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text-classification
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transformers
|
<!-- 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. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8976
- Mae: 0.4268
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.092 | 1.0 | 235 | 0.9514 | 0.5122 |
| 0.9509 | 2.0 | 470 | 0.8976 | 0.4268 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]}
|
d4niel92/xlm-roberta-base-finetuned-marc-en
| null |
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-generation
|
transformers
|
# Harry
|
{"tags": ["conversational"]}
|
d4rk/harry
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
d8oss/cybergan
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-generation
|
transformers
|
{}
|
d8oss/gamio-small
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-generation
|
transformers
|
{}
|
d8oss/giw-medium
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
dadada/mbart-finetuned-few-task
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
<!-- 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. -->
# opus-mt-zh-en-ep1-renri-zh-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2192
- Bleu: 18.2579
- Gen Len: 28.4817
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 2.2194 | 1.0 | 59472 | 2.2192 | 18.2579 | 28.4817 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model_index": [{"name": "opus-mt-zh-en-ep1-renri-zh-to-en", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "metric": {"name": "Bleu", "type": "bleu", "value": 18.2579}}]}]}
|
dadada/opus-mt-zh-en-ep1-renri-zh-to-en
| null |
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
dadangheksa/finetuning-sentiment-model-3000-samples
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
sentence-similarity
|
transformers
|
# Similarity between two sentences (fine-tuning with KoELECTRA-Small-v3 model and KorSTS dataset)
## Usage (Amazon SageMaker inference applicable)
It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.
### inference_korsts.py
```python
import json
import sys
import logging
import torch
from torch import nn
from transformers import ElectraConfig
from transformers import ElectraModel, AutoTokenizer, ElectraTokenizer, ElectraForSequenceClassification
logging.basicConfig(
level=logging.INFO,
format='[{%(filename)s:%(lineno)d} %(levelname)s - %(message)s',
handlers=[
logging.FileHandler(filename='tmp.log'),
logging.StreamHandler(sys.stdout)
]
)
logger = logging.getLogger(__name__)
max_seq_length = 128
tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/koelectra-small-v3-korsts")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Huggingface pre-trained model: 'monologg/koelectra-small-v3-discriminator'
def model_fn(model_path):
####
# If you have your own trained model
# Huggingface pre-trained model: 'monologg/koelectra-small-v3-discriminator'
####
#config = ElectraConfig.from_json_file(f'{model_path}/config.json')
#model = ElectraForSequenceClassification.from_pretrained(f'{model_path}/model.pth', config=config)
model = ElectraForSequenceClassification.from_pretrained('daekeun-ml/koelectra-small-v3-korsts')
model.to(device)
return model
def input_fn(input_data, content_type="application/jsonlines"):
data_str = input_data.decode("utf-8")
jsonlines = data_str.split("\n")
transformed_inputs = []
for jsonline in jsonlines:
text = json.loads(jsonline)["text"]
logger.info("input text: {}".format(text))
encode_plus_token = tokenizer.encode_plus(
text,
max_length=max_seq_length,
add_special_tokens=True,
return_token_type_ids=False,
padding="max_length",
return_attention_mask=True,
return_tensors="pt",
truncation=True,
)
transformed_inputs.append(encode_plus_token)
return transformed_inputs
def predict_fn(transformed_inputs, model):
predicted_classes = []
for data in transformed_inputs:
data = data.to(device)
output = model(**data)
prediction_dict = {}
prediction_dict['score'] = output[0].squeeze().cpu().detach().numpy().tolist()
jsonline = json.dumps(prediction_dict)
logger.info("jsonline: {}".format(jsonline))
predicted_classes.append(jsonline)
predicted_classes_jsonlines = "\n".join(predicted_classes)
return predicted_classes_jsonlines
def output_fn(outputs, accept="application/jsonlines"):
return outputs, accept
```
### test.py
```python
>>> from inference_korsts import model_fn, input_fn, predict_fn, output_fn
>>> with open('./samples/korsts.txt', mode='rb') as file:
>>> model_input_data = file.read()
>>> model = model_fn()
>>> transformed_inputs = input_fn(model_input_data)
>>> predicted_classes_jsonlines = predict_fn(transformed_inputs, model)
>>> model_outputs = output_fn(predicted_classes_jsonlines)
>>> print(model_outputs[0])
[{inference_korsts.py:44} INFO - input text: ['맛있는 라면을 먹고 싶어요', '후루룩 쩝쩝 후루룩 쩝쩝 맛좋은 라면']
[{inference_korsts.py:44} INFO - input text: ['뽀로로는 내친구', '머신러닝은 러닝머신이 아닙니다.']
[{inference_korsts.py:71} INFO - jsonline: {"score": 4.786738872528076}
[{inference_korsts.py:71} INFO - jsonline: {"score": 0.2319069355726242}
{"score": 4.786738872528076}
{"score": 0.2319069355726242}
```
### Sample data (samples/korsts.txt)
```
{"text": ["맛있는 라면을 먹고 싶어요", "후루룩 쩝쩝 후루룩 쩝쩝 맛좋은 라면"]}
{"text": ["뽀로로는 내친구", "머신러닝은 러닝머신이 아닙니다."]}
```
## References
- KoELECTRA: https://github.com/monologg/KoELECTRA
- KorNLI and KorSTS Dataset: https://github.com/kakaobrain/KorNLUDatasets
|
{"language": ["ko"], "license": "cc-by-4.0", "tags": ["sentence-similarity", "transformers"], "datasets": ["korsts"], "metrics": ["accuracy", "f1", "precision", "recall"], "pipeline_tag": "sentence-similarity"}
|
daekeun-ml/koelectra-small-v3-korsts
| null |
[
"transformers",
"pytorch",
"electra",
"text-classification",
"sentence-similarity",
"ko",
"dataset:korsts",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
# Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)
## Usage (Amazon SageMaker inference applicable)
It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.
### inference_nsmc.py
```python
import json
import sys
import logging
import torch
from torch import nn
from transformers import ElectraConfig
from transformers import ElectraModel, AutoTokenizer, ElectraTokenizer, ElectraForSequenceClassification
logging.basicConfig(
level=logging.INFO,
format='[{%(filename)s:%(lineno)d} %(levelname)s - %(message)s',
handlers=[
logging.FileHandler(filename='tmp.log'),
logging.StreamHandler(sys.stdout)
]
)
logger = logging.getLogger(__name__)
max_seq_length = 128
classes = ['Neg', 'Pos']
tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/koelectra-small-v3-nsmc")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def model_fn(model_path=None):
####
# If you have your own trained model
# Huggingface pre-trained model: 'monologg/koelectra-small-v3-discriminator'
####
#config = ElectraConfig.from_json_file(f'{model_path}/config.json')
#model = ElectraForSequenceClassification.from_pretrained(f'{model_path}/model.pth', config=config)
# Download model from the Huggingface hub
model = ElectraForSequenceClassification.from_pretrained('daekeun-ml/koelectra-small-v3-nsmc')
model.to(device)
return model
def input_fn(input_data, content_type="application/jsonlines"):
data_str = input_data.decode("utf-8")
jsonlines = data_str.split("\n")
transformed_inputs = []
for jsonline in jsonlines:
text = json.loads(jsonline)["text"][0]
logger.info("input text: {}".format(text))
encode_plus_token = tokenizer.encode_plus(
text,
max_length=max_seq_length,
add_special_tokens=True,
return_token_type_ids=False,
padding="max_length",
return_attention_mask=True,
return_tensors="pt",
truncation=True,
)
transformed_inputs.append(encode_plus_token)
return transformed_inputs
def predict_fn(transformed_inputs, model):
predicted_classes = []
for data in transformed_inputs:
data = data.to(device)
output = model(**data)
softmax_fn = nn.Softmax(dim=1)
softmax_output = softmax_fn(output[0])
_, prediction = torch.max(softmax_output, dim=1)
predicted_class_idx = prediction.item()
predicted_class = classes[predicted_class_idx]
score = softmax_output[0][predicted_class_idx]
logger.info("predicted_class: {}".format(predicted_class))
prediction_dict = {}
prediction_dict["predicted_label"] = predicted_class
prediction_dict['score'] = score.cpu().detach().numpy().tolist()
jsonline = json.dumps(prediction_dict)
logger.info("jsonline: {}".format(jsonline))
predicted_classes.append(jsonline)
predicted_classes_jsonlines = "\n".join(predicted_classes)
return predicted_classes_jsonlines
def output_fn(outputs, accept="application/jsonlines"):
return outputs, accept
```
### test.py
```python
>>> from inference_nsmc import model_fn, input_fn, predict_fn, output_fn
>>> with open('samples/nsmc.txt', mode='rb') as file:
>>> model_input_data = file.read()
>>> model = model_fn()
>>> transformed_inputs = input_fn(model_input_data)
>>> predicted_classes_jsonlines = predict_fn(transformed_inputs, model)
>>> model_outputs = output_fn(predicted_classes_jsonlines)
>>> print(model_outputs[0])
[{inference_nsmc.py:47} INFO - input text: 이 영화는 최고의 영화입니다
[{inference_nsmc.py:47} INFO - input text: 최악이에요. 배우의 연기력도 좋지 않고 내용도 너무 허접합니다
[{inference_nsmc.py:77} INFO - predicted_class: Pos
[{inference_nsmc.py:84} INFO - jsonline: {"predicted_label": "Pos", "score": 0.9619030952453613}
[{inference_nsmc.py:77} INFO - predicted_class: Neg
[{inference_nsmc.py:84} INFO - jsonline: {"predicted_label": "Neg", "score": 0.9994170665740967}
{"predicted_label": "Pos", "score": 0.9619030952453613}
{"predicted_label": "Neg", "score": 0.9994170665740967}
```
### Sample data (samples/nsmc.txt)
```
{"text": ["이 영화는 최고의 영화입니다"]}
{"text": ["최악이에요. 배우의 연기력도 좋지 않고 내용도 너무 허접합니다"]}
```
## References
- KoELECTRA: https://github.com/monologg/KoELECTRA
- Naver Sentiment Movie Corpus Dataset: https://github.com/e9t/nsmc
|
{"language": ["ko"], "license": "mit", "tags": ["classification"], "datasets": ["nsmc"], "metrics": ["accuracy", "f1", "precision", "recall- accuracy"], "widget": [{"text": "\ubd88\ud6c4\uc758 \uba85\uc791\uc785\ub2c8\ub2e4! \uc774\ub807\uac8c \uac10\ub3d9\uc801\uc778 \ub0b4\uc6a9\uc740 \ucc98\uc74c\uc774\uc5d0\uc694", "example_title": "Positive"}, {"text": "\uc2dc\uac04\uc774 \uc815\ub9d0 \uc544\uae5d\uc2b5\ub2c8\ub2e4. 10\uc810 \ub9cc\uc810\uc5d0 1\uc810\ub3c4 \uc544\uae4c\uc6cc\uc694..", "example_title": "Negative"}]}
|
daekeun-ml/koelectra-small-v3-nsmc
| null |
[
"transformers",
"pytorch",
"electra",
"text-classification",
"classification",
"ko",
"dataset:nsmc",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
dahele/tip
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
daisymzhang/bert-base-uncased-finetuned-swag
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
daito/test
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
feature-extraction
|
transformers
|
{}
|
dakshvar22/LaBSE
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
dalchob/distilbert-base-uncased-finetuned-ner
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-to-image
|
transformers
|
# DALL·E Mini Model Card
This model card focuses on the model associated with the DALL·E mini space on Hugging Face, available [here](https://huggingface.co/spaces/dalle-mini/dalle-mini). The app is called “dalle-mini”, but incorporates “[DALL·E Mini](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy)’’ and “[DALL·E Mega](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega-Training-Journal--VmlldzoxODMxMDI2)” models (further details on this distinction forthcoming).
The DALL·E Mega model is the largest version of DALLE Mini. For more information specific to DALL·E Mega, see the [DALL·E Mega model card](https://huggingface.co/dalle-mini/dalle-mega).
## Model Details
* **Developed by:** Boris Dayma, Suraj Patil, Pedro Cuenca, Khalid Saifullah, Tanishq Abraham, Phúc Lê, Luke, Luke Melas, Ritobrata Ghosh
* **Model type:** Transformer-based text-to-image generation model
* **Language(s):** English
* **License:** Apache 2.0
* **Model Description:** This is a model that can be used to generate images based on text prompts. As the model developers wrote in the [project report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy) about DALL·E mini, “OpenAI had the first impressive model for generating images with [DALL·E](https://openai.com/blog/dall-e/). DALL·E mini is an attempt at reproducing those results with an open-source model.”
* **Resources for more information:** See OpenAI’s website for more information about [DALL·E](https://openai.com/blog/dall-e/), including the [DALL·E model card](https://github.com/openai/DALL-E/blob/master/model_card.md). See the [project report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy) for more information from the model’s developers. To learn more about DALL·E Mega, see the DALL·E Mega [training journal](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega-Training--VmlldzoxODMxMDI2#training-parameters).
* **Cite as:**
```bib text
@misc{Dayma_DALL·E_Mini_2021,
author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata},
doi = {10.5281/zenodo.5146400},
month = {7},
title = {DALL·E Mini},
url = {https://github.com/borisdayma/dalle-mini},
year = {2021}
}
```
## Uses
### Direct Use
The model is intended to be used to generate images based on text prompts for research and personal consumption. Intended uses include supporting creativity, creating humorous content, and providing generations for people curious about the model’s behavior. Intended uses exclude those described in the [Misuse and Out-of-Scope Use](#misuse-malicious-use-and-out-of-scope-use) section.
