- DIGGINGS
- FRAZER
- POLLUTION
- LAURENT
- VIOLIN
- WAISTBAND
- FINDLAY
- INCE
- CONTINGENCIES
- TWEED
- ULSTER
- CAIRNGORM
- BOULDER
- VERACITY
- FUNNEL
- ATHLETES
- RACIAL
- SIOUX
- INFLUENTIAL
- ASSIDUOUSLY
- AROUSING
- PRECEPTS
- SECTARIES
- CURTIUS
- RIVULETS
- IMPRACTICABLE
- FOUNDERS
- LOWELL
- MORTON'S
- DILLSBOROUGH
- SWITCHED
- MARVELLED
- SHAHRAZAD
- WIGHT
- KHORASANI
- SUCCOUR
- HATTIE
- SKAGGSY
- ALLINGTON
- ENVIES
- CROFTS
- ANIMOSITY
- AIL
- RECITATION
- QUAINTLY
- NORA'S
- LEVICE
- HARMLESSLY
- FREIGHTED
- COLLECTOR'S
- WHEW
- MUSHA
- ENDOWMENTS
- INARTICULATE
- CATHOLICS
- WEALTHIER
- PAPISTS
- BOTTLED
- COLLOQUY
- HOOPER
- DICKY
- BLINDFOLDED
- HOLMES
- THUMP
- PIPER
- LECOQ'S
- EUGENE
- OAKEN
- LOITER
- POLYTE
- UNREASONING
- JOSIANA'S
- STUART
- ESSEX
- CUPIDITY
- FADES
- DIMINISHES
- DEFORMITY
- HURTFUL
- PRICK
- POULTRY
- PUGILIST
- NECKLACES
- CHARTERS
- GIBRALTAR
- MULTIPLICATION
- VALUATION
- DISUSE
- SHEDS
- SHREDS
- DUKES
- SUB
- SHREWDLY
- BARGAINS
- EMBITTERED
- FATHERLESS
- GARRET
- CASSY
- GRAND'THER
- SPONGE
- MERCE
- SAFFRON
- UNFALTERING
- OSBORNE
- WHOOPED
- HELIOTROPE
- PHENOMENAL
- CORLEONE
- ATTICS
- BURNISHED
- ALCOVES
- PROW
- LUMBERING
- TYPEWRITER
- STOCKTON
- UPLIFT
- SEDATIVE
- NOVELIST
- CHESTNUT
- WHISTLES
- WINNINGS
- DIETERLI
- GARRETS
- MARDEN
- WALLACE'S
- PLAYFELLOWS
- UNCHRISTIAN
- INSTRUCTIVE
- GNOMES
- MOULDERING
- CORK
- YE'D
- CAROLS
- HOWLAND
- METROPOLITAN
- INKLING
- WITCHES
- MISDEEDS
- MINAMOTO
- PROVIDES
- TOKIWA
- IOLCUS
- INITIAL
- GRAZE
- PALISADE
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- KEENER
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- BACKBONE
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- INFREQUENTLY
- MOCCASINED
- WHOOPS
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- TRACKLESS
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- PLAYIN
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- MUMMER
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- RAREST
- UNPRINCIPLED
- JIGGING
- UNWINKING
- FATUOUS
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- VICAR'S
- SQUEALING
- WHISK
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- HAMBURG
- NAT
- CAMERON
- FIREFLY
- LURED
- DEMI
- SCRUBBING
- FARING
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- TARES
- DILIGENTLY
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- TINIEST
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- UNPROPITIOUS
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- DISCRIMINATIONS
- FREQUENCY
- FIDGETY
- PICCADILLY
- ATHENS
- CONSUMMATION
- INCOMING
- CONFERRING
- SMARTEST
- ANSON
- WILLY
- O'REILLY
- JEWELRY
- ACTRESSES
- PRODIGIES
- SUBSERVIENCE
- TOY
- MULATTO
- CUFFED
- BOXWOOD
- UNMERCIFULLY
- MEDDLING
- PRIMEVAL
- EXPERIMENTED
- GLOATING
- WALTZ
- DUSAK
- GIGGLING
- ROOMFUL
- TOPAZ
- COLOGNE
- WHELP
- DISGUSTINGNESS
- INTERROGATE
- WAYMORE
- RAKE
- DEBAUCHERY
- ESCAPADE
- INDEFINABLE
- COMPLICATION
- LAMENESS
- DEBAUCHERIES
- ROADSTEAD
- DREAMERS
- ATTUNED
- SIAMESE
- CHINAMAN
- GANGWAYS
- CONFINING
- CREVICES
- GRIME
- TURBAN
- ASTERN
- UNBELIEVERS
- VISCOUS
- AWNINGS
- IMMENSITY
- FLICKED
- ENSLAVED
- COLLIDED
- AWASH
- SCORNFUL
- BLOTTING
- FOREPEAK
- METICULOUS
- TRUTH'S
- EDDIED
- VOLUMINOUS
- DRILL
- DRAPED
- USELESSLY
- DELIBERATING
- COURAGEOUSLY
- DOWNRIGHT
- SOLIDARITY
- AGGRIEVED
- OAKBOURNE
- BLIGHTED
- WRAPPING
- EFFULGENT
- OUTGROWTH
- SEARCHINGLY
- SLOMAN'S
- CHATTY
- SPAWN
- TAMMY
- CONDIMENTS
- OZ
- FLAVOURED
- FORCEMEAT
- MALES
- ADHERING
- BIVALVES
- ANCHOVY
- STEW
- LISTLESSLY
- SHIELDING
- MOCKINGLY
- CLIPS
- SPLASHES
- CYCLIST
- DOER
- SURPRISINGLY
- QUARRELED
- JUPP
- UTTERS
- COLLAPSED
- IMPERVIOUS
- MUTTERINGS
- VALERIE
- ENGULF
- SLANTED
- STUTTERED
- LAK
- YAS
- MO
- FARNSWORTH
- UNTIED
- GENTILES
- DOVES
- BAPTIZE
- SCRIBES
- RANSOMED
- HIGHWAYMAN
- TRUE'S
- FRASER
- THRIVE
- CHOPS
- RASPBERRIES
- PELLUCID
- GLEN
- SOJOURNED
- OLIVIA
- SCOFFED
- FURTIVE
- CHIMES
- NETTLES
- DASHWOOD
- INCONSIDERATELY
- HENCEFORWARD
- MILSOM
- INCIVILITY
- RIGOURS
- RANKED
- ARTLESS
- CONSEQUENTIAL
- TRANSPORTS
- EMBELLISH'D
- ACCOUTRED
- PROCLAIM'D
- ATCHIEVEMENTS
- PERSIA
- ITABOD
- SYCOPHANTS
- REMOUNTED
- CATCH'D
- ESQUIRES
- CONVEY'D
- PLAY'D
- REBUFFS
- EXCEPTED
- STRATAGEM
- ARTFULLY
- SABRE
- CHIEFTAINS
- PUSH'D
- MUTES
- SWEATED
- SLUGGARD
- AGGRAVATION
- OPPRESS
- JOT
- BENNET'S
- PIES
- LUCASES
- NOURISHES
- SONNET
- GRACIOUSNESS
- MERYTON
- SOLWAY
- BLINDFOLD
- FORDS
- DECEMBER'S
- MOONLESS
- MATIN
- SALUTES
- PARTICIPATED
- GESTICULATIONS
- LOQUACIOUS
- REMOUNTING
- HAUGHTILY
- WHARTONS
- APPREHEND
- SUSPENSE
- BASKING
- METEOR
- DELL
- ROOTED
- INFINITY
- SHRINKS
- TENURE
- INSECURE
- IMPEDIMENTS
- CONTAGIOUS
- PREVENTION
- SUBSCRIPTIONS
- INTERCHANGE
- CARGOES
- ABODES
- CINNAMON
- SPURNS
- DELLS
- POLLUTED
- WEEPS
- EXPORTS
- OPULENCE
- BEGGARY
- GLORIED
- CONCILIATE
- ABANDONING
- ALLEVIATION
- HURRIES
- REMITTANCES
- AFFORDING
- PENSIONERS
- OLDEN
- INDIGENT
- HOE
- LAVISH
- BREEDS
- VISITATION
- SIGNORS
- TRIMMINGS
- BODED
- LUDOVICO
- JEERINGLY
- ARCHLY
- SEBASTIAN
- SLASHING
- CASEMENT
- EXCELLENZA
- EMILY'S
- APPEASE
- IMPLORE
- FOOL'S
- RAMPARTS
- BASEMENT
- FITZGERALD
- MOY'S
- DRINKER
- MONEYED
- SILVERWARE
- AUGMENTED
- GLASSWARE
- MANAGERIAL
- CASHIER
- TAILORED
- VEST
- DRESSY
- COMMENTARY
- BASK
- SEQUESTERED
- EARED
- SENSORY
- PALAVER
- PUFFED
- JULES
- MAYST
- BLYTH
- WRESTLING
- RANGERS
- SHERWOOD
- JUBILEE
- COWHIDE
- MAYHAP
- DARLINGS
- SCURVY
- FUME
- TAN
- METHINKS
- TWAIN
- TESTILY
- MANCHA
- OWNER'S
- MONTERA
- CARDENIO'S
- COMB
- RELIEVES
- DEPICTED
- FERNANDO'S
- PROTECTORS
- PROSTRATION
- CHERISHING
- KLETKE
- MAIMED
- SIGHTLESS
- DIABOLICAL
- QUAKED
- UNINHABITED
- MAIDSERVANT
- GASCONY
- GERMAIN
- BRAWL
- D'ARTAGNAN'S
- DEVIATED
- SUCCOR
- SORTIE
- DISSOLVE
- INCARCERATION
- DUNGEONS
- GRATINGS
- PRETENSE
- MYSTIFIER
- GLITTERS
- TENACIOUS
- LACKEYS
- TRAITOROUS
- REVERIES
- HOAR
- WHIPPER
- BALUSTRADE
- PEDESTAL
- REOPENED
- EXPOSTULATION
- FOUNDER
- KEYHOLE
- COURTESIED
- SMIRK
- INTANGIBLE
- WIXTED
- PRECIPITATELY
- LAUDANUM
- INCENSED
- DISTORTION
- BIZARRE
- VRAIMENT
- WAT
- BRYERLY
- FEIGN
- PETITE
- WONTED
- ENQUIRIES
- MILLINER
- GARRULOUS
- HOUSEMAID
- LES
- WINCED
- HEIGHTENING
- IDIOM
- SMACK
- ODDITY
- SCALED
- SLUMBERING
- BROOMSTICK
- SEMICIRCLE
- UNCLASP
- VOMIT
- FLINT'S
- CHAFFED
- PURCHASER
- GLAZED
- DIFFIDENT
- BOLDER
- IMPRINTED
- RESOUNDED
- EXTINGUISH
- EVENING'S
- HARANGUED
- SURGED
- MAGNETIZER
- PRICKING
- WAGNER'S
- OPERAS
- SUPERSEDE
- RESPIRATION
- REALISTIC
- INCONSISTENCY
- CHARLATAN
- CHARLATANISM
- OPERATORS
- CLAIRVOYANT
- EATER
- LETHARGY
- ACCOUNTABLE
- LOURDES
- PERSONATED
- STRIKINGLY
- MYLES'S
- ARMORY
- BOYHOOD'S
- GRACED
- COARSER
- CHEERLESS
- MARROW
- PUDDINGS
- SWEETENED
- MINSTRELS
- LOATHE
- MUSTARD
- CROCUSES
- GASCOYNE
- COOK'S
- ARBOR
- SPLINTERING
- PROPRIETRESS
- KINDNESSES
- JAKE
- SOMETIME
- FELLERS
- ORTER
- OLE
- CUM
- MORNIN
- WITHERING
- PICTUR
- TIPTON
- SPEEDING
- JOGGINS
- SYSTEMATIZED
- PATRONIZING
- CHEMISTRY
- VOGUE
- DISTILLATION
- SULPHURIC
- PERPETUATION
- ATTESTED
- SUPPLEMENTED
- PERPETUATED
- UNCHARITABLE
- INEXTRICABLY
- ENSUES
- LOOM
- MESMER
- IMBUED
- KROGER
- HYPNOTHERAPY
- AUTHOR'S
- EXPERIENTIAL
- ATAVISTIC
- KLINE
- CONSTITUTES
- INHERENTLY
- CEREBRUM
- CONDITIONED
- REFLEX
- FOURTHLY
- UNITS
- GLADSTONE
- CATO
- DECORATING
- DRAWINGS
- FIG
- BEAUTIFY
- FULFILLING
- ENLARGE
- UNREALITY
- LOBE
- EMBLEM
- WAGING
- SUREST
- PROGRESSING
- DOLING
- DIVED
- CONTRABAND
- SLANDER
- UNWAVERING
- IRRITATING
- GOSSIPS
- SAUNTERED
- EQUESTRIAN
- EDGING
- DUPLICITY
- PLODDING
- QUARRELLING
- REPROVE
- INNUENDOES
- DERISIVELY
- PASTORAL
- DISPLEASING
- DEFEATING
- SCOURGE
- EUDOXIA
- BEGS
- INVITES
- MAJORIAN
- CARTHAGENA
- HOSPITABLY
- BASILICUS
- CLOVIS
- BULGARIA
- GOTHS
- JUSTIN
- GELIMER
- VITIGES
- GRANDEST
- MANUFACTURES
- MOHAMMEDANS
- ISLAM
- INTENDS
- KHADIJAH
- IDOLS
- GABRIEL
- MOHAMMEDAN
- DIETH
- QUADRANGLE
- BA
- MOSLEM
- NORTHWESTERN
- CEYLON'S
- BENGAL
- LUCRATIVE
- OARSMEN
- APOPLEXY
- WHIMS
- EMPLOYERS
- CAREFREE
- SWISS
- JUNGLES
- NOOSE
- SIRR'S
- TERRAIN
- ORIENTALS
- SOLIDIFIED
- PROTEIN
- TESTACEA
- SAXONY
- SECRETING
- SHELLFISH
- CREATURE'S
- ROTTED
- IMMERSED
- SPHERICAL
- EARRINGS
- ERRATICALLY
- VESTMENTS
- SIEVES
- CLASSIFYING
- HARVESTING
- CARP
- PHILOSOPHICALLY
- UH
- DIDACTIC
- NEMO'S
- HARPOON
- COMPLETENESS
- GRIFFITH'S
- SQUIRMING
- AFIRE
- INTERMINGLED
- MADLY
- STYLISH
- COAXING
- SIDEWALKS
- UNRIGHTEOUS
- UNDRAWN
- PANACEA
- FOURSCORE
- TRIBUNALS
- INCOMPETENT
- NOMINALLY
- MASSACRED
- ABANDONMENT
- BURGLARY
- OBSESSION
- TAMELY
- CONFEDERACIES
- REALIZES
- BIGOTRY
- ZIGZAGS
- DORMITORY
- PULP
- BUNKS
- EYEBOLTS
- METRE
- DEPOTS
- NANSEN
- HICKORY
- TAR
- HUITFELDT
- HOEYER
- ELLEFSEN
- STIFFEST
- ASSORTMENT
- BERGEN
- PENETRATES
- RIME
- SPREADS
- ELABORATELY
- TIRES
- PATENTS
- UPPERS
- MEASUREMENTS
- PRIMUS
- STOCKHOLM
- HORIZONS
- MERCURY
- MAKER'S
- AILMENT
- RUST
- GROCER'S
- NOURISHING
- OATMEAL
- EMBANKMENT
- SPORTSMEN
- UNNUMBERED
- CORNICE
- ERECTING
- COPSE
- RADIATED
- SHRUBBERY
- RENTED
- COOPERS
- PULPITS
- NAMESAKE
- MANSFIELD
- INTUITIVE
- RECOMMENDATIONS
- RETICENCE
- EMBELLISH
- RECEIPTS
- MEAD
- LAMENTABLY
- APPENDAGE
- ENCUMBER
- PERIODICALS
- FASTIDIOUSNESS
- MINUET
- GYRATIONS
- ADDISON
- HORNPIPES
- SUPERINTENDED
- CONCOCTION
- DISTILLING
- LEIGH
- FLAX
- BALLAD
- SPINNING
- SINGSONG
- SPORADIC
- SUBCONSCIOUSLY
- IMPOTENT
- MEDLEY
- STARES
- JOSTLED
- ELBOWED
- ESCORTS
- STRAGGLERS
- LOITERED
- UNOPENED
- CURB
- APPROVING
- NONCHALANTLY
- FASTIDIOUSLY
- MUSINGLY
- UNCERTAINLY
- DIAL
- IMMACULATE
- EMPLOYEES
- HARLEM
- BLUNTLY
- BANKNOTES
- LURCH
- PENITENTIARY
- SPURTING
- HALTINGLY
- VIAL
- ALMONDS
- SWIFTEST
- INSIGNIA
- UNGUARDED
- ORB
- FETISHISM
- HAHN'S
- TROTTER
- SILKY
- AMAZON
- PLAITED
- BALCH
- SECLUDED
- EARSHOT
- MAGICIANS
- MESH
- DEFLECTING
- DUD
- FINGERED
- WHATEVER'S
- CARTER'S
- ANITA'S
- LUNG
- CUBBY
- BUZZER
- CYLINDER
- MATERIALIZED
- MIKO
- IDYLLIC
- FETTERS
- ARCHERY
- JUSTIFYING
- CAPABILITY
- DAVILOW
- TOLERANT
- RAINY
- FLACCID
- EUPHONIOUS
- SHORTNESS
- ANTIQUATED
- FLUFF
- WHIMPERED
- HOLLIS
- GOGOFFS
- ARROWPOINT
- MARQUESS
- LUSH'S
- STINTED
- BLOSSOMED
- CRAMP
- UNINTENTIONAL
- SUBMISSIVELY
- DISCOVERABLE
- ACCOMMODATED
- WOODY
- EQUIP
- BERESFORD
- DEDUCTION
- BYES
- ORBITS
- DRAWBACKS
- WAFTED
- PARISHIONERS
- CLASSICS
- MIDDLETON'S
- NEWEST
- HALE'S
- BELLES
- LESSENING
- WOODLANDS
- THORNTON'S
- CAPTIVATED
- IMPERTINENTLY
- TELEGRAPHIC
- LANCASTER
- SUPERNATURALLY
- IMPUTED
- DENOUNCING
- BRIBES
- GLOSS
- BRUMMAGEM
- IMPOTENCE
- PARADE
- IMPECCABLE
- RELEGATED
- RAKED
- EXPANSIVE
- INSISTENTLY
- LOWDER'S
- FLASHLIGHT
- WINCING
- INVOKED
- INHALING
- BENEVOLENTLY
- APPRECIATING
- PROPOUNDED
- CELEBRITIES
- EXPONENT
- ARENA
- CARESSINGLY
- MARTYRED
- NOSING
- AMERICAN'S
- GRIST
- EXPEDITIOUS
- SPANGLES
- CIVILISED
- ENUNCIATED
- SHIRK
- CEREBRAL
- HANDICAPPED
- CHOP
- SPLICE
- KERCHIEF
- WELLED
- OUTWIT
- HEARTEDNESS
- UNTRAMMELLED
- SCUFFLE
- QUALM
- NARROWEST
- CRANNY
- CHIDING
- BESOTTED
- MASKEW'S
- GIDDINESS
- HUMMOCKY
- APACE
- FRESHENED
- SCARING
- ROOKS
- CLOTTED
- FIRELOCK
- OWLS
- EXCAVATIONS
- STEEPLY
- VESTMENT
- GOOSEBERRY
- GASES
- STRANGLE
- OVERGROWN
- TINDER
- ENIGMATICAL
- GARGANTUA
- DEPRAVED
- MERLIN
- ALLEGORIES
- GOETH
- SAITH
- BATTALION
- REVENGING
- JOAQUIN
- PENNED
- SMOLDERING
- MOORE
- HUTCHINGS
- MALL
- UNDERGRADUATE
- COMPETE
- UNGENEROUS
- COSMOPOLITAN
- INSENSITIVENESS
- JUDICIOUSLY
- ICARUS
- ACCLAIMED
- CRUMPLED
- HUGGING
- HERRING
- GRUEL
- BRAT
- TAILED
- PELAGUEYA
- LEVANT
- CYPRUS
- BURIALS
- ABATED
- WAGGONS
- FRIGHTED
- FOOLHARDY
- CRIPPLEGATE
- CLARKENWELL
- WEALTHIEST
- ROTHERHITHE
- COMPUTED
- ACCUSERS
- DONNER
- TORTURING
- STONED
- BREWING
- PRIVATIONS
- FREMONT
- LATHERED
- CLEANSED
- POPPIES
- AIMLESSLY
- BROWNING
- BLIGHT
- SUTTER'S
- KETTLES
- MASON'S
- WATCHWORDS
- PARCELS
- OBSTRUCTIONS
- GRANDMA'S
- SOLDIERLY
- HOMELIKE
- HONEYS
- PUNCTIONS
- TAKIN
- JAKIE'S
- SONOMA
- HARDWOOD
- MODELLED
- DUCKLINGS
- SOCKETS
- MESHES
- EXUBERANT
- CENTREPIECE
- WIRTHIN
- LADLE
- EMBELLISHED
- HARROWING
- JAKIE
- CASTILIAN
- HORNED
- SUPERINTEND
- EVOLUTIONS
- MONMOUTH
- SANGUINARY
- MURRAY
- CONDOLENCE
- UNQUESTIONABLE
- HAGUE
- UNPLEASING
- TARDILY
- FRUSTRATED
- EVASIONS
- SKELTON
- ORKNEYS
- KIRKWALL
- MISADVENTURE
- REPEL
- DUNSTAFFNAGE
- MANIFESTO
- JOACHIM
- HEREIN
- MUSTERED
- ASCENTS
- VOUCHSAFE
- INIQUITY
- ACHIOR
- BETHULIA
- ISRAELITES
- ADORING
- SYNAGOGUE
- WRECKAGE
- SOLUTIONS
- BATTERING
- TRANSOCEANIC
- QUARTO
- HERALD
- FORMULATE
- REFUTED
- ADMISSIBLE
- GENERA
- CETACEAN
- IRONCLAD
- PROFESSORIAL
- LOOPHOLE
- GAZETTE
- INSURANCE
- ARSENALS
- ARMING
- WAYLAID
- VOCATION
- BOTANICAL
- MANSERVANT
- FLEMISH
- UNSOLICITED
- ENTHUSIAST
- BALEEN
- EMOTIONLESS
- SOCKS
- SKELETONS
- UNPREDICTABLE
- MAMMALS
- CONTAINERS
- FUNNELS
- ACCOMMODATIONS
- SKEPTICISM
- CHURNED
- GAFF
- WHALERS
- MANEUVERED
- SPYGLASSES
- POPULATED
- UNCONCERN
- MOLDY
- TACKLED
- CONTINENTS
- TROPIC
- OPTICAL
- GEARS
- IMMENSENESS
- UNSOLVED
- DISK
- SHROUDS
- PROBING
- MURKY
- SATCHELL
- CHURCHYARD
- AGGRAVATED
- DAB
- BETTERS
- CATECHISM
- PINAFORE
- JAM
- STAN
- IN'T
- SATCHELL'S
- FOLKS'S
- SEATING
- THURLE
- ON'T
- ULL
- CHURN
- INS
- OUTS
- INT
- GALLONS
- SCOURING
- EXPIRES
- HANNA
- CAUSEWAY
- NAME'S
- SIGHTEDNESS
- SOLO
- QUARTET
- CORKED
- ANYBODY'S
- ISOLATE
- EGREGIOUS
- FAVORING
- CIRCUMLOCUTION
- GUTTERS
- PRECLUDED
- ASPIRE
- HECTIC
- BARONETCY
- CORRECTING
- CUTLETS
- SNOWING
- SNOWED
- ACCLAMATION
- NEGLECTS
- DISPERSING
- UNPLEASANTNESS
- BEACONS
- THREATENINGLY
- LANDMARKS
- DIARY
- HELMER
- ROPED
- BATTLEFIELD
- HELL'S
- BOTTOMLESS
- RISKING
- QUILLS
- PORCUPINE
- TENACIOUSLY
- DITTIES
- SHELLEY
- DISPROVE
- PERRAULT
- DISTAFF
- VERGOOSE
- VERTIGOOSE
- GRANDCHILD
- GOOSE'S
- WM
- WHITMORE
- MONOGRAPH
- SUSSEX
- CHARLEMAGNE
- CREATIONS
- JUICY
- WRINKLES
- DEARIE
- BLEATING
- VE
- HOBBLING
- RABBITS
- INTERMITTENTLY
- ARIGHT
- OVERLAPPING
- DOMINATED
- CONTINGENT
- DOMINANT
- CLAMOURING
- INTRUDERS
- DEAFENED
- UPBORNE
- ARCHWAYS
- TOOTHLESS
- SHRIVELLED
- CADENCES
- UNISON
- SPACIOUSNESS
- DISTINCTIVE
- FELSPAR
- FEEDER
- RESTRAINTS
- ELIMINATING
- SPLENDOURS
- INDUCEMENTS
- NOURISHED
- LABOURING
- DIFFERENTIATING
- SLITS
- DISFIGUREMENT
- BARBARIC
- ARCHINGS
- UNLOADED
- POWDERED
- INKY
- THUNDERED
- SHRUG
- LOOMS
- REEKS
- TITANS
- SWARTHY
- HUNCHBACK
- RIDICULOUSLY
- STREWED
- VASES
- ADMONITIONS
- VOCAL
- UNSEEMLY
- CERES
- HOUSEWIFERY
- MILLET
- LENTILS
- FLEECES
- NOONTIDE
- EREBUS
- GROVELLING
- BLISSFUL
- FABLES
- WORSHIPPER
- OLYMPUS
- CENSOR
- MOORE'S
- SALES
- SNACK
- GRUYERE
- ANJOU
- FORTISSIMO
- TAWNY
- BRITTLE
- NIPS
- DOCTORING
- DANZIG
- CARAWAY
- SIP
- TRIBAL
- TILLED
- MAIZE
- MOHAWK
- GOADED
- PROLOGUE
- ALGONQUINS
- VOYAGER
- WANDERERS
- CURBED
- GARRISONS
- DEPLETED
- LOYOLA
- ABSOLUTISM
- HEARKEN
- HEARKENING
- SUPPOSITIONS
- TATTOO
- VEX
- KYUSHU
- WITHSTOOD
- MUGEN
- VERB
- MIMETIC
- BUDDHA
- VALUABLES
- SOLES
- IMAGINATIVELY
- JIKININKI
- DEVOURS
- OBLIGES
- AGREES
- ANJITSU
- ROADSIDE
- SKIRTING
- ATONEMENT
- AQUEDUCT
- GNASHED
- SQUATTING
- FOULED
- MURDERER'S
- COLLEAGUES
- KAI
- JOCOSELY
- TOLERATED
- PARLORS
- PEDDLER
- JEANETTE
- INDISCRIMINATE
- DOWER
- EMPIRIC
- ARRESTING
- SIMPLES
- CHECKING
- DISRESPECT
- PERSEVERED
- LAWTON'S
- PRESUMPTUOUS
- BANDAGES
- COMPLACENT
- EXCEEDS
- VIRGINIANS
- VALE
- DESPONDENCY
- RENOVATED
- EXERTING
- PROFFER
- SOMERSAULT
- RESTORING
- SLIPPER
- PORTIONED
- NICETIES
- WIELD
- LORDSHIPS
- ACCOMPANIMENTS
- GOALS
- CARRASCO
- UNHINGE
- APPERTAINING
- ENAMOURED
- ASCERTAINING
- SLACKEN
- SURNAME
- CAMACHO'S
- TOLEDANS
- CORCHUELO
- DISMOUNT
- DESPISING
- ONSET
- DEVOUTLY
- STRIPS
- CUTTLEFISH
- HILT
- AURORA
- FERVENT
- COUNTERPOISE
- ENLIVEN
- SPICES
- CAPTIVATE
- BASHFUL
- GALA
- LATERALLY
- FANGS
- TRANSCENDING
- VISE
- GLIMPSED
- FROTHING
- OVERWHELMINGLY
- FIGURED
- ERSTWHILE
- BATTLED
- TUSK
- PURGATORY
- GEHENNA
- STRAPPED
- UNMANAGEABLE
- THOAT
- SKULLS
- WARHOONS
- TRANSCENDS
- INSUBORDINATE
- RIPPED
- HERCULEAN
- JAILER
- VICTIM'S
- THOATS
- THRONES
- ENCRUSTED
- DIGNITARIES
- PYGMIES
- JAILERS
- LABYRINTHINE
- LOOT
- HELIUM
- ALARMS
- REAPING
- DUSKILY
- TORCHLIGHT
- LUBBER
- ROPE'S
- BUOY
- RUINATION
- HOSTAGE
- HITCH
- TAIN'T
- RACER
- COOLEST
- BANDAGE
- HAWKINS
- WRIGGLING
- AXES
- SILVER'S
- SMOLLETT
- GAYER
- ONSLAUGHT
- RAVING
- BULWARKS
- SOJOURN
- DEDUCED
- CONDUCE
- EXPOUNDED
- CONSUME
- CONFORMATION
- CORRESPOND
- VENA
- INAPPROPRIATELY
- CANALS
- PELLICLES
- PRECLUDE
- LIGATURE
- SMALLNESS
- PERFORATED
- DISTRIBUTING
- IRRATIONAL
- EMITS
- IDIOTS
- BRAINED
- POSTSCRIPT
- LANDLEAGUERS
- KEPPEL
- RUSSELL
- WYKAMIST
- BARRISTER
- SUICIDAL
- BOARDERS
- SUNBURY
- UNFURNISHED
- FOES
- UNATTRACTIVE
- WEALD
- BOARDER
- COWSHEDS
- JOCUND
- FOLIO
- CANTERBURY
- PRODIGAL
- CEASELESS
- BEDSTEAD
- TUGGING
- ADJUTANT'S
- RHYTHMICALLY
- CHILDLIKE
- CALECHE
- DRESSINGS
- DELIRIOUS
- COCKROACHES
- RUSTLED
- SEQUENCE
- ENJOIN
- SPLINTERS
- BOLKONSKI
- BROWNIE'S
- REFRIGERATOR
- BEAVER'S
- FATTY
- COON
- WOODCHUCK
- FELLING
- SWUM
- SPECULATOR
- CURRER
- ALTHESA
- CHATTEL
- DEM
- TOBIAS
- LOUISVILLE
- SLAVE'S
- WASHER
- BATON
- ROUGE
- FETTERED
- E'ER
- PEERLESS
- REND
- FLAY
- BEGRUDGED
- LAVRUSHKA
- BELTS
- LIBERATED
- MATTING
- BROADSHEETS
- COUSINE
- IVANOVNA
- MISINFORMED
- IRRESOLUTION
- JULIE
- VORONTSOVO
- LEPPICH
- COURIER
- FLOGGING
- OUTPOST
- STEADFASTLY
- REJECTING
- INTERFERING
- EFFUSION
- REQUESTING
- DISMISSION
- ASSIDUOUS
- SHORTEN
- WICKHAM
- INSPIRES
- MISLEADING
- ADMIRES
- DISOBLIGING
- CAROLINE'S
- LAMENTING
- IRKSOME
- PRESERVATIVE
- OFFENDING
- CIVILITIES
- SYNCOPATION
- TEMPO
- QUADRUPLE
- TEMPI
- QUINTUPLE
- SPICK
- CARPETS
- SERGE
- DIGESTIVE
- RECITATIONS
- DANCER
- ANTIDOTE
- SWISHING
- TRUTHFULLY
- ASLANT
- BEHRING
- COMIN
- COMMONEST
- SHRUB
- OBSTRUCTED
- BIRTHPLACE
- KEECHAWIK
- LETTUCE
- YUKON
- YAHKUK
- APENNINES
- METEOROLOGICAL
- BIRMINGHAM
- HURRICANES
- ASSERTING
- NIXON
- STICKLEBACKS
- GRAY'S
- AARON
- ACCEPTANCES
- ATTRIBUTABLE
- MORADABAD
- FERIDPOOR
- DISREGARDING
- TORRENTIAL
- PUTREFY
- ATMOSPHERIC
- BUIST
- HELTER
- SKELTER
- HINDON
- ACCURSED
- TORNADO
- SPRINKLE
- SPECIFY
- THEORETICALLY
- ACCEPTS
- CORRELATION
- EQUIVALENCE
- IRRESPECTIVE
- NEWTONIAN
- HARMONIZES
- SARGASSO
- IMMATURE
- STAGNATION
- HATFUL
- APPROXIMATION
- INVESTIGATOR
- WETTER
- CONCEIVABLY
- SNATCHING
- WINGLESS
- FRILLS
- SAUSAGE
- GANGS
- CHECKER
- REGISTERING
- COPYING
- PLUGGING
- WRITER'S
- OVERTIME
- WALLOWED
- SALARIES
- PHILANTHROPIST
- REFRESHMENTS
- TRANCE
- SCALPS
- PONE
- PUYA
- REFILL
- UNGRATEFULLY
- COMBINES
- ORGANIZATIONS
- HUB
- RAIDERS
- TERRORISM
- MANAGEABLE
- PERSONNEL
- COHEN
- WHITCOMB
- ELLA
- FINDEISEN
- KENT
- GRAM
- QUAY
- ROBERTSON
- COLORADO
- INDIANAPOLIS
- STAFFORD
- EMORY
- JACKSONVILLE
- ILLOGICALLY
- SUFFRAGISTS
- HINTING
- SUFFRAGER
- WHITTAKER'S
- COMMITMENT
- OUTSTRIP
- BLACKEST
- DOROTHY
- BRUTALLY
- BANGING
- CLANGING
- SKIMMED
- COGNIZANT
- MAINMAST
- CHEAPER
- DISTEMPERED
- STRAITENED
- PALLET
- ABATING
- INVIOLABLE
- PRACTITIONERS
- BRAWNY
- PUG
- QUELLED
- QUELL
- SESSIONS
- BLOODED
- ALLOY
- AMUCK
- BROWED
- MARINER
- BEDRAGGLED
- BUCCANEERING
- PROVOKING
- OVERT
- RIGGING
- PILLAGE
- UNSUSPECTING
- LIFELIKE
- FANNED
- SENSUAL
- TORMENTING
- HOVER
- DALES
- MISUNDERSTANDINGS
- MANIFOLD
- ABOLISHING
- INFLEXIBLY
- INSERTING
- DITHYRAMBIC
- EVANESCENT
- INNERMOST
- HARSHEST
- WEDLOCK
- PROFOUNDEST
- WITTY
- COMPRISING
- SORCERER
- REASSUMED
- ENTICE
- ACME
- UNENDING
- UNATTAINABLE
- MADONNA
- YEARNED
- POETICALLY
- BRADEMAGUS
- SMITTEN
- RESCUING
- GAWAIN
- CONSIGNED
- TURRETS
- PLEASANTRY
- ENFEEBLED
- DISGRACEFULLY
- SKILFUL
- JEER
- FOOTNOTE
- MIEN
- OVERTHROWN
- UNFASTENED
- VASSAL
- PECHEUR
- SOLICIT
- KINSMEN
- DINSMORE'S
- ENCOURAGINGLY
- FORGAVE
- ROSELANDS
- TEMPERS
- SLOVENLY
- UNFAVORABLE
- STEVENS
- SAFEGUARD
- TAPERS
- EVASION
- PORTAL
- WHIRLPOOL
- GLINT
- DULLY
- MING'S
- DIKE
- DRILLING
- PHOSPHORESCENT
- SENSED
- EERY
- VALLEY'S
- VOMITED
- CUBES
- GEOMETRIC
- PRODIGY
- FLEXING
- SICKENED
- FLAIL
- TOLL
- TWOS
- TRIPOD
- APEX
- TENTACLE
- PLAYFULNESS
- DRAKE'S
- CLUSTERED
- TOTTERED
- DAZEDLY
- HIMALAYAS
- SURGES
- NUDITY
- PITEOUSNESS
- BLOSSOMING
- RECOILED
- CLEFT
- RIFT
- PURGED
- PORTRESS
- SOFAS
- PENDENT
- INVENTORY
- SHRUBBERIES
- WARILY
- ANNUM
- EVINCED
- SELFSAME
- BLUEBERRY
- LOAM
- UNTIE
- BURROWING
- KETCH
- DRAM
- GENTLER
- SPIRAL
- HAW
- WHITTLED
- SCUDDING
- GROOVE
- CAREERING
- KINGSTOWN
- MOTORING
- COVERTLY
- PIANIST
- DEFT
- JIMMY'S
- NUDGES
- INHERITOR
- FREAK
- LORDLY
- MOTORISTS
- SNORTING
- SEGOUIN'S
- GRAFTON
- TWINED
- ENGLISHMAN'S
- SIGNIFICANTLY
- DEVISING
- BUNDLED
- HOPPERSON
- DAFT
- LEVITY
- CURMUDGEON
- GAUGES
- BOOKSTORE
- EVERLASTINGLY
- PRAISING
- SELLERS
- HICKS
- STAMPS
- PANTALOONS
- WARDROBES
- INJUNS
- SINFUL
- QUACK
- ABSOLUTION
- FRANCESCO
- FEVERED
- VIGILS
- ALTERING
- RENEWING
- FESTIVALS
- FLATTERERS
- UNALTERED
- SPRINGFIELD
- FORTE
- ACCOMMODATING
- BACKERS
- FASTEST
- SUPERINTENDENTS
- ORD
- PLATTE
- PHERSON
- STAGER
- HECKSHER
- GORDON
- BREVOORT
- BELMONT
- BACKWOODS
- EMPHATIC
- LOOK'D
- TENET
- WHIMSICALITY
- DIDIUS
- TRIBONIUS
- WARMEST
- NEPHEW'S
- JOSTLING
- SKIRMISHING
- CONTRARIWISE
- INTERROGATIVELY
- FURBISH'D
- POPISH
- UNGRACIOUS
- AIDING
- TARNISH'D
- COCKADE
- THONG
- TASSEL
- MARCH'D
- CELIBACY
- DECLAMATION
- PHIPPS
- MANUFACTORY
- LANCERS
- ANGLING
- CONSIDERS
- PIMPLES
- SATIRICAL
- CLERICAL
- PHERORAS
- COHORTS
- FORTRESSES
- SAMARIA
- OVERRAN
- CONQUERORS
- MIDLAND
- ROBBERIES
- EXHORTATIONS
- THREATENINGS
- SPOILING
- FRIGID
- ARS
- BIRTHRIGHT
- TRIUMVIRATE
- FOULNESS
- GIVEST
- CONTRITION
- UNDERSTANDINGS
- IDOLATORS
- BAPTIZED
- SACRAMENT
- LESCAUT
- DISTRACTIONS
- FRASCATI
- ISOLATING
- GAMBLER
- MARLY
- CLOSES
- COURTESAN
- SEMICIRCULAR
- JOUR
- OSPREY
- NIGHTMARE
- ERST
- THWART
- SULKILY
- GASHED
- GRIMES
- YE'LL
- OFFENCES
- LEVELLED
- MAINLAND
- COMMANDANT
- SCOOPED
- ABYSMAL
- GIRDING
- GORGED
- RETAKEN
- JEM
- SHORTENED
- DUDS
- FORGING
- BUILDER
- DOZE
- OLIVER'S
- CONTENDING
- RIP
- TROLLING
- FLEDGED
- STALWART
- SLACKENING
- BUSHEL
- BREAD'S
- ARID
- INVIGORATED
- SURLY
- EXPENDING
- ENSIGN
- CONNECTICUT
- SUCCESSORS
- TESTIFIES
- CLEAREST
- WOODBURY
- CONGREGATIONAL
- SALISBURY
- GATHERINGS
- HOUSATONIC
- OUTCASTS
- KINSHIP
- VERMIN
- BROADLY
- UNDREAMED
- DYNASTIES
- SIMIANS
- UNLOVELY
- FLIGHTY
- OPERATIVE
- CONVERSELY
- INITIATIVE
- SPEECHLESS
- BANQUETS
- EXTERMINATE
- PUSHES
- APPALACHE
- WEDGES
- HATCHET
- CANAVERAL
- REEFS
- OTTIGNY
- OUTINA'S
- CONJURER
- WELLNIGH
- HOWLINGS
- OUTINA
- TRIBESMEN
- EXULTED
- HOMESICK
- CLEAVE
- COLIGNY
- RIPEN
- YELLS
- BOROUGH
- JOURNEYMAN
- CHILTERN
- NEEDY
- LENDER
- INSERT
- EXPECTANT
- BUCKLED
- DRESDEN
- ASCENDANT
- UNITE
- ILLNESSES
- UNRESERVE
- FRIENDLINESS
- FORETELL
- CLEVERER
- BOASTS
- RANDALLS
- WOODHOUSE'S
- PREDICAMENT
- COMMUNICATIONS
- FIRESIDE
- LOREEN'S
- DISTRUSTED
- ROLLIN'S
- ARNOLD
- REDEEM
- CONSTITUENCY
- DISPLAYS
- INCREDULOUSLY
- DESPAIRINGLY
- RUEFULLY
- CHEAPENED
- MAUSOLEUM
- GRAFTER
- STEALS
- GROCERS
- PLUTORIA
- SLUGGISHNESS
- LAWLESSNESS
- O'HOOLIGAN
- GRATH
- INGRATIATING
- CENTRES
- DAVIDSON
- CLERK'S
- DISCERNED
- UNWELL
- PURPORT
- CLOSETED
- AGHAST
- ESCUTCHEON
- KICKS
- DEMONIAC
- BUFFETED
- JUVENILE
- PEA
- RUEFUL
- HARE'S
- SCORNING
- VASTLY
- CONVULSIONS
- AFY
- JOYCE'S
- YEARN
- DERANGED
- LEGIBLE
- PENS
- PENKNIFE
- CURIOSITIES
- SULPHUR
- COMPRESSION
- MERCEDES
- TRANSPIRE
- SPOTLESS
- DISPOSITIONS
- GUILELESS
- SMOKESTACK
- RAPPING
- JACKKNIFE
- SANCTUM
- RELAPSED
- SIGNALING
- KNOCKOUT
- STOUTNESS
- LAGGED
- BRIGHTON
- WHIN
- POSITIVENESS
- EVERETT
- SHRAPNEL
- POISONOUS
- BOCHES
- FATEFUL
- TRUCKS
- IRREGULARLY
- BRIGADES
- SPASMODIC
- AIRCRAFT
- RIGHTED
- ENCIRCLE
- MOMENTUM
- JUBILATION
- SUBMARINE
- AMIABLY
- PERMEATED
- DEBUTANTES
- ELABORATED
- MORNINGS
- NATALIE
- EVADED
- BARRACK
- CLUSTERING
- HIVES
- WISTFULLY
- BATE
- COPS
- COP
- FINES
- YARNS
- WEAL
- SCRUPULOUSLY
- TRAFFORDS
- EGREMONT
- SCANNING
- INCONVENIENT
- ABSTRACTED
- DUPES
- ASSEMBLIES
- CLAMOROUS
- DEGRADE
- TERRIFY
- OPPRESSORS
- ERMINTRUDE
- JOLLIGINKI
- THICKEST
- POLYNESIA
- DOLITTLE
- CONCIERGERIE
- UNCONNECTED
- ACCEPTATION
- LUCIE
- CITIZEN'S
- THOUGHTLESSNESS
- ASPHYXIATED
- BORINGS
- ICEBERG
- WATERLINE
- STAKED
- SUPERVISED
- WIELDED
- SUPERVISING
- INJECTED
- SCARCER
- SUSTAINING
- PRAISEWORTHY
- NAVIGATORS
- PREDICTS
- NAVIGATED
- BULB
- MIXING
- SPECIFICALLY
- DECIMETERS
