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jonatasgrosman/exp_w2v2r_fr_xls-r_age_teens-2_sixties-8_s598
|
jonatasgrosman
|
wav2vec2
| 10 | 0 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['fr']
|
['mozilla-foundation/common_voice_7_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'fr']
| false | true | true | 475 | false |
# exp_w2v2r_fr_xls-r_age_teens-2_sixties-8_s598
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
ba4deea330bbb081effbd88d617f9df5
|
hirohiroz/wav2vec2-base-timit-demo-google-colab
|
hirohiroz
|
wav2vec2
| 12 | 7 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,998 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5173
- Wer: 0.3399
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5684 | 1.0 | 500 | 2.1662 | 1.0068 |
| 0.9143 | 2.01 | 1000 | 0.5820 | 0.5399 |
| 0.439 | 3.01 | 1500 | 0.4596 | 0.4586 |
| 0.3122 | 4.02 | 2000 | 0.4623 | 0.4181 |
| 0.2391 | 5.02 | 2500 | 0.4243 | 0.3938 |
| 0.1977 | 6.02 | 3000 | 0.4421 | 0.3964 |
| 0.1635 | 7.03 | 3500 | 0.5076 | 0.3977 |
| 0.145 | 8.03 | 4000 | 0.4639 | 0.3754 |
| 0.1315 | 9.04 | 4500 | 0.5181 | 0.3652 |
| 0.1131 | 10.04 | 5000 | 0.4496 | 0.3778 |
| 0.1005 | 11.04 | 5500 | 0.4438 | 0.3664 |
| 0.0919 | 12.05 | 6000 | 0.4868 | 0.3865 |
| 0.0934 | 13.05 | 6500 | 0.5163 | 0.3694 |
| 0.076 | 14.06 | 7000 | 0.4543 | 0.3719 |
| 0.0727 | 15.06 | 7500 | 0.5296 | 0.3807 |
| 0.0657 | 16.06 | 8000 | 0.4715 | 0.3699 |
| 0.0578 | 17.07 | 8500 | 0.4927 | 0.3699 |
| 0.057 | 18.07 | 9000 | 0.4767 | 0.3660 |
| 0.0493 | 19.08 | 9500 | 0.5306 | 0.3623 |
| 0.0425 | 20.08 | 10000 | 0.4828 | 0.3561 |
| 0.0431 | 21.08 | 10500 | 0.4875 | 0.3620 |
| 0.0366 | 22.09 | 11000 | 0.4984 | 0.3482 |
| 0.0332 | 23.09 | 11500 | 0.5375 | 0.3477 |
| 0.0348 | 24.1 | 12000 | 0.5406 | 0.3361 |
| 0.0301 | 25.1 | 12500 | 0.4954 | 0.3381 |
| 0.0294 | 26.1 | 13000 | 0.5033 | 0.3424 |
| 0.026 | 27.11 | 13500 | 0.5254 | 0.3384 |
| 0.0243 | 28.11 | 14000 | 0.5189 | 0.3402 |
| 0.0221 | 29.12 | 14500 | 0.5173 | 0.3399 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
542d95dd1e638bb6a8114dbe0a8dd4fd
|
juro95/xlm-roberta-finetuned-ner-cased_1_ratio
|
juro95
|
xlm-roberta
| 8 | 2 |
transformers
| 0 |
token-classification
| false | true | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,489 | false |
<!-- 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. -->
# juro95/xlm-roberta-finetuned-ner-cased_1_ratio
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0633
- Validation Loss: 0.0940
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 14272, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.3166 | 0.1533 | 0 |
| 0.1376 | 0.1114 | 1 |
| 0.0909 | 0.0988 | 2 |
| 0.0633 | 0.0940 | 3 |
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.6.5
- Datasets 2.3.2
- Tokenizers 0.13.2
|
afb56fc19fbff6c2885bb89cf2627060
|
doddle124578/wav2vec2-base-timit-demo-colab-3
|
doddle124578
|
wav2vec2
| 14 | 3 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,402 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab-3
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6622
- Wer: 0.5082
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.2195 | 8.77 | 500 | 0.9187 | 0.6635 |
| 0.5996 | 17.54 | 1000 | 0.6569 | 0.5347 |
| 0.2855 | 26.32 | 1500 | 0.6622 | 0.5082 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
c79351f7c2ad4844f35281f2ae47ab4a
|
joaoluislins/wmt-ptt5-colab-base-finetuned-en-to-pt
|
joaoluislins
|
t5
| 12 | 5 |
transformers
| 0 |
text2text-generation
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,688 | false |
<!-- 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. -->
# wmt-mbart50-large-finetuned-en-to-pt
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the WMT dataset (bi and mono-backtranslated)
It achieves the following results on the evaluation set:
- Loss: 0.2510
- Bleu: 62.7011
- Gen Len: 19.224
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 1.6426 | 1.0 | 433 | 0.5323 | 4.484 | 10.5635 |
| 0.2571 | 2.0 | 866 | 0.1965 | 47.6449 | 19.164 |
| 0.1043 | 3.0 | 1299 | 0.1723 | 53.6231 | 19.1455 |
| 0.058 | 4.0 | 1732 | 0.1908 | 52.9831 | 18.5543 |
| 0.0382 | 5.0 | 2165 | 0.1801 | 58.4418 | 19.0808 |
| 0.0244 | 6.0 | 2598 | 0.2014 | 56.0197 | 20.0485 |
| 0.0195 | 7.0 | 3031 | 0.2029 | 56.7903 | 18.642 |
| 0.0138 | 8.0 | 3464 | 0.2015 | 57.6855 | 19.0 |
| 0.0126 | 9.0 | 3897 | 0.2095 | 58.5733 | 18.7644 |
| 0.0095 | 10.0 | 4330 | 0.1946 | 60.3165 | 19.6097 |
| 0.0067 | 11.0 | 4763 | 0.2094 | 60.2691 | 18.9561 |
| 0.0055 | 12.0 | 5196 | 0.2202 | 60.375 | 19.3025 |
| 0.0046 | 13.0 | 5629 | 0.2153 | 60.7254 | 19.0855 |
| 0.0035 | 14.0 | 6062 | 0.2239 | 61.458 | 19.0647 |
| 0.0054 | 15.0 | 6495 | 0.2250 | 61.5297 | 19.164 |
| 0.0025 | 16.0 | 6928 | 0.2458 | 61.263 | 19.0531 |
| 0.002 | 17.0 | 7361 | 0.2354 | 62.4404 | 19.2102 |
| 0.0015 | 18.0 | 7794 | 0.2403 | 62.0235 | 19.1293 |
| 0.0011 | 19.0 | 8227 | 0.2477 | 62.6301 | 19.2494 |
| 0.0009 | 20.0 | 8660 | 0.2510 | 62.7011 | 19.224 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
441eace720226a280107694549207b32
|
xezpeleta/whisper-small-eu-v2
|
xezpeleta
|
whisper
| 26 | 14 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['eu']
|
['mozilla-foundation/common_voice_11_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['whisper-event', 'generated_from_trainer']
| true | true | true | 1,564 | false |
<!-- 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. -->
# Whisper Small Basque
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 eu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3580
- Wer: 18.9337
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1372 | 2.04 | 1000 | 0.3166 | 22.2335 |
| 0.0175 | 4.07 | 2000 | 0.3356 | 19.9862 |
| 0.0055 | 7.02 | 3000 | 0.3580 | 18.9337 |
| 0.0015 | 9.06 | 4000 | 0.3803 | 18.9581 |
| 0.0013 | 12.01 | 5000 | 0.3908 | 18.9541 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
6b94877a39c93e4dde4e963ea7f938cf
|
shi-labs/oneformer_ade20k_swin_large
|
shi-labs
|
oneformer
| 10 | 394 |
transformers
| 0 |
image-segmentation
| true | false | false |
mit
| null |
['scene_parse_150']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['vision', 'image-segmentation', 'universal-image-segmentation']
| false | true | true | 3,329 | false |
# OneFormer
OneFormer model trained on the ADE20k dataset (large-sized version, Swin backbone). It was introduced in the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jain et al. and first released in [this repository](https://github.com/SHI-Labs/OneFormer).
## Model description
OneFormer is the first multi-task universal image segmentation framework. It needs to be trained only once with a single universal architecture, a single model, and on a single dataset, to outperform existing specialized models across semantic, instance, and panoptic segmentation tasks. OneFormer uses a task token to condition the model on the task in focus, making the architecture task-guided for training, and task-dynamic for inference, all with a single model.
## Intended uses & limitations
You can use this particular checkpoint for semantic, instance and panoptic segmentation. See the [model hub](https://huggingface.co/models?search=oneformer) to look for other fine-tuned versions on a different dataset.
### How to use
Here is how to use this model:
```python
from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
from PIL import Image
import requests
url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/ade20k.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
# Loading a single model for all three tasks
processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_swin_large")
model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_large")
# Semantic Segmentation
semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
semantic_outputs = model(**semantic_inputs)
# pass through image_processor for postprocessing
predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
# Instance Segmentation
instance_inputs = processor(images=image, task_inputs=["instance"], return_tensors="pt")
instance_outputs = model(**instance_inputs)
# pass through image_processor for postprocessing
predicted_instance_map = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
# Panoptic Segmentation
panoptic_inputs = processor(images=image, task_inputs=["panoptic"], return_tensors="pt")
panoptic_outputs = model(**panoptic_inputs)
# pass through image_processor for postprocessing
predicted_semantic_map = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
```
For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer).