### Downstream Use
The model could also be used for downstream use cases, including:
* Research efforts, such as probing and better understanding the limitations and biases of generative models to further improve the state of science
* Development of educational or creative tools
* Generation of artwork and use in design and artistic processes.
* Other uses that are newly discovered by users. This currently includes poetry illustration (give a poem as prompt), fan art (putting a character in various other visual universes), visual puns, fairy tale illustrations (give a fantasy situation as prompt), concept mashups (applying a texture to something completely different), style transfers (portraits in the style of), … We hope you will find your own application!
Downstream uses exclude the uses described in [Misuse and Out-of-Scope Use](#misuse-malicious-use-and-out-of-scope-use).
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes:
* Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
* Intentionally promoting or propagating discriminatory content or harmful stereotypes.
* Impersonating individuals without their consent.
* Sexual content without consent of the people who might see it.
* Mis- and disinformation
* Representations of egregious violence and gore
* Sharing of copyrighted or licensed material in violation of its terms of use.
* Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
The model developers discuss the limitations of the model further in the DALL·E Mini [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA):
* Faces and people in general are not generated properly.
* Animals are usually unrealistic.
* It is hard to predict where the model excels or falls short…Good prompt engineering will lead to the best results.
* The model has only been trained with English descriptions and will not perform as well in other languages
### Bias
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
The model was trained on unfiltered data from the Internet, limited to pictures with English descriptions. Text and images from communities and cultures using other languages were not utilized. This affects all output of the model, with white and Western culture asserted as a default, and the model’s ability to generate content using non-English prompts is observably lower quality than prompts in English.
While the capabilities of image generation models are impressive, they may also reinforce or exacerbate societal biases. The extent and nature of the biases of DALL·E Mini and DALL·E Mega models have yet to be fully documented, but initial testing demonstrates that they may generate images that contain negative stereotypes against minoritized groups. Work to analyze the nature and extent of the models’ biases and limitations is ongoing.
Our current analyses demonstrate that:
* Images generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
* When the model generates images with people in them, it tends to output people who we perceive to be white, while people of color are underrepresented.
* Images generated by the model can contain biased content that depicts power differentials between people of color and people who are white, with white people in positions of privilege.
* The model is generally only usable for generating images based on text in English, limiting accessibility of the model for non-English speakers and potentially contributing to the biases in images generated by the model.
The [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA) discusses these issues in more detail, and also highlights potential sources of bias in the model development process.
### Limitations and Bias Recommendations
* Users (both direct and downstream) should be made aware of the biases and limitations.
* Content that is potentially problematic should be filtered out, e.g., via automated models that detect violence or pornography.
* Further work on this model should include methods for balanced and just representations of people and cultures, for example, by curating the training dataset to be both diverse and inclusive.
## Training
### Training Data
The model developers used 3 datasets for the model:
* [Conceptual Captions Dataset](https://aclanthology.org/P18-1238/), which contains 3 million image and caption pairs.
* [Conceptual 12M](https://arxiv.org/abs/2102.08981), which contains 12 million image and caption pairs.
* The [OpenAI subset](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md) of [YFCC100M](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/), which contains about 15 million images and that we further sub-sampled to 2 million images due to limitations in storage space. They used both title and description as caption and removed html tags, new lines and extra spaces.
For fine-tuning the image encoder, a subset of 2 million images were used.
All images (about 15 million) were used for training the Seq2Seq model.
### Training Procedure
As described further in the [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA#our-dall-e-model-architecture) for DALL·E Mini, during training, images and descriptions are both available and pass through the system as follows:
* Images are encoded through a [VQGAN](https://arxiv.org/abs/2012.09841) encoder, which turns images into a sequence of tokens.
* Descriptions are encoded through a [BART](https://arxiv.org/abs/1910.13461) encoder.
* The output of the BART encoder and encoded images are fed through the BART decoder, which is an auto-regressive model whose goal is to predict the next token.
* Loss is the [softmax cross-entropy](https://wandb.ai/sauravm/Activation-Functions/reports/Activation-Functions-Softmax--VmlldzoxNDU1Njgy#%F0%9F%93%A2-softmax-+-cross-entropy-loss-(caution:-math-alert)) between the model prediction logits and the actual image encodings from the VQGAN.
The simplified training procedure for DALL·E Mega is as follows:
* **Hardware:** 1 pod TPU v3-256 = 32 nodes of TPU VM v3-8 (8 TPU per node) = 256 TPU v3
* **Optimizer:** Distributed Shampoo
* **Model Partition Specificiations:** 8 model parallel x 32 data parallel
* **Batch:** 44 samples per model x 32 data parallel x 3 gradient accumulation steps = 4224 increasing samples per update
* **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant until plateau
* Gradient checkpointing used on each Encoder/Decoder layer (ie, MHA + FFN)
* Distributed Shampoo + Normformer Optimizations have proved to be effective and efficiently scaling this model.
* It should also be noted that the learning rate and other parameters are sometimes adjusted on the fly, and batch size increased over time as well.
There is more information about the full procedure and technical material in the DALL·E Mega [training journal](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega-Training--VmlldzoxODMxMDI2#training-parameters).
## Evaluation Results
The model developers discuss their results extensively in their [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA#the-results-of-our-dall-e-experiment) for DALL·E Mini, which provides comparisons between DALL·E Mini’s results with [DALL·E-pytorch](https://github.com/lucidrains/DALLE-pytorch), OpenAI’s [DALL·E](https://openai.com/blog/dall-e/), and models consisting of a generator coupled with the [CLIP neural network model](https://openai.com/blog/clip/).
For evaluation results related to DALL·E Mega, see this [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy).
## Environmental Impact
### DALL·E Mini Estimated Emissions
*The model is 27 times smaller than the original DALL·E and was trained on a single TPU v3-8 for only 3 days.*
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
* **Hardware Type:** TPU v3-8
* **Hours used:** 72 (3 days)
* **Cloud Provider:** GCP (as mentioned in the technical report)
* **Compute Region:** us-east1 (provided by model developers)
* **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 30.16 kg CO2 eq.
### DALL·E Mega Estimated Emissions
DALL·E Mega is still training. So far, as on June 9, 2022, the model developers report that DALL·E Mega has been training for about 40-45 days on a TPU v3-256. Using those numbers, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
* **Hardware Type:** TPU v3-256
* **Hours used:** 960 - 1080 hours (40-45 days)
* **Cloud Provider:** Unknown
* **Compute Region:** Unknown
* **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** Unknown
## Citation
```bibtext
@misc{Dayma_DALL·E_Mini_2021,
author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata},
doi = {10.5281/zenodo.5146400},
month = {7},
title = {DALL·E Mini},
url = {https://github.com/borisdayma/dalle-mini},
year = {2021}
}
```
*This model card was written by: Boris Dayma, Margaret Mitchell, Ezi Ozoani, Marissa Gerchick, Irene Solaiman, Clémentine Fourrier, Sasha Luccioni, Emily Witko, Nazneen Rajani, and Julian Herrera.*
|
{"language": "en", "license": "apache-2.0", "tags": ["text-to-image"], "inference": false, "co2_eq_emissions": {"emissions": 7540, "source": "MLCo2 Machine Learning Impact calculator", "geographical_location": "East USA", "hardware_used": "TPU v3-8"}, "model-index": [{"name": "dalle-mini", "results": []}]}
|
dalle-mini/dalle-mini
| null |
[
"transformers",
"jax",
"dallebart",
"text-to-image",
"en",
"arxiv:2102.08981",
"arxiv:2012.09841",
"arxiv:1910.13461",
"arxiv:1910.09700",
"license:apache-2.0",
"co2_eq_emissions",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null |
transformers
|
## VQGAN-f16-16384
### Model Description
This is a Flax/JAX implementation of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in [Taming Transformers for High-Resolution Image Synthesis](https://compvis.github.io/taming-transformers/) ([CVPR paper](https://openaccess.thecvf.com/content/CVPR2021/html/Esser_Taming_Transformers_for_High-Resolution_Image_Synthesis_CVPR_2021_paper.html)).
The model allows the encoding of images as a fixed-length sequence of tokens taken from the codebook.
This version of the model uses a reduction factor `f=16` and a vocabulary of `16,384` tokens.
As an example of how the reduction factor works, images of size `256x256` are encoded to sequences of `256` tokens: `256/16 * 256/16`. Images of `512x512` would result in sequences of `1024` tokens.
This model was ported to JAX using [a checkpoint trained on ImageNet](https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/).
### How to Use
The checkpoint can be loaded using [Suraj Patil's implementation](https://github.com/patil-suraj/vqgan-jax) of `VQModel`.
### Other
This model can be used as part of the implementation of [DALL·E mini](https://github.com/borisdayma/dalle-mini). Our [report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA) contains more details on how to leverage it in an image encoding / generation pipeline.
|
{}
|
dalle-mini/vqgan_imagenet_f16_16384
| null |
[
"transformers",
"jax",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null |
transformers
|
{}
|
damien-ir/ko-rest-electra-discriminator
| null |
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
damien-ir/ko-rest-electra-generator
| null |
[
"transformers",
"pytorch",
"electra",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
damien-ir/kosentelectra-discriminator-v1
| null |
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
{}
|
damien-ir/kosentelectra-discriminator-v2-mixed
| null |
[
"transformers",
"pytorch",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
damien-ir/kosentelectra-discriminator-v2-small
| null |
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
damien-ir/kosentelectra-discriminator-v2
| null |
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
damien-ir/kosentelectra-discriminator-v3
| null |
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
damien-ir/kosentelectra-discriminator-v4
| null |
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
damien-ir/kosentelectra-discriminator-v5
| null |
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
damien-ir/kosentelectra-generator-v1
| null |
[
"transformers",
"pytorch",
"electra",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
damien-ir/kosentelectra-generator-v2
| null |
[
"transformers",
"pytorch",
"electra",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
damien-ir/kosentelectra-generator-v3
| null |
[
"transformers",
"pytorch",
"electra",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
damien-ir/kosentelectra-generator-v4
| null |
[
"transformers",
"pytorch",
"electra",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
damien-ir/kosentelectra-generator-v5
| null |
[
"transformers",
"pytorch",
"electra",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
# 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]
|
{"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"}]}
|
damlab/HIV_BERT
| null |
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"dataset:damlab/HIV_FLT",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
# HIV_PR_resist 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-Protease-Resistance model was trained as a refinement of the HIV-BERT model (insert link) and serves to better predict whether an HIV protease sequence will be resistant to certain protease inhibitors. 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 protease sequences from the [Stanford HIV Genotype-Phenotype Database](https://hivdb.stanford.edu/pages/genotype-phenotype.html), allowing even more precise prediction protease inhibitor resistance than the HIV-BERT model can provide.
## Model Description
The HIV-BERT-Protease-Resistance model is intended to predict the likelihood that an HIV protease sequence will be resistant to protease inhibitors. The protease gene is responsible for cleaving viral proteins into their active states, and as such is an ideal target for antiretroviral therapy. Annotation programs designed to predict and identify protease resistance using known mutations already exist, however with varied results. The HIV-BERT-Protease-Resistance model is designed to provide an alternative, NLP-based mechanism for predicting resistance mutations when provided with an HIV protease sequence.
## Intended Uses & Limitations
This tool can be used as a predictor of protease resistance mutations within an HIV genomic sequence. It should not be considered a clinical diagnostic tool.
## How to use
*Prediction example of protease sequences*
## Training Data
This model was trained using the [damlab/HIV-PI dataset](https://huggingface.co/datasets/damlab/HIV_PI) using the 0th fold. The dataset consists of 1959 sequences (approximately 99 tokens each) extracted from the Stanford HIV Genotype-Phenotype Database.
## Training Procedure
### Preprocessing
As with the [rostlab/Prot-bert-bfd model](https://huggingface.co/Rostlab/prot_bert_bfd), 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](https://huggingface.co/damlab/HIV_BERT) 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 resistant to multiple drugs) 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]
|
{"license": "mit"}
|
damlab/HIV_PR_resist
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
# HIV_V3_coreceptor 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-Coreceptor model was trained as a refinement of the [HIV-BERT model](https://huggingface.co/damlab/HIV_BERT) and serves to better predict HIV V3 coreceptor tropism. 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 V3 coreceptor tropism than the HIV-BERT model can provide.
## Model Description
The HIV-BERT-Coreceptor model is intended to predict the Co-receptor tropism of HIV from a segment of the envelope protein. These envelope proteins encapsulate the virus and interact with the host cell through the human CD4 receptor. HIV then requires the interaction of one, of two, co-receptors: CCR5 or CXCR4. The availability of these co-receptors on different cell types allows the virus to invade different areas of the body and evade antiretroviral therapy. The 3rd variable loop of the envelope protein, the V3 loop, is responsible for this interaction. Given a V3 loop sequence, the HIV-BERT-Coreceptor model will predict the likelihood of binding to each of these co-receptors.
## Intended Uses & Limitations
This tool can be used as a predictor of HIV tropism from the Env-V3 loop. It can recognize both R5, X4, and dual tropic viruses natively. 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
*Need to add*
## Training Data
This model was trained using the [damlab/HIV_V3_coreceptor dataset](https://huggingface.co/datasets/damlab/HIV_V3_coreceptor) using the 0th fold. The dataset consists of 2935 V3 sequences (approximately 35 tokens each) extracted from the [Los Alamos HIV Sequence database](https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html).
