modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
Ayham/distilbert_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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} | 5 | null | ---
license: cc-by-nc-4.0
---
https://civitai.com/models/5973
Don't sell anything using my Lora
-
Don't claim it to be your's
-
at least Credit me if you used it, (my ego is fragile)
-
Do not create anything Illegal with my lora (-_-)
-
and
good luck using my lora :D
have a good day to any one reading this
-
 |
Ayham/distilbert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 6 | null | ---
license: unknown
language:
- en
tags:
- Cybersecurity
- Information Security
- Computer Science
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base model for cybersecurity Tasks.
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Markus Bayer, Philipp Kuehn, Ramin Shanehsaz, and Christian Reuter
- **Model type:** BERT-base
- **Language(s) (NLP):** English
- **Finetuned from model:** bert-base-uncased.
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** Will be added later
- **Paper:** https://arxiv.org/abs/2212.02974
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
@misc{https://doi.org/10.48550/arxiv.2212.02974,
doi = {10.48550/ARXIV.2212.02974},
url = {https://arxiv.org/abs/2212.02974},
author = {Bayer, Markus and Kuehn, Philipp and Shanehsaz, Ramin and Reuter, Christian},
keywords = {Cryptography and Security (cs.CR), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
|
Ayham/distilbert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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} | 8 | 2023-01-31T07:59:50Z | ---
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8601
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.2968 | 1.0 | 3765 | 3.8488 |
| 3.1549 | 2.0 | 7530 | 3.8771 |
| 3.1105 | 3.0 | 11295 | 3.8601 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Ayham/distilbert_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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} | 14 | 2023-01-31T08:01:07Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-uncased-finetuned-DA-Zero-shot-20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-finetuned-DA-Zero-shot-20
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0118
## 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: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6214 | 1.0 | 435 | 1.1818 |
| 0.6285 | 2.0 | 870 | 1.2124 |
| 0.713 | 3.0 | 1305 | 1.1673 |
| 0.7902 | 4.0 | 1740 | 1.1342 |
| 0.8051 | 5.0 | 2175 | 1.1042 |
| 0.8167 | 6.0 | 2610 | 1.1086 |
| 0.8412 | 7.0 | 3045 | 1.0797 |
| 0.8885 | 8.0 | 3480 | 1.0575 |
| 0.918 | 9.0 | 3915 | 1.0749 |
| 0.9765 | 10.0 | 4350 | 1.0565 |
| 1.0009 | 11.0 | 4785 | 1.0509 |
| 0.986 | 12.0 | 5220 | 1.0564 |
| 0.9819 | 13.0 | 5655 | 1.0527 |
| 0.9786 | 14.0 | 6090 | 1.0064 |
| 0.9689 | 15.0 | 6525 | 1.0038 |
| 0.9481 | 16.0 | 6960 | 1.0186 |
| 0.955 | 17.0 | 7395 | 0.9860 |
| 0.9481 | 18.0 | 7830 | 0.9914 |
| 0.9452 | 19.0 | 8265 | 1.0173 |
| 0.9452 | 20.0 | 8700 | 1.0050 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Ayham/ernie_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
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} | 13 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-base-extraction-cnndm_fs0.01-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-extraction-cnndm_fs0.01-all
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8747
## 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: 32
- eval_batch_size: 32
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3573 | 2.25 | 200 | 1.9379 |
| 1.9645 | 4.49 | 400 | 1.9068 |
| 1.862 | 6.74 | 600 | 1.8823 |
| 1.7958 | 8.99 | 800 | 1.8796 |
| 1.7493 | 11.24 | 1000 | 1.8759 |
| 1.7053 | 13.48 | 1200 | 1.8747 |
| 1.6773 | 15.73 | 1400 | 1.8786 |
| 1.6631 | 17.98 | 1600 | 1.8796 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Ayham/xlnet_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 10 | null | ---
tags:
- spacy
- token-classification
language:
- grc
model-index:
- name: grc_homercy_treebanks_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8635029354
- name: NER Recall
type: recall
value: 0.8847117794
- name: NER F Score
type: f_score
value: 0.8739787076
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.0
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.0
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.0
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.0
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.0
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.0
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.0
---
| Feature | Description |
| --- | --- |
| **Name** | `grc_homercy_treebanks_sm` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.5.0,<3.6.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `trainable_lemmatizer`, `frequency_lemmatizer`, `ner` |
| **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `trainable_lemmatizer`, `frequency_lemmatizer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [marton & jan]() |
### Label Scheme
<details>
<summary>View label scheme (2302 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `---------`, `--p---fa-`, `--s---ma-`, `-3paia---`, `-3paim---`, `-3siia---`, `A-`, `C-`, `Df`, `Dq`, `Du`, `F-`, `G-`, `I-`, `Ma`, `Mo`, `Nb`, `Ne`, `Pc`, `Pd`, `Pi`, `Pk`, `Pp`, `Pr`, `Ps`, `Px`, `R-`, `S-`, `V-`, `a--------`, `a-------s`, `a-d---fa-`, `a-d---fd-`, `a-d---fg-`, `a-d---fn-`, `a-d---ma-`, `a-d---md-`, `a-d---mg-`, `a-d---mn-`, `a-d---mnc`, `a-d---mv-`, `a-d---na-`, `a-d---ng-`, `a-d---nn-`, `a-p----dc`, `a-p---fa-`, `a-p---fac`, `a-p---fas`, `a-p---fd-`, `a-p---fdc`, `a-p---fds`, `a-p---fg-`, `a-p---fgc`, `a-p---fn-`, `a-p---fnc`, `a-p---fns`, `a-p---fv-`, `a-p---m--`, `a-p---m-c`, `a-p---ma-`, `a-p---mac`, `a-p---mas`, `a-p---md-`, `a-p---mdc`, `a-p---mds`, `a-p---mg-`, `a-p---mgc`, `a-p---mgs`, `a-p---mn-`, `a-p---mnc`, `a-p---mns`, `a-p---mv-`, `a-p---mvs`, `a-p---na-`, `a-p---nac`, `a-p---nas`, `a-p---nd-`, `a-p---ndc`, `a-p---nds`, `a-p---ng-`, `a-p---ngs`, `a-p---nn-`, `a-p---nnc`, `a-p---nns`, `a-p---nv-`, `a-s----d-`, `a-s----dc`, `a-s----g-`, `a-s----gc`, `a-s---fa-`, `a-s---fac`, `a-s---fas`, `a-s---fd-`, `a-s---fds`, `a-s---fg-`, `a-s---fgc`, `a-s---fgs`, `a-s---fn-`, `a-s---fnc`, `a-s---fns`, `a-s---fv-`, `a-s---m--`, `a-s---ma-`, `a-s---mac`, `a-s---mas`, `a-s---md-`, `a-s---mdc`, `a-s---mds`, `a-s---mg-`, `a-s---mgc`, `a-s---mgs`, `a-s---mn-`, `a-s---mnc`, `a-s---mns`, `a-s---mv-`, `a-s---mvc`, `a-s---mvs`, `a-s---na-`, `a-s---nac`, `a-s---nas`, `a-s---nd-`, `a-s---ndc`, `a-s---nds`, `a-s---ng-`, `a-s---nn-`, `a-s---nnc`, `a-s---nns`, `a-s---nv-`, `a-s---nvs`, `c--------`, `d--------`, `d-------c`, `d-------s`, `g--------`, `i--------`, `l--------`, `l-d---fa-`, `l-d---fg-`, `l-d---mg-`, `l-d---mn-`, `l-d---na-`, `l-d---nn-`, `l-p---fa-`, `l-p---fd-`, `l-p---fg-`, `l-p---fn-`, `l-p---ma-`, `l-p---md-`, `l-p---mg-`, `l-p---mn-`, `l-p---na-`, `l-p---nd-`, `l-p---ng-`, `l-p---nn-`, `l-s---fa-`, `l-s---fd-`, `l-s---fg-`, `l-s---fn-`, `l-s---ma-`, `l-s---md-`, `l-s---mg-`, `l-s---mn-`, `l-s---na-`, `l-s---nd-`, `l-s---ng-`, `l-s---nn-`, `m--------`, `m-p---m--`, `m-p---md-`, `m-p---nn-`, `n-----fg-`, `n-----na-`, `n-----nn-`, `n-d----a-`, `n-d---fa-`, `n-d---fd-`, `n-d---fg-`, `n-d---fn-`, `n-d---ma-`, `n-d---md-`, `n-d---mg-`, `n-d---mn-`, `n-d---mv-`, `n-d---na-`, `n-d---nn-`, `n-p----d-`, `n-p----g-`, `n-p---fa-`, `n-p---fd-`, `n-p---fg-`, `n-p---fn-`, `n-p---fv-`, `n-p---ma-`, `n-p---md-`, `n-p---mg-`, `n-p---mn-`, `n-p---mv-`, `n-p---na-`, `n-p---nd-`, `n-p---ng-`, `n-p---nn-`, `n-p---nv-`, `n-s----d-`, `n-s----g-`, `n-s----n-`, `n-s----v-`, `n-s---fa-`, `n-s---fd-`, `n-s---fg-`, `n-s---fn-`, `n-s---fv-`, `n-s---m--`, `n-s---ma-`, `n-s---md-`, `n-s---mg-`, `n-s---mn-`, `n-s---mv-`, `n-s---na-`, `n-s---nd-`, `n-s---ng-`, `n-s---nn-`, `n-s---nv-`, `p--------`, `p-d----d-`, `p-d----n-`, `p-d---fa-`, `p-d---fd-`, `p-d---fg-`, `p-d---fn-`, `p-d---ma-`, `p-d---md-`, `p-d---mg-`, `p-d---mn-`, `p-d---mv-`, `p-p----a-`, `p-p----d-`, `p-p----g-`, `p-p----n-`, `p-p---fa-`, `p-p---fd-`, `p-p---fg-`, `p-p---fn-`, `p-p---ma-`, `p-p---md-`, `p-p---mg-`, `p-p---mn-`, `p-p---na-`, `p-p---nd-`, `p-p---ng-`, `p-p---nn-`, `p-s----a-`, `p-s----d-`, `p-s----g-`, `p-s----n-`, `p-s---fa-`, `p-s---fd-`, `p-s---fg-`, `p-s---fn-`, `p-s---ma-`, `p-s---md-`, `p-s---mg-`, `p-s---mn-`, `p-s---mv-`, `p-s---na-`, `p-s---nd-`, `p-s---ng-`, `p-s---nn-`, `p1p---fa-`, `p1p---ma-`, `p1p---md-`, `p1p---mg-`, `p1p---mn-`, `p1s---fa-`, `p1s---fd-`, `p1s---fg-`, `p1s---fn-`, `p1s---ma-`, `p1s---md-`, `p1s---mg-`, `p1s---mn-`, `p2p----a-`, `p2p----d-`, `p2p---ma-`, `p2p---mg-`, `p2p---mn-`, `p2s----a-`, `p2s----d-`, `p2s----g-`, `p2s----n-`, `p2s---ma-`, `p2s---md-`, `p2s---mg-`, `p3s---fa-`, `p3s---ma-`, `r--------`, `u--------`, `v---na---`, `v--amm---`, `v--an----`, `v--ana---`, `v--ane---`, `v--anm---`, `v--anp---`, `v--fna---`, `v--fne---`, `v--fnm---`, `v--fnp---`, `v--pna---`, `v--pnd---`, `v--pne---`, `v--pnp---`, `v--ppefa-`, `v--ppemn-`, `v--rn----`, `v--rna---`, `v--rne---`, `v--rnp---`, `v--tna---`, `v-dapafn-`, `v-dapama-`, `v-dapamg-`, `v-dapamn-`, `v-dapmfn-`, `v-dapmmn-`, `v-dappma-`, `v-dappmn-`, `v-dppafg-`, `v-dppama-`, `v-dppamn-`, `v-dppefn-`, `v-dppema-`, `v-dppemd-`, `v-dppemn-`, `v-dpppmn-`, `v-drpama-`, `v-drpamn-`, `v-drpefn-`, `v-drpemn-`, `v-p-pmma-`, `v-pap-mn-`, `v-papafa-`, `v-papafg-`, `v-papafn-`, `v-papama-`, `v-papamd-`, `v-papamg-`, `v-papamn-`, `v-papana-`, `v-papand-`, `v-papann-`, `v-papefn-`, `v-papema-`, `v-papemn-`, `v-papmfa-`, `v-papmfg-`, `v-papmfn-`, `v-papmma-`, `v-papmmd-`, `v-papmmg-`, `v-papmmn-`, `v-papmna-`, `v-papmng-`, `v-papmnn-`, `v-pappfd-`, `v-pappfg-`, `v-pappfn-`, `v-pappma-`, `v-pappmd-`, `v-pappmg-`, `v-pappmn-`, `v-pappna-`, `v-pappng-`, `v-pappnn-`, `v-pfpama-`, `v-pfpamg-`, `v-pfpamn-`, `v-pfpema-`, `v-pfpemn-`, `v-pfpmfa-`, `v-pfpmfn-`, `v-pfpmma-`, `v-pfpmmd-`, `v-pfpmmg-`, `v-pfpmmn-`, `v-pfpmnn-`, `v-pfppmn-`, `v-ppp-mn-`, `v-pppafa-`, `v-pppafd-`, `v-pppafg-`, `v-pppafn-`, `v-pppafv-`, `v-pppama-`, `v-pppamd-`, `v-pppamg-`, `v-pppamn-`, `v-pppamv-`, `v-pppana-`, `v-pppand-`, `v-pppang-`, `v-pppann-`, `v-pppefa-`, `v-pppefd-`, `v-pppefg-`, `v-pppefn-`, `v-pppefv-`, `v-pppema-`, `v-pppemd-`, `v-pppemg-`, `v-pppemn-`, `v-pppemv-`, `v-pppena-`, `v-pppend-`, `v-pppeng-`, `v-pppenn-`, `v-ppppma-`, `v-ppppmd-`, `v-ppppmn-`, `v-prp-mn-`, `v-prpafa-`, `v-prpafd-`, `v-prpafn-`, `v-prpama-`, `v-prpamd-`, `v-prpamg-`, `v-prpamn-`, `v-prpana-`, `v-prpang-`, `v-prpefa-`, `v-prpefd-`, `v-prpefg-`, `v-prpefn-`, `v-prpema-`, `v-prpemd-`, `v-prpemg-`, `v-prpemn-`, `v-prpena-`, `v-prpend-`, `v-prpeng-`, `v-prpenn-`, `v-prppfn-`, `v-prppmn-`, `v-sagamn-`, `v-saiamn-`, `v-samp---`, `v-sap-mg-`, `v-sap-mn-`, `v-sapafa-`, `v-sapafd-`, `v-sapafg-`, `v-sapafn-`, `v-sapama-`, `v-sapamd-`, `v-sapamg-`, `v-sapamn-`, `v-sapamv-`, `v-sapana-`, `v-sapang-`, `v-sapann-`, `v-sapanv-`, `v-sapema-`, `v-sapemn-`, `v-sapmfa-`, `v-sapmfd-`, `v-sapmfg-`, `v-sapmfn-`, `v-sapmma-`, `v-sapmmd-`, `v-sapmmg-`, `v-sapmmn-`, `v-sapmna-`, `v-sapmng-`, `v-sapmnn-`, `v-sappfa-`, `v-sappfd-`, `v-sappfg-`, `v-sappfn-`, `v-sappma-`, `v-sappmd-`, `v-sappmg-`, `v-sappmn-`, `v-sappna-`, `v-sappng-`, `v-sappnn-`, `v-sappnv-`, `v-sfpafa-`, `v-sfpafd-`, `v-sfpafn-`, `v-sfpama-`, `v-sfpamd-`, `v-sfpamg-`, `v-sfpamn-`, `v-sfpmfa-`, `v-sfpmfd-`, `v-sfpmfg-`, `v-sfpmfn-`, `v-sfpmma-`, `v-sfpmmg-`, `v-sfpmmn-`, `v-sfpmna-`, `v-sfppma-`, `v-spiamn-`, `v-spp-mn-`, `v-spp-nn-`, `v-sppa---`, `v-sppafa-`, `v-sppafd-`, `v-sppafg-`, `v-sppafn-`, `v-sppafv-`, `v-sppama-`, `v-sppamd-`, `v-sppamg-`, `v-sppamn-`, `v-sppamv-`, `v-sppana-`, `v-sppand-`, `v-sppang-`, `v-sppann-`, `v-sppanv-`, `v-sppefa-`, `v-sppefd-`, `v-sppefg-`, `v-sppefn-`, `v-sppema-`, `v-sppemd-`, `v-sppemg-`, `v-sppemn-`, `v-sppemv-`, `v-sppena-`, `v-sppend-`, `v-sppeng-`, `v-sppenn-`, `v-spppfa-`, `v-spppfd-`, `v-spppfg-`, `v-spppfn-`, `v-spppma-`, `v-spppmn-`, `v-srp-mn-`, `v-srpafa-`, `v-srpafd-`, `v-srpafg-`, `v-srpafn-`, `v-srpama-`, `v-srpamd-`, `v-srpamg-`, `v-srpamn-`, `v-srpamv-`, `v-srpana-`, `v-srpand-`, `v-srpang-`, `v-srpann-`, `v-srpefa-`, `v-srpefd-`, `v-srpefg-`, `v-srpefn-`, `v-srpema-`, `v-srpemd-`, `v-srpemg-`, `v-srpemn-`, `v-srpemv-`, `v-srpena-`, `v-srpend-`, `v-srpeng-`, `v-srpenn-`, `v-srppfn-`, `v-srppma-`, `v-srppmn-`, `v-srppmv-`, `v1paia---`, `v1paim---`, `v1paip---`, `v1paoa---`, `v1paom---`, `v1paop---`, `v1pasa---`, `v1pase---`, `v1pasm---`, `v1pasp---`, `v1pfia---`, `v1pfim---`, `v1pfom---`, `v1piia---`, `v1piie---`, `v1plia---`, `v1plie---`, `v1ppia---`, `v1ppie---`, `v1ppip---`, `v1ppoa---`, `v1ppoe---`, `v1ppsa---`, `v1ppse---`, `v1pria---`, `v1prie---`, `v1prsa---`, `v1prse---`, `v1ptie---`, `v1s-sa---`, `v1sa-a---`, `v1saia---`, `v1saie---`, `v1saim---`, `v1saip---`, `v1sao----`, `v1saoa---`, `v1saoe---`, `v1saom---`, `v1saop---`, `v1sasa---`, `v1sase---`, `v1sasm---`, `v1sasp---`, `v1sfi----`, `v1sfia---`, `v1sfie---`, `v1sfim---`, `v1sfip---`, `v1siia---`, `v1siie---`, `v1slia---`, `v1slie---`, `v1slim---`, `v1spia---`, `v1spie---`, `v1spoa---`, `v1spoe---`, `v1spsa---`, `v1spse---`, `v1sria---`, `v1srie---`, `v1sroa---`, `v1sroe---`, `v1srsa---`, `v1stie---`, `v1stim---`, `v2daia---`, `v2dama---`, `v2dasa---`, `v2dase---`, `v2dfia---`, `v2dfim---`, `v2diia---`, `v2diie---`, `v2dpia---`, `v2dpma---`, `v2dpme---`, `v2dria---`, `v2drma---`, `v2paia---`, `v2paim---`, `v2paip---`, `v2pama---`, `v2pame---`, `v2pamm---`, `v2paoa---`, `v2paom---`, `v2paop---`, `v2pasa---`, `v2pase---`, `v2pasm---`, `v2pasp---`, `v2pfia---`, `v2pfim---`, `v2piia---`, `v2piie---`, `v2ppia---`, `v2ppie---`, `v2ppma---`, `v2ppme---`, `v2ppoa---`, `v2ppoe---`, `v2ppsa---`, `v2pria---`, `v2prie---`, `v2prma---`, `v2prmp---`, `v2proa---`, `v2prsa---`, `v2saia---`, `v2saie---`, `v2saim---`, `v2saip---`, `v2sam----`, `v2sama---`, `v2same---`, `v2samm---`, `v2samp---`, `v2saoa---`, `v2saoe---`, `v2saom---`, `v2saop---`, `v2sasa---`, `v2sase---`, `v2sasm---`, `v2sasp---`, `v2sfi----`, `v2sfia---`, `v2sfie---`, `v2sfim---`, `v2sfip---`, `v2siia---`, `v2siie---`, `v2siip---`, `v2slia---`, `v2slie---`, `v2slim---`, `v2spia---`, `v2spie---`, `v2spma---`, `v2spme---`, `v2spoa---`, `v2spoe---`, `v2spsa---`, `v2spse---`, `v2sria---`, `v2srie---`, `v2srma---`, `v2srme---`, `v2sroa---`, `v2srsa---`, `v2stie---`, `v3-roe---`, `v3daia---`, `v3daim---`, `v3daip---`, `v3daoa---`, `v3dfia---`, `v3dfim---`, `v3diia---`, `v3diie---`, `v3dlia---`, `v3dlie---`, `v3dlim---`, `v3dpia---`, `v3dpie---`, `v3dpma---`, `v3dpme---`, `v3dpsa---`, `v3dria---`, `v3pai----`, `v3paia---`, `v3paie---`, `v3paim---`, `v3paip---`, `v3pamm---`, `v3paoa---`, `v3paoe---`, `v3paom---`, `v3paop---`, `v3pasa---`, `v3pase---`, `v3pasm---`, `v3pasp---`, `v3pfia---`, `v3pfie---`, `v3pfim---`, `v3piia---`, `v3piie---`, `v3piip---`, `v3plia---`, `v3plie---`, `v3plim---`, `v3plip---`, `v3ppia---`, `v3ppie---`, `v3ppip---`, `v3ppma---`, `v3ppme---`, `v3ppoa---`, `v3ppoe---`, `v3ppsa---`, `v3ppse---`, `v3pria---`, `v3prie---`, `v3prip---`, `v3sai----`, `v3saia---`, `v3saie---`, `v3saim---`, `v3saip---`, `v3sama---`, `v3samm---`, `v3samp---`, `v3sana---`, `v3sao----`, `v3saoa---`, `v3saoe---`, `v3saom---`, `v3saop---`, `v3sas----`, `v3sasa---`, `v3sase---`, `v3sasm---`, `v3sasp---`, `v3sfi----`, `v3sfia---`, `v3sfie---`, `v3sfim---`, `v3sfip---`, `v3sfoa---`, `v3sii----`, `v3siia---`, `v3siie---`, `v3siip---`, `v3sli----`, `v3slia---`, `v3slie---`, `v3slim---`, `v3slip---`, `v3spia---`, `v3spie---`, `v3spip---`, `v3spma---`, `v3spme---`, `v3spoa---`, `v3spoe---`, `v3spop---`, `v3spsa---`, `v3spse---`, `v3sria---`, `v3srie---`, `v3srip---`, `v3srma---`, `v3sroa---`, `v3srsa---`, `v3stie---`, `v3stim---`, `v3stip---`, `x--------`, `x-p----d-`, `x-p---nn-` |