- MARVELED
- JELLYFISH
- FESTOONS
- DANGLE
- FERRETING
- APPALLED
- PHRIXUS
- DETHRONED
- CHIRON
- CADMUS
- COXCOMB
- ENCHANTRESS
- SHRIVELS
- GRIPE
- ASSAILS
- GREENSWARD
- BROADCAST
- RIPENED
- CLASHING
- HEWING
- SIMPLETONS
- KINGLY
- ENCHANTRESSES
- FORBIDS
- LOWING
- SNOUTS
- ANTELOPE
- SHATTERING
- BEGONE
- EMBARK
- GRENADES
- CONCENTRATING
- WEAKEST
- UNTENABLE
- INSCRIPTIONS
- VIRGIL
- AMPLIFICATION
- PROGRESSIVES
- PENDING
- CHESS
- CONSERVATION
- AMEND
- RHODE
- MARSHALING
- LETTERED
- DENT
- LIBERATE
- APPOINTEE
- REPORTERS
- FLAUNTED
- BULLETIN
- CONCLUDES
- POLITICALLY
- GRAZED
- DETECTIVES
- SUMMED
- DELEGATION
- FLAMBEAU
- STATUARY
- BROODS
- COSY
- REPENTED
- RITUAL
- FANTASTICALLY
- ADJURATION
- UNLOADING
- BAZAAR
- UNWRAPPED
- COMPANIONABLE
- FINANCIER
- DISARMING
- SPURTED
- FRAYED
- FLORIAN
- GODCHILD
- ANONYMOUS
- SMASHING
- BELVANE'S
- CORONEL'S
- MAIDENLY
- ARMOURER
- ARMOURER'S
- ARCHITECT
- HYACINTH'S
- ELVIRA
- HUMOUREDLY
- COMPUNCTION
- DUGALD
- PRISCILLA'S
- AFGHAN
- BUTTONING
- GASLIGHT
- BRUNWALDE
- SERENADING
- TALENTED
- HANSOM
- JERVIS
- DEPRECIATION
- PREMISE
- DUPLICATE
- CARROZZA
- CABMAN
- UNIFORMED
- WHEREAT
- UNABASHED
- RHAPSODIES
- SPANKING
- STEEDS
- PITIFULLY
- OPPORTUNELY
- TOURIST
- CANVASS
- BRADLEY
- POPPY
- NICKLE
- REEVES'S
- GLEEFULLY
- REEVES
- WAILS
- STOW
- CURING
- SIPPING
- COUNTRYFIED
- REVELED
- MESSY
- BEDROOMS
- CROCK
- SNAPS
- PRIMLY
- GILLIS
- WHITE'S
- SAM'S
- TASSELS
- MAMIE
- TUMBLERFULS
- WATERED
- DETERMINEDLY
- DIZZILY
- DIANA'S
- THOMAS'S
- SERE
- INTOXICATE
- IRRITATE
- UNHOLY
- PESTERING
- OUTSPOKEN
- WILDFIRE
- WHEELBARROWS
- VEERED
- GERANIUM
- PRETTIER
- CHUM
- CANONIZED
- CLAUDE
- BICYCLES
- UNKLE
- CLAIR'S
- REPRODUCE
- WILLIE
- IRVING
- DONNELL
- OFTENEST
- MOONGLADE
- SCUTTLE
- HIRAM
- PRILLIE
- ANGERED
- INSOLENTLY
- ANTHONY'S
- REPENTANT
- HUMILIATIONS
- COMRADESHIP
- CONTINGENCY
- QUIETNESS
- CAMPFIRE
- HENDRY'S
- KNOCKER
- SWARMS
- ANTICIPATING
- MARM
- TANTRUMS
- GRAFTIN
- AIRTH
- ETARNAL
- ABED
- GRAINED
- GRENADIERS
- WHOPPER
- KNOWLES
- WHARTON'S
- REVERSAL
- PROFESSING
- ATTACHES
- MANFULLY
- RATTLERS
- ROBYS
- UNBLUSHING
- INSEPARABLE
- SECURES
- ABSOLVE
- OMNIUM
- AUSPICES
- DROUGHT
- SELECTIONS
- SILVERBRIDGE
- LUCKIEST
- RECEPTIONS
- PICNICS
- FEATHERLESS
- ROBINS
- CATCHER
- RESENTING
- ENTICING
- TUMBLERS
- WINKS
- TORMENTOR
- PERCHING
- TAGGING
- FLAPPING
- LAPPED
- PLUCKY
- SCRUBBED
- GOLDENRODS
- RAVINES
- LIMESTONE
- ADVISERS
- MOUNTAINEERING
- CONE
- ARMPITS
- FATIGUING
- AVALANCHES
- WALLOWING
- HOLLOWED
- DIVERSIFIED
- SIFTING
- NOTEBOOK
- DEPOSITION
- BOSSY
- MATTED
- FLUFFY
- LASSEN'S
- BOSSES
- ENCIRCLING
- DISSOLVING
- FABRICS
- FUMAROLES
- SCALDING
- FINENESS
- COMPELS
- WREATHING
- NORTHEASTERLY
- ENABLING
- VAUNTED
- MORMON
- ZION
- TAINTED
- SAUNTERING
- SAGEY
- COMPARABLE
- LARKS
- SEDIMENTS
- ERYTHRONIUMS
- FRITILLARIAS
- BATTLEMENTS
- ERYTHRONIUM
- SHOWY
- AGLOW
- BULBS
- ATROPURPUREA
- TULIPS
- MORMONS
- GRASSHOPPERS
- SUBSISTED
- GRANDDAUGHTER
- TRANSPLANTED
- COMPOSITORS
- UNWARRANTED
- LOGGING
- BREAKAGE
- PROSPECTOR
- ASPIRING
- BOULEVARDS
- LOGGERS
- ATTRACTIONS
- CORDUROY
- PLUNGES
- HUNT'S
- PARTICLE
- HALLOWED
- INFIDELS
- HAULING
- DAUNTED
- NARRAGANSETT
- HOOPING
- FILTHY
- PSALMIST
- PARCHING
- BEAR'S
- UNSATISFIED
- SAVORY
- TRUMPERY
- PORTUGUEZ
- IRONS
- FORD
- ALLIGATORS
- RESURRECTED
- IMPECUNIOUS
- PLYING
- FURROW
- INSTANT'S
- QUOTATION
- DREARILY
- OMENS
- WA'N'T
- DERNED
- SNAPPY
- JADED
- JACKASSES
- FALK'S
- HURTING
- DOWRY
- DORCAS
- CRESTFALLEN
- SHOEMAKERS
- PRINTERS
- LEVIN'S
- COQUETTISH
- FALANDER'S
- REHNHJELM'S
- RIPPLED
- DISCONCERTED
- COUP
- HJALMAR
- STIPEND
- RENTING
- LEAKING
- SKETCHING
- DIRECTORY
- ADJUNCT
- PROUDER
- EVE'S
- LASCIVIOUS
- BEAUTY'S
- RETENTION
- HERETIC
- ARIZONA
- QUARTZ
- SNUFFED
- INCARNATION
- RADIUS
- IMBUE
- ARMLET
- WITHDRAWING
- ROSTRUM
- BULKS
- DENIZENS
- CAPTOR
- PERFUNCTORY
- PEALS
- JUMPS
- SENSATIONALISM
- LAIR
- BRIGANDAGE
- ENVIRONMENTS
- RECLINED
- BEAUT
- DIVERSIONS
- BANKING
- SOR
- CUDDLE
- MAVOURNEEN
- BREAKIN
- LIVELIHOOD
- SUPREMELY
- HOBOKEN
- FLYAWAY
- FONT
- CHRISTENING
- THREES
- SANNA
- RAVEN'S
- SHOVELS
- SENTENCED
- SALARIED
- ESSAYS
- NOTHINGS
- DRONE
- PEPPERLEIGH
- HAREM
- DROOPED
- MOP
- LENDS
- NEWSPACKET
- JEFFERSON'S
- STROPPED
- EARNINGS
- SCRIP
- JAMMED
- NIPPEWA
- TULIP
- PROSPECTUS
- NETLEY'S
- CAPITALISTS
- CORONA
- INCOMPETENCE
- INSURRECTOS
- RECLAIMED
- DIRECTORS
- FRAUDS
- RECONSTRUCTED
- MABEUF
- TREMBLES
- REVERTED
- SHAKSPEARE
- HIVE
- NUMBERING
- SUICIDES
- FASTING
- VOLUPTUOUSNESS
- EXPIRATION
- PLENITUDE
- THINKERS
- HYDRA
- FICTIONS
- AMPHICTYONS
- SOVEREIGNTIES
- 'NULL'
- CONSCIENCES
- DISMEMBERMENT
- DISPLACE
- EVOKES
- BRUJON
- EPONINE'S
- SPECTRE
- PLUMET
- PARDI
- ICES
- PROUVAIRES
- WHISPERINGS
- VIBRATION
- PROFILES
- ENGULFED
- PONTMERCY
- WIDENING
- UNANSWERED
- LAGGING
- INIQUITOUS
- DEFENDER
- AMBIORIX
- ARTEVELDE
- VIOLATES
- SUFFICES
- PHILIPPE
- REPLACES
- SUPPRESSES
- REASSURING
- SUNSHINY
- GLEESON
- GOODBYE
- UNTRUTH
- MENDER
- EMANUEL
- DIME
- STORIED
- POINTSMAN
- CAUSATION
- WELD
- WELLINGTON
- INFERENCE
- INDUCTION
- HAMILTON'S
- REVIEWING
- RELATIVITY
- REQUISITES
- NOMINAL
- COLLECTIVE
- LOCALLY
- SUCCESSIVELY
- APPETISING
- MANFRED
- STOREY
- SNUB
- SERVILE
- APPLAUDED
- REFINEMENTS
- CURRICULUM
- SUBJUGATE
- TENTHS
- DISDAINFULLY
- PAROXYSM
- DOMINATING
- SWIMS
- LEVELED
- CRESTS
- SIGNIFYING
- DISTINGUISHES
- NEPONSET
- WENDELL
- SUFFOLK
- RESOLVES
- VICISSITUDES
- NURTURED
- RUSKIN'S
- CURSORY
- SOMERVILLE
- LYNDEBORO
- ROLLICKING
- DOMINATE
- MISCELLANEOUS
- TOILSOME
- TOLLING
- DELINQUENTS
- EXACTNESS
- TITHING
- IDOLATRY
- HYMNS
- STIRS
- DEPRESSING
- PEOPLED
- SEVERITIES
- STAMINA
- COCKADOODLE
- PECKED
- BANTAM
- RIPPLING
- BIDDY
- CROAK
- POUNCED
- MATERNAL
- MINT
- DISOBEYING
- SNOWFLAKES
- BLOT'S
- LEAFLESS
- HAWTHORN
- TEASED
- BUN
- VILLAINOUS
- PROMENADE
- KNOTTED
- PAGODA
- CURLING
- EATABLE
- SOBERED
- LAZARETTE
- CHAFING
- PINION
- WEIGHTY
- STAVE
- MANGLED
- SWAMPED
- SABLE
- FOREMAST
- STAVES
- FENDERS
- RANSACKED
- DAZZLE
- WHIRLPOOLS
- INFERRED
- CELEBRATING
- CAMEO
- BIOGRAPHICAL
- HISTORY'S
- GERVINUS
- INTERACTIONS
- EXECUTIONS
- DECORATION
- FOREHEADS
- PARISHIONER
- GOERS
- TAKU
- SOUTHWEST
- NAVIGABLE
- HONEYSUCKLE
- DELTA
- FORESTED
- ABOUNDING
- NORTHEASTERN
- TERRACES
- HEMLOCK
- SPIRES
- HUCKLEBERRIES
- NOTCHES
- MUIR
- FORDWITCH
- DRAWBACK
- INFLICTION
- TOLLER'S
- FOREWARNED
- DEVILISH
- SIXES
- SEVENS
- FAVOURING
- SUPERBLY
- TURBID
- ASSOCIATING
- REMBRANDT
- BLOCKING
- RECONNOITRED
- TROOPERS
- CAPRON'S
- GAUDY
- KEENEST
- KRAG
- DISTILLERY
- ROWLAND
- UPHILL
- CANTEEN
- GAUZE
- STORMING
- DRIFTWOOD
- HEARTSICK
- OLAF
- HARALD
- GREENLANDERS
- GUDRID
- VARIEGATED
- AFFABLY
- FOREARMED
- CROPPERS
- SLATES
- STEELY
- SAPLINGS
- DUCE
- PRIESTLY
- STIPULATION
- PULL'D
- PUZZLE
- NOAH'S
- HARM'S
- POO
- ACCOUTREMENTS
- CHRONOLOGY
- PLOUGHS
- MINERALS
- MARLBOROUGH
- BELBURG
- KERPENORD
- KALSAKEN
- NEWDORF
- LANDENBOURG
- MILDENHEIM
- ELCHINGEN
- GINGEN
- BALMERCHOFFEN
- DEFENCES
- SCHWARTZ
- HAPPEN'D
- LANDEN
- INFUSING
- CLAMBERING
- STAG'S
- DMITRI
- BUZZED
- STEPPE
- TRUMP
- SEDATE
- GODSON
- GROZNOE
- DEALERS
- HAYTERSBANK
- HAMMERS
- WHALING
- COULSON'S
- NEWCASSEL
- PRENTICE
- FAILINGS
- URANIA
- CONJURE
- REID
- ABACK
- YO'RE
- NOAN
- CHARITABLY
- FOSTER'S
- INSOLVENCY
- FORESTALLED
- CULMINATING
- MAR
- LADDIE
- LUCK'S
- THOU'RT
- MOLLIFIED
- BRONZED
- MAK
- THOU'S
- BARABBAS
- JOSEPHUS
- TALMUD
- CORRECTIONS
- SCRIBE
- EGYPTIAN
- LENDING
- WASTEFUL
- DISCLOSURE
- TOL
- WHOA
- ETHEL'S
- CHERUBIM
- CUPIDS
- PELL
- MELL
- UNCHAINED
- SHAMELESSNESS
- SATURNALIA
- CUCKOO
- LANDAU
- APOTHEOSIS
- TRIUMPHAL
- ENTICED
- CONFRONTS
- TINSEL
- PREFECTURE
- CANDOR
- MIRTHFUL
- SUPPOSES
- POTTERY
- BASQUE
- DESSERT
- CAJOLE
- WEIGHS
- ROBESPIERRE
- POMUM
- IMPASSIONED
- MANHATTAN
- RATAPLAN
- MUSCOVADO
- GRANDISSIMO
- BASTINADO
- RENTAL
- EMERALDS
- CREME
- COMMONERS
- FACADE
- SATINS
- HAPLY
- SHEDDING
- PHRONY
- TURNT
- HISSE'F
- INTER
- F'UM
- MO'NFUL
- GOO
- SWOOP
- BLUFFS
- COPPERAS
- CAPTING
- PADDLED
- HEADWAY
- SOWERBY'S
- PREBENDARY
- RECURRING
- PERQUISITES
- JAUNTY
- ENTRAPPED
- APPERTAIN
- EARNS
- CHARIOTEER
- GRISELDA
- CORNISH
- CLERGYMEN
- RAIMENT
- DEANERY
- DELECTUS
- SAVOURED
- GUAVA
- UNLOAD
- INCUMBENT
- DISPENSED
- PLATOON
- INSURGENT
- LYNCH
- SIMONIANS
- MEMOIRS
- UNAIDED
- AUDACIOUS
- ABOUND
- INTERMITTENCES
- MISCARRIED
- ENJOLRAS
- ROLAND
- USHER
- ORDNANCE
- BOSSUET
- CARTOUCHES
- SHARPSHOOTERS
- PECKING
- GAMIN
- URCHIN
- PYGMY
- CROPOLE
- CORTEGE
- FLAMBEAUX
- PANOPLY
- VIVE
- ROI
- PITTRINO
- FATIGUES
- PIERSON
- SORORITY
- MISDEMEANOR
- HIPPY
- APPALLINGLY
- TEAMS
- SPRINTING
- NETTED
- SWOOPED
- EUPHRASIE
- MOIRE
- WATERWORKS
- STRASBURG
- INSIPID
- TROY
- ACHILLES
- HECTOR
- HEW
- NESTOR
- BYGONE
- CAROUSE
- CABBAGES
- BOURGEOISE
- BALLET
- MOUNTEBANK
- SYLPHS
- SWANS
- CAREFULNESS
- EXAMINES
- URINE
- DIFFERENTIATION
- ANALYZING
- SCHEMATIC
- RECOGNIZABLE
- APPENDIX
- GROWTHS
- NEURASTHENIC
- HYGIENE
- REACT
- UNDERMINE
- OVERBUSY
- PLEASANTNESS
- MASONIC
- MASONS
- OUTWARDS
- VICTORIANS
- ENUMERATED
- REFORMS
- PLACARDS
- TRANQUILLY
- INCALCULABLE
- COMMUNISM
- MABEL'S
- FELSENBURGH'S
- BENNINSCHEIN
- UNIMAGINABLE
- PRECEDENTS
- BEGAT
- GOADING
- PROGRAMME
- GULLY
- LICENCES
- BULLOCK
- GROG
- ALLSORTS
- MISCONCEPTION
- SEMITES
- DEFILED
- MENTIONS
- CHILDBIRTH
- SECLUDE
- COMPARES
- SECLUDING
- EMANATE
- ZEALAND
- TABOOS
- DURKHEIM
- PUBERTY
- REAPPEARS
- CHRONICALLY
- BOAS
- MEDIAEVAL
- SURVIVES
- VATS
- ABDOMINAL
- ORCHESTRAL
- TUNED
- REFERS
- PRIESTESS
- PROPHETESS
- SCOTCHWOMAN
- ROMANINOV
- BUSIER
- MASQUERADING
- ADORNMENT
- MOOR
- SHADOWING
- BOATMEN
- GINTLEMAN
- ILLUSTRATIONS
- QUALIFICATION
- PICOTTE
- CURTIS
- UNEQUALLED
- COOLIDGE
- HELPERS
- SAC
- BONNEY
- INTERCOLLEGIATE
- JOURNALISTS
- KENZIE
- CAVIL
- AUBANUS
- BOHEMUS
- HEALTHFUL
- ORACLES
- AESCULAPIUS
- DEITY
- COMMENTATOR
- MOUNTEBANKS
- DISAGREEING
- MEDICORUM
- GUSTY
- MOCCASIN
- ACORN
- NICOLAS
- ROCKETS
- HOGARTH
- UNRESTRICTED
- INQUEST
- FEMININITY
- CURSING
- IBRAHIM
- GOODWILL
- SEEKETH
- GIVER
- SCONES
- BLEMISH
- ASKER
- MAKETH
- BAZAR
- TRENCHER
- HADDEST
- BURDENSOME
- PURVEYORS
- LIABILITIES
- BOUNTIES
- DISHONOUR
- HARDEN
- STRUTTING
- PU
- MAUDIE
- MENACINGLY
- SERPENTS
- REALISATION
- WERMIN
- OLLAYS
- EAMES'S
- PRECAUTIONARY
- BESEECHINGLY
- VERIFY
- DIVIDES
- EMPHASIZING
- VIBRATED
- GIGGLE
- REHEARSAL
- TYRRELL
- SQUALL
- GRUNDY
- SPLEEN
- AGROUND
- STRAYING
- PULSATING
- COWERING
- IMMEASURABLY
- VILLEFORT'S
- TIPTOE
- AFFABILITY
- LAMPREYS
- IMPUDENTLY
- CICERONE
- HONORABLY
- AISY
- WHAT'LL
- GI
- JABERS
- DREAMER'S
- PARTAKES
- HARRELSTEIN
- VOLUPTUOUS
- INTERCEPTED
- WEISHAUPT'S
- WEISHAUPT
- PRUDES
- GUESSES
- SCRUTINIZING
- SERVER
- COGITATIONS
- TWINKLED
- LOANED
- GARAGE
- GREENE
- RODDY
- CALLER
- DISINCLINATION
- SCHOOLMATE
- OUTSIDER
- JACK'S
- HAZEL
- SWASH
- FANNING
- FAUSTUS
- CHEMIST
- ASTRONOMER
- ABLAZE
- SUP
- BABA
- BOURGOGNE
- HEY
- PICARD
- HUSSIES
- CIVILLY
- PAPILLON
- SARDINES
- DISGUSTING
- IDENTIFICATION
- ILLEGIBLE
- DOORKEEPER
- TOINON
- MAGISTRATE'S
- POLYTE'S
- FASCINATIONS
- UNAPPROACHABLE
- BASTARDY
- PRETENSION
- CREATES
- CIRCUMVALLATION
- BOLEYN'S
- ORTHOGRAPHY
- DICTATION
- IMPOSSIBILITIES
- DIRRY
- MOIR
- BREASTPLATES
- TOASTED
- THATCH
- SCRAPED
- REDOUBTABLE
- UNSHORN
- THRASH
- TRAINER
- HERCULES
- CIRCUSES
- FREQUENTED
- FLESHY
- FELONY
- PROSTITUTE
- ANNUITIES
- SUBSCRIBE
- INCIPIENT
- LEGITIMATELY
- DETRIMENTAL
- TACITUS
- MONOPOLY
- JUS
- AGREEMENTS
- ACQUIRES
- DISINHERITED
- OVERSTEP
- LEGISTS
- EQUITABLE
- BIRTHS
- USUFRUCT
- DEATHLESS
- THENCEFORWARD
- STY
- SLEEPERS
- PHANTOMS
- DISSOLVES
- WANDERER
- BRACKETS
- BROWSES
- GRANARY
- RECONSTRUCTION
- HOEING
- FERTILIZERS
- OAKEY
- PLANTERS
- FENCED
- INFLEXIBLE
- BOLTON
- INCLUDES
- GRABBING
- STURDILY
- PREACHER'S
- NEWBORN
- IDEALIZED
- COAX
- UNCLOTHED
- DUBBED
- BUILDED
- VINEYARD
- BARMOUTH
- GRAND'THER'S
- BANNOCK
- PETTISHLY
- FEATHERY
- MORGESONS
- DISTANTLY
- BOMBAZINE
- FLACON
- SALTS
- UNSWERVING
- HOMELAND
- DISILLUSION
- RAMBLES
- DIMNESS
- GIST
- FRAMPTON
- VACATIONS
- MAILED
- COMPREHENSIBLE
- PHOTOGRAPHY
- ILLOGICAL
- RECEPTACLES
- RAMIFICATIONS
- MONALDESCHI
- SKEIN
- SCALY
- HOPE'S
- VOGRAAT
- ROTTERDAM
- PARALYTIC
- MOULDY
- REGISTRATION
- FRAEULEIN
- SCHERIN
- DUES
- DICTIONARY
- FACILE
- RUMPLED
- REGISTRAR
- FERRIS'S
- COMPOSING
- UNTAMED
- MUSINGS
- CYNIC
- COLYUMIST'S
- WILDE
- COLYUMISTS
- JEWELLERY
- SCRAWLED
- CONRAD
- PARAGRAPHS
- FOHRENSEE
- DIETRICH'S
- EXPOSTULATED
- SANGUINE
- SUPPORTER
- RIVERMOUTH
- ASPIRED
- CENTIPEDE
- LANGDON
- VESTIGES
- INITIATIONS
- TEARFULLY
- CLAPHAM
- MEEKS'S
- UNPROTECTED
- PROHIBITED
- SHIELDED
- UNCEREMONIOUSLY
- PATRICK
- CLASSIFY
- JAMIESON
- INFORMANT
- HOBGOBLINS
- CHIMBLEY
- GERAGHTY
- NYMPHS
- DA
- HALIBURTON
- CHRYSOSTOM
- ADJOURNMENT
- MADGE
- RETAINER
- KAJI
- YEARNS
- VIE
- THUNDERSTRUCK
- DIRK
- DANZAYEMON
- TANNERS
- ETAS
- BANJO
- YOSHITOMO
- TIPHYS
- NAUPLIUS
- ARCAS
- ADMETUS
- THESEUS
- AFFRIGHT
- REPORTER'S
- TABOR
- PENCROFT'S
- EMBRASURES
- FABRICATION
- INCONTESTABLY
- RESISTS
- ACCOMPLICES
- CONTUSED
- STRANGULATION
- PERFORATION
- SUPPURATION
- COMPRESSES
- SUBSIDE
- DOZED
- HAMATH
- NAIRI
- ARMENIA
- INAUGURATED
- TIGRIS
- PRETENDER
- FLAYED
- PAL'S
- SUKHI
- CHALDAEA
- NINEVEH
- PHOENICIAN
- EXCAVATED
- LAYARD
- HEBREWS
- MOLTEN
- BETHEL
- REBELLIONS
- ZIMRI
- TIRZAH
- GIBBETHON
- CONSPIRED
- QARQAR
- CLAIMANT
- HADAD
- SHALMANESER'S
- INAUGURATING
- THWARTED
- WHOSO
- INVENTOR'S
- WURTEMBERG
- LOTTERY
- BALLOONS
- ELIMINATED
- PRUSSIAN
- AWARD
- MEASURABLE
- GALL
- DESCENTS
- SYMPATHISED
- FORGE
- TEUTONIC
- RAIDING
- PERTAINING
- ILLUSTRATIVE
- CAXTON
- PAMPHLET
- FURNISHING
- ENLARGEMENT
- DOCKETED
- CRAVAT
- BECKY'S
- URN
- REGENT
- GRUDGED
- COUNSELLOR
- CURZON
- TOILETTE
- AMELIA
- JOS
- HUMBLEST
- BOOMERANG
- BADGE
- WARES
- OPPORTUNE
- OPPONENT'S
- SUBSIDIZED
- POLICIES
- SOUTHERNERS
- DISOWNED
- MUTILATION
- CONFEDERATES
- DAMNING
- COWED
- CRAVEN
- CALHOUN'S
- CREOLE
- DIAZ
- RUFFIAN
- DESTROYER
- RESTORER
- TABOOED
- HUCKLEBERRY
- GOSHEN
- HUMBOLDT
- BUCKEYE
- DELIVERS
- POPULARLY
- FREDONIA
- GRITTY
- CATTARAUGUS
- DEIGNING
- PEASANTRY
- DISPLACING
- VISCOUNT
- PEEVISHLY
- LANCET
- RESPIRATORY
- OUNCES
- CRAYTURE
- GALLON
- UTTERMOST
- TAY
- SHEFFIELD
- JANIUS
- DISCOORSIN
- ARISTOPHANES
- TAXED
- LOVELIER
- CELTS
- GLASTONBURY
- TITULARY
- METAPHYSICS
- PROGRAMMES
- HEEDS
- APPLAUD
- SOPHIST
- ANATHEMAS
- ECONOMISTS
- SPARING
- KNEAD
- POETICAL
- FANATICS
- PRECEPT
- POLYGON
- COLLATERAL
- HINDUSTAN
- SWIRL
- WITCHLAND
- WASHERWOMAN'S
- MARTYRDOM
- NORWICH
- TYRANNICAL
- PROCLAMATIONS
- BELGIANS
- BISSING
- LANCKEN
- LEGATION
- THEREABOUTS
- ANNIE'S
- REDS
- LEATHERN
- NORTHWICK
- CONSECUTIVELY
- BANGS
- EXPLICIT
- GROUNDED
- HAWTHORNE
- REVEL
- BIGNESS
- ROSELEAVES
- SENTRIES
- BELINDA
- OVERDOING
- GRATIFIES
- SWEETS
- CONTROLLING
- REVOLUTIONARIES
- MASCULINIZATION
- UTOPIA
- CIRCE
- CAMPDEN
- HOTHOUSE
- MILKY
- DECLARATIONS
- BASENESS
- TILT
- BUTTONHOLE
- DIO
- DAUB
- COMMISSIONS
- OXTED
- ECHOING
- SWEETMEATS
- GIRDLES
- BENI
- PUPPETS
- UNALTERABLE
- MINX
- BELITTLE
- SANS
- VERSION
- VERSIONS
- HYPERION
- EMERGES
- ANALYTIC
- BOLEYN
- PATE
- OFFENDER
- WICKET
- THREADING
- DURABILITY
- LINNAEUS
- HUNKERVILLE
- IMMUNITIES
- UNDERSIGNED
- LAUSANNE
- VEVEY
- PROHIBITIONS
- GAIZDORRA
- CAPGAROUPE
- GOURD
- WICKER
- ADHERED
- UNSEALED
- EXECUTIONER'S
- SERJEANT
- WAIF
- TALBOT
- INSCRIPTION
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- CHRISTINA
- ASSASSINATIONS
- POISONS
- DEDUCTIONS
- COUNTRYSIDE
- QUARLES'S
- MADHOUSE
- GENTLEMEN'S
- TANTALUS
- SIDEBOARD
- UNDERESTIMATE
- SUBVERT
- CONSECUTIVE
- JUT
- COUNTERACTING
- LYNDON
- PENT
- WINNEBAGO
- BOWLDERS
- HULK
- FERRYMAN'S
- EJACULATIONS
- ORGIN
- PEDALS
- NECESSITATING
- CROONING
- DAMPER
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- SAVANNAS
- RETRACING
- ARMFUL
- PARALLELOGRAM
- DAILIES
- JOURNALS
- PLUTOCRATIC
- CLIQUE
- UNMAKE
- ENTHUSIASMS
- DISABILITIES
- ICONOCLASTS
- DISPARATE
- DISJOINTED
- MOUTHPIECE
- STIFLE
- METTLE
- LOUDEST
- HEEDING
- RECONCILE
- COLERIDGE
- GODLIKE
- UNPRACTISED
- GENDARMES
- IMPORTUNATE
- SMACKED
- ALIENATE
- DEADLIEST
- VOCIFERATED
- PALERMO
- STRIPLINGS
- INMATES
- STUPEFACTION
- BRAVO
- SACRA
- DECESARIS
- BLUNDERBUSSES
- PARISIAN
- PAMPINARA
- BORGO
- FRIENDSHIPS
- DIAVOLACCIO
- PORTRAITURE
- CHARACTERIZATION
- DEPICTS
- EFFECTIVELY
- MOLIERE'S
- SATIRIST
- DISPARAGE
- REIN
- SEASHORE
- LEVERS
- CLAVICHORD
- VIRGINALS
- ELIZABETHAN
- IMMORTALIZED
- BYRD
- SCARLATTI
- SONATAS
- MODULATIONS
- SOULLESS
- PROGRESSIONS
- CHROMATIC
- EXPLOITATION
- CONSECRATE
- CONFIDENCES
- ELEVATING
- ZWYNY
- HARKENED
- GOSSAMER
- NUANCES
- STACCATO
- INVALUABLE
- FLUCTUATION
- REGULATE
- AUDIENCES
- REPLACING
- GRATIFIERS
- RAILWAYS
- FRANCHISES
- UNEARNED
- INCREMENT
- ALGONKIN
- UNIFORMITY
- SYNONYMOUS
- CONEY
- HOLTS
- BUCKBOARD
- ASPENS
- SUPPOSIN
- SORENSON
- LICENSES
- LIZ'BETH
- QUEEREST
- SPIKE
- JERRINE
- RUMBLING
- GROS
- VENTRE
- TRIANGLE
- MURRY'S
- ACCORDION
- MURRY
- ALDENHAM'S
- SAMUEL
- FINANCIALLY
- BULGER
- DESGAS
- BRANDED
- GAGGED
- BLOODLESS
- JEW'S
- SOUNDEST
- MEDDLESOME
- BLAKENEY
- SECTS
- CREDULITY
- FRUGALITY
- ADHERE
- MONASTERIES
- ACQUISITIONS
- AUTHENTICITY
- LUTHERANS
- SECONDING
- BLANDEST
- FREAKS
- ANCESTOR
- BIAS
- INGLORIOUS
- RIGIDLY
- INSULATED
- SUSCEPTIBILITY
- STYLES
- DETECTING
- FERVID
- ELEVATE
- CHRYSALIS
- BU
- CUB'S
- GOODWIFE
- SCORPIONS
- RENAL
- CLINGS
- ABHORRED
- CLERVAL
- ERNEST
- ADVERSITY
- KNEEL
- GIBE
- WALTON
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- DARREL
- CLENCHING
- RAVAGED
- VEHEMENT
- SEER
- HERETICAL
- CYCLE
- OMAR
- ABOMINATION
- JUSTINIAN'S
- ABBOT
- EXPEDIENTS
- NORTHERNMOST
- KHAN
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- BOSPHORUS
- ICONOCLASTIC
- CONTROVERSIES
- CONSTANTINE
- GRADES
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- FILLETS
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- COMBS
- LEGGINGS
- KILT
- CLUSIUM
- ETRURIANS
- LARTIUS
- HERMINIUS
- SCHRINER
- BREADS
- BAKERIES
- PATRIOTICALLY
- RATIONING
- BRISTLES
- CHANTS
- PAMPERED
- REARS
- TINNEKONK
- INFLATED
- MIGHTINESSES
- TWANGING
- COMMODITIES
- FESTIVITY
- NEDERLANDS
- MOSQUITOS
- LOMBARDS
- MANEUVER
- MILLSTONES
- UNHESITATINGLY
- CELESTIN
- DIGGER
- MENDICANT
- LITTERED
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- ELECTRODES
- VISUALIZE
- SEAWEEDS
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- DERELICT
- PROCURING
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- COKE
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- SEYMOUR
- WENTWORTH
- PREPOSTEROUSLY
- ENCROACHMENTS
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- GARNERED
- INEXPENSIVE
- HANDCART
- CREPE
- THOAP
- BRANDS
- COMPANY'S
- VENDER'S
- IMMERSE
- ELIJAH
- ELISHA
- RIGMAROLE
- RO
- COSM
- SIMPSON'S
- RUSTLY
- NOKOMIS
- APOSTROPHIZED
- DREST
- UNSTOPPING
- UNGLUING
- HUSKING
- FATS
- JOYLESS
- CAPSIZING
- UNBUSINESSLIKE
- REMINISCENCE
- ELERGANT
- DRAB
- COMFORTINGLY
- READINGS
- OUTGROW
- MANSIONS
- BEECHER
- STOWE
- HOME'PATH
- GAB
- MAKIN
- INTEMPERANCE
- YOUS
- PERSPIRED
- EXCUSING
- PAINSTAKINGLY
- REWROTE
- DEARBORN'S
- FONDLES
- WASHES
- SMELLIE'S
- SMELLIE
- PRAYIN
- SWEARIN
- MINNIE'S
- BEATIN'EST
- SAWYERS
- IMPROVIN
- ABNER
- SWAPPIN
- MERCIES
- ACCOUNTIN
- STACKED
- KEROSENE
- WICKS
- CRANBERRIES
- DIGNIFYING
- PIANOLA
- BEIN
- TUBBS
- RAPTUROUS
- GOIN'S
- UNCONVINCED
- TENEMENTS
- LADDS
- MAYFLOWER
- COCHERE
- WAGONETTE
- URSULA'S
- POSTILIONS
- YOUSE
- EQUESTRIENNES
- SOUVENIR
- JELLINGS
- CHINS
- WHERE'D
- LAPS
- MAM'SELLE
- NURSEMAID
- DISAPPOINTEDLY
- COOMSDALE
- FEELINGLY
- PORTENTOUSLY
- INTERSTICES
- PASTED
- TOBACCOEY
- RUMMISH
- PATTON
- HISSOP
- DARINGLY
- SQUEALED
- HARTWELL
- PAJAMAS
- UNKINDLY
- WYATT
- RELOCKED
- BANISTERS
- GLADDEN'S
- COUNTRYWOMEN
- EXCULPATE
- RESPELL
- DISPLEASE
- AVAILING
- NOURON
- NIHAR
- MINIONS
- IMPUTE
- UNBOUND
- OBJETS
- FORGIVES
- DISPELLED
- DEFER
- AFRICANS
- KOOLLOOB'S
- CAUZEE
- HISTORIOGRAPHER
- GROSBEAKS
- BROADWING
- FUSSED
- CONTORT
- OGRESS
- ENGRAFTED
- GENDARME
- HANGMAN
- COQUETRY
- CARTERS
- SWINDLER
- LILLE
- ENTAILS
- SUTLERS
- SUTLER
- RECALLS
- SEMINARY
- ORTHOGRAPHICAL
- SLOTHFUL
- GIANTESS
- BANKRUPTCIES
- MAMMIFEROUS
- MATERNITY
- BROWSE
- HITCHED
- POACHER
- APHORISMS
- TRUSS
- EXHORTATION
- BESEEMETH
- CONSTRAINETH
- NIHIL
- GAINSAID
- REASONINGS
- CONCISELY
- PLAINWARD
- WEND
- TORRENT'S
- GRANDFATHERS
- FIRSTLINGS
- MULTIFARIOUSNESS
- LABYRINTHS
- STELLAR
- MORALITIES
- SUBLIMER
- VOLTAIREAN
- FREETHINKER
- LITANIES
- GOODY
- FLAUBERT
- ASTUTENESS
- MEDIOCRITY
- SPIRITUALISES
- SPIRITUALISING
- DESINTERESSE
- DISINTERESTEDLY
- SACRIFICER
- RARER
- MORALISTIC
- BONHOMME
- EXHORTED
- OVERSHADOWING
- UGLIFYING
- DOCUMENTARILY
- SUITING
- BAROCCO
- MORIBUS
- ARTIBUS
- ARISTOPHANIC
- PARODISTS
- CULTURES
- SUPERIMPOSED
- ESPRIT
- VASTE
- DETERMINES
- UNFAVOURABLY
- TRUCKLING
- ATHENIAN
- SHAKESPEARE'S
- CHIAJA
- GOLDENNESS
- GODSENDS
- GLORIFICATIONS
- HEDONISM
- UTILITARIANISM
- EUDAEMONISM
- MISFORTUNED
- HEREDITARILY
- INVENTIVENESS
- ANNEALED
- CAPTIOUS
- DISENGAGE
- HONESTY'
- DEVILRY
- TEDIOUSNESS
- PHILOSOPHIZING
- UTILITARIANS
- RESPECTABLY
- HOMERIC
- BENTHAM
- SENATEUR
- POCOCURANTE
- MORALISTS
- MORALIST
- AUTHORITATIVE
- BIFURCATION
- MAIRE
- STATENLAND
- SPOKESMAN
- PATAGONIANS
- FREISCHUTZ
- MIMICS
- SQUINT
- MIMICRY
- CAFFRES
- AUSTRALIANS
- HOSTAGES
- JEOPARDY
- MATTHEWS
- AQUATIC
- DIRTIED
- SKYLARK
- LANDSMAN
- UNTRIMMED
- OURANGOUTANG
- THRIVING
- BETULOIDES
- SOLANDER
- GUANACOS
- GLOOMINESS
- BARNEVELTS
- ANCHORS
- GREENSTONE
- HAYCOCK
- SOLSTICE
- WOLLASTON
- OTTER
- BAITED
- FUNGI
- CANNIBALS
- CONCURRENT
- DOGGIES
- FIRESIDES
- LIMPET
- CORDILLERA
- CHILE
- CALIBAN
- SETEBOS
- FREAKISH
- VAGARIES
- BLEND
- HALED
- ELF
- PORTENTS
- STUFFILY
- UNBLENCHING
- TOMAHAWKED
- IDIOSYNCRASY
- PITILESSLY
- HAZELS
- PHANTASMS
- IMPORTANCES
- ILLUMINATI
- CONSPIRACIES
- TROW
- NEIGHBOUR'S
- FRIENDLIEST
- PORKER
- CULPRIT
- OLYMPIAN
- SADDENING
- ARCADIA
- AWAKENINGS
- BLUEST
- GERMINATING
- KINDLE
- PRIMROSE
- CRAZE
- STAUNCHLY
- FAD
- WAYFARERS
- COS
- MISPLACED
- GROWLINGS
- UNRECORDED
- HEROISMS
- EFFLUENCE
- SQUELCHING
- SPLASHED
- SKYWARDS
- UNRHYTHMIC
- COMPANIONABLY
- EXPRESSIONLESS
- TRICKSTER
- BLUSTER
- MISRULE
- SHEERED
- THWARTWISE
- CHAINLESS
- TOMFOOLERY
- KINDLINESS
- GYRATING
- LARCENY
- PLUMMET
- PLAYBILLS
- CHAFFINCH
- HEDGEHOG
- DECADENT
- JACKETED
- CANCELLED
- NIBBLED
- EARTHBOUND
- SLUNK
- NOTHINGNESS
- SHACKLES
- TETHERED
- STERNE
- OUTPUT
- COVETING
- DUCKWEED
- BEDABBLED
- FRITZ
- SHIPOWNERS
- BHAERS
- LEIPZIG
- BONS
- SWEETEN
- JOSIE'S
- NAN
- DOSED
- DAISY'S
- BESS'S
- MEG
- PLUMFIELD
- STUDIOUS
- TEDDY'S
- OCTOO
- SNOWDROPS
- DUSTING
- EMIL
- CASABLANCA
- FIDDLED
- HEIMWEH
- BEARABLE
- HERR
- LINDENS
- MINNA
- NAT'S
- STEADFASTNESS
- CADDIS
- BROOKSIDE
- CONFIDINGLY
- THROATED
- FLYCATCHERS
- PEWEE'S
- CRESTY'S
- BLUEBIRD
- TIT
- WOODCOCK
- TUSSOCK
- LONGBILL'S
- FREEZES
- SNIPE
- FUNNIEST
- TEETERED
- SANDPIPER
- TULES
- OBSIDIAN
- GRANITIC
- TOTEM
- MYTHOLOGICAL
- QUAVERING
- UNSOUGHT
- SCURRYING
- CRUSTS
- RETRACE
- FIERCER
- SNAKELIKE
- MONOPOLIZED
- UNTRODDEN
- INCONGRUITY
- PALPABLE
- ROUNDING
- SOAKS
- SYMBOLIZES
- INEXORABLENESS
- INVERTED
- JUTS
- CONSTRUCTS
- SOLOMON'S
- MATING
- PULSELESS
- BENDINGS
- ENCHANTS
- LIMBLESS
- UNRESTFUL
- FULFILS
- WAVY
- SHAMBLED
- CANTER
- UPGRADES
- SURENESS
- DOWNGRADE
- ASKIN
- Y
- LEARNIN
- REJOINDER
- OVERHEARING
- BUNKING
- GIMME
- MA'S
- CUSSING
- CHOW
- SQUATTERS
- LEASTWAYS
- STRAIGHTER
- MIMICKED
- WONDERIN
- NOTHIN'
- JOSIAH
- EDDICATED
- BUCKER
- FIGGERS
- LYIN
- AW
- POLO
- OFF'N
- FLOPS
- OUT'N
- B'LIEVE
- RAISIN
- PERFORMIN
- BUCKIN
- TWISTIN
- DRAWIN
- S'LONG
- ROOMING
- THUMPS
- IDLING
- UNPRESSED
- MODELED
- CHISELING
- HOOKED
- ERECTNESS
- CLUMPED
- SOURLY
- SKUNK
- HARRY'S
- TRYST
- UNEVENLY
- RONICKY'S
- ABDUCT
- MERRYMAKING
- RELIGHT
- ADIEUS
- LAGREE'S
- PERSECUTOR
- TIRING
- MELODIOUSLY
- SYREN
- RAINBOW'S
- BRAVES
- POUCH
- UNHURT
- BOOKSELLER
- DORMOUSE
- CANARY
- PREFERRING
- BEGGAR'S
- SHIRKED
- PROFLIGACY
- PLUMPNESS
- BEETLE
- SUFFERANCE
- WILTSHIRE
- LISTLESSNESS
- UNCANDID
- SARAH'S
- HEROINE'S
- OVERRATED
- DOUBLING
- TREBLING
- LEGACIED
- OPENNESS
- VOUCHERS
- WEAKENING
- RECONCILIATION
- RHODOMONTADE
- OVERTURE
- RELATOR
- DEVOLVE
- READER'S
- RETRACTION
- INTERSTICE
- FORESTALL
- LUDLOWS
- WONDERMENTS