## Training Procedure
### Preprocessing
As with the [rostlab/Prot-bert-bfd model](https://huggingface.co/Rostlab/prot_bert_bfd), 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](https://huggingface.co/damlab/HIV_BERT) 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 bind to CCR5, CXCR4, neither, or both) 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]
|
{"license": "mit", "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"}, {"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"}, {"text": "C T R P N N N T R R S I R I G P G Q A F Y A T G D I I G D I R Q A H C"}, {"text": "C G R P N N H R I K G L R I G P G R A F F A M G A I G G G E I R Q A H C"}]}
|
damlab/HIV_V3_Coreceptor
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
# 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]
|
{"datasets": ["damlab/HIV_V3_bodysite"], "metrics": ["accuracy"], "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"}]}
|
damlab/HIV_V3_bodysite
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"dataset:damlab/HIV_V3_bodysite",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
damondanieli/tweettest
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
{}
|
danasone/bart-small-ru-en
| null |
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
token-classification
|
transformers
|
{}
|
danasone/rubert-tiny-essay
| null |
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
token-classification
|
transformers
|
{}
|
danasone/rubert-tiny-grammar
| null |
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
token-classification
|
transformers
|
{}
|
danasone/rubert-tiny-speech
| null |
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
danasone/testpush
| null |
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
danbri/HelloWorld
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-generation
|
transformers
|
#dialogue
|
{"tags": ["text-generation"]}
|
danchang11/GPT2-TraditionalChat
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null |
transformers
|
# Vision-and-Language Transformer (ViLT), fine-tuned on COCO
Vision-and-Language Transformer (ViLT) model fine-tuned on [COCO](https://cocodataset.org/#home). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT).
Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Intended uses & limitations
You can use the model for image and text retrieval.
### How to use
Here is how to use the model in PyTorch:
```
from transformers import ViltProcessor, ViltForImageAndTextRetrieval
import requests
from PIL import Image
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"]
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-coco")
model = ViltForImageAndTextRetrieval.from_pretrained("dandelin/vilt-b32-finetuned-coco")
# prepare inputs
encoding = processor(image, text, return_tensors="pt")
# forward pass
scores = dict()
for text in texts:
encoding = processor(image, text, return_tensors="pt")
outputs = model(**encoding)
scores[text] = outputs.logits[0, :].item()
```
## Training data
(to do)
## Training procedure
### Preprocessing
(to do)
### Pretraining
(to do)
## Evaluation results
(to do)
### BibTeX entry and citation info
```bibtex
@misc{kim2021vilt,
title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision},
author={Wonjae Kim and Bokyung Son and Ildoo Kim},
year={2021},
eprint={2102.03334},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
```
|
{"license": "apache-2.0"}
|
dandelin/vilt-b32-finetuned-coco
| null |
[
"transformers",
"pytorch",
"vilt",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null |
transformers
|
# Vision-and-Language Transformer (ViLT), fine-tuned on Flickr30k
Vision-and-Language Transformer (ViLT) model fine-tuned on [Flickr30k](https://arxiv.org/abs/1505.04870#:~:text=The%20Flickr30k%20dataset%20has%20become,for%20sentence%2Dbased%20image%20description.&text=Such%20annotations%20are%20essential%20for,entity%20mentions%20in%20an%20image.). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT).
Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Intended uses & limitations
You can use the model for image and text retrieval.
### How to use
Here is how to use the model in PyTorch:
```
from transformers import ViltProcessor, ViltForImageAndTextRetrieval
import requests
from PIL import Image
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"]
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-flickr30k")
model = ViltForImageAndTextRetrieval.from_pretrained("dandelin/vilt-b32-finetuned-flickr30k")
# prepare inputs
encoding = processor(image, text, return_tensors="pt")
# forward pass
scores = dict()
for text in texts:
encoding = processor(image, text, return_tensors="pt")
outputs = model(**encoding)
scores[text] = outputs.logits[0, :].item()
```
## Training data
(to do)
## Training procedure
### Preprocessing
(to do)
### Pretraining
(to do)
## Evaluation results
(to do)
### BibTeX entry and citation info
```bibtex
@misc{kim2021vilt,
title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision},
author={Wonjae Kim and Bokyung Son and Ildoo Kim},
year={2021},
eprint={2102.03334},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
```
|
{"license": "apache-2.0"}
|
dandelin/vilt-b32-finetuned-flickr30k
| null |
[
"transformers",
"pytorch",
"vilt",
"arxiv:1505.04870",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null |
transformers
|
# Vision-and-Language Transformer (ViLT), fine-tuned on NLVR2
Vision-and-Language Transformer (ViLT) model fine-tuned on [NLVR2](https://lil.nlp.cornell.edu/nlvr/). It was introduced in the paper [ViLT: Vision-and-Language Transformer
Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT).
Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Intended uses & limitations
You can use the model to determine whether a sentence is true or false given 2 images.
### How to use
Here is how to use the model in PyTorch:
```
from transformers import ViltProcessor, ViltForImagesAndTextClassification
import requests
from PIL import Image
image1 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg", stream=True).raw)
image2 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_1.jpg", stream=True).raw)
text = "The left image contains twice the number of dogs as the right image."
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2")
model = ViltForImagesAndTextClassification.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2")
# prepare inputs
encoding = processor([image1, image2], text, return_tensors="pt")
# forward pass
outputs = model(input_ids=encoding.input_ids, pixel_values=encoding.pixel_values.unsqueeze(0))
logits = outputs.logits
idx = logits.argmax(-1).item()
print("Predicted answer:", model.config.id2label[idx])
```
## Training data
(to do)
## Training procedure
### Preprocessing
(to do)
### Pretraining
(to do)
## Evaluation results
(to do)
### BibTeX entry and citation info
```bibtex
@misc{kim2021vilt,
title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision},
author={Wonjae Kim and Bokyung Son and Ildoo Kim},
year={2021},
eprint={2102.03334},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
```
|
{"license": "apache-2.0"}
|
dandelin/vilt-b32-finetuned-nlvr2
| null |
[
"transformers",
"pytorch",
"vilt",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
visual-question-answering
|
transformers
|
# Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2
Vision-and-Language Transformer (ViLT) model fine-tuned on [VQAv2](https://visualqa.org/). It was introduced in the paper [ViLT: Vision-and-Language Transformer
Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT).
Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Intended uses & limitations
You can use the raw model for visual question answering.
### How to use
Here is how to use this model in PyTorch:
```python
from transformers import ViltProcessor, ViltForQuestionAnswering
import requests
from PIL import Image
# prepare image + question
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
text = "How many cats are there?"
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
# prepare inputs
encoding = processor(image, text, return_tensors="pt")
# forward pass
outputs = model(**encoding)
logits = outputs.logits
idx = logits.argmax(-1).item()
print("Predicted answer:", model.config.id2label[idx])
```
## Training data
(to do)
## Training procedure
### Preprocessing
(to do)
### Pretraining
(to do)
## Evaluation results
(to do)
### BibTeX entry and citation info
```bibtex
@misc{kim2021vilt,
title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision},
author={Wonjae Kim and Bokyung Son and Ildoo Kim},
year={2021},
eprint={2102.03334},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
```
|
{"license": "apache-2.0", "tags": ["visual-question-answering"], "widget": [{"text": "What's the animal doing?", "src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg"}, {"text": "What is on top of the building?", "src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg"}]}
|
dandelin/vilt-b32-finetuned-vqa
| null |
[
"transformers",
"pytorch",
"vilt",
"visual-question-answering",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null |
transformers
|
# Vision-and-Language Transformer (ViLT), pre-trained only
Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT).
Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
(to do)
## Intended uses & limitations
You can use the raw model for visual question answering.
### How to use
(to do)
## Training data
(to do)
## Training procedure
### Preprocessing
(to do)
### Pretraining
(to do)
## Evaluation results
(to do)
### BibTeX entry and citation info
```bibtex
@misc{kim2021vilt,
title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision},
author={Wonjae Kim and Bokyung Son and Ildoo Kim},
year={2021},
eprint={2102.03334},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
```
|
{"license": "apache-2.0"}
|
dandelin/vilt-b32-mlm-itm
| null |
[
"transformers",
"pytorch",
"vilt",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
# Vision-and-Language Transformer (ViLT), pre-trained only
Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT). Note: this model only includes the language modeling head.
Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Intended uses & limitations
You can use the raw model for masked language modeling given an image and a piece of text with [MASK] tokens.
### How to use
Here is how to use this model in PyTorch:
```
from transformers import ViltProcessor, ViltForMaskedLM
import requests
from PIL import Image
import re
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
text = "a bunch of [MASK] laying on a [MASK]."
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-mlm")
model = ViltForMaskedLM.from_pretrained("dandelin/vilt-b32-mlm")
# prepare inputs
encoding = processor(image, text, return_tensors="pt")
# forward pass
outputs = model(**encoding)
tl = len(re.findall("\[MASK\]", text))
inferred_token = [text]
# gradually fill in the MASK tokens, one by one
with torch.no_grad():
for i in range(tl):
encoded = processor.tokenizer(inferred_token)
input_ids = torch.tensor(encoded.input_ids).to(device)
encoded = encoded["input_ids"][0][1:-1]
outputs = model(input_ids=input_ids, pixel_values=pixel_values)
mlm_logits = outputs.logits[0] # shape (seq_len, vocab_size)
# only take into account text features (minus CLS and SEP token)
mlm_logits = mlm_logits[1 : input_ids.shape[1] - 1, :]
mlm_values, mlm_ids = mlm_logits.softmax(dim=-1).max(dim=-1)
# only take into account text
mlm_values[torch.tensor(encoded) != 103] = 0
select = mlm_values.argmax().item()
encoded[select] = mlm_ids[select].item()
inferred_token = [processor.decode(encoded)]
selected_token = ""
encoded = processor.tokenizer(inferred_token)
processor.decode(encoded.input_ids[0], skip_special_tokens=True)
```
## Training data
(to do)
## Training procedure
### Preprocessing
(to do)
### Pretraining
(to do)
## Evaluation results
(to do)
### BibTeX entry and citation info
```bibtex
@misc{kim2021vilt,
title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision},
author={Wonjae Kim and Bokyung Son and Ildoo Kim},
year={2021},
eprint={2102.03334},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
```
|
{"license": "apache-2.0"}
|
dandelin/vilt-b32-mlm
| null |
[
"transformers",
"pytorch",
"vilt",
"fill-mask",
"arxiv:2102.03334",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
danf0/MADE
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
# GPT-2 Fine-tuning in Vietnamese Wikipedia
## Model description
This is a Vietnamese GPT-2 model which is finetuned on the [Latest pages articles of Vietnamese Wikipedia](https://dumps.wikimedia.org/viwiki/latest/viwiki-latest-pages-articles.xml.bz2).
## Dataset
The dataset is about 800MB, includes many articles from Wikipedia.
## How to use
You can use this model to:
- Tokenize Vietnamese sentences with GPT2Tokenizer.
- Generate text seems like a Wikipedia article.
- Finetune it to other downstream tasks.
Here is how to use the model to generate text in Pytorch:
```python
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained('danghuy1999/gpt2-viwiki')
model = GPT2LMHeadModel.from_pretrained('danghuy1999/gpt2-viwiki').to('cuda')
text = "Albert Einstein là nhà vật lý học tạo ra thuyết lượng tử"
input_ids = tokenizer.encode(text, return_tensors='pt').to('cuda')
max_length = 100
sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
do_sample=True,
max_length=max_length,
min_length=max_length,
top_k=40,
num_beams=5,
early_stopping=True,
no_repeat_ngram_size=2,
num_return_sequences=3)
for i, sample_output in enumerate(sample_outputs):
print(">> Generated text {}\n\n{}".format(i+1, tokenizer.decode(sample_output.tolist())))
print('\n---')
```
And the results are:
```bash
>> Generated text 1
Albert Einstein là nhà vật lý học tạo ra thuyết lượng tử.
Mặc dù thuyết tương đối tổng quát không được áp dụng rộng rãi trong nhiều lĩnh vực khác nhau, nhưng các nhà lý thuyết đã đưa ra khái niệm rộng hơn về tính chất của vật chất. Một trong những nghiên cứu của Albert Einstein về sự tồn tại của hệ quy chiếu quán tính, ông đã đề xuất rằng một lực hấp dẫn có thể có khối lượng bằng năng lượng của nó. Tuy nhiên, những người cho rằng
---
>> Generated text 2
Albert Einstein là nhà vật lý học tạo ra thuyết lượng tử. Tuy nhiên, thuyết tương đối hẹp không phải là lý thuyết của Einstein.
Cho đến tận cuối thế kỷ 19, Albert Einstein đã chứng minh được sự tồn tại của lực hấp dẫn trong một số trường hợp đặc biệt. Năm 1915, ông đưa ra khái niệm "khối lượng" để miêu tả chuyển động lượng của một hạt bằng khối lượng nghỉ của nó. Ông cho rằng năng lượng "m" là một thành phần của
---
>> Generated text 3
Albert Einstein là nhà vật lý học tạo ra thuyết lượng tử. Tuy nhiên, thuyết tương đối hẹp không được chấp nhận rộng rãi bởi các nhà lý thuyết.