### Citation
```bibtex
@article{jain2022oneformer,
title={{OneFormer: One Transformer to Rule Universal Image Segmentation}},
author={Jitesh Jain and Jiachen Li and MangTik Chiu and Ali Hassani and Nikita Orlov and Humphrey Shi},
journal={arXiv},
year={2022}
}
```
|
a8177b7a18302f46119a2a33ecce841f
|
lmqg/mbart-large-cc25-ruquad-qg
|
lmqg
|
mbart
| 20 | 63 |
transformers
| 0 |
text2text-generation
| true | false | false |
cc-by-4.0
|
['ru']
|
['lmqg/qg_ruquad']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['question generation']
| true | true | true | 6,815 | false |
# Model Card of `lmqg/mbart-large-cc25-ruquad-qg`
This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
- **Language:** ru
- **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="ru", model="lmqg/mbart-large-cc25-ruquad-qg")
# model prediction
questions = model.generate_q(list_context="Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.", list_answer="в мае 1860 года")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-ruquad-qg")
output = pipe("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 87.18 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_1 | 35.25 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_2 | 28.1 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_3 | 22.87 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_4 | 18.8 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| METEOR | 29.3 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| MoverScore | 65.88 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| ROUGE_L | 34.18 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
- ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_ruquad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 92.08 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedF1Score (MoverScore) | 71.45 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedPrecision (BERTScore) | 92.09 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedPrecision (MoverScore) | 71.46 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedRecall (BERTScore) | 92.08 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedRecall (MoverScore) | 71.45 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
- ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mbart-large-cc25-ruquad-ae`](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-ae). [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_ruquad.default.lmqg_mbart-large-cc25-ruquad-ae.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 79.14 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedF1Score (MoverScore) | 56.25 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedPrecision (BERTScore) | 75.88 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedPrecision (MoverScore) | 54.01 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedRecall (BERTScore) | 82.85 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedRecall (MoverScore) | 58.93 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_ruquad
- dataset_name: default
- input_types: ['paragraph_answer']
- output_types: ['question']
- prefix_types: None
- model: facebook/mbart-large-cc25
- max_length: 512
- max_length_output: 32
- epoch: 17
- batch: 4
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 16
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qg/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
b2816cee53af91e0ce3e10c761a19c78
|
steysie/bert-base-multilingual-cased-tuned-smartcat
|
steysie
|
bert
| 6 | 4 |
transformers
| 0 |
text-generation
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,297 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-tuned-smartcat
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0006 | 1.0 | 11586 | 0.0000 |
| 0.0003 | 2.0 | 23172 | 0.0000 |
| 0.0 | 3.0 | 34806 | 0.0000 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
|
35c2a2d7a1094087ff828a3ad151694a
|
chinhon/distilgpt2-sgnews
|
chinhon
|
gpt2
| 16 | 3 |
transformers
| 0 |
text-generation
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,235 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-sgnews
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1516
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3558 | 1.0 | 23769 | 3.2316 |
| 3.2558 | 2.0 | 47538 | 3.1683 |
| 3.2321 | 3.0 | 71307 | 3.1516 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
a88eaf255d9cb1e69f010113bfea6c03
|
emilios/whisper-medium-el
|
emilios
|
whisper
| 106 | 7 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['el']
|
['mozilla-foundation/common_voice_11_0', 'google/fleurs']
| null | 3 | 0 | 3 | 0 | 0 | 0 | 0 |
['hf-asr-leaderboard', 'whisper-medium', 'mozilla-foundation/common_voice_11_0', 'greek', 'whisper-event', 'generated_from_trainer', 'whisper-event']
| true | true | true | 1,225 | false |
<!-- 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. -->
# Whisper Medium El Greco
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4245
- eval_wer: 10.7448
- eval_runtime: 1107.1212
- eval_samples_per_second: 1.532
- eval_steps_per_second: 0.096
- epoch: 33.98
- step: 7000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 7000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
a767e91f6fe50fa588640760792ce815
|
nellchic/test
|
nellchic
| null | 12 | 0 | null | 0 | null | false | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,319 | false |