| **`morphologizer`** | `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET`, `POS=SCONJ`, `POS=CCONJ`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `POS=ADP`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Def\|Gender=Masc,Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADV`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Mid`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid,Pass`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=VERB\|Tense=Pres\|VerbForm=Inf\|Voice=Mid,Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET`, `POS=VERB\|Tense=Pres\|VerbForm=Inf\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Masc,Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|Tense=Pres\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Degree=Sup\|POS=ADV`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=VERB\|Tense=Pres\|VerbForm=Inf\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=ADV\|Polarity=Neg`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Mood=Opt\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=VERB\|Tense=Fut\|VerbForm=Inf\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid,Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Definite=Def\|Gender=Masc,Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid,Pass`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Degree=Pos\|POS=ADV`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|Tense=Pres\|VerbForm=Inf\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Pos\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Voc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `POS=INTJ`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `POS=ADV\|PronType=Rel`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|Tense=Fut\|VerbForm=Inf\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Degree=Cmp\|POS=ADV`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=DET`, `POS=ADV\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Opt\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `POS=AUX\|Tense=Fut\|VerbForm=Inf\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Degree=Pos\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Degree=Pos\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=DET`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Cmp\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=DET`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Definite=Def\|Gender=Masc,Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Degree=Cmp\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Sup\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Degree=Pos\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Degree=Sup\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid,Pass`, `Case=Dat\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Acc\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Gdv`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=NUM`, `Case=Dat\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `POS=PROPN`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Dat\|Definite=Def\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Fem,Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Voc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Gen\|Degree=Pos\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Degree=Cmp\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `POS=VERB\|Tense=Fut\|VerbForm=Inf\|Voice=Pass`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid,Pass`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `POS=VERB`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Dat\|Gender=Fem,Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Gdv`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Mood=Opt\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Gdv`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Degree=Pos\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Degree=Cmp\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Voc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Opt\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Gdv`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Degree=Cmp\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Mood=Opt\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Voc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=NUM`, `POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Voc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=X`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=DET`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid,Pass`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Voc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=NUM`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=DET`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Sup\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Perf\|POS=AUX\|Tense=Past\|VerbForm=Inf\|Voice=Mid`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=NUM`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=DET`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Voc\|Degree=Pos\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Nom\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid,Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Dat\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid,Pass`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Voc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|POS=AUX\|Tense=Past\|VerbForm=Inf\|Voice=Act`, `POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Act`, `POS=PUNCT`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=1`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON`, `POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=PART`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON`, `POS=DET`, `Case=Gen\|Number=Sing\|POS=PRON`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `Case=Nom\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Number=Sing\|POS=PRON`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=PRON`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Voc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Dual\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=PRON`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=X`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=PRON`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Number=Sing\|POS=PRON`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Number=Sing\|POS=NOUN`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `POS=VERB\|Tense=Past\|VerbForm=Inf`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Nom\|Number=Sing\|POS=PRON`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Dual\|POS=PRON`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=X`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Neut\|Number=Dual\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=PRON`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=PRON`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=DET`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Imp\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=PRON`, `Case=Nom\|Gender=Neut\|Number=Dual\|POS=NOUN`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Dual\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Dual\|POS=PRON`, `Case=Nom\|Number=Dual\|POS=PRON`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|POS=NOUN`, `Case=Acc\|Gender=Neut\|POS=NOUN`, `Case=Dat\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Voc\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Dual\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=PRON`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Dual\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=PRON`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Inf`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Dual\|POS=DET`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=X\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Dual\|POS=DET`, `Case=Nom\|Gender=Neut\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|POS=VERB\|Tense=Fut\|VerbForm=Inf\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PRON`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Voc\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Mood=Sub\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=X\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Opt\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Dual\|POS=PRON`, `Mood=Sub\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Imp\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=X\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Voc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Number=Plur\|POS=PRON`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1`, `Degree=Sup\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2`, `Case=Nom\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Plur\|POS=X`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Voc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `POS=VERB\|Tense=Pres\|VerbForm=Inf`, `Aspect=Perf\|Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Number=Dual\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Inf\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=X`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `dislocated`, `fixed`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`ner`** | `GROUP`, `PERSON`, `PLACE` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TAG_ACC` | 0.00 |
| `POS_ACC` | 0.00 |
| `MORPH_ACC` | 0.00 |
| `MORPH_PER_FEAT` | 0.00 |
| `DEP_UAS` | 0.00 |
| `DEP_LAS` | 0.00 |
| `DEP_LAS_PER_TYPE` | 0.00 |
| `SENTS_P` | 0.00 |
| `SENTS_R` | 0.00 |
| `SENTS_F` | 0.00 |
| `LEMMA_ACC` | 0.00 |
| `ENTS_F` | 87.40 |
| `ENTS_P` | 86.35 |
| `ENTS_R` | 88.47 |
| `TOK2VEC_LOSS` | 42181.56 |
| `TAGGER_LOSS` | 0.00 |
| `MORPHOLOGIZER_LOSS` | 0.00 |
| `PARSER_LOSS` | 0.00 |
| `TRAINABLE_LEMMATIZER_LOSS` | 0.00 |
| `NER_LOSS` | 32227.48 | |
Ayham/xlnetgpt2_xsum7 | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
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} | 8 | 2023-01-31T08:50:49Z | ---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9952305246
- name: NER Recall
type: recall
value: 0.9984051037
- name: NER F Score
type: f_score
value: 0.9968152866
---
| Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.4.4,<3.5.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (9 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `AGENT`, `ASSET`, `ASSET STATE`, `DATE`, `DETERMINAND`, `FLOW LEVEL`, `MEASUREMENT`, `OPERATION`, `PROCCESS` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 99.68 |
| `ENTS_P` | 99.52 |
| `ENTS_R` | 99.84 |
| `TOK2VEC_LOSS` | 21054.32 |
| `NER_LOSS` | 27455.52 | |
Ayjayo/DialoGPT-medium-AyjayoAI | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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}
} | 12 | null | Access to model junujunu/roberta_128_spacing is restricted and you are not in the authorized list. Visit https://huggingface.co/junujunu/roberta_128_spacing to ask for access. |
Ayta/Haha | []
| null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: test-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# test-model
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Tokenizers 0.13.2
|
Ayu/Shiriro | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-cased-finetuned-prompt-20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-finetuned-prompt-20
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7142
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3173 | 1.0 | 280 | 1.1293 |
| 1.087 | 2.0 | 560 | 0.9716 |
| 1.0064 | 3.0 | 840 | 0.9606 |
| 0.9341 | 4.0 | 1120 | 0.8887 |
| 0.8881 | 5.0 | 1400 | 0.8654 |
| 0.8662 | 6.0 | 1680 | 0.8181 |
| 0.8331 | 7.0 | 1960 | 0.8286 |
| 0.8206 | 8.0 | 2240 | 0.7941 |
| 0.8017 | 9.0 | 2520 | 0.7677 |
| 0.772 | 10.0 | 2800 | 0.7711 |
| 0.76 | 11.0 | 3080 | 0.7314 |
| 0.7436 | 12.0 | 3360 | 0.7479 |
| 0.7305 | 13.0 | 3640 | 0.7354 |
| 0.7204 | 14.0 | 3920 | 0.7143 |
| 0.7102 | 15.0 | 4200 | 0.7366 |
| 0.7034 | 16.0 | 4480 | 0.7036 |
| 0.6937 | 17.0 | 4760 | 0.7049 |
| 0.695 | 18.0 | 5040 | 0.7080 |
| 0.6923 | 19.0 | 5320 | 0.7110 |
| 0.6886 | 20.0 | 5600 | 0.6969 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Ayumi/Jovana | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-squad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0867
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.5824 | 1.0 | 5485 | 4.4179 |
| 4.2525 | 2.0 | 10970 | 4.1442 |
| 4.1607 | 3.0 | 16455 | 4.0867 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
AyushPJ/ai-club-inductions-21-nlp-ELECTRA-base-squad | [
"pytorch",
"electra",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"ElectraForQuestionAnswering"
],
"model_type": "electra",
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}
} | 12 | 2023-01-31T09:30:43Z | ---
license: bigscience-bloom-rail-1.0
language:
- en
library_name: diffusers
tags:
- stable-diffusion
- text-to-image
---
# pony-diffusion-g5 - a new generation ~~of waifus~~
**UPDATE:** Plot twist I made a new version of this model which has much higher quality and is based on LoCon and with Mane 5 + Misty! [pony-diffusion-g5-lora](https://huggingface.co/GrieferPig/pony-diffusion-g5-lora)
~~**UPDATE:** due to lots of poor results generated from the model and I will no longer update this model, please use [pony-diffusion-v4](https://huggingface.co/AstraliteHeart/pony-diffusion-v4) instead which is way better trust me they've got experts doing training stuff~~
pony-diffusion-g5 is a latent text-to-image diffusion model that has been conditioned on high quality pony images through fine-tuning.
Finetuned for MLP G5 main characters, based on [AstraliteHeart/pony-diffusion](https://huggingface.co/AstraliteHeart/pony-diffusion)
__!!IMPORTANT: DUE TO LACK OF DATASETS ONLY SUNNY AND IZZY CAN GENERATE QUALITY IMAGES__
__!!IMPORTANT: TRY NEGATIVE PROMPT "3d, sfm"__
<img src="https://huggingface.co/GrieferPig/pony-diffusion-g5/resolve/main/doc/demo5.png" width=50% height=50%>
<img src="https://huggingface.co/GrieferPig/pony-diffusion-g5/resolve/main/doc/demo1.png" width=50% height=50%>
<img src="https://huggingface.co/GrieferPig/pony-diffusion-g5/resolve/main/doc/demo4.png" width=50% height=50%>
<img src="https://huggingface.co/GrieferPig/pony-diffusion-g5/resolve/main/doc/demo3.png" width=50% height=50%>
<img src="https://huggingface.co/GrieferPig/pony-diffusion-g5/resolve/main/doc/demo2.png" width=50% height=50%>
## Dataset criteria
All training images are from Derpibooru using the search criteria below
- General: "g5, safe, solo, score.gte:250, -webm, -animate || g5, suggestive, solo, score.gte:250, -webm, -animate", 856 entries wo/ gifs, ~15 epochs
## Why the model's quality is uh, meh?
The amount of G5 pony images within the search criteria is little, so don't really expect the quality to be as high as the original model is
~~_Also bcs im new to ai stuff i don't know how to train datasets correctly if u could help me great thx_~~
## Example code
```python
from diffusers import StableDiffusionPipeline
import torch
from diffusers import DDIMScheduler
model_path = "GrieferPig/pony-diffusion-g5"
prompt = "((((sunny starscout)))), pony, smiling, looking away, running in forest, cute, portrait, digital painting, dawn, smooth, sharp, focus, depth of field, bright, Unreal Engine, 4k, cinematic"
negative= "3d sfm"
# torch.manual_seed(1145141919810)
pipe = StableDiffusionPipeline.from_pretrained(
model_path,
torch_dtype=torch.float16,
scheduler=DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=True,
),
# safety_checker=None
)
pipe = pipe.to("cuda")
images = pipe(prompt, width=512, height=512, num_inference_steps=50, num_images_per_prompt=5, negative_prompt=negative).images
for i, image in enumerate(images):
image.save(f"test-{i}.png")
```
## Thanks
[AstraliteHeart/pony-diffusion](https://huggingface.co/AstraliteHeart/pony-diffusion), for providing a solid start-point to train on
This project would not have been possible without the incredible work by the [CompVis Researchers](https://ommer-lab.com/).
With special thanks to [Waifu-Diffusion](https://huggingface.co/hakurei/waifu-diffusion) for providing finetuning expertise and [Novel AI](https://novelai.net/) for providing necessary compute.