Một trong những nghiên cứu của Einstein về tính chất của lực hấp dẫn là vào năm 1905, ông đã đưa ra một khái niệm về lực học. Ông đã phát biểu rằng nếu một hạt mang điện tích dương, nó có thể chuyển đổi năng lượng của nó thành các hạt khác. Năm 1915, Arthur Eddington phát minh ra
---
```
You can do the same with **Tensorflow** by using the model **TFGPT2Tokenizer** instead.
|
{"language": "vi", "license": "mit", "tags": ["gpt2-viwiki"]}
|
danghuy1999/gpt2-viwiki
| null |
[
"transformers",
"pytorch",
"tf",
"gpt2",
"gpt2-viwiki",
"vi",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
sentence-similarity
|
transformers
|
## Description:
[**Sentence-CamemBERT-Large**](https://huggingface.co/dangvantuan/sentence-camembert-large) is the Embedding Model for French developed by [La Javaness](https://www.lajavaness.com/). The purpose of this embedding model is to represent the content and semantics of a French sentence in a mathematical vector which allows it to understand the meaning of the text-beyond individual words in queries and documents, offering a powerful semantic search.
## Pre-trained sentence embedding models are state-of-the-art of Sentence Embeddings for French.
The model is Fine-tuned using pre-trained [facebook/camembert-large](https://huggingface.co/camembert/camembert-large) and
[Siamese BERT-Networks with 'sentences-transformers'](https://www.sbert.net/) on dataset [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train)
## Usage
The model can be used directly (without a language model) as follows:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("dangvantuan/sentence-camembert-large")
sentences = ["Un avion est en train de décoller.",
"Un homme joue d'une grande flûte.",
"Un homme étale du fromage râpé sur une pizza.",
"Une personne jette un chat au plafond.",
"Une personne est en train de plier un morceau de papier.",
]
embeddings = model.encode(sentences)
```
## Evaluation
The model can be evaluated as follows on the French test data of stsb.
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.readers import InputExample
from datasets import load_dataset
def convert_dataset(dataset):
dataset_samples=[]
for df in dataset:
score = float(df['similarity_score'])/5.0 # Normalize score to range 0 ... 1
inp_example = InputExample(texts=[df['sentence1'],
df['sentence2']], label=score)
dataset_samples.append(inp_example)
return dataset_samples
# Loading the dataset for evaluation
df_dev = load_dataset("stsb_multi_mt", name="fr", split="dev")
df_test = load_dataset("stsb_multi_mt", name="fr", split="test")
# Convert the dataset for evaluation
# For Dev set:
dev_samples = convert_dataset(df_dev)
val_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')
val_evaluator(model, output_path="./")
# For Test set:
test_samples = convert_dataset(df_test)
test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='sts-test')
test_evaluator(model, output_path="./")
```
**Test Result**:
The performance is measured using Pearson and Spearman correlation:
- On dev
| Model | Pearson correlation | Spearman correlation | #params |
| ------------- | ------------- | ------------- |------------- |
| [dangvantuan/sentence-camembert-large](https://huggingface.co/dangvantuan/sentence-camembert-large)| 88.2 |88.02 | 336M|
| [dangvantuan/sentence-camembert-base](https://huggingface.co/dangvantuan/sentence-camembert-base) | 86.73|86.54 | 110M |
| [distiluse-base-multilingual-cased](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased) | 79.22 | 79.16|135M |
| [GPT-3 (text-davinci-003)](https://platform.openai.com/docs/models) | 85 | NaN|175B |
| [GPT-(text-embedding-ada-002)](https://platform.openai.com/docs/models) | 79.75 | 80.44|NaN |
- On test
| Model | Pearson correlation | Spearman correlation |
| ------------- | ------------- | ------------- |
| [dangvantuan/sentence-camembert-large](https://huggingface.co/dangvantuan/sentence-camembert-large)| 85.9 | 85.8|
| [dangvantuan/sentence-camembert-base](https://huggingface.co/dangvantuan/sentence-camembert-base)| 82.36 | 81.64|
| [distiluse-base-multilingual-cased](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased) | 78.62 | 77.48|
| [GPT-3 (text-davinci-003)](https://platform.openai.com/docs/models) | 82 | NaN|175B |
| [GPT-(text-embedding-ada-002)](https://platform.openai.com/docs/models) | 79.05 | 77.56|NaN |
## Citation
@article{reimers2019sentence,
title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
author={Nils Reimers, Iryna Gurevych},
journal={https://arxiv.org/abs/1908.10084},
year={2019}
}
@article{martin2020camembert,
title={CamemBERT: a Tasty French Language Mode},
author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
journal={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020}
}
|
{"language": "fr", "license": "apache-2.0", "tags": ["Text", "Sentence Similarity", "Sentence-Embedding", "camembert-large"], "datasets": ["stsb_multi_mt"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "sentence-camembert-large by Van Tuan DANG", "results": [{"task": {"type": "Text Similarity", "name": "Sentence-Embedding"}, "dataset": {"name": "Text Similarity fr", "type": "stsb_multi_mt", "args": "fr"}, "metrics": [{"type": "Pearson_correlation_coefficient", "value": "xx.xx", "name": "Test Pearson correlation coefficient"}]}]}]}
|
dangvantuan/sentence-camembert-large
| null |
[
"transformers",
"pytorch",
"tf",
"safetensors",
"camembert",
"feature-extraction",
"Text",
"Sentence Similarity",
"Sentence-Embedding",
"camembert-large",
"sentence-similarity",
"fr",
"dataset:stsb_multi_mt",
"arxiv:1908.10084",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
danhsf/mt5-small-finetuned-en-to-ro-fp_false
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
danhsf/mt5-small-finetuned-en-to-ro
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
{}
|
danhsf/mt5-small-finetuned-hi-to-en
| null |
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
danhsf/t5-base-finetuned-en-to-hi
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
danhsf/t5-small-finetuned-en-to-hi-maxlength-256
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
danhsf/t5-small-finetuned-en-to-hi
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
<!-- 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. -->
# t5-small-finetuned-en-to-pt
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3295
- Bleu: 5.6807
- Gen Len: 18.6772
## 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: 0.005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 0.5787 | 1.0 | 6250 | 0.4928 | 4.1007 | 18.638 |
| 0.5089 | 2.0 | 12500 | 0.4463 | 4.3492 | 18.663 |
| 0.4652 | 3.0 | 18750 | 0.4215 | 4.68 | 18.6652 |
| 0.4353 | 4.0 | 25000 | 0.3980 | 4.8172 | 18.6708 |
| 0.4042 | 5.0 | 31250 | 0.3799 | 4.9719 | 18.6514 |
| 0.3734 | 6.0 | 37500 | 0.3676 | 5.2226 | 18.6572 |
| 0.3396 | 7.0 | 43750 | 0.3513 | 5.2693 | 18.6596 |
| 0.308 | 8.0 | 50000 | 0.3400 | 5.4546 | 18.676 |
| 0.2767 | 9.0 | 56250 | 0.3331 | 5.5649 | 18.6708 |
| 0.2424 | 10.0 | 62500 | 0.3295 | 5.6807 | 18.6772 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-pt", "results": []}]}
|
danhsf/t5-small-finetuned-en-to-pt
| null |
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
danhsf/t5-small-finetuned-en-to-ro-fp_false
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
<!-- 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. -->
# t5-small-finetuned-en-to-ro-lr_2e-3-fp_false
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4239
- Bleu: 7.1921
- Gen Len: 18.2611
## 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: 0.002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 0.8922 | 0.05 | 2000 | 1.7000 | 6.5274 | 18.2656 |
| 0.8621 | 0.1 | 4000 | 1.6409 | 6.6411 | 18.2311 |
| 0.8433 | 0.16 | 6000 | 1.6396 | 6.6601 | 18.2596 |
| 0.8297 | 0.21 | 8000 | 1.6304 | 6.7129 | 18.2581 |
| 0.8006 | 0.26 | 10000 | 1.6022 | 6.6067 | 18.2816 |
| 0.793 | 0.31 | 12000 | 1.5999 | 6.551 | 18.2631 |
| 0.774 | 0.37 | 14000 | 1.5586 | 6.7105 | 18.2661 |
| 0.7618 | 0.42 | 16000 | 1.5769 | 6.7278 | 18.2526 |
| 0.7463 | 0.47 | 18000 | 1.5625 | 6.6972 | 18.2201 |
| 0.7394 | 0.52 | 20000 | 1.5377 | 6.936 | 18.2491 |
| 0.7203 | 0.58 | 22000 | 1.5191 | 7.0205 | 18.2731 |
| 0.7158 | 0.63 | 24000 | 1.5055 | 6.835 | 18.2506 |
| 0.688 | 0.68 | 26000 | 1.4779 | 7.0534 | 18.2716 |
| 0.678 | 0.73 | 28000 | 1.4691 | 6.9735 | 18.2616 |
| 0.6677 | 0.79 | 30000 | 1.4702 | 7.0359 | 18.2496 |
| 0.6568 | 0.84 | 32000 | 1.4534 | 6.9982 | 18.2556 |
| 0.6475 | 0.89 | 34000 | 1.4427 | 7.0443 | 18.2466 |
| 0.6395 | 0.94 | 36000 | 1.4265 | 7.1205 | 18.2721 |
| 0.6319 | 1.0 | 38000 | 1.4239 | 7.1921 | 18.2611 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-ro-lr_2e-3-fp_false", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "args": "ro-en"}, "metrics": [{"type": "bleu", "value": 7.1921, "name": "Bleu"}]}]}]}
|
danhsf/t5-small-finetuned-en-to-ro-lr_2e-3-fp_false
| null |
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
danhsf/t5-small-finetuned-en-to-ro-lr_2e-3
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
danhsf/t5-small-finetuned-en-to-ro-weight_decay
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
danhsf/t5-small-finetuned-en-to-ro
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
danhsf/t5-small-finetuned-hi-to-en
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
{}
|
danhsf/t5-small-finetuned-ro-to-en
| null |
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 457311749
- CO2 Emissions (in grams): 10.148805588432941
## Validation Metrics
- Loss: 1.647747278213501
- Rouge1: 32.4854
- Rouge2: 19.8974
- RougeL: 30.0602
- RougeLsum: 29.9377
- Gen Len: 46.6556
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/danicodes/autonlp-legal-text-summary-457311749
```
|
{"language": "unk", "tags": "autonlp", "datasets": ["danicodes/autonlp-data-legal-text-summary"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 10.148805588432941}
|
danicodes/autonlp-legal-text-summary-457311749
| null |
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autonlp",
"unk",
"dataset:danicodes/autonlp-data-legal-text-summary",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
danie08/distilbert-base-uncased-finetuned-cola
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text2text-generation
|
transformers
|
<!-- 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. -->
# t5-small-finetuned-fi-to-en
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt19 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5235
- Bleu: 1.129
- Gen Len: 17.088
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-----:|:-------:|
| 3.414 | 1.0 | 6250 | 3.5235 | 1.129 | 17.088 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt19"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-fi-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt19", "type": "wmt19", "args": "fi-en"}, "metrics": [{"type": "bleu", "value": 1.129, "name": "Bleu"}]}]}]}
|
danielbispov/t5-small-finetuned-fi-to-en
| null |
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt19",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- 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. -->
# bangla_asr
This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 157.8652
- Wer: 0.4507
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2601.5363 | 7.46 | 500 | 259.6630 | 0.6863 |
| 417.7386 | 14.93 | 1000 | 156.6117 | 0.5275 |
| 262.9455 | 22.39 | 1500 | 155.0886 | 0.5006 |
| 178.7715 | 29.85 | 2000 | 155.1077 | 0.4840 |
| 132.448 | 37.31 | 2500 | 163.8623 | 0.4770 |
| 116.3943 | 44.78 | 3000 | 161.5531 | 0.4609 |
| 87.1653 | 52.24 | 3500 | 165.6857 | 0.4597 |
| 80.5606 | 59.7 | 4000 | 157.8652 | 0.4507 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "bangla_asr", "results": []}]}
|
danielbubiola/bangla_asr
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- 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. -->
# daniel_asr
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: 0.4565
- Wer: 0.3423
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4909 | 4.0 | 500 | 1.3485 | 0.8887 |
| 0.5887 | 8.0 | 1000 | 0.4957 | 0.4641 |
| 0.2207 | 12.0 | 1500 | 0.4621 | 0.3971 |
| 0.125 | 16.0 | 2000 | 0.4339 | 0.3756 |
| 0.0829 | 20.0 | 2500 | 0.4618 | 0.3613 |
| 0.0601 | 24.0 | 3000 | 0.4564 | 0.3535 |
| 0.0456 | 28.0 | 3500 | 0.4565 | 0.3423 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "daniel_asr", "results": []}]}
|
danielbubiola/daniel_asr
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
danielf0123/distilbert-base-uncased-finetuned-ner
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
danielferreira/emotion-text-classification
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
daniellee93/text_summarization
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
danielo200/model_danielo
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
danielrama/QuestionAnswering
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