### NOTE: USED WAIFU DIFFUSION
<https://huggingface.co/hakurei/waifu-diffusion>
### hitokomoru-style
Artist: <https://www.pixiv.net/en/users/30837811>
This is the `<hitokomoru-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
35cfe0910c9d86237e52578ebcd505be
|
explosion/ko_udv25_koreankaist_trf
|
explosion
| null | 28 | 3 |
spacy
| 0 |
token-classification
| false | false | false |
cc-by-sa-4.0
|
['ko']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['spacy', 'token-classification']
| false | true | true | 61,725 | false |
UD v2.5 benchmarking pipeline for UD_Korean-Kaist
| Feature | Description |
| --- | --- |
| **Name** | `ko_udv25_koreankaist_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (5329 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ecs`, `etm`, `f`, `f+f+jcj`, `f+f+jcs`, `f+f+jct`, `f+f+jxt`, `f+jca`, `f+jca+jp+ecc`, `f+jca+jp+ep+ef`, `f+jca+jxc`, `f+jca+jxc+jcm`, `f+jca+jxt`, `f+jcj`, `f+jcm`, `f+jco`, `f+jcs`, `f+jct`, `f+jct+jcm`, `f+jp+ef`, `f+jp+ep+ef`, `f+jp+etm`, `f+jxc`, `f+jxt`, `f+ncn`, `f+ncn+jcm`, `f+ncn+jcs`, `f+ncn+jp+ecc`, `f+ncn+jxt`, `f+ncpa+jcm`, `f+npp+jcs`, `f+nq`, `f+xsn`, `f+xsn+jco`, `f+xsn+jxt`, `ii`, `jca`, `jca+jcm`, `jca+jxc`, `jca+jxt`, `jcc`, `jcj`, `jcm`, `jco`, `jcr`, `jcr+jxc`, `jcs`, `jct`, `jct+jcm`, `jct+jxt`, `jp+ecc`, `jp+ecs`, `jp+ef`, `jp+ef+jcr`, `jp+ef+jcr+jxc`, `jp+ep+ecs`, `jp+ep+ef`, `jp+ep+etm`, `jp+ep+etn`, `jp+etm`, `jp+etn`, `jp+etn+jco`, `jp+etn+jxc`, `jxc`, `jxc+jca`, `jxc+jco`, `jxc+jcs`, `jxt`, `mad`, `mad+jxc`, `mad+jxt`, `mag`, `mag+jca`, `mag+jcm`, `mag+jcs`, `mag+jp+ef+jcr`, `mag+jxc`, `mag+jxc+jxc`, `mag+jxt`, `mag+xsn`, `maj`, `maj+jxc`, `maj+jxt`, `mma`, `mmd`, `nbn`, `nbn+jca`, `nbn+jca+jcj`, `nbn+jca+jcm`, `nbn+jca+jp+ef`, `nbn+jca+jxc`, `nbn+jca+jxt`, `nbn+jcc`, `nbn+jcj`, `nbn+jcm`, `nbn+jco`, `nbn+jcr`, `nbn+jcs`, `nbn+jct`, `nbn+jct+jcm`, `nbn+jct+jxt`, `nbn+jp+ecc`, `nbn+jp+ecs`, `nbn+jp+ecs+jca`, `nbn+jp+ecs+jcm`, `nbn+jp+ecs+jco`, `nbn+jp+ecs+jxc`, `nbn+jp+ecs+jxt`, `nbn+jp+ecx`, `nbn+jp+ef`, `nbn+jp+ef+jca`, `nbn+jp+ef+jco`, `nbn+jp+ef+jcr`, `nbn+jp+ef+jcr+jxc`, `nbn+jp+ef+jcr+jxt`, `nbn+jp+ef+jcs`, `nbn+jp+ef+jxc`, `nbn+jp+ef+jxc+jco`, `nbn+jp+ef+jxf`, `nbn+jp+ef+jxt`, `nbn+jp+ep+ecc`, `nbn+jp+ep+ecs`, `nbn+jp+ep+ecs+jxc`, `nbn+jp+ep+ef`, `nbn+jp+ep+ef+jcr`, `nbn+jp+ep+etm`, `nbn+jp+ep+etn`, `nbn+jp+ep+etn+jco`, `nbn+jp+ep+etn+jcs`, `nbn+jp+etm`, `nbn+jp+etn`, `nbn+jp+etn+jca`, `nbn+jp+etn+jca+jxt`, `nbn+jp+etn+jco`, `nbn+jp+etn+jcs`, `nbn+jp+etn+jxc`, `nbn+jp+etn+jxt`, `nbn+jxc`, `nbn+jxc+jca`, `nbn+jxc+jca+jxc`, `nbn+jxc+jca+jxt`, `nbn+jxc+jcc`, `nbn+jxc+jcm`, `nbn+jxc+jco`, `nbn+jxc+jcs`, `nbn+jxc+jp+ef`, `nbn+jxc+jxc`, `nbn+jxc+jxt`, `nbn+jxt`, `nbn+nbn`, `nbn+nbn+jp+ef`, `nbn+xsm+ecs`, `nbn+xsm+ef`, `nbn+xsm+ep+ef`, `nbn+xsm+ep+ef+jcr`, `nbn+xsm+etm`, `nbn+xsn`, `nbn+xsn+jca`, `nbn+xsn+jca+jp+ef+jcr`, `nbn+xsn+jca+jxc`, `nbn+xsn+jca+jxt`, `nbn+xsn+jcm`, `nbn+xsn+jco`, `nbn+xsn+jcs`, `nbn+xsn+jct`, `nbn+xsn+jp+ecc`, `nbn+xsn+jp+ecs`, `nbn+xsn+jp+ef`, `nbn+xsn+jp+ef+jcr`, `nbn+xsn+jp+ep+ef`, `nbn+xsn+jxc`, `nbn+xsn+jxt`, `nbn+xsv+etm`, `nbu`, `nbu+jca`, `nbu+jca+jxc`, `nbu+jca+jxt`, `nbu+jcc`, `nbu+jcc+jxc`, `nbu+jcj`, `nbu+jcm`, `nbu+jco`, `nbu+jcs`, `nbu+jct`, `nbu+jct+jxc`, `nbu+jp+ecc`, `nbu+jp+ecs`, `nbu+jp+ef`, `nbu+jp+ef+jcr`, `nbu+jp+ef+jxc`, `nbu+jp+ep+ecc`, `nbu+jp+ep+ecs`, `nbu+jp+ep+ef`, `nbu+jp+ep+ef+jcr`, `nbu+jp+ep+etm`, `nbu+jp+ep+etn+jco`, `nbu+jp+etm`, `nbu+jxc`, `nbu+jxc+jca`, `nbu+jxc+jcs`, `nbu+jxc+jp+ef`, `nbu+jxc+jp+ep+ef`, `nbu+jxc+jxt`, `nbu+jxt`, `nbu+ncn`, `nbu+ncn+jca`, `nbu+ncn+jcm`, `nbu+xsn`, `nbu+xsn+jca`, `nbu+xsn+jca+jxc`, `nbu+xsn+jca+jxt`, `nbu+xsn+jcm`, `nbu+xsn+jco`, `nbu+xsn+jcs`, `nbu+xsn+jp+ecs`, `nbu+xsn+jp+ep+ef`, `nbu+xsn+jxc`, `nbu+xsn+jxc+jxt`, `nbu+xsn+jxt`, `nbu+xsv+ecc`, `nbu+xsv+etm`, `ncn`, `ncn+f+ncpa+jco`, `ncn+jca`, `ncn+jca+jca`, `ncn+jca+jcc`, `ncn+jca+jcj`, `ncn+jca+jcm`, `ncn+jca+jcs`, `ncn+jca+jct`, `ncn+jca+jp+ecc`, `ncn+jca+jp+ecs`, `ncn+jca+jp+ef`, `ncn+jca+jp+ep+ef`, `ncn+jca+jp+etm`, `ncn+jca+jp+etn+jxt`, `ncn+jca+jxc`, `ncn+jca+jxc+jcc`, `ncn+jca+jxc+jcm`, `ncn+jca+jxc+jxc`, `ncn+jca+jxc+jxt`, `ncn+jca+jxt`, `ncn+jcc`, `ncn+jcc+jxc`, `ncn+jcj`, `ncn+jcj+jxt`, `ncn+jcm`, `ncn+jco`, `ncn+jcr`, `ncn+jcr+jxc`, `ncn+jcs`, `ncn+jcs+jxt`, `ncn+jct`, `ncn+jct+jcm`, `ncn+jct+jxc`, `ncn+jct+jxt`, `ncn+jcv`, `ncn+jp+ecc`, `ncn+jp+ecc+jct`, `ncn+jp+ecc+jxc`, `ncn+jp+ecs`, `ncn+jp+ecs+jcm`, `ncn+jp+ecs+jco`, `ncn+jp+ecs+jxc`, `ncn+jp+ecs+jxt`, `ncn+jp+ecx`, `ncn+jp+ef`, `ncn+jp+ef+jca`, `ncn+jp+ef+jcm`, `ncn+jp+ef+jco`, `ncn+jp+ef+jcr`, `ncn+jp+ef+jcr+jxc`, `ncn+jp+ef+jcr+jxt`, `ncn+jp+ef+jp+etm`, `ncn+jp+ef+jxc`, `ncn+jp+ef+jxf`, `ncn+jp+ef+jxt`, `ncn+jp+ep+ecc`, `ncn+jp+ep+ecs`, `ncn+jp+ep+ecs+jxc`, `ncn+jp+ep+ecx`, `ncn+jp+ep+ef`, `ncn+jp+ep+ef+jcr`, `ncn+jp+ep+ef+jcr+jxc`, `ncn+jp+ep+ef+jxc`, `ncn+jp+ep+ef+jxf`, `ncn+jp+ep+ef+jxt`, `ncn+jp+ep+ep+etm`, `ncn+jp+ep+etm`, `ncn+jp+ep+etn`, `ncn+jp+ep+etn+jca`, `ncn+jp+ep+etn+jca+jxc`, `ncn+jp+ep+etn+jco`, `ncn+jp+ep+etn+jcs`, `ncn+jp+ep+etn+jxt`, `ncn+jp+etm`, `ncn+jp+etn`, `ncn+jp+etn+jca`, `ncn+jp+etn+jca+jxc`, `ncn+jp+etn+jca+jxt`, `ncn+jp+etn+jco`, `ncn+jp+etn+jcs`, `ncn+jp+etn+jct`, `ncn+jp+etn+jxc`, `ncn+jp+etn+jxt`, `ncn+jxc`, `ncn+jxc+jca`, `ncn+jxc+jca+jxc`, `ncn+jxc+jca+jxt`, `ncn+jxc+jcc`, `ncn+jxc+jcm`, `ncn+jxc+jco`, `ncn+jxc+jcs`, `ncn+jxc+jct+jxt`, `ncn+jxc+jp+ef`, `ncn+jxc+jp+ef+jcr`, `ncn+jxc+jp+ep+ecs`, `ncn+jxc+jp+ep+ef`, `ncn+jxc+jp+etm`, `ncn+jxc+jxc`, `ncn+jxc+jxt`, `ncn+jxt`, `ncn+jxt+jcm`, `ncn+jxt+jxc`, `ncn+nbn`, `ncn+nbn+jca`, `ncn+nbn+jcm`, `ncn+nbn+jcs`, `ncn+nbn+jp+ecc`, `ncn+nbn+jp+ep+ef`, `ncn+nbn+jxc`, `ncn+nbn+jxt`, `ncn+nbu`, `ncn+nbu+jca`, `ncn+nbu+jcm`, `ncn+nbu+jco`, `ncn+nbu+jp+ef`, `ncn+nbu+jxc`, `ncn+nbu+ncn`, `ncn+ncn`, `ncn+ncn+jca`, `ncn+ncn+jca+jcc`, `ncn+ncn+jca+jcm`, `ncn+ncn+jca+jxc`, `ncn+ncn+jca+jxc+jcm`, `ncn+ncn+jca+jxc+jxc`, `ncn+ncn+jca+jxt`, `ncn+ncn+jcc`, `ncn+ncn+jcj`, `ncn+ncn+jcm`, `ncn+ncn+jco`, `ncn+ncn+jcr`, `ncn+ncn+jcs`, `ncn+ncn+jct`, `ncn+ncn+jct+jcm`, `ncn+ncn+jct+jxc`, `ncn+ncn+jct+jxt`, `ncn+ncn+jp+ecc`, `ncn+ncn+jp+ecs`, `ncn+ncn+jp+ef`, `ncn+ncn+jp+ef+jcm`, `ncn+ncn+jp+ef+jcr`, `ncn+ncn+jp+ef+jcs`, `ncn+ncn+jp+ep+ecc`, `ncn+ncn+jp+ep+ecs`, `ncn+ncn+jp+ep+ef`, `ncn+ncn+jp+ep+ef+jcr`, `ncn+ncn+jp+ep+ep+etm`, `ncn+ncn+jp+ep+etm`, `ncn+ncn+jp+ep+etn`, `ncn+ncn+jp+etm`, `ncn+ncn+jp+etn`, `ncn+ncn+jp+etn+jca`, `ncn+ncn+jp+etn+jco`, `ncn+ncn+jp+etn+jxc`, `ncn+ncn+jxc`, `ncn+ncn+jxc+jca`, `ncn+ncn+jxc+jcc`, `ncn+ncn+jxc+jcm`, `ncn+ncn+jxc+jco`, `ncn+ncn+jxc+jcs`, `ncn+ncn+jxc+jxc`, `ncn+ncn+jxt`, `ncn+ncn+nbn`, `ncn+ncn+ncn`, `ncn+ncn+ncn+jca`, `ncn+ncn+ncn+jca+jcm`, `ncn+ncn+ncn+jca+jxt`, `ncn+ncn+ncn+jcj`, `ncn+ncn+ncn+jcm`, `ncn+ncn+ncn+jco`, `ncn+ncn+ncn+jcs`, `ncn+ncn+ncn+jct+jxt`, `ncn+ncn+ncn+jp+etn+jxc`, `ncn+ncn+ncn+jxt`, `ncn+ncn+ncn+ncn+jca`, `ncn+ncn+ncn+ncn+jca+jxt`, `ncn+ncn+ncn+ncn+jco`, `ncn+ncn+ncn+xsn+jp+etm`, `ncn+ncn+ncpa`, `ncn+ncn+ncpa+jca`, `ncn+ncn+ncpa+jcm`, `ncn+ncn+ncpa+jco`, `ncn+ncn+ncpa+jcs`, `ncn+ncn+ncpa+jxc`, `ncn+ncn+ncpa+jxt`, `ncn+ncn+ncpa+ncn`, `ncn+ncn+ncpa+ncn+jca`, `ncn+ncn+ncpa+ncn+jcj`, `ncn+ncn+ncpa+ncn+jcm`, `ncn+ncn+ncpa+ncn+jxt`, `ncn+ncn+xsn`, `ncn+ncn+xsn+jca`, `ncn+ncn+xsn+jca+jxt`, `ncn+ncn+xsn+jcj`, `ncn+ncn+xsn+jcm`, `ncn+ncn+xsn+jco`, `ncn+ncn+xsn+jcs`, `ncn+ncn+xsn+jct`, `ncn+ncn+xsn+jp+ecs`, `ncn+ncn+xsn+jp+ep+ef`, `ncn+ncn+xsn+jp+etm`, `ncn+ncn+xsn+jxc`, `ncn+ncn+xsn+jxc+jcs`, `ncn+ncn+xsn+jxt`, `ncn+ncn+xsv+ecc`, `ncn+ncn+xsv+etm`, `ncn+ncpa`, `ncn+ncpa+jca`, `ncn+ncpa+jca+jcm`, `ncn+ncpa+jca+jxc`, `ncn+ncpa+jca+jxt`, `ncn+ncpa+jcc`, `ncn+ncpa+jcj`, `ncn+ncpa+jcm`, `ncn+ncpa+jco`, `ncn+ncpa+jcr`, `ncn+ncpa+jcs`, `ncn+ncpa+jct`, `ncn+ncpa+jct+jcm`, `ncn+ncpa+jct+jxt`, `ncn+ncpa+jp+ecc`, `ncn+ncpa+jp+ecc+jxc`, `ncn+ncpa+jp+ecs`, `ncn+ncpa+jp+ecs+jxc`, `ncn+ncpa+jp+ef`, `ncn+ncpa+jp+ef+jcr`, `ncn+ncpa+jp+ef+jcr+jxc`, `ncn+ncpa+jp+ep+ef`, `ncn+ncpa+jp+ep+etm`, `ncn+ncpa+jp+ep+etn`, `ncn+ncpa+jp+etm`, `ncn+ncpa+jxc`, `ncn+ncpa+jxc+jca+jxc`, `ncn+ncpa+jxc+jco`, `ncn+ncpa+jxc+jcs`, `ncn+ncpa+jxt`, `ncn+ncpa+nbn+jcs`, `ncn+ncpa+ncn`, `ncn+ncpa+ncn+jca`, `ncn+ncpa+ncn+jca+jcm`, `ncn+ncpa+ncn+jca+jxc`, `ncn+ncpa+ncn+jca+jxt`, `ncn+ncpa+ncn+jcj`, `ncn+ncpa+ncn+jcm`, `ncn+ncpa+ncn+jco`, `ncn+ncpa+ncn+jcs`, `ncn+ncpa+ncn+jct`, `ncn+ncpa+ncn+jct+jcm`, `ncn+ncpa+ncn+jp+ef+jcr`, `ncn+ncpa+ncn+jp+ep+etm`, `ncn+ncpa+ncn+jxc`, `ncn+ncpa+ncn+jxt`, `ncn+ncpa+ncn+xsn+jcm`, `ncn+ncpa+ncn+xsn+jxt`, `ncn+ncpa+ncpa`, `ncn+ncpa+ncpa+jca`, `ncn+ncpa+ncpa+jcj`, `ncn+ncpa+ncpa+jcm`, `ncn+ncpa+ncpa+jco`, `ncn+ncpa+ncpa+jcs`, `ncn+ncpa+ncpa+jp+ep+ef`, `ncn+ncpa+ncpa+jxt`, `ncn+ncpa+ncpa+ncn`, `ncn+ncpa+xsn`, `ncn+ncpa+xsn+jcm`, `ncn+ncpa+xsn+jco`, `ncn+ncpa+xsn+jcs`, `ncn+ncpa+xsn+jp+ecc`, `ncn+ncpa+xsn+jp+etm`, `ncn+ncpa+xsn+jxt`, `ncn+ncpa+xsv+ecc`, `ncn+ncpa+xsv+ecs`, `ncn+ncpa+xsv+ecx`, `ncn+ncpa+xsv+ecx+px+etm`, `ncn+ncpa+xsv+ef`, `ncn+ncpa+xsv+ef+jcm`, `ncn+ncpa+xsv+ef+jcr`, `ncn+ncpa+xsv+etm`, `ncn+ncpa+xsv+etn`, `ncn+ncpa+xsv+etn+jco`, `ncn+ncps`, `ncn+ncps+jca`, `ncn+ncps+jcm`, `ncn+ncps+jco`, `ncn+ncps+jcs`, `ncn+ncps+jp+ecs`, `ncn+ncps+jxt`, `ncn+ncps+ncn+jcs`, `ncn+ncps+ncpa+ncn`, `ncn+ncps+xsm+ef`, `ncn+ncps+xsm+etm`, `ncn+nnc`, `ncn+nnc+jcs`, `ncn+nnc+nnc`, `ncn+nno`, `ncn+nq`, `ncn+nq+jca`, `ncn+nq+jca+jxc`, `ncn+nq+jca+jxt`, `ncn+nq+jcm`, `ncn+nq+jcs`, `ncn+nq+jxt`, `ncn+nq+ncn+jcm`, `ncn+nq+ncn+xsn+jcs`, `ncn+nq+xsn+jxt`, `ncn+xsa`, `ncn+xsm+ecc`, `ncn+xsm+ecs`, `ncn+xsm+ecs+jxc`, `ncn+xsm+ecx`, `ncn+xsm+ecx+jcs`, `ncn+xsm+ecx+px+ep+etm`, `ncn+xsm+ef`, `ncn+xsm+ef+jcr`, `ncn+xsm+etm`, `ncn+xsm+etn+jcm`, `ncn+xsm+etn+jp+ef+jcr`, `ncn+xsn`, `ncn+xsn+jca`, `ncn+xsn+jca+jcj`, `ncn+xsn+jca+jxc`, `ncn+xsn+jca+jxc+jxc`, `ncn+xsn+jca+jxt`, `ncn+xsn+jcc`, `ncn+xsn+jcj`, `ncn+xsn+jcm`, `ncn+xsn+jco`, `ncn+xsn+jcs`, `ncn+xsn+jcs+jxt`, `ncn+xsn+jct`, `ncn+xsn+jct+jcm`, `ncn+xsn+jct+jxc`, `ncn+xsn+jct+jxt`, `ncn+xsn+jcv`, `ncn+xsn+jp+ecc`, `ncn+xsn+jp+ecc+jxc`, `ncn+xsn+jp+ecs`, `ncn+xsn+jp+ecs+jxc`, `ncn+xsn+jp+ecx`, `ncn+xsn+jp+ecx+jxt`, `ncn+xsn+jp+ef`, `ncn+xsn+jp+ef+jca`, `ncn+xsn+jp+ef+jcr`, `ncn+xsn+jp+ep+ecc`, `ncn+xsn+jp+ep+ecs`, `ncn+xsn+jp+ep+ef`, `ncn+xsn+jp+ep+ef+jcr`, `ncn+xsn+jp+ep+etm`, `ncn+xsn+jp+ep+etn`, `ncn+xsn+jp+etm`, `ncn+xsn+jp+etn`, `ncn+xsn+jp+etn+jca`, `ncn+xsn+jp+etn+jca+jxt`, `ncn+xsn+jp+etn+jxc`, `ncn+xsn+jp+etn+jxt`, `ncn+xsn+jxc`, `ncn+xsn+jxc+jcm`, `ncn+xsn+jxc+jco`, `ncn+xsn+jxc+jcs`, `ncn+xsn+jxc+jxc`, `ncn+xsn+jxt`, `ncn+xsn+ncn+jca`, `ncn+xsn+ncn+jca+jxt`, `ncn+xsn+ncn+jcs`, `ncn+xsn+ncpa+jca`, `ncn+xsn+xsn`, `ncn+xsn+xsn+jca`, `ncn+xsn+xsn+jcm`, `ncn+xsn+xsn+jp+ecs`, `ncn+xsn+xsn+jxc`, `ncn+xsn+xsn+jxc+jcc`, `ncn+xsn+xsn+jxc+jcs`, `ncn+xsn+xsv+ecc`, `ncn+xsn+xsv+etm`, `ncn+xsn+xsv+etn`, `ncn+xsv+ecc`, `ncn+xsv+ecs`, `ncn+xsv+ecx`, `ncn+xsv+ef`, `ncn+xsv+ep+ecs