---
license: bigscience-bloom-rail-1.0
--- |
Azaghast/GPT2-SCP-Descriptions | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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"GPT2LMHeadModel"
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}
} | 5 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="liweiliu/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Azaghast/GPT2-SCP-Miscellaneous | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
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}
} | 5 | 2023-01-31T09:55:19Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="liweiliu/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Azuris/DialoGPT-medium-envy | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 12 | null | ---
license: other
language:
- en
inference: false
widget:
- text: "How do I download this model?"
example_title: "Text Gen Example"
---
# OPT-19M-ChatSalad
This is an experimental OPT-based model with 19 million parameters trained entirely **from scratch** as a datasetting practice.
Thus, it should not be subject to the usual OPT license. You are free to use this model for any purpose.
The model is small enough (under 40mb) that it should run at very fast speeds even entirely on CPU.
It is recommend to use this model with the KoboldAI software, with the following parameters:
- Temperature: 0.9
- Repetition Penalty: 1.1
- Repetition Penalty Slope: 0.7
- Repetition Penalty Range: 1024
- Top-P Sampling: 0.9
- Disable all other samplers
All feedback and comments can be directed to Concedo on the KoboldAI discord. |
Azuris/DialoGPT-medium-senorita | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
} | 14 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-th-v7_0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-th-v7_0
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4099
- Wer: 0.9988
- Cer: 0.7861
- Clean Cer: 0.7617
- Learning Rate: 0.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Clean Cer | Rate |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:---------:|:------:|
| 8.5484 | 0.4 | 500 | 3.6234 | 1.0 | 1.0 | 1.0 | 0.0000 |
| 3.2275 | 0.8 | 1000 | 2.2960 | 0.9998 | 0.7081 | 0.6540 | 0.0000 |
| 0.9955 | 1.2 | 1500 | 1.2224 | 0.9549 | 0.4327 | 0.3756 | 0.0000 |
| 0.66 | 1.61 | 2000 | 0.9559 | 0.9232 | 0.3651 | 0.3040 | 0.0000 |
| 0.546 | 2.01 | 2500 | 0.9207 | 0.9481 | 0.3585 | 0.2826 | 0.0000 |
| 0.4459 | 2.41 | 3000 | 0.7701 | 0.8693 | 0.2940 | 0.2383 | 0.0000 |
| 0.4041 | 2.81 | 3500 | 0.7756 | 0.8224 | 0.2949 | 0.2634 | 0.0000 |
| 0.3637 | 3.21 | 4000 | 0.6015 | 0.7015 | 0.2064 | 0.1807 | 0.0000 |
| 0.334 | 3.61 | 4500 | 0.5615 | 0.6675 | 0.1907 | 0.1638 | 0.0000 |
| 0.3283 | 4.02 | 5000 | 0.6205 | 0.7073 | 0.2092 | 0.1803 | 0.0000 |
| 0.3762 | 4.42 | 5500 | 0.7517 | 0.6366 | 0.1778 | 0.1600 | 0.0000 |
| 0.4954 | 4.82 | 6000 | 0.9374 | 0.7073 | 0.2023 | 0.1735 | 0.0000 |
| 0.5568 | 5.22 | 6500 | 0.8859 | 0.7027 | 0.1982 | 0.1666 | 0.0000 |
| 0.6756 | 5.62 | 7000 | 1.0252 | 0.6802 | 0.1920 | 0.1628 | 0.0000 |
| 0.7752 | 6.02 | 7500 | 1.1259 | 0.7657 | 0.2309 | 0.1908 | 0.0000 |
| 0.8305 | 6.43 | 8000 | 1.3857 | 0.9029 | 0.3252 | 0.2668 | 0.0000 |
| 1.7385 | 6.83 | 8500 | 3.2320 | 0.9998 | 0.9234 | 0.9114 | 0.0000 |
| 2.7839 | 7.23 | 9000 | 3.3238 | 0.9999 | 0.9400 | 0.9306 | 0.0000 |
| 2.8307 | 7.63 | 9500 | 3.2678 | 0.9998 | 0.9167 | 0.9053 | 0.0000 |
| 2.7672 | 8.03 | 10000 | 3.2435 | 0.9995 | 0.8992 | 0.8867 | 0.0000 |
| 2.7426 | 8.43 | 10500 | 3.2396 | 0.9995 | 0.8720 | 0.8561 | 0.0000 |
| 2.7608 | 8.84 | 11000 | 3.2689 | 0.9993 | 0.8399 | 0.8202 | 0.0000 |
| 2.8195 | 9.24 | 11500 | 3.3283 | 0.9989 | 0.8084 | 0.7865 | 0.0000 |
| 2.9044 | 9.64 | 12000 | 3.4099 | 0.9988 | 0.7861 | 0.7617 | 0.0000 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BJTK2/model_name | []
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}
} | 0 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: pretrained-m-bert-500
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pretrained-m-bert-500
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: nan
- Validation Loss: nan
- Epoch: 499
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.2739 | 10.9467 | 0 |
| 7.7645 | 10.9288 | 1 |
| 6.8555 | 11.5279 | 2 |
| 6.4632 | 11.6263 | 3 |
| 6.3355 | 11.4757 | 4 |
| 6.3058 | 12.0991 | 5 |
| 6.4287 | 11.7486 | 6 |
| 6.0511 | 12.1714 | 7 |
| 5.9225 | 12.1125 | 8 |
| 6.0564 | 12.0840 | 9 |
| 5.9826 | 12.5151 | 10 |
| 5.9158 | 12.8370 | 11 |
| 5.9259 | 12.4708 | 12 |
| 5.9949 | 12.9119 | 13 |
| 6.0393 | 12.3977 | 14 |
| 5.8529 | 12.5782 | 15 |
| 5.8925 | 12.8304 | 16 |
| 5.9375 | 13.0765 | 17 |
| 5.8301 | 12.9770 | 18 |
| 5.8228 | 13.0910 | 19 |
| 5.8745 | 13.1173 | 20 |
| 5.7530 | 13.1872 | 21 |
| 5.8580 | 13.2372 | 22 |
| 5.8831 | 13.1819 | 23 |
| 5.7337 | 13.0628 | 24 |
| 5.6735 | 13.2866 | 25 |
| 5.8513 | 13.4646 | 26 |
| 5.7785 | 14.1611 | 27 |
| 5.8023 | 13.6074 | 28 |
| 5.9448 | 13.0115 | 29 |
| 5.8186 | 13.0827 | 30 |
| 5.7649 | 13.5381 | 31 |
| 5.9589 | 13.5486 | 32 |
| 5.6677 | 13.6187 | 33 |
| 5.7742 | 13.5131 | 34 |
| 5.7498 | 13.3843 | 35 |
| 5.7025 | 13.6528 | 36 |
| 5.8219 | 13.5450 | 37 |
| 5.7838 | 13.7073 | 38 |
| 5.8232 | 13.6628 | 39 |
| 5.6149 | 13.8447 | 40 |
| 5.7623 | 13.8217 | 41 |
| 5.7256 | 14.2310 | 42 |
| 5.6833 | 13.8236 | 43 |
| 5.6959 | 14.1115 | 44 |
| 5.6337 | 14.1601 | 45 |
| 5.6291 | 14.0644 | 46 |
| 5.6384 | 14.2376 | 47 |
| 5.6432 | 14.0575 | 48 |
| 5.7842 | 14.0357 | 49 |
| 5.8243 | 14.0310 | 50 |
| 5.6608 | 14.5138 | 51 |
| 5.6837 | 14.3153 | 52 |
| 5.6851 | 14.0351 | 53 |
| 5.9102 | 14.3691 | 54 |
| 5.6353 | 13.7349 | 55 |
| 5.8288 | 14.1780 | 56 |
| 5.6853 | 14.5344 | 57 |
| 5.6768 | 14.1037 | 58 |
| 5.7125 | 14.2650 | 59 |
| 5.7264 | 13.9798 | 60 |
| 5.6180 | 14.2999 | 61 |
| 5.6552 | 14.6840 | 62 |
| 5.5937 | 12.6319 | 63 |
| 5.6968 | 14.4662 | 64 |
| 5.7412 | 14.5146 | 65 |
| 5.6894 | 14.7825 | 66 |
| 5.7429 | 14.4179 | 67 |
| 5.8970 | 14.5220 | 68 |
| 5.8363 | 14.7431 | 69 |
| 5.6723 | 14.3854 | 70 |
| 5.6331 | 14.3606 | 71 |
| 5.8211 | 14.2819 | 72 |
| 5.6573 | 14.1377 | 73 |
| 5.6679 | 15.0708 | 74 |
| 5.8098 | 14.2208 | 75 |
| 5.7651 | 14.5841 | 76 |
| 5.7128 | 14.4615 | 77 |
| 5.7240 | 14.3851 | 78 |
| 5.6542 | 14.1252 | 79 |
| 5.6956 | 14.5275 | 80 |
| 5.7033 | 14.2555 | 81 |
| 5.7193 | 14.5061 | 82 |
| 5.5942 | 15.0025 | 83 |
| 5.7616 | 14.7460 | 84 |
| 5.7584 | 15.0039 | 85 |
| 5.7949 | 14.6488 | 86 |
| 5.6070 | 15.1781 | 87 |
| 5.7464 | 14.9159 | 88 |
| 5.6865 | 14.4204 | 89 |
| 5.6934 | 14.2373 | 90 |
| 5.7053 | 15.2664 | 91 |
| 5.6629 | 15.0062 | 92 |
| 5.6186 | 14.8861 | 93 |
| 5.8011 | 14.5602 | 94 |
| 5.5920 | 14.8159 | 95 |
| 5.7014 | 14.7589 | 96 |
| 5.5643 | 14.8814 | 97 |
| 5.6404 | 14.7907 | 98 |
| 5.7086 | 15.2773 | 99 |
| 5.6854 | 14.9998 | 100 |
| 5.6626 | 15.3495 | 101 |
| 5.7779 | 14.7495 | 102 |
| 5.7254 | 14.1843 | 103 |
| 5.5688 | 15.4027 | 104 |
| 5.7299 | 15.1292 | 105 |
| 5.7637 | 15.0089 | 106 |
| 5.7381 | 14.7808 | 107 |
| 5.6771 | 15.2646 | 108 |
| 5.6818 | 15.2837 | 109 |
| 5.7686 | 14.9571 | 110 |
| 5.7576 | 15.1920 | 111 |
| 5.7726 | 15.1617 | 112 |
| 5.6316 | 15.1758 | 113 |
| 5.8113 | 14.7105 | 114 |
| 5.6426 | 15.1844 | 115 |
| 5.7508 | 14.8047 | 116 |
| 5.6051 | 15.1107 | 117 |
| 5.7612 | 15.4183 | 118 |
| 5.6339 | 15.2419 | 119 |
| 5.6624 | 15.3083 | 120 |
| 5.7811 | 15.0736 | 121 |
| 5.6642 | 15.1613 | 122 |
| 5.7181 | 15.4360 | 123 |
| 5.6678 | 14.9387 | 124 |
| 5.5912 | 15.0978 | 125 |
| 5.6615 | 15.6547 | 126 |
| 5.6578 | 15.3756 | 127 |
| 5.6066 | 15.0189 | 128 |
| 5.8590 | 15.0346 | 129 |
| 5.4730 | 15.3790 | 130 |
| 5.7071 | 15.5029 | 131 |
| 5.8147 | 15.2418 | 132 |
| 5.7221 | 15.5273 | 133 |
| 5.6042 | 15.1112 | 134 |
| 5.6864 | 15.0770 | 135 |
| 5.6760 | 15.3917 | 136 |
| 5.6659 | 15.1614 | 137 |
| 5.6877 | 15.2246 | 138 |
| 5.7605 | 14.7458 | 139 |
| 5.6002 | 15.4996 | 140 |
| 5.5900 | 15.1735 | 141 |
| 5.6669 | 14.8704 | 142 |
| 5.7044 | 15.2206 | 143 |
| 5.6527 | 15.2784 | 144 |
| 5.7957 | 14.6124 | 145 |
| 5.7027 | 15.1526 | 146 |
| 5.6535 | 15.6537 | 147 |
| 5.5724 | 15.6795 | 148 |
| 5.8004 | 15.0675 | 149 |
| 5.7325 | 15.7633 | 150 |
| 5.7628 | 15.2577 | 151 |
| 5.6989 | 15.5838 | 152 |
| 5.5965 | 14.6995 | 153 |
| 5.7098 | 15.5871 | 154 |
| 5.7223 | 15.9051 | 155 |
| 5.6590 | 14.8577 | 156 |
| 5.6875 | 15.5673 | 157 |
| 5.4954 | 15.8485 | 158 |
| 5.6731 | 16.3177 | 159 |
| 5.6663 | 15.4642 | 160 |
| 5.7220 | 15.7394 | 161 |
| 5.6401 | 15.3949 | 162 |
| 5.7438 | 15.1514 | 163 |
| 5.7138 | 15.3914 | 164 |
| 5.6213 | 16.1217 | 165 |
| 5.5700 | 15.8129 | 166 |
| 5.7998 | 15.9362 | 167 |
| 5.6577 | 15.5407 | 168 |
| 5.6681 | 15.6683 | 169 |
| 5.5963 | 16.1492 | 170 |
| 5.6245 | 15.7006 | 171 |
| 5.6382 | 15.7842 | 172 |
| 5.8126 | 15.1912 | 173 |
| 5.6222 | 16.0619 | 174 |
| 5.6766 | 15.8941 | 175 |
| 5.6306 | 15.5406 | 176 |
| 5.6549 | 15.9326 | 177 |
| 5.6167 | 15.8319 | 178 |
| 5.6650 | 16.0879 | 179 |
| 5.6494 | 15.7430 | 180 |
| 5.6033 | 15.9896 | 181 |
| 5.6986 | 15.5833 | 182 |
| 5.6200 | 15.5568 | 183 |
| 5.7038 | 15.4953 | 184 |
| 5.6138 | 15.4238 | 185 |
| 5.6298 | 15.3535 | 186 |
| 5.7825 | 15.5319 | 187 |
| 5.6837 | 15.8585 | 188 |
| 5.6850 | 15.7378 | 189 |
| 5.6308 | 15.9983 | 190 |
| 5.7468 | 15.2695 | 191 |
| 5.6929 | 15.6588 | 192 |
| 5.5980 | 15.8079 | 193 |
| 5.6585 | 15.1795 | 194 |
| 5.7155 | 15.4172 | 195 |
| 5.4967 | 16.0720 | 196 |
| 5.6457 | 15.8192 | 197 |
| 5.5944 | 16.0818 | 198 |
| 5.5993 | 15.7492 | 199 |
| 5.6399 | 15.5226 | 200 |
| 5.6213 | 15.2274 | 201 |
| 5.5596 | 15.9212 | 202 |
| 5.7005 | 16.0254 | 203 |
| 5.6509 | 16.0489 | 204 |
| 5.6330 | 15.6095 | 205 |
| 5.6937 | 15.4836 | 206 |
| 5.6139 | 15.9037 | 207 |
| 5.6990 | 15.8835 | 208 |
| 5.7159 | 15.4055 | 209 |
| 5.7064 | 16.1568 | 210 |
| 5.5811 | 15.8374 | 211 |
| 5.5380 | 15.9787 | 212 |
| 5.7873 | 16.3566 | 213 |
| 5.6803 | 16.1136 | 214 |
| 5.7022 | 15.9423 | 215 |
| 5.6328 | 16.2375 | 216 |
| 5.5515 | 16.1874 | 217 |
| 5.5606 | 15.8988 | 218 |
| 5.6255 | 15.0519 | 219 |
| 5.6009 | 16.9413 | 220 |
| 5.6156 | 16.3816 | 221 |
| 5.5921 | 15.8814 | 222 |
| 5.6362 | 16.4681 | 223 |
| 5.5992 | 16.4140 | 224 |
| 5.7476 | 15.9326 | 225 |
| 5.7070 | 16.2967 | 226 |
| 5.6355 | 16.0066 | 227 |
| 5.6825 | 16.1857 | 228 |
| 5.7157 | 16.4286 | 229 |
| 5.6292 | 16.1983 | 230 |
| 5.6856 | 16.1762 | 231 |
| 5.5922 | 16.3408 | 232 |
| 5.6552 | 16.4230 | 233 |
| 5.6409 | 16.3617 | 234 |
| 5.7150 | 15.9821 | 235 |
| 5.6426 | 16.2628 | 236 |
| 5.6254 | 16.2317 | 237 |
| 5.6533 | 15.8563 | 238 |
| 5.7265 | 16.1139 | 239 |
| 5.5244 | 16.2591 | 240 |
| 5.5972 | 16.5940 | 241 |
| 5.6212 | 16.3575 | 242 |
| 5.6620 | 16.2986 | 243 |
| 5.7014 | 15.7731 | 244 |
| 5.5571 | 16.3621 | 245 |
| 5.5364 | 15.8728 | 246 |
| 5.6137 | 15.9289 | 247 |
| 5.6210 | 15.8208 | 248 |
| 5.6127 | 16.4034 | 249 |
| 5.5398 | 15.4969 | 250 |
| 5.4860 | 16.3688 | 251 |
| 5.5070 | 15.2843 | 252 |
| 5.5852 | 16.6199 | 253 |
| 5.6538 | 16.2439 | 254 |
| 5.6297 | 16.4628 | 255 |
| 5.5851 | 16.2025 | 256 |
| 5.5888 | 16.1771 | 257 |
| 5.5863 | 16.1895 | 258 |
| 5.5985 | 15.6048 | 259 |
| 5.5132 | 16.3437 | 260 |
| 5.5057 | 16.2532 | 261 |
| 5.4660 | 15.8150 | 262 |
| 5.4307 | 15.9858 | 263 |
| 5.4938 | 15.8380 | 264 |
| 5.5093 | 15.9445 | 265 |
| 5.3760 | 16.1691 | 266 |
| 5.4411 | 16.3107 | 267 |
| 5.4771 | 15.2124 | 268 |
| 5.4363 | 15.7446 | 269 |
| 5.3159 | 15.2140 | 270 |
| 5.3206 | 16.5491 | 271 |
| 5.4283 | 15.8891 | 272 |
| 5.4417 | 15.7291 | 273 |
| 5.2961 | 15.7117 | 274 |
| 5.3599 | 15.7950 | 275 |
| 5.3951 | 15.7102 | 276 |
| 5.2708 | 16.0568 | 277 |
| 5.2460 | 15.8950 | 278 |
| 5.2299 | 15.7666 | 279 |
| 5.2599 | 16.0815 | 280 |
| 5.2435 | 15.6292 | 281 |
| 5.2287 | 15.8015 | 282 |
| 5.1863 | 15.7617 | 283 |
| 5.2478 | 15.2488 | 284 |
| 5.2081 | 15.8984 | 285 |
| 5.2845 | 15.4205 | 286 |
| 5.1488 | 15.6027 | 287 |
| 5.1796 | 15.4884 | 288 |
| 5.1620 | 15.8486 | 289 |
| 5.0873 | 15.6319 | 290 |
| 5.1490 | 15.8943 | 291 |
| 5.3411 | 15.4410 | 292 |
| 5.1213 | 15.4223 | 293 |
| 5.1529 | 15.7270 | 294 |
| 5.0584 | 15.8027 | 295 |
| 5.1333 | 15.3475 | 296 |
| 5.0771 | 16.0088 | 297 |
| 5.1865 | 15.6306 | 298 |
| 5.1647 | 14.5357 | 299 |
| 5.1321 | 15.5258 | 300 |
| 5.0660 | 15.1154 | 301 |
| 4.9785 | 15.7435 | 302 |
| 5.1288 | 15.2501 | 303 |
| 4.9958 | 16.2107 | 304 |
| 5.0186 | 14.8714 | 305 |
| 5.0534 | 16.0393 | 306 |
| 5.1283 | 15.8092 | 307 |
| 5.0487 | 15.1757 | 308 |
| 5.0059 | 14.9999 | 309 |
| 5.0254 | 15.2550 | 310 |
| 5.0700 | 14.6593 | 311 |
| 4.9701 | 14.9625 | 312 |
| 4.9914 | 15.1240 | 313 |
| 5.1482 | 15.1512 | 314 |
| 5.0386 | 15.0771 | 315 |
| 5.0796 | 15.3972 | 316 |
| 5.0037 | 15.2716 | 317 |
| 5.0123 | 15.5238 | 318 |
| 4.9456 | 15.3506 | 319 |
| 4.9821 | 15.1408 | 320 |