- LUDLOW'S
- OSMOND
- INHERITING
- NEPHEWS
- EUSTON
- BANTLING
- CANDOUR
- CEASES
- ACROPOLIS
- SALAMIS
- HARLINGS
- SKATED
- BONFIRES
- CHOPPY
- HARLING
- IMMOBILITY
- FIELD'S
- BOOTH
- BARRETT
- PAPERY
- KENTUCKY
- BUXOM
- LAUNDRESS
- D'ARNAULTS
- FIDGETS
- MARTHA'S
- LILACS
- PICKANINNY
- HOLLYHOCK
- STRADDLED
- MASTIFF
- MASTIFF'S
- PRESENCES
- VITALIZED
- SPECT
- TRANSOM
- TONY
- SODERBALL
- RESOURCEFUL
- USURY
- HATRACK
- POULTICING
- HANDBAG
- AVOUCHED
- HORSELIKE
- CUTTER'S
- EXACTIONS
- PROSAIC
- ELEMENTAL
- PAIRFECT
- MEERACLE
- OVERVALUED
- BATTENED
- DEVASTATION
- TAMIL
- SMUGGLE
- UNWEARIED
- PYJAMAS
- FRONDS
- THOROUGHFARE
- GARLANDED
- ISLETS
- UNDEFACED
- SEVERER
- UNSUBSTANTIAL
- RENEGADE
- BISMARCK'S
- BRUTALISED
- HATCHWAYS
- CISTERN
- CRANNIES
- COASTING
- PRAUS
- CAMPONGS
- FULGOR
- STAGNANT
- PATNA
- AMIDSHIPS
- SMOULDERING
- SPELLBOUND
- PRESIDING
- IMPASSIBLE
- ASSESSORS
- LOGGED
- FOREHOLD
- BULKHEAD'LL
- MALEVOLENT
- SERRIED
- PITH
- PEONS
- WAYFARER
- VERANDAH
- UNRUFFLED
- MARLOW'S
- REKINDLED
- INDEFINITENESS
- DISENCHANTMENT
- IMPRECATION
- PROD
- PLACATED
- INTERJECTED
- NAUSEOUS
- HAIR'S
- INCISIVELY
- CONFOUNDEDLY
- THROVE
- EXCELLENTLY
- DWINDLE
- DILUTED
- DISCOURAGE
- HUNGERING
- BURGE'S
- GREYSTONE
- INTERPRETERS
- BLASPHEMOUS
- REJOICES
- EVIL'S
- SORROW'S
- CRUDER
- WOODLESS
- OVERARCHING
- WEBLIKE
- UNSCREENED
- OVERSTARTLED
- MONITIONS
- CUMIN
- PEPPERCORNS
- SPAWNING
- OPPIAN
- STARFISH
- WIDEN'D
- PRAWN
- PINTS
- TOMATOES
- VERMICELLI
- SHRIMP
- STUPIFIED
- WHITESIDE
- LYNE
- SMIRKING
- EXTRADITION
- COMPLICITY
- UNLOCK
- MILBURGH'S
- PROVIDENTIALLY
- YONKERS
- UNRELIEVED
- POCK
- UNCHALLENGED
- OVERRIDE
- MOTORCYCLE
- SCHEDULE
- HAB
- OHDAHD
- DARKY
- BRUNG
- POWFUL
- TELEPHOME
- AST
- AFTAH
- HONGRY
- DRAT
- LIAH
- DOLLAH
- CAIN'T
- GROGAN
- FELDERSON'S
- PARALLELING
- SYNAGOGUES
- SOMEDAY
- CRAFTIEST
- AFRITES
- JINNS
- WORKADAY
- CORANTO
- BUSKIRK'S
- IMPERSONATION
- IMPERSONATED
- CHANCING
- ABJECTLY
- INORDINATE
- THEREON
- JANET'S
- JUGS
- BASKETFUL
- FOREGATHERED
- TELLERS
- MASTERFULLY
- FINGERTIPS
- MILESTONE
- DAFFODIL
- SUNSETS
- SKEERED
- EERIE
- ALEC'S
- HEN'S
- VISITANT
- EYEHOLES
- WOEFULLY
- REEKING
- SAUCERFUL
- RESERVEDNESS
- ENSURED
- PROMONTORIES
- THISTLES
- INTERNALLY
- CENSURED
- FERRARS
- DASHWOODS
- CAREYS
- WHITAKERS
- EDGAR'S
- UNFORGIVINGNESS
- AMENABLE
- TILNEYS
- ADMITTANCE
- BEDFORD
- CLEANEST
- RECEIV'D
- EXPRESS'D
- HYRCANIA
- DETERMIN'D
- PROCUR'D
- ACKNOWLEDG'D
- AMPHITHEATRICAL
- COMPLEATLY
- MAGI
- PERPLEXT
- TOURNAMENTS
- CONTINU'D
- WATCH'D
- ALLOW'D
- COVER'D
- VAIL
- INDULG'D
- ABOVEMENTION'D
- MARTIAL
- INSCRIB'D
- CADOR
- COMPLEAT
- ENRICH'D
- AMPHITHEATRES
- ACQUIR'D
- HOVER'D
- FLATTER'D
- ENAMELL'D
- RIBBANDS
- ITOBAD'S
- HEAV'N
- PITCH'D
- BABYLONISH
- SCEPTER
- CRUPPER
- QUIV'RING
- ITOBAD
- DISDAINING
- UNHORS'D
- CONQUER'D
- VANQUISH'D
- GAIN'D
- MIXT
- PALPITATION
- VOLTA'S
- WISH'D
- BUTTOCKS
- GRASP'D
- JUMP'D
- WHEEL'D
- INCENS'D
- OFFER'D
- ADVANC'D
- CLOS'D
- DISARM'D
- DESTIN'D
- RECONDUCTED
- PRESCRIB'D
- ORDER'D
- FATIGU'D
- ZADIG'S
- REPAIR'D
- OVERWHELM'D
- OBLIG'D
- HISS'D
- RIVAL'S
- RECTIFY
- PLUNG'D
- IMPROPITIOUS
- BALEFUL
- IRRETRIEVABLE
- COQUET
- PROV'D
- DISPENSATIONS
- GOVERN'D
- FAIL'D
- CROWN'D
- FRANTICK
- INDOLENT
- RAGOUT
- ABUSING
- CHEAPSIDE
- LOO
- DESPISES
- DERBYSHIRE
- INATTENTION
- CAPTIVATION
- DESPICABLE
- SOLACED
- DUETS
- TRESPASS
- ESTIMABLE
- STUDIER
- NEIGHBOURHOODS
- GARDINER'S
- FORSTER
- TAROSS
- PERCY'S
- ESKE
- LIDDEL
- JULY'S
- UNREFRESHED
- UNTASTED
- PEYTON'S
- AEOLIAN
- MASSA
- VISITOR'S
- SUBALTERNS
- BENIGNANT
- PLAGUES
- NAVIES
- OCEAN'S
- WHELMS
- LIGHTSOME
- UNGENIAL
- IMBIBE
- SUPERABUNDANT
- UNVEILED
- THRONED
- COMEST
- SHORELESS
- SWEEPEST
- VIEWLESS
- VALLIES
- HOLDEST
- GOVERNANCE
- ROARINGS
- DESPOILED
- WRENCH
- MEREST
- WIELDERS
- INTEGRAL
- DOMESTICATE
- VISAGED
- DISQUISITION
- HUNDREDTH
- IMMEDICABLE
- GULLIVER
- BROBDIGNAGIANS
- ERUPTIONS
- RIFE
- QUITO
- PARTIZANS
- HINDOSTAN
- EMPOISONED
- INHALES
- UNINFECTED
- UNCULTIVATED
- DESTROYERS
- CLIMES
- CELT
- UNCOMMUNICATED
- INNOXIOUS
- SPICY
- CIRCASSIA
- ENCREASED
- EQUALIZATION
- PROTECTORATE
- TWELVEMONTHS
- UNLAMENTED
- QUAILING
- UNERASEABLE
- REVULSIVE
- SPECIE
- UNPAID
- NURSLINGS
- PENURY
- PARSIMONIOUSLY
- IMPORTS
- ANTLERED
- PROTEGES
- OFFCASTS
- PLEADINGS
- AGRICULTURIST
- INDULGENCIES
- PALANQUINS
- SECEDE
- PARTERRES
- EMIGRATION
- CONCOMITANTS
- LUDOVICO'S
- CARLO'S
- CARLO
- ROBERTO
- RAMPART
- UDOLPHO
- SIGNORA
- LIVONA
- CONJURED
- SIGNOR'S
- RANSOMS
- ANIMATE
- RETRACTING
- NIGHT'
- DETER
- MONTONI'S
- ACQUAINTANCESHIP
- BROKERS
- ROUNDERS
- DROUET'S
- STUCCO
- STEWARDSHIP
- SOLITAIRE
- ENGRAVING
- INFORMALITY
- FREQUENTING
- TACTFUL
- BARKEEPER
- EVANS
- MILWAUKEE
- BAREST
- MODIFYING
- SELTZER
- PACER
- SCHEMERS
- AUGUR
- GAINSAY
- INFESTED
- SPIRITUALIST
- THINNING
- ALARUM
- LIKINGS
- BIDED
- PIKESTAFF
- BOUTS
- LONGBOW
- BLAND'S
- APLENTY
- HALFSCORE
- COWHIDES
- TANNER'S
- CLOVEN
- BROADSWORD
- WOODCRAFT
- VARLET
- CRAFTSMAN
- BACKTALK
- SOVEREIGN'S
- TALKEST
- CALF'S
- ANCASTER
- FORFEND
- WINDOWPANE
- BELIKE
- MURRAIN
- CUDGELED
- NOTTINGHAMSHIRE
- ELY
- JOCK
- SCATHELOCK
- BELABORED
- COWSKIN
- TWANG
- BOWSTRING
- EPISODES
- AUBURN
- BEHOLDERS
- COMPLYING
- GENTLEFOLK
- BEEHIVES
- OSTENTATION
- COYNESS
- PERJURED
- UNLACE
- BEFOOLED
- SLIGHTED
- ABHOR
- CRIER
- HERDSMAN
- FASTED
- BATHES
- DEWDROPS
- LIMPING
- KNOCKERS
- HYACINTHS
- LOVEABLE
- WARRANTY
- SCABBARDS
- VIEUVILLE
- CARMES
- DESCHAUX
- CAHUSAC
- EDICTS
- SWORDSMEN
- HILTS
- LUNGE
- EJECT
- REPRIMAND
- ADJURE
- EMINENCE'S
- VILLE
- REREAD
- REKISSED
- MUSKETOON
- ENTERTAINS
- EXPATIATED
- WICKETS
- EXEMPLARY
- TINE
- RECOMPENSED
- DELAYING
- SWORDSMAN
- DOMICILE
- EXPLICATIVE
- SAVORS
- PARDIEU
- COINING
- GARDES
- MOONLIGHTED
- SOLITARILY
- MOVELESS
- UNDULATIONS
- SADDENED
- CAEN
- STEMS
- GLADES
- SYLVAN
- PILLARED
- FRONTED
- LICHEN
- HAGAR'S
- QUINCE
- SWEDENBORGIANS
- RUSK'S
- INACCURATE
- SUPERSEDING
- GOGGLED
- UNDERCHAMBERMAID
- IMPOUNDED
- ROUGIERRE
- JUSTER
- ORDAINED
- GRIMACING
- RUTHYN
- GOUT
- AV
- SWEDENBORGIAN
- DURE
- SPITEFULLY
- HOITY
- TOITY
- ELICIT
- FRENCHWOMAN
- OAKLEY
- QUAITE
- APPY
- VOUS
- SAVEZ
- MALADES
- TARTNESS
- PARFOIS
- SHAMMING
- TRANSITIONS
- TESSELLATION
- DETESTABLY
- AUSTIN
- IRREVERENT
- VIALS
- COURTING
- REPUGNANT
- UPROARIOUSLY
- COURSED
- IMPERSONATING
- BLISTER
- MALVOLIO
- WOOING
- HUG
- FAKIR
- SUPPOSITIOUS
- MARTHIEU'S
- BELIEVER
- NUMB
- UNCONSCIOUSNESS
- DEVOTEES
- O'SHANTER
- WARTHIN
- WOTAN
- SIGMUND
- VALHALLA
- CLAIRVOYANCE
- HEALERS
- PROFESSIONALS
- PROSTITUTED
- COCKE'S
- QUINCEY'S
- PROFUNDIS
- MESMER'S
- PROLONGING
- ZOLA'S
- CRUSADES
- POET'S
- DEMONIACAL
- DERVISHES
- FAKIRS
- IMPERSONATIONS
- SUMNER
- CUSHMAN
- COOKE
- PELS
- BUTTS
- MACKWORTH'S
- BLURRING
- JOAN
- FISHIEST
- GRIMNESS
- SCORCHING
- CLOAKING
- MINSTREL
- GROSSER
- SLUSHY
- SNOWBALLS
- MOAT
- SKATES
- GROAT
- JACKDAWS
- YULE
- SUET
- CALDRONS
- MALMSEY
- BROACHED
- DAFFODILS
- FARAWAY
- FALWORTH'S
- BAREHANDED
- BAREARMED
- OUTFIELDERS
- DICCON
- DEVLEN
- SHINNED
- CLEMATIS
- YEW
- GREENNESS
- SUNSTROKE
- TRADE'LL
- THANKY
- BRINGIN
- YE'VE
- THORT
- AN'
- ARRYSTOCRACY
- UNCOILING
- FURLED
- WHARFMEN
- TRADE'S
- TRYIN
- HANKERCHER
- GYPSY
- DERE
- FREND
- THEVES
- WIL
- PARDNERS
- ILE
- CUMS
- ENNY
- FELER
- TRISE
- THATS
- THERES
- YURE
- PERVIDED
- FUST
- RISTOCRAT
- MINNIT
- HARRISON'S
- DUBIOUSNESS
- DORINCOURT'S
- OFTTIMES
- GIRDERS
- HYDROGRAPHIC
- SHIPMASTERS
- VENTURESOME
- COLLATED
- TABULATED
- BASER
- SUBLIMATION
- FILTRATION
- CRYSTALLIZATION
- ANTIMONY
- PHOSPHORUS
- OILS
- REDMAN
- GENOESE
- NAVIGATOR
- LIVINGSTONE
- EVANGEL
- UNIFIED
- PURGING
- DROSS
- TRIVIALITY
- PIVOT
- SWINGS
- DEPRIVATION
- TRUER
- RESURRECTION
- SHOWINGS
- EVOLVING
- COMMENTATORS
- DISHEARTEN
- UNSTRAINED
- FLUIDS
- LODESTONE
- CHARCOT
- METALOGY
- PSYCHIATRICALLY
- ORIENTED
- OBSTETRICIAN
- GYNECOLOGIST
- CATALYZED
- VISUAL
- IMAGERY
- SEMANTIC
- HYPERACUTE
- INTERPERSONAL
- CRUX
- INHIBITED
- ASIANS
- JANET
- WOLBERG
- PSYCHOANALYST
- THEORETICAL
- RETROGRESSIVE
- UNCOVER
- TRAUMATIC
- DUPLICATING
- SCHNECK
- CLINICAL
- EQUATED
- IMMOBILIZATION
- EQUATE
- ANALOGY
- DISSOCIATION
- DREDGED
- AUTOMATICISM
- NEGATE
- OPERATIONAL
- THERAPIST
- THERAPIST'S
- CONNOTATES
- VERNAL
- DONALD
- DAVIS
- SELFLESSNESS
- WITHERS
- COMPLAININGS
- INERTIA
- GLORIFY
- FUNLESS
- PITTSBURGH
- REHEARSING
- INSTITUTIONALIZED
- UNDIVIDED
- STAINLESS
- BRINDISI
- GARTH
- OCULIST
- DALMAIN
- CURT
- PENCE
- APENNY
- TEARLESS
- STEAMED
- CHARING
- STANDSTILL
- CHILDHOOD'S
- DUCHESS'S
- PILOTED
- BROUGHAM
- TRAFALGAR
- HOMICIDAL
- MONGERS
- UNSIGHTLY
- UNCORDIAL
- MEDIATOR
- ACCOSTING
- INCLINING
- PERFIDIOUS
- UNCLOSING
- UNCIVIL
- WILSONS
- MAMMA'S
- LATISH
- FATHERLY
- OFFICIOUSLY
- AFFIRMATORY
- IRATE
- BREWED
- AUDITOR
- SLAMMING
- AT'TI
- ATTILA'S
- TIEW
- THOR'IS
- MOND
- THEODORIC
- THORISMOND
- AETIUS
- HIPPO
- VALENTINIAN
- ALARIC
- JO'RI
- PLUNDERING
- LEO'S
- VANDAL
- TOWING
- JUSTINUS
- STEAMSHIPS
- OSTROGOTHS
- VIT'I
- GES
- RAVENNA
- OSTROGOTH
- TRIB
- O'NI
- HAM'ME
- MUS'SUL
- MANS
- AMIN
- WORSHIPED
- HIRA
- DI'NA
- MOHAMMED'S
- HEJ'I
- RA
- MUSSULMAN
- GIVETH
- KA'A
- SULTANS
- WHIMSICALLY
- SCULPTED
- SINGHALESE
- REENTERING
- REAPS
- INDUSTRIALIZED
- PERCIVAL
- KAFFIR
- WORKDAY
- NOW'S
- CRESPO
- ANDAMAN
- FANTASIZING
- ENVISIONING
- PELVIC
- OFFHAND
- PERENNIAL
- LEAFED
- GLEEFUL
- NATURALIST
- INSINUATING
- OYSTERBANK
- OCCUPATIONAL
- PHOSPHATE
- CARBONATE
- GELATIN
- FESTERING
- SECRETION
- MOLLUSCA
- ACEPHALA
- ABALONE
- TURBO
- SALTWATER
- SCALLOPS
- MOLLUSK
- MELEAGRINA
- MARGARITIFERA
- GLOBULAR
- EMBEDDED
- NUCLEUS
- CONCENTRIC
- YELPED
- PLIERS
- SORTERS
- MEATY
- DAPPLED
- PARAGONS
- MOLLUSK'S
- FARMED
- FIANCEE
- WOW
- AMMONIA
- REWARDING
- SWALLOWING
- GULPS
- HARPOONER
- SWIVEL
- UNDERBODY
- UGLIEST
- GIRTH
- DEGRADEMENT
- BLEARY
- BITCHES
- BITCH
- TRIMMER
- MILLINERY
- EXPECTANTLY
- SICKENS
- DENTIST
- DISPATCHER
- INSTALLMENT
- EXALT
- OPPRESSING
- HOMOLOGY
- INFAMIES
- TURKS
- OUTRAGES
- MEDDLED
- ARBITRATION
- DISBAND
- INTENTIONED
- WRONGING
- OUTSIDERS
- DISARM
- IMMUNITY
- SUDANESE
- INTOLERANCE
- SONNETEER
- VANTAGE
- DISARMAMENT
- ARBITRATE
- VEGETARIANISM
- VACCINATION
- ILLS
- REPUDIATED
- CLAMORS
- BUNDEFJORD
- JOERGEN
- STUBBERUD
- LINDSTROEM'S
- INSULATION
- CELLULOSE
- OBLIQUE
- ROOFING
- LINOLEUM
- LUX
- VENTILATING
- ROENNE
- SEWED
- OUTFITTERS
- CLAMPS
- SHOEING
- EBONITE
- NARRATIVES
- PALSGAARD
- JUTLAND
- ALUMINIUM
- HANDIER
- WINTERED
- FRAM
- LAPPS
- NETCHELLI
- ANORAKS
- BRANDT
- FURRIER
- BURBERRY
- ANORAK
- ROOMY
- WINDPROOF
- FRAMHEIM
- TASMANIA
- WOOLLEN
- TRANSITIONAL
- CRAMPONS
- BOOTMAKER'S
- SHOEMAKER'S
- COOKERS
- COOKER
- UTILIZES
- TANDEM
- UTILIZING
- SEXTANTS
- DILUTE
- INCONVENIENCED
- BINOCULARS
- ZEISS
- GOERTZ
- GOGGLES
- SCHANZ
- CAMERAS
- ANEROIDS
- HYPSOMETERS
- HYPSOMETER
- VENDOR'S
- JAEDEREN
- HANTS
- BASINGSTOKE
- POPHAM
- REJECTS
- HENLEY
- PARSONAGES
- WHITEWASH
- QUICKSET
- SYCAMORES
- RECTORIES
- ASSESSED
- BUILDERS
- SONNING
- PUBLICATIONS
- HANCOCK
- GUILLOTINE
- INCIVISM
- ARABLE
- DETENUS
- BUONAPARTE'S
- TOURNELLE'S
- PROLOGUES
- EPILOGUES
- REVIEWER
- BERTRAM
- UNSUSPICIOUS
- TINGES
- UNNAMED
- CANDLESTICKS
- FURMITY
- TANSEY
- NOVELTIES
- TENANT'S
- CARPETING
- INVALIDS
- BOOKCASES
- COURTESIES
- PALMY
- GRANDISON
- CLOWNISH
- MINUETS
- LAPPET
- IMMACULATELY
- COTILLONS
- REELS
- RECONDITE
- COOKERY
- SCANTILY
- PERFORMING
- PANTRIES
- UNPACKED
- LANTHORN
- CLIPPING
- GAMEKEEPERS
- STUD
- GRANDSONS
- INFORMS
- PATTENS
- TRIVIA
- ASCRIBES
- GALOSHE
- EPIGRAM
- PERROT
- FOOTE
- PLAITING
- GADDING
- HANDMAIDS
- MYTHOLOGY
- OBLIVIOUS
- LUDDY'S
- DIATRIBES
- INDORSEMENT
- DOPE
- TELEGRAPHING
- INDIVIDUALISE
- ELITE
- PROFITLESS
- UNREHEARSED
- EXHILARATION
- PREMONITION
- PRONOUNCEMENT
- BURTON
- FLUKE
- INDULGENTLY
- DISCONCERTING
- FASHIONABLY
- MOYNE'S
- GRITTED
- LAVISHNESS
- CASHIER'S
- MONOTONE
- MOYNES
- CIRCUMSPECT
- MONOTONOUSLY
- SPLUTTERED
- TELLTALE
- PRUSSIC
- HYDROCYANIC
- ADHESIVE
- TOPMOST
- GRILL
- VISUALLY
- SERO
- ACCENTUATED
- PLATINUM
- ENTWINED
- MYSTICS
- CONISTON
- ACKNOWLEDGEMENT
- HALSEY
- INTERSTELLAR
- CONISTON'S
- PSEUDOMAIL
- SPINDLY
- CLANKING
- THROATY
- LINGUISTS
- SEAR
- PLANETARA'S
- HANDLER
- CALCULATORS
- LOCATING
- CHARTING
- MIKO'S
- STARLIT
- PORTHOLE
- NEARBY
- HOLA
- SHEATHED
- PLANETARA
- LISTED
- ENTERTAINER
- OILER
- HALSEY'S
- MASQUERADERS
- PARRIED
- SOMETHING'S
- GAMBLERS
- ARDLEY
- STACK
- CUTE
- MICROPHONE
- DRAPE
- PURSER'S
- SHAC'S
- PROJECTOR
- MOA
- NIGHTROBE
- RASPED
- AUDIPHONED
- TAMPERING
- MARAUDER
- UNSEAL
- ELECTRONIZED
- CIRCUITED
- NUMBED
- SPOTLIGHT
- CARBIDE
- PENNICOTE
- MAIDENHOOD
- GASCOIGNE'S
- WITCHING
- UNARGUMENTATIVELY
- FULFILLMENT
- SUED
- IRREVOCABLY
- HUMDRUM
- PROMOTIONS
- SYLPH
- ARROWPOINTS
- LIMPNESS
- KNOWABLE
- IMPRESSES
- LAUNDRESS'S
- HARLETH
- CRISPEST
- DECAMPMENT
- UNUSUALNESS
- UNHESITATING
- CREASES
- DREGS
- COMPUNCTIOUS
- IRIDESCENCE
- MACBETH'S
- BASELY
- DIAL'S
- HENLEIGH
- MALLINGER
- STILLER
- VACILLATING
- SPANIEL
- FOREPAWS
- MALTESE
- UNIMPASSIONED
- INTERRUPTEDLY
- RELIGHTING
- CUSHATS
- DRAWL
- KLESMER
- ORATORY
- RUMPUS
- MARKEDLY
- INSPECTING
- HANDINESS
- GRANDCOURT'S
- CONFIDANT
- RELISHING
- MARCUS
- AURELIUS
- HINDERING
- VITIATION
- DERONDA'S
- DOOMS
- FULFILLS
- HUGO
- DIPLOW
- LEUBRONN
- INAUGURAL
- LECTURESHIP
- UPTON
- IMPLICITS
- UNDERLIE
- KEMPIS
- TRANCES
- AUTISTIC
- CULTS
- EDUCATIONISTS
- CORPORATE
- UNFAILING
- OLDNESS
- DESCANT
- FIGARO
- COMELINESS
- UNDECIDED
- AN'T
- BROODINGLY
- MESSES
- KEENNESS
- CHANGEABLE
- UNHEALTHINESS
- REPINED
- HUME
- HOULDSWORTH
- TRAMMELS
- SENSUOUS
- OVERBALANCED
- STANSFIELDS
- SOUTHAMPTON
- SHOPPY
- SEVERALLY
- WEEDED
- THOMSON'S
- HAYLEY'S
- WARDS
- BERESFORDS
- RUTLANDSHIRE
- BAFFLE
- WITNESSING
- ARBUTUS
- CRAMPTON
- CRITICISING
- KNICK
- KNACKS
- SHRINKAGE
- COHERENT
- BLAMELESS
- OSTENSIBLY
- OSTENSIBLE
- CONSUMMATELY
- PONDERABLE
- MAUD'S
- PERFORMER'S
- NOTATION
- PREPONDERANTLY
- MERTON
- SPECTATORSHIP
- AMPLIFIED
- SHARERS
- CONTRACTILE
- INNOCUOUS
- PACIFIED
- PARAGRAPHED
- THEALE'S
- WEARERS
- EXPERTNESS
- UNMISTAKEABLY
- SAMPLED
- CHALKED
- BRITON
- BRITON'S
- FORMULATION
- DENSHER'S
- THEALE
- SEASON'S
- GREGARIOUS
- SCRIBBLING
- JOURNALISING
- BOOMABLE
- PREDICATED
- TWADDLE
- KATE'S
- OVERTLY
- DISCLAIMER
- CHARMER
- PLEASANTRIES
- CROYS
- SUSIE
- CROY
- MANNINGHAM
- WAIVING
- UNOBJECTIONABLE
- MATCHLOCKS
- NUMBNESS
- BLEEDS
- DORCHESTER
- SHERLOCK'S
- FEAR'ST
- CLIMBS
- SLANTS
- LANDER
- DOT
- FAUGH
- REBUKING
- ELZEVIR'S
- FRESHER
- PICKABACK
- FRESHENING
- GUILLEMOTS
- BUDGE
- ZOUNDS
- SUNPATH
- SPANGLED
- MACKEREL'S
- BRASHY
- BUTTRESSED
- PRYING
- THIMBLEFUL
- SOUND'
- BUTTED
- THEREABOUT
- POWDERY
- NUMERATION
- BLUNDERBUSS
- TOPP
- BUZZARD
- TEMPTER
- FOWLING
- WESTERING
- ANVIL
- ASKEW
- QUARRYMEN
- WINCHES
- MANDRIVE
- HEWED
- OVERGREW
- LOTH
- SUBORNING
- JARRINGS
- GARBOILS
- VERIEST
- LOUT
- INNOCENTEST
- EXHORT
- GAOL
- INARIME
- TYPHEUS
- DISPERST
- SHRINKETH
- GODERAN
- GLOSSES
- DOTE
- SUBORNERS
- CHASES
- RACKETS
- GUTS
- FRESNO
- TENAYA'S
- CAPITAN
- POHONO
- BOLING'S
- MONO
- MONOS
- TOURIST'S
- MANN
- HITE
- NEAL
- CUNNINGHAM
- RECESSION
- INTIMATES
- SEASICKNESS
- OVERSEAS
- THIRTIES
- TERRIFYINGLY
- MONOLOGUE
- CONNOISSEUR
- TATTOOED
- DEPLIS
- PINCINI'S
- PACKLETIDE'S
- SHOEMAKER
- ALIAKHIN
- EMPLOYER'S
- IKON
- LARDER
- SNEEZE
- BELABOURED
- VODKA
- CUCUMBERS
- GUZZLE
- FLOG
- FEDYA
- UNDERHERDSMAN
- SHEAT
- SNOWDRIFT
- QUADRILLE
- ALIONA
- TEGOR
- TROIKAS
- CANDIA
- CLERKENWELL
- EXTENUATIONS
- KNAVERY
- COLLUSION
- WHITECHAPPEL
- RUMOURED
- TURNPIKES
- CONFUTED
- PROVIDENCES
- COMPLEXLY
- LISBON
- SNARE
- FOWLER
- BUCKINGHAMSHIRE
- BEDFORDSHIRE
- VILLAINIES
- LEVITIES
- DESOLATING
- ABSTRACTLY
- INTERMITTED
- UNENCUMBERED
- WAPPING
- RATCLIFF
- STEPNEY
- PREFERMENTS
- CONFLUX
- CONDOLE
- ORDEALS
- CANNIBAL
- ORPHANED
- WANTONLY
- CURDLING
- CAIN
- FALLON'S
- DIEM
- RESCUERS
- KESEBERG'S
- CACHE
- WINNERS
- RANCHERIA
- MOULDERS
- ELITHA
- LEANNA
- COON'S
- SHIPMENT
- BROTHS
- APPETIZERS
- METE
- NIMBLER
- LAGGARDS
- PROVOKINGLY
- COYOTES
- OFFICERED
- STONEMAN
- DARKEY
- LAWD
- TOLE
- MUDDER
- SPRINKLING
- HOLLYHOCKS
- PANTALETS
- PREARRANGED
- MUS
- YOS
- CAISSON
- CHAPEAU
- GAUNTLETS
- FANNIE
- COUCHED
- LAMB'S
- JOSEPHINE
- BUGLERS
- POODLE
- SNUGGLED
- EMPRESS'S
- CRIB
- FLEECY
- FITCH
- LEESE
- HEMMING
- VALLEJOS
- HOED
- LUXURIANTLY
- GEORGIA'S
- STINGING
- WELTS
- WABBLING
- PALING
- DROVERS
- BRUNNERS
- LEVYING
- DOLE
- WHIGS
- CORONATION
- PARABLES
- BOSWORTH
- SCOT
- BARONS
- LOCKE'S
- DISSEMBLED
- INVEIGLE
- CLANSMEN
- CLANS
- CAMPBELL
- ATHOL
- EVERARD
- DYKVELT
- CUTTHROATS
- BUTCHERY
- ANNOYING
- BATAVIAN
- UTRECHT
- COMMONWEALTHS
- NASSAU
- FUNCTIONARIES
- BEVIL
- ZUYDER
- ZEE
- STADTHOUSE
- MISINFORMATION
- TEXEL
- TELESCOPES
- UNWISELY
- ARGYLESHIRE
- CAMPBELLS
- HERDSMEN
- CALLUM
- ARMAMENT
- CAMPBELLTOWN
- KINTYRE
- SCURRILITY
- ELIACHIM
- MANASSES
- NABUCHODONOSOR
- ANGE
- PILLAGED
- THARSIS
- MAMBRE
- DESTROYETH
- SOBAL
- LIBYA
- APAMEA
- ESDRELON
- FASTINGS
- PROFANED
- DWELLETH
- CHARAN
- HETHITES
- HEVITES
- AMORRHITES
- HESEBON
- FORSAKEST
- BESIEGETH
- LOOKETH
- DOTHAIN
- BELMA
- CHELMON
- SCOTIA'S
- DISCREDITED
- MONITOR
- SURFACED
- EXCERPT
- POPULATING
- ICHTHYOLOGICAL
- CETACEANS
- BASICALLY
- CATALOGED
- FIVEFOLD
- SHANNON
- PERFORATE
- CENTIMETERS
- FRIGATES
- RAUCOUSLY
- MILLENNIA
- FANTASIES
- FABLED
- DAUNTING
- LLOYD'S
- FRANCE'S
- PACKETBOAT
- WAGS
- TAMPICO
- SHANGHAI
- BREATHER
- BUNKERS
- CREWMAN
- STOKE
- UNFORGIVABLE
- HOBSON'S
- GOVERNMENT'S
- PUNCTILIOUS
- HARDWORKING
- BIOLOGICAL
- ACROBATIC
- SUBCLASSES
- SUBGENERA
- SUITCASE
- CONGO
- AILMENTS
- UNDERHANDED
- ARCHAEOTHERIUM
- HYRACOTHERIUM
- OREODONTS
- CHEIROPOTAMUS
- EXPERTLY
- MEZZANINE
- VOMITING
- COMPLEMENTED
- WHELK
- MOORINGS
- RELAYED
- ACTIVATED
- PISTONS
- STEAMBOATS
- LINERS
- MIZZEN
- LIGHTSHIP
- SKILLS
- AUTHORIZED
- HARPOONED
- CABO
- LAS
- VIRGENES
- HOMETOWN
- HOORN
- NYCTALOPIC
- PERCENT
- STERNRAIL
- COWLS
- COMPANIONWAYS
- CETACEAN'S
- SOUTHERNMOST
- PERIMETER
- CHIDED
- NOTHING'S
- SPOTTING
- NARWHALE'S
- VIOLATE
- BOSUN
- SHIPBOARD
- CROSSTREES
- WHALES
- VEERING
- REVERSING
- BEACHES
- MESSROOM
- FARRAGUT'S
- STUBBORNNESS
- CREW'S
- FUNCTIONED
- LONGBOATS
- LEEWARD
- STEMPOST
- ROOSTING
- CASSON'S
- CORROBORATING
- HUP
- HIN
- PIG'S
- BUGLE
- MIKE
- HOLDSWORTH'S
- LOAMSNIRE
- SHOULDNA
- TENANTRY
- ALLAYS
- MISBEHAVE
- RICK
- HETTY'S
- CURTSY
- TOTTY
- CHISELLED
- SUGARY
- BOARDING'S
- COVETOUS
- SMELL'S
- UNROLLING
- BLANKNESS
- SPEARING
- WORRET
- OVERWORK
- DINGALL
- MEASLES
- WELLY
- BETHELL
- GENTLEFOLKS'S
- GELLS
- SHANNA
- HOPPIN
- TUMBLES
- ON'Y
- MAGGOT
- SPINNIN
- UNDERHAND
- ISNA
- PORRIDGE
- WAGGONER
- GANDER
- PONY'S
- UNSPEARING
- THEE'ST
- DRIBBLE
- LEAKY
- TH
- KNOW'ST
- WORRETING
- UNFASTENING
- APPORTION
- FORESAW
- ENTAIL
- DONNITHORNE'S
- POYSERS
- METHODISTS
- PRINCIPLED
- PERSUADER'S
- COMMANDMENTS
- IRREVOCABLE
- GRENETTE
- DANDIES
- SLOVENS
- APATHETICALLY
- SUPERVENED
- VS
- DEMISE
- BART
- SUPERSCRIBED
- ESQUIRE
- LEGITIMACY
- SLEETY
- SLOPPY
- IGNITING
- MILADY
- UNGLOSSED
- DETERMINATELY
- FALSENESS
- UNBORN
- DIVORCED
- INFANT'S
- OUTRAGEOUSLY
- STRATAGEMS
- SEVERN'S
- VORACITY
- DAUNTLESSNESS
- CAPSIZE
- WISTING'S
- HANSSEN'S
- RECOMMENCED
- SAHARA
- PROMISINGLY
- ADAMANTINE
- OLAV
- OVERLYING
- DOOMSDAY
- ICEWAVE
- HUMMOCKS
- TACKING
- HASSEL
- CIRCUMSPECTION
- CIRCUITS
- MERGING
- SNATCHES
- LULLABIES
- CROONS
- MEMORY'S
- MEW
- OFFSHOOTS
- LULLA
- COLE
- PERRAULT'S
- RIQUET
- CINDERELLA
- HURD
- HOUGHTON'S
- TRANSCRIPT
- REPRINT
- BARCLAY'S
- UNSUPPORTED
- CROWNINSHIELD
- ANTIQUARIAN
- BOSTON'S
- CROON
- GOOSEFOOT
- RONALD
- BARCLAY
- WORTHINGTON
- MELODIOUS
- MEAGER
- NEWBURY
- QUOTA
- OYE
- BAUM
- EWES
- SHEPHERDESSES
- LAMBKINS
- TIPPETS
- BEWAIL
- PUSSYCATS
- THUNDEROUS
- COUNCILLORS
- TYRANNISED
- HYPNOTISE
- ORGANISATIONS
- FLAUNTING
- PAGEANTRY
- AMPHITHEATRAL
- CONGESTED
- BRANDISHING
- THEATRICALLY
- CHINAMEN
- CLAMBERED
- UPLANDS
- REPLACEMENT
- VIADUCT
- OVERARCHED
- STARRED
- CARCASSES
- POTTERS
- OVERLOOKERS
- VIGOURS
- PHYSIQUE
- MANAGERS
- FOREWOMEN
- DRAY
- PRODUCERS
- MINDER
- CHESTED
- DRUDGE
- EUTHANASY
- AGGREGATED
- SPARSELY
- FILIGREE
- GROTESQUES
- GEOMETRICAL
- MOTIF
- APOLOGISED
- CRUDITIES
- FLOURY
- LIFTS
- ARMATURE
- TANNING
- REEK
- BREWERY
- MASSIVENESS
- BRICKWORK
- ANEMIC
- HOOTING
- OSTROG'S
- TIERS
- IMPASSABLE
- CHAPLETS
- PERVERSION
- AMBROSIAL
- PALLAS
- ROUSES
- PROVOKES
- CONTUMACIOUS
- VENUS'S
- SUSPENDING
- DEPLORED
- BEHOLDER
- NECTAREOUS
- UNGRATIFIED
- INHABITS
- SHEAVES
- RAKES
- REAPERS
- ALLAY
- PROPITIATE
- STOREHOUSE
- VETCHES
- GODDESSES
- CARRIER
- STYGIAN
- AMBROSIA
- ALLEGORICAL
- PURIFIED
- COMUS
- UNSPOTTED
- FANCY'S
- GROTTO'S
- TRACERY
- SPARS
- NEVERMORE
- APULEIUS
- PHOEBE'S
- REGIONED
- VESPER
- EPICUREAN
- TIPPLE
- SHEEP'S
- FETA
- MIXER
- CELLARED
- VINTAGES
- TASTERS
- PALATES
- NARY
- BURGUNDY
- COTES
- BAUNE
- BURGUNDIAN
- SALUT
- MARGAUX
- NOTABLES
- SAINTE
- MAURE
- VENDOME
- LOIRE
- VOUVRAY
- CHABLIS
- CLARET
- PROVOLONE
- CHIANTI
- NEUFCHATEL
- UNIQUELY
- BRINZAS
- TOKAY
- OLOROSO
- SHERRY
- LEICESTER
- AMONTILLADO
- APPETIZER
- STILTON
- CHEDDARS
- FRAUDULENT
- COUNTERFEIT
- GOLDWASSER
- LIQUEUR
- SEEDED
- SAUCER
- JUNIPER
- FROMAGE
- HOMEMADE
- REDUNDANT
- BUTTERMILK
- MUTABLE
- CARTIER
- QUEBEC
- EXTERMINATED
- CAROLINAS
- SOUTHEASTERN
- ERIES
- CHAMPLAIN
- HARVESTS
- VERMONT
- PROLIFIC
- MICMACS
- PAPINACHOIS
- BERSIAMITES
- TADOUSSAC
- MUSTERING
- CANNIBALISM
- ATTICAMEGUES
- MARAUDERS
- OTTAWA
- UBIQUITOUS
- NIPISSINGS
- SAGUENAY
- CATHOLICITY
- INSENSATE
- POPULATIONS
- HEALTHILY
- DISSIMULATION
- UNDID
- STIMULATES
- DISMEMBERED
- SCANTLINGS
- SUAVITY
- DEFINITENESS
- HYPOCRITICAL
- HARK
- JODO
- HAKATA
- SHO
- CHIKU
- BAI
- PLUMFLOWER
- MYSTICALLY
- WEIRDER
- ASSAULTS
- LIKEN
- ESOTERIC
- TEXTS
- WINDLASS
- MOXA
- KAJIWARA
- UMEGAE'S
- CHOZUBACHI
- TATAITE
- KOKUSHI
- HERMITAGES
- PRIEST'S
- TOMYO
- HEADMAN'S
- UNHARMED
- RECLUSE
- IMPIETY
- REBORN
- KUNSHI
- AYAYUKI
- NI
- CHIKAYORAZU
- DAIMYO
- REESTABLISH
- KARMA
- UNBARRING
- TOPKNOT
- REUNITED
- SHAMELESSLY
- NAPE
- LEVERANCE
- DETACHES
- ISOGAI
- HEIDAZAEMON
- TAKETSURA
- MIYAGE
- KOROMO
- GOBLIN'S
- TOMBSTONE
- SEGAKI
- SKINNERS
- DEPREDATIONS
- COMMISERATION
- TRITE
- LOWLAND
- BESPOKE
- DEERSKIN
- CHINKING
- ATTAINMENTS
- PHIALS
- APPLICANT
- PREDICARY
- SECUNDEM
- ARTEM
- INDISPOSITION
- DOCTORED
- YARBS
- UNLETTERED
- RIG'LARS
- RIG'LAR
- COUNSELORS
- BEDRIDDEN
- CONDEMNABLE
- FEBRILE
- HOARFROST
- PHLEBOTOMY
- CONSTITUTIONS
- MASON
- DEBILITATED
- COGENT
- WELLMERE'S
- WELLMERE
- CLINTON
- LEECH
- SINGLETON'S
- FORBEARING
- CONSANGUINITY
- AFFIXED
- IMPROBABLY
- UNIFORMLY
- VARIABILITY
- GAZER
- SOLILOQUY
- SHAMBLING
- QUALIFY
- FATNESS
- MANDATE
- CONVALESCENT
- FORBORE
- UNTOWARD
- YANKEES
- INCLEMENCIES
- VANQUISH
- TRENCHANT
- SPIKES
- BLASPHEMY
- BOBBINS
- PINCHBECK
- CABALLERO
- CENSORIOUS
- MARAVEDIS
- DISTRIBUTES
- ORDAINS
- UNREACHED
- FALTER
- KNICKKNACK
- APOLLONIA
- FOCILE
- BENE
- QUIDEM
- IMPEDE
- DEFRAUDING
- ERRANT'S
- SHATTER
- PANZAS
- HOOPS
- CASK
- REVOKED
- SAMSON'S
- ALFORJAS
- DIEGO'S
- ASSES
- GIBBERISH
- PAR
- PEDIGREES
- SOLDER
- FLAWS
- UNGRUDGINGLY
- THROWER
- WRESTLER
- BOWLS
- EWE
- GAZES
- GRAVELLING
- TANNERIES
- MAJALAHONDA
- CANON
- SALAMANCA
- SWORDSMANSHIP
- LICENTIATE'S
- CASSOCK
- WRESTLE
- FENCERS
- FLUTES
- PSALTERIES
- GAMBOLLING
- PHOEBUS
- SLEEPEST
- UNSTINTING
- WORSHIP'S
- STEWPOTS
- WHITEST
- CAULDRONS
- SEWN
- SKIM
- SKIMMINGS
- ARCADE
- JABBERED
- VOUCHSAFED
- EGGSHELL
- APE'S
- DENIZEN
- RESCUER
- ELICITING
- ZEALOUSLY
- UNSCATHED
- HOLSTER
- CASING
- BOLTING
- BREASTPLATE
- MAUDLIN
- WEAKLING
- DEFAMER
- CLINCH
- KOVA'S
- SINUOUS
- INTENTNESS
- BOMBARDED
- JIBBERING
- LOOTING
- NEGOTIATED
- IMPREGNABLE
- JEDDAK'S
- SALVERS
- PADLOCK
- ENSHROUDING
- LONGSWORD
- ZODANGAN
- SHAMBLES
- FIGHTERS
- KAN'S
- ZODANGANS
- BARSOOM
- DEPOSED'
- BUNGLING
- DUFF
- HORNPIPE
- VEHEMENCE
- GUM
- NECK'S
- JANGLE
- CLOVE
- TOON
- CUR'OSITY
- DEPPOSED
- OVERTURN
- MERRY'S
- CRUTCH
- TEETOTUM
- HAFT
- MALARIA
- DIAGONAL
- LIVESEY
- GIGS
- INLET
- ANCHORAGE
- GIG
- DERELICTION
- QUADRILATERALS
- AMASSING
- SCUTTLED
- ME'LL
- ABSENCES
- WISPS
- SLIGHTS
- UNWEARYING
- CONNIVED
- CHEAPLY
- SMIT
- RIGGED
- SEAFARING
- DEMONSTRATING
- GEOMETERS
- COMPENSE
- REFUTE
- AGITATE
- DIGRESSION
- PERTAINS
- REDUCES
- HARDENS
- TRANSMUTATION
- VEGETATIVE
- ANNEXED