`, `ncn+xsv+ep+ef`, `ncn+xsv+ep+etm`, `ncn+xsv+etm`, `ncn+xsv+etn+jca`, `ncpa`, `ncpa+jca`, `ncpa+jca+jcm`, `ncpa+jca+jct`, `ncpa+jca+jp+ecs`, `ncpa+jca+jp+ef`, `ncpa+jca+jp+ep+ef`, `ncpa+jca+jxc`, `ncpa+jca+jxc+jcm`, `ncpa+jca+jxc+jxc`, `ncpa+jca+jxc+jxt`, `ncpa+jca+jxt`, `ncpa+jcc`, `ncpa+jcj`, `ncpa+jcm`, `ncpa+jco`, `ncpa+jcr`, `ncpa+jcs`, `ncpa+jct`, `ncpa+jct+jcm`, `ncpa+jct+jxc`, `ncpa+jct+jxt`, `ncpa+jp+ecc`, `ncpa+jp+ecs`, `ncpa+jp+ecs+jxc`, `ncpa+jp+ecx`, `ncpa+jp+ecx+jxc`, `ncpa+jp+ef`, `ncpa+jp+ef+jca`, `ncpa+jp+ef+jco`, `ncpa+jp+ef+jcr`, `ncpa+jp+ef+jxc`, `ncpa+jp+ef+jxf`, `ncpa+jp+ep+ecc`, `ncpa+jp+ep+ecs`, `ncpa+jp+ep+ef`, `ncpa+jp+ep+ef+jca`, `ncpa+jp+ep+ef+jcr`, `ncpa+jp+ep+ef+jxt`, `ncpa+jp+ep+etm`, `ncpa+jp+ep+etn+jca`, `ncpa+jp+ep+etn+jca+jxc`, `ncpa+jp+ep+etn+jcs`, `ncpa+jp+etm`, `ncpa+jp+etn`, `ncpa+jp+etn+jca`, `ncpa+jp+etn+jca+jxt`, `ncpa+jp+etn+jco`, `ncpa+jp+etn+jcs`, `ncpa+jp+etn+jxc`, `ncpa+jp+etn+jxt`, `ncpa+jxc`, `ncpa+jxc+jca`, `ncpa+jxc+jca+jxc`, `ncpa+jxc+jca+jxt`, `ncpa+jxc+jcc`, `ncpa+jxc+jcm`, `ncpa+jxc+jco`, `ncpa+jxc+jcs`, `ncpa+jxc+jxc`, `ncpa+jxt`, `ncpa+jxt+jxc`, `ncpa+jxt+jxt`, `ncpa+nbn+jca`, `ncpa+nbn+jct`, `ncpa+nbn+jp+ef`, `ncpa+nbn+jp+ep+ef`, `ncpa+nbn+jp+etm`, `ncpa+nbn+jxc+jcc`, `ncpa+nbu+jca`, `ncpa+ncn`, `ncpa+ncn+jca`, `ncpa+ncn+jca+jcm`, `ncpa+ncn+jca+jxc`, `ncpa+ncn+jca+jxc+jcm`, `ncpa+ncn+jca+jxt`, `ncpa+ncn+jcc`, `ncpa+ncn+jcj`, `ncpa+ncn+jcm`, `ncpa+ncn+jco`, `ncpa+ncn+jcr`, `ncpa+ncn+jcs`, `ncpa+ncn+jct`, `ncpa+ncn+jct+jcm`, `ncpa+ncn+jct+jxc`, `ncpa+ncn+jp+ecc`, `ncpa+ncn+jp+ecs`, `ncpa+ncn+jp+ef`, `ncpa+ncn+jp+ef+jcr`, `ncpa+ncn+jp+ef+jcr+jxc`, `ncpa+ncn+jp+ep+ef`, `ncpa+ncn+jp+ep+etm`, `ncpa+ncn+jp+etm`, `ncpa+ncn+jp+etn+jca+jxt`, `ncpa+ncn+jp+etn+jco`, `ncpa+ncn+jp+etn+jxc`, `ncpa+ncn+jxc`, `ncpa+ncn+jxc+jcc`, `ncpa+ncn+jxc+jco`, `ncpa+ncn+jxt`, `ncpa+ncn+nbn`, `ncpa+ncn+ncn`, `ncpa+ncn+ncn+jca`, `ncpa+ncn+ncn+jca+jxt`, `ncpa+ncn+ncn+jcm`, `ncpa+ncn+ncn+jco`, `ncpa+ncn+ncn+jcs`, `ncpa+ncn+ncn+jp+ep+ef`, `ncpa+ncn+ncn+jp+etm`, `ncpa+ncn+ncn+jxt`, `ncpa+ncn+ncn+ncn`, `ncpa+ncn+ncn+xsn+jxt`, `ncpa+ncn+ncpa`, `ncpa+ncn+ncpa+jca`, `ncpa+ncn+ncpa+jcj`, `ncpa+ncn+ncpa+jco`, `ncpa+ncn+ncpa+ncn`, `ncpa+ncn+ncpa+ncn+jco`, `ncpa+ncn+xsn`, `ncpa+ncn+xsn+jca`, `ncpa+ncn+xsn+jca+jxc`, `ncpa+ncn+xsn+jcj`, `ncpa+ncn+xsn+jcm`, `ncpa+ncn+xsn+jco`, `ncpa+ncn+xsn+jcs`, `ncpa+ncn+xsn+jct`, `ncpa+ncn+xsn+jp+ep+ef`, `ncpa+ncn+xsn+jp+etm`, `ncpa+ncn+xsn+jxt`, `ncpa+ncpa`, `ncpa+ncpa+jca`, `ncpa+ncpa+jca+jcm`, `ncpa+ncpa+jca+jxc`, `ncpa+ncpa+jca+jxt`, `ncpa+ncpa+jcj`, `ncpa+ncpa+jcm`, `ncpa+ncpa+jco`, `ncpa+ncpa+jcs`, `ncpa+ncpa+jct`, `ncpa+ncpa+jct+jxc`, `ncpa+ncpa+jct+jxt`, `ncpa+ncpa+jp+ecc`, `ncpa+ncpa+jp+ecs`, `ncpa+ncpa+jp+ecx`, `ncpa+ncpa+jp+ef`, `ncpa+ncpa+jp+ef+jca`, `ncpa+ncpa+jp+ef+jcr`, `ncpa+ncpa+jp+ef+jcr+jxc`, `ncpa+ncpa+jp+ep+ecs`, `ncpa+ncpa+jp+etm`, `ncpa+ncpa+jxc`, `ncpa+ncpa+jxt`, `ncpa+ncpa+ncn`, `ncpa+ncpa+ncn+jca`, `ncpa+ncpa+ncn+jcj`, `ncpa+ncpa+ncn+jcm`, `ncpa+ncpa+ncn+jco`, `ncpa+ncpa+ncn+jcs`, `ncpa+ncpa+ncn+jxt`, `ncpa+ncpa+ncpa+jcm`, `ncpa+ncpa+ncpa+jcs`, `ncpa+ncpa+ncpa+ncpa+jco`, `ncpa+ncpa+xsn`, `ncpa+ncpa+xsn+jca`, `ncpa+ncpa+xsn+jcj`, `ncpa+ncpa+xsn+jco`, `ncpa+ncpa+xsn+jcs`, `ncpa+ncpa+xsn+jxc`, `ncpa+ncpa+xsn+jxt`, `ncpa+ncpa+xsv+ecc`, `ncpa+ncpa+xsv+ecs`, `ncpa+ncpa+xsv+ef`, `ncpa+ncpa+xsv+ep+ef`, `ncpa+ncpa+xsv+ep+etm`, `ncpa+ncpa+xsv+etm`, `ncpa+ncpa+xsv+etn+jca`, `ncpa+ncps`, `ncpa+ncps+jca`, `ncpa+ncps+jcm`, `ncpa+ncps+jco`, `ncpa+ncps+jcs`, `ncpa+ncps+jxt`, `ncpa+ncps+xsm+etm`, `ncpa+nq+jca`, `ncpa+xsa`, `ncpa+xsn`, `ncpa+xsn+jca`, `ncpa+xsn+jca+jxc`, `ncpa+xsn+jca+jxt`, `ncpa+xsn+jcc`, `ncpa+xsn+jcj`, `ncpa+xsn+jcm`, `ncpa+xsn+jco`, `ncpa+xsn+jcs`, `ncpa+xsn+jct`, `ncpa+xsn+jp+ecc`, `ncpa+xsn+jp+ecs`, `ncpa+xsn+jp+ecs+jxc`, `ncpa+xsn+jp+ecx`, `ncpa+xsn+jp+ecx+jxt`, `ncpa+xsn+jp+ef`, `ncpa+xsn+jp+ef+jcr`, `ncpa+xsn+jp+ef+jxf`, `ncpa+xsn+jp+ep+ecc`, `ncpa+xsn+jp+ep+ef`, `ncpa+xsn+jp+ep+ef+jco`, `ncpa+xsn+jp+ep+ef+jcr`, `ncpa+xsn+jp+etm`, `ncpa+xsn+jp+etn`, `ncpa+xsn+jp+etn+jco`, `ncpa+xsn+jp+etn+jxc`, `ncpa+xsn+jxc`, `ncpa+xsn+jxt`, `ncpa+xsv+ecc`, `ncpa+xsv+ecc+jcm`, `ncpa+xsv+ecc+jxc`, `ncpa+xsv+ecc+jxt`, `ncpa+xsv+ecs`, `ncpa+xsv+ecs+jca`, `ncpa+xsv+ecs+jco`, `ncpa+xsv+ecs+jp+ef`, `ncpa+xsv+ecs+jxc`, `ncpa+xsv+ecs+jxc+jxt`, `ncpa+xsv+ecs+jxt`, `ncpa+xsv+ecx`, `ncpa+xsv+ecx+jco`, `ncpa+xsv+ecx+jxc`, `ncpa+xsv+ecx+jxt`, `ncpa+xsv+ecx+px+ecc`, `ncpa+xsv+ecx+px+ecs`, `ncpa+xsv+ecx+px+ecx`, `ncpa+xsv+ecx+px+ecx+jxc`, `ncpa+xsv+ecx+px+ecx+px+ecs`, `ncpa+xsv+ecx+px+ef`, `ncpa+xsv+ecx+px+ef+jcr`, `ncpa+xsv+ecx+px+ep+ecc`, `ncpa+xsv+ecx+px+ep+ecs`, `ncpa+xsv+ecx+px+ep+ef`, `ncpa+xsv+ecx+px+ep+ef+jcr`, `ncpa+xsv+ecx+px+ep+etm`, `ncpa+xsv+ecx+px+ep+etn+jca`, `ncpa+xsv+ecx+px+ep+etn+jco`, `ncpa+xsv+ecx+px+ep+etn+jxc`, `ncpa+xsv+ecx+px+ep+etn+jxt`, `ncpa+xsv+ecx+px+etm`, `ncpa+xsv+ecx+px+etn`, `ncpa+xsv+ecx+px+etn+jca`, `ncpa+xsv+ecx+px+etn+jco`, `ncpa+xsv+ef`, `ncpa+xsv+ef+jca`, `ncpa+xsv+ef+jcj`, `ncpa+xsv+ef+jcm`, `ncpa+xsv+ef+jco`, `ncpa+xsv+ef+jcr`, `ncpa+xsv+ef+jcr+jxt`, `ncpa+xsv+ef+jcs`, `ncpa+xsv+ef+jxc`, `ncpa+xsv+ef+jxf`, `ncpa+xsv+ef+jxt`, `ncpa+xsv+ep+ecc`, `ncpa+xsv+ep+ecs`, `ncpa+xsv+ep+ecs+jco`, `ncpa+xsv+ep+ecs+jxc`, `ncpa+xsv+ep+ecs+jxt`, `ncpa+xsv+ep+ecx`, `ncpa+xsv+ep+ecx+jxc`, `ncpa+xsv+ep+ef`, `ncpa+xsv+ep+ef+jca`, `ncpa+xsv+ep+ef+jca+jxt`, `ncpa+xsv+ep+ef+jco`, `ncpa+xsv+ep+ef+jcr`, `ncpa+xsv+ep+ef+jcr+jxc`, `ncpa+xsv+ep+ef+jcr+jxc+jxt`, `ncpa+xsv+ep+ef+jxc`, `ncpa+xsv+ep+ef+jxf`, `ncpa+xsv+ep+ef+jxt`, `ncpa+xsv+ep+ep+ecs`, `ncpa+xsv+ep+ep+ef`, `ncpa+xsv+ep+etm`, `ncpa+xsv+ep+etn`, `ncpa+xsv+ep+etn+jca`, `ncpa+xsv+ep+etn+jca+jxc`, `ncpa+xsv+ep+etn+jcj`, `ncpa+xsv+ep+etn+jco`, `ncpa+xsv+ep+etn+jcs`, `ncpa+xsv+ep+etn+jxt`, `ncpa+xsv+etm`, `ncpa+xsv+etn`, `ncpa+xsv+etn+jca`, `ncpa+xsv+etn+jca+jxc`, `ncpa+xsv+etn+jca+jxt`, `ncpa+xsv+etn+jco`, `ncpa+xsv+etn+jcs`, `ncpa+xsv+etn+jct`, `ncpa+xsv+etn+jxc`, `ncpa+xsv+etn+jxc+jcm`, `ncpa+xsv+etn+jxc+jcs`, `ncpa+xsv+etn+jxc+jxc`, `ncpa+xsv+etn+jxc+jxt`, `ncpa+xsv+etn+jxt`, `ncps`, `ncps+jca`, `ncps+jca+jcm`, `ncps+jca+jxc`, `ncps+jca+jxc+jcm`, `ncps+jcc`, `ncps+jcj`, `ncps+jcm`, `ncps+jco`, `ncps+jcs`, `ncps+jct`, `ncps+jct+jcm`, `ncps+jct+jxt`, `ncps+jp+ecc`, `ncps+jp+ecs`, `ncps+jp+ecs+jxt`, `ncps+jp+ef`, `ncps+jp+ef+jcr`, `ncps+jp+ep+ef`, `ncps+jp+ep+etn`, `ncps+jp+etm`, `ncps+jp+etn+jcs`, `ncps+jp+etn+jxt`, `ncps+jxc`, `ncps+jxc+jxc`, `ncps+jxt`, `ncps+nbn+jp+etm`, `ncps+nbn+jxc`, `ncps+ncn`, `ncps+ncn+jca`, `ncps+ncn+jca+jcm`, `ncps+ncn+jcm`, `ncps+ncn+jco`, `ncps+ncn+jcs`, `ncps+ncn+jct+jxt`, `ncps+ncn+jp+ef`, `ncps+ncn+jp+ef+jcr`, `ncps+ncn+jp+etm`, `ncps+ncn+jxc+jco`, `ncps+ncn+jxt`, `ncps+ncn+ncn`, `ncps+ncn+ncn+jca+jxc`, `ncps+ncn+ncn+jcm`, `ncps+ncn+ncn+jco`, `ncps+ncn+ncn+jxt`, `ncps+ncn+xsn`, `ncps+ncn+xsn+jca`, `ncps+ncn+xsn+jcj`, `ncps+ncn+xsn+jco`, `ncps+ncn+xsn+jp+ecc`, `ncps+ncn+xsn+jp+etm`, `ncps+ncpa`, `ncps+ncpa+jca`, `ncps+ncpa+jcc`, `ncps+ncpa+jcj`, `ncps+ncpa+jcm`, `ncps+ncpa+jco`, `ncps+ncpa+jcs`, `ncps+ncpa+jp+etm`, `ncps+ncpa+jxt`, `ncps+ncpa+xsv+etm`, `ncps+ncps+jca`, `ncps+ncps+jcm`, `ncps+ncps+xsm+ecc`, `ncps+ncps+xsm+ecs`, `ncps+ncps+xsm+etm`, `ncps+xsa`, `ncps+xsa+jxc`, `ncps+xsm+ecc`, `ncps+xsm+ecc+jxc`, `ncps+xsm+ecc+jxt`, `ncps+xsm+ecs`, `ncps+xsm+ecs+jxc`, `ncps+xsm+ecs+jxt`, `ncps+xsm+ecx`, `ncps+xsm+ecx+jcs`, `ncps+xsm+ecx+jxc`, `ncps+xsm+ecx+jxt`, `ncps+xsm+ecx+px+ecc`, `ncps+xsm+ecx+px+ecs`, `ncps+xsm+ecx+px+ecx`, `ncps+xsm+ecx+px+ecx+jxt`, `ncps+xsm+ecx+px+ef`, `ncps+xsm+ecx+px+ep+ecs`, `ncps+xsm+ecx+px+ep+ef`, `ncps+xsm+ecx+px+ep+etm`, `ncps+xsm+ecx+px+ep+etn+jco`, `ncps+xsm+ecx+px+etm`, `ncps+xsm+ecx+px+etn`, `ncps+xsm+ecx+px+etn+jca`, `ncps+xsm+ecx+px+etn+jcj`, `ncps+xsm+ecx+px+etn+jco`, `ncps+xsm+ef`, `ncps+xsm+ef+jco`, `ncps+xsm+ef+jcr`, `ncps+xsm+ef+jcr+jxc`, `ncps+xsm+ef+jcr+jxt`, `ncps+xsm+ef+jxf`, `ncps+xsm+ef+jxt`, `ncps+xsm+ep+ecc`, `ncps+xsm+ep+ecs`, `ncps+xsm+ep+ecs+etm`, `ncps+xsm+ep+ef`, `ncps+xsm+ep+ef+jco`, `ncps+xsm+ep+ef+jcr`, `ncps+xsm+ep+ef+jxf`, `ncps+xsm+ep+ep+ef`, `ncps+xsm+ep+etm`, `ncps+xsm+ep+etn`, `ncps+xsm+ep+etn+jxt`, `ncps+xsm+etm`, `ncps+xsm+etn`, `ncps+xsm+etn+jca`, `ncps+xsm+etn+jca+jxt`, `ncps+xsm+etn+jcj`, `ncps+xsm+etn+jcm`, `ncps+xsm+etn+jco`, `ncps+xsm+etn+jcs`, `ncps+xsm+etn+jct`, `ncps+xsm+etn+jct+jcm`, `ncps+xsm+etn+jp+ef+jcr`, `ncps+xsm+etn+jp+etm`, `ncps+xsm+etn+jxc`, `ncps+xsm+etn+jxc+jxt`, `ncps+xsm+etn+jxt`, `ncps+xsn`, `ncps+xsn+jca`, `ncps+xsn+jca+jxt`, `ncps+xsn+jcm`, `ncps+xsn+jco`, `ncps+xsn+jcs`, `ncps+xsn+jp+ecc`, `ncps+xsn+jp+ep+ecs`, `ncps+xsn+jp+etm`, `ncps+xsn+jxc`, `ncps+xsn+jxt`, `ncps+xsv+etm`, `nnc`, `nnc+f`, `nnc+f+jca`, `nnc+f+jp+ef`, `nnc+jca`, `nnc+jca+jxc`, `nnc+jca+jxt`, `nnc+jcc`, `nnc+jcj`, `nnc+jcm`, `nnc+jco`, `nnc+jcs`, `nnc+jp+ecc`, `nnc+jp+ecs`, `nnc+jp+ef`, `nnc+jp+ef+jcr`, `nnc+jp+ep+ef`, `nnc+jp+ep+etm`, `nnc+jp+etm`, `nnc+jp+etn+jca`, `nnc+jxc`, `nnc+jxt`, `nnc+nbn`, `nnc+nbn+jcm`, `nnc+nbn+jco`, `nnc+nbn+nbu+jcc`, `nnc+nbn+nbu+jcs`, `nnc+nbn+xsn`, `nnc+nbu`, `nnc+nbu+jca`, `nnc+nbu+jca+jxc`, `nnc+nbu+jcc`, `nnc+nbu+jcj`, `nnc+nbu+jcm`, `nnc+nbu+jco`, `nnc+nbu+jcs`, `nnc+nbu+jp+ef`, `nnc+nbu+jp+ef+jcr`, `nnc+nbu+jp+ep+ecs`, `nnc+nbu+jp+ep+ef`, `nnc+nbu+jp+etm`, `nnc+nbu+jxc`, `nnc+nbu+jxc+jcs`, `nnc+nbu+jxc+jxt`, `nnc+nbu+jxt`, `nnc+nbu+nbu`, `nnc+nbu+nbu+jcm`, `nnc+nbu+nbu+jp+ef+jcr`, `nnc+nbu+ncn`, `nnc+nbu+ncn+jca`, `nnc+nbu+ncn+jcj`, `nnc+nbu+ncn+jcm`, `nnc+nbu+ncn+jxc`, `nnc+nbu+xsn`, `nnc+nbu+xsn+jca`, `nnc+nbu+xsn+jcm`, `nnc+nbu+xsn+jco`, `nnc+nbu+xsn+jcs`, `nnc+nbu+xsn+jp+ecc`, `nnc+nbu+xsn+jp+ef`, `nnc+nbu+xsn+jxc`, `nnc+nbu+xsn+jxc+jcm`, `nnc+nbu+xsn+jxt`, `nnc+nbu+xsv+etm`, `nnc+ncn`, `nnc+ncn+jca`, `nnc+ncn+jca+jxt`, `nnc+ncn+jcj`, `nnc+ncn+jcm`, `nnc+ncn+jco`, `nnc+ncn+jcs`, `nnc+ncn+jct`, `nnc+ncn+jp+ef`, `nnc+ncn+jp+etm`, `nnc+ncn+jxc`, `nnc+ncn+jxt`, `nnc+ncn+nbu`, `nnc+ncn+nbu+xsn+jca`, `nnc+ncn+ncn+jca+jxt`, `nnc+ncn+ncn+xsn`, `nnc+ncn+nnc+nnc`, `nnc+ncn+xsn`, `nnc+ncn+xsn+jp+etm`, `nnc+ncn+xsn+jxt`, `nnc+ncpa`, `nnc+ncpa+jcs`, `nnc+nnc`, `nnc+nnc+jca`, `nnc+nnc+jca+jxt`, `nnc+nnc+jcm`, `nnc+nnc+jco`, `nnc+nnc+jp+ef`, `nnc+nnc+nbu`, `nnc+nnc+nbu+jca`, `nnc+nnc+nbu+jcc`, `nnc+nnc+nbu+jcm`, `nnc+nnc+nbu+jco`, `nnc+nnc+nbu+jcs`, `nnc+nnc+nbu+jp+ep+ef`, `nnc+nnc+nbu+jp+etm`, `nnc+nnc+nbu+jxc`, `nnc+nnc+nbu+xsn`, `nnc+nnc+nbu+xsn+jcm`, `nnc+nnc+nbu+xsn+jxc`, `nnc+nnc+ncn+jco`, `nnc+nnc+nnc`, `nnc+nnc+nnc+nnc`, `nnc+nnc+su+jp+ef`, `nnc+nnc+xsn`, `nnc+nnc+xsn+jcm`, `nnc+nnc+xsn+nbu+jca`, `nnc+nnc+xsn+nbu+jcm`, `nnc+nnc+xsn+nbu+jco`, `nnc+nnc+xsn+nbu+jcs`, `nnc+nno+nbu`, `nnc+nno+nbu+jcc`, `nnc+su`, `nnc+su+jca`, `nnc+su+jcm`, `nnc+su+jco`, `nnc+su+jcs`, `nnc+su+jxc`, `nnc+su+xsn`, `nnc+xsn`, `nnc+xsn+jca`, `nnc+xsn+jca+jxt`, `nnc+xsn+jcm`, `nnc+xsn+jco`, `nnc+xsn+jcs`, `nnc+xsn+jp+ef`, `nnc+xsn+jxc`, `nnc+xsn+nbn+jca`, `nnc+xsn+nbu`, `nnc+xsn+nbu+jca`, `nnc+xsn+nbu+jcm`, `nnc+xsn+nbu+jco`, `nnc+xsn+nbu+jcs`, `nnc+xsn+nnc+nbu`, `nnc+xsn+nnc+nbu+jcm`, `nno`, `nno+jca`, `nno+jca+jxt`, `nno+jcj`, `nno+jcm`, `nno+jco`, `nno+jcs`, `nno+jxt`, `nno+nbn`, `nno+nbn+jcm`, `nno+nbn+xsn`, `nno+nbu`, `nno+nbu+jca`, `nno+nbu+jca+jxc`, `nno+nbu+jca+jxt`, `nno+nbu+jcc`, `nno+nbu+jcj`, `nno+nbu+jcm`, `nno+nbu+jco`, `nno+nbu+jcs`, `nno+nbu+jct`, `nno+nbu+jp+ecc`, `nno+nbu+jp+ecs`, `nno+nbu+jp+ef`, `nno+nbu+jp+ep+ecc`, `nno+nbu+jp+ep+ecs`, `nno+nbu+jp+ep+ef`, `nno+nbu+jp+etm`, `nno+nbu+jxc`, `nno+nbu+jxc+jca`, `nno+nbu+jxc+jcm`, `nno+nbu+jxc+jp+ef`, `nno+nbu+jxc+jp+etm`, `nno+nbu+jxc+jxc`, `nno+nbu+jxc+jxt`, `nno+nbu+jxt`, `nno+nbu+nbu`, `nno+nbu+ncn`, `nno+nbu+ncn+jp+ep+ef`, `nno+nbu+ncn+ncn`, `nno+nbu+xsn`, `nno+nbu+xsn+jca`, `nno+nbu+xsn+jcc`, `nno+nbu+xsn+jcm`, `nno+nbu+xsn+jxc`, `nno+nbu+xsn+jxt`, `nno+ncn`, `nno+ncn+jca`, `nno+ncn+jca+jxc`, `nno+ncn+jca+jxt`, `nno+ncn+jcm`, `nno+ncn+jco`, `nno+ncn+jcs`, `nno+ncn+jct`, `nno+ncn+jp+ef`, `nno+ncn+jp+etm`, `nno+ncn+jxc`, `nno+ncn+jxc+jxt`, `nno+ncn+ncn+jp+etm`, `nno+ncn+xsn`, `nno+ncn+xsn+jca`, `nno+ncn+xsn+jp+ep+ef`, `nno+ncn+xsn+jp+etm`, `nno+ncpa+jp+ep+etn+jca+jxc`, `nno+nnc`, `nno+xsn`, `nno+xsn+jca`, `nno+xsn+jca+jxc`, `nno+xsn+jxc`, `nno+xsn+jxc+jcs`, `nno+xsn+nbu`, `nno+xsn+nbu+jcm`, `npd`, `npd+jca`, `npd+jca+jcm`, `npd+jca+jp+ef`, `npd+jca+jp+ef+jca`, `npd+jca+jxc`, `npd+jca+jxc+jcm`, `npd+jca+jxt`, `npd+jcc`, `npd+jcj`, `npd+jcm`, `npd+jco`, `npd+jcs`, `npd+jct`, `npd+jct+jcm`, `npd+jct+jxt`, `npd+jp+ecc`, `npd+jp+ecs`, `npd+jp+ecs+jco`, `npd+jp+ecs+jxt`, `npd+jp+ef`, `npd+jp+ef+jca`, `npd+jp+ef+jcm`, `npd+jp+ef+jco`, `npd+jp+ef+jcr`, `npd+jp+ef+jcs`, `npd+jp+ef+jp+ef`, `npd+jp+ef+jp+etm`, `npd+jp+ef+jxc`, `npd+jp+ef+jxt`, `npd+jp+ep+ef`, `npd+jp+etm`, `npd+jxc`, `npd+jxc+jca`, `npd+jxc+jca+jxc`, `npd+jxc+jcc`, `npd+jxc+jcr`, `npd+jxc+jp+ef`, `npd+jxc+jxc`, `npd+jxc+jxt`, `npd+jxt`, `npd+nbn`, `npd+nbn+jca`, `npd+nbn+jcs`, `npd+nbn+jxc`, `npd+nbn+jxc+jxt`, `npd+ncn`, `npd+ncn+jca`, `npd+ncn+jca+jxc`, `npd+ncn+jcm`, `npd+ncn+jco`, `npd+ncn+jcs`, `npd+ncn+jxt`, `npd+npd`, `npd+xsn`, `npd+xsn+jca`, `npd+xsn+jca+jxc`, `npd+xsn+jca+jxt`, `npd+xsn+jcm`, `npd+xsn+jco`, `npd+xsn+jcs`, `npd+xsn+jct`, `npd+xsn+jp+ef`, `npd+xsn+jxc`, `npd+xsn+jxt`, `npp`, `npp+jca`, `npp+jca+jcm`, `npp+jca+jxc`, `npp+jca+jxc+jcm`, `npp+jca+jxt`, `npp+jcc`, `npp+jcj`, `npp+jcm`, `npp+jco`, `npp+jcs`, `npp+jcs+jxt`, `npp+jct`, `npp+jct+jcm`, `npp+jct+jxc`, `npp+jct+jxt`, `npp+jp+ecs`, `npp+jp+ecs+jco`, `npp+jp+ef`, `npp+jp+ef+jcs`, `npp+jp+ef+jxc+jcs`, `npp+jp+ef+jxt`, `npp+jp+ep+ecc`, `npp+jp+ep+ef`, `npp+jp+ep+etm`, `npp+jp+etm`, `npp+jxc`, `npp+jxc+jcc`, `npp+jxc+jcm`, `npp+jxc+jco`, `npp+jxt`, `npp+nbn+jca`, `npp+nbn+jcs`, `npp+ncn`, `npp+ncn+jca`, `npp+ncn+jca+jxc`, `npp+ncn+jca+jxt`, `npp+ncn+jcm`, `npp+ncn+jco`, `npp+ncn+jcs`, `npp+ncn+jct`, `npp+ncn+jct+jxt`, `npp+ncn+jp+ecs`, `npp+ncn+jxc`, `npp+ncn+jxt`, `npp+ncn+xsn`, `npp+ncpa`, `npp+ncpa+jca`, `npp+ncpa+jca+jxc`, `npp+ncpa+jcj`, `npp+ncpa+jcm`, `npp+ncpa+jco`, `npp+ncpa+jcs`, `npp+ncpa+jxt`, `npp+ncpa+ncpa+jca`, `npp+ncpa+xsn+jp+ecc`, `npp+ncpa+xsn+jp+etm`, `npp+npp+jco`, `npp+xsn`, `npp+xsn+jca`, `npp+xsn+jca+jxc`, `npp+xsn+jca+jxc+jxc`, `npp+xsn+jca+jxt`, `npp+xsn+jcj`, `npp+xsn+jcm`, `npp+xsn+jco`, `npp+xsn+jcs`, `npp+xsn+jcs+jxt`, `npp+xsn+jct`, `npp+xsn+jct+jcm`, `npp+xsn+jct+jxt`, `npp+xsn+jp+ecs`, `npp+xsn+jp+ef`, `npp+xsn+jp+etm`, `npp+xsn+jxc`, `npp+xsn+jxc+jcs`, `npp+xsn+jxc+jxt`, `npp+xsn+jxt`, `npp+xsn+ncn`, `npp+xsn+xsn`, `npp+xsn+xsn+jca`, `npp+xsn+xsn+jca+jxt`, `nq`, `nq+jca`, `nq+jca+jca`, `nq+jca+jca+jxc`, `nq+jca+jcm`, `nq+jca+jxc`, `nq+jca+jxc+jcm`, `nq+jca+jxc+jxc`, `nq+jca+jxt`, `nq+jcc`, `nq+jcj`, `nq+jcm`, `nq+jco`, `nq+jcr`, `nq+jcs`, `nq+jcs+jca+jxc`, `nq+jcs+jxt`, `nq+jct`, `nq+jct+jcm`, `nq+jct+jxt`, `nq+jp+ecc`, `nq+jp+ecs`, `nq+jp+ef`, `nq+jp+ef+jcr`, `nq+jp+ef+jcr+jxc`, `nq+jp+ep+ecc`, `nq+jp+ep+ecs`, `nq+jp+ep+ef`, `nq+jp+ep+etm`, `nq+jp+ep+etn`, `nq+jp+etm`, `nq+jp+etn+jco`, `nq+jxc`, `nq+jxc+jca+jxt`, `nq+jxc+jcm`, `nq+jxc+jcs`, `nq+jxc+jp+ef`, `nq+jxc+jp+ef+jcr`, `nq+jxc+jxc`, `nq+jxc+jxc+jxt`, `nq+jxc+jxt`, `nq+jxt`, `nq+nbn`, `nq+nbn+jca`, `nq+nbn+jcm`, `nq+nbn+jp+ep+ef`, `nq+ncn`, `nq+ncn+jca`, `nq+ncn+jca+jcm`, `nq+ncn+jca+jxc`, `nq+ncn+jca+jxt`, `nq+ncn+jcc`, `nq+ncn+jcj`, `nq+ncn+jcm`, `nq+ncn+jco`, `nq+ncn+jcs`, `nq+ncn+jct`, `nq+ncn+jct+jcm`, `nq+ncn+jct+jxc`, `nq+ncn+jct+jxt`, `nq+ncn+jp+ef`, `nq+ncn+jp+ep+ef`, `nq+ncn+jp+ep+etm`, `nq+ncn+jp+etm`, `nq+ncn+jxc`, `nq+ncn+jxc+jxt`, `nq+ncn+jxt`, `nq+ncn+ncn`, `nq+ncn+ncn+jca`, `nq+ncn+ncn+jca+jxt`, `nq+ncn+ncn+jcm`, `nq+ncn+ncn+jco`, `nq+ncn+ncn+jp+etm`, `nq+ncn+ncn+jxc`, `nq+ncn+ncn+ncn`, `nq+ncn+ncn+ncn+jca`, `nq+ncn+ncn+ncn+jcs`, `nq+ncn+ncn+xsn+jxt`, `nq+ncn+ncpa+jca`, `nq+ncn+ncpa+jcs`, `nq+ncn+ncpa+jxt`, `nq+ncn+ncpa+ncn`, `nq+ncn+ncpa+ncn+jcm`, `nq+ncn+xsn`, `nq+ncn+xsn+jca`, `nq+ncn+xsn+jca+jxt`, `nq+ncn+xsn+jcm`, `nq+ncn+xsn+jco`, `nq+ncn+xsn+jcs`, `nq+ncn+xsn+jct`, `nq+ncn+xsn+jp+etm`, `nq+ncn+xsn+jxt`, `nq+ncpa`, `nq+ncpa+jca`, `nq+ncpa+jcm`, `nq+ncpa+jco`, `nq+ncpa+jxt`, `nq+ncpa+ncn+jcm`, `nq+ncpa+ncn+jp+ef`, `nq+ncpa+ncn+jp+etm`, `nq+nq`, `nq+nq+jca`, `nq+nq+jcj`, `nq+nq+jcm`, `nq+nq+jcs`, `nq+nq+jct`, `nq+nq+jxc+jcs`, `nq+nq+jxt`, `nq+nq+ncn`, `nq+nq+ncn+jca`, `nq+nq+nq+jxt`, `nq+nq+nq+nq+jcm`, `nq+xsm+ecs`, `nq+xsm+etm`, `