danielvasic/SemCorBert
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
danielvasic/distilbert-wordnet-uncased
| null |
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
token-classification
|
spacy
|
| Feature | Description |
| --- | --- |
| **Name** | `en_acnl_electra_pipeline` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.3,<3.2.0` |
| **Default Pipeline** | `transformer`, `tagger`, `parser` |
| **Components** | `transformer`, `tagger`, `parser` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | GPL |
| **Author** | Daniel Vasić() |
### Label Scheme
<details>
<summary>View label scheme (87 labels for 2 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`, `VERB`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `dative`, `dep`, `det`, `dobj`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nummod`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TAG_ACC` | 97.69 |
| `DEP_UAS` | 95.77 |
| `DEP_LAS` | 94.52 |
| `SENTS_P` | 95.09 |
| `SENTS_R` | 94.81 |
| `SENTS_F` | 94.95 |
| `TRANSFORMER_LOSS` | 6123357.72 |
| `TAGGER_LOSS` | 338995.26 |
| `PARSER_LOSS` | 4101825.66 |
|
{"language": ["en"], "tags": ["spacy", "token-classification"]}
|
danielvasic/en_acnl_electra_pipeline
| null |
[
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
spacy
|
| Feature | Description |
| --- | --- |
| **Name** | `en_acnl_roberta_pipeline` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.3,<3.2.0` |
| **Default Pipeline** | `transformer`, `tagger`, `parser` |
| **Components** | `transformer`, `tagger`, `parser` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | OntoNotes |
| **License** | CC BY-SA 4.0 |
| **Author** | Daniel Vasić |
### Label Scheme
<details>
<summary>View label scheme (87 labels for 2 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `$`, `''`, `,`, `-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$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `VERB`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `dative`, `dep`, `det`, `dobj`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nummod`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TAG_ACC` | 98.05 |
| `DEP_UAS` | 95.98 |
| `DEP_LAS` | 94.83 |
| `SENTS_P` | 93.80 |
| `SENTS_R` | 95.42 |
| `SENTS_F` | 94.61 |
| `TRANSFORMER_LOSS` | 3784861.59 |
| `TAGGER_LOSS` | 698704.80 |
| `PARSER_LOSS` | 5540167.00 |
|
{"language": ["en"], "license": "cc-by-4.0", "library_name": "spacy", "tags": ["spacy", "token-classification"], "datasets": ["conll2012_ontonotesv5"], "metrics": ["f1"], "pipeline_tag": "text-classification"}
|
danielvasic/en_acnl_roberta_pipeline
| null |
[
"spacy",
"token-classification",
"text-classification",
"en",
"dataset:conll2012_ontonotesv5",
"license:cc-by-4.0",
"model-index",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
token-classification
|
spacy
|
| Feature | Description |
| --- | --- |
| **Name** | `hr_bertic_pipeline` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.3,<3.2.0` |
| **Default Pipeline** | `transformer`, `morphologizer`, `tagger`, `parser` |
| **Components** | `transformer`, `morphologizer`, `tagger`, `parser` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (1392 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`morphologizer`** | `Case=nominative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|POS=ADP`, `Case=locative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=instrumental\|POS=ADP`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Degree=positive\|POS=ADV\|Type=general`, `Number=singular\|POS=VERB\|Person=third\|Type=main\|VForm=present`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `POS=PUNCT`, `POS=PART\|Type=modal`, `Case=locative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `POS=SCONJ\|Type=subordinating`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=accusative\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `POS=CCONJ\|Type=coordinating`, `Case=genitive\|POS=ADP`, `Case=dative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Number=plural\|POS=VERB\|Person=third\|Type=main\|VForm=present`, `Number=singular\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=present`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|POS=ADP`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Animate=no\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `POS=VERB\|Type=main\|VForm=infinitive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `POS=PART\|Type=negative`, `Case=accusative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Degree=comparative\|POS=ADV\|Type=general`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Gender=masculine\|Number=singular\|POS=VERB\|Type=main\|VForm=participle`, `Case=locative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Form=digit\|POS=ADJ\|Type=ordinal`, `Number=singular\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=present`, `Number=plural\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=present`, `Case=accusative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Gender=feminine\|Number=plural\|POS=VERB\|Type=main\|VForm=participle`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Gender=neuter\|Number=singular\|POS=VERB\|Type=main\|VForm=participle`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Degree=superlative\|POS=ADV\|Type=general`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Number=plural\|POS=VERB\|Person=first\|Type=main\|VForm=present`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Gender=feminine\|Number=plural\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Gender=masculine\|Number=singular\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Gender=masculine\|Number=plural\|POS=VERB\|Type=main\|VForm=participle`, `Form=digit\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Gender=feminine\|Number=singular\|POS=VERB\|Type=main\|VForm=participle`, `Case=accusative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Gender=neuter\|Number=singular\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=locative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Number=plural\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=aorist`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=dative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=locative\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=aorist`, `POS=X`, `Case=genitive\|Form=letter\|POS=NUM\|Type=cardinal`, `Case=genitive\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Form=letter\|POS=NUM\|Type=cardinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `POS=X\|Type=foreign`, `Number=plural\|POS=VERB\|Person=second\|Type=main\|VForm=present`, `POS=PART\|Type=interrogative`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `POS=ADV\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=VERB\|Person=first\|Type=main\|VForm=present`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=dative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Animate=yes\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Number=plural\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=present`, `POS=AUX\|Type=auxiliary\|VForm=infinitive`, `Case=locative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Gender=feminine\|Number=singular\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Animate=no\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=dative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Number=plural\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=aorist`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=dative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `POS=NOUN`, `Case=vocative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=accusative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=neuter\|POS=PRON\|Person=third\|Type=interrogative`, `Case=nominative\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Number=plural\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=present`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=dative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Number=singular\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=aorist`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=accusative\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Type=interrogative`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Animate=no\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=instrumental\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=dative\|POS=ADP`, `Case=instrumental\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Gender=neuter\|Number=plural\|POS=VERB\|Type=main\|VForm=participle`, `Case=nominative\|Form=letter\|Gender=neuter\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=dative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=accusative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Form=letter\|POS=NUM\|Type=special`, `Case=accusative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=genitive\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=genitive\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Type=interrogative`, `Case=nominative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=locative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=dative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Gender=masculine\|Number=plural\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=genitive\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=genitive\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `POS=PROPN`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=locative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=accusative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=locative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Gender=neuter\|Number=plural\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Number=plural\|POS=VERB\|Person=second\|Type=main\|VForm=imperative`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=locative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Form=digit\|POS=SYM\|Type=special`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=possessive`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=nominative\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Form=digit\|POS=NUM\|Type=special`, `Case=genitive\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=locative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=genitive\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=genitive\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=aorist`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `POS=SYM`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Case=vocative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Form=digit\|POS=NUM\|Type=multiple`, `Case=instrumental\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Animate=yes\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Number=plural\|POS=VERB\|Person=first\|Type=main\|VForm=imperative`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=plural\|POS=NUM\|Type=cardinal`, `Case=accusative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=interrogative`, `Case=accusative\|Gender=neuter\|POS=PRON\|Person=third\|Type=interrogative`, `Case=locative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=locative\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Animate=no\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=dative\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=accusative\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=PRON\|Type=indefinite`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=genitive\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=locative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=locative\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=accusative\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=locative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=VERB\|Person=third\|Type=main\|VForm=aorist`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=instrumental\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `POS=ADJ`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=instrumental\|Form=letter\|POS=NUM\|Type=cardinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=instrumental\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=dative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Number=singular\|POS=VERB\|Person=second\|Type=main\|VForm=imperative`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Form=roman\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=dative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=plural\|POS=NUM\|Type=special`, `Case=locative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=accusative\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=genitive\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Form=letter\|POS=NUM\|Type=cardinal`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=genitive\