- VENTRICLES
- CAVA
- ARTERIOSA
- ARTERIA
- VENOSA
- WINDPIPE
- VENTRICLE
- REFLUX
- ORIFICE
- AURICLES
- VERISIMILITUDES
- COUNTERWEIGHTS
- STRAITNESS
- COPIOUSLY
- COVERINGS
- COMPRESS
- EVINCE
- RAREFIES
- REPASSING
- SENSUS
- COMMUNIS
- AUTOMATA
- FABRICATED
- INCOMPARABLY
- VOCABLES
- APPOSITELY
- EDUCED
- EDITED
- INTRODUCTORY
- CONFORMABLY
- HEADINGS
- MASTIFFS
- THACKERAY
- AYALA'S
- WORTLE'S
- FROHMANN
- BLACKWOOD
- HARTING
- GARRULITY
- DIRECTOR
- CHANCERY
- JULIANS
- LIVERED
- CURS
- CINCINNATI
- SUBMITS
- TUITION
- DRACO
- THRASHINGS
- BI
- COLUMNED
- SUPERVEILLANCE
- PARIAH
- STOPPAGE
- DEFALCATION
- APPURTENANCES
- BAILIFF'S
- SIZAR
- ACERBITIES
- ADOLPHUS
- DENOMINATIONS
- SUBDIVISIONS
- UNFLAGGING
- ALPHABET
- LEXICON
- GRADUS
- HOOKHAM'S
- ORLEY
- EXTRAS
- FERULE
- FERULES
- SCOURGINGS
- PROSE
- PETYA
- TROITSA
- DISBELIEVING
- TAPES
- REPLAITING
- UNPLAITED
- REPLAITED
- MAMONOV'S
- SONYA'S
- FLEXIBLE
- ORDERLIES
- BOLSTER
- WRY
- MORTIFYING
- CHARRED
- SEMIDARKNESS
- COLLAPSING
- COMPASSIONATELY
- DAMMING
- SQUIRREL'S
- MISSPELLED
- SPELLED
- CLOTEL
- TRADER'S
- COUNTEFIT
- GENEWINE
- ARTEKIL
- HEARTRENDING
- GENTMAN
- GIB
- SHINEY
- DUS
- FIREMEN
- VALVE
- BOILERS
- SCALDED
- REDEEMING
- STEAMBOAT'S
- BOWIE
- NECKCLOTH
- WALKER'S
- TRAFFICKER
- AFRIC'S
- DEPLORE
- SUFFERER'S
- PIRATE'S
- HAVOC
- PROTECTS
- PRESUMES
- SCAN
- PROTEUS
- UNROLL
- MANTLED
- ASSAIL
- DISSIMULATION'S
- ILYIN
- OVERRESIST
- UNCONCIOUSLY
- ROSTOV'S
- FATTENING
- COMMUNE
- CONSCRIPTED
- WRATHFUL
- UNMEANINGLY
- BICKERING
- YAKOV
- PROPRIETOR'S
- BAST
- DICTIONARIES
- OBTRUDE
- YANKOVO
- BLUSHINGLY
- DUNYASHA
- HEIRESSES
- ROSTOPCHIN'S
- TRADESMEN'S
- VYAZMA
- WITTGENSTEIN
- MUTINOUS
- BONAPARTE'S
- MALEVOLENTLY
- LOCKUP
- CAPTURES
- CAJOLERY
- RELEASING
- FRENCHMAN'S
- PELISSES
- PUCKERED
- LUBYANKA
- LOBNOE
- EVSTAFEY
- PERKHUSHKOVO
- CAISSONS
- IRRATIONALLY
- ACCUSTOM
- MILORADOVICH
- VOYNA
- SEMENOVSK
- GRIDNEVA
- PONIATOWSKI'S
- UVAROV'S
- FELICITATIONS
- PERSISTS
- HERTFORDSHIRE
- GEORGIANA
- INTERMARRIAGE
- BOURGH
- WILFULLY
- DESPONDING
- BENNETS
- LUCAS'S
- SLYNESS
- PATRONESS
- PLURAL
- SPACED
- NORMALLY
- SUBJECTIVELY
- ACCENTING
- CONSTITUENT
- CLASSIFIES
- SPECIFYING
- SEXTUPLE
- TRIPLET
- SEPTUPLE
- STEWARDESS
- BERTHA
- BRAID
- COY
- STRINGED
- RISIBLES
- MAL
- MER
- LIQUIDS
- GOONIES
- ALEUTIAN
- UNIMAK
- GREENS
- SUPPOSEDLY
- TUNDRA
- MISCREANTS
- GORGES
- GULLIED
- ALASKAN
- FLOES
- SCHOONERS
- ELMORE
- EPISODE
- HEARTFUL
- NEUKLUK
- PROSPECTING
- AGEETUK
- SALADS
- MEATS
- DIETING
- DAWSON
- OASIS
- FROGEITY
- MALADJUSTMENT
- TOULOUSE
- PONTUS
- ARAGO
- RAINFALL
- INDISTINGUISHABLES
- EJECTAMENTA
- TAHITI
- HOMOGENEITY
- EXCLUSIONIST
- IDENTIFIES
- SYMONS
- ACCELERATIVE
- ANCESTRALLY
- EXCLUSIONISTS
- ABEDARE
- EXCLUSIONISM
- PUBLISHES
- APOLOGIZES
- PAILFULS
- AXIALLY
- TANGENTIALLY
- THINKABLE
- CARMATHON
- GRIFFITH
- LEIRUS
- GASTEROSTEUS
- BIFURCATE
- FIFESHIRE
- SHIRE
- SERIAL
- MAGNET'S
- DISLODGMENT
- REPULSIONS
- CONVENTIONALISTS
- CONCEPT
- HAYSTACKS
- FERREL
- CALCUTTA
- CUBIT
- FUTTEPOOR
- CUTTERCOATS
- SUNDERLAND
- DIGESTIONS
- UNASSIMILABLE
- BOVINA
- HAILSTORM
- PRESBYTERIANISM
- HAILSTORMS
- INCREDIBILITIES
- POUNCING
- DIVERGENCE
- WAFTING
- HAILSTONES
- UNACCEPTABLE
- INOPERATIVE
- DODOES
- MOAS
- PTERODACTYLS
- CARBONIFEROUS
- DISINTEGRATE
- MUDS
- TROVES
- PALAEONTOLOGISTS
- CYCLONES
- OMNIPRESENCE
- HETEROGENEITY
- TENTATIVELY
- PROVISIONALLY
- LARVAL
- STATIONARINESS
- REDRUTH
- FABULOUS
- RUSTICS
- SHOVELED
- DAS
- PADERBORN
- SIGNIFICANCES
- SEGREGATIONS
- LOCALIZED
- SEGREGATE
- GRADATION
- SELECTIVENESS
- HIBERNATING
- INFINITUDE
- ENORMOUSNESS
- MIGRATORY
- MIGRATE
- FLUTTERINGS
- STRASBOURG
- SAVOY
- ADS
- CONDEMNS
- FRACTIONS
- ASTRONOMY
- LOCATES
- DECIMAL
- THERE'RE
- LOADER
- KANKAKEE
- HAMMERLESS
- HOUND'S
- JAG
- FOOTERS
- TIERCES
- STOREROOMS
- TIMEKEEPER
- TIMEKEEPERS
- EFFECTING
- TYPEWRITERS
- SICKING
- TYPEWRITING
- JIM'S
- CONSUMER
- STEERS
- REPRIEVE
- GAUGED
- FERTILIZER
- ELABORATING
- GUNNING
- OBSEQUIES
- SHORTS
- WORKINGMAN'S
- SYRUPY
- HEIFER
- PAVILION
- DIVIDENDS
- REFINERY
- CUCUMBER
- SUNBEAM
- DRAGS
- CATFISH
- CLYTEMNESTRA
- VOODOO
- SNIFTY
- JINKS
- STRINGING
- AMAZONS
- MEMORIAL
- SLAT
- CLIP
- TRIGGER'S
- SUBSPACE
- MANTELISH'S
- SPECIALTIES
- CORRELATED
- GALACTICS
- BASIC
- BEASTIES
- PLANETARY
- SUBPLANETARY
- UMPTEEN
- LORDY
- BIOLOGIST
- MACCADON
- CUBICLE
- FIDGETED
- NOSY
- INDUCES
- MODIFIES
- FAYLE'S
- FEDS
- SPILLED
- REFLECTIVELY
- SHEINBERG
- HORNSBY
- CYN
- THIA
- TILDEN
- BURRITT
- BROOKLINE
- CAMILLA
- DANIELS
- MONTCLAIR
- SHANE
- KENTFIELD
- DECKER
- GENEVIEVE
- MANITOU
- BARNES
- MARTINETTE
- WILEY
- EASTON
- CONSECRATION
- MULLOWNY
- ADVISEMENT
- SUMMARILY
- HOUSING
- ILLEGALLY
- MALONE
- DISPENSARY
- MATILDA
- MARINES
- QUANTICO
- RIVALED
- BURN'S
- SMUGGLED
- SARDINE
- HANDCUFFED
- MORALE
- STRIKERS
- INTERROGATED
- INDICTED
- TUMULTY'S
- INTERCEDE
- TUMULTY
- INADEQUATELY
- DANA
- COUNSELOR
- LIBERTARIAN
- NARRATING
- TERRORIZE
- O'BRIEN'S
- WADDILL
- MOOT
- JURISTS
- HAVEN
- RECRUIT
- REDRIFF
- SLUR
- NARROWNESS
- MISMANAGING
- ENGROSS
- DISTASTED
- LANDLADY'S
- SHEPTON
- BED'S
- IMMODEST
- RAVISHER
- BUNTING
- BULLION
- DESPERADO
- MUNDANE
- IMPROPERLY
- THOROUGHBRED
- FORBEARANCE
- BOSWELL
- ASSURES
- JAWED
- MARINERS
- SCRAPES
- DISQUIETED
- RECREATIONS
- DISPORTED
- YUCATAN
- TRIBUTOR
- CHRONICLER
- TRUCULENTLY
- CHRONICLES
- DRAPES
- POESY
- TROPE
- BLENDINGS
- INTERTWININGS
- IMPAIR
- LUCINDA
- UNDULATES
- CRADLED
- FRIVOLITIES
- REFASHION
- INCONTESTABLE
- BECKONS
- PROGENY
- THIRSTS
- WOMANLINESS
- SEPARATIONS
- WANTONNESS
- ANTITHESIS
- FRIVOLITY
- MUTUALITY
- CONSTRAINING
- DESECRATES
- SANCTITIES
- CLARITY
- FUSE
- ILLUMINATES
- GERMINATED
- DOUBLES
- WILHELMINA
- FATHOMLESS
- DISSONANCES
- WALKERS
- SICKLINESS
- SUNDERED
- IRRADIATED
- IDOLIZED
- GODLINESS
- REASSURANCE
- MOCK'D
- AGRIVAINE
- BRANDILES
- SAGRAMOUR
- DESIRUS
- DODYNAS
- SAUVAGE
- OZANNA
- LADYNAS
- PERSANT
- INDE
- IRONSIDE
- PELLEAS
- MAYED
- LASHED
- PRIVILY
- LOVETH
- LAMBETH
- DITCHES
- CUMBERED
- OFFAL
- VORACIOUSLY
- CARTED
- MISREPRESENTATION
- LORDES
- FAIRE
- GERE
- SAWEST
- SWOONED
- DESIREST
- PELENORE
- PATER
- NOSTER
- FLASKS
- BOAR'S
- STOMACHER
- BAWBLE
- ORACULAR
- URIEN
- FOREBORE
- SANGREAL
- HERMIT'S
- SURNAMED
- UNADVISEDLY
- UNCOURTEOUSLY
- THEREAT
- UNGENTLE
- UPRIGHTNESS
- HANDMAIDENS
- PRAYS
- RENEW
- SINNER'S
- ASKETH
- MEEKNESS
- TRAVILLA
- HEALTHIER
- CARRINGTONS
- FLATLY
- WEEPER
- CROSSEST
- BANISHMENT
- UNLADYLIKE
- PENITENT
- PLEADINGLY
- DOLL'S
- FLORA'S
- GLUE
- UNTRUTHFUL
- FLATTERETH
- SPREADETH
- ARNOTT
- LESLIE'S
- LORA
- IMMENSITIES
- RAYED
- POPPIED
- UNBELIEVING
- CAROLING
- WARBLERS
- AMBERING
- CORSELETS
- BATTLECRIES
- MILLING
- MART
- TUNNEL'S
- OUTFLANKED
- WOLFLIKE
- DYNAMITE
- WHINED
- LACQUERED
- CLUBBED
- AWESOME
- RELAXING
- PENNONED
- LUCENT
- SPEARSMEN
- PIKEMEN
- AUTOMATONS
- VIBRANT
- WAILINGS
- DRAGONED
- COLUMN'S
- IMMOBILE
- UNLEASHED
- BOXER
- SPIKED
- GLADIATORS
- PREENING
- GAPS
- JAVELINED
- HUDDLING
- DISINTEGRATED
- SCAMPERING
- FLAILING
- PRONGS
- TRIDENT
- TEETERING
- MADDENED
- SCYTHE
- FROTH
- JE
- LAMMERGEIERS
- SCAVENGERS
- LINGERINGLY
- SATED
- SCANTINESS
- OLDSTER
- RIMMING
- CINCTURE
- CHIMINGS
- VASTNESSES
- THRUMMING
- EFFORTLESS
- STRIDING
- TAPS
- DRUMMING
- ENIGMATICALLY
- ETERNITIES
- CRESCENDO
- FACETS
- AIRLESS
- WATERLESS
- SUNLESS
- SMARTER
- REUTER
- CURSORILY
- PLAT
- PENDULE
- MANTELPIECE
- CONSOLES
- CHIFFONNIERE
- TENDRILS
- DEMESNE
- PARTERRE
- ENGLISHWOMAN
- RESTE
- STEEPS
- FURRINERS
- RHODODENDRON
- COVERT
- SLOUCHED
- HOODED
- UNDERMIST
- FOOTFALLS
- DRIP
- TINKLED
- LIMBER
- GILLS
- HOWDYE
- HAIN'T
- AFFIRMATIVELY
- HIT'S
- SHET
- PURTY
- NONCHALANT
- HUMOUROUS
- RUMBLED
- HYEH
- TWUSN'T
- GITTIN
- DEBATING
- UNAVAILINGLY
- SPECTACLED
- NAIVE
- THAR'S
- REMONSTRATING
- KINDLIEST
- UNJOINTED
- CRAWFISH
- CHINKED
- GOAD
- SPURNED
- PELLETS
- NAAS
- VIRTUAL
- TRIMLY
- ELECTRICIAN
- OPTIMIST
- NATIONALIST
- REMONSTRATIVE
- VILLONA'S
- CONFUSE
- ELATES
- CONTINENTALS
- GONGS
- TRAM
- GAZERS
- EQUATION
- UNPURCHASEABLE
- SUPPED
- VOLUBLY
- MADRIGAL
- INGENUOUSLY
- MECHANICIANS
- LUTES
- SHEPHERDED
- TORPID
- NOISIEST
- ROUSSEL
- HOHE
- FORM'S
- HUNGARY
- VOLUNTARIES
- OBSCURELY
- LOSERS
- COMMITTEES
- CONCEALMENTS
- ENNOBLED
- DROLLERY
- PUB
- DOCS
- CONGRESSMEN
- ERASING
- HOPPERSON'S
- SANCTIMONIOUS
- COMMITTEEMEN
- COMMINGLING
- PERSIFLAGE
- FRANKING
- FACETIOUS
- OFFICIO
- ENNOBLE
- BRIBERY
- LEONI
- SPOLETO
- HANGINGS
- GIRALAMO
- USURPER
- COMMANDANTE
- GIOVANNI
- ORGY
- FERRARA
- NOVICE
- PRONUNCIATION
- ABSTINENCE
- PENANCES
- REVERENTIAL
- LUTHER'S
- THEOLOGIAN
- DEATHLIKE
- VANITIES
- ADULTEROUS
- OBSCENE
- FRESCOBALDI
- MONK'S
- PIERO
- CONSTITUTIONALLY
- ENERVATED
- KILLER
- NOISED
- ARTA
- BREECH
- WINDWARD
- SCALPED
- GROVER
- FLEETNESS
- CENTERS
- HAYES
- AVOCATION
- RAFFLE
- CONTESTANTS
- BONHAM
- POKY
- THOROUGHBREDS
- WILCOX'S
- ALEXIS
- SHERIDAN'S
- RUCKER
- LEONARD
- INFORMAL
- OVERTON
- BUNTLINE'S
- BENNETT'S
- CODY
- BELMONT'S
- LIEDERKRANZ
- INSPIRITING
- NIBLO'S
- JARRETT
- PALMER
- MAEDER
- DRAMATIZED
- STUDLEY
- FRELEIGH
- GUSS
- S'DEATH
- PANTAGRUEL
- ENNASIN
- UNTWISTED
- JOINTURE
- ENTER'D
- CAWL
- SUCK'D
- PICK'D
- CHEW'D
- PEEL'D
- DIGESTED
- LAWYER'S
- EXSUDATIONS
- BACKSIDE
- DIGESTING
- INTRENCH
- CIRCUMVALLATIONS
- TRANSGRESSION
- DEVOUREDST
- AMBLE
- WEEDER
- DISPUTATIONS
- CIRCUMVENTION
- PASS'D
- PROWESSE
- BEGUINE
- BEGUINES
- FUSTY
- BAGGAGES
- NEGLECTEST
- ENTERPRIZE
- OFTNER
- WEAVES
- FACETIOUSNESS
- ENTEREST
- RESOLV'D
- FAG
- VENTED
- CONJUGALLY
- CALL'D
- SENSIBILITIES
- CHRYSTAL
- MOTE
- ABETTING
- THEREUNTO
- TRAGICOMICAL
- REVERENCES
- RAMALLIE
- CURL'D
- ASSIMILATED
- SOVEREIGNLY
- TAFFETA
- METALLICK
- TUCK'D
- PUFF'D
- CLIPP'D
- VAPOURINGLY
- BEGUILED
- STEDFASTLY
- DRAGG'D
- SYLLOGISMS
- NULLIFIED
- PREMISS
- LANDOR
- COMPENSATING
- PHIPPS'S
- STUMPY
- AUREOLE
- CZERLASKI'S
- MISDEMEANOURS
- UNIMPEACHED
- QUADRAGENARIAN
- MATHEMATICIAN
- PACHYDERMS
- BRIDMAIN'S
- DEBATABLE
- COMMONALTY
- PAROCHIAL
- INITIATED
- PAS
- QUADRILLES
- PROBLEMATIC
- COUSINSHIP
- BLEMISHES
- DISQUALIFICATIONS
- SEVEREST
- UNDENIABLY
- PAUCITY
- CONGREGATIONS
- WEDNESDAYS
- SHUNS
- GORGON
- SOPHOCLES
- ABSORBINGLY
- NEE
- UNSKILLFUL
- IDUMEA
- GITTHA
- DAPHNE
- ANTIOCH
- FOREBODED
- IRRUPTION
- FOREFRONT
- CANA
- HINDERMOST
- PAPPUS'S
- ARISTOBULUS
- MARIAMNE
- PHOENICIA
- AMBUSHES
- SOSIUS'S
- CENTURIONS
- PROPORTIONABLY
- CALUMNIATED
- MALICHUS
- PARTHIANS
- APAMIA
- PACIFY
- PARTHIA
- MINISTRANT
- NATURA
- INSTILLANT
- HOWSOEVER
- PRIMOGENITURE
- DEPENDANCE
- DICTATES
- MEUM
- TUUM
- OVERALL
- INTERMIT
- SAKES
- PESTILENTIAL
- EXALTS
- FILIATION
- SOUGHTEST
- FOUNDEST
- JOYFULNESS
- DISEASEFUL
- MALIGN
- PESTILENT
- PASSETH
- FILLEST
- SACRAMENTS
- DISLOYAL
- HYPOCRITES
- INFECTIONS
- BEDCHAMBER
- RELUCTATION
- VIPERS
- CATECHISED
- RECEVEUR
- DIPLOMA
- NAIVETE
- PAYMENTS
- D'ANTIN
- SUBSTITUTING
- HYGIENIC
- CHAMPS
- ELYSEES
- GAMING
- NANINE
- STEWED
- ARNOULD'S
- LAMARTINE
- SCUDO
- EFFACE
- EMBODY
- PRUDENCE'S
- PROFESSEDLY
- UNSUSPECTINGLY
- GAINER
- SERMONIZED
- GAUTIER
- DUPRAT
- BOUQUETS
- USELESSNESS
- SHIRKING
- BOMBAST
- PRISTINE
- BATES'S
- GENERIC
- TAUNTER
- BARKED
- BLOODTHIRSTY
- UNHEEDING
- STERNWAY
- SPLINTERED
- BRIG'S
- CHESHIRE'S
- TWITCH
- BEGOTTEN
- UNRISEN
- GABBETT
- NAB
- TROUGHS
- LEVIATHAN
- WALLOW
- CREAMED
- BANDAGING
- VILLAINY
- BEACHED
- LAGGARD
- HUMOROUSLY
- UNBUCKLING
- HELMSMAN
- BISECTED
- VETCH'S
- BLUNT'S
- IMPENITENTLY
- SYDNEY
- STUMPED
- CHARTS
- KILN
- PURFOY'S
- WAXING
- HOYSTERIN
- CANARIES
- NOTION'S
- MADAM'S
- EAGLEHAWK
- WIND'S
- ISTHMUS
- LAMBENT
- VULCAN'S
- SMITHY
- DEMIGOD
- ROUSTABOUT
- DICKERING
- MELVILLE
- BARSTON
- SWERVED
- HOMESITE
- LAZIEST
- SIGHTSEEING
- HIGHWATER
- CALDRON
- RAINIER
- WINTHROP
- PUYALLUP
- TOWNSEND
- SHACKS
- RASCALLY
- DEBAUCH
- BUFFETING
- MILESTONES
- SLOUCH
- VENTILATION
- PIONEERS
- BLOOMER
- STUMPER
- HARDTACK
- PROVENDER
- BIGHT
- YAKIMA
- TETHERING
- ROUSING
- HOBBLES
- NATCHESS
- WHEELMEN
- OBSTRUCTION
- OBTAINABLE
- NISQUALLY
- UNEVENTFUL
- BACCALAUREATE
- NATHANIEL
- AUGUSTUS
- FENN
- VAILL
- HAZZARD
- MISSOURI
- BERKELEY
- BRIDGEPORT
- GLAZIER
- HOPSON
- GARWOOD
- MERWIN
- REUNIONS
- REGIMENT'S
- ARLINGTON
- NAUGATUCK
- UPRISINGS
- ANCESTRY
- AMPHIOXUS
- DINOSAURS
- BARBELLION
- DISGUSTEDLY
- VAUDEVILLE
- MONKEYISH
- GREATGRANDSONS
- WRYLY
- COMPETING
- HEADSHIP
- COSMOS
- LEMURS
- AESTHETICALLY
- UNEMPLOYED
- EUGENICS
- DARWIN
- REPRESSIVELY
- HYPER
- RUCK
- PLAUSIBLY
- TRUSTFUL
- EXPLOITABLE
- PARASITIC
- GROSSNESS
- STUPORS
- COSMOPOLITANS
- TOLERANCE
- OUTLAWS
- TERRORIZED
- SLINK
- URBANELY
- PARVENUS
- PLUCKS
- SWISHES
- FLANKS
- UNLEARNED
- ATROPHIES
- QUENCHLESS
- SLOGS
- CONFORM
- ENERGIZED
- PERPETUATE
- ITCH
- MANTLES
- GROTAUT
- HOSTAQUA
- HUMBLER
- GAMBIE
- EDELANO
- VERGED
- SUPERNAL
- CABOOSA
- OATHCAQUA
- CALOS
- FASTNESS
- THIRNAGOAS
- BARRENS
- CONTORTIONS
- SATOURIONA
- MALTREATED
- RIBAUT
- MISERIE
- FINDE
- BEATE
- EFTSOONES
- BEGANNE
- NEERE
- SKINNE
- SOULDIERS
- SKINNES
- THOROW
- PARTES
- EXORBITANT
- TOOKE
- VILLAINES
- ANSWERE
- CHURLISHLY
- MARCHANDISE
- VASSEUR'S
- BRIGANTINE
- ASTINA
- VASSEUR
- DEEPELY
- QUICKE
- PARED
- SCRIVENER
- STATIONER'S
- LENDER'S
- GAUDS
- VAPID
- FLAVOURLESS
- INTIMATING
- CHANCELLOR'S
- DISESTABLISHMENT
- BARSETSHIRE
- DOWNING
- BALDOCK
- BRIBED
- ELECTOR
- DREADS
- RETRICKED
- LOUGHLINTER
- COMPLAINS
- EMMA'S
- UNEXCEPTIONABLE
- TENDERER
- VALETUDINARIAN
- LIEU
- MITCHELL'S
- YORKSHIRE
- SURPRIZED
- ENSCOMBE
- COMPETENCE
- PORTIONLESS
- TYRANNIC
- WESTON'S
- SURPRIZE
- WESTONS
- ELEGANCIES
- QUICKSIGHTED
- SIXPENCES
- CONTRIVING
- PROSINGS
- GODDARD'S
- SHEWING
- ARTLESSLY
- BETWEENS
- CONSTRAIN
- LITIGATION
- SLUMS
- LOREEN
- DISPENSER
- FURNISHES
- ORCHESTRAS
- CONVERSIONS
- SPECIFICATIONS
- TOYNBEE
- SLUM
- DISCIPLESHIP
- CHRISTIANIZING
- CRANK
- MEMBERSHIP
- RIGOR
- TRANSFORMING
- REDEMPTIVE
- CESSPOOL
- WARINESS
- PERAMBULATOR
- SUNDAYFIED
- HOW'D
- FELLA
- AFFIRMATION
- DESOLATELY
- SIBYL'S
- THOROUGHGOING
- LAMHORN
- ROSCOE
- MISTED
- SHAMEFACEDNESS
- STENOGRAPHERS
- STENOGRAPHER
- FASCINATE
- SACRILEGE
- FLICKING
- ARGUMENTATIVELY
- DECADENCE
- WITENAGEMOT
- PHEW
- BYWORD
- BLAZES
- CHECKERED
- REASSERTION
- EXPROPRIATE
- EXPROPRIATION
- UNDERCUTT
- PARENTAGE
- GORFINKEL
- EUREKA
- KOSSUTH
- DAVIDSON'S
- LOVELORN
- DILL'S
- AFY'S
- REPAYMENT
- LABORER'S
- PREMISED
- EQUANIMITY
- DELIBERATED
- BROADCUT
- BETHEL'S
- ARCHIE
- FERRET
- FLURRIED
- GRAVER
- GIBBETED
- DESERVINGS
- LYNNE'S
- TAGRAG'S
- POKES
- BUCKETFULS
- RAGTAG
- SHIVERY
- TAGRAG
- RIMS
- INQUISITIVELY
- TATTERS
- HISSES
- UNWASHED
- JIG
- PINNER'S
- PLOWMAN
- LATIMER
- DUCKING
- GOVERNESSES
- IRASCIBLY
- ANNUITY
- LYNNEBOROUGH
- LIGHTENING
- ELLIPSE
- GUZERAT
- GOLCONDA
- DISUSED
- DEPOSITORY
- PAPYRUS
- FINIS
- NIB
- ILLUMINATIONS
- FARIA'S
- FENESTRELLE
- UNHEMMED
- IMPUTING
- EVAPORATED
- LECLERE
- NUPTUAL
- D'IF
- RADCLIFFE'S
- IMITATORS
- RHUBARB
- SPECKS
- LENIENT
- MEMENTO
- CIRCUMSTANCED
- COCOA
- HOUSEMAIDS
- WAIVE
- INCONSTANCY
- DEFUNCT
- ANYONE'S
- UNDERVALUE
- ABHORRENT
- COIL
- TRANSMITTER
- HURTLED
- BLURTED
- CONNECTS
- BLASTS
- OILERS
- LIEUTENANT'S
- SUMMING
- TELEGRAPHS
- WAR'S
- COUNSELED
- SLIM'S
- CONVALESCING
- PERISCOPE
- WINCE
- MACHINIST
- FOAMY
- CORRALLED
- WIRY
- AMBUSHED
- OUTNUMBERED
- GRENADE
- STARK
- RECUPERATING
- DETONATIONS
- BATTLESHIPS
- FLASHLIGHTS
- ROCKET
- BREASTWORKS
- REPORTING
- FORMATIONS
- CARNAGE
- CLOCKWORK
- REPULSES
- PUNCTURED
- MOBILIZATION
- AEROPLANES
- BOCHE
- MANNING
- ONCOMING
- ARMADA
- OBSERVER'S
- PARACHUTES
- VERBIAGE
- ENTOMBED
- NEUTRALITY
- WAGERS
- SINKINGS
- BILLIARDS
- MARION'S
- CALLERS
- PUNGENT
- HIGHBALLS
- CIGARET
- TOOTS
- ARBITRARILY
- RANKLING
- INDISCREET
- CALCULATING
- PREEMPTED
- PERCENTAGE
- DIVINELY
- CUSTOMARILY
- CLAYTON'S
- CLAYTON
- BARONIAL
- INTERSECTED
- SQUARES
- SUBTERRANEOUS
- PURCHASERS
- SARVED
- KNAVES
- BOTTOMED
- SNICKS
- SCREW'S
- SNICKEY
- SCRAG
- WINDER
- TREACLE
- BABBY
- VEAL
- IMPERENCE
- HUCKSTER'S
- TRAFFORD'S
- WURNO
- SHODDY
- FAULTERING
- SHUTTLE
- PREPOSSESSING
- DETRACT
- RECOLLECTING
- CHARTIST
- DELEGATE
- FORETHOUGHT
- UNRESERVEDLY
- PSEUDO
- REFINE
- LAWFULLY
- MEMORANDA
- BUMPO'S
- COCOANUTS
- MAMMOTHS
- JIP
- GOLLY
- PROSECUTOR
- GUILLOTINED
- GRATES
- REFILLED
- DARNAY'S
- DAGGERS
- OVERLADEN
- THEOPHILE
- GABELLE
- ALEXANDRE
- CITIZENESS
- MANETTE'S
- ABBAYE
- JURYMEN
- REFERABLE
- PREDOMINATING
- ACQUITTAL
- STREW
- FORASMUCH
- COMPENSATE
- CONCOURSE
- DYE
- BRAWLING
- PROSS
- UNKINDNESS
- GLOUCESTERSHIRE
- ENTREATING
- GUESSWORK
- SHORTAGE
- EMOTIONLESSNESS
- THINNEST
- NED'S
- DETACHING
- CHUNK
- WIELDING
- ATMOSPHERES
- FORECASTS
- SOLIDIFY
- DAMPENING
- WARDING
- SOLIDIFIES
- DOGGEDLY
- SATURATING
- NOXIOUS
- POTASSIUM
- HYDROXIDE
- MINER'S
- UNDERBELLY
- CONGEALING
- SOLIDIFICATION
- BREATHABLE
- CENTIGRADE
- MONITORED
- OPERATION'S
- INSPECTIONS
- DISLOCATED
- ENKINDLING
- DAZE
- WINDED
- EXPERIENCING
- VIBRATIONS
- CONVULSION
- CANNONBALL
- VACUUM
- EXPEL
- RIVETS
- WHIFFS
- HEEDLESSLY
- LUNGFUL
- AHHH
- INHALATIONS
- CIRCULATING
- THROATFULS
- BANALITY
- COASTLINE
- SARMIENTO
- SHALE
- CRUSTACEANS
- STEWS
- ALGAE
- FUCUS
- GOBY
- GUDGEON
- MEDUSAS
- SEMISPHERIC
- PARASOLS
- QUIVERINGS
- LEAFLIKE
- TENTACLES
- EVAPORATING
- CAPRICORN
- FRIO
- BRAZIL'S
- DIZZYING
- PERTURBED
- VRONSKY
- UPSETS
- FERRETS
- UNLUCKILY
- BREATHINGS
- FLEECED
- WRONGFULLY
- VULCAN
- REPROBATES
- SUITABLY
- SCHOOLMASTER
- COMPLAISANTLY
- CUDS
- SICKLE
- CLANGOR
- BELLOW
- PROTRUDED
- STABBING
- LOPPING
- SURVIVOR
- CONCEIT
- IOLCHOS
- TAMING
- REFUSES
- SULPHUROUS
- MEDEA'S
- GAPE
- DUSKINESS
- DISPORTING
- LYNCEUS
- PERPENDICULARLY
- DEDICATE
- FURL
- IMPERIALISTIC
- EDITORIALLY
- VOTING
- ENFRANCHISING
- SUBMARINES
- PERTINENT
- INSCRIBED
- ENHANCED
- BALFOUR
- VIVIANI
- RUMBLINGS
- ANDREAS
- SPARGO
- HINSHAW
- AUTHORIZATION
- ALABAMA
- UNEX
- PECTED
- FEDERATIONS
- SUF
- FRAGE
- MENT
- UNIVER
- ANDS
- MICHIGAN
- CONTROVERSIAL
- CONSCRIPTION
- ESPIONAGE
- NATIONALLY
- CZARIST
- AMERICA'S
- IMPOVERISHED
- AMERI
- PRESI
- TIONAL
- ENFRANCHISEMENT
- HOODLUMS
- ROUGHS
- EMBODYING
- CONFEREES
- ARNEIL
- LAVINIA
- DECLAIMING
- VACATE
- SUFFRAGETTES
- DISSEMINATION
- EXHORTS
- IMPRISONMENTS
- MYER
- ROWDIES
- GEORGINA
- STURGIS
- EMBEDDING
- FLATHER
- STONE'S
- TESTIMONIALS
- EVANGELICAL
- CITE
- JAMISON
- PANELLED
- RICHE
- DICKENS'S
- TODDLING
- BLOOMLESS
- UNOBTRUSIVELY
- BESTRIDDEN
- CLARION
- AUSTERELY
- SCARVES
- FURRY
- FEZ
- SIRDARS
- SATURNINE
- RADIANTLY
- AFRICAN
- SUPERCILIOUS
- CONJUROR
- EXTERNALLY
- ACROBAT
- PREDATORY
- MAGISTERIAL
- BLACKING
- BONFIRE
- SNIVELLING
- MORALISING
- BEARD'S
- SOCIALISM
- HARLEQUINADE'S
- TOWEL
- POLICEMAN'S
- THIGH
- FLORIAN'S
- COSTUMIER
- PHONE
- PHONES
- BOSH
- FLAMED
- TAMENESS
- BENEFACTORS
- CHANDELIERS
- WIGGS'S
- DOTAGE
- WIGGS
- MEGRIMS
- CURTSEY
- UNDECIDEDLY
- AMU
- SMALLNOSE
- BOWMAN
- MERRIWIG'S
- LANDMARK
- BAYED
- H'R'M
- APOLOGISE
- ABDICATED
- FONS
- ORIGO
- SMALLNOSE'S
- FLAGSTAFF
- EAVESDROPPERS
- COUN
- SUITORS
- BARODIANS
- SWINEHERD
- TREGONG
- LEONORA
- VESTAL
- GOWERS
- ALBEIT
- UNENTHUSIASTIC
- OBSTREPEROUS
- COLLECTEDLY
- ANTIMACASSAR
- EULOGIES
- ABASEMENT
- CEREMONIOUS
- EMBARRASSINGLY
- UNGIRLISH
- MINERVA
- UNADORNED
- EULOGIZING
- UNBIASED
- SAFEST
- DUGALD'S
- GUILELESSLY
- DOWNPORT
- SUCCUMBED
- CONVERSATIONALLY
- PAMELA'S
- PAM
- CROCHET
- RESET
- DESPERATENESS
- EFFUSIVE
- PAM'S
- ELDERBERRY
- SACQUE
- INTERJECTION
- SPAS
- REDLY
- HENCHMAN
- LANGDALE
- INTENTIONAL
- COLLEAGUE'S
- EMANCIPATED
- GALTON
- EVIDENTIAL
- TRANSFERS
- HORNBY'S
- BESTA
- INA
- DESERTO
- ILLUSTRISSIMO
- VETTURIO
- BELLISSIMA
- CONFRERES
- SIGNORINI
- BELLA
- VETTURE
- DILAPIDATION
- NAGS
- MISNOMER
- APOSTROPHIZING
- SALERNO
- OVERHANGS
- HEADFOREMOST
- WEDGED
- POSITANO
- ARBORED
- FORMER'S
- ANNULLING
- BEQUESTS
- PATRICIA'S
- SUPERCEDES
- INHERITS
- MERRICKS
- BOOKKEEPING
- VIOLET'S
- BRACKET
- SQUANDERING
- SMOKES
- BAKERY
- SEEDY
- EGRESS
- SHIRTSLEEVES
- CHEF
- SECURITIES
- UPTOWN
- DOWNTOWN
- WHEEDLED
- TUCKING
- BRONZY
- SUNNED
- AFTERMATHS
- DECORATE
- CLUTTER
- ADDLEPATED
- UPPISH
- DRYAD'S
- SWEETINGS
- GERTIE
- WARTS
- SLOANE'S
- BOULTER
- SASSED
- MATTIE
- CROSSOVER
- WILSON'S
- TUMBLERFUL
- BRAGS
- PROB'LY
- ROSEBUSH
- PITCHERFUL
- NUN
- CLOISTERED
- STORMIER
- FIDDLESTICKS
- STRICTER
- GLASSFULS
- SUPPLIANT
- PREPENSE
- TRIBULATIONS
- MINDING
- BUSYBODY
- TIMOTHY
- LIBERALLY
- INSTILL
- ITALICS
- SHINGLING
- MOPING
- DAVY'S
- UNSOCIABLE
- BANDBOX
- HEARTBREAKING
- UNMERCIFUL
- IMPROVER
- CONTRARIES
- ANIMADVERTED
- SASSIETY
- SHINGLED
- LOTTIE
- STELLA
- BECKET
- TYNDALE
- MARJORY
- BOSSED
- NAUGHTIEST
- OLL
- FROZ
- WOUDENT
- CHARE
- ORGINALITY
- LESSUNS
- HALLOW
- NEWBRIDGE
- AFECKSIONATE
- FESSED
- BLOTS
- WHACKED
- INAGINARY
- MERMAIDS
- KELPIES
- RAINBOWY
- MUSSEL
- CLARICE
- ALMIRA'S
- FIRECRACKERS
- PINWHEELS
- DONNELL'S
- ROGERSON
- PAILFUL
- PRILLIE'S
- TEACHER'S
- ODORIFEROUS
- F'S
- IRVING'S
- UNASHAMED
- NIPPED
- HEARTEN
- IMPISH
- OUTFLASHING
- CULMINATION
- VOLCANOES
- ERUPT
- PREDICTIONS
- FORKING
- OPPRESSIVENESS
- THUNDERCLAP
- INGOT
- FOUNDRY
- TUNS
- INCOMPLETION
- BARMAIDS
- TYCK
- APOLLINARIS
- KATHARINE
- WASHINGTON'S
- SQUASHED
- BISHOP'S
- GLOAMING
- LORNA
- DOONE'S
- EVENSONG
- SCHUYLER
- ABBREVIATE
- KITTY'S
- OUTGREW
- AMHERST
- ONSARTIN
- GOSLIN'S
- BUDDIN
- NOHOW
- GRAFTED
- CHOICEST
- PUCKERY
- FRONTISPIECE
- COPAL
- KICKIN
- PASTERN
- EXTINGUISHING
- CLOCKMAKER'S
- ROGUE
- YALLER
- MAMA
- CRITTERS
- ARTER
- NATUR
- CRITTUR
- T'OTHER
- SNEEZER
- CHARLESTOWN
- AMAZIN
- HANDSOM
- GALS
- RECIPROCATE
- CLIPPER
- CLAPPER
- SPARKLIN
- TWINKLIN
- TEETOTALLY
- DEFLESHED
- WALKIN
- DYIN
- SHAKIN
- GRIT
- LEFTENANT
- OBY
- FOSTERING
- IMPALPABLE
- EMIT
- ALLURE
- TROWSERS
- UNCHANGEABLY
- UNFITTING
- DRUMMOND
- WOOLSACK
- GREGORY
- INEFFICIENT
- RAMSDEN
- FITZGIBBONS
- MACPHERSONS
- MACPHERSON
- BUNGAY
- CARLTON
- MILDMAY
- CONSOLIDATING
- RETICENT
- STRUGGLERS
- COALITIONS
- CREDITING
- UTILISED
- SPEAKER'S
- SECRETARYSHIP
- DEMONSTRATIVE
- ERLE
- SYMPATHISE
- READJUSTED
- RAMPANT
- BLITHELY
- THRUSHES
- WRENS
- UNINITIATED
- MADDEST
- BATTING
- GRUFFEST
- SQUABBLING
- PROXY
- UMPIRE
- FUSSING
- KITTERIDGE
- DEFRAUDED
- PYROTECHNICS
- TOGGERY
- WAGGLED
- CUTTIN
- SMITHERS
- BERRYVILLE
- MANES
- CONGLOMERATION
- GIRAFFE
- ELEPHANT'S
- ZEBRA
- LUNCHING
- CROCODILES
- CRICKY
- DODGES
- MONEY'S
- DOUBTER
- GROWLS
- SISSY
- SANCH'S
- BAB'S
- MISSIS
- CHICKERBERRY
- LOZENGERS
- CANES
- GRIG
- EXCITEMENTS
- BAKER'S
- MUNCHING
- COOKIE
- MOOING
- BECKON
- PLAYFROCK
- CHIRRUP
- PLAYFULLY
- TICKLISH
- MUSK
- SPATTING
- POISONED
- SHASTA'S
- SNOWBOUND
- AFTERGLOW
- PEDESTRIAN
- INTERROGATIONS
- SLIDES
- SISSON'S
- FROSTS
- LOOSER
- MEALY
- LOATH
- STEEPNESS
- STEEPEST
- MAPLIKE
- PATHWAYS
- EXULTINGLY
- ROSIN
- FIERCEST
- SNOWSHOES
- SIFTED
- WEARILESS
- WOODPILE
- LENS
- TRACHYTE
- BUMBLEBEE
- ZIGZAGGED
- RHETT
- KLAMATH
- DISKS
- MODOC
- SISKIYOU
- CLOUDLAND
- DUSTINGS
- SHOWERING
- PROMPTINGS
- FIBROUS
- WEBS
- CONVOLUTIONS
- SPRAYS
- PRECIPITOUS
- INCONCEIVABLY
- WHITNEY
- EVINCING
- FORDING
- OVERSWEEPING
- FLOWERING
- GLINTING
- STUPEFY
- SUBSIDENCE
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- UNLITERARY
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- LIONESSES
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- FLOUNDER
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- HOLLOAED
- RUDEST
- REASSERTED
- CREAMING
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- SOAKING
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- HANDSPIKE
- PRISED
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- FLATTENING
- UNSETTLING
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- HEAVE
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- CRUSHINGLY
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- UPRAISED
- TREMORLESS
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- VOZDVIZHENKA
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- UNEXPECTEDNESS
- INTERROGATIVE
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- SERF
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- UNDEFINED
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- TINGLE
- ATTENDS
- UNCOMMONLY
- SEVENFOLD
- KIRK
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- HOOPED
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- UNFREQUENTED
- DEACON
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- PURSUES
- WESTERLY