nq+xsn`, `nq+xsn+jca`, `nq+xsn+jca+jxc`, `nq+xsn+jca+jxt`, `nq+xsn+jcj`, `nq+xsn+jcm`, `nq+xsn+jco`, `nq+xsn+jcs`, `nq+xsn+jcs+jxt`, `nq+xsn+jct`, `nq+xsn+jct+jcm`, `nq+xsn+jp+ef`, `nq+xsn+jp+ef+jcr`, `nq+xsn+jp+ep+ef`, `nq+xsn+jp+etm`, `nq+xsn+jp+etn+jco`, `nq+xsn+jxc`, `nq+xsn+jxt`, `nq+xsn+xsn`, `nq+xsn+xsn+jcj`, `nq+xsn+xsn+jcs`, `nq+xsn+xsv+ep+etm`, `nq+xsv+ecs`, `paa+ecc`, `paa+ecc+jxc`, `paa+ecc+jxt`, `paa+ecs`, `paa+ecs+etm`, `paa+ecs+jca`, `paa+ecs+jcm`, `paa+ecs+jco`, `paa+ecs+jct`, `paa+ecs+jp+ecc`, `paa+ecs+jp+ep+ef`, `paa+ecs+jxc`, `paa+ecs+jxc+jxt`, `paa+ecs+jxt`, `paa+ecx`, `paa+ecx+jco`, `paa+ecx+jcs`, `paa+ecx+jxc`, `paa+ecx+jxt`, `paa+ecx+px+ecc`, `paa+ecx+px+ecs`, `paa+ecx+px+ecx`, `paa+ecx+px+ecx+jxc`, `paa+ecx+px+ecx+px+ecc`, `paa+ecx+px+ecx+px+ecx`, `paa+ecx+px+ecx+px+ef`, `paa+ecx+px+ecx+px+ep+ef`, `paa+ecx+px+ecx+px+etm`, `paa+ecx+px+ef`, `paa+ecx+px+ef+jcr`, `paa+ecx+px+ep+ecc`, `paa+ecx+px+ep+ecs`, `paa+ecx+px+ep+ef`, `paa+ecx+px+ep+ef+jcr`, `paa+ecx+px+ep+etm`, `paa+ecx+px+ep+etn`, `paa+ecx+px+ep+etn+jco`, `paa+ecx+px+etm`, `paa+ecx+px+etn`, `paa+ecx+px+etn+jca`, `paa+ecx+px+etn+jco`, `paa+ecx+px+etn+jcs`, `paa+ecx+px+etn+jxc`, `paa+ecx+px+etn+jxt`, `paa+ef`, `paa+ef+ecc`, `paa+ef+ecs`, `paa+ef+ecs+jxc`, `paa+ef+jca`, `paa+ef+jcm`, `paa+ef+jco`, `paa+ef+jcr`, `paa+ef+jcr+jxc`, `paa+ef+jcr+jxt`, `paa+ef+jxf`, `paa+ep+ecc`, `paa+ep+ecs`, `paa+ep+ecs+jxc`, `paa+ep+ef`, `paa+ep+ef+jcr`, `paa+ep+ef+jxc`, `paa+ep+ef+jxf`, `paa+ep+ef+jxt`, `paa+ep+ep+ecs`, `paa+ep+ep+ef`, `paa+ep+ep+etm`, `paa+ep+etm`, `paa+ep+etn`, `paa+ep+etn+jca`, `paa+ep+etn+jca+jxc`, `paa+ep+etn+jco`, `paa+ep+etn+jcs`, `paa+ep+etn+jxt`, `paa+etm`, `paa+etn`, `paa+etn+jca`, `paa+etn+jca+jxc`, `paa+etn+jca+jxt`, `paa+etn+jcc`, `paa+etn+jcj`, `paa+etn+jcm`, `paa+etn+jco`, `paa+etn+jcs`, `paa+etn+jct`, `paa+etn+jp+ecc`, `paa+etn+jp+ef`, `paa+etn+jp+ep+ecs`, `paa+etn+jp+ep+ef`, `paa+etn+jxc`, `paa+etn+jxt`, `paa+jxt`, `pad+ecc`, `pad+ecc+jxt`, `pad+ecs`, `pad+ecs+jxc`, `pad+ecs+jxt`, `pad+ecx`, `pad+ecx+jcs`, `pad+ecx+jxc`, `pad+ecx+jxt`, `pad+ecx+px+ecs`, `pad+ecx+px+ecx+px+ecc+jxt`, `pad+ef`, `pad+ef+jcr`, `pad+ef+jcr+jxt`, `pad+ef+jxf`, `pad+ef+jxt`, `pad+ep+ecc`, `pad+ep+ecs`, `pad+ep+ef`, `pad+ep+ef+jco`, `pad+ep+etm`, `pad+etm`, `pad+etn`, `pad+etn+jxt`, `pvd+ecc+jxc`, `pvd+ecs`, `pvd+ecs+jp+ecs`, `pvd+ecs+jxc`, `pvd+ecs+jxt`, `pvd+ecx`, `pvd+ep+ef`, `pvd+ep+etm`, `pvd+etm`, `pvd+etn`, `pvd+etn+jca`, `pvd+etn+jca+jxc`, `pvg+ecc`, `pvg+ecc+jxc`, `pvg+ecc+jxt`, `pvg+ecs`, `pvg+ecs+ecs`, `pvg+ecs+jca`, `pvg+ecs+jca+jxt`, `pvg+ecs+jcc`, `pvg+ecs+jcm`, `pvg+ecs+jco`, `pvg+ecs+jcs`, `pvg+ecs+jct`, `pvg+ecs+jp+ecs`, `pvg+ecs+jp+ef`, `pvg+ecs+jp+ep+ecs`, `pvg+ecs+jp+ep+ef`, `pvg+ecs+jp+ep+ef+jcr`, `pvg+ecs+jxc`, `pvg+ecs+jxc+jcc`, `pvg+ecs+jxc+jp+ef`, `pvg+ecs+jxc+jp+ep+ef`, `pvg+ecs+jxt`, `pvg+ecx`, `pvg+ecx+jco`, `pvg+ecx+jxc`, `pvg+ecx+jxt`, `pvg+ecx+jxt+px+ep+ef`, `pvg+ecx+px+ecc`, `pvg+ecx+px+ecc+jxc`, `pvg+ecx+px+ecc+jxt`, `pvg+ecx+px+ecs`, `pvg+ecx+px+ecs+jxc`, `pvg+ecx+px+ecs+jxt`, `pvg+ecx+px+ecx`, `pvg+ecx+px+ecx+jco`, `pvg+ecx+px+ecx+jxc`, `pvg+ecx+px+ecx+jxt`, `pvg+ecx+px+ecx+px+ecc`, `pvg+ecx+px+ecx+px+ecs`, `pvg+ecx+px+ecx+px+ecs+jxt`, `pvg+ecx+px+ecx+px+ecx`, `pvg+ecx+px+ecx+px+ecx+px+ecc`, `pvg+ecx+px+ecx+px+ef`, `pvg+ecx+px+ecx+px+ep+ecc`, `pvg+ecx+px+ecx+px+ep+ef`, `pvg+ecx+px+ecx+px+ep+etm`, `pvg+ecx+px+ecx+px+ep+etn+jco`, `pvg+ecx+px+ecx+px+etm`, `pvg+ecx+px+ecx+px+etn`, `pvg+ecx+px+ecx+px+etn+jca`, `pvg+ecx+px+ef`, `pvg+ecx+px+ef+jca`, `pvg+ecx+px+ef+jcm`, `pvg+ecx+px+ef+jcr`, `pvg+ecx+px+ep+ecc`, `pvg+ecx+px+ep+ecs`, `pvg+ecx+px+ep+ecs+jxc`, `pvg+ecx+px+ep+ef`, `pvg+ecx+px+ep+ef+jcm`, `pvg+ecx+px+ep+ef+jcr`, `pvg+ecx+px+ep+ef+jxf`, `pvg+ecx+px+ep+ep+ecs`, `pvg+ecx+px+ep+etm`, `pvg+ecx+px+ep+etn`, `pvg+ecx+px+ep+etn+jca`, `pvg+ecx+px+ep+etn+jca+jxc`, `pvg+ecx+px+ep+etn+jco`, `pvg+ecx+px+etm`, `pvg+ecx+px+etn`, `pvg+ecx+px+etn+jca`, `pvg+ecx+px+etn+jca+jxc`, `pvg+ecx+px+etn+jca+jxt`, `pvg+ecx+px+etn+jco`, `pvg+ecx+px+etn+jcs`, `pvg+ecx+px+etn+jct`, `pvg+ecx+px+etn+jxc`, `pvg+ecx+px+etn+jxc+jxt`, `pvg+ecx+px+etn+jxt`, `pvg+ef`, `pvg+ef+jca`, `pvg+ef+jcm`, `pvg+ef+jco`, `pvg+ef+jcr`, `pvg+ef+jcr+jxc`, `pvg+ef+jcr+jxt`, `pvg+ef+jcs`, `pvg+ef+jp+ef+jcr`, `pvg+ef+jp+etm`, `pvg+ef+jxc`, `pvg+ef+jxf`, `pvg+ef+jxt`, `pvg+ep+ecc`, `pvg+ep+ecc+jxt`, `pvg+ep+ecs`, `pvg+ep+ecs+jca+jxt`, `pvg+ep+ecs+jco`, `pvg+ep+ecs+jxc`, `pvg+ep+ecs+jxt`, `pvg+ep+ecx`, `pvg+ep+ecx+px+ef`, `pvg+ep+ef`, `pvg+ep+ef+jca`, `pvg+ep+ef+jcm`, `pvg+ep+ef+jco`, `pvg+ep+ef+jcr`, `pvg+ep+ef+jcr+jxc`, `pvg+ep+ef+jcr+jxt`, `pvg+ep+ef+jct`, `pvg+ep+ef+jxc`, `pvg+ep+ef+jxf`, `pvg+ep+ef+jxt`, `pvg+ep+ep+ef`, `pvg+ep+ep+ef+jco`, `pvg+ep+ep+ef+jxf`, `pvg+ep+etm`, `pvg+ep+etn`, `pvg+ep+etn+jca`, `pvg+ep+etn+jca+jxc`, `pvg+ep+etn+jca+jxt`, `pvg+ep+etn+jco`, `pvg+ep+etn+jcs`, `pvg+ep+etn+jxt`, `pvg+etm`, `pvg+etn`, `pvg+etn+jca`, `pvg+etn+jca+jxc`, `pvg+etn+jca+jxt`, `pvg+etn+jcc`, `pvg+etn+jcj`, `pvg+etn+jcm`, `pvg+etn+jco`, `pvg+etn+jcr`, `pvg+etn+jcs`, `pvg+etn+jct`, `pvg+etn+jct+jxt`, `pvg+etn+jp+ecc`, `pvg+etn+jp+ecs`, `pvg+etn+jp+ef`, `pvg+etn+jp+ef+jcr`, `pvg+etn+jp+ef+jcs`, `pvg+etn+jp+ep+ef`, `pvg+etn+jp+ep+ef+jcr`, `pvg+etn+jp+etm`, `pvg+etn+jxc`, `pvg+etn+jxc+jca+jxt`, `pvg+etn+jxc+jcm`, `pvg+etn+jxc+jco`, `pvg+etn+jxc+jcs`, `pvg+etn+jxc+jxt`, `pvg+etn+jxt`, `pvg+etn+xsm+ecs`, `pvg+etn+xsn+jcm`, `px+ecc`, `px+ecc+jxc`, `px+