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Form=roman\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=PROPN\|Type=proper`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Number=plural\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=aorist`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|POS=SYM`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=nominative\|Gender=masculine\|POS=PRON\|Person=third\|Type=interrogative`, `Case=locative\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `POS=PART`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=genitive\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=dative\|Gender=masculine\|POS=PRON\|Person=third\|Type=interrogative`, `Case=instrumental\|Definiteness=no\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `POS=INTJ`, `Case=locative\|Gender=neuter\|POS=PRON\|Person=third\|Type=interrogative`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `POS=PART\|Type=affirmative`, `Number=singular\|POS=VERB\|Person=second\|Type=main\|VForm=present`, `Case=dative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=instrumental\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=neuter\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=neuter\|POS=NUM\|Type=special`, `Case=locative\|Form=letter\|Gender=masculine\|Number=plural\|POS=NUM\|Type=cardinal`, `Case=accusative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=locative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Number=singular\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=present`, `Case=vocative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=vocative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=vocative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=vocative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=vocative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=genitive\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Number=plural\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=imperative`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=vocative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Number=singular\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=imperfect`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=nominative\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `POS=ADV`, `Case=locative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=vocative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=vocative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=PROPN\|Type=proper`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=vocative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=vocative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=interrogative`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=vocative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=dative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Case=dative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=vocative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=vocative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive` |
| **`tagger`** | `Agcfpay`, `Agcfpdy`, `Agcfpgy`, `Agcfpiy`, `Agcfply`, `Agcfpny`, `Agcfsay`, `Agcfsdy`, `Agcfsgy`, `Agcfsiy`, `Agcfsly`, `Agcfsny`, `Agcmpay`, `Agcmpgy`, `Agcmpiy`, `Agcmply`, `Agcmpny`, `Agcmsany`, `Agcmsay`, `Agcmsayn`, `Agcmsdy`, `Agcmsgy`, `Agcmsiy`, `Agcmsly`, `Agcmsny`, `Agcnpay`, `Agcnpdy`, `Agcnpgy`, `Agcnpny`, `Agcnsay`, `Agcnsdy`, `Agcnsgy`, `Agcnsiy`, `Agcnsly`, `Agcnsny`, `Agpfpay`, `Agpfpdy`, `Agpfpgy`, `Agpfpiy`, `Agpfply`, `Agpfpny`, `Agpfsay`, `Agpfsdy`, `Agpfsgy`, `Agpfsin`, `Agpfsiy`, `Agpfsly`, `Agpfsny`, `Agpfsvy`, `Agpmpay`, `Agpmpdy`, `Agpmpgy`, `Agpmpiy`, `Agpmply`, `Agpmpny`, `Agpmpvy`, `Agpmsan`, `Agpmsann`, `Agpmsany`, `Agpmsay`, `Agpmsayn`, `Agpmsayy`, `Agpmsdy`, `Agpmsgn`, `Agpmsgy`, `Agpmsiy`, `Agpmsln`, `Agpmsly`, `Agpmsnn`, `Agpmsny`, `Agpmsvy`, `Agpnpay`, `Agpnpdy`, `Agpnpgy`, `Agpnpiy`, `Agpnply`, `Agpnpny`, `Agpnsay`, `Agpnsdy`, `Agpnsgn`, `Agpnsgy`, `Agpnsiy`, `Agpnsln`, `Agpnsly`, `Agpnsny`, `Agsfpay`, `Agsfpdy`, `Agsfpgy`, `Agsfpiy`, `Agsfply`, `Agsfpny`, `Agsfsay`, `Agsfsgy`, `Agsfsiy`, `Agsfsly`, `Agsfsny`, `Agsmpay`, `Agsmpdy`, `Agsmpgy`, `Agsmpiy`, `Agsmply`, `Agsmpny`, `Agsmpvy`, `Agsmsany`, `Agsmsay`, `Agsmsayn`, `Agsmsayy`, `Agsmsdy`, `Agsmsgy`, `Agsmsiy`, `Agsmsly`, `Agsmsny`, `Agsnpay`, `Agsnpgy`, `Agsnply`, `Agsnpny`, `Agsnsay`, `Agsnsdy`, `Agsnsiy`, `Agsnsly`, `Agsnsny`, `Appfpay`, `Appfpdy`, `Appfpgy`, `Appfpiy`, `Appfply`, `Appfpny`, `Appfsay`, `Appfsgy`, `Appfsiy`, `Appfsly`, `Appfsny`, `Appmpay`, `Appmpdy`, `Appmpgy`, `Appmpiy`, `Appmply`, `Appmpny`, `Appmsann`, `Appmsany`, `Appmsayn`, `Appmsayy`, `Appmsdy`, `Appmsgn`, `Appmsgy`, `Appmsiy`, `Appmsly`, `Appmsnn`, `Appmsny`, `Appnpay`, `Appnpdy`, `Appnpgy`, `Appnpiy`, `Appnply`, `Appnpny`, `Appnsay`, `Appnsgy`, `Appnsly`, `Appnsny`, `Aspfpay`, `Aspfpgy`, `Aspfpiy`, `Aspfply`, `Aspfpny`, `Aspfsay`, `Aspfsdy`, `Aspfsgy`, `Aspfsiy`, `Aspfsly`, `Aspfsny`, `Aspmpay`, `Aspmpgy`, `Aspmply`, `Aspmpny`, `Aspmsayn`, `Aspmsayy`, `Aspmsdy`, `Aspmsgn`, `Aspmsgy`, `Aspmsiy`, `Aspmsln`, `Aspmsly`, `Aspmsnn`, `Aspnpay`, `Aspnpgy`, `Aspnpny`, `Aspnsay`, `Aspnsdn`, `Aspnsgn`, `Aspnsgy`, `Aspnsly`, `Aspnsny`, `Cc`, `Cs`, `I`, `Mdc`, `Mdm`, `Mdo`, `Mds`, `Mlc`, `Mlc--g`, `Mlc--i`, `Mlc--l`, `Mlcf-a`, `Mlcf-d`, `Mlcf-g`, `Mlcf-n`, `Mlcfsa`, `Mlcfsd`, `Mlcfsg`, `Mlcfsi`, `Mlcfsl`, `Mlcfsn`, `Mlcm-a`, `Mlcm-g`, `Mlcm-l`, `Mlcm-n`, `Mlcmpl`, `Mlcmpn`, `Mlcmsan`, `Mlcmsay`, `Mlcmsg`, `Mlcmsi`, `Mlcmsl`, `Mlcmsn`, `Mlcn-n`, `Mlcnsa`, `Mlcnsg`, `Mlcnsl`, `Mlcnsn`, `Mlofpa`, `Mlofpd`, `Mlofpg`, `Mlofpi`, `Mlofpl`, `Mlofpn`, `Mlofsa`, `Mlofsd`, `Mlofsg`, `Mlofsi`, `Mlofsl`, `Mlofsn`, `Mlompa`, `Mlompd`, `Mlompg`, `Mlompi`, `Mlompl`, `Mlompn`, `Mlomsan`, `Mlomsay`, `Mlomsd`, `Mlomsg`, `Mlomsi`, `Mlomsl`, `Mlomsn`, `Mlomsv`, `Mlonpa`, `Mlonpg`, `Mlonpl`, `Mlonpn`, `Mlonsa`, `Mlonsd`, `Mlonsg`, `Mlonsi`, `Mlonsl`, `Mlonsn`, `Mls`, `Mlsf-a`, `Mlsf-d`, `Mlsf-g`, `Mlsf-i`, `Mlsf-l`, `Mlsf-n`, `Mlsm-a`, `Mlsm-g`, `Mlsm-l`, `Mlsm-n`, `Mlsmpn`, `Mlsn-n`, `Mrc`, `Mro`, `Ncfpa`, `Ncfpd`, `Ncfpg`, `Ncfpi`, `Ncfpl`, `Ncfpn`, `Ncfpv`, `Ncfsa`, `Ncfsd`, `Ncfsg`, `Ncfsi`, `Ncfsl`, `Ncfsn`, `Ncfsv`, `Ncmpa`, `Ncmpd`, `Ncmpg`, `Ncmpi`, `Ncmpl`, `Ncmpn`, `Ncmpv`, `Ncmsan`, `Ncmsay`, `Ncmsd`, `Ncmsg`, `Ncmsi`, `Ncmsl`, `Ncmsn`, `Ncmsv`, `Ncnpa`, `Ncnpd`, `Ncnpg`, `Ncnpi`, `Ncnpl`, `Ncnpn`, `Ncnsa`, `Ncnsd`, `Ncnsg`, `Ncnsi`, `Ncnsl`, `Ncnsn`, `Ncnsv`, `Npfpa`, `Npfpg`, `Npfpl`, `Npfpn`, `Npfsa`, `Npfsd`, `Npfsg`, `Npfsi`, `Npfsl`, `Npfsn`, `Npmpa`, `Npmpd`, `Npmpg`, `Npmpi`, `Npmpl`, `Npmpn`, `Npmsan`, `Npmsay`, `Npmsd`, `Npmsg`, `Npmsi`, `Npmsl`, `Npmsn`, `Npmsv`, `Npnpg`, `Npnpn`, `Npnsa`, `Npnsd`, `Npnsg`, `Npnsi`, `Npnsl`, `Npnsn`, `Pd-fpa`, `Pd-fpd`, `Pd-fpg`, `Pd-fpi`, `Pd-fpl`, `Pd-fpn`, `Pd-fsa`, `Pd-fsd`, `Pd-fsg`, `Pd-fsi`, `Pd-fsl`, `Pd-fsn`, `Pd-mpa`, `Pd-mpd`, `Pd-mpg`, `Pd-mpi`, `Pd-mpl`, `Pd-mpn`, `Pd-msan`, `Pd-msay`, `Pd-msd`, `Pd-msg`, `Pd-msi`, `Pd-msl`, `Pd-msn`, `Pd-npa`, `Pd-npd`, `Pd-npg`, `Pd-npi`, `Pd-npn`, `Pd-nsa`, `Pd-nsd`, `Pd-nsg`, `Pd-nsi`, `Pd-nsl`, `Pd-nsn`, `Pi-fpa`, `Pi-fpd`, `Pi-fpg`, `Pi-fpi`, `Pi-fpl`, `Pi-fpn`, `Pi-fsa`, `Pi-fsd`, `Pi-fsg`, `Pi-fsi`, `Pi-fsl`, `Pi-fsn`, `Pi-mpa`, `Pi-mpd`, `Pi-mpg`, `Pi-mpi`, `Pi-mpl`, `Pi-mpn`, `Pi-msan`, `Pi-msay`, `Pi-msd`, `Pi-msg`, `Pi-msi`, `Pi-msl`, `Pi-msn`, `Pi-npa`, `Pi-npd`, `Pi-npg`, `Pi-npi`, `Pi-npl`, `Pi-npn`, `Pi-nsa`, `Pi-nsd`, `Pi-nsg`, `Pi-nsi`, `Pi-nsl`, `Pi-nsn`, `Pi3m-a`, `Pi3m-d`, `Pi3m-g`, `Pi3m-i`, `Pi3m-n`, `Pi3n-a`, `Pi3n-d`, `Pi3n-g`, `Pi3n-i`, `Pi3n-l`, `Pi3n-n`, `Pp1-pa`, `Pp1-pd`, `Pp1-pg`, `Pp1-pi`, `Pp1-pl`, `Pp1-pn`, `Pp1-sa`, `Pp1-sd`, `Pp1-sg`, `Pp1-si`, `Pp1-sl`, `Pp1-sn`, `Pp2-pa`, `Pp2-pd`, `Pp2-pg`, `Pp2-pn`, `Pp2-sa`, `Pp2-sd`, `Pp2-sg`, `Pp2-sl`, `Pp2-sn`, `Pp2-sv`, `Pp3-pa`, `Pp3-pd`, `Pp3-pg`, `Pp3-pi`, `Pp3-pl`, `Pp3fpn`, `Pp3fsa`, `Pp3fsd`, `Pp3fsg`, `Pp3fsi`, `Pp3fsl`, `Pp3fsn`, `Pp3mpn`, `Pp3msa`, `Pp3msd`, `Pp3msg`, `Pp3msi`, `Pp3msl`, `Pp3msn`, `Pp3npn`, `Pp3nsa`, `Pp3nsi`, `Pp3nsn`, `Pq-fpa`, `Pq-fpn`, `Pq-fsa`, `Pq-fsi`, `Pq-fsl`, `Pq-fsn`, `Pq-mpn`, `Pq-msn`, `Pq-nsn`, `Pq3m-d`, `Pq3m-n`, `Pq3n-a`, `Pq3n-l`, `Pq3n-n`, `Ps1fpa`, `Ps1fpd`, `Ps1fpg`, `Ps1fpl`, `Ps1fpn`, `Ps1fsa`, `Ps1fsd`, `Ps1fsg`, `Ps1fsi`, `Ps1fsl`, `Ps1fsn`, `Ps1fsv`, `Ps1mpa`, `Ps1mpd`, `Ps1mpg`, `Ps1mpi`, `Ps1mpl`, `Ps1mpn`, `Ps1mpv`, `Ps1msan`, `Ps1msay`, `Ps1msd`, `Ps1msg`, `Ps1msi`, `Ps1msl`, `Ps1msn`, `Ps1msv`, `Ps1npd`, `Ps1npn`, `Ps1nsa`, `Ps1nsg`, `Ps1nsi`, `Ps1nsl`, `Ps1nsn`, `Ps2fpa`, `Ps2fpl`, `Ps2fpn`, `Ps2fsa`, `Ps2fsd`, `Ps2fsg`, `Ps2fsn`, `Ps2mpa`, `Ps2mpg`, `Ps2mpl`, `Ps2mpn`, `Ps2msan`, `Ps2msd`, `Ps2msg`, `Ps2msi`, `Ps2msl`, `Ps2msn`, `Ps2npn`, `Ps2nsa`, `Ps2nsg`, `Ps2nsi`, `Ps2nsl`, `Ps2nsn`, `Ps3fpa`, `Ps3fpg`, `Ps3fpl`, `Ps3fpn`, `Ps3fsa`, `Ps3fsd`, `Ps3fsg`, `Ps3fsi`, `Ps3fsl`, `Ps3fsn`, `Ps3mpa`, `Ps3mpd`, `Ps3mpg`, `Ps3mpi`, `Ps3mpl`, `Ps3mpn`, `Ps3msan`, `Ps3msay`, `Ps3msd`, `Ps3msg`, `Ps3msi`, `Ps3msl`, `Ps3msn`, `Ps3npa`, `Ps3npg`, `Ps3npl`, `Ps3npn`, `Ps3nsa`, `Ps3nsg`, `Ps3nsi`, `Ps3nsl`, `Ps3nsn`, `Px--sa`, `Px--sd`, `Px--sg`, `Px--si`, `Px--sl`, `Px-fpa`, `Px-fpg`, `Px-fpi`, `Px-fpl`, `Px-fpn`, `Px-fsa`, `Px-fsd`, `Px-fsg`, `Px-fsi`, `Px-fsl`, `Px-mpa`, `Px-mpd`, `Px-mpg`, `Px-mpi`, `Px-mpl`, `Px-msan`, `Px-msay`, `Px-msd`, `Px-msg`, `Px-msi`, `Px-msl`, `Px-npa`, `Px-npg`, `Px-npi`, `Px-npl`, `Px-nsa`, `Px-nsg`, `Px-nsi`, `Px-nsl`, `Qo`, `Qq`, `Qr`, `Qz`, `Rgc`, `Rgp`, `Rgs`, `Rr`, `Sa`, `Sd`, `Sg`, `Si`, `Sl`, `Vaa1p`, `Vaa1s`, `Vaa2p`, `Vaa2s`, `Vaa3p`, `Vaa3s`, `Vae3s`, `Vam2p`, `Van`, `Vap-pf`, `Vap-pm`, `Vap-pn`, `Vap-sf`, `Vap-sm`, `Vap-sn`, `Var1p`, `Var1s`, `Var2p`, `Var2s`, `Var3p`, `Var3s`, `Vma3s`, `Vmm1p`, `Vmm2p`, `Vmm2s`, `Vmn`, `Vmp-pf`, `Vmp-pm`, `Vmp-pn`, `Vmp-sf`, `Vmp-sm`, `Vmp-sn`, `Vmr1p`, `Vmr1s`, `Vmr2p`, `Vmr2s`, `Vmr3p`, `Vmr3s`, `X`, `Xf`, `Y`, `Z` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `goeswith`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `xcomp` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `POS_ACC` | 98.70 |
| `MORPH_ACC` | 95.55 |
| `TAG_ACC` | 95.52 |
| `DEP_UAS` | 91.29 |
| `DEP_LAS` | 86.17 |
| `SENTS_P` | 95.36 |
| `SENTS_R` | 96.16 |
| `SENTS_F` | 95.76 |
| `TRANSFORMER_LOSS` | 24668298.17 |
| `MORPHOLOGIZER_LOSS` | 362811.40 |
| `TAGGER_LOSS` | 349660.11 |
| `PARSER_LOSS` | 2088768.64 |
|
{"language": ["hr"], "tags": ["spacy", "token-classification"]}
|
danielvasic/hr_bertic_pipeline
| null |
[
"spacy",
"token-classification",
"hr",
"model-index",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
token-classification
|
spacy
|
| Feature | Description |
| --- | --- |
| **Name** | `hr_hroberta_pipeline` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.3,<3.2.0` |
| **Default Pipeline** | `transformer`, `morphologizer`, `tagger`, `parser` |
| **Components** | `transformer`, `morphologizer`, `tagger`, `parser` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | HR500k |
| **License** | CC BY-SA 4.0 |
| **Author** | [Daniel Vasić](https://github.com/danielvasic) |
### Label Scheme
<details>