| 5.0723 | 14.7902 | 321 |
| 4.9550 | 15.1561 | 322 |
| 4.8579 | 15.6329 | 323 |
| 4.9479 | 15.1341 | 324 |
| 4.9533 | 15.0210 | 325 |
| 4.9578 | 15.3307 | 326 |
| 4.9989 | 15.5646 | 327 |
| 4.9010 | 15.2794 | 328 |
| 4.8946 | nan | 329 |
| 4.9502 | 15.3112 | 330 |
| 4.9215 | 15.4103 | 331 |
| 4.7364 | 15.1038 | 332 |
| 4.9434 | 15.4464 | 333 |
| 4.8433 | 15.2240 | 334 |
| 4.8293 | 15.4287 | 335 |
| 4.9500 | 15.3020 | 336 |
| 4.9363 | 15.1094 | 337 |
| 4.8763 | 15.6272 | 338 |
| 4.9083 | 15.1468 | 339 |
| 4.7588 | 15.4358 | 340 |
| 4.7691 | 14.9748 | 341 |
| 4.8005 | 15.5152 | 342 |
| nan | nan | 343 |
| nan | nan | 344 |
| nan | nan | 345 |
| nan | nan | 346 |
| nan | nan | 347 |
| nan | nan | 348 |
| nan | nan | 349 |
| nan | nan | 350 |
| nan | nan | 351 |
| nan | nan | 352 |
| nan | nan | 353 |
| nan | nan | 354 |
| nan | nan | 355 |
| nan | nan | 356 |
| nan | nan | 357 |
| nan | nan | 358 |
| nan | nan | 359 |
| nan | nan | 360 |
| nan | nan | 361 |
| nan | nan | 362 |
| nan | nan | 363 |
| nan | nan | 364 |
| nan | nan | 365 |
| nan | nan | 366 |
| nan | nan | 367 |
| nan | nan | 368 |
| nan | nan | 369 |
| nan | nan | 370 |
| nan | nan | 371 |
| nan | nan | 372 |
| nan | nan | 373 |
| nan | nan | 374 |
| nan | nan | 375 |
| nan | nan | 376 |
| nan | nan | 377 |
| nan | nan | 378 |
| nan | nan | 379 |
| nan | nan | 380 |
| nan | nan | 381 |
| nan | nan | 382 |
| nan | nan | 383 |
| nan | nan | 384 |
| nan | nan | 385 |
| nan | nan | 386 |
| nan | nan | 387 |
| nan | nan | 388 |
| nan | nan | 389 |
| nan | nan | 390 |
| nan | nan | 391 |
| nan | nan | 392 |
| nan | nan | 393 |
| nan | nan | 394 |
| nan | nan | 395 |
| nan | nan | 396 |
| nan | nan | 397 |
| nan | nan | 398 |
| nan | nan | 399 |
| nan | nan | 400 |
| nan | nan | 401 |
| nan | nan | 402 |
| nan | nan | 403 |
| nan | nan | 404 |
| nan | nan | 405 |
| nan | nan | 406 |
| nan | nan | 407 |
| nan | nan | 408 |
| nan | nan | 409 |
| nan | nan | 410 |
| nan | nan | 411 |
| nan | nan | 412 |
| nan | nan | 413 |
| nan | nan | 414 |
| nan | nan | 415 |
| nan | nan | 416 |
| nan | nan | 417 |
| nan | nan | 418 |
| nan | nan | 419 |
| nan | nan | 420 |
| nan | nan | 421 |
| nan | nan | 422 |
| nan | nan | 423 |
| nan | nan | 424 |
| nan | nan | 425 |
| nan | nan | 426 |
| nan | nan | 427 |
| nan | nan | 428 |
| nan | nan | 429 |
| nan | nan | 430 |
| nan | nan | 431 |
| nan | nan | 432 |
| nan | nan | 433 |
| nan | nan | 434 |
| nan | nan | 435 |
| nan | nan | 436 |
| nan | nan | 437 |
| nan | nan | 438 |
| nan | nan | 439 |
| nan | nan | 440 |
| nan | nan | 441 |
| nan | nan | 442 |
| nan | nan | 443 |
| nan | nan | 444 |
| nan | nan | 445 |
| nan | nan | 446 |
| nan | nan | 447 |
| nan | nan | 448 |
| nan | nan | 449 |
| nan | nan | 450 |
| nan | nan | 451 |
| nan | nan | 452 |
| nan | nan | 453 |
| nan | nan | 454 |
| nan | nan | 455 |
| nan | nan | 456 |
| nan | nan | 457 |
| nan | nan | 458 |
| nan | nan | 459 |
| nan | nan | 460 |
| nan | nan | 461 |
| nan | nan | 462 |
| nan | nan | 463 |
| nan | nan | 464 |
| nan | nan | 465 |
| nan | nan | 466 |
| nan | nan | 467 |
| nan | nan | 468 |
| nan | nan | 469 |
| nan | nan | 470 |
| nan | nan | 471 |
| nan | nan | 472 |
| nan | nan | 473 |
| nan | nan | 474 |
| nan | nan | 475 |
| nan | nan | 476 |
| nan | nan | 477 |
| nan | nan | 478 |
| nan | nan | 479 |
| nan | nan | 480 |
| nan | nan | 481 |
| nan | nan | 482 |
| nan | nan | 483 |
| nan | nan | 484 |
| nan | nan | 485 |
| nan | nan | 486 |
| nan | nan | 487 |
| nan | nan | 488 |
| nan | nan | 489 |
| nan | nan | 490 |
| nan | nan | 491 |
| nan | nan | 492 |
| nan | nan | 493 |
| nan | nan | 494 |
| nan | nan | 495 |
| nan | nan | 496 |
| nan | nan | 497 |
| nan | nan | 498 |
| nan | nan | 499 |
### Framework versions
- Transformers 4.27.0.dev0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BME-TMIT/foszt2oszt | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"hu",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 15 | 2023-01-31T10:14:50Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 277.91 +/- 23.07
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BSC-LT/RoBERTalex | [
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:legal_ES",
"dataset:temu_legal",
"arxiv:2110.12201",
"transformers",
"legal",
"spanish",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 24 | null | ---
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-muchocine
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clasificador-muchocine
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3788
- Accuracy: 0.4555
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 388 | 1.3559 | 0.3961 |
| 1.4414 | 2.0 | 776 | 1.3217 | 0.4258 |
| 1.1139 | 3.0 | 1164 | 1.3788 | 0.4555 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BSC-LT/gpt2-large-bne | [
"pytorch",
"gpt2",
"text-generation",
"es",
"dataset:bne",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"license:apache-2.0"
]
| text-generation | {
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],
"model_type": "gpt2",
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}
} | 11 | null | ---
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-muchocine
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clasificador-muchocine
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3877
- Accuracy: 0.4439
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 388 | 1.3596 | 0.3884 |
| 1.4301 | 2.0 | 776 | 1.2666 | 0.4323 |
| 1.0491 | 3.0 | 1164 | 1.3877 | 0.4439 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BSC-LT/roberta-base-biomedical-es | [
"pytorch",
"roberta",
"fill-mask",
"es",
"arxiv:2109.03570",
"arxiv:2109.07765",
"transformers",
"biomedical",
"spanish",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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}
} | 161 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: gpt_trinity_2_4_3e-5_lp5_nb5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt_trinity_2_4_3e-5_lp5_nb5
This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0291
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.5765 | 0.05 | 1000 | 4.1247 |
| 3.19 | 0.09 | 2000 | 4.0578 |
| 3.1177 | 0.14 | 3000 | 4.0708 |
| 3.1116 | 0.19 | 4000 | 4.0654 |
| 3.0777 | 0.24 | 5000 | 4.0857 |
| 3.1105 | 0.28 | 6000 | 4.1127 |
| 3.1018 | 0.33 | 7000 | 4.1410 |
| 3.0728 | 0.38 | 8000 | 4.1834 |
| 3.1248 | 0.42 | 9000 | 4.2058 |
| 3.1035 | 0.47 | 10000 | 4.2048 |
| 3.0943 | 0.52 | 11000 | 4.1892 |
| 3.0724 | 0.57 | 12000 | 4.2063 |
| 3.0517 | 0.61 | 13000 | 4.1923 |
| 3.0372 | 0.66 | 14000 | 4.2112 |
| 3.0235 | 0.71 | 15000 | 4.2043 |
| 3.0329 | 0.76 | 16000 | 4.1630 |
| 3.0171 | 0.8 | 17000 | 4.1631 |
| 2.9997 | 0.85 | 18000 | 4.1563 |
| 2.9913 | 0.9 | 19000 | 4.1616 |
| 2.9579 | 0.94 | 20000 | 4.1494 |
| 2.9576 | 0.99 | 21000 | 4.1367 |
| 2.7461 | 1.04 | 22000 | 4.1593 |
| 2.7637 | 1.09 | 23000 | 4.1453 |
| 2.741 | 1.13 | 24000 | 4.1624 |
| 2.7514 | 1.18 | 25000 | 4.1357 |
| 2.755 | 1.23 | 26000 | 4.1524 |
| 2.7365 | 1.27 | 27000 | 4.1399 |
| 2.7356 | 1.32 | 28000 | 4.1285 |
| 2.7386 | 1.37 | 29000 | 4.1286 |
| 2.7489 | 1.42 | 30000 | 4.1231 |
| 2.7518 | 1.46 | 31000 | 4.1104 |
| 2.7317 | 1.51 | 32000 | 4.1202 |
| 2.7378 | 1.56 | 33000 | 4.1132 |
| 2.7309 | 1.6 | 34000 | 4.1047 |
| 2.7791 | 1.65 | 35000 | 4.0976 |
| 2.7427 | 1.7 | 36000 | 4.0874 |
| 2.7184 | 1.75 | 37000 | 4.0953 |
| 2.7107 | 1.79 | 38000 | 4.0963 |
| 2.7122 | 1.84 | 39000 | 4.0841 |
| 2.7172 | 1.89 | 40000 | 4.0852 |
| 2.7126 | 1.94 | 41000 | 4.0632 |
| 2.7063 | 1.98 | 42000 | 4.0643 |
| 2.5311 | 2.03 | 43000 | 4.0848 |
| 2.4496 | 2.08 | 44000 | 4.0943 |
| 2.4597 | 2.12 | 45000 | 4.0799 |
| 2.4472 | 2.17 | 46000 | 4.0802 |
| 2.4628 | 2.22 | 47000 | 4.0880 |
| 2.4508 | 2.27 | 48000 | 4.0791 |
| 2.4743 | 2.31 | 49000 | 4.0765 |
| 2.4692 | 2.36 | 50000 | 4.0739 |
| 2.4651 | 2.41 | 51000 | 4.0690 |
| 2.4885 | 2.45 | 52000 | 4.0723 |
| 2.5023 | 2.5 | 53000 | 4.0675 |
| 2.4651 | 2.55 | 54000 | 4.0649 |
| 2.4774 | 2.6 | 55000 | 4.0695 |
| 2.4717 | 2.64 | 56000 | 4.0559 |
| 2.4856 | 2.69 | 57000 | 4.0512 |
| 2.4572 | 2.74 | 58000 | 4.0473 |
| 2.486 | 2.79 | 59000 | 4.0438 |
| 2.449 | 2.83 | 60000 | 4.0385 |
| 2.456 | 2.88 | 61000 | 4.0355 |
| 2.4802 | 2.93 | 62000 | 4.0378 |
| 2.4635 | 2.97 | 63000 | 4.0308 |
| 2.3742 | 3.02 | 64000 | 4.0488 |
| 2.2371 | 3.07 | 65000 | 4.0579 |
| 2.2496 | 3.12 | 66000 | 4.0630 |
| 2.2758 | 3.16 | 67000 | 4.0516 |
| 2.2489 | 3.21 | 68000 | 4.0585 |
| 2.2374 | 3.26 | 69000 | 4.0715 |
| 2.2862 | 3.3 | 70000 | 4.0507 |
| 2.2502 | 3.35 | 71000 | 4.0512 |
| 2.238 | 3.4 | 72000 | 4.0545 |
| 2.2407 | 3.45 | 73000 | 4.0459 |
| 2.2529 | 3.49 | 74000 | 4.0452 |
| 2.2453 | 3.54 | 75000 | 4.0459 |
| 2.2314 | 3.59 | 76000 | 4.0416 |
| 2.2408 | 3.63 | 77000 | 4.0379 |
| 2.2497 | 3.68 | 78000 | 4.0348 |
| 2.2475 | 3.73 | 79000 | 4.0374 |
| 2.2376 | 3.78 | 80000 | 4.0319 |
| 2.244 | 3.82 | 81000 | 4.0331 |
| 2.2611 | 3.87 | 82000 | 4.0306 |
| 2.237 | 3.92 | 83000 | 4.0301 |
| 2.2337 | 3.97 | 84000 | 4.0291 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.9.0+cu102
- Datasets 2.8.0
- Tokenizers 0.13.2
|
BSC-LT/roberta-base-bne | [
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
],
"model_type": "roberta",
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}
}
} | 594 | null | ---
datasets:
- allenai/soda
language:
- de
metrics:
- bleu
pipeline_tag: text-classification
tags:
- code
--- |
BSC-LT/roberta-base-ca | [
"pytorch",
"roberta",
"fill-mask",
"ca",
"transformers",
"masked-lm",
"BERTa",
"catalan",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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}
}
} | 18 | null | ---
language:
- tr
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: base Turkish Whisper (bTW)
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# base Turkish Whisper (bTW)
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Ermetal Meetings dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8800
- Wer: 0.8060
- Cer: 0.7585
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.8904 | 1.32 | 100 | 1.5873 | 0.8893 | 0.5437 |
| 0.8039 | 2.63 | 200 | 0.9239 | 0.9076 | 0.5721 |
| 0.5988 | 3.95 | 300 | 0.7970 | 0.7850 | 0.4821 |
| 0.384 | 5.26 | 400 | 0.7586 | 0.7164 | 0.5206 |
| 0.2643 | 6.58 | 500 | 0.7578 | 0.9130 | 0.6843 |
| 0.2026 | 7.89 | 600 | 0.7627 | 0.9147 | 0.7228 |
| 0.1091 | 9.21 | 700 | 0.8043 | 0.8363 | 0.8283 |
| 0.0623 | 10.53 | 800 | 0.8342 | 0.7615 | 0.7619 |
| 0.0436 | 11.84 | 900 | 0.8577 | 0.7079 | 0.6824 |
| 0.0348 | 13.16 | 1000 | 0.8800 | 0.8060 | 0.7585 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.0+cu102
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BSC-LT/roberta-large-bne-capitel-pos | [
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"pos",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
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}
} | 13 | null | ---
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-muchocine
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clasificador-muchocine
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4813
- Accuracy: 0.4439
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 388 | 1.3269 | 0.4155 |
| 1.4007 | 2.0 | 776 | 1.3847 | 0.4258 |
| 0.9989 | 3.0 | 1164 | 1.4813 | 0.4439 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BSen/wav2vec2-base-timit-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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} | 4 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- ashutoshmondal/autotrain-data-pneumo
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 1.9594067819084715
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 3177689678
- CO2 Emissions (in grams): 1.9594
## Validation Metrics
- Loss: 0.017
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000 |
BW/TEST | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
} | 14 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.40 +/- 35.81
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Badr/model1 | []
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} | 0 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ScareCrow432/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Bagus/ser-japanese | []
| null | {
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}
} | 0 | null | ---
language:
- de
license: apache-2.0
tags:
- voice
- classification
- emotion
- speech
- audio
datasets:
- emo-DB
widget:
- src: >-
https://huggingface.co/padmalcom/wav2vec2-large-emotion-detection-german/resolve/main/test.wav
example_title: Sample 1
pipeline_tag: audio-classification
metrics:
- accuracy
---
This wav2vec2 based emotion detection model is trained on the [emo-DB dataset](http://emodb.bilderbar.info/start.html).