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- UNCLEARABLE
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- NORTHEASTWARD
- DISINTEGRATING
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- FAWNED
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- DEEN
- PREFATORY
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- LURID
- INTERFUSIONS
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- KYLAM
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- DISEMBARKED
- SEARCHLIGHTS
- IMPEDING
- SHOREWARD
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- SLEDS
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- SIGNALIZED
- SHAMING
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- DEPLOYED
- BRIGADE
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- BUCKY
- O'NEILL
- LUNA'S
- BURROW
- TAMPA
- GREWSOME
- TOURNIQUETS
- MEXICANS
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- SURGEON'S
- TAWDRY
- ANTONIO
- UNCONCERNEDLY
- JORGENSEN
- RETALIATE
- UNSELFISHLY
- AGUARDIENTE
- ALIGNMENT
- TOLERANTLY
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- INTRENCHED
- SMOKELESS
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- SCARFS
- PASTURED
- FIORDS
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- ASGARD'S
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- FOCUSSED
- RECKLESSLY
- LITTLEST
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- HIRING
- INTERESTEDLY
- CHARLEY'S
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- GENTLEMANLY
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- IMPERVIOUSNESS
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- ALF
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- BRIDGET
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- FIX'D
- FASTEN'D
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- INDENTINGS
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- LOOKER
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- EXEMPT
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- HAPPENEST
- SQUAT
- UNFORCED
- FOREFINGERS
- INSENSIBLY
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- FLATUS
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- FRAY'D
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- ORMOND
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- SCARFE
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- CONTI
- RECALL'D
- TALMASH
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- UNCOCKED
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- NOONTIME
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- SU
- OUTPOSTS
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- DEVOLVING
- ENTRIES
- BANDANAS
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- BETTER'
- FORGIVED
- DARLEY
- FRA
- SPECKSIONEER
- UNMENDED
- REDD
- DITTY
- BEFITTING
- WORSTED
- YO'VE
- KITH
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- HISSELF
- HORNY
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- MATTERIMONY
- WE'N
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- UNBOLTED
- LAMER
- LATENESS
- UNREAD
- QUAKER
- SPITALFIELDS
- PHILANTHROPIC
- DICKINSON'S
- PLEDGING
- DICKINSON
- HARTLEPOOL
- INACCESSIBILITY
- ROSE'S
- IVERY
- TEASPOON
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- LIEFER
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- LASSES
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- UNDERANGED
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- WHOSOEVER
- BUTTING
- PIMPLE
- CYCLOPS
- ATHLETE'S
- PUPIL'S
- GOUGES
- MERRYMEN
- FARCES
- TAVERNS
- CINQUE
- JOWL
- TOPMAN
- CALKER
- FAVOURITES
- BIGOT
- BRAWLER
- EXUDED
- FORETROCHE
- CRIMPS
- OVENS
- EMANATING
- ASTROLOGERS
- ASTROLOGY
- PRIMATE
- VIRGINITY
- VIRGINITAS
- EMPTA
- MOT
- SCROLL
- EXCLUDES
- EMBODIES
- CATALONIA
- BARCELONA
- CHEFS
- D'OEUVRE
- LULLI
- ENSEMBLE
- PASSABLE
- MANSARD
- LAMOIGNON
- RACINE
- DRYDEN
- LOUVOIS
- PEMBROKE
- EFFEMINATE
- PERE
- TELLIER
- TARTUFFE
- IMITATES
- HYDE
- INCORRECTNESS
- MESALLIANCE
- PRINCIPIUM
- DOMINI
- MADEST
- GENESIS
- TRANSFERABLE
- POSSESSOIRE
- PETITOIRE
- CULTIVATORS
- REM
- DOMINIUM
- POTEST
- NISI
- CAUSA
- OCCUPANCY
- OFFSETS
- THEMIS
- RENNES
- TOULLIER
- ESCHEAT
- CULTIVATES
- INDORSED
- IMPIOUS
- REINSTATED
- REJUVENATED
- PALLADIUM
- DEIFY
- SPOLIATION
- DISPOSSESSED
- FORMULATES
- LITERALITIES
- RETAINABLE
- NUDO
- ANIMO
- LEVY
- EXCLUSIONS
- CONTROLLER
- SUBSTANTIATE
- NILE
- GANGES
- PARTITIONING
- APPRAISING
- CONTRADICTS
- DITHYRAMB
- NEGATION
- IMMORALITY
- AXIOMS
- ROUGHER
- ASSUAGED
- DELUGES
- DECLIVITIES
- STIFFENING
- ICICLES
- GLUED
- UNSLEEPING
- BINCLEAVES
- SEPARATES
- SILHOUETTE
- SLATED
- CARVE
- FOUNDLING
- INTERSECTIONS
- SCRAMBRIDGE
- BACKWATER
- SUCTION
- LETHARGIES
- SLUMBERS
- DECOMPOSED
- TRANSPARENCIES
- IMPALPABILITY
- EXISTENCES
- AMALGAMATE
- LARVAE
- IMPEDES
- WENDING
- DIFFUSION
- ALBAN'S
- JERKY
- TOLLED
- VAGABONDS
- UNRELENTING
- JOHNSTONE
- RADIPOLE
- REFUSALS
- MISANTHROPY
- GRAINING
- GRANULATORS
- QUICKS
- IMPRECATIONS
- PROPRIA
- PERSONA
- BANQUETING
- RUBENS
- DEITIES
- CRUNCH
- SPONGERS
- PARASITE
- RABID
- VIRUS
- GATESBY
- ENGLANDER
- PROSTITUTES
- BLACKS
- HARBORED
- EKE
- CORNFIELDS
- HICKORIES
- PALMETTOS
- WHOLESALERS
- REDDEN
- RENTERS
- MISTREATMENT
- IMMIGRANT
- SHUFFLES
- DEBTORS
- RUTHLESSLY
- UNSHADED
- UNFENCED
- SEARS
- FLIPPED
- STRAPPING
- DEVASTATING
- UNHINGED
- SOBERLY
- SANFORD
- LADSON
- CORLISS
- WILLIS
- WEAZENED
- FEATURED
- DELSON
- SENNET
- OVERSEERS
- GILLONSVILLE
- FARMHOUSES
- STOREKEEPER
- PATCHING
- MATRONLY
- SWINBURNE
- FATHERHOOD
- BERKSHIRE
- WHIMPERING
- FORMLESS
- SNEEZING
- TWITTER
- NEGRO'S
- UNBOWED
- UNHOPEFUL
- UNVOICED
- FRILL
- PHANTASM
- ATLANTA
- PATTERED
- GLINTED
- WRITHED
- QUAIL
- COIGN
- UNCOLORED
- DARKLY
- DEFORMED
- CRINGE
- PRISONED
- FOREGONE
- WED
- UNMOTHERED
- PATTER
- TRANSFERRING
- VUM
- PALMIRY
- EXPIATION
- DELF
- BABYSHIP
- TASSELED
- SALINE
- BRANCHED
- SNIPPED
- CANDELABRA
- COUNTERPANE
- PLAINER
- WASHSTAND
- REARING
- UNREMITTING
- APPOINTMENTS
- AVERRING
- NELL
- SNELL
- CONDOLED
- INSPECTED
- ANNOYANCES
- RELAYS
- PANDEMONIUM
- DRESSMAKER
- VERONICA'S
- PALPITATED
- PALINGS
- LICHENED
- DOMINANTLY
- PITTING
- SACRIFICES
- DAWNS
- PURPOSEFUL
- EVENTIDE
- SAGA
- UNBLURRED
- BEGGARED
- BOVINELY
- RESPONDING
- WIFELESS
- CONFIDANTE
- EAVED
- OFFENSIVELY
- WINNOWED
- HOMELIGHT
- SANDSTONE
- UNFEIGNEDLY
- SCARFED
- STAUNCH
- UNBELIEVINGLY
- UNTARNISHED
- ASPIRATION
- UNHINDERED
- BLUNDERINGLY
- YEARNINGLY
- FOOTER
- MUSHY
- FOOZLE
- CAD
- TODDLED
- PURPLY
- PROM
- FIENDISHLY
- SNUBBING
- FLATTISH
- RUGGLES'S
- STUNNING
- NUDGED
- ASSERTS
- SILLIEST
- SPOONEY
- FLATFISH
- IDIOTICALLY
- SIMILE
- QUR'AN
- FECUNDATION
- PAIRING
- UNACHIEVABLE
- PHONOGRAPHY
- GILDING
- WAINSCOTING
- AZULEJOS
- LEBEL
- ABDUCTIONS
- DISAPPEARANCES
- CAVERNS
- CHAROLAIS
- COURCHAMP
- PENNYWELL
- VI
- CLAM
- PRECARIO
- OUBLIETTES
- SAVOURING
- STAIRCASES
- EXITS
- CONTRIVANCES
- NICHES
- ELTZ
- RIZZIO
- CROSSWAY
- QUAINTER
- FITTINGS
- ENAMELS
- MINIATURES
- HYPOCHONDRIASIS
- BOUDOIRS
- LACEWORK
- QUEENS
- TRITONS
- TERMINATIONS
- PRISMATIC
- SPARKLES
- PIGEON'S
- BERYLS
- MAB
- GEO
- RETARD
- ENCOMPASSED
- MINOTAUR
- CLEMENCY
- OMNISCIENT
- SURMOUNT
- BALLASTED
- PORTHOLES
- STOWING
- FLAPS
- SLOOPS
- TILLER
- POINTI'S
- CARNERO
- AXLETREES
- HOMO'S
- GWYNPLAINE'S
- HESITATES
- REOPEN
- SEMESTER
- UNPREMEDITATED
- ABSTRACTEDLY
- GRAMMATICAL
- REINSCRIBED
- REENROLLED
- OVERDUE
- HOSTESSES
- REENTERTAINING
- HALLUCINATION
- INCLOSE
- OUTGROWS
- PALTRINESS
- ADDLED
- SLUTTISH
- TINGLED
- ENGENDERS
- LINEAL
- DESCENDENT
- FRANCOIS
- RABELAIS
- EDITORIAL
- PARAGRAPHERS
- LASTLINE
- SPOOFING
- ISAIAH
- IMPRECATING
- CHRONICLING
- MUDDLED
- JESTER
- TICKS
- PESSIMIST
- BIDDLE
- CAPTION
- ODDITIES
- PLUMPLY
- ABAFT
- PENCILLED
- PINKISH
- RACKING
- HURLY
- INTELLIGIBLY
- FRONTAGE
- SCRIBBLERS
- TRAVAIL
- TETCHY
- SCHOOLED
- STEWING
- LAUREATE
- NICOTINE
- SAINTED
- BLASI'S
- REHBOCK
- EXPLAINS
- ARRANGES
- BOUNCED
- DEALER'S
- FATHOMED
- ONEROUS
- INITIATION
- LANGDON'S
- PRONGED
- PITCHFORK
- COMPUTE
- DIRGE
- ASPIRANT
- CENTIPEDAL
- TRICKED
- DIABOLUS
- BLAKE'S
- MYSTIFY
- RIVERMOUTHIANS
- TOWNSFOLK
- SIGNBOARDS
- TRUSTFULLY
- PEANUT
- CLAPBAM
- DISCLAIMED
- HAYMOW
- COOPS
- EXHUMED
- SELECTMAN
- MUDGE
- CRONIES
- STAGECOACH
- PETTINGIL'S
- ICECREAMS
- MARDEN'S
- ABLY
- SYRUPS
- WENDED
- PESTLE
- PLACARD
- LIVELIER
- ANDREWS'S
- LUSTILY
- WINDMILL
- TEMPLARS
- STAGGERER
- MULATTER
- GALLOWSES
- BRACES
- PHILISTINE
- UNJUSTIFIABLE
- CASTE
- TRADITIONARY
- POMMELLED
- DUMPLING
- COCOANUT
- NUTTER
- CONSISTENTLY
- DRO
- PUGILISM
- BLINDERS
- UNPREPARED
- VERSUS
- GRIMSHAW'S
- MALTREATS
- TAMEST
- MUMBLED
- MOLESTATION
- ABIGAIL'S
- SANITARY
- OPODELDOC
- REQUISITION
- FLIRTATIONS
- SWEENEYS
- CASSIDYS
- PRESBYTERY
- INERNEYS
- SCRATCHINGS
- BACKSTAIRS
- FRIEN'S
- INCRIMINATING
- SHAMED
- SECRET'S
- TURGID
- MISSHAPEN
- CLANE
- CHILDER
- STINKIN
- BUSTED
- PINSIONS
- HAN'S
- SWEEPERS
- WESKIT
- CONNIVANCE
- FOULLY
- SOMEONE'S
- MISLAID
- BRICKS
- SOOTY
- LACERATED
- WHILES
- SCREECHING
- EGGED
- DOMNED
- SCREECHIN
- DEASEY'S
- HANKERING
- MORBIDITY
- CAROLLINGS
- MERCIANS
- PLOD
- THAW
- SPREE
- BOLGIE
- LYCIDAS
- NATIVITY
- CAPO
- FAIRFIELD
- HALIBURTON'S
- CLEMENT'S
- PINCKNEY
- WALTER'S
- EPHREM
- STITCHES
- HANOVER
- GERRY'S
- UNCTION
- CONFECTIONERS
- MILLIONNAIRES
- FROTHINGHAM'S
- MANGER
- CAMP'S
- SISERA
- RETORTING
- APPROPRIATELY
- BLUFFED
- GRABBED
- BRIDLES
- LOPE
- AGENT'S
- BATCH
- REVOLVERS
- LIABILITY
- OUTWITTING
- STATE'S
- SIDING
- CULLEN
- RALLES
- KANDA
- ADZUMA
- OJI
- PROFLIGATE
- AMAZE
- GULLED
- SMARTENED
- CONSENTING
- RONIN
- CHOKICHI'S
- HATAMOTO
- ASAKUSA
- YEDO
- PEDIGREE
- MENDICANTS
- SORCERERS
- DIVINERS
- HERMITS
- DISOBEDIENT
- COBBLERS
- TORIOI
- SHAMISEN
- BURYING
- CONCUBINE
- MUNEMORI
- LEDA
- CENTAUR
- PELION
- POSEIDON
- WINGING
- AGAMEMNON
- PELEUS
- TELAMON
- OREITHYIA
- ERECHTHEUS
- BOREAS
- THESEUS'S
- ANAURUS
- METALWORKERS
- SMITHS
- ZEUS'S
- PELIAS
- ENCIRCLES
- PECCARY
- DUGONG
- NEB'S
- HAYMAKING
- HOUSED
- PULLEYS
- TRANSIT
- GREASING
- POLISHING
- JUP
- CYLINDROCONIC
- RICOCHETED
- MANDIBLE
- JAGUARS
- THOUGHTLESSLY
- MAYN'T
- HUMANITY'S
- HARDING'S
- PROPS
- AYRTON'S
- STANCHED
- HERBERTS
- INFLAMMATORY
- STYPTICS
- ANTIPHLOGISTICS
- TEPID
- COMPRESSING
- SUPPURATE
- LINT
- CICATRIZATION
- COAPTATION
- TOP'S
- SHAMASH
- SUBBILULIUMA
- CARCHEMISH
- TABAL
- CILICIA
- KHILAKKU
- TARSUS
- TIANA
- COMANA
- KAMMANU
- THRACO
- PHRYGIAN
- MOBILITY
- RAIDED
- TUKULTI
- NINIP
- REVOLTING
- BUBU
- NISHTUN
- AKHIABABA
- KHABAR
- BALIKH
- ADINI
- KIRKHI
- REPRISALS
- METED
- KINABU
- UNFAITHFUL
- DAMDAMUSA
- TELA
- TELLO
- DEVASTATED
- STRATEGICAL
- SUPPLANTED
- ZABDANU
- KASHSHI
- KALDU
- REDECORATED
- JAIF
- KESHAF
- INDISTINCTLY
- KURDISH
- SUMERIAN
- AKKADIAN
- ASSYRIA'S
- DELINEATING
- CANAANITES
- AHIJAH
- ABIJAH
- JESHANAH
- RAMAH
- MESOPOTAMIAN
- TABRIMON
- HEZION
- ARZA
- ZIMRI'S
- SHORTLIVED
- PHILISTINES
- MICAH
- BACKSLIDERS
- PUTTETH
- AKHUNI
- HITTITE
- ORONTES
- AKHABBU
- REPULSING
- OVERLORD
- BORSIPPA
- CUTHAH
- UNCONQUERED
- CONSPIRE
- DEPOSE
- JEZREEL
- NIMSHI
- DRIVETH
- HERMON
- TYRIANS
- SIDONIANS
- YAUA
- UNPRONOUNCED
- ALEPH
- JEHUA
- BAAL
- BACKSLIDER
- CULT
- ISRAELITISH
- MUSICK
- FOLLOWETH
- JEHOAHAZ
- DANIN
- APLI
- IMGURBEL
- BALAT
- DEPENDENCIES
- RECONQUEST
- BABYLONIAN
- DABAN
- BLASTING
- MORTGAGING
- PROPELLERS
- STRUCTURAL
- DIRIGIBLE
- DEFLATION
- GAMELY
- REMEDIED
- KAISER
- LIQUIDATE
- CRUCIAL
- COLLIDING
- CLIMATIC
- CONFLAGRATION
- UNPREJUDICED
- SYNONYM
- LEVELHEADED
- ZEPPELINS
- FRIEDRICHSHAFEN
- DOCKS
- AIRMEN
- CONVINCINGLY
- RATED
- TANTAMOUNT
- PARSEVAL
- AERONAUTICAL
- RUTHEMBERG
- SIEMENS
- SCHUKERT
- SERVICEABILITY
- CONCLUSIVELY
- COLONELS
- ROUGHEST
- RESPLENDENTLY
- SUMPTUOSITY
- ELIOT'S
- MATHER
- ATLASES
- PRINTER'S
- UNPRIZED
- MOULDERED
- SURPLUS
- UNWRITTEN
- COMPILER
- LIBRARIAN
- SPOFFORD
- SUPERINTENDING
- TOILETTES
- BARONET'S
- SYMMETRICAL
- PITT'S
- BUTTERED
- RAWDING
- CRAWLEYS
- DAMPED
- SMUGLY
- STARCHED
- RAGLAND
- FONDER
- VILLAIN'S
- SURMISED
- BAILIFFS
- ENSUE
- RAWDON'S
- AUGURING
- LIVERIES
- SILENUS
- GREENGROCER
- LOLLING
- JOKED
- KNIGHTSBRIDGE
- CONSORTED
- FANCIERS
- BRUISERS
- BRISTLY
- MARKER
- CAMPAIGNER
- QUOD
- INCOHERENTLY
- KEP
- MACMURDO'S
- LORDSHIP'S
- SHINTY
- LANDLORD'S
- DRINKIN
- DRAWINGROOM
- MUSTACHIOS
- INEXPRESSIBLY
- TAPEWORM
- GLUM
- GEORGY
- AMELIA'S
- CONCLAVES
- DISTRAITE
- WEBER'S
- RUMMAGING
- GEORGY'S
- MONOGRAM
- EDIBLES
- RUSSE
- JELLIES
- MOUSSES
- CAVIARS
- DAUBED
- SOAPS
- TOBACCOS
- PROPOSES
- CIRCUMVENT
- BETH'S
- ERASTUS
- CHALLENGES
- FAIRVIEW
- VOTER
- NOMINATIONS
- DISTRIBUTORS
- TIRADE
- FRIZZLE
- MISSES
- EDUCATING
- SOUTHERNER
- GALLING
- UNTENANTED
- VERGING
- CRISPLY
- COLLINGWOODS
- INTRUDING
- UNEXPLAINABLE
- CYNTHIA'S
- UNUTTERABLE
- CANOPIED
- PORTENT
- SYMBOLISES
- CYNOSURE
- CRIMINALITY
- CANTONMENT
- POINDEXTER'S
- PAYED
- SYMPATHISER
- BULLYISM
- UNBURDEN
- CICADAS
- FORMALISED
- INTERROGATORY
- CONJECTURING
- CULMINATES
- GLINTS
- RICOCHET
- CORPOREAL
- VULTURES
- PLANTER
- MUSTANGER
- CASA
- CORVO
- WOODLEY
- HACIENDA
- COVARUBIO
- LLANOS
- SADDLING
- JOYED
- LAZO
- REGULATORS
- SUMMARY
- SWARTH
- TEXANS
- ISIDORA
- PATHLESS
- WITHAL
- CORRALES
- PHELIM
- O'NEAL
- YCLEPT
- FLORINDA
- GOBBLER
- EXCITES
- BENIGHTED
- CRAM'S
- LINGERS
- CRUM'S
- MISHAWAKA
- GOSHENITES
- HOOSIER
- JUMBO
- DELIGHTFULLY
- JAUNDICE
- PHLEGMATIC
- PUMPKIN
- YOUNGSTER'S
- RELINQUISHING
- UNBIDDEN
- SAGEBRUSH
- REBEAUTIFIED
- OHIOANS
- RESOUNDS
- PERRYSBURG
- BELLEVUE
- GARFLELD'S
- MENTOR
- GARFIELD'S
- FRUCTIFEROUS
- GLISTEN
- INTERSPACES
- GIRARD
- TAHOE
- NEVADA
- WHEELMAN
- TORTUOUS
- ANGOLA
- SWASHING
- MISFIT
- AUDACIOUSLY
- SWIPES
- JOGS
- ISHAM'S
- TRAYNOR
- NELLY
- CONGESTION
- MEMBRANES
- ACCENTUATION
- PROVINCIALISM
- AFEAR'D
- COWLD
- SLUICE
- INTERCOSTIALS
- SANGUINEOUS
- WEATHERIN
- CHAPS
- HEXAMETERS
- GRIDDLE
- RAYSON
- MEDICATRIX
- CONTIGIT
- HYDRAULIC
- THEM'S
- DISAZE
- YE'RE
- TEMPORIAL
- SALTPETRE
- DIAPHORESIS
- PHENOMENONS
- INORGANIC
- SHINDY
- PUER
- INGENUUS
- FRONTAL
- RELISHED
- TANKARD
- BAYCON
- TROTH
- SORRA
- ILLIGANT
- PRETENTIOUSLY
- OBSTRUCKT
- IMPADE
- CORRESPONDIENCE
- SARVE
- SINTRY
- YAWL
- GRENADIER
- CARBINEER
- FRINCH
- BRIGADED
- ARENTSCHILD'S
- HANOVERIANS
- SCREWING
- SCRAPING
- SURROUNDERS
- CHANSONS
- RHINE
- RHONE
- SATIS
- HEARTIEST
- FLATTERIES
- IRRITABILE
- TUPPENCE
- DANCIN
- HANDIN
- EMPRESSES
- SACRET
- PENKNIVES
- BODKINS
- BEGINNIN
- FORNINT
- FASTIN
- THRIVES
- DROPPIN
- FILTERIN
- GLITTERIN
- REFULGENT
- DISTURBS
- O'ERCASTS
- GILD
- YELLOWER
- MOUNTAIN'S
- VALES
- SWAINS
- ACCUSES
- DOABLE
- THRAVELS
- HOLLANDIA
- UNDEFENDED
- MEGANTIC
- INEXPRESSIVENESS
- INEXPRESSIVE
- DELUSIONS
- LAKE'S
- SUCHLIKE
- CASTON
- SHELVED
- PROTECTIVE
- SWEETHEARTS
- FALMOUTH
- LINER
- SOMERSET
- STONEHENGE
- ROMANIZED
- BRITONS
- DYKES
- GLOUCESTERS
- TEWKESBURY
- NORTHMEN
- CORINTHIAN
- TRIENNIAL
- SUCCINCT
- AMELIORATING
- CANDIDACY
- PHILOLOGY
- LINGUISTICS
- ENUMERATION
- INSEPARABLY
- INCLUSIVE
- REDOUND
- PARDONS
- ANACHRONISM
- BEHOOVES
- CONFOUNDING
- INDISCRIMINATELY
- PROPAGATORS
- DISAVOW
- COMPATRIOTS
- FULMINATING
- BROCHURE
- PUBLICIST
- DISGRACING
- FORWARDING
- VIOLATING
- RECONSTRUCT
- EQUALIZE
- IMPROVISATIONS
- UTOPIST
- ROUSSEAU
- INCENDIARY
- GRAVEST
- PERORATION
- PRUNING
- CHARACTERIZE
- MANIFESTOES
- IMPARTIALITY
- BLANQUI
- TERMINATES
- WEARIES
- RECAPITULATING
- ADMINISTRATIVE
- PELLET
- GALAIL
- EDDY
- EVILDOER
- OGRESSES
- CLAMBER
- NIMBLY
- PARROT'S
- GEW
- GAWS
- MAIDEN'S
- CRIMEAN
- SWARDESTON
- CONFORMANCE
- HOXTON
- PANCRAS
- COMMENTING
- FOOTSORE
- OFFENSES
- FABRICATIONS
- EXPOSTULATE
- LEVAL
- HEROICALLY
- ENLISTMENT
- SILVERED
- PEARLED
- BUTTRESS
- TRANSGRESS
- REFECTORY
- MOULDING
- WRING
- DEVOTIONAL
- TEENS
- PROVISIONAL
- PUP
- WRONGER
- ILLUMINATIVE
- STETHOSCOPE
- PALPITATING
- CONVENTIONALLY
- UNDOING
- WILMINGTON'S
- FLAGMAN
- DEAFENINGLY
- JOUNCING
- GRASSLESS
- WINDOWY
- CINDERS
- ASPHALT
- WATTEAU
- RUSSETS
- VURRY
- CORNERWISE
- AUNTY
- MORRELL
- JULIET'S
- CARVEN
- NORAH
- AFFLUENT
- NORTHWICK'S
- HAUTEUR
- NORTHWICKS
- IMPROBABILITY
- OPPOSITES
- FILIAL
- IMPOSINGLY
- FEINTS
- SASHES
- SPLICED
- FROWSED
- LYRA'S
- SUPERFLUITY
- ROMEO
- REHEARSALS
- INCARNATIONS
- MEDIOCRE
- DOMINATES
- IRONIC
- CUSSEDNESS
- ROMANTICISTS
- SALEM
- LONGFELLOW
- AMOS
- BRONSON
- ALCOTT
- EUCALYPTUS
- POMEGRANATE
- SORDIDNESS
- CATHAY
- BROBDINGNAGIAN
- DAMPENS
- ALKALI
- PHOTOPLAYS
- THRILLINGLY
- THINNESS
- ENUMERATE
- ROCKIES
- EXISTENT
- SHOUTER
- HURRAHING
- CONFETTI
- RELEGATES
- EPIC
- OVERWHELMS
- PHOTO
- PLAYWRIGHT
- MOBS
- ROMANESQUE
- REDWOODS
- REPROVED
- MELODRAMATICS
- ASHTON
- TREADER
- SCENARIOS
- INCONSIDERATE
- ULTIMATUM
- UNCONGENIAL
- COUPLING
- FLIRTATIOUS
- KENELM
- BOURNEMOUTH
- HEARTHRUG
- ADORES
- BUTCHERED
- WOMENKIND
- HARMFUL
- CONDESCENSIONS
- UNEXACTING
- FIXITY
- HA'PORTH
- INTERPENETRATING
- UPBRINGING
- PLEASINGLY
- BEHINDS
- RESHAPE
- PIGGY
- WIGGYS
- SEIZES
- REALIZATIONS
- MEDITATIVELY
- LASSO
- REVIEWED
- WHO'D
- SPENDERS
- WASTERS
- SKYLINE
- JUDGMENTS
- GRAMMONT'S
- REGRETTABLE
- COMPLETEST
- PRACTICABILITY
- REFT
- HARVESTER
- MENSERVANTS
- PIECED
- ASCRIBING
- COMPLETER
- SCIENTIFICALLY
- COMMONSENSE
- REPLACEABLE
- UNWONTED
- OUTRUNS
- STIFLES
- DAIS
- PERSEIS
- STRAYS
- CUPIDON
- CRATE
- APROPOS
- ILLUSTRATING
- DRIVELLED
- GRANDILOQUENT
- INGENUE
- BOHEMIANS
- DEBUT
- ARSENIC
- FENDER
- PALETTE
- BOHEMIENNE
- REVILING
- POUTING
- CHIMED
- UNLOCKING
- SEALSKIN
- SOURED
- TYPHOID
- FATALLY
- CRAYON
- INFATUATED
- SOPRANO
- TROTERE'S
- GARLANDS
- WISHERS
- REVIEWERS
- MINGLES
- ENDORSED
- GUSHING
- INANE
- JEUNESSE
- DOREE
- RECOGNISING
- ZELIE
- SPRAWLY
- DISFIGURING
- VALUELESS
- OBLITERATION
- APERTURES
- DIABLERIE
- ENHANCE
- LAUDATORY
- VOSGES
- METHODICAL
- ENMESHED
- CHANTREY
- ADIEUX
- RESUSCITATE
- GLEAN
- OVERDOSE
- MORPHIA
- HOUSEHOLDER
- EVILDOERS
- NOTS
- RINGED
- SPRITE
- NIGHTINGALES
- DISPEL
- PLAYFELLOW
- BRUNHILDA
- CRONES
- TRELLIS
- CARROT
- RADISH
- SPROUTING
- FORSOOK
- GNOME'S
- BRAIDED
- BRIDLED
- SUBMISSIVE
- MOSSY
- ENCUMBERED
- THUNDERCLOUDS
- CORNISHMAN
- ARNOLD'S
- CELTIC
- FINSBURY
- CENSORIOUSNESS
- GUY'S
- COWDEN
- CLARKE
- INTENSIFIER
- QUALIFIES
- VULGARLY
- YARMOUTH
- MERCI
- INDICATOR
- UNWARRANTABLE
- DEPRECIATING
- COLVIN'S
- MORBIDNESS
- MAWKISHNESS
- VIRILITY
- RECASTING
- PAGANS
- REFORMER
- FELICITATE
- UNSUSCEPTIBLE
- PLEASURABLE
- WOFUL
- COLERIDGE'S
- IRREMEDIABLE
- INEXPUGNABLE
- UNREMITTINGLY
- AVERSIONS
- UNDIMINISHED
- ANALYSING
- COMPLEMENTS
- CORRECTIVES
- SEQUENCES
- COHERE
- HAL
- BOWNTANCE
- ARRAGON
- BLUFFLY
- ADDLE
- DOFF
- BESHREW
- MINION
- MARK'S
- GRAPPLED
- HERNE
- FORTIFICATION
- EIGHTH'S
- OCTANGULAR
- LOOPHOLES
- ARQUEBUSIER
- CONVEYS
- NINETIETH
- ANN
- BRADSHAW
- TOLERATIONS
- BUCCA
- FISSA
- COMPRACHICOS
- CHEYLAS
- HARDQUANONNE'S
- INDICTMENTS
- FULMINATIONS
- HANDWRITINGS
- HEBRIDES
- QUARTOURZE
- NARBONNAIS
- LUC
- GALDEAZUN
- INTERSPERSING
- MATUTINA
- BISCAY
- TILE
- GERNARDUS
- OAKUM
- MALEFACTOR
- PLAGIARY
- WRETCH'S
- CRUCIFIED
- DENZILL
- VERIFICATION
- ATTESTATIONS
- PURSY
- ERMINE
- WAIFS
- UNCORKED
- ANATOMIST
- BIDLOO
- YELVERTON
- LONGUEVILLE
- ROTS
- LINNAEUS'S
- CHANCELLORSHIP
- PATHOLOGICALLY
- LACENAIRE
- COMMITS
- DISSIMULATE
- SIMANCAS
- PAYMASTER
- ENDORSEMENT
- JUSSU
- UNFEMININE
- MONARCHIES
- AULIC
- CARLOVINGIAN
- AURICULARIUS
- PALATINE
- LAPWING
- HUDBUD
- SENIORATU
- ERIPIMUS
- ROTURAGIO
- CADAT
- READJUSTING
- TARNISH
- BLAZON
- ABDOLMUMEN
- REINSTATEMENT
- MISCALLED
- ZENA'S
- UNRAVELED
- ACUMEN
- THREADBARE
- DODDERING
- KNOLL
- TRESPASSERS
- HILLY
- CLIMBABLE
- BRACKEN
- NOTCHED
- UNERRING
- SCRUB
- TAINT
- MANIA
- BORNEO
- DETECTION
- LUNATIC
- ELUCIDATED
- BURGLARIES
- AMATEURISHNESS
- BURGLE
- BURGLED
- FACEACHE
- REPETITIONS
- FLOATERS
- MUDBANKS
- BLACKBIRDS
- HERONS
- PLOVERS
- FORAGING
- HAWK'S
- PROPHET'S
- CLOUD'S
- SNAGS
- SPRING'S
- TRAUT
- BOGGY
- EDDYING
- BREAKNECK
- AWNING
- MUDBANK
- HALLOO
- ODOROUS
- FERRYING
- AVOWEDLY
- FERRYMEN
- GUARANTIED
- FERRYMAN
- ERIE'S
- MILE'S
- PARLEY
- DEVIOUS
- HINGELESS
- SHIFTLESSNESS
- UNTASTEFUL
- PRETENTIOUSNESS
- WHEEZY
- DIAGONALLY
- SIDELONG
- JOCKEY
- OSTRICH
- PINEAPPLES
- SANKEY
- WHEEZES
- MORNIN'S
- MILKIN
- HUL
- BLOWIN
- FASHI'N
- BUSTLES
- HOOPSKIRTS
- UNDERWEAR
- CREAKY
- CLEAT
- HI
- DUMPY
- SHOVELLED
- COB
- CUSPADORE
- VINEGARY
- SLANGY
- GLIB
- COTERIE
- USIN
- SOME'N
- JINED
- KNOWIN
- KIDS
- NOUGH
- KEM
- LOWED
- AGINT
- WORKIN
- DUCK'S
- HAMPTIN
- KEER
- DRUV
- GOV'MENT
- AMMERNITION
- WAGIN
- VETERAN'S
- QUONDAM
- CANOEISTS
- TENS
- ASTONISHINGLY
- INEDIBLE
- DOUGHY
- PASTEY
- GRAVY
- LEATHERY
- ADULTERATED
- SALERATUS
- ATKINSON
- HYGIENISTS
- WIDEN
- MORASSES
- DIREFUL
- CAPSIZED
- INTERVENE
- WORT
- LOBELIA
- SUNBAKED
- ASHY
- BURLINGTON
- QUINCY
- SPURT
- VOYAGED
- PORTAGE
- CHURLISH
- CANOEIST
- CHAINING
- PAYMASTERS
- SWINDLING
- JEOPARDIZING
- EXCHANGERS
- DYNAMICS
- ADVERSELY
- SHAREHOLDER
- PILLS
- GAMBLED
- INTERMIXED
- ECONOMICALLY
- SUBSIDY
- NOMINATING
- USURPING
- DEPOSED
- CONTROLLERS
- HARMSWORTH
- ADJECTIVE
- MISDEMEANOUR
- CLAMOURS
- ELIMINATION
- AUDITING
- GOVERNS
- CRITICIZE
- DUPED
- WARPS
- DEPLETES
- SNIPES
- PARVENU
- CONNOTE
- VAGUEST
- PLATITUDES
- INVERTEBRATE
- FORMATIVE
- AGNOSTIC
- NATIONALIZING
- OPPRESSOR
- COGNATE
- BREEDERS
- DEBATS
- ECCENTRICITY
- CONNOTES
- PENALTIES
- CENTRICS
- PLAINEST
- SUPPRESSIONS
- SPECIALITY
- CENTRIC
- TEETOTAL
- DIABOLISTS
- RATIONALIST
- ATHEIST
- PARTICULARISM
- CANCEL
- PARTICULARIST
- REVOCABLE
- DUAL
- EDITOR'S
- VULNERABILITY
- VULGARIAN
- DISTORT
- CONTRASTS
- NOBODIES
- LIMELIGHT
- NOMINATE
- NUMEROUSLY
- MULTIPLYING
- HUMANITARIAN
- OVERAWE
- DISPIRIT
- VINDICTIVENESS
- STRATUM
- SYMPATHIZING
- MISSILES
- GLASCOCK
- JARVIS
- BANTERED
- INNOVATORS
- IRREDEEMABLE
- HIRELING
- EXTREMISTS
- FEASIBILITY
- DISQUALIFY
- ASSORTING
- FILING
- ABOLITIONIST
- INDEPENDENCE'
- INDWELLING
- BLESSEDNESS
- OLASTON
- WITHSTANDING
- STILLING
- GLASTON
- HEAVINGS
- REPASSED
- APOSTOLIC
- MISREPRESENTATIONS
- TASTEFUL
- UNHOMELIKE
- ALMSHOUSE
- PAUPER'S
- CLIME
- CARVING
- WARWICKSHIRE
- RITTENHOUSE
- PLANETARIUM
- POTTS
- IRONSIDES
- ALLIBONE
- ALMANAC
- SUBSCRIBER
- BOOKSHELVES
- CHAILLU
- LOWELL'S
- IDYL
- FENIMORE
- EDGAR
- ALLAN
- POE'S
- POE
- ERASURES
- DANTE'S
- DIVINA
- COMMEDIA
- WORDSWORTH
- PRESIDENTS
- PEABODY
- WOOTTON
- FLINTSTONE
- MILKROOM
- DUFFERIN
- NORTHCOTE
- WALLER
- HUGHES
- EVARTS
- CENTENNIAL
- GRANT'S
- NEWSBOYS
- INGOLSTADT
- SLAKED
- ALLURED
- INCLEMENCY
- PURLOINED
- THRUSH
- COTTAGER
- INTERCHANGING
- POIGNANTLY
- ENDEARED
- SADDEST
- PERIODICALLY
- RECOMMENCING
- DISPELLING
- ENTRANCINGLY
- ENRAPTURED
- PEACEABLY
- VOLNEY'S
- DECLAMATORY
- DEGENERATING
- NARRATIONS
- DOTED
- DEIGNS
- SHARPENING
- PLUMING
- NIGGER'S
- QUELLING
- CADEROUSSE
- HEELED
- ALLIANCES
- PINCETTE
- BAILED
- RESIDES
- MENACES
- COACHMEN
- DISSIPATING
- TARQUIN
- LINDEN
- HELIOTROPES
- FLICKERINGS
- NOIRTIER'S
- DENTED
- IMPASSIBILITY
- POISONERS
- BEVERAGES
- UNBLEMISHED
- WHIRLS
- SUCCINCTLY
- RALLYING
- FELONS
- PERVADED
- PIAZZI
- BARBERINI
- MAZZINI
- BELGIOJOSO
- QUIRINAL
- ENDEARMENT
- PINCIAN
- PORTER'S
- RIETI
- CORSO
- DORIA
- SEQUINS
- GONDOLAS
- STILTS
- PULCHINELLOS
- AFFETTATORE
- CONFESSES
- CONVEYANCE
- LAUDATION
- ANTONINUS
- FAUSTINA
- PIQUED
- BLOCKHEADS
- HELDER
- GAND
- COLOSSEO
- GASPARONES
- FOREWARN
- CREDENCE
- PORTA
- POPOLO
- ALBERT'S
- PRESERVERS
- PASTRINI'S
- TERRACINA
- CORNEILLE
- LACRYMA
- CHRISTI
- BUGABOO
- LARA
- FERENTINO
- ALATRI
- PESTE
- PRECOCITY
- STYLUS
- FELICE
- IMITATIVE
- GIOTTO
- PINELLI
- VALMONTONE
- FELICE'S
- LIVERIED
- BRESCHIA
- OLIVETREE
- SABINE
- CONTADINO
- SABINES
- EXTIRPATED
- GARIGLIANO
- AMASINE
- SONNINO
- JUPERNO
- GASPERONE
- DISQUIETUDE
- SURVEYOR
- BANDIT'S
- LASCIVIOUSNESS
- ENTREATIES'
- CLINCHED
- DIOVOLACCIO
- DIOVALACCIO
- PICKAXES
- PICKPOCKET
- UNFEELINGLY
- UNPOPULARITY
- VACUOUS
- BILLINGSGATE
- EXASPERATION
- POSTER
- PEARS'S
- ARGUABLE
- TRAMPS
- IRISHMEN
- BROADBENT
- REFRACTING
- DOCTRINAIRE
- JUSTIFIES
- FIZZING
- ANGELO
- VELASQUEZ
- ADMIXTURE
- FALSIFICATION
- DUBEDAT'S
- FARCICAL
- COMEDIES
- FALSIFICATIONS
- DOCTRINAL
- COMICALLY
- PARAMORE
- PHILANDERER
- MISANTHROPE
- EPIGRAMS
- OUTRAGING
- BELLIED
- CONTROVERSIALISTS
- DISILLUSIONIST
- SCEPTIC
- CHALLENGING
- SCOLDINGS
- BEATINGS
- CRAW'S
- BLUEBELLS
- OVERSLEPT
- MELON
- ZIZZ
- WARDER
- SMOOTHER
- PEEK
- SIMPLETON'S
- ZENZA'S
- CHARGER
- TILDA'S
- HARNESSES
- CARTWHEEL
- WHIRRED
- SHABBILY