<summary>View label scheme (1392 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`morphologizer`** | `Case=nominative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|POS=ADP`, `Case=locative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=instrumental\|POS=ADP`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Degree=positive\|POS=ADV\|Type=general`, `Number=singular\|POS=VERB\|Person=third\|Type=main\|VForm=present`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `POS=PUNCT`, `POS=PART\|Type=modal`, `Case=locative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `POS=SCONJ\|Type=subordinating`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=accusative\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `POS=CCONJ\|Type=coordinating`, `Case=genitive\|POS=ADP`, `Case=dative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Number=plural\|POS=VERB\|Person=third\|Type=main\|VForm=present`, `Number=singular\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=present`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|POS=ADP`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Animate=no\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `POS=VERB\|Type=main\|VForm=infinitive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `POS=PART\|Type=negative`, `Case=accusative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Degree=comparative\|POS=ADV\|Type=general`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Gender=masculine\|Number=singular\|POS=VERB\|Type=main\|VForm=participle`, `Case=locative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Form=digit\|POS=ADJ\|Type=ordinal`, `Number=singular\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=present`, `Number=plural\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=present`, `Case=accusative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Gender=feminine\|Number=plural\|POS=VERB\|Type=main\|VForm=participle`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Gender=neuter\|Number=singular\|POS=VERB\|Type=main\|VForm=participle`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Degree=superlative\|POS=ADV\|Type=general`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Number=plural\|POS=VERB\|Person=first\|Type=main\|VForm=present`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Gender=feminine\|Number=plural\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Gender=masculine\|Number=singular\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Gender=masculine\|Number=plural\|POS=VERB\|Type=main\|VForm=participle`, `Form=digit\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Gender=feminine\|Number=singular\|POS=VERB\|Type=main\|VForm=participle`, `Case=accusative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Gender=neuter\|Number=singular\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=locative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Number=plural\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=aorist`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=dative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=locative\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=aorist`, `POS=X`, `Case=genitive\|Form=letter\|POS=NUM\|Type=cardinal`, `Case=genitive\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Form=letter\|POS=NUM\|Type=cardinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `POS=X\|Type=foreign`, `Number=plural\|POS=VERB\|Person=second\|Type=main\|VForm=present`, `POS=PART\|Type=interrogative`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `POS=ADV\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=VERB\|Person=first\|Type=main\|VForm=present`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=dative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Animate=yes\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Number=plural\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=present`, `POS=AUX\|Type=auxiliary\|VForm=infinitive`, `Case=locative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Gender=feminine\|Number=singular\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Animate=no\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=dative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Number=plural\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=aorist`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=dative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `POS=NOUN`, `Case=vocative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=accusative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=neuter\|POS=PRON\|Person=third\|Type=interrogative`, `Case=nominative\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Number=plural\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=present`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=dative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Number=singular\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=aorist`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=accusative\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Type=interrogative`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Animate=no\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=instrumental\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=dative\|POS=ADP`, `Case=instrumental\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Gender=neuter\|Number=plural\|POS=VERB\|Type=main\|VForm=participle`, `Case=nominative\|Form=letter\|Gender=neuter\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=dative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=accusative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Form=letter\|POS=NUM\|Type=special`, `Case=accusative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=genitive\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=genitive\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Type=interrogative`, `Case=nominative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=locative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=dative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Gender=masculine\|Number=plural\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=genitive\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=genitive\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `POS=PROPN`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=locative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=accusative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=locative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Gender=neuter\|Number=plural\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Number=plural\|POS=VERB\|Person=second\|Type=main\|VForm=imperative`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=locative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Form=digit\|POS=SYM\|Type=special`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=possessive`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=nominative\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Form=digit\|POS=NUM\|Type=special`, `Case=genitive\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=locative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=genitive\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=genitive\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=aorist`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `POS=SYM`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Case=vocative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Form=digit\|POS=NUM\|Type=multiple`, `Case=instrumental\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Animate=yes\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Number=plural\|POS=VERB\|Person=first\|Type=main\|VForm=imperative`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=plural\|POS=NUM\|Type=cardinal`, `Case=accusative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=interrogative`, `Case=accusative\|Gender=neuter\|POS=PRON\|Person=third\|Type=interrogative`, `Case=locative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=locative\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Animate=no\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=dative\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=accusative\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=PRON\|Type=indefinite`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=genitive\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=locative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=locative\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=accusative\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=locative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=VERB\|Person=third\|Type=main\|VForm=aorist`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=instrumental\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `POS=ADJ`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=instrumental\|Form=letter\|POS=NUM\|Type=cardinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=instrumental\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=dative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Number=singular\|POS=VERB\|Person=second\|Type=main\|VForm=imperative`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Form=roman\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=dative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=plural\|POS=NUM\|Type=special`, `Case=locative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=accusative\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=genitive\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Form=letter\|POS=NUM\|Type=cardinal`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=genitive\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Form=roman\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=PROPN\|Type=proper`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Number=plural\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=aorist`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|POS=SYM`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=nominative\|Gender=masculine\|POS=PRON\|Person=third\|Type=interrogative`, `Case=locative\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `POS=PART`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=genitive\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=dative\|Gender=masculine\|POS=PRON\|Person=third\|Type=interrogative`, `Case=instrumental\|Definiteness=no\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `POS=INTJ`, `Case=locative\|Gender=neuter\|POS=PRON\|Person=third\|Type=interrogative`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `POS=PART\|Type=affirmative`, `Number=singular\|POS=VERB\|Person=second\|Type=main\|VForm=present`, `Case=dative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=instrumental\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=neuter\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=neuter\|POS=NUM\|Type=special`, `Case=locative\|Form=letter\|Gender=masculine\|Number=plural\|POS=NUM\|Type=cardinal`, `Case=accusative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=locative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Number=singular\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=present`, `Case=vocative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=vocative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=vocative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=vocative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=vocative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=genitive\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Number=plural\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=imperative`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=vocative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Number=singular\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=imperfect`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=nominative\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `POS=ADV`, `Case=locative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=vocative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=vocative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=PROPN\|Type=proper`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=vocative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=vocative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=interrogative`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=vocative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=dative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Case=dative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=vocative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=vocative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive` |