Code for training can be found [here](https://github.com/padmalcom/wav2vec2-emotion-detection-ger).
Emotion classes are:
- 0: 'anger'
- 1: 'boredom'
- 2: 'disgust'
- 3: 'fear'
- 4: 'happiness'
- 5: 'sadness'
- 6: 'neutral'
*inference.py* shows, how the model can be used. |
Bagus/wav2vec2-large-xlsr-bahasa-indonesia | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"el",
"dataset:common_voice_id_6.1",
"transformers",
"audio",
"speech",
"bahasa-indonesia",
"license:apache-2.0"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"prefix": null
},
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 12 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Q-Learning-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ScareCrow432/Q-Learning-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition | [
"pytorch",
"tensorboard",
"wav2vec2",
"el",
"dataset:aesdd",
"transformers",
"audio",
"audio-classification",
"speech",
"license:apache-2.0"
]
| audio-classification | {
"architectures": [
"Wav2Vec2ForSpeechClassification"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
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"do_sample": null,
"max_length": null
},
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}
}
} | 21 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: result
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# result
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BalajiSathesh/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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"length_penalty": null,
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},
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}
}
} | 8 | null | ---
license: apache-2.0
datasets:
- squad_v2
language:
- en
metrics:
- f1
- accuracy
library_name: adapter-transformers
pipeline_tag: question-answering
--- |
Banshee/LukeSkywalker | []
| null | {
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}
} | 0 | 2023-01-31T11:47:26Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: iammartian0/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Barleysack/AERoberta | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
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},
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},
"translation_en_to_fr": {
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},
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}
} | 7 | 2023-02-01T11:57:33Z | ---
tags:
- molecular language model
- SELFIES
- molecule generation
widget:
- text: '[C][=C][C][=C][C][=C][Ring1][=Branch1]'
inference: false
---
# MolGen-large
MolGen-large was introduced in the paper ["Molecular Language Model as Multi-task Generator"](https://arxiv.org/pdf/2301.11259.pdf) and first released in [this repository](https://github.com/zjunlp/MolGen). It is a pre-trained molecular generative model built using the 100\% robust molecular language representation, SELFIES.
## Model description
MolGen-large is the first pre-trained model that only produces chemically valid molecules.
With a training corpus of over 100 million molecules in SELFIES representation, MolGen-large learns the intrinsic structural patterns of molecules by mapping corrupted SELFIES to their original forms.
Specifically, MolGen-large employs a bidirectional Transformer as its encoder and an autoregressive Transformer as its decoder.
Through its carefully designed multi-task molecular prefix tuning (MPT), MolGen-large can generate molecules with desired properties, making it a valuable tool for molecular optimization.
## Intended uses
You can use the raw model for molecule generation or fine-tune it to a downstream task. Please take note that the following examples only demonstrate the utilization of our pre-trained model for molecule generation. See the [repository](https://github.com/zjunlp/MolGen) to look for fine-tune details on a task that interests you.
### How to use
Molecule generation example:
```python
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-large")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/MolGen-large")
>>> sf_input = tokenizer("[C][=C][C][=C][C][=C][Ring1][=Branch1]", return_tensors="pt")
>>> # beam search
>>> molecules = model.generate(input_ids=sf_input["input_ids"],
attention_mask=sf_input["attention_mask"],
max_length=15,
min_length=5,
num_return_sequences=5,
num_beams=5)
>>> sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]
['[C][=C][C][=C][C][=C][Ring1][=Branch1]',
'[C][=C][C][=C][C][=C][C][=C][Ring1][=Branch1]',
'[C][=C][C][=C][C][=C][Ring1][=Branch1][C][=C][C][=C]',
'[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][C][=C][C]',
'[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][=C][C][=C]']
```
### BibTeX entry and citation info
```bibtex
@article{fang2023molecular,
title={Molecular Language Model as Multi-task Generator},
author={Fang, Yin and Zhang, Ningyu and Chen, Zhuo and Fan, Xiaohui and Chen, Huajun},
journal={arXiv preprint arXiv:2301.11259},
year={2023}
}
``` |
Barytes/hellohf | [
"tf",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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}
}
} | 2 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Shanghai Dreambooth model trained by WoodRoof with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Batsy24/DialoGPT-medium-Twilight_BellaBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 8 | null | ---
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-muchocine-distilbert
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clasificador-muchocine-distilbert
This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3952
- Accuracy: 0.4710
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 388 | 1.2939 | 0.4465 |
| 1.2888 | 2.0 | 776 | 1.2575 | 0.4684 |
| 0.8575 | 3.0 | 1164 | 1.3952 | 0.4710 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BatuhanYilmaz/bert-finetuned-mrpc | []
| null | {
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}
} | 0 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: roberta-finetuned-subjqa-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-finetuned-subjqa-2
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BatuhanYilmaz/mlm-finetuned-imdb | []
| null | {
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}
} | 0 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- ashutoshmondal/autotrain-data-pneumo-v3
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 3.4021330886626298
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 3180589690
- CO2 Emissions (in grams): 3.4021
## Validation Metrics
- Loss: 0.131
- Accuracy: 0.964
- Precision: 0.964
- Recall: 0.964
- AUC: 0.994
- F1: 0.964 |
Baybars/wav2vec2-xls-r-1b-turkish | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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},
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}
} | 13 | null |
---
tags:
- TensorRT
- Text2Image
- Stable Diffusion
- Image2Image
- SDA
---
# andite/anything-v4.0 converted into TensorRT
<a href="https://github.com/chavinlo/sda-node/"><img src="https://i.imgur.com/fQS926g.png"></a>
Model converted from diffusers into TensorRT for accelerated inference up to 4x faster.
For how to use the model check https://github.com/chavinlo/sda-node
This model was automatically converted by SDA-node
Compilation configuration:
```json
{
"_class_name": "StableDiffusionAccelerated_Base",
"_sda_version": "0.1.2",
"_trt_version": "8.5.1",
"_cuda_version": "11.6",
"_cudnn_version": "7.5",
"_onnx2trt_version": "8.5.1",
"UNET": {
"precision": "fp16",
"path": "engine/unet_fp16.plan"
},
"CLIP": {
"path": "engine/clip.plan"
},
"DE_VAE": {
"path": "engine/de_vae.plan"
}
}
``` |
Baybars/wav2vec2-xls-r-300m-cv8-turkish | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"license:apache-2.0"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
} | 5 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 685 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 68,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
BeIR/query-gen-msmarco-t5-base-v1 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 1,816 | null | ---
tags:
- spacy
- text-classification
language:
- en
model-index:
- name: en_textcat_sales_in
results: []
---
| Feature | Description |
| --- | --- |
| **Name** | `en_textcat_sales_in` |
| **Version** | `0.0.4` |
| **spaCy** | `>=3.4.3,<3.5.0` |
| **Default Pipeline** | `textcat` |
| **Components** | `textcat` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (2 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`textcat`** | `OTHER`, `2100 - Sales` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `CATS_SCORE` | 95.26 |
| `CATS_MICRO_P` | 95.47 |
| `CATS_MICRO_R` | 95.47 |
| `CATS_MICRO_F` | 95.47 |
| `CATS_MACRO_P` | 95.26 |
| `CATS_MACRO_R` | 95.25 |
| `CATS_MACRO_F` | 95.26 |
| `CATS_MACRO_AUC` | 98.83 |
| `CATS_MACRO_AUC_PER_TYPE` | 0.00 |
| `TEXTCAT_LOSS` | 438.02 | |
BeIR/query-gen-msmarco-t5-large-v1 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 1,225 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xlcost-text-to-code
model-index:
- name: flan-t5-xl-codeparrot-xlcost-text-to-code
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-xl-codeparrot-xlcost-text-to-code
This model is a fine-tuned version of [epinnock/flan-t5-xl-codeparrot-xlcost-text-to-code](https://huggingface.co/epinnock/flan-t5-xl-codeparrot-xlcost-text-to-code) on the xlcost-text-to-code dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.9876
- eval_rouge1: 43.1227
- eval_rouge2: 25.6539
- eval_rougeL: 41.8635
- eval_rougeLsum: 41.8883
- eval_gen_len: 9.0445
- eval_runtime: 1137.2469
- eval_samples_per_second: 7.17
- eval_steps_per_second: 0.897
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 144
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.0+cu116
- Datasets 2.9.0
- Tokenizers 0.12.1
|
Bee-Garbs/DialoGPT-real-cartman-small | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 10 | null | # 禁止将他人成品图进行转绘，除非你有对方的授权！
**hi，你好 ~**
这个项目最初是由于天刀明月刀生产同人图极为困难而建立的，用AI来代替P图、画图的工作，大幅度减少同人创作的难度及工作量，AI绘画具有泛用性，理论上本帖所有资源都可以用于国风游戏，例如**剑网三、逆水寒、仙剑**等。
所有文件都是网络搜集而来，我将资源整合分享给你，程序与教程文件你都可以在**Files and versions（手机上是Files）**中下载获取，下载时推荐用第三方下载器，例如IDM与XDown，可以更快的下载，但在哪之前，请先来看下文件的简介说明。
**推荐显卡是N卡的电脑使用，**不是的话也可以用，就是CPU生成速度非常慢，**贴子底部有更多模型的下载方式，**如有其他问题可联系Q群：**524980709**，注意是其它问题，而不是把这里写的东西再复制粘贴给你。
[](https://postimg.cc/CdS0LVnF)**[点击查看大图]**
# 程序整合包：
## WuXia-NovelAI-WebUI-\[简易版\]
**解压后大小为12.0GB，仅支持N卡及CPU！转绘尝鲜首选！**基于B站UP@秋葉aaaki制作的NovelAIv3整合包进改行修，添加了常用咒语预设，**精简了放大模型**（只能使用R-ESRGAN进行放大），**精简了模型体积**（半精度模型，若报错请下载额外内容里的国风2.5D模型），**移除了反推tag的功能**（有也不好用不如删了）**解压即用！注意！！！程序必须是纯英文路径，不含空格！额外内容见贴子下方。**
原作者主页：[https://space.bilibili.com/12566101/](https://space.bilibili.com/12566101/)
## WuXia-NovelAI-WebUI-\[高级版\]
**解压后大小为25.7GB，仅支持N卡及CPU！追求好效果的首选！且可以用作其他创意用途！基于B站UP@秋葉aaaki制作的NovelAIv4整合包进改行修，**添加了常用咒语预设，支持输入中文转换tag、常用咒语预设、增强的放大模型、高级嵌入式模型训练、深度图库、高级模型转换、高级模型合并、高级大模型训练、深度图处理、词元处理、高级tag反推、CFG模拟、分割拼接放大脚本、面部识别修复以及精确绘图插件（含全部半精度模型），**解压即用！注意！！！程序必须是纯英文路径，不含空格！额外内容见贴子下方。**
## WuXia-NovelAI-WebUI-\[专业版\]
**解压后大小为38.6GB，支持N卡/A卡/I卡/CPU！追求好效果的首选！且可以用作其他创意用途！【部分A/I卡用户由于无法释放内存，连续多次跑图需要重启WebUI后台释放内存。同时A/I卡用户无法使用DDIM与DPM系列采样，无法训练模型，其余功能可正常使用(i卡疑似无法使用controlnet功能，可在使用时转为cpu模式)】**基于NovelAI中文频道v1.71整合包进改行修，支持输入中文转换tag、常用咒语预设、增强的放大模型、高级嵌入式模型训练、深度图库、高级模型转换、高级模型合并、高级大模型训练、深度图处理、词元处理、高级tag反推以及精确绘图插件（含全部半精度模型），解压即用！**注意！！！程序必须是纯英文路径，不含空格！且以命令行方式运行，需要具有自己排查错误的能力！额外内容见贴子下方。**
NovelAI中文QQ频道：[https://pd.qq.com/s/b42y674c8](https://pd.qq.com/s/b42y674c8)
# AI绘画程序使用教程
## Stable Diffusion WebUI教程
AI绘画软件SD_WebUI的入门向教程，由B站@阿宅LENS制作，适合0基础的入门。（是个合集，可以切换集数）
AI绘画非正式教程：[https://www.bilibili.com/video/BV1Dv4y1W7tw/](https://www.bilibili.com/video/BV1Dv4y1W7tw/)
# 教程文件：
## 天刀AI绘画教学
利用AI绘画辅助天刀转绘的教程，包含启动教程、图生图教程、局部重绘教程、AI图片放大教程以及游戏截图实战流程，均为PDF格式，图片因排版问题，手动放大后就能看清了。
[](https://postimg.cc/LgVdkB97)
**\[点击图片查看大图\]**
## 天刀编辑器FBX捏动作教程及资源
含有Maya与iClone捏天刀编辑器支持的FBX动作教程与对应资源，Maya教程是@白菜竹录制的，非常的详细；iClone的教程不完全**（主要是录制视频的是个封号斗罗）**，是@十七录制的；建议结合起来学习，目前公认iClone简易且快速，Maya捏出来的更精细，压缩包内有你所需要的所有内容。
原作者@白菜竹主页：[https://space.bilibili.com/19497515](https://space.bilibili.com/19497515)
原作者@十七主页：号没了
# tag对照表：
## AI绘画Tag生成器离线版
纯网页版应用，**解压后双击index.html即可打开浏览器并使用，**类似标签超市一样的用法，可以快速的生成你需要的tag，**注意可以选择性开启NSFW内容**
## tag百科大典(4万多)
好消息：有39195行tag可供查找。坏消息：谷歌机翻。但确实是很全面了，可以在记事本里按Ctrl+F快速找到目标tag。**（建议先用微软翻译生成图，如果图上有元素没被体现再翻找tag词典，这样会比较方便组合）**
# 额外内容：
可用于本整合包的额外资源。
## 主模型：
**本人整理的模型**：[https://huggingface.co/Azhai-FX/WuXia-NovelAI_Stable-Diffusion](https://huggingface.co/Azhai-FX/WuXia-NovelAI_Stable-Diffusion)
## LoRA模型：
**本人整理的LoRA**：[https://huggingface.co/Azhai-FX/WuXia-NovelAI_LoRA](https://huggingface.co/Azhai-FX/WuXia-NovelAI_LoRA)
更多LoRA链接：[https://huggingface.co/xiaozhangMJXXZ](https://huggingface.co/xiaozhangMJXXZ)
## Hypernetwork模型：
**本人整理的Hypernetwork**：[https://huggingface.co/Azhai-FX/WuXia-NovelAI_Hypernetwork](https://huggingface.co/Azhai-FX/WuXia-NovelAI_Hypernetwork)
## Embedding模型：
**本人整理的Embedding**：[https://huggingface.co/Azhai-FX/WuXia-NovelAI_Embedding](https://huggingface.co/Azhai-FX/WuXia-NovelAI_Embedding)
## VAE模型：
本人整理的VAE：[https://huggingface.co/Azhai-FX/WuXia-NovelAI_VAE](https://huggingface.co/Azhai-FX/WuXia-NovelAI_VAE)
## Tag在线查找：
可以在线查找很多tag，也有很多成品tag可以直接复制粘贴使用。
链接：[https://aitag.top/](https://aitag.top/)
## AI绘画图站：
收录了很多AI画好的成品图，可以直接用来生成图片以及学习tag写作，当然也有很多被压缩丢失了tag信息，需要在AI绘画界面中的图片信息自行辨别。
链接：[https://aibooru.online/](https://aibooru.online/)
### 最后祝你玩的愉快！
|
Bhumika/roberta-base-finetuned-sst2 | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"model-index"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
} | 85 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: ThomasSimonini/poca-SoccerTwosTest3
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Bhuvana/t5-base-spellchecker | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 93 | null | ---
license: gpl-3.0
---
Pre-trained word embeddings using the text of published clinical case reports. These embeddings use 600 dimensions and were trained using the word2vec algorithm on published clinical case reports found in the [PMC Open Access Subset](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/). See the paper here: https://pubmed.ncbi.nlm.nih.gov/34920127/
Citation:
```
@article{flamholz2022word,
title={Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information},
author={Flamholz, Zachary N and Crane-Droesch, Andrew and Ungar, Lyle H and Weissman, Gary E},
journal={Journal of Biomedical Informatics},
volume={125},
pages={103971},
year={2022},
publisher={Elsevier}
}
```
## Quick start
Word embeddings are compatible with the [`gensim` Python package](https://radimrehurek.com/gensim/) format.