- TANGENTS
- OCTAVES
- VOLATILE
- WORKBOX
- BURNEY
- EXECUTANT
- CLAVICEMBALO
- CLAVECIN
- SPINET
- PROGENITOR
- TRANSCRIBED
- BAPTISTE
- LULLY
- EXTENSIVELY
- ORCHESTRATION
- ADVISES
- IMPLICIT
- BEGINNERS
- PRACTICING
- RAMEAU
- COUPERIN'S
- VIRTUOSITY
- DOMENICO
- JOHANN
- MIRRORING
- HARMONIES
- EXPRESSIVENESS
- SPONTANEITY
- DEFACE
- PIANIST'S
- EXEMPLIFY
- TUNING
- TUNER
- FORKEL
- SUITES
- FANTASIA
- RHAPSODIST
- RUBINSTEIN
- SOULFUL
- POLAND'S
- COMPLEMENTING
- LANGUOROUS
- SCINTILLATING
- HOMESICKNESS
- DIVINEST
- UNFETTERED
- MELODIC
- SPIRITUALIZED
- TONAL
- UNBARED
- JARRING
- POLAND
- TONALITY
- UHLAND
- PREDOMINATES
- SOFTENS
- UPLIFTS
- STRENGTHENS
- TRAVAILING
- ORGANIST
- THEORIST
- DUNCE
- REVERING
- LISZT
- RAPHAEL
- INDIVIDUALIZED
- PIANISSIMO
- GRADED
- FLUCTUATIONS
- BALZAC
- CHOPIN'S
- KLECZYNSKI
- REPRODUCTION
- ANDANTE
- PRESTISSIMO
- ARPEGGIO
- INDIVIDUALIZATION
- HARMONICS
- PEDAL
- ADVOCATED
- MISAPPREHENSIONS
- MISINTERPRETED
- ASPIRANTS
- SENTIMENTALISM
- DISFIGURES
- RUBARE
- PLIABLE
- INTONING
- GREGORIAN
- BEETHOVEN
- UNSYMPATHETIC
- SPIRITUALIZING
- TIMBRE
- LIBERATING
- CHORAL
- EXTENSIONS
- ARABESQUES
- CANTILENA
- NOCTURNES
- FAERY
- POLONAISES
- VALSES
- MAZURKAS
- IMPROMPTUS
- RECAPTURING
- SCHUMANN
- PROUDEST
- BIE
- VESTURE
- ALGEBRAIC
- UTILITIES
- DEDUCTING
- EXPENDITURES
- STORING
- INDIRECT
- TRANSPORTING
- CONTRIBUTING
- UNFUNDED
- REDUCIBLE
- PARADOXES
- INCONSISTENTLY
- CAMPUS
- MULTIPLIES
- PRODUCER
- DREDGING
- COLLECTIVELY
- DITCHED
- DIKED
- HILLSIDES
- IMPROVES
- GROUPINGS
- UTILIZATION
- FELDSPAR
- HECKLING
- MINERAL
- COLLAPSES
- LATHE
- DYNAMOS
- CONVERTING
- GUSHED
- IOSKEHA
- SARAMA
- VEDA
- SANSCRIT
- RESCUES
- PACHACAMA
- MERITING
- PACHAYACHACHIC
- VIRACOCHA
- REBUS
- YOLCUAT
- RATTLESNAKE
- TOHIL
- RUMBLER
- HUEMAC
- DUALISM
- ANALOGUES
- TULA
- TLAPALLAN
- FIGURATIVELY
- PRECURSORS
- INDELIBLE
- NATTY
- HOLT
- FORESTRY
- DARLINT
- INSPECT
- WOODSMEN
- ILK
- TABLEAU
- GAWKING
- CHUB
- ADMONISHING
- GIRUL
- YOWLING
- SCURRY
- STOVES
- PINON
- STRAWBERRIES
- GROUSE
- GLENHOLDT
- BRONTOSAURUS
- TESCHALL
- HARKRUDDER
- BEL
- TINWARE
- GUY
- LULLS
- CUPLIKE
- GRUMBLINGLY
- HUNTER'S
- HAYNES'S
- WEAKLY
- CHIRK
- WINDFALL
- BREWSTER
- SECRETED
- BRISTLE
- GOLIAR
- BIFF
- BLACKTHORN
- SHILALEY
- CLYDE
- FRAID
- CAYUSE
- TEARIN
- RIBALD
- ZOO
- DESIGNATION
- UNPRETENTIOUS
- PEDANTIC
- LAMELY
- ALDENHAM
- CASTERBRIDGE
- HABAKKUK
- UNNATURALLY
- LYRICAL
- QUOTES
- BRAWN
- APRONED
- SHORTBREADS
- PILCHARDS
- GETHER
- GOLFING
- ATHENAEUM
- HOMER'S
- OOZE
- SIXPENNY
- ROBINSON'S
- WHIPS
- RACONTEUR
- INDIARUBBER
- HASTENS
- BUMPS
- ICH
- DIEN
- ARMAND'S
- SULLENLY
- SCHOONER'S
- CREEKS
- DIPLOMATIST'S
- APERTURE
- STOOLS
- FISHERMAN'S
- CITOYEN'S
- ENDANGERING
- PROBLEMATICAL
- PIMPERNEL
- PARALYZES
- VENTING
- MALICIOUSLY
- BELABOUR
- TRUMPS
- ENROLLED
- ANATHEMA
- OVERBURDENED
- STOLID
- ROSENBAUM
- FINANCES
- PERVERT
- INCULCATED
- DISORDERLY
- CONVENTICLE
- OBLATIONS
- LIBERALITIES
- INDULGENCES
- JUSTIFICATION
- UNEXHAUSTED
- RETAIL
- SCHISMATICS
- FALSITY
- IMPOSTURES
- LUTHERAN
- SCRUPLED
- VALID
- CHARTER
- DAMNABLE
- ANTICHRIST
- WHORE
- PROPAGATION
- SOVEREIGNS
- INVEIGHED
- ENCROACHING
- MONASTIC
- CONVENTS
- LIBERTINISM
- INVADER
- CONCUR
- WOLSEY
- BRUGES
- SEMBLED
- FLOW'RS
- LUCE
- SPARKLE
- EV'NING
- WORDLY
- FORESHADOWING
- TURGOT
- PRIESTLEY
- CONDORCET
- EXEMPLIFYING
- PERFECTIONISTS
- UNWROUGHT
- FORTUITOUS
- PRECONCERT
- VORTEX
- PREEMINENT
- ATTAINABLE
- TILLERS
- PROVERBIALLY
- INSTANCED
- INTUITION
- RADICALLY
- ELLISON'S
- AMASSED
- FACTO
- ABORTIVE
- SEABRIGHT
- EXTRAVAGANCES
- BUSYING
- MINISTERIAL
- VIRTU
- ENDOWING
- SUPERABUNDANCE
- ENAMORED
- EXPATIATING
- MULTIFORM
- MULTICOLOR
- SOLVING
- ARTISTICAL
- SCULPTURAL
- GLADDENS
- GENERALIZATION
- COMPEERS
- COLLOCATIONS
- ADAPTING
- RETIREMENTS
- CAPABILITIES
- INCONGRUITIES
- APPERTAINS
- ODYSSEY
- INFERNO
- PROMETHEUS
- SOPHISTS
- INCONTROVERTIBLE
- TECHNICALITY
- HARMONIZED
- DEFINITIVENESS
- INTELLIGENCES
- EMANATION
- EXEMPTION
- SCRUFF
- SAGES
- SHINTO
- YOSHIDA
- FUSHIMI
- KANJUJI
- PERQUISITE
- DAIMIOS
- PHARMACOPOEIA
- PEKING
- DECOCTION
- DYSENTERY
- ACUPUNCTURE
- VESICAL
- CALCULI
- CORROSIVE
- DECOCTIONS
- MUMMIES
- COMMENDED
- PULVERIZED
- CALCULUS
- PETRI
- ANDREAE
- MATTHIOLI
- PROFUSE
- BADGER'S
- POSTHUMOUS
- REPENTING
- FUSED
- THROTTLES
- PEST
- ERELONG
- FIEND'S
- JUSTINE
- MADDENING
- WREAK
- DEVIATING
- INVECTIVE
- HOVERS
- CHAMOIS
- INTIMIDATED
- PROPORTIONATE
- FLIT
- PRESIDE
- IMPASSIVE
- HOVELS
- WILDNESS
- RUGGEDNESS
- ADVERSARY'S
- INSTIGATED
- PROTRACTION
- UNFULFILLED
- SASSY
- EXTRICATING
- MISHAPS
- SCAPEGOAT
- DECLINES
- MEASLY
- IMPETUOUSLY
- OBJECTING
- STUBBORNLY
- ROWDY
- REENTER
- SCAPEGOATS
- DODGED
- TRAPPING
- MILTON'S
- AFTERTHOUGHT
- BULL'S
- WILL'S
- LUMBERMAN
- LUMBERJACKS
- PORTENDED
- HOOT
- LIMPED
- PALS
- DRUDGERY
- DASTARDLY
- TOTE
- TRE
- MENDOUS
- ANTLERS
- SHRILLED
- CAMERA
- WHIFFING
- FRISK
- FORERUNNER
- BUCKSHOT
- WOMANISH
- REDEEMED
- SWEATERS
- OUTDOORS
- EYETEETH
- MARCHLAND
- HARRIED
- AVARS
- THRACE
- PORE
- SHEAR
- DISUNITED
- AUSTERITY
- POLYTHEISTS
- SCOFF
- GIBBON
- CHEAPEST
- PROVINCIALS
- MONOPHYSITES
- JACOBITES
- GOVERNMENTAL
- RECONQUERED
- RECORDING
- GABATHA
- ITURAEA
- LEGIONS
- BOSTRA
- HIEROMAX
- KHALED
- EMESA
- HELIOPOLIS
- SOPHRONIUS
- SEBASTOPOLIS
- ACCOUNTANT
- EUNUCH
- FLAMBARD
- RUFUS
- FLAGRANT
- PAYERS
- UNPOPULAR
- IMPRISON
- DECIMATE
- DICTATORSHIP
- HELLAS
- SOPHIA
- ABDALMALIK
- BEFEL
- CRIMEA
- SEBASTOPOL
- SUZERAINTY
- AZOF
- INGRATIATED
- KHAN'S
- EUXINE
- WEATHERED
- TERBEL
- BULGARIAN
- UNREADY
- RELENTLESS
- CATHISMA
- HIPPODROME
- TRAMPLE
- APSIMARUS
- WREAKING
- MEANER
- ANARCHICAL
- DEMORALIZATION
- ADRAMMYTIUM
- PHRYGIA
- ANATOLIC
- CAPPADOCIA
- LYCAONIA
- AMORIUM
- BUREAUCRACY
- MONOTHELITE
- MONOPHYSITE
- SLAVS
- PERSECUTING
- PARDONED
- ACQUIESCING
- IMMURED
- ONUS
- KARL
- HONORIUS
- ROMULUS
- AUGUSTULUS
- EQUIPOISE
- ANTIQUARIES
- LONGMANS
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- UNOPPOSED
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- UNMITIGATED
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- COWERED
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- PRESUMING
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- UNEMOTIONAL
- ABRASION
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- REDNESS
- STIPPLED
- SHOPMAN
- CONTUSIONS
- IMPACT
- BEGINNER
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- TREADLE
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- APPRENTICED
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- WASHABILITY
- UNFADING
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- PRITCHARD
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- CLAMOURED
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- VOCIFERATING
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- BECHAMEL'S
- MONOMANIAC
- KNUCKLE
- FAIRISH
- EIGH
- MARVELLING
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- BLASPHEMIES
- SULLIED
- MIDHURST
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- INCONSOLABLE
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- GAOLER
- ADORNING
- BEMOANING
- AMETHYSTS
- LYNX
- WARBLED
- MINETTA
- GLOWERED
- LINNET
- POSTILION
- VALETS
- SOUSSIO'S
- JIGGLING
- SLEEKER
- TWINE
- RUMPLING
- HINDFOOT
- BUSHIEST
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- UNDERSIDES
- ESTUARY
- OBITUARY
- INMAN
- ISIS
- PARAMATTA
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- SEDIMENTATION
- STREAM'S
- COUNTERCURRENT
- BREAKUP
- BONEYARD
- BILLIONS
- LUMPFISH
- MONOGAMY
- EELPOUT
- MORAY
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- VIVIPAROUS
- GOBIO
- PRICKLES
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- BULLHEAD
- NODULES
- BANDED
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- MULLET
- NORWEGIANS
- DEPOPULATED
- CODFISH
- GRAPPLING
- SPIDERWEB
- ALERTED
- GEARING
- TRANSMITTING
- METRIC
- RETRIEVED
- RESUBMERGED
- PADDING
- TEXTILE
- SHEATH
- SADOVA
- SEASHELL
- SEA'S
- BLANC
- FASTNET
- GRAVITATING
- LOWERMOST
- SCILLY
- GLOOMIER
- RELIVING
- HUNCH
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- FACILITATED
- VERTICAL
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- ENSHROUDED
- SEASHELLS
- POYPE
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- PRESTON
- D'ESTAING
- GRENADA
- GRASSE
- BREST
- STABEL
- PREENED
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- BELOSTOMA'S
- FORELEG
- DUCKLING
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- WIGGLY
- LITTLER
- KATYDID
- UNHOOKED
- FROLICKED
- TEDDER
- CHANGEFUL
- INDISCRIBABLE
- FITTEST
- UMBRAGE
- HUSK
- AVAILETH
- UNRECEPTIVE
- INHARMONIOUS
- GLOOMED
- CURATE'S
- UNLOVED
- THEREFROM
- DUCO
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- RUMMAGE
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- SATURN
- FLUTED
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- TOGA'D
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- UNWITTING
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- BAEDEKERED
- BUSTS
- TORSOS
- FRAILNESS
- ACTUALITY
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- LIRE
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- CORNELIE
- RETZ
- SAYINGS
- PIECEMEAL
- WOUTER
- TWILLER
- ACKNOWLEDGMENTS
- SALLUST
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- WAYFARING
- COMPILED
- WINNOWING
- DISCARDING
- PITHY
- ENTHRALLED
- PIOUSLY
- REUNITE
- SHRILLING
- FANFARE
- LILTING
- DINNING
- FLARE'S
- RAUCOUS
- LIGHT'S
- BAND'S
- DIMMING
- GARDEN'S
- OVERLAID
- DEARTH
- BUCKWHEAT
- SHEEN
- MARGINAL
- JANGLED
- STARRING
- FIREFLIES
- ASPEN
- RAM'S
- STEAMS
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- NUMBS
- ETCHES
- PATTERNED
- WHAN
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- DOORYARDS
- RHYTHMED
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- SUNRISES
- LAPPING
- INQUISITORIAL
- GENERALITIES
- POLWARTH
- REFRACTION
- EXECRATION
- ACE
- DIVISIONS
- SCATTERINGS
- COUNSELLING
- SEVEIRITY
- HEARTLESSNESS
- BASCOMBE
- NOWISE
- TRUISMS
- RESTORATIVES
- DISMAYFULLY
- JILT
- EXECUTIONERS
- LINGARD
- SPECTRES
- LOATHES
- WINGFOLD'S
- GAWKY
- UNPOLISHED
- UNORNAMENTAL
- EMMELINE
- RAINDROPS
- SUNNING
- MEA
- LAGS
- WADING
- CUDDLED
- FIREBRANDS
- DIFFUSING
- BRIMFUL
- EMANCIPATOR
- INDOCTRINATE
- AMISTAD
- FREEDMEN
- BEFRIENDING
- OSTRACISED
- TAPPANS
- INVOICE
- FREEDMAN
- ODIUM
- OBLOQUY
- APPRIZED
- DICTATING
- AMANUENSIS
- NARRATE
- GA
- DOUBTFULNESS
- BIDDER
- JOURNEYMEN
- PURSUER
- EVERYONE'S
- GRATIS
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- KITE
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- CHECKERBERRY
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- VARLETS
- JEERING
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- OPERATIC
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- APING
- ADMONISH
- UNBEFITTING
- INTERLOCKING
- GIZZARD
- VOL
- COMPARTMENTS
- CAPERING
- FEEDERS
- SPEEDED
- RUDDERS
- STATEROOMS
- WHIZZER
- TRANSMITTERS
- WORRIMENT
- SWIFT'S
- BOWLED
- HATBAND
- YORKE'S
- FROCK'S
- FANNY'S
- CHAPERON
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- REV
- AFFLICTS
- CHANNINGS
- CONTRIBUTOR
- PRESCRIPTIVE
- UNCHECKED
- LEGALITIES
- CHURCHWARDEN'S
- ERUDITION
- EPISCOPACY
- HEARSAY
- ARROGATE
- SHELLINESS
- BROWNRIGG'S
- ADDICEHEAD
- DISTRAINT
- TEMPLETON'S
- RUBICUND
- QUENCH
- UNMINGLED
- PERPLEXES
- AUTHORIZES
- ORDINANCES
- DISSENTERS
- REPOSITORY
- TABERNACLES
- COMPULSORY
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- COMMEMORATE
- CHURCHMAN
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- REMOTEST
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- RIGHTFULLY
- UNCALLED
- IMPROPRIETY
- PROVISO
- ZABEL
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- COMMUNICABLE
- INGRATITUDES
- EMPHASISED
- INFERENCES
- MISDOINGS
- LOATHED
- TRICKING
- ESCAPADES
- CATECHUMEN
- RIVALLED
- GLADIATORIAL
- SMASHERS
- UPSETTERS
- OUTSTRIPPED
- GERMS
- TRUSTFULNESS
- STAINS
- SOLACING
- FRIENDLESS
- MOTHERLESS
- TOMBS
- GEOMETRY
- STUDENT'S
- EMPTINESS
- DISDAINED
- VAINGLORY
- PASSWORDS
- HONORATUS
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- THIRSTING
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- GYRE
- TRANSCEND
- CLARIONS
- HANDMAID
- VAGRANT
- THITHERWARD
- CALAHORRA
- PARAMOUR
- ESPOUSALS
- DOWERED
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- DOMINIC
- JOANNA
- OSTIENSE
- TADDEO
- MANNA
- FADETH
- DRESSER
- DECIMAS
- QUAE
- SUNT
- PAUPERUM
- APOSTOLICAL
- RUNNELS
- ORBIT
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- DISCOURSES
- BEGINNETH
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- MOVETH
- OUTSPEEDS
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- OPE
- FOUNT
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- INTRINED
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- SUPREMEST
- NOTEST
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- AFFIRMS
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- LAMENTETH
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- UPGATHERED
- RAPT
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- TIGHTEN
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- ELYSIUM
- BENEDIGHT
- TRINE
- THINKEST
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- DIVERSELY
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- E'EN
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- BARES
- UGHI
- SANNELLA
- ARCA
- SOLDANIER
- ARDINGHI
- BOSTICHI
- RAVIGNANI
- GUIDO
- WHOE'ER
- BELLINCIONE
- PRESSA
- GALIGAJO
- POMMEL
- VAIR
- SACCHETTI
- GIUOCHI
- FIFANT
- BARUCCI
- GALLI
- CALFUCCI
- CURULE
- SIZII
- ARRIGUCCI
- ENFLOWERED
- CONSISTORY
- UBERTIN
- DONATO
- CAPONSACCO
- GIUDA
- INFANGATO
- DELLA
- PERA
- KEEPETH
- GUALTEROTTI
- IMPORTUNI
- UNFED
- EMA
- CAMEST
- BEHOVED
- MASQUERADED
- MEEKER
- HURSETON
- PLANTAGENET'S
- MALLESON'S
- PUPPET
- TABERLEY
- SNEERINGLY
- PITTANCE
- BLACKMAILING
- SPITFIRE
- UNDERSIZED
- CHERUB
- NIP
- CHRYSANTHEMUMS
- TOBACCONIST'S
- OSTENTATIOUSLY
- SNUFFLED
- TROWEL
- STEPSISTER
- DRUNKARD'S
- WASHINGTONIAN
- EULOGY
- SONNY
- IMBECILE
- GORILLA
- TURNCOAT
- DELEGATIONS
- COMET
- ANTIETAM
- FLATBOAT
- BUFFOONERY
- GENTRYVILLE
- CONSTABLE'S
- SEWARDS
- FREDERICKSBURG
- MEADE
- GUNBOAT
- BISECT
- DEDICATORY
- NOAH
- GARDNER'S
- EVERETT'S
- LISPING
- FLOWERTH
- PRETHIDENT
- COBBLER
- TANTRUM
- PIGEONHOLE
- ASSASSINATION
- INBRED
- NOTHER
- EVACUATION
- APPOMATTOX
- PROMULGATED
- OUTLINING
- PENDEL
- GURGLE
- ECSTACY
- ROMPING
- TADDIE
- SURGICAL
- INEQUITIES
- ARBITER
- WHILENESS
- FASTNESSES
- DEERLIKE
- GARMENTED
- KOREANS
- CEASELESSLY
- SPRINGY
- WEEDING
- RIVERBED
- PIGTAILS
- KUELIAN
- CHING
- SOWERS
- PEKIN
- PEDLARS
- CONDENSED
- MUGS
- HORSESHOERS
- SHAMPOOED
- TINS
- COGNAC
- INEFFICIENCY
- WORTHLESSNESS
- EPITOMIZES
- INTERMINABLY
- PREPARES
- ENDURES
- SQUADS
- SLIVERS
- BULLIED
- SEN
- ETHICS
- ADAPTABLE
- CHANG
- MANCHURIAN
- INSCRIBES
- DIVERGED
- MONGOL
- DIFFERENTIATIONS
- INFUSIONS
- SAMENESS
- MALAY
- AMBITIOUSLY
- NAPOLEONIC
- NIPPON
- BANZAI
- UNANIMITY
- DESPOIL
- SUBSTANTIALITY
- PLANET'S
- ORGANIZER
- BALKED
- INCOMPREHENSION
- TWISTS
- PRESTO
- DOZING
- AFRIKANDER
- UNAFRAID
- DUPLICATES
- RIVALLING
- INTERCHANGEABLE
- HIEROGLYPHICS
- THUMBED
- LUSTS
- VIOLENCES
- LOGARITHMS
- BETOKENS
- SEERS
- HARKED
- CONSTABLE
- REICHSSTAAT
- KULTURSTAAT
- INDIVIDUALIST
- PROMPTING
- CONQUEST'S
- POSTULATE
- ADVENTURING
- CHENG
- CYCLOPEAN
- SPOUTED
- BANKED
- POLLUTING
- DESPONDENT
- RASPING
- STOCKY
- OVERBEARING
- UNPROVOKED
- WHIZZING
- UPPERCUT
- DOMN
- MOPPING
- FLAPPIN
- LOOKY
- SORLEY
- NONPAREIL
- COALPIT
- MAGDALENE
- BOXCLOTH
- NOWT
- STANDIN
- RESPEC
- ASSISTANT'S
- BICEPS
- WILLIN
- SLAUGHTERER
- SHILLIN
- NIEF
- QUEERED
- QUEENSBERRY
- WRITIN
- BRADFORD
- SPARRING
- OGILVY
- MEDAL
- PROMOTERS
- EFFEMINACY
- LOR
- RUMMAGED
- THYSEL
- PUGILIST'S
- OWD
- LOITERERS
- MIDLANDER
- DUNN
- FERNIE
- WILLOX
- UNDISMAYED
- NORTON
- LEVI
- CONCEDING
- QUIRE
- BETTING
- UNTRIED
- UNDERRATE
- GUTTA
- PERCHA
- PUNCHED
- SLOGGER
- THOU'LT
- DEPRECATED
- FACER
- RUFFIANLY
- BLACKGUARDS
- PUND
- SCRATTED
- POTMAN
- CHEQUERS
- SPARRIN
- SPYIN
- DOAN'T
- BRAY
- BRAYED
- MAISTER'S
- FINISHER
- TURBLE
- DENOTES
- GRAEUBEN
- ANEROID
- THERMOMETERS
- SPADES
- HEADLAND
- BREAKFASTING
- GEYSER
- COMPUTATIONS
- OSCILLATIONS
- PERTINACIOUSLY
- DEFYING
- UNMEASURED
- SURTURBRAND
- REFITTED
- FEUDALISED
- CARAPACES
- RIDGED
- RESERVOIR
- SEDIMENTARY
- CONTORTED
- PREHISTORIC
- FOSSILS
- CUVIERS
- LEPTOTHERIA
- MERICOTHERIA
- LOPHIODIA
- ANOPLOTHERIA
- MEGATHERIA
- PROTOPITHECAE
- PTERODACTYLES
- BIBLIOMANIAC
- ALEXANDRIAN
- APOSTROPHE
- SAVANTS
- ABBEVILLE
- DEFENDANTS
- CUVIER
- MAXILLARIES
- GROTTOES
- GOLGOTHA
- BORDEAUX
- DESICCATED
- THOMASES
- PALAEONTOLOGY
- BARNUM
- KNEEPAN
- AJAX
- ORESTES
- SPARTANS
- ASTERIUS
- CUBITS
- TRAPANI
- POLYPHEMUS
- LUCERNE
- PLATER
- SCHEUCHZER'S
- ADAMITE
- CAMPET
- GIGANTOSTEOLOGIE
- MAMMOTH
- INCRUSTED
- SOLVENT
- OVOID
- CHEEKBONES
- PROGNATHISM
- JAPHETIC
- ECCENTRICITIES
- CLEFTS
- CATACOMB
- SCEPTICS
- COMMINGLED
- SLIDED
- PAPERED
- UNCHEERFUL
- UNPIN
- TARNISHED
- HASP
- UNFOUNDED
- PREPONDERATED
- FAGGOT
- AWFULNESS
- INSTANTS
- CELERITY
- UNSEARCHED
- LININGS
- EXEMPLIFICATION
- REKINDLING
- MAID'S
- UNDISCOVERED
- DOOR'S
- TEACHABLENESS
- LUCIDLY
- STAFFORDSHIRE
- DISENGAGED
- CONVERTS
- KNOLLS
- UNFIXED
- ENDEARS
- ENDEAR
- OVERDRAWN
- LASSITUDE
- REVERT
- TRANSFORMS
- REGRESSIVE
- FIRSTLY
- RETOLD
- FORMATIVELY
- SURROUNDS
- SELECTIVELY
- DISCARDS
- LICENTIOUS
- SUBSTANTIATED
- STIMULATOR
- DERIVATION
- PREDISPOSED
- SUPERSEDED
- GLOSSED
- ACCOMMODATES
- PROPINQUITY
- NURSERIES
- AROUSES
- HEMS
- APPROXIMATING
- RECEDES
- ISOLATES
- SACHS
- OVERESTIMATE
- OUTWORN
- PSYCHOANALYTIC
- PROSCRIBED
- INTERPRETATIONS
- DERIVATIONS
- CASTRATION
- INTIMIDATION
- SEXUALITY
- COMMENCES
- GENITALS
- DIFFERS
- HOMOSEXUALITY
- UNBRIDGABLE
- EXCREMENT
- ACCREDITS
- GENITAL
- POLYMORPHUS
- MISREPRESENTING
- ANSWERABLE
- FURTHERANCES
- REDISCOVER
- EVOLUTIONARY
- PSYCHICALLY
- INBREEDING
- DETERIORATE
- INCESTUOUS
- SLIGHTNESS
- REGRESSES
- DECEPTIVE
- UNDISTORTED
- UNINTERPRETED
- TRANSLATES
- REAWAKENS
- PREDOMINANCE
- ORIGINATES
- COMPLETES
- PROPOUND
- OUTGROWN
- CONVULSE
- FERMENTS
- CABAL
- PROVIDENT
- PREEXISTING
- CORRUPTING
- UNSAFE
- PRECARIOUSNESS
- CONSPIRING
- INTRUST
- UNQUALIFIED
- ADULATOR
- ARTIFICES
- COMPORTS
- DEPARTMENTS
- INDUED
- COEQUAL
- ASSIGNS
- DISBURSEMENT
- APPROPRIATIONS
- DEPUTIES
- NOMINATION
- ATTACHMENTS
- PERMANENCY
- EXCLUDING
- PECULATION
- EMOLUMENTS
- PROPENSITY
- ADAGE
- BAN
- FELLOWCITIZENS
- BANISHING
- ESSENTIALITY
- INTERDICTION
- NECESSITATE
- OPTION
- OBVIATE
- READMISSION
- COUNTERBALANCE
- RESENTMENTS
- DISABLING
- MATERIA
- PRIMA
- ANGERS
- COMICALITY
- PRECIOUSNESS
- INVALIDATING
- ANALYZED
- SUFFUSES
- INTERPENETRATES
- INTERRELATION
- IMPENETRABILITY
- RETICULATIONS
- TIGRESS
- INACTIVE
- ILLUSTRATES
- SUBJECTIVITY
- OBJECTIVITY
- CLASSIFICATIONS
- AMBIGUOUSLY
- CLASSING
- OBJECTORS
- CITING
- ADJECTIVES
- SANTAYANA
- OBJECTIFIED
- MASTERLY
- ESTHETIC
- RHETORICAL
- CONNOTING
- VERTIGO
- SIDIS
- GOODHART
- EQUIVOCALITY
- CONVENIENCES
- COEFFICIENTS
- DISPLACES
- ENGENDERING
- TRANSLOCATING
- GALILEO
- DESCARTES
- ATOMIC
- KANTIANS
- ILLUSORY
- TRANSLOCATION
- RATIONALISM
- MIND'
- ANTIPATHETIC
- PLOTTED
- AFFINITIES
- TENSIONS
- ANTHROPOMORPHIC
- DANGEROUSNESS
- VASCULAR
- DISCRETE
- EXTRACORPOREALLY
- SUBSERVE
- CONSECUTION
- FIXES
- INFALLIBLY
- SORTED
- WOOES
- PALMARY
- DETERMINATIONS
- SENSORIAL
- PERTURBATIONS
- INTROSPECTION
- HUTIBUDI
- CRUNCHING
- HEADMAN
- SAL
- MERAL
- <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
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: default
frontend_conf:
n_fft: 512
win_length: 400
hop_length: 160
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: 5
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_en_word_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
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: conv2d
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: 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}
}
```
|
Santiagot1105/wav2vec2-lar-xlsr-es-col
|
Santiagot1105
| 2022-02-22T20:58:23Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-lar-xlsr-es-col
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-lar-xlsr-es-col
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-spanish](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0947
- Wer: 0.1884
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.8446 | 8.51 | 400 | 2.8174 | 0.9854 |
| 0.5146 | 17.02 | 800 | 0.1022 | 0.2020 |
| 0.0706 | 25.53 | 1200 | 0.0947 | 0.1884 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
yancong/distilbert-base-uncased-finetuned-existence
|
yancong
| 2022-02-22T20:56:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-existence
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-existence
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: 1.7925
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9532 | 1.0 | 221 | 2.1697 |
| 2.0959 | 2.0 | 442 | 1.9725 |
| 1.9277 | 3.0 | 663 | 1.7944 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.1
- Datasets 1.18.3
- Tokenizers 0.11.0
|
shibli/wav2vec2-large-xls-r-300m-pun-colab
|
shibli
| 2022-02-22T18:51:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-pun-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-pun-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.
## 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
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer
|
espnet
| 2022-02-22T18:25:19Z | 1 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:iemocap",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- iemocap
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer`
This model was trained by Yushi Ueda using iemocap recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout dfa2868243a897c2a6c34b7407eaea5e4b5508a5
pip install -e .
cd egs2/iemocap/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Thu Feb 17 11:25:22 EST 2022`
- python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.9.0+cu102`
- Git hash: `f6cde1c419c814a14ccd40abe557a780508cbcdf`
- Commit date: `Fri Feb 11 12:25:33 2022 -0500`
## Using Conformer based encoder and Transformer based decoder with spectral augmentation and predicting transcript along with sentiment
- ASR config: [conf/tuning/train_asr_conformer.yaml](conf/tuning/train_asr_conformer.yaml)
- token_type: word
- labels: Positive, Neutral, Negative
|dataset|Snt|Intent Classification Macro F1 (%)| Weighted F1 (%)| Micro F1 (%)|
|---|---|---|---|---|
|decode_asr_model_valid.acc.ave_10best/valid|754|53.9|65.7|66.4|
|decode_asr_model_valid.acc.ave_10best/test|1650|50.3|54.5|55.7|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_conformer.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_raw_en_word
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 200
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 64
valid_batch_size: null
batch_bins: 1000000
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:
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token_list:
- <blank>
- <unk>
- i
- you
- Negative
- to
- it
- '''s'
- the
- '''t'
- that
- and
- Neutral
- Positive
- a
- know
- what
- of
- like
- we
- don
- just
- is
- do
- this
- '''m'
- me
- have
- can
- in
- for
- 'no'
- so
- not
- '''re'
- my
- but
- mean
- be
- going
- all
- was
- they
- well
- want
- yeah
- right
- get
- 'on'
- there
- he
- oh
- here
- go
- out
- with
- your
- if
- okay
- are
- she
- at
- '''ll'
- '''ve'
- got
- think
- about
- up
- see
- then
- why
- how
- time
- really
- one
- now
- or
- as
- back
- look
- her
- him
- been
- because
- 'yes'
- would
- didn
- little
- did
- good
- some
- them
- something
- need
- maybe
- never
- um
- come
- take
- god
- had
- could
- will
- uh
- am
- people
- thing
- when
- very
- let
- much
- sorry
- from
- again
- long
- give
- anything
- too
- make
- fish
- years
- where
- isn
- three
- said
- things
- nothing
- help
- work
- tell
- guess
- over
- 'off'
- business
- even
- sir
- any
- his
- around
- were
- way
- who
- new
- kind
- '''d'
- our
- everything
- more
- came
- an
- should
- down
- understand
- only
- great
- else
- man
- line
- us
- ask
- last
- doing
- say
- waiting
- other
- lot
- job
- feel
- yourself
- point
- thought
- day
- whole
- away
- coming
- better
- marry
- always
- these
- still
- wrong
- two
- sure
- care
- phone
- probably
- remember
- annie
- life