| **`tagger`** | `Agcfpay`, `Agcfpdy`, `Agcfpgy`, `Agcfpiy`, `Agcfply`, `Agcfpny`, `Agcfsay`, `Agcfsdy`, `Agcfsgy`, `Agcfsiy`, `Agcfsly`, `Agcfsny`, `Agcmpay`, `Agcmpgy`, `Agcmpiy`, `Agcmply`, `Agcmpny`, `Agcmsany`, `Agcmsay`, `Agcmsayn`, `Agcmsdy`, `Agcmsgy`, `Agcmsiy`, `Agcmsly`, `Agcmsny`, `Agcnpay`, `Agcnpdy`, `Agcnpgy`, `Agcnpny`, `Agcnsay`, `Agcnsdy`, `Agcnsgy`, `Agcnsiy`, `Agcnsly`, `Agcnsny`, `Agpfpay`, `Agpfpdy`, `Agpfpgy`, `Agpfpiy`, `Agpfply`, `Agpfpny`, `Agpfsay`, `Agpfsdy`, `Agpfsgy`, `Agpfsin`, `Agpfsiy`, `Agpfsly`, `Agpfsny`, `Agpfsvy`, `Agpmpay`, `Agpmpdy`, `Agpmpgy`, `Agpmpiy`, `Agpmply`, `Agpmpny`, `Agpmpvy`, `Agpmsan`, `Agpmsann`, `Agpmsany`, `Agpmsay`, `Agpmsayn`, `Agpmsayy`, `Agpmsdy`, `Agpmsgn`, `Agpmsgy`, `Agpmsiy`, `Agpmsln`, `Agpmsly`, `Agpmsnn`, `Agpmsny`, `Agpmsvy`, `Agpnpay`, `Agpnpdy`, `Agpnpgy`, `Agpnpiy`, `Agpnply`, `Agpnpny`, `Agpnsay`, `Agpnsdy`, `Agpnsgn`, `Agpnsgy`, `Agpnsiy`, `Agpnsln`, `Agpnsly`, `Agpnsny`, `Agsfpay`, `Agsfpdy`, `Agsfpgy`, `Agsfpiy`, `Agsfply`, `Agsfpny`, `Agsfsay`, `Agsfsgy`, `Agsfsiy`, `Agsfsly`, `Agsfsny`, `Agsmpay`, `Agsmpdy`, `Agsmpgy`, `Agsmpiy`, `Agsmply`, `Agsmpny`, `Agsmpvy`, `Agsmsany`, `Agsmsay`, `Agsmsayn`, `Agsmsayy`, `Agsmsdy`, `Agsmsgy`, `Agsmsiy`, `Agsmsly`, `Agsmsny`, `Agsnpay`, `Agsnpgy`, `Agsnply`, `Agsnpny`, `Agsnsay`, `Agsnsdy`, `Agsnsiy`, `Agsnsly`, `Agsnsny`, `Appfpay`, `Appfpdy`, `Appfpgy`, `Appfpiy`, `Appfply`, `Appfpny`, `Appfsay`, `Appfsgy`, `Appfsiy`, `Appfsly`, `Appfsny`, `Appmpay`, `Appmpdy`, `Appmpgy`, `Appmpiy`, `Appmply`, `Appmpny`, `Appmsann`, `Appmsany`, `Appmsayn`, `Appmsayy`, `Appmsdy`, `Appmsgn`, `Appmsgy`, `Appmsiy`, `Appmsly`, `Appmsnn`, `Appmsny`, `Appnpay`, `Appnpdy`, `Appnpgy`, `Appnpiy`, `Appnply`, `Appnpny`, `Appnsay`, `Appnsgy`, `Appnsly`, `Appnsny`, `Aspfpay`, `Aspfpgy`, `Aspfpiy`, `Aspfply`, `Aspfpny`, `Aspfsay`, `Aspfsdy`, `Aspfsgy`, `Aspfsiy`, `Aspfsly`, `Aspfsny`, `Aspmpay`, `Aspmpgy`, `Aspmply`, `Aspmpny`, `Aspmsayn`, `Aspmsayy`, `Aspmsdy`, `Aspmsgn`, `Aspmsgy`, `Aspmsiy`, `Aspmsln`, `Aspmsly`, `Aspmsnn`, `Aspnpay`, `Aspnpgy`, `Aspnpny`, `Aspnsay`, `Aspnsdn`, `Aspnsgn`, `Aspnsgy`, `Aspnsly`, `Aspnsny`, `Cc`, `Cs`, `I`, `Mdc`, `Mdm`, `Mdo`, `Mds`, `Mlc`, `Mlc--g`, `Mlc--i`, `Mlc--l`, `Mlcf-a`, `Mlcf-d`, `Mlcf-g`, `Mlcf-n`, `Mlcfsa`, `Mlcfsd`, `Mlcfsg`, `Mlcfsi`, `Mlcfsl`, `Mlcfsn`, `Mlcm-a`, `Mlcm-g`, `Mlcm-l`, `Mlcm-n`, `Mlcmpl`, `Mlcmpn`, `Mlcmsan`, `Mlcmsay`, `Mlcmsg`, `Mlcmsi`, `Mlcmsl`, `Mlcmsn`, `Mlcn-n`, `Mlcnsa`, `Mlcnsg`, `Mlcnsl`, `Mlcnsn`, `Mlofpa`, `Mlofpd`, `Mlofpg`, `Mlofpi`, `Mlofpl`, `Mlofpn`, `Mlofsa`, `Mlofsd`, `Mlofsg`, `Mlofsi`, `Mlofsl`, `Mlofsn`, `Mlompa`, `Mlompd`, `Mlompg`, `Mlompi`, `Mlompl`, `Mlompn`, `Mlomsan`, `Mlomsay`, `Mlomsd`, `Mlomsg`, `Mlomsi`, `Mlomsl`, `Mlomsn`, `Mlomsv`, `Mlonpa`, `Mlonpg`, `Mlonpl`, `Mlonpn`, `Mlonsa`, `Mlonsd`, `Mlonsg`, `Mlonsi`, `Mlonsl`, `Mlonsn`, `Mls`, `Mlsf-a`, `Mlsf-d`, `Mlsf-g`, `Mlsf-i`, `Mlsf-l`, `Mlsf-n`, `Mlsm-a`, `Mlsm-g`, `Mlsm-l`, `Mlsm-n`, `Mlsmpn`, `Mlsn-n`, `Mrc`, `Mro`, `Ncfpa`, `Ncfpd`, `Ncfpg`, `Ncfpi`, `Ncfpl`, `Ncfpn`, `Ncfpv`, `Ncfsa`, `Ncfsd`, `Ncfsg`, `Ncfsi`, `Ncfsl`, `Ncfsn`, `Ncfsv`, `Ncmpa`, `Ncmpd`, `Ncmpg`, `Ncmpi`, `Ncmpl`, `Ncmpn`, `Ncmpv`, `Ncmsan`, `Ncmsay`, `Ncmsd`, `Ncmsg`, `Ncmsi`, `Ncmsl`, `Ncmsn`, `Ncmsv`, `Ncnpa`, `Ncnpd`, `Ncnpg`, `Ncnpi`, `Ncnpl`, `Ncnpn`, `Ncnsa`, `Ncnsd`, `Ncnsg`, `Ncnsi`, `Ncnsl`, `Ncnsn`, `Ncnsv`, `Npfpa`, `Npfpg`, `Npfpl`, `Npfpn`, `Npfsa`, `Npfsd`, `Npfsg`, `Npfsi`, `Npfsl`, `Npfsn`, `Npmpa`, `Npmpd`, `Npmpg`, `Npmpi`, `Npmpl`, `Npmpn`, `Npmsan`, `Npmsay`, `Npmsd`, `Npmsg`, `Npmsi`, `Npmsl`, `Npmsn`, `Npmsv`, `Npnpg`, `Npnpn`, `Npnsa`, `Npnsd`, `Npnsg`, `Npnsi`, `Npnsl`, `Npnsn`, `Pd-fpa`, `Pd-fpd`, `Pd-fpg`, `Pd-fpi`, `Pd-fpl`, `Pd-fpn`, `Pd-fsa`, `Pd-fsd`, `Pd-fsg`, `Pd-fsi`, `Pd-fsl`, `Pd-fsn`, `Pd-mpa`, `Pd-mpd`, `Pd-mpg`, `Pd-mpi`, `Pd-mpl`, `Pd-mpn`, `Pd-msan`, `Pd-msay`, `Pd-msd`, `Pd-msg`, `Pd-msi`, `Pd-msl`, `Pd-msn`, `Pd-npa`, `Pd-npd`, `Pd-npg`, `Pd-npi`, `Pd-npn`, `Pd-nsa`, `Pd-nsd`, `Pd-nsg`, `Pd-nsi`, `Pd-nsl`, `Pd-nsn`, `Pi-fpa`, `Pi-fpd`, `Pi-fpg`, `Pi-fpi`, `Pi-fpl`, `Pi-fpn`, `Pi-fsa`, `Pi-fsd`, `Pi-fsg`, `Pi-fsi`, `Pi-fsl`, `Pi-fsn`, `Pi-mpa`, `Pi-mpd`, `Pi-mpg`, `Pi-mpi`, `Pi-mpl`, `Pi-mpn`, `Pi-msan`, `Pi-msay`, `Pi-msd`, `Pi-msg`, `Pi-msi`, `Pi-msl`, `Pi-msn`, `Pi-npa`, `Pi-npd`, `Pi-npg`, `Pi-npi`, `Pi-npl`, `Pi-npn`, `Pi-nsa`, `Pi-nsd`, `Pi-nsg`, `Pi-nsi`, `Pi-nsl`, `Pi-nsn`, `Pi3m-a`, `Pi3m-d`, `Pi3m-g`, `Pi3m-i`, `Pi3m-n`, `Pi3n-a`, `Pi3n-d`, `Pi3n-g`, `Pi3n-i`, `Pi3n-l`, `Pi3n-n`, `Pp1-pa`, `Pp1-pd`, `Pp1-pg`, `Pp1-pi`, `Pp1-pl`, `Pp1-pn`, `Pp1-sa`, `Pp1-sd`, `Pp1-sg`, `Pp1-si`, `Pp1-sl`, `Pp1-sn`, `Pp2-pa`, `Pp2-pd`, `Pp2-pg`, `Pp2-pn`, `Pp2-sa`, `Pp2-sd`, `Pp2-sg`, `Pp2-sl`, `Pp2-sn`, `Pp2-sv`, `Pp3-pa`, `Pp3-pd`, `Pp3-pg`, `Pp3-pi`, `Pp3-pl`, `Pp3fpn`, `Pp3fsa`, `Pp3fsd`, `Pp3fsg`, `Pp3fsi`, `Pp3fsl`, `Pp3fsn`, `Pp3mpn`, `Pp3msa`, `Pp3msd`, `Pp3msg`, `Pp3msi`, `Pp3msl`, `Pp3msn`, `Pp3npn`, `Pp3nsa`, `Pp3nsi`, `Pp3nsn`, `Pq-fpa`, `Pq-fpn`, `Pq-fsa`, `Pq-fsi`, `Pq-fsl`, `Pq-fsn`, `Pq-mpn`, `Pq-msn`, `Pq-nsn`, `Pq3m-d`, `Pq3m-n`, `Pq3n-a`, `Pq3n-l`, `Pq3n-n`, `Ps1fpa`, `Ps1fpd`, `Ps1fpg`, `Ps1fpl`, `Ps1fpn`, `Ps1fsa`, `Ps1fsd`, `Ps1fsg`, `Ps1fsi`, `Ps1fsl`, `Ps1fsn`, `Ps1fsv`, `Ps1mpa`, `Ps1mpd`, `Ps1mpg`, `Ps1mpi`, `Ps1mpl`, `Ps1mpn`, `Ps1mpv`, `Ps1msan`, `Ps1msay`, `Ps1msd`, `Ps1msg`, `Ps1msi`, `Ps1msl`, `Ps1msn`, `Ps1msv`, `Ps1npd`, `Ps1npn`, `Ps1nsa`, `Ps1nsg`, `Ps1nsi`, `Ps1nsl`, `Ps1nsn`, `Ps2fpa`, `Ps2fpl`, `Ps2fpn`, `Ps2fsa`, `Ps2fsd`, `Ps2fsg`, `Ps2fsn`, `Ps2mpa`, `Ps2mpg`, `Ps2mpl`, `Ps2mpn`, `Ps2msan`, `Ps2msd`, `Ps2msg`, `Ps2msi`, `Ps2msl`, `Ps2msn`, `Ps2npn`, `Ps2nsa`, `Ps2nsg`, `Ps2nsi`, `Ps2nsl`, `Ps2nsn`, `Ps3fpa`, `Ps3fpg`, `Ps3fpl`, `Ps3fpn`, `Ps3fsa`, `Ps3fsd`, `Ps3fsg`, `Ps3fsi`, `Ps3fsl`, `Ps3fsn`, `Ps3mpa`, `Ps3mpd`, `Ps3mpg`, `Ps3mpi`, `Ps3mpl`, `Ps3mpn`, `Ps3msan`, `Ps3msay`, `Ps3msd`, `Ps3msg`, `Ps3msi`, `Ps3msl`, `Ps3msn`, `Ps3npa`, `Ps3npg`, `Ps3npl`, `Ps3npn`, `Ps3nsa`, `Ps3nsg`, `Ps3nsi`, `Ps3nsl`, `Ps3nsn`, `Px--sa`, `Px--sd`, `Px--sg`, `Px--si`, `Px--sl`, `Px-fpa`, `Px-fpg`, `Px-fpi`, `Px-fpl`, `Px-fpn`, `Px-fsa`, `Px-fsd`, `Px-fsg`, `Px-fsi`, `Px-fsl`, `Px-mpa`, `Px-mpd`, `Px-mpg`, `Px-mpi`, `Px-mpl`, `Px-msan`, `Px-msay`, `Px-msd`, `Px-msg`, `Px-msi`, `Px-msl`, `Px-npa`, `Px-npg`, `Px-npi`, `Px-npl`, `Px-nsa`, `Px-nsg`, `Px-nsi`, `Px-nsl`, `Qo`, `Qq`, `Qr`, `Qz`, `Rgc`, `Rgp`, `Rgs`, `Rr`, `Sa`, `Sd`, `Sg`, `Si`, `Sl`, `Vaa1p`, `Vaa1s`, `Vaa2p`, `Vaa2s`, `Vaa3p`, `Vaa3s`, `Vae3s`, `Vam2p`, `Van`, `Vap-pf`, `Vap-pm`, `Vap-pn`, `Vap-sf`, `Vap-sm`, `Vap-sn`, `Var1p`, `Var1s`, `Var2p`, `Var2s`, `Var3p`, `Var3s`, `Vma3s`, `Vmm1p`, `Vmm2p`, `Vmm2s`, `Vmn`, `Vmp-pf`, `Vmp-pm`, `Vmp-pn`, `Vmp-sf`, `Vmp-sm`, `Vmp-sn`, `Vmr1p`, `Vmr1s`, `Vmr2p`, `Vmr2s`, `Vmr3p`, `Vmr3s`, `X`, `Xf`, `Y`, `Z` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `goeswith`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `xcomp` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `POS_ACC` | 97.94 |
| `MORPH_ACC` | 93.45 |
| `TAG_ACC` | 93.42 |
| `DEP_UAS` | 88.33 |
| `DEP_LAS` | 82.92 |
| `SENTS_P` | 96.67 |
| `SENTS_R` | 96.83 |
| `SENTS_F` | 96.75 |
| `TRANSFORMER_LOSS` | 3301725.19 |
| `MORPHOLOGIZER_LOSS` | 410128.51 |
| `TAGGER_LOSS` | 393243.89 |
| `PARSER_LOSS` | 3074279.42 |
|
{"language": ["hr"], "license": "cc", "library_name": "spacy", "tags": ["spacy", "token-classification"], "datasets": ["classla/hr500k"], "metrics": ["f1", "accuracy"], "pipeline_tag": "token-classification"}
|
danielvasic/hr_hroberta_pipeline
| null |
[
"spacy",
"token-classification",
"hr",
"dataset:classla/hr500k",
"license:cc",
"model-index",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
danielvasic/hr_sroberta_pipeline
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-generation
|
transformers
|
# Michael Scott DialoGPT Model
|
{"tags": ["conversational"]}
|
danildany/DialoGPT-small-MichaelScott
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
danimlk93/bert-finetuned-squad-accelerate
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
danish-col3/model_name
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
multiple-choice
|
transformers
|
<!-- 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. -->
# albert-xxlarge-v2-finetuned-csqa-ih
This model is a fine-tuned version of [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5694
- Accuracy: 0.8026
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8032 | 1.0 | 532 | 0.5217 | 0.8043 |
| 0.3182 | 2.0 | 1064 | 0.6313 | 0.7985 |
| 0.0668 | 3.0 | 1596 | 1.2971 | 0.7969 |
| 0.0131 | 4.0 | 2128 | 1.4671 | 0.8026 |
| 0.0046 | 5.0 | 2660 | 1.5694 | 0.8026 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0
- Datasets 1.10.2
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model_index": {"name": "albert-xxlarge-v2-finetuned-csqa-ih"}}
|
danlou/albert-xxlarge-v2-finetuned-csqa-ih
| null |
[
"transformers",
"pytorch",
"albert",
"multiple-choice",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
multiple-choice
|
transformers
|
<!-- 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. -->
# albert-xxlarge-v2-finetuned-csqa
This model is a fine-tuned version of [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2) on the commonsense_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6177
- Accuracy: 0.7871
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7464 | 1.0 | 609 | 0.5319 | 0.7985 |
| 0.3116 | 2.0 | 1218 | 0.6422 | 0.7936 |
| 0.0769 | 3.0 | 1827 | 1.2674 | 0.7952 |
| 0.0163 | 4.0 | 2436 | 1.4839 | 0.7903 |
| 0.0122 | 5.0 | 3045 | 1.6177 | 0.7871 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0
- Datasets 1.10.2
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["commonsense_qa"], "metrics": ["accuracy"], "model_index": [{"name": "albert-xxlarge-v2-finetuned-csqa", "results": [{"dataset": {"name": "commonsense_qa", "type": "commonsense_qa", "args": "default"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.7870597839355469}}]}]}
|
danlou/albert-xxlarge-v2-finetuned-csqa
| null |
[
"transformers",
"pytorch",
"albert",
"multiple-choice",
"generated_from_trainer",
"dataset:commonsense_qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
multiple-choice
|
transformers
|
<!-- 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. -->
# aristo-roberta-finetuned-csqa
This model is a fine-tuned version of [LIAMF-USP/aristo-roberta](https://huggingface.co/LIAMF-USP/aristo-roberta) on the commonsense_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2187
- Accuracy: 0.7305
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.131 | 1.0 | 609 | 0.7109 | 0.7232 |
| 0.6957 | 2.0 | 1218 | 0.6912 | 0.7346 |
| 0.459 | 3.0 | 1827 | 0.8364 | 0.7305 |
| 0.3063 | 4.0 | 2436 | 1.0595 | 0.7322 |
| 0.2283 | 5.0 | 3045 | 1.2187 | 0.7305 |
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0
- Datasets 1.10.2
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["commonsense_qa"], "metrics": ["accuracy"], "model_index": [{"name": "aristo-roberta-finetuned-csqa", "results": [{"dataset": {"name": "commonsense_qa", "type": "commonsense_qa", "args": "default"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.7305487394332886}}]}]}
|
danlou/aristo-roberta-finetuned-csqa
| null |
[
"transformers",
"pytorch",
"roberta",
"multiple-choice",
"generated_from_trainer",
"dataset:commonsense_qa",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
{}
|
danlou/distilbert-base-uncased-finetuned-cola
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
Testing
|
{}
|
danlou/distilbert-base-uncased-finetuned-rte
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
danlou/roberta-large-finetuned-csqa-ih
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
multiple-choice
|
transformers
|
<!-- 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-large-finetuned-csqa
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the commonsense_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9146
- Accuracy: 0.7330
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3903 | 1.0 | 609 | 0.8845 | 0.6642 |
| 0.8939 | 2.0 | 1218 | 0.7054 | 0.7281 |
| 0.6163 | 3.0 | 1827 | 0.7452 | 0.7314 |
| 0.4245 | 4.0 | 2436 | 0.8369 | 0.7355 |
| 0.3258 | 5.0 | 3045 | 0.9146 | 0.7330 |
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0
- Datasets 1.10.2
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["commonsense_qa"], "metrics": ["accuracy"], "model_index": [{"name": "roberta-large-finetuned-csqa", "results": [{"dataset": {"name": "commonsense_qa", "type": "commonsense_qa", "args": "default"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.7330057621002197}}]}]}
|
danlou/roberta-large-finetuned-csqa
| null |
[
"transformers",
"pytorch",
"roberta",
"multiple-choice",
"generated_from_trainer",
"dataset:commonsense_qa",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
{}
|
dannersm/wav2vec2-large-xlsr-53-chilean-lessons
| null |
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
danny48/DialoGPT-small-harrypotter
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-generation
|
transformers
|
#datnguyen
|
{"tags": ["conversational"]}
|
danny481/DialoGPT-small-datnguyenchatbot
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-generation
|
transformers
|
#Harry Potter DialoGPT
|
{"tags": ["conversational"]}
|
danny481/DialoGPT-small-harrypotter
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-generation
|
transformers
|
#ChatBot updated by datng
|
{"tags": ["conversational"]}
|
danny481/Final_ChatBot
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
## CALM
This model is for ICLR2021 paper: [Pre-training Text-to-Text Transformers for Concept-centric Common Sense](https://openreview.net/forum?id=3k20LAiHYL2).
Checkout our [Project website](https://inklab.usc.edu/calm-project) for details!
```bibtex
@inproceedings{CALM2021,
title={Pre-training Text-to-Text Transformers for Concept-centric Common Sense},
author={Wangchunshu Zhou and Dong-Ho Lee and Ravi Kiran Selvam and Seyeon Lee and Bill Yuchen Lin and Xiang Ren},
booktitle={ICLR},
year={2021}
}
```
|
{}
|
danny911kr/calm-base
| null |
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
## CALM
This model is for ICLR2021 paper: [Pre-training Text-to-Text Transformers for Concept-centric Common Sense](https://openreview.net/forum?id=3k20LAiHYL2).
Checkout our [Project website](https://inklab.usc.edu/calm-project) for details!
```bibtex
@inproceedings{CALM2021,
title={Pre-training Text-to-Text Transformers for Concept-centric Common Sense},
author={Wangchunshu Zhou and Dong-Ho Lee and Ravi Kiran Selvam and Seyeon Lee and Bill Yuchen Lin and Xiang Ren},
booktitle={ICLR},
year={2021}
}
```
|
{}
|
danny911kr/calm-large
| null |
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
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