First download the files from this archive. Then load the embeddings into Python.
```python
from gensim.models import FastText, Word2Vec, KeyedVectors # KeyedVectors are used to load the GloVe models
# Load the model
model = Word2Vec.load('w2v_oa_cr_600d.bin')
# Return 100-dimensional vector representations of each word
model.wv.word_vec('diabetes')
model.wv.word_vec('cardiac_arrest')
model.wv.word_vec('lymphangioleiomyomatosis')
# Try out cosine similarity
model.wv.similarity('copd', 'chronic_obstructive_pulmonary_disease')
model.wv.similarity('myocardial_infarction', 'heart_attack')
model.wv.similarity('lymphangioleiomyomatosis', 'lam')
``` |
Biasface/DDDC2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
}
} | 10 | null | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -122.49 +/- 51.19
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'stinoco/PPO-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
BigBoy/model | []
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}
} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: from_scratch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# from_scratch
This model is a fine-tuned version of [tokenizer/config.json](https://huggingface.co/tokenizer/config.json) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4744
## 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: 360
- eval_batch_size: 360
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-------:|:---------------:|
| 1.0952 | 0.05 | 20000 | 1.0383 |
| 0.936 | 0.1 | 40000 | 0.8852 |
| 0.8679 | 0.14 | 60000 | 0.8207 |
| 0.8276 | 0.19 | 80000 | 0.7796 |
| 0.796 | 0.24 | 100000 | 0.7519 |
| 0.7756 | 0.29 | 120000 | 0.7299 |
| 0.7545 | 0.33 | 140000 | 0.7103 |
| 0.7395 | 0.38 | 160000 | 0.6947 |
| 0.7236 | 0.43 | 180000 | 0.6809 |
| 0.7143 | 0.48 | 200000 | 0.6705 |
| 0.705 | 0.52 | 220000 | 0.6585 |
| 0.6904 | 0.57 | 240000 | 0.6479 |
| 0.6835 | 0.62 | 260000 | 0.6388 |
| 0.672 | 0.67 | 280000 | 0.6290 |
| 0.665 | 0.72 | 300000 | 0.6217 |
| 0.6581 | 0.76 | 320000 | 0.6136 |
| 0.6466 | 0.81 | 340000 | 0.6071 |
| 0.6396 | 0.86 | 360000 | 0.6000 |
| 0.6343 | 0.91 | 380000 | 0.5940 |
| 0.6286 | 0.95 | 400000 | 0.5880 |
| 0.6183 | 1.0 | 420000 | 0.5809 |
| 0.6134 | 1.05 | 440000 | 0.5757 |
| 0.6094 | 1.1 | 460000 | 0.5693 |
| 0.6032 | 1.15 | 480000 | 0.5641 |
| 0.5954 | 1.19 | 500000 | 0.5596 |
| 0.5915 | 1.24 | 520000 | 0.5532 |
| 0.5845 | 1.29 | 540000 | 0.5489 |
| 0.5823 | 1.34 | 560000 | 0.5437 |
| 0.5754 | 1.38 | 580000 | 0.5393 |
| 0.573 | 1.43 | 600000 | 0.5345 |
| 0.5643 | 1.48 | 620000 | 0.5309 |
| 0.5627 | 1.53 | 640000 | 0.5262 |
| 0.56 | 1.57 | 660000 | 0.5220 |
| 0.5554 | 1.62 | 680000 | 0.5186 |
| 0.5507 | 1.67 | 700000 | 0.5152 |
| 0.5494 | 1.72 | 720000 | 0.5117 |
| 0.5445 | 1.77 | 740000 | 0.5076 |
| 0.5396 | 1.81 | 760000 | 0.5051 |
| 0.5363 | 1.86 | 780000 | 0.5026 |
| 0.5356 | 1.91 | 800000 | 0.4998 |
| 0.5303 | 1.96 | 820000 | 0.4982 |
| 0.5583 | 2.0 | 840000 | 0.5195 |
| 0.5565 | 2.05 | 860000 | 0.5180 |
| 0.5535 | 2.1 | 880000 | 0.5158 |
| 0.5497 | 2.15 | 900000 | 0.5133 |
| 0.5511 | 2.19 | 920000 | 0.5110 |
| 0.5439 | 2.24 | 940000 | 0.5085 |
| 0.5413 | 2.29 | 960000 | 0.5060 |
| 0.5376 | 2.34 | 980000 | 0.5023 |
| 0.5333 | 2.39 | 1000000 | 0.5004 |
| 0.5322 | 2.43 | 1020000 | 0.4973 |
| 0.5312 | 2.48 | 1040000 | 0.4941 |
| 0.5281 | 2.53 | 1060000 | 0.4921 |
| 0.5267 | 2.58 | 1080000 | 0.4902 |
| 0.5257 | 2.62 | 1100000 | 0.4871 |
| 0.5174 | 2.67 | 1120000 | 0.4849 |
| 0.5183 | 2.72 | 1140000 | 0.4825 |
| 0.5181 | 2.77 | 1160000 | 0.4807 |
| 0.5116 | 2.81 | 1180000 | 0.4784 |
| 0.5092 | 2.86 | 1200000 | 0.4769 |
| 0.5109 | 2.91 | 1220000 | 0.4757 |
| 0.5102 | 2.96 | 1240000 | 0.4739 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
BigSalmon/BertaMyWorda | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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}
}
} | 8 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('A3itor/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
BigSalmon/BestMask2 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 10 | null | ---
license: mit
datasets:
- natural_questions
language:
- en
--- |
BigSalmon/BlankSlots | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 4 | 2023-01-31T14:09:33Z | ---
license: creativeml-openrail-m
---
Fantasy.ai is the official and exclusive hosted AI generation platform that holds a commercial use license for GalaxyTimeMachine, you can use their service at https://fantasy.ai/
Please report any unauthorized commercial use.
No models using any license other than the standard creativeml-openrail-m license were used in this merge.
The VAE is baked into the model, but if you need one, then I would HIGHLY recommend using the standard SD VAE.
https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt
If you want to help me out, or just have a look at the public images, then please hop over to my Patreon: https://www.patreon.com/galaxytimemachine |
BigSalmon/FormalBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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}
}
} | 10 | 2023-01-31T14:13:58Z | ---
language:
- es
library_name: nemo
datasets:
- mozilla-foundation/common_voice_12_0
tags:
- automatic-speech-recognition
model-index:
- name: stt_es_citrinet_512_gamma_0_25
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 12.0
type: mozilla-foundation/common_voice_12_0
config: clean
split: test
args:
language: es
metrics:
- name: Test WER
type: wer
value: 9.549
license: bsd-3-clause
---
# NVIDIA Streaming Citrinet 512 (es-ES)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [](#datasets) |
## Attribution
As initial checkpoint used [stt_en_citrinet_512_gamma_0_25](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_citrinet_512_gamma_0_25) by [NVIDIA](https://github.com/NVIDIA) licensed under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) |
BigSalmon/FormalBerta2 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
} | 16 | 2023-01-31T14:14:11Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-audio-generation
- diffusion-models-class
---
# Model Card for Unit 4 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional audio generation of music in the genre Electronic
## Usage
```python
from IPython.display import Audio
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("akanametov/audio-diffusion-electronic")
output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
```
|
BigSalmon/FormalRobertaa | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"translation_en_to_de": {
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"translation_en_to_fr": {
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}
}
} | 5 | null | ---
tags:
- generated_from_trainer
model-index:
- name: xlm-twitter-toxicity-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-twitter-toxicity-test
This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1046
- eval_rmse: 0.3234
- eval_runtime: 3.4121
- eval_samples_per_second: 146.538
- eval_steps_per_second: 4.689
- epoch: 1.0
- step: 157
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BigSalmon/FormalRobertaaa | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"min_length": null,
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},
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"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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}
} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BigSalmon/GPT2HardandEasy | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"text-generation": {
"do_sample": true,
"max_length": 50
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}
} | 9 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 259.24 +/- 13.64
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BigSalmon/GPTNeo350MInformalToFormalLincoln2 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
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}
} | 8 | null | Access to model Wazzko/berdnikovich is restricted and you are not in the authorized list. Visit https://huggingface.co/Wazzko/berdnikovich to ask for access. |
BigSalmon/GPTNeo350MInformalToFormalLincoln5 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
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}
}
} | 11 | null | ---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-diffusers
---
|
BigSalmon/GPTT | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 9 | null | Access to model junujunu/ko-roberta-128 is restricted and you are not in the authorized list. Visit https://huggingface.co/junujunu/ko-roberta-128 to ask for access. |
BigSalmon/InformalToFormalLincoln14 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
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}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikitext
metrics:
- accuracy
model-index:
- name: distilbert_add_pre-training-dim-96
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: wikitext wikitext-103-raw-v1
type: wikitext
config: wikitext-103-raw-v1
split: validation
args: wikitext-103-raw-v1
metrics:
- name: Accuracy
type: accuracy
value: 0.14942141332434558
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_add_pre-training-dim-96
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wikitext wikitext-103-raw-v1 dataset.
It achieves the following results on the evaluation set:
- Loss: 6.6092
- Accuracy: 0.1494
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 14.685 | 1.0 | 3573 | 9.3922 | 0.1240 |
| 8.0255 | 2.0 | 7146 | 7.1510 | 0.1315 |
| 7.0152 | 3.0 | 10719 | 6.7861 | 0.1482 |
| 6.8127 | 4.0 | 14292 | 6.7053 | 0.1493 |
| 6.74 | 5.0 | 17865 | 6.6695 | 0.1474 |
| 6.7067 | 6.0 | 21438 | 6.6431 | 0.1491 |
| 6.6871 | 7.0 | 25011 | 6.6204 | 0.1483 |
| 6.6748 | 8.0 | 28584 | 6.6250 | 0.1473 |
| 6.6649 | 9.0 | 32157 | 6.6108 | 0.1486 |
| 6.6596 | 10.0 | 35730 | 6.6140 | 0.1497 |
| 6.6536 | 11.0 | 39303 | 6.6067 | 0.1493 |
| 6.6483 | 12.0 | 42876 | 6.6140 | 0.1489 |
| 6.6463 | 13.0 | 46449 | 6.6096 | 0.1484 |
| 6.6434 | 14.0 | 50022 | 6.5570 | 0.1526 |
| 6.6414 | 15.0 | 53595 | 6.5836 | 0.1526 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BigSalmon/InformalToFormalLincoln19 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
],
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}
} | 11 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="james0248/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
BigSalmon/InformalToFormalLincoln21 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 8 | null | ---
license: cc-by-2.0
---
+ Kudou_Chitose:
+ Kudou_Chitose_v2:
+ Kudou_Chitose_SD:
+ additional prompts you may need to get better results:
`yellow eyes`, `white hair`, `white bloomers`, `hat`
|
BigSalmon/InformalToFormalLincoln22 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 6 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 276.12 +/- 12.91
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BigSalmon/InformalToFormalLincoln24 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: result
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# result
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BigSalmon/InformalToFormalLincoln25 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 10 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 633.00 +/- 142.74
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bnowak1831 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bnowak1831 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bnowak1831
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BigSalmon/MrLincoln | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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} | 7 | null | Access to model Owishiboo/correctnesschorus_v2 is restricted and you are not in the authorized list. Visit https://huggingface.co/Owishiboo/correctnesschorus_v2 to ask for access. |
BigSalmon/MrLincoln10 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.938
- name: F1
type: f1
value: 0.9382222634823464
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1611
- Accuracy: 0.938
- F1: 0.9382
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2043 | 1.0 | 250 | 0.1804 | 0.9275 | 0.9270 |
| 0.1334 | 2.0 | 500 | 0.1611 | 0.938 | 0.9382 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BigSalmon/MrLincoln13 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 9 | null | ---
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: tr
split: test
args: tr
metrics:
- name: Wer
type: wer
value: 0.4423450107241344
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model was trained from scratch on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5094
- Wer: 0.4423
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1344 | 3.32 | 400 | 0.5839 | 0.5715 |
| 0.1998 | 6.64 | 800 | 0.6102 | 0.5864 |
| 0.1714 | 9.96 | 1200 | 0.5624 | 0.5618 |
| 0.1485 | 13.28 | 1600 | 0.5720 | 0.5217 |
| 0.1055 | 16.6 | 2000 | 0.5338 | 0.4709 |
| 0.0731 | 19.92 | 2400 | 0.5094 | 0.4423 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.10.0+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
|
BigSalmon/MrLincoln5 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 9 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: Liapunov/ppo-SnowballTargetFR
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
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} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 10240 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 20480,
"warmup_steps": 2048,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
BigSalmon/MrLincolnBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
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} | 8 | 2023-01-31T15:43:59Z | ---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- ' stable-diffusion'
- stable-diffusion-diffusers
duplicated_from: xiaolxl/Gf_style
---
# Gf_style - 介绍
欢迎使用Gf_style模型 - 这是一个中国华丽古风风格模型，也可以说是一个古风游戏角色模型，具有2.5D的质感。这是一个模型系列，会在未来不断更新模型。
2.0版本已发布：[https://huggingface.co/xiaolxl/Gf_style2](https://huggingface.co/xiaolxl/Gf_style2)
3.0版本已发布：[https://huggingface.co/xiaolxl/Gf_style3](https://huggingface.co/xiaolxl/Gf_style3)
--
Welcome to Gf_style - This is a model of Chinese gorgeous ancient style, which can also be said to be an ancient game character model, with the effect of 2.5D texture. This is a series of models that will be updated in the future.
# install - 安装教程
1. 将XXX.saftensors模型和XXX.yaml放入SD目录 - Put the XXX.safetensors model and XXX.yaml into the SD directory
2. 请记住选择任何VAE文件，否则图形将为灰色 - Remember to select any VAE file, otherwise the drawing will be gray
# How to use - 如何使用
(TIP:人物是竖图炼制，理论上生成竖图效果更好)
如果您想使图片尽可能更好，请尝试以下配置 - If you want to make the picture better as possible, please try the following configuration
- Sampling steps:**30 or 50**
- Sampler:**DDIM** or **(DPM++ 2M Karras, DPM++ SDE Karras)** - These two have different surprises - 这两个有不一样的惊喜
- The size of the picture should be at least **768**, otherwise it will collapse - 图片大小至少768，不然会崩图
- Turn on Hires fix:**R-ESRGAN 4x+ Anime6B** and **Upscale by 2**
- If the face is deformed, try to Open **face repair**
- **key word(Start):**
```
{best quality}, {{masterpiece}}, {highres}, {an extremely delicate and beautiful}, original, extremely detailed wallpaper,
```
- **Negative words:**
```
NSFW, lowres,bad anatomy,bad hands, text, error, missing fingers,extra digit, fewer digits, cropped, worstquality, low quality, normal quality,jpegartifacts,signature, watermark, username,blurry,bad feet
```
# Examples - 例图
(可在文件列表中找到原图，并放入WebUi查看关键词等信息) - (You can find the original image in the file list, and put WebUi to view keywords and other information)
Town building map -- 镇楼图
<img src=https://huggingface.co/xiaolxl/Gf_style/resolve/main/examples/f1.png>
<img src=https://huggingface.co/xiaolxl/Gf_style/resolve/main/examples/f2.png>
Graph generated by keywords in How to use -- How to use中的关键词所生成的图
<img src=https://huggingface.co/xiaolxl/Gf_style/resolve/main/examples/e1.png>
<img src=https://huggingface.co/xiaolxl/Gf_style/resolve/main/examples/e2.png>
<img src=https://huggingface.co/xiaolxl/Gf_style/resolve/main/examples/e3.png>
<img src=https://huggingface.co/xiaolxl/Gf_style/resolve/main/examples/e4.png>
Ending figure -- 收尾图
<img src=https://huggingface.co/xiaolxl/Gf_style/resolve/main/examples/g1.png>
<img src=https://huggingface.co/xiaolxl/Gf_style/resolve/main/examples/g2.png>
|
BigSalmon/Neo | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
]
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} | 13 | null | ---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-diffusers
duplicated_from: xiaolxl/GuoFeng3
---
# 介绍 - GuoFeng3
欢迎使用GuoFeng3模型 - (TIP:这个版本的名字进行了微调),这是一个中国华丽古风风格模型，也可以说是一个古风游戏角色模型，具有2.5D的质感。第三代大幅度减少上手难度，增加了场景元素与男性古风人物，除此之外为了模型能更好的适应其它TAG，还增加了其它风格的元素。这一代对脸和手的崩坏有一定的修复，同时素材大小也提高到了最长边1024。
--
Welcome to the GuoFeng3 model - (TIP: the name of this version has been fine-tuned). This is a Chinese gorgeous antique style model, which can also be said to be an antique game character model with a 2.5D texture. The third generation greatly reduces the difficulty of getting started, and adds scene elements and male antique characters. In addition, in order to better adapt the model to other TAGs, other style elements are also added. This generation has repaired the broken face and hands to a certain extent, and the size of the material has also increased to the longest side of 1024.