- year
- believe
- gonna
- supposed
- went
- first
- talk
- listen
- alright
- before
- thinking
- after
- stuff
- happy
- ever
- turn
- thank
- home
- fine
- into
- than
- call
- money
- stay
- actually
- every
- hope
- love
- huh
- married
- wait
- somewhere
- has
- being
- father
- larry
- hell
- wanted
- trying
- getting
- guys
- name
- saying
- bag
- hear
- girl
- hey
- flashlight
- beach
- put
- leave
- dollars
- mind
- augie
- does
- won
- fifty
- excited
- hate
- four
- done
- through
- their
- keep
- car
- lost
- doesn
- happen
- wouldn
- school
- big
- calm
- night
- '''cause'
- id
- another
- though
- myself
- nobody
- somebody
- best
- might
- same
- form
- mom
- nice
- matter
- spot
- stop
- told
- by
- shut
- enough
- five
- joe
- hard
- find
- course
- chris
- drunk
- snap
- luggage
- rather
- standing
- someone
- laugh
- took
- those
- please
- live
- six
- ridiculous
- minute
- looking
- bring
- show
- start
- brought
- days
- must
- pretty
- sort
- talking
- sand
- child
- working
- send
- next
- hundred
- whatever
- many
- moon
- moment
- champagne
- s
- problem
- end
- real
- dear
- happened
- person
- place
- fill
- awesome
- house
- such
- cool
- c
- haven
- knew
- die
- finally
- glasses
- stupid
- least
- dad
- supervisor
- totally
- each
- try
- waited
- idea
- u
- party
- asked
- anymore
- sick
- evening
- license
- kid
- wow
- flight
- felt
- pay
- since
- single
- miss
- without
- different
- mmhmm
- free
- sometimes
- yet
- couldn
- view
- hour
- knows
- drive
- themselves
- swim
- ah
- brandy
- fact
- ma
- '''am'
- already
- part
- sit
- thanks
- comes
- check
- everyone
- started
- kiss
- weren
- hotel
- own
- beast
- bad
- above
- run
- worst
- grunions
- darling
- seem
- baby
- turned
- gone
- shouldn
- exactly
- reason
- full
- both
- crazy
- pack
- bit
- swimming
- liquor
- seemed
- serious
- cause
- peter
- burden
- gosh
- forgot
- happens
- alone
- pass
- letters
- heard
- manager
- hours
- baggage
- card
- number
- argue
- seen
- walk
- forget
- kids
- family
- blanket
- honey
- open
- quite
- gotta
- forms
- mother
- old
- needs
- times
- airline
- which
- once
- service
- week
- together
- twenty
- stand
- made
- fun
- dead
- sake
- men
- kate
- today
- plane
- most
- carla
- driving
- deal
- information
- wanna
- definitely
- while
- yea
- certificate
- particular
- lots
- calling
- fortune
- write
- entire
- found
- trouble
- use
- forever
- woman
- enjoy
- room
- damn
- war
- meaning
- longer
- jacket
- ticket
- twice
- sent
- wonder
- small
- amanda
- cannot
- able
- half
- ha
- saw
- bus
- ago
- hmm
- hi
- kidding
- giving
- gave
- move
- women
- ahead
- york
- guy
- suppose
- company
- incredible
- either
- minutes
- tonight
- shoes
- utterly
- wasn
- filled
- gets
- amazing
- beautiful
- hello
- birth
- prove
- choice
- friend
- expect
- says
- blue
- anywhere
- died
- weird
- umm
- blood
- d
- face
- body
- alive
- diagram
- goes
- read
- far
- race
- wind
- fly
- interested
- california
- coast
- news
- past
- charles
- floor
- idiotic
- indeed
- absolutely
- softball
- answer
- somehow
- having
- campus
- completely
- file
- everybody
- given
- fair
- front
- telling
- tried
- sign
- helping
- dollar
- used
- takes
- hair
- behind
- head
- also
- question
- pull
- brother
- nonsense
- kill
- pocket
- cold
- mine
- watching
- shall
- divorce
- driver
- m
- makes
- cried
- security
- suitcase
- seems
- control
- set
- letter
- realized
- paper
- weeks
- address
- sweet
- lose
- huge
- death
- ones
- living
- glad
- bed
- until
- thinks
- wedding
- pieces
- parents
- ready
- almost
- forgive
- kissed
- silver
- during
- forty
- lives
- grow
- arrive
- eyes
- putting
- quiet
- poor
- presents
- sting
- tired
- row
- anyhow
- window
- v
- thousand
- watch
- ashamed
- figure
- vacation
- application
- left
- certainly
- calls
- months
- student
- close
- helpful
- called
- welcome
- major
- match
- morning
- fit
- reach
- door
- wife
- faith
- noticed
- several
- killed
- accident
- rat
- flop
- hands
- ear
- dancing
- hairs
- bugging
- dinner
- bills
- worked
- bored
- conversation
- tunis
- overbearing
- grand
- nine
- amusing
- vile
- tempered
- obviously
- tomorrow
- taken
- eight
- venice
- worth
- boy
- realize
- midnight
- evil
- sixteen
- gotten
- paying
- bottle
- smart
- cindy
- excuse
- along
- seven
- children
- figured
- jobs
- joke
- charge
- memorial
- sitting
- hardly
- young
- story
- feels
- pronouncing
- insane
- forgotten
- fast
- inspire
- grub
- tough
- arguing
- air
- toss
- instance
- raining
- pair
- dry
- socks
- selfish
- included
- yours
- mystery
- mindedness
- urgency
- pure
- urge
- insulting
- ideas
- herself
- period
- missed
- backwards
- dance
- worms
- pop
- except
- perfect
- blow
- funny
- listening
- sadistic
- bully
- cruel
- 'true'
- second
- acting
- lucky
- handle
- loved
- hit
- shaking
- destroyed
- changed
- book
- eleven
- animals
- ice
- cream
- brings
- frustrating
- otherwise
- onto
- pregnant
- operator
- baltimore
- san
- diego
- contract
- brown
- friends
- pictures
- internet
- piece
- high
- anyone
- tickets
- inconvenience
- gift
- usually
- green
- city
- couple
- chuck
- growing
- pick
- throw
- yay
- walking
- grave
- considerate
- inspired
- looked
- mistake
- believes
- avoid
- sucker
- rock
- strangers
- missing
- hide
- geez
- imagination
- overseas
- command
- earth
- monument
- difference
- zipped
- kansas
- reservations
- ahh
- formed
- barefoot
- shower
- running
- garage
- knickerbocker
- locker
- wasting
- roses
- peaches
- rosy
- mention
- shh
- behave
- exquisitely
- beautifully
- rolling
- biting
- scratching
- panthers
- suddenly
- ought
- dreadfully
- pity
- eye
- world
- making
- bark
- roll
- hoops
- insufferable
- weak
- upstairs
- insist
- boorish
- conceited
- impossible
- torment
- brute
- perfectly
- wicked
- crawling
- top
- wish
- wants
- bank
- plan
- soon
- plenty
- bags
- congratulations
- play
- carry
- ignore
- sudden
- refrigerator
- loot
- fight
- lights
- swallows
- goose
- bumps
- keeps
- fighting
- massive
- celebration
- sex
- human
- ours
- light
- minded
- social
- needed
- anyway
- words
- problems
- claim
- reimburse
- checked
- airport
- meet
- e
- responsibility
- grunion
- knees
- thousands
- important
- shows
- goddamn
- strong
- law
- sara
- brent
- passport
- aren
- month
- romantic
- leaving
- random
- applied
- interesting
- regular
- taking
- harder
- hurt
- movie
- freaking
- record
- airlines
- responsible
- honestly
- grew
- proud
- hang
- mrs
- fellow
- terrible
- contradict
- infuriate
- throws
- afraid
- suffer
- bloody
- settled
- thrash
- may
- son
- faithful
- moments
- act
- sleep
- detroit
- planning
- yard
- particularly
- natural
- phenomenon
- highlight
- flopping
- laying
- eggs
- mating
- orgy
- magic
- unexplainable
- instincts
- seaweed
- instinctual
- firecracker
- spent
- clasped
- intimate
- special
- wishes
- seriously
- refreshments
- ooh
- pinpoint
- marge
- dishes
- fat
- ring
- later
- shivers
- spine
- sillier
- poise
- trumpets
- squeakers
- sockets
- allure
- contrary
- violently
- glass
- temperamental
- fiend
- loathe
- adder
- riotous
- mentioned
- intemperate
- tots
- downstairs
- mad
- loose
- lived
- yelling
- happening
- promise
- known
- exciting
- finish
- college
- atlanta
- searching
- fired
- drinking
- jesus
- lock
- plans
- hole
- santa
- kitchen
- invite
- believing
- ann
- landing
- eats
- panties
- sore
- throat
- unmistakable
- capistrano
- lemmings
- cliffs
- invitation
- map
- heaven
- carpet
- poodle
- suicide
- pact
- turns
- court
- dies
- mustn
- vampire
- identification
- places
- danger
- hand
- middle
- situation
- option
- willing
- paid
- horrible
- pain
- anybody
- paperwork
- difficult
- dream
- sakes
- matters
- toes
- become
- habit
- hold
- survive
- break
- babe
- shit
- contact
- land
- water
- transfer
- backersen
- desk
- wallet
- stolen
- credit
- cards
- clearly
- appreciate
- complicated
- uhuh
- bucks
- win
- theatre
- resume
- riding
- helps
- less
- planes
- means
- future
- ran
- red
- wrote
- loans
- spend
- dreaming
- proof
- shooting
- crack
- cracked
- dares
- invited
- breaks
- embarrassed
- wondering
- aw
- style
- granted
- embarrassing
- mixed
- su
- spawning
- stubbed
- toe
- bodies
- expectantly
- meant
- beginning
- traumatized
- freda
- sooner
- applies
- philosophers
- rots
- trivial
- torture
- stiff
- venom
- fangs
- wake
- bended
- voice
- build
- unbelievable
- hiring
- resumes
- eventually
- aggressive
- awhile
- especially
- further
- mass
- pointless
- claus
- neither
- mmm
- cannes
- figures
- burnt
- debate
- exception
- busy
- safe
- possible
- spring
- starting
- buy
- rest
- office
- complaint
- accepted
- ten
- area
- seats
- foam
- vibrations
- drives
- popped
- slightly
- exaggerated
- scientific
- proposed
- bathroom
- awful
- scene
- adders
- afford
- packet
- forward
- customer
- brand
- yellow
- fifteen
- brian
- asking
- percent
- girlfriend
- acceptance
- patient
- patience
- dishonest
- cheese
- restaurant
- t
- sixty
- direct
- holiday
- inn
- refund
- hmmm
- receiving
- sim
- browns
- unacceptable
- northwest
- dorky
- putt
- change
- filling
- z
- x
- simple
- mail
- request
- raise
- town
- hadn
- played
- pennies
- visa
- visit
- loves
- list
- environment
- frustrated
- ride
- imagine
- flew
- nash
- replace
- paris
- personal
- issue
- flights
- track
- angry
- headstone
- cemetery
- cancer
- poetry
- palm
- l
- dropped
- bunch
- p
- chair
- broke
- o
- allow
- nights
- talent
- ignoring
- center
- lovely
- sneaking
- whose
- es
- naturally
- stays
- wide
- bought
- arm
- exact
- curtsy
- wiggle
- superficial
- paint
- naked
- vendome
- rouser
- younger
- jealous
- fascinating
- duty
- photographer
- studio
- cad
- restraint
- ill
- knee
- applying
- questions
- picture
- fake
- apartment
- cash
- drink
- upset
- sending
- flying
- speak
- details
- wherever
- unfortunate
- education
- leaves
- basically
- hospital
- messed
- sounds
- pinch
- malibu
- drop
- team
- professional
- till
- ambiguous
- seeing
- ugh
- wet
- heading
- release
- fire
- inside
- pr
- includes
- rub
- ludicrous
- wriggle
- flippancy
- acid
- sweetness
- curling
- dressing
- gown
- broach
- enjoyable
- original
- '''em'
- early
- ok
- daughter
- age
- steps
- rejected
- starts
- competitive
- hired
- worse
- itself
- nowhere
- unfortunately
- process
- fault
- decision
- package
- easy
- transferred
- straight
- suckers
- none
- returning
- throwing
- cork
- softest
- breathe
- road
- catch
- threw
- canal
- comb
- towels
- sacred
- savor
- delight
- needn
- late
- web
- website
- rough
- daddy
- talked
- feeling
- talented
- interview
- food
- looks
- misplaced
- theft
- likely
- stuck
- tags
- cult
- everywhere
- menu
- choose
- press
- lady
- bill
- department
- online
- immediately
- miles
- notice
- vote
- heavens
- yell
- anna
- tables
- hasn
- stole
- losing
- unfair
- positive
- boston
- celebrate
- system
- turning
- newspapers
- pays
- dare
- jokes
- swine
- demand
- building
- finished
- staying
- cheap
- anyways
- okey
- lobster
- wonderful
- harvard
- engineering
- summer
- lawyer
- mr
- lax
- delta
- funeral
- report
- property
- whoever
- corporate
- miso
- soup
- holy
- olivia
- camera
- power
- sold
- testing
- greens
- explain
- agreement
- undecided
- access
- babies
- street
- vegas
- slot
- honeymoon
- husband
- penny
- slots
- wheel
- cat
- citizenship
- england
- fan
- spending
- craig
- services
- monster
- baloney
- saving
- necessarily
- carousel
- cameras
- airplane
- sentimental
- value
- incredibly
- shopping
- jet
- clothes
- apologize
- allowed
- amount
- candy
- redlands
- sprinklers
- whenever
- brain
- park
- holding
- memorized
- surgery
- audience
- joy
- scholarships
- commuting
- h
- ruined
- mm
- bet
- neighborhood
- sticking
- woo
- teach
- class
- confused
- clock
- foolish
- ocean
- distinctly
- whispered
- wishing
- white
- elliott
- strange
- quest
- ultimate
- truth
- shan
- word
- disagreeable
- wench
- birthday
- national
- thin
- rent
- colors
- citizen
- account
- '''til'
- hire
- short
- fuse
- america
- audition
- sponge
- language
- arriving
- reimbursement
- computer
- cover
- ass
- dealing
- quick
- freaks
- pitch
- hitting
- housing
- force
- scholarship
- dirty
- depends
- helicopter
- wild
- sport
- games
- streets
- although
- mi
- trust
- cracker
- curtsey
- bicker
- irons
- besides
- splendid
- born
- weekends
- letting
- tear
- apart
- touch
- flipped
- hot
- outside
- flowers
- candles
- approve
- surprised
- lead
- ends
- worthless
- apparently
- worker
- annoy
- belongings
- disappeared
- under
- case
- checking
- admit
- risk
- agreed
- yesterday
- country
- financial
- aid
- within
- automated
- systems
- specific
- rate
- star
- aisle
- afternoon
- maui
- machine
- waste
- available
- confirmed
- thinkin
- liked
- kicked
- intermittently
- burned
- desire
- fade
- passion
- laughable
- cunning
- mirrors
- painted
- wooden
- snake
- suspicious
- nosey
- silly
- wonders
- order
- standard
- site
- sense
- dangerous
- cute
- whether
- considering
- opinion
- f
- few
- guarantee
- possessions
- claims
- sue
- easier
- cared
- expected
- trip
- europe
- its
- circles
- large
- store
- macy
- rotary
- instead
- showed
- hundreds
- planned
- someplace
- sensitive
- popping
- opened
- backrub
- fantasy
- damned
- sheet
- cut
- purchase
- amy
- quit
- clapping
- onstage
- eighteen
- auditioning
- rejection
- prepared
- thirty
- master
- kelly
- natalie
- pants
- isabella
- verizon
- goodbye
- fucking
- challenge
- slept
- created
- checkbook
- argument
- uhh
- perhaps
- loath
- complete
- sad
- priorities
- between
- moving
- song
- temporary
- pulling
- smith
- receptionist
- extra
- lodging
- eh
- la
- cost
- boss
- peanuts
- doctor
- production
- downtown
- april
- contracts
- incompetent
- realtor
- fix
- payphone
- verify
- electrical
- outage
- symptoms
- nature
- pilot
- hook
- realizes
- bother
- trade
- event
- meadow
- faint
- blues
- bananas
- overnight
- station
- attention
- purchasing
- terms
- taser
- excellent
- counsel
- sorority
- golfing
- library
- dork
- taco
- branch
- separate
- sacrifices
- mothers
- kicking
- videotape
- stream
- sitters
- moved
- computers
- machines
- bride
- cruise
- likes
- tabs
- plays
- giant
- renamed
- brenda
- lumber
- janet
- state
- quarters
- costs
- escort
- reliable
- board
- posting
- trail
- following
- fantastic
- mighty
- recommending
- generally
- outline
- affords
- save
- carpool
- frustration
- refuse
- anger
- fourth
- lines
- fourteen
- mileage
- candid
- packed
- replaced
- expensive
- lawsuit
- cruising
- bruising
- president
- mistakenly
- behalf
- listed
- liable
- held
- sean
- badge
- employee
- impression
- cemeteries
- urban
- oasis
- wandering
- hers
- pathetic
- ground
- stones
- tumors
- heather
- built
- prospect
- garden
- section
- parties
- feet
- poems
- curly
- tree
- crown
- john
- dunn
- begin
- wheelchair
- reciting
- envelope
- grants
- mold
- minds
- mess
- rapper
- ho
- masters
- teacher
- dash
- popular
- seasoning
- messing
- ruin
- woke
- darkest
- beating
- bush
- porch
- fresh
- rooms
- sweetest
- pets
- cheeked
- brooch
- however
- jones
- voices
- berating
- christmas
- shame
- bunker
- guard
- spread
- companies
- shipping
- shock
- group
- dual
- unattached
- engagement
- sock
- dude
- lucked
- blush
- beige
- loaded
- craziest
- offered
- spoke
- english
- accent
- illegal
- jail
- caught
- hardcore
- tropical
- bahamas
- tahiti
- wealthy
- royalty
- removed
- attitude
- extremely
- hostile
- cutting
- sentence
- jumping
- produce
- field
- shake
- across
- soaked
- dying
- georgia
- educated
- boarding
- attendance
- seat
- offer
- publicize
- abuse
- insinuating
- smug
- mouth
- tossing
- hanky
- black
- wheels
- easily
- overhead
- compartment
- data
- collecting
- lip
- coffee
- smoking
- cigarettes
- union
- differently
- numb
- sickness
- boom
- mortality
- affecting
- slow
- books
- per
- diem
- victorian
- houses
- west
- sider
- commute
- practice
- neon
- softballs
- glow
- co
- ed
- nationally
- ranked
- ping
- pong
- denigrate
- rookie
- donuts
- recently
- pitcher
- hitter
- mostly
- shortstop
- ex
- trojans
- sports
- nicer
- monica
- player
- type
- helipad
- fell
- literally
- doubt
- cares
- mustache
- papers
- crying
- floorboards
- sorted
- everyday
- seas
- bringing
- sacrifice
- guilty
- opening
- return
- jumped
- distinctively
- direction
- tiny
- action
- passed
- cheeks
- darn
- urgh
- restrain
- self
- centered
- registration
- lunch
- documents
- identifications
- deadline
- carries
- official
- documentation
- government
- wireless
- crucial
- pulls
- kinda
- girly
- radiant
- ya
- shine
- invitations
- response
- mcdonald
- level
- member
- pavement
- indicators
- prejudice
- against
- applications
- hating
- physically
- amateur
- crawl
- dumber
- cases
- etiquette
- bug
- opinions
- magically
- irresponsible
- carrousel
- contents
- main
- liability
- provides
- shops
- reimbursed
- investigate
- provide
- uncommon
- johnny
- conscious
- stories
- africa
- image
- hurts
- goout
- gradual
- impact
- subside
- heals
- parts
- football
- recognizable
- accomplished
- prestige
- load
- worrying
- decide
- tour
- friendly
- ivy
- walls
- collegiate
- g
- choices
- math
- prestigious
- departments
- orientation
- graduate
- shiloh
- valued
- customers
- previous
- purchases
- scheduling
- highly
- discounted
- uses
- corporation
- hotels
- rated
- aisles
- switch
- fortunately
- allows
- spare
- shuttle
- appropriate
- traveling
- deals
- shuttles
- sleeps
- gee
- futile
- moralists
- unbearable
- flippant
- shibboleths
- rush
- madly
- piazza
- iron
- dri
- counter
- applica
- lonely
- disappear
- video
- definitive
- magazine
- boyfriend
- stage
- golly
- concert
- crew
- freak
- guaranteed
- nervous
- hah
- persistence
- factors
- types
- male
- female
- consideration
- cooking
- reconsidering
- uhm
- retirement
- foot
- persistent
- table
- skewed
- painting
- outer
- employment
- unlucky
- planet
- normal
- peoples
- reading
- difficulties
- loading
- mishap
- cart
- shipped
- tracking
- reim
- tight
- error
- continue
- 'false'
- compensate
- policy
- gifts
- nobodies
- tag
- originally
- shoe
- core
- memories
- kathy
- lasted
- gary
- closed
- surreal
- troops
- loving
- los
- angeles
- schools
- kinds
- secrets
- explore
- rip
- nuts
- champions
- leaning
- towards
- communications
- broad
- confined
- ropes
- recording
- depending
- leads
- bypass
- zero
- pleasant
- ebay
- bye
- steve
- hint
- asks
- tone
- pretend
- protection
- rid
- submit
- print
- regarding
- grievance
- sites
- protected
- processed
- careful
- secure
- unreliable
- trash
- kept
- spotting
- certain
- specifically
- pushing
- headed
- ears
- watched
- sends
- ceaseless
- wear
- often
- pleasure
- sonya
- promoted
- nurses
- mommy
- va
- videotaped
- cousin
- postpone
- performance
- swear
- cast
- spotlight
- microphone
- tripped
- surprise
- scored
- points
- members
- loser
- marrying
- weddings
- carats
- lousy
- chaperone
- drowsy
- deserve
- cry
- tears
- happiness
- marriage
- commercials
- refection
- financially
- studied
- passing
- russel
- crowe
- pooling
- funds
- owe
- learning
- role
- auditions
- denny
- tip
- teaching
- oof
- france
- steal
- keys
- laughing
- rosenkrantz
- thingy
- bopper
- limit
- whoa
- ways
- suffered
- disease
- handsome
- gifted
- parent
- ripped
- uveny
- tricia
- chemo
- baseball
- benny
- nat
- nation
- bread
- eat
- beer
- dorm
- sometime
- mattresses
- reserved
- grauman
- scale
- whooooo
- acti
- film
- art
- academy
- films
- fuck
- ethiopia
- cuddle
- profanity
- provider
- satellites
- average
- compensating
- unbeknownst
- satellite
- exaggerate
- advising
- addressed
- fax
- dumb
- fritz
- incoming
- million
- grown
- fella
- shootin
- travel
- sat
- instinct
- goosebumps
- arms
- danced
- intimately
- spart
- strumpets
- bristling
- diamonds
- taste
- portion
- side
- stairs
- condescending
- copy
- proceed
- remove
- missy
- behaving
- sweetie
- deploy
- specialist
- increase
- triple
- promotion
- retire
- quiets
- faster
- career
- lame
- drew
- barrymore
- nasty
- mouse
- cheesy
- jane
- tarzan
- engaged
- esmeralda
- hitched
- spontaneous
- character
- conga
- dim
- pulled
- chucky
- sarah
- guiding
- graduated
- apply
- colleges
- energy
- busing
- clerk
- excuses
- qualified
- chang
- investment
- banking
- deloitte
- touche
- temp
- degrading
- smarter
- astronaut
- biomedical
- internship
- plus
- breaking
- evicting
- typing
- shoot
- degree
- science
- club
- joking
- doomed
- maryland
- cooperate
- emergency
- pounds
- urn
- deduction
- sherlock
- holmes
- vessel
- burst
- caption
- therefore
- placed
- firing
- lobby
- fastest
- ibm
- misplace
- count
- hanging
- explanation
- follow
- footsteps
- overboard
- paralyzed
- coma
- fucked
- studying
- countries
- goal
- met
- greatest
- hopefully
- mmmm
- cinema
- chapter
- professionals
- sipping
- martinis
- sushi
- vat
- assistance
- starve
- south
- central
- firm
- police
- officer
- viacom
- digits
- speaking
- network
- charging
- connect
- outages
- hurricane
- katrina
- chose
- maam
- proven
- failing
- receive
- cuts
- using
- flip
- writing
- ms
- fall
- older
- game
- orange
- pink
- goodies
- battling
- sees
- flat
- stronger
- acted
- deserves
- hats
- shore
- pokes
- nah
- paul
- boats
- dammit
- enjoys
- bound
- harm
- pleasured
- lure
- devil
- rile
- topic
- initialed
- lets
- correctly
- spelled
- signed
- shitty
- timing
- susie
- tours
- emotionally
- bullshit
- enlist
- lie
- traditional
- church
- cabins
- flowery
- naturey
- midsummer
- excitement
- hoping
- attacked
- bears
- trim
- cooler
- dog
- tanish
- contrast
- cake
- buffet
- fried
- chicken
- mashed
- potatoes
- happier
- thrilled
- ecstatic
- rushed
- pressure
- interviews
- favors
- bite
- excessive
- unemployed
- cab
- gas
- possibly
- extreme
- trained
- presentable
- quote
- buck
- chugging
- engine
- realm
- minimum
- wage
- fry
- flipper
- bottom
- clear
- affect
- cle
- dressed
- shave
- legs
- presentation
- eighty
- success
- position
- training
- mcdonalds
- tv
- rainbow
- colored
- crap
- safely
- destination
- percoes
- equivalent
- amends
- courtesy
- inconveniencing
- near
- communicate
- conditions
- frequently
- current
- expecting
- pissed
- honor
- grandmother
- condition
- inevitable
- peace
- general
- mace
- present
- knife
- puny
- underwater
- basket
- weaving
- lying
- decided
- works
- worried
- occasion
- cruisers
- vibe
- greek
- lessons
- suck
- celebrating
- crush
- throughout
- test
- waters
- movies
- vermont
- cruiser
- abused
- frat
- boys
- dorms
- dell
- requests
- fixed
- dealt
- worries
- refunded
- situa
- relevant
- ordered
- orders
- others
- incorrectly
- tomatoes
- del
- cents
- attached
- cuz
- hoped
- opportunity
- rushing
- goods
- skipped
- breath
- kleenex
- alaska
- bearing
- hated
- holes
- calf
- witch
- whore
- <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: 0.5
ignore_id: -1
lsm_weight: 0.0
length_normalized_loss: false
report_cer: true
report_wer: true
sym_space: <space>
sym_blank: <blank>
extract_feats_in_collect_stats: true
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: 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: utterance_mvn
normalize_conf: {}
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 4
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 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: 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}
}
```
|
oguzhanolm/loodos-bert-base-uncased-QA-fine-tuned
|
oguzhanolm
| 2022-02-22T18:22:01Z | 14 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"loodos-bert-base",
"TQuAD",
"tr",
"dataset:TQuAD",
"model-index",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: tr
tags:
- question-answering
- loodos-bert-base
- TQuAD
- tr
datasets:
- TQuAD
model-index:
- name: loodos-bert-base-uncased-QA-fine-tuned
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: TQuAD
type: question-answering
args: tr
metrics:
- name: Accuracy
type: acc
value: 0.91
---
# Turkish SQuAD Model : Question Answering
I fine-tuned Loodos-Turkish-Bert-Model for Question-Answering problem with TQuAD dataset. Since the "loodos/bert-base-turkish-uncased" model gave the best results for the Turkish language in classification in the "Auto-tagging of Short Conversational Sentences using Transformer Methods" research we conducted with my teammates, I used this model because I thought that the success rate could be high in the question-answering.