# 安装教程 - install
1. 将GuoFeng3.ckpt模型放入SD目录 - Put GuoFeng3.ckpt model into SD directory
2. 此模型自带VAE，如果你的程序不支持，请记得选择任意一个VAE文件，否则图形将为灰色 - This model comes with VAE. If your program does not support it, please remember to select any VAE file, otherwise the graphics will be gray
# 如何使用 - How to use
**TIP：经过一天的测试，发现很多人物可能出现红眼问题，可以尝试在负面词添加red eyes。如果色彩艳丽可以尝试降低CFG - After a day of testing, we found that many characters may have red-eye problems. We can try to add red eyes to negative words。Try to reduce CFG if the color is bright**
简单：第三代大幅度减少上手难度 - Simple: the third generation greatly reduces the difficulty of getting started
- **关键词 - key word:**
```
best quality, masterpiece, highres, 1girl,china dress,Beautiful face
```
- **负面词 - Negative words:**
```
NSFW, lowres,bad anatomy,bad hands, text, error, missing fingers,extra digit, fewer digits, cropped, worstquality, low quality, normal quality,jpegartifacts,signature, watermark, username,blurry,bad feet
```
---
高级：如果您还想使图片尽可能更好，请尝试以下配置 - senior：If you also want to make the picture as better as possible, please try the following configuration
- Sampling steps:**50**
- Sampler:**DPM++ SDE Karras or DDIM**
- The size of the picture should be at least **1024** - 图片大小至少1024
- CFG:**4-6**
- **更好的负面词 Better negative words - 感谢群友提供的负面词:**
```
(((simple background))),monochrome ,lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, lowres, bad anatomy, bad hands, text, error, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ugly,pregnant,vore,duplicate,morbid,mut ilated,tran nsexual, hermaphrodite,long neck,mutated hands,poorly drawn hands,poorly drawn face,mutation,deformed,blurry,bad anatomy,bad proportions,malformed limbs,extra limbs,cloned face,disfigured,gross proportions, (((missing arms))),(((missing legs))), (((extra arms))),(((extra legs))),pubic hair, plump,bad legs,error legs,username,blurry,bad feet
```
- **如果想元素更丰富，可以添加下方关键词 - If you want to enrich the elements, you can add the following keywords**
```
Beautiful face,
hair ornament, solo,looking at viewer,smile,closed mouth,lips
china dress,dress,hair ornament, necklace, jewelry, long hair, earrings, chinese clothes,
architecture,east asian architecture,building,outdoors,rooftop,city,cityscape
```
# 例图 - Examples
(可在文件列表中找到原图，并放入WebUi查看关键词等信息) - (You can find the original image in the file list, and put WebUi to view keywords and other information)
<img src=https://huggingface.co/xiaolxl/GuoFeng3/resolve/main/examples/e1.png>
<img src=https://huggingface.co/xiaolxl/GuoFeng3/resolve/main/examples/e2.png>
<img src=https://huggingface.co/xiaolxl/GuoFeng3/resolve/main/examples/e3.png>
<img src=https://huggingface.co/xiaolxl/GuoFeng3/resolve/main/examples/e4.png> |
BigSalmon/ParaphraseParentheses2.0 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 13 | null | ---
language:
- en
---
# S5: Simplified State Space Layers for Sequence Modeling
This repository provides the implementation for the
paper: Simplified State Space Layers for Sequence Modeling. The preprint is available [here](https://arxiv.org/abs/2208.04933).
<p style="text-align: center;">
Figure 1: S5 uses a single multi-input, multi-output linear state-space model, coupled with non-linearities, to define a non-linear sequence-to-sequence transformation. Parallel scans are used for efficient offline processing.
</p>
The S5 layer builds on the prior S4 work ([paper](https://arxiv.org/abs/2111.00396)). While it has departed considerably, this repository originally started off with much of the JAX implementation of S4 from the
Annotated S4 blog by Rush and Karamcheti (available [here](https://github.com/srush/annotated-s4)).
## Requirements & Installation
To run the code on your own machine, run either `pip install -r requirements_cpu.txt` or `pip install -r requirements_gpu.txt`. The GPU installation of JAX can be tricky, and so we include requirements that should work for most people, although further instructions are available [here](https://github.com/google/jax#installation).
Run from within the root directory `pip install -e .` to install the package.
## Data Download
Downloading the raw data is done differently for each dataset. The following datasets require no action:
- Text (IMDb)
- Image (Cifar black & white)
- sMNIST
- psMNIST
- Cifar (Color)
The remaining datasets need to be manually downloaded. To download _everything_, run `./bin/download_all.sh`. This will download quite a lot of data and will take some time.
Below is a summary of the steps for each dataset:
- ListOps: run `./bin/download_lra.sh` to download the full LRA dataset.
- Retrieval (AAN): run `./bin/download_aan.sh`
- Pathfinder: run `./bin/download_lra.sh` to download the full LRA dataset.
- Path-X: run `./bin/download_lra.sh` to download the full LRA dataset.
- Speech commands 35: run `./bin/download_sc35.sh` to download the speech commands data.
*With the exception of SC35.* When the dataset is used for the first time, a cache is created in `./cache_dir`. Converting the data (e.g. tokenizing) can be quite slow, and so this cache contains the processed dataset. The cache can be moved and specified with the `--dir_name` argument (i.e. the default is `--dir_name=./cache_dir`) to avoid applying this preprocessing every time the code is run somewhere new.
SC35 is slightly different. SC35 doesn't use `--dir_name`, and instead requires that the following path exists: `./raw_datasets/speech_commands/0.0.2/SpeechCommands` (i.e. the directory `./raw_datasets/speech_commands/0.0.2/SpeechCommands/zero` must exist). The cache is then stored in `./raw_datasets/speech_commands/0.0.2/SpeechCommands/processed_data`. This directory can then be copied (preserving the directory path) to move the preprocessed dataset to a new location.
## Repository Structure
Directories and files that ship with GitHub repo:
```
s5/ Source code for models, datasets, etc.
dataloading.py Dataloading functions.
layers.py Defines the S5 layer which wraps the S5 SSM with nonlinearity, norms, dropout, etc.
seq_model.py Defines deep sequence models that consist of stacks of S5 layers.
ssm.py S5 SSM implementation.
ssm_init.py Helper functions for initializing the S5 SSM .
train.py Training loop code.
train_helpers.py Functions for optimization, training and evaluation steps.
dataloaders/ Code mainly derived from S4 processing each dataset.
utils/ Range of utility functions.
bin/ Shell scripts for downloading data and running example experiments.
requirements_cpu.txt Requirements for running in CPU mode (not advised).
requirements_gpu.txt Requirements for running in GPU mode (installation can be highly system-dependent).
run_train.py Training loop entrypoint.
```
Directories that may be created on-the-fly:
```
raw_datasets/ Raw data as downloaded.
cache_dir/ Precompiled caches of data. Can be copied to new locations to avoid preprocessing.
wandb/ Local WandB log files.
```
## Experiments
The configurations to run the LRA and 35-way Speech Commands experiments from the paper are located in `bin/run_experiments`. For example,
to run the LRA text (character level IMDB) experiment, run `./bin/run_experiments/run_lra_imdb.sh`.
To log with W&B, adjust the default `USE_WANDB, wandb_entity, wandb_project` arguments.
Note: the pendulum
regression dataloading and experiments will be added soon.
## Citation
Please use the following when citing our work:
```
@misc{smith2022s5,
doi = {10.48550/ARXIV.2208.04933},
url = {https://arxiv.org/abs/2208.04933},
author = {Smith, Jimmy T. H. and Warrington, Andrew and Linderman, Scott W.},
keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Simplified State Space Layers for Sequence Modeling},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
Please reach out if you have any questions.
-- The S5 authors.
|
BigSalmon/PhraseBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
} | 10 | 2023-01-31T15:50:09Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.30 +/- 21.03
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BigSalmon/SimplifyText | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
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},
"translation_en_to_fr": {
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},
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}
}
} | 17 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 604.00 +/- 234.61
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ArtYac -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ArtYac -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ArtYac
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BigSalmon/T5F | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | null | Full notebook:
https://github.com/MustafaAlahmid/hugging_face_models/blob/main/layoutlm_funsd.ipynb
---
tags:
- generated_from_keras_callback
model-index:
- name: layoutlm-funsd-tf
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# layoutlm-funsd-tf
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0691
- Validation Loss: 0.7709
- Train Overall Precision: 0.7410
- Train Overall Recall: 0.7953
- Train Overall F1: 0.7672
- Train Overall Accuracy: 0.8057
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Train Overall Precision | Train Overall Recall | Train Overall F1 | Train Overall Accuracy | Epoch |
|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:|
| 1.1546 | 0.6939 | 0.6387 | 0.7381 | 0.6848 | 0.7761 | 0 |
| 0.6170 | 0.5872 | 0.7099 | 0.7832 | 0.7448 | 0.7949 | 1 |
| 0.4005 | 0.6761 | 0.6766 | 0.7777 | 0.7236 | 0.7729 | 2 |
| 0.2921 | 0.6447 | 0.7169 | 0.7852 | 0.7495 | 0.7934 | 3 |
| 0.2029 | 0.7472 | 0.7019 | 0.7953 | 0.7457 | 0.7852 | 4 |
| 0.1383 | 0.7195 | 0.7327 | 0.7938 | 0.7620 | 0.8048 | 5 |
| 0.0932 | 0.7851 | 0.7272 | 0.7998 | 0.7618 | 0.8063 | 6 |
| 0.0691 | 0.7709 | 0.7410 | 0.7953 | 0.7672 | 0.8057 | 7 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.10.0
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BigSalmon/T5Salmon | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartoole-01
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 487.60 +/- 37.20
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
BigSalmon/T5Salmon2 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 13 | 2023-01-31T16:16:01Z |
---
tags:
- TensorRT
- Text2Image
- Stable Diffusion
- Image2Image
- SDA
---
# Linaqruf/anything-v3.0 converted into TensorRT
<a href="https://github.com/chavinlo/sda-node/"><img src="https://i.imgur.com/fQS926g.png"></a>
Model converted from diffusers into TensorRT for accelerated inference up to 4x faster.
For how to use the model check https://github.com/chavinlo/sda-node
This model was automatically converted by SDA-node
Compilation configuration:
```json
{
"_class_name": "StableDiffusionAccelerated_Base",
"_sda_version": "0.1.2",
"_trt_version": "8.5.1",
"_cuda_version": "11.6",
"_cudnn_version": "7.5",
"_onnx2trt_version": "8.5.1",
"unet": {
"precision": "fp16",
"path": "engine/unet.plan"
},
"clip": {
"path": "engine/clip.plan"
},
"de_vae": {
"path": "engine/de_vae.plan"
}
}
``` |
BigTooth/DialoGPT-Megumin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
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}
}
} | 16 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: robotman0/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BigTooth/Megumin-v0.2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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}
} | 13 | null | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-31jan-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-31jan-4
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5165
- Rouge1: 19.31
- Rouge2: 6.34
- Rougel: 19.06
- Rougelsum: 19.09
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 6.4223 | 1.0 | 217 | 2.8162 | 13.51 | 3.46 | 13.13 | 13.26 |
| 3.4986 | 2.0 | 434 | 2.7158 | 15.95 | 4.09 | 15.66 | 15.77 |
| 3.2297 | 3.0 | 651 | 2.6552 | 16.82 | 4.3 | 16.4 | 16.52 |
| 3.0796 | 4.0 | 868 | 2.6526 | 17.99 | 5.02 | 17.6 | 17.79 |
| 2.969 | 5.0 | 1085 | 2.6005 | 18.05 | 5.22 | 17.78 | 17.79 |
| 2.8939 | 6.0 | 1302 | 2.5879 | 18.22 | 5.17 | 17.93 | 18.01 |
| 2.8147 | 7.0 | 1519 | 2.5569 | 18.25 | 5.56 | 18.03 | 18.14 |
| 2.7642 | 8.0 | 1736 | 2.5541 | 18.24 | 5.38 | 18.07 | 18.19 |
| 2.724 | 9.0 | 1953 | 2.5493 | 18.86 | 5.7 | 18.51 | 18.63 |
| 2.6962 | 10.0 | 2170 | 2.5320 | 19.12 | 5.72 | 18.93 | 19.01 |
| 2.6499 | 11.0 | 2387 | 2.5224 | 18.78 | 5.69 | 18.6 | 18.66 |
| 2.6242 | 12.0 | 2604 | 2.5272 | 19.23 | 5.82 | 18.96 | 18.99 |
| 2.6088 | 13.0 | 2821 | 2.5122 | 19.51 | 6.16 | 19.26 | 19.36 |
| 2.5976 | 14.0 | 3038 | 2.5218 | 19.06 | 6.23 | 18.82 | 18.87 |
| 2.5775 | 15.0 | 3255 | 2.5165 | 19.31 | 6.34 | 19.06 | 19.09 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BigeS/DialoGPT-small-Rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
}
} | 10 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="bdrighes/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Bilz/DialoGPT-small-harrypotter | []
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-tiny-10M
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-tiny-10M
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7391
- Accuracy: 0.5148
## 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: 0.0004
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 7.8031 | 1.04 | 50 | 7.3560 | 0.0606 |
| 7.1948 | 2.08 | 100 | 6.7374 | 0.1182 |
| 6.8927 | 3.12 | 150 | 6.5022 | 0.1415 |
| 6.7339 | 4.16 | 200 | 6.4005 | 0.1483 |
| 6.6609 | 5.21 | 250 | 6.3535 | 0.1510 |
| 6.1972 | 6.25 | 300 | 6.3324 | 0.1519 |
| 6.1685 | 7.29 | 350 | 6.3029 | 0.1528 |
| 6.1302 | 8.33 | 400 | 6.2828 | 0.1521 |
| 6.093 | 9.37 | 450 | 6.2568 | 0.1536 |
| 6.0543 | 10.41 | 500 | 6.2430 | 0.1544 |
| 6.0479 | 11.45 | 550 | 6.2346 | 0.1541 |
| 6.0372 | 12.49 | 600 | 6.2232 | 0.1546 |
| 6.0127 | 13.53 | 650 | 6.2139 | 0.1541 |
| 5.968 | 14.58 | 700 | 6.2053 | 0.1547 |
| 5.9635 | 15.62 | 750 | 6.1996 | 0.1549 |
| 5.9479 | 16.66 | 800 | 6.1953 | 0.1548 |
| 5.9371 | 17.7 | 850 | 6.1887 | 0.1545 |
| 5.9046 | 18.74 | 900 | 6.1613 | 0.1545 |
| 5.8368 | 19.78 | 950 | 6.0952 | 0.1557 |
| 5.7914 | 20.82 | 1000 | 6.0330 | 0.1569 |
| 5.7026 | 21.86 | 1050 | 5.9430 | 0.1612 |
| 5.491 | 22.9 | 1100 | 5.6100 | 0.1974 |
| 4.9289 | 23.95 | 1150 | 4.9607 | 0.2702 |
| 4.5214 | 24.99 | 1200 | 4.5795 | 0.3051 |
| 4.5663 | 26.04 | 1250 | 4.3454 | 0.3265 |
| 4.3717 | 27.08 | 1300 | 4.1738 | 0.3412 |
| 4.1483 | 28.12 | 1350 | 4.0336 | 0.3555 |
| 3.9988 | 29.16 | 1400 | 3.9180 | 0.3677 |
| 3.8695 | 30.21 | 1450 | 3.8108 | 0.3782 |
| 3.5017 | 31.25 | 1500 | 3.7240 | 0.3879 |
| 3.4311 | 32.29 | 1550 | 3.6426 | 0.3974 |
| 3.3517 | 33.33 | 1600 | 3.5615 | 0.4068 |
| 3.2856 | 34.37 | 1650 | 3.4915 | 0.4156 |
| 3.227 | 35.41 | 1700 | 3.4179 | 0.4255 |
| 3.1675 | 36.45 | 1750 | 3.3636 | 0.4325 |
| 3.0908 | 37.49 | 1800 | 3.3083 | 0.4394 |
| 3.0561 | 38.53 | 1850 | 3.2572 | 0.4473 |
| 3.0139 | 39.58 | 1900 | 3.2159 | 0.4525 |
| 2.9837 | 40.62 | 1950 | 3.1789 | 0.4575 |
| 2.9387 | 41.66 | 2000 | 3.1431 | 0.4618 |
| 2.9034 | 42.7 | 2050 | 3.1163 | 0.4654 |
| 2.8822 | 43.74 | 2100 | 3.0842 | 0.4694 |
| 2.836 | 44.78 | 2150 | 3.0583 | 0.4727 |
| 2.8129 | 45.82 | 2200 | 3.0359 | 0.4760 |
| 2.7733 | 46.86 | 2250 | 3.0173 | 0.4776 |
| 2.7589 | 47.9 | 2300 | 2.9978 | 0.4812 |
| 2.7378 | 48.95 | 2350 | 2.9788 | 0.4831 |
| 2.7138 | 49.99 | 2400 | 2.9674 | 0.4844 |
| 2.8692 | 51.04 | 2450 | 2.9476 | 0.4874 |
| 2.8462 | 52.08 | 2500 | 2.9342 | 0.4893 |
| 2.8312 | 53.12 | 2550 | 2.9269 | 0.4900 |
| 2.7834 | 54.16 | 2600 | 2.9111 | 0.4917 |
| 2.7822 | 55.21 | 2650 | 2.8987 | 0.4934 |
| 2.584 | 56.25 | 2700 | 2.8844 | 0.4949 |
| 2.5668 | 57.29 | 2750 | 2.8808 | 0.4965 |
| 2.5536 | 58.33 | 2800 | 2.8640 | 0.4982 |
| 2.5403 | 59.37 | 2850 | 2.8606 | 0.4982 |
| 2.5294 | 60.41 | 2900 | 2.8441 | 0.5008 |
| 2.513 | 61.45 | 2950 | 2.8402 | 0.5013 |
| 2.5105 | 62.49 | 3000 | 2.8316 | 0.5022 |
| 2.4897 | 63.53 | 3050 | 2.8237 | 0.5027 |
| 2.4974 | 64.58 | 3100 | 2.8187 | 0.5040 |
| 2.4799 | 65.62 | 3150 | 2.8129 | 0.5044 |
| 2.4741 | 66.66 | 3200 | 2.8056 | 0.5057 |
| 2.4582 | 67.7 | 3250 | 2.8025 | 0.5061 |
| 2.4389 | 68.74 | 3300 | 2.7913 | 0.5076 |
| 2.4539 | 69.78 | 3350 | 2.7881 | 0.5072 |
| 2.4252 | 70.82 | 3400 | 2.7884 | 0.5082 |
| 2.4287 | 71.86 | 3450 | 2.7784 | 0.5093 |
| 2.4131 | 72.9 | 3500 | 2.7782 | 0.5099 |
| 2.4016 | 73.95 | 3550 | 2.7724 | 0.5098 |
| 2.3998 | 74.99 | 3600 | 2.7659 | 0.5111 |
| 2.5475 | 76.04 | 3650 | 2.7650 | 0.5108 |
| 2.5443 | 77.08 | 3700 | 2.7620 | 0.5117 |
| 2.5381 | 78.12 | 3750 | 2.7631 | 0.5115 |
| 2.5269 | 79.16 | 3800 | 2.7578 | 0.5122 |
| 2.5288 | 80.21 | 3850 | 2.7540 | 0.5124 |