* Loodos-BERT-base-uncased: https://huggingface.co/loodos/bert-base-turkish-uncased
* TQuAD dataset: https://github.com/TQuad/turkish-nlp-qa-dataset
# Training Code
```
!python3 Turkish-QA.py \
--model_type bert \
--model_name_or_path loodos/bert-base-turkish-uncased
--do_train \
--do_eval \
--train_file trainQ.json \
--predict_file dev1.json \
--per_gpu_train_batch_size 8 \
--learning_rate 5e-5 \
--num_train_epochs 6 \
--max_seq_length 384 \
--output_dir "./model"
```
# Example Usage
> Load Model
```
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("oguzhanolm/loodos-bert-base-uncased-QA-fine-tuned")
model = AutoModelForQuestionAnswering.from_pretrained("oguzhanolm/loodos-bert-base-uncased-QA-fine-tuned")
nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)
```
> Apply the model
```
def ask(question,context):
temp = nlp(question=question, context=context)
start_idx = temp["start"]
end_idx = temp["end"]
return context[start_idx:end_idx]
istanbul="İstanbul, Türkiye'de Marmara Bölgesi'nde yer alan şehir ve Türkiye Cumhuriyeti Devletinin 81 ilinden biridir. Ülkenin nüfus bakımından en çok göç alan ve en kalabalık ilidir. Ekonomik, tarihî ve sosyo-kültürel açıdan önde gelen şehirlerden biridir. Şehir, iktisadi büyüklük açısından dünyada 34. sırada yer alır. Nüfuslarına göre şehirler listesinde belediye sınırları göz önüne alınarak yapılan sıralamaya göre Avrupa'da birinci, dünyada ise altıncı sırada yer almaktadır."
soru1 = "İstanbul büyüklük açısından kaçıncı sıradadır?"
print(ask(soru1,istanbul))
soru2 = "İstanbul nerede bulunur?"
print(ask(soru2,istanbul))
```
|
elena-soare/t5-base-ecommerce
|
elena-soare
| 2022-02-22T18:19:10Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
T5 pre-trained on e-commerce data
|
ronanki/ml_mpnet_768_MNR
|
ronanki
| 2022-02-22T18:16:43Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# ronanki/ml_mpnet_768_MNR
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('ronanki/ml_mpnet_768_MNR')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('ronanki/ml_mpnet_768_MNR')
model = AutoModel.from_pretrained('ronanki/ml_mpnet_768_MNR')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ronanki/ml_mpnet_768_MNR)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 29 with parameters:
```
{'batch_size': 32}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
ronanki/ml_mpnet_768_MNR_10
|
ronanki
| 2022-02-22T18:14:36Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# ronanki/ml_mpnet_768_MNR_10
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('ronanki/ml_mpnet_768_MNR_10')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('ronanki/ml_mpnet_768_MNR_10')
model = AutoModel.from_pretrained('ronanki/ml_mpnet_768_MNR_10')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ronanki/ml_mpnet_768_MNR_10)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 29 with parameters:
```
{'batch_size': 32}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
NeonBohdan/stt-polyglot-it
|
NeonBohdan
| 2022-02-22T17:49:20Z | 0 | 0 | null |
[
"tflite",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
license: apache-2.0
---
|
NeonBohdan/stt-polyglot-es
|
NeonBohdan
| 2022-02-22T17:47:26Z | 0 | 0 | null |
[
"tflite",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
license: apache-2.0
---
|
NeonBohdan/stt-polyglot-de
|
NeonBohdan
| 2022-02-22T17:39:43Z | 0 | 0 | null |
[
"tflite",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
license: apache-2.0
---
|
NeonBohdan/stt-polyglot-pl
|
NeonBohdan
| 2022-02-22T17:27:31Z | 0 | 0 | null |
[
"tflite",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
license: apache-2.0
---
|
NeonBohdan/stt-polyglot-fr
|
NeonBohdan
| 2022-02-22T17:23:49Z | 0 | 0 | null |
[
"tflite",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
license: apache-2.0
---
|
vocab-transformers/msmarco-distilbert-word2vec256k-MLM_400k
|
vocab-transformers
| 2022-02-22T17:03:11Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# Model
This model is based on [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 400k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs. The word embedding matrix was frozen.
|
vocab-transformers/dense_encoder-msmarco-distilbert-word2vec256k-MLM_785k_emb_updated
|
vocab-transformers
| 2022-02-22T12:09:18Z | 87 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# dense_encoder-msmarco-distilbert-word2vec256k-MLM_785k_emb_updated
**Note: Token embeddings where updated!**
This model is based on [vocab-transformers/msmarco-distilbert-word2vec256k-MLM_785k_emb_updated](https://huggingface.co/vocab-transformers/msmarco-distilbert-word2vec256k-MLM_785k_emb_updated) with a 256k sized vocabulary initialized with word2vec that has been trained with MLM for 785k.
It has been trained on MS MARCO using [MarginMSELoss](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_margin-mse.py). See the train_script.py in this repository.
Performance:
- MS MARCO dev: 35.20 (MRR@10)
- TREC-DL 2019: 67.61 (nDCG@10)
- TREC-DL 2020: 69.62 (nDCG@10)
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7858 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MarginMSELoss.MarginMSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 30,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 250, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic
|
mbeukman
| 2022-02-22T11:42:08Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"am",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- am
tags:
- NER
- token-classification
datasets:
- masakhaner
metrics:
- f1
- precision
- recall
widget:
- text: "ቀዳሚው የሶማሌ ክልል በአወዳይ ከተማ ለተገደሉ የክልሉ ተወላጆች ያከናወነው የቀብር ስነ ስርዓትን የተመለከተ ዘገባ ነው ፡፡"
---
# xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-swahili](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Amharic part.
More information, and other similar models can be found in the [main Github repository](https://github.com/Michael-Beukman/NERTransfer).
## About
This model is transformer based and was fine-tuned on the MasakhaNER dataset. It is a named entity recognition dataset, containing mostly news articles in 10 different African languages.
The model was fine-tuned for 50 epochs, with a maximum sequence length of 200, 32 batch size, 5e-5 learning rate. This process was repeated 5 times (with different random seeds), and this uploaded model performed the best out of those 5 seeds (aggregate F1 on test set).
This model was fine-tuned by me, Michael Beukman while doing a project at the University of the Witwatersrand, Johannesburg. This is version 1, as of 20 November 2021.
This model is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Contact & More information
For more information about the models, including training scripts, detailed results and further resources, you can visit the the [main Github repository](https://github.com/Michael-Beukman/NERTransfer). You can contact me by filing an issue on this repository.
### Training Resources
In the interest of openness, and reporting resources used, we list here how long the training process took, as well as what the minimum resources would be to reproduce this. Fine-tuning each model on the NER dataset took between 10 and 30 minutes, and was performed on a NVIDIA RTX3090 GPU. To use a batch size of 32, at least 14GB of GPU memory was required, although it was just possible to fit these models in around 6.5GB's of VRAM when using a batch size of 1.
## Data
The train, evaluation and test datasets were taken directly from the MasakhaNER [Github](https://github.com/masakhane-io/masakhane-ner) repository, with minimal to no preprocessing, as the original dataset is already of high quality.
The motivation for the use of this data is that it is the "first large, publicly available, high quality dataset for named entity recognition (NER) in ten African languages" ([source](https://arxiv.org/pdf/2103.11811.pdf)). The high-quality data, as well as the groundwork laid by the paper introducing it are some more reasons why this dataset was used. For evaluation, the dedicated test split was used, which is from the same distribution as the training data, so this model may not generalise to other distributions, and further testing would need to be done to investigate this. The exact distribution of the data is covered in detail [here](https://arxiv.org/abs/2103.11811).
## Intended Use
This model are intended to be used for NLP research into e.g. interpretability or transfer learning. Using this model in production is not supported, as generalisability and downright performance is limited. In particular, this is not designed to be used in any important downstream task that could affect people, as harm could be caused by the limitations of the model, described next.
## Limitations
This model was only trained on one (relatively small) dataset, covering one task (NER) in one domain (news articles) and in a set span of time. The results may not generalise, and the model may perform badly, or in an unfair / biased way if used on other tasks. Although the purpose of this project was to investigate transfer learning, the performance on languages that the model was not trained for does suffer.
Because this model used xlm-roberta-base as its starting point (potentially with domain adaptive fine-tuning on specific languages), this model's limitations can also apply here. These can include being biased towards the hegemonic viewpoint of most of its training data, being ungrounded and having subpar results on other languages (possibly due to unbalanced training data).
As [Adelani et al. (2021)](https://arxiv.org/abs/2103.11811) showed, the models in general struggled with entities that were either longer than 3 words and entities that were not contained in the training data. This could bias the models towards not finding, e.g. names of people that have many words, possibly leading to a misrepresentation in the results. Similarly, names that are uncommon, and may not have been found in the training data (due to e.g. different languages) would also be predicted less often.
Additionally, this model has not been verified in practice, and other, more subtle problems may become prevalent if used without any verification that it does what it is supposed to.
### Privacy & Ethical Considerations
The data comes from only publicly available news sources, the only available data should cover public figures and those that agreed to be reported on. See the original MasakhaNER paper for more details.
No explicit ethical considerations or adjustments were made during fine-tuning of this model.
## Metrics
The language adaptive models achieve (mostly) superior performance over starting with xlm-roberta-base. Our main metric was the aggregate F1 score for all NER categories.
These metrics are on the test set for MasakhaNER, so the data distribution is similar to the training set, so these results do not directly indicate how well these models generalise.
We do find large variation in transfer results when starting from different seeds (5 different seeds were tested), indicating that the fine-tuning process for transfer might be unstable.
The metrics used were chosen to be consistent with previous work, and to facilitate research. Other metrics may be more appropriate for other purposes.
## Caveats and Recommendations
In general, this model performed worse on the 'date' category compared to others, so if dates are a critical factor, then that might need to be taken into account and addressed, by for example collecting and annotating more data.
## Model Structure
Here are some performance details on this specific model, compared to others we trained.
All of these metrics were calculated on the test set, and the seed was chosen that gave the best overall F1 score. The first three result columns are averaged over all categories, and the latter 4 provide performance broken down by category.
This model can predict the following label for a token ([source](https://huggingface.co/Davlan/xlm-roberta-large-masakhaner)):
Abbreviation|Description
-|-
O|Outside of a named entity
B-DATE |Beginning of a DATE entity right after another DATE entity
I-DATE |DATE entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
| Model Name | Staring point | Evaluation / Fine-tune Language | F1 | Precision | Recall | F1 (DATE) | F1 (LOC) | F1 (ORG) | F1 (PER) |
| -------------------------------------------------- | -------------------- | -------------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- |
| [xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic) (This model) | [swa](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) | amh | 70.34 | 69.72 | 70.97 | 72.00 | 75.00 | 51.00 | 73.00 |
| [xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic) | [amh](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-amharic) | amh | 79.55 | 76.71 | 82.62 | 70.00 | 84.00 | 62.00 | 91.00 |
| [xlm-roberta-base-finetuned-ner-amharic](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-ner-amharic) | [base](https://huggingface.co/xlm-roberta-base) | amh | 72.63 | 70.49 | 74.91 | 76.00 | 75.00 | 52.00 | 78.00 |
## Usage
To use this model (or others), you can do the following, just changing the model name ([source](https://huggingface.co/dslim/bert-base-NER)):
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
model_name = 'mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "ቀዳሚው የሶማሌ ክልል በአወዳይ ከተማ ለተገደሉ የክልሉ ተወላጆች ያከናወነው የቀብር ስነ ስርዓትን የተመለከተ ዘገባ ነው ፡፡"
ner_results = nlp(example)
print(ner_results)
```
|
MahsaShahidi/Persian-Image-Captioning
|
MahsaShahidi
| 2022-02-22T10:49:24Z | 55 | 2 |
transformers
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
model-index:
name: Persian-Image-Captioning
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Persian-Image-Captioning
This model is a fine-tuned version of [Vision Encoder Decoder](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder) on coco-flickr-farsi.
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
cammy/bart-large-cnn-finetuned-weaksup-1000-pad
|
cammy
| 2022-02-22T09:29:33Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-weaksup-1000-pad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-weaksup-1000-pad
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4168
- Rouge1: 26.2506
- Rouge2: 10.7802
- Rougel: 19.2236
- Rougelsum: 22.6883
- Gen Len: 68.74
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.1434 | 1.0 | 1000 | 0.4168 | 26.2506 | 10.7802 | 19.2236 | 22.6883 | 68.74 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
cammy/distilbart-cnn-12-6-finetuned-weaksup-1000
|
cammy
| 2022-02-22T08:49:00Z | 40 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-cnn-12-6-finetuned-weaksup-1000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbart-cnn-12-6-finetuned-weaksup-1000
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6818
- Rouge1: 25.9199
- Rouge2: 11.2697
- Rougel: 20.3598
- Rougelsum: 22.8242
- Gen Len: 66.44
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.644 | 1.0 | 1000 | 1.6818 | 25.9199 | 11.2697 | 20.3598 | 22.8242 | 66.44 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
vocab-transformers/msmarco-distilbert-word2vec256k-MLM_230k
|
vocab-transformers
| 2022-02-22T08:25:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# Model
This model is based on [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 230k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs. The word embedding matrix was frozen.
|
z3c1f4/distilbert-base-uncased-finetuned-cola
|
z3c1f4
| 2022-02-22T07:48:31Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5320879841803337
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
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: 0.7400
- Matthews Correlation: 0.5321
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5298 | 1.0 | 535 | 0.5168 | 0.4092 |
| 0.349 | 2.0 | 1070 | 0.4993 | 0.5099 |
| 0.2345 | 3.0 | 1605 | 0.6194 | 0.5046 |
| 0.1731 | 4.0 | 2140 | 0.7400 | 0.5321 |
| 0.1282 | 5.0 | 2675 | 0.8724 | 0.5078 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
cammy/bart-large-cnn-finetuned-weaksup-1000
|
cammy
| 2022-02-22T06:34:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-weaksup-1000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-weaksup-1000
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6325
- Rouge1: 26.1954
- Rouge2: 10.7128
- Rougel: 19.3873
- Rougelsum: 22.785
- Gen Len: 66.85
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.3896 | 1.0 | 1000 | 1.6325 | 26.1954 | 10.7128 | 19.3873 | 22.785 | 66.85 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Fan-s/reddit-tc-bert
|
Fan-s
| 2022-02-22T05:25:39Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-uncased-base
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-uncased-base
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an Reddit-dialogue dataset.
This model can be used for Text Classification: Given two sentences, see if they are related.
It achieves the following results on the evaluation set:
- Loss: 0.2297
- Accuracy: 0.9267
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 320
- eval_batch_size: 80
- 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 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.11.0
## Usage (HuggingFace Transformers)
You can use the model like this:
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# label_list
label_list = ['matched', 'unmatched']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("Fan-s/reddit-tc-bert", use_fast=True)
model = AutoModelForSequenceClassification.from_pretrained("Fan-s/reddit-tc-bert")
# Set the input
post = "don't make gravy with asbestos."
response = "i'd expect someone with a culinary background to know that. since we're talking about school dinner ladies, they need to learn this pronto."
# Predict whether the two sentences are matched
def predict(post, response, max_seq_length=128):
with torch.no_grad():
args = (post, response)
input = tokenizer(*args, padding="max_length", max_length=max_seq_length, truncation=True, return_tensors="pt")
output = model(**input)
logits = output.logits
item = torch.argmax(logits, dim=1)
predict_label = label_list[item]
return predict_label, logits
predict_label, logits = predict(post, response)
# Matched
print("predict_label:", predict_label)
```
|
yancong/distilbert-base-uncased-finetuned-quantifier
|
yancong
| 2022-02-22T02:57:28Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-quantifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-quantifier
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: 1.7478
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2007 | 1.0 | 94 | 2.3496 |
| 2.2332 | 2.0 | 188 | 1.8656 |
| 2.0141 | 3.0 | 282 | 1.8479 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.1
- Datasets 1.18.3
- Tokenizers 0.11.0
|
speech-seq2seq/wav2vec2-2-bart-large-no-adapter-frozen-enc
|
speech-seq2seq
| 2022-02-22T01:08:44Z | 33 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 18.7898
- Wer: 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.5396 | 0.28 | 500 | 9.0401 | 1.0120 |
| 5.898 | 0.56 | 1000 | 9.3199 | 1.0 |
| 4.9595 | 0.84 | 1500 | 8.4434 | 1.4563 |
| 5.7082 | 1.12 | 2000 | 15.1805 | 1.0000 |
| 5.4377 | 1.4 | 2500 | 15.7984 | 1.0021 |
| 5.5941 | 1.68 | 3000 | 18.4928 | 1.0 |
| 5.0662 | 1.96 | 3500 | 17.4886 | 1.0000 |
| 4.8363 | 2.24 | 4000 | 18.9458 | 1.0 |
| 4.7908 | 2.52 | 4500 | 18.2794 | 1.0006 |
| 4.679 | 2.8 | 5000 | 18.7898 | 1.0 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|
anas-awadalla/spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-42
|
anas-awadalla
| 2022-02-21T23:04:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-42
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
{'exact_match': 12.573320719016083, 'f1': 22.855895753681814}
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
pritoms/gpt2-group2
|
pritoms
| 2022-02-21T23:03:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-group2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-group2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6769
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 6 | 3.7517 |
| No log | 2.0 | 12 | 3.6951 |
| No log | 3.0 | 18 | 3.6769 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
anas-awadalla/spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-42
|
anas-awadalla
| 2022-02-21T22:44:41Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-42
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
{'exact_match': 64.02081362346263, 'f1': 75.36439229517165}
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
anas-awadalla/spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-42
|
anas-awadalla
| 2022-02-21T22:04:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-42
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
{'exact_match': 4.541154210028382, 'f1': 10.04181288563879}
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
anas-awadalla/roberta-base-few-shot-k-128-finetuned-squad-seed-42
|
anas-awadalla
| 2022-02-21T21:29:23Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-128-finetuned-squad-seed-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-few-shot-k-128-finetuned-squad-seed-42
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
{'exact_match': 39.04446546830653, 'f1': 49.90230650794353}
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Santiagot1105/wav2vec2-large-xlsr-finetune-es-col
|
Santiagot1105
| 2022-02-21T21:19:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-finetune-es-col
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-finetune-es-col
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6514
- Wer: 0.9874
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.9709 | 3.25 | 400 | 2.9673 | 1.0 |
| 2.9488 | 6.5 | 800 | 2.9075 | 0.9973 |
| 2.907 | 9.76 | 1200 | 2.8772 | 0.9688 |
| 2.886 | 13.01 | 1600 | 2.8245 | 0.9484 |
| 2.8043 | 16.26 | 2000 | 2.7134 | 0.9874 |
| 2.7288 | 19.51 | 2400 | 2.6750 | 0.9874 |
| 2.7072 | 22.76 | 2800 | 2.6651 | 0.9874 |
| 2.6892 | 26.02 | 3200 | 2.6573 | 0.9874 |
| 2.683 | 29.27 | 3600 | 2.6514 | 0.9874 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
ajrae/bert-base-uncased-finetuned-mrpc
|
ajrae
| 2022-02-21T21:19:06Z | 4 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8578431372549019
- name: F1
type: f1
value: 0.9003436426116839
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4520
- Accuracy: 0.8578
- F1: 0.9003
## 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 230 | 0.4169 | 0.8039 | 0.8639 |
| No log | 2.0 | 460 | 0.4299 | 0.8137 | 0.875 |
| 0.4242 | 3.0 | 690 | 0.4520 | 0.8578 | 0.9003 |
| 0.4242 | 4.0 | 920 | 0.6323 | 0.8431 | 0.8926 |
| 0.1103 | 5.0 | 1150 | 0.6163 | 0.8578 | 0.8997 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
bettertextapp/bart_large_paraphrase_generator_en_de_v2
|
bettertextapp
| 2022-02-21T21:11:51Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
model-index:
- name: bart_large_paraphrase_generator_en_de_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart_large_paraphrase_generator_en_de_v2
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
{'eval_loss': 0.9200083613395691, 'eval_score': 49.97448884411352, 'eval_counts': [100712, 72963, 57055, 41578], 'eval_totals': [133837, 130839, 127841, 124843], 'eval_precisions': [75.24974409169363, 55.76548276889918, 44.6296571522438, 33.30423011302196], 'eval_bp': 1.0, 'eval_sys_len': 133837, 'eval_ref_len': 130883, 'eval_runtime': 138.6871, 'eval_samples_per_second': 21.617, 'eval_steps_per_second': 0.678}
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.16.2
- Pytorch 1.11.0a0+bfe5ad2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
anas-awadalla/roberta-base-few-shot-k-16-finetuned-squad-seed-42
|
anas-awadalla
| 2022-02-21T20:54:21Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-16-finetuned-squad-seed-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-few-shot-k-16-finetuned-squad-seed-42
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
{'exact_match': 8.618732261116367, 'f1': 14.074017518582023}
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
anas-awadalla/roberta-base-few-shot-k-1024-finetuned-squad-seed-42
|
anas-awadalla
| 2022-02-21T20:36:42Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-1024-finetuned-squad-seed-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-few-shot-k-1024-finetuned-squad-seed-42
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
{'exact_match': 66.90633869441817, 'f1': 77.54482247690522}
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
vocab-transformers/dense_encoder-msmarco-distilbert-word2vec256k-MLM_210k_emb_updated
|
vocab-transformers
| 2022-02-21T20:13:56Z | 92 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# dense_encoder-msmarco-distilbert-word2vec256k-MLM_210k
**Note: Token embeddings where updated!**
This model is based on [vocab-transformers/msmarco-distilbert-word2vec256k-MLM_210k](https://huggingface.co/vocab-transformers/msmarco-distilbert-word2vec256k-MLM_210k) with a 256k sized vocabulary initialized with word2vec that has been trained with MLM for 210k.
It has been trained on MS MARCO using [MarginMSELoss](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_margin-mse.py). See the train_script.py in this repository.
Performance:
- MS MARCO dev: 34.91 (MRR@10)
- TREC-DL 2019: 67.56 (nDCG@10)
- TREC-DL 2020: 68.18 (nDCG@10)
## Usage (Sentence-Transformers)
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7858 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MarginMSELoss.MarginMSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 30,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 250, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
vocab-transformers/dense_encoder-msmarco-bert-base-word2vec256k_emb_updated
|
vocab-transformers
| 2022-02-21T20:13:25Z | 11 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# dense_encoder-msmarco-bert-base-word2vec256k
**Note: Token embeddings where updated!**
This model is based on [msmarco-word2vec256000-bert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-bert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
It has been trained on MS MARCO using [MarginMSELoss](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_margin-mse.py). See the train_script.py in this repository.
Performance:
- MS MARCO dev: (evaluating) (MRR@10)
- TREC-DL 2019: 67.56 (nDCG@10)
- TREC-DL 2020: 71.26 (nDCG@10)
## Usage (Sentence-Transformers)
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 15716 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MarginMSELoss.MarginMSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 30,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 250, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
vocab-transformers/msmarco-distilbert-word2vec256k-MLM_785k_emb_updated
|
vocab-transformers
| 2022-02-21T20:12:43Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# Model
This model is based on [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 785k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs.
**Note: Token embeddings where updated!**
|
vocab-transformers/msmarco-distilbert-word2vec256k-MLM_210k_emb_updated
|
vocab-transformers
| 2022-02-21T20:12:32Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# Model
This model is based on [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 210k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs.
**Note: Token embeddings where updated!**
|
vocab-transformers/dense_encoder-msmarco-distilbert-word2vec256k-MLM_445k_emb_updated
|
vocab-transformers
| 2022-02-21T20:09:42Z | 98 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# dense_encoder-msmarco-distilbert-word2vec256k-MLM_445k
This model is based on [vocab-transformers/msmarco-distilbert-word2vec256k-MLM_445k](https://huggingface.co/vocab-transformers/msmarco-distilbert-word2vec256k-MLM_445k) with a 256k sized vocabulary initialized with word2vec that has been trained with MLM for 445k steps. **Note: Token embeddings where updated!**
It has been trained on MS MARCO using [MarginMSELoss](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_margin-mse.py). See the train_script.py in this repository. **Note: Token embeddings where updated!**
Performance:
- MS MARCO dev: 34.94 (MRR@10)
- TREC-DL 2019: 66.72 (nDCG@10)
- TREC-DL 2020: 69.14 (nDCG@10)
## Usage (Sentence-Transformers)
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7858 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MarginMSELoss.MarginMSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 30,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 250, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
anas-awadalla/bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-42
|
anas-awadalla
| 2022-02-21T19:28:40Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-42
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 3e-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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
{'exact_match': 40.91769157994324, 'f1': 52.89154394730339}
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
speech-seq2seq/wav2vec2-2-bert-large-no-adapter
|
speech-seq2seq
| 2022-02-21T17:49:39Z | 11 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 6.9251
- Wer: 1.7858
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.6487 | 0.28 | 500 | 6.8354 | 1.4719 |
| 6.5662 | 0.56 | 1000 | 6.7877 | 0.9371 |
| 6.4309 | 0.84 | 1500 | 6.7640 | 1.1317 |
| 6.7123 | 1.12 | 2000 | 6.7907 | 1.9354 |
| 6.7547 | 1.4 | 2500 | 6.7830 | 1.8854 |
| 6.6726 | 1.68 | 3000 | 6.8211 | 1.9203 |
| 6.6538 | 1.96 | 3500 | 6.8444 | 1.8235 |
| 6.5693 | 2.24 | 4000 | 6.8873 | 1.8606 |
| 6.7234 | 2.52 | 4500 | 6.8649 | 1.8126 |
| 6.5104 | 2.8 | 5000 | 6.9251 | 1.7858 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|
espnet/Karthik_DSTC2_asr_train_asr_transformer
|
espnet
| 2022-02-21T14:51:23Z | 1 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:sinhala",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- sinhala
license: cc-by-4.0
---
## ESPnet2 ASR pretrained model
### `espnet/Karthik_DSTC2_asr_train_asr_transformer`
This model was trained by Karthik using DSTC2/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
GunnarThor/talromur_f_tacotron2
|
GunnarThor
| 2022-02-21T13:57:35Z | 11 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:04Z |
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- talromur
license: cc-by-4.0
---
## ESPnet2 TTS model
### `GunnarThor/talromur_f_tacotron2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 81522029063e42ce807d9d145b64d3f9aca45987
pip install -e .
cd egs2/talromur/tts1
./run.sh --skip_data_prep false --skip_train true --download_model GunnarThor/talromur_f_tacotron2
```
## TTS config
<details><summary>expand</summary>
```
config: ./conf/tuning/train_tacotron2.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp_f/tts_train_tacotron2_raw_phn_none
ngpu: 1
seed: 0
num_workers: 1
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: 55005
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: 200
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
- - train
- loss
- min
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: 1.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: 500
batch_size: 20
valid_batch_size: null
batch_bins: 5120000
valid_batch_bins: null
train_shape_file:
- exp_f/tts_stats_raw_phn_none/train/text_shape.phn
- exp_f/tts_stats_raw_phn_none/train/speech_shape
valid_shape_file:
- exp_f/tts_stats_raw_phn_none/valid/text_shape.phn
- exp_f/tts_stats_raw_phn_none/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_f_phn/text
- text
- text
- - dump/raw/train_f_phn/wav.scp
- speech
- sound
valid_data_path_and_name_and_type:
- - dump/raw/dev_f_phn/text
- text
- text
- - dump/raw/dev_f_phn/wav.scp
- speech
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
eps: 1.0e-06
weight_decay: 0.0
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ','
- .
- r
- t
- n
- a0
- s
- I0
- D
- l
- Y0
- m
- v
- h
- k
- E1
- a:1
- E:1
- f
- G
- j
- a1
- T
- p
- c
- au:1
- E0
- i:1
- O:1
- I:1
- I1
- r_0
- t_h
- k_h
- Y1
- ei1
- i0
- ei:1
- ou:1
- u:1
- O1
- N
- l_0
- '91'
- ai0
- au1
- ou0
- ai:1
- n_0
- ei0
- O0
- ou1
- i1
- '9:1'
- ai1
- '90'
- au0
- x
- c_h
- 9i:1
- C
- p_h
- u0
- Y:1
- J
- 9i1
- u1
- 9i0
- N_0
- m_0
- J_0
- Yi0
- Oi1
- Yi1
- Oi0
- au:0
- '9:0'
- E:0
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
feats_extract: fbank
feats_extract_conf:
n_fft: 1024
hop_length: 256
win_length: null
fs: 22050
fmin: 80
fmax: 7600
n_mels: 80
normalize: global_mvn
normalize_conf:
stats_file: exp_f/tts_stats_raw_phn_none/train/feats_stats.npz
tts: tacotron2
tts_conf:
embed_dim: 512
elayers: 1
eunits: 512
econv_layers: 3
econv_chans: 512
econv_filts: 5
atype: location
adim: 512
aconv_chans: 32
aconv_filts: 15
cumulate_att_w: true
dlayers: 2
dunits: 1024
prenet_layers: 2
prenet_units: 256
postnet_layers: 5
postnet_chans: 512
postnet_filts: 5
output_activation: null
use_batch_norm: true
use_concate: true
use_residual: false
dropout_rate: 0.5
zoneout_rate: 0.1
reduction_factor: 1
spk_embed_dim: null
use_masking: true
bce_pos_weight: 5.0
use_guided_attn_loss: true
guided_attn_loss_sigma: 0.4
guided_attn_loss_lambda: 1.0
pitch_extract: null
pitch_extract_conf: {}
pitch_normalize: null
pitch_normalize_conf: {}
energy_extract: null
energy_extract_conf: {}
energy_normalize: null
energy_normalize_conf: {}
required:
- output_dir
- token_list
version: 0.10.5a1
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}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson 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}
}
```
|
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