| 2.3669 | 81.25 | 3900 | 2.7529 | 0.5125 |
| 2.3631 | 82.29 | 3950 | 2.7498 | 0.5132 |
| 2.3499 | 83.33 | 4000 | 2.7454 | 0.5136 |
| 2.3726 | 84.37 | 4050 | 2.7446 | 0.5141 |
| 2.3411 | 85.41 | 4100 | 2.7403 | 0.5144 |
| 2.3321 | 86.45 | 4150 | 2.7372 | 0.5146 |
| 2.3456 | 87.49 | 4200 | 2.7389 | 0.5146 |
| 2.3372 | 88.53 | 4250 | 2.7384 | 0.5151 |
| 2.343 | 89.58 | 4300 | 2.7398 | 0.5144 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.6.1
- Tokenizers 0.12.1
|
BinksSachary/DialoGPT-small-shaxx | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 12 | null | ---
tags:
- classification
- generated_from_trainer
datasets:
- rotten_tomatoes
model-index:
- name: clasificador-rotten-tomatoes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clasificador-rotten-tomatoes
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the rotten_tomatoes dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BinksSachary/ShaxxBot2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 12 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: robotman0/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Blerrrry/Kkk | []
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}
} | 0 | 2023-01-31T17:19:47Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- funsd-layoutlmv3
model-index:
- name: my-lilt-en-funsd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-lilt-en-funsd
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7942
- Answer: {'precision': 0.8597914252607184, 'recall': 0.9082007343941249, 'f1': 0.8833333333333333, 'number': 817}
- Header: {'precision': 0.6666666666666666, 'recall': 0.5714285714285714, 'f1': 0.6153846153846153, 'number': 119}
- Question: {'precision': 0.9046746104491292, 'recall': 0.9164345403899722, 'f1': 0.9105166051660516, 'number': 1077}
- Overall Precision: 0.8740
- Overall Recall: 0.8927
- Overall F1: 0.8833
- Overall Accuracy: 0.8042
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.1935 | 26.32 | 500 | 1.2125 | {'precision': 0.8702830188679245, 'recall': 0.9033047735618115, 'f1': 0.8864864864864864, 'number': 817} | {'precision': 0.6296296296296297, 'recall': 0.5714285714285714, 'f1': 0.5991189427312775, 'number': 119} | {'precision': 0.8748921484037964, 'recall': 0.9415041782729805, 'f1': 0.9069767441860466, 'number': 1077} | 0.8605 | 0.9041 | 0.8818 | 0.8024 |
| 0.0063 | 52.63 | 1000 | 1.4406 | {'precision': 0.8732394366197183, 'recall': 0.9106487148102815, 'f1': 0.8915518274415818, 'number': 817} | {'precision': 0.632183908045977, 'recall': 0.46218487394957986, 'f1': 0.5339805825242718, 'number': 119} | {'precision': 0.8827708703374778, 'recall': 0.9229340761374187, 'f1': 0.902405810258738, 'number': 1077} | 0.8683 | 0.8907 | 0.8794 | 0.8175 |
| 0.002 | 78.95 | 1500 | 1.6624 | {'precision': 0.861904761904762, 'recall': 0.8861689106487148, 'f1': 0.8738684369342186, 'number': 817} | {'precision': 0.6363636363636364, 'recall': 0.5294117647058824, 'f1': 0.5779816513761468, 'number': 119} | {'precision': 0.8920863309352518, 'recall': 0.9210770659238626, 'f1': 0.9063499314755596, 'number': 1077} | 0.8674 | 0.8838 | 0.8755 | 0.7998 |
| 0.0006 | 105.26 | 2000 | 1.7942 | {'precision': 0.8597914252607184, 'recall': 0.9082007343941249, 'f1': 0.8833333333333333, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5714285714285714, 'f1': 0.6153846153846153, 'number': 119} | {'precision': 0.9046746104491292, 'recall': 0.9164345403899722, 'f1': 0.9105166051660516, 'number': 1077} | 0.8740 | 0.8927 | 0.8833 | 0.8042 |
| 0.0002 | 131.58 | 2500 | 1.8161 | {'precision': 0.8591385331781141, 'recall': 0.9033047735618115, 'f1': 0.8806682577565632, 'number': 817} | {'precision': 0.6346153846153846, 'recall': 0.5546218487394958, 'f1': 0.5919282511210763, 'number': 119} | {'precision': 0.9047619047619048, 'recall': 0.9173630454967502, 'f1': 0.9110189027201475, 'number': 1077} | 0.8720 | 0.8902 | 0.8810 | 0.8021 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BlightZz/DialoGPT-medium-Kurisu | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 19 | 2023-01-31T17:20:16Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: Liapunov/ppo-pyramidsFR2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BlightZz/MakiseKurisu | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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"task_specific_params": {
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} | 14 | 2023-01-31T17:20:58Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -160.05 +/- 82.57
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BlindMan820/Sarcastic-News-Headlines | [
"pytorch",
"distilbert",
"text-classification",
"English",
"dataset:Kaggle Dataset",
"transformers",
"Text",
"Sequence-Classification",
"Sarcasm",
"DistilBert"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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"max_length": null,
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},
"translation_en_to_fr": {
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}
} | 28 | null | ---
license: gpl-3.0
---
Pre-trained word embeddings using the text of published clinical case reports. These embeddings use 100 dimensions and were trained using the fasttext algorithm on published clinical case reports found in the [PMC Open Access Subset](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/). See the paper here: https://pubmed.ncbi.nlm.nih.gov/34920127/
Citation:
```
@article{flamholz2022word,
title={Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information},
author={Flamholz, Zachary N and Crane-Droesch, Andrew and Ungar, Lyle H and Weissman, Gary E},
journal={Journal of Biomedical Informatics},
volume={125},
pages={103971},
year={2022},
publisher={Elsevier}
}
```
## Quick start
Word embeddings are compatible with the [`gensim` Python package](https://radimrehurek.com/gensim/) format.
First download the files from this archive. Then load the embeddings into Python.
```python
from gensim.models import FastText, Word2Vec, KeyedVectors # KeyedVectors are used to load the GloVe models
# Load the model
model = FastText.load('ft_oa_corp_100d.bin')
# Return 100-dimensional vector representations of each word
model.wv.word_vec('diabetes')
model.wv.word_vec('cardiac_arrest')
model.wv.word_vec('lymphangioleiomyomatosis')
# Try out cosine similarity
model.wv.similarity('copd', 'chronic_obstructive_pulmonary_disease')
model.wv.similarity('myocardial_infarction', 'heart_attack')
model.wv.similarity('lymphangioleiomyomatosis', 'lam')
``` |
Bloodwarrior/Chikfalay | []
| null | {
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},
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}
} | 0 | 2023-01-31T17:25:00Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1813.99 +/- 152.68
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BlueGamerBeast/DialoGPT-small-Morgana | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 12 | 2023-01-31T17:25:14Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 479.20 +/- 47.66
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
BobBraico/distilbert-base-uncased-finetuned-imdb-accelerate | []
| null | {
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}
}
} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 3624 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 3624,
"warmup_steps": 363,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
BogdanKuloren/continual-learning-paper-embeddings-model | [
"pytorch",
"mpnet",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"MPNetModel"
],
"model_type": "mpnet",
"task_specific_params": {
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},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 11 | 2023-01-31T17:45:31Z | ---
license: gpl-3.0
---
Pre-trained word embeddings using the text of published clinical case reports. These embeddings use 300 dimensions and were trained using the fasttext algorithm on published clinical case reports found in the [PMC Open Access Subset](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/)
. See the paper here: https://pubmed.ncbi.nlm.nih.gov/34920127/
Citation:
```
@article{flamholz2022word,
title={Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information},
author={Flamholz, Zachary N and Crane-Droesch, Andrew and Ungar, Lyle H and Weissman, Gary E},
journal={Journal of Biomedical Informatics},
volume={125},
pages={103971},
year={2022},
publisher={Elsevier}
}
```
## Quick start
Word embeddings are compatible with the [`gensim` Python package](https://radimrehurek.com/gensim/) format.
First download the files from this archive. Then load the embeddings into Python.
```python
from gensim.models import FastText, Word2Vec, KeyedVectors # KeyedVectors are used to load the GloVe models
# Load the model
model = FastText.load('ft_oa_corp_300d.bin')
# Return 100-dimensional vector representations of each word
model.wv.word_vec('diabetes')
model.wv.word_vec('cardiac_arrest')
model.wv.word_vec('lymphangioleiomyomatosis')
# Try out cosine similarity
model.wv.similarity('copd', 'chronic_obstructive_pulmonary_disease')
model.wv.similarity('myocardial_infarction', 'heart_attack')
model.wv.similarity('lymphangioleiomyomatosis', 'lam')
``` |
Bosio/full-sentence-distillroberta3-finetuned-wikitext2 | []
| null | {
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}
} | 0 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="mallycrip/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
BossLee/t5-gec | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | null | ---
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-muchocine
results: []
datasets:
- muchocine
language:
- es
---
# clasificador-muchocine
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the 'muchocine' dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3389
- Accuracy: 0.4671
## Model description
This model predicts a 1-5 star_rating for a movie based on a short review in Spanish.
## Training and evaluation data
The model uses the train split of the 'muchocine' dataset, containing 3,872 reviews.
## Training procedure
The original dataset was randomized and subsequently split into a training set (80%) and a testing set (20%).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 388 | 1.2937 | 0.4335 |
| 1.4261 | 2.0 | 776 | 1.2515 | 0.4839 |
| 1.0492 | 3.0 | 1164 | 1.3389 | 0.4671 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2 |
BotterHax/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 8 | null | Getting started and UI.
https://www.cognitionai.org/hdhowtogetstarted |
Brunomezenga/NN | []
| null | {
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"model_type": null,
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}
}
} | 0 | 2023-01-31T18:40:25Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Lakoc/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Bwehfuk/Ron | []
| null | {
"architectures": null,
"model_type": null,
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},
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}
} | 0 | null | ---
license: openrail
---
# information & usage
lora trained from 185 hand-picked and signature/text removed images of artist LAM (https://www.pixiv.net/en/users/17429)
weight at `0.6` for less style, enable to mix with other styles. `0.8` - `1` make the style closer to artist's.
recommended tags:
```
colorful, multicolored hair, multicolored eyes, eyeshadow, makeup
```
nice to play with:
```
gradient eyes
```
model used for training is `anything-v4.5`
model for previews is `AbyssOrangeMix2`
# preview
<img src="https://huggingface.co/Faber8/lam-lora/resolve/main/examples/xyz_grid-0008-1804075986-masterpiece%2C%20best%20quality%2C%201girl%2C%20solo%2C%20highres%2C%20_lora_lam-000010-v3_0.6_%2C%20upper%20body%2C%20portrait%2C%20colorful%2C%20multicolored%20hair%2C%20mu.png">
<img src="https://huggingface.co/Faber8/lam-lora/resolve/main/examples/xyz_grid-0009-1394024648-masterpiece%2C%20best%20quality%2C%201girl%2C%20solo%2C%20highres%2C%20_lora_lam-000010-v3_0.6_%2C%20upper%20body%2C%20portrait%2C%20colorful%2C%20multicolored%20hair%2C%20mu.png">
parameters:
```
masterpiece, best quality, 1girl, solo, highres, <lora:lam-000010-v3:0.6>, upper body, portrait, colorful, multicolored hair, multicolored eyes, eyeshadow, makeup
Negative prompt: (worst quality, low quality:1.4), medium quality
Steps: 22, Sampler: DPM++ SDE, CFG scale: 6, Seed: 1394024648 & 1804075986, Size: 768x512, Model hash: f39a8b9f97, Model: AbyssOrangeMix2_nsfw, Eta: 0.2, Clip skip: 2, ENSD: 31337, Script: X/Y/Z plot, X Type: Prompt S/R, X Values: "0.6, 0.8, 1"
```
<br/>
<img src="https://huggingface.co/Faber8/lam-lora/resolve/main/examples/xyz_grid-0012-3429112048-masterpiece%2C%20best%20quality%2C%201girl%2C%20solo%2C%20highres%2C%20_lora_lam-000010-v3_0.6_%2C%20upper%20body%2C%20portrait%2C%20colorful%2C%20multicolored%20hair%2C%20mu.png">
<img src="https://huggingface.co/Faber8/lam-lora/resolve/main/examples/xyz_grid-0013-503129266-masterpiece%2C%20best%20quality%2C%201girl%2C%20solo%2C%20highres%2C%20_lora_lam-000010-v3_0.6_%2C%20upper%20body%2C%20portrait%2C%20colorful%2C%20multicolored%20hair%2C%20mu.png">
parameters:
```
masterpiece, best quality, 1girl, solo, highres, <lora:lam-000010-v3:0.6>, upper body, portrait, colorful, multicolored hair, multicolored eyes, eyeshadow, makeup
Negative prompt: (worst quality, low quality:1.4), medium quality
Steps: 22, Sampler: DPM++ SDE, CFG scale: 6, Seed: 3429112048 & 503129266, Size: 512x768, Model hash: f39a8b9f97, Model: AbyssOrangeMix2_nsfw, Eta: 0.2, Clip skip: 2, ENSD: 31337
```
|
CAMeL-Lab/bert-base-arabic-camelbert-da-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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} | 42 | 2023-01-31T19:23:05Z | ---
license: gpl-3.0
datasets:
- detection-datasets/coco
tags:
- object detection
- computer vision
- machine learning
- yolo
- yolov8
---
### Model Description
[Ultralytics:](https://github.com/ultralytics/ultralytics/) YOLOv8 in PyTorch > ONNX > CoreML > TFLite
### Installation
```
pip install ultralytics
```
### Yolov8 Inference
```python
from ultralytics import YOLO
model = YOLO('techzizou/yolov8m')
model.conf = conf_threshold
model.iou = iou_threshold
prediction = model.predict(image, imgsz=image_size, show=False, save=False)
``` |
CAMeL-Lab/bert-base-arabic-camelbert-da-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 37 | 2023-01-31T19:23:23Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: RTT/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CAMeL-Lab/bert-base-arabic-camelbert-mix | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"Arabic",
"Dialect",
"Egyptian",
"Gulf",
"Levantine",
"Classical Arabic",
"MSA",
"Modern Standard Arabic",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 20,880 | null | ---
tags:
- generated_from_trainer
model-index:
- name: fine_tuned_theme2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine_tuned_theme2
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Capreolus/bert-base-msmarco | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"arxiv:2008.09093",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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},
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}
} | 238 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: tiny-mlm-glue-qnli-from-scratch-custom-tokenizer-expand-vocab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mlm-glue-qnli-from-scratch-custom-tokenizer-expand-vocab
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.5220
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 9.5281 | 0.4 | 500 | 8.3592 |
| 7.6191 | 0.8 | 1000 | 7.1164 |
| 6.8775 | 1.2 | 1500 | 6.7262 |
| 6.6762 | 1.6 | 2000 | 6.6561 |
| 6.6116 | 2.0 | 2500 | 6.6190 |
| 6.5919 | 2.4 | 3000 | 6.6003 |
| 6.575 | 2.8 | 3500 | 6.5831 |
| 6.5329 | 3.2 | 4000 | 6.5478 |
| 6.5152 | 3.6 | 4500 | 6.5384 |
| 6.5249 | 4.0 | 5000 | 6.5220 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
Capreolus/birch-bert-large-car_mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
]
| null | {
"architectures": [
"BertForNextSentencePrediction"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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},
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},
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}
} | 4 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 253.80 +/- 22.65
name: mean_reward
verified: false
---
# **ppo** Agent playing **LunarLander-v2**
This is a trained model of a **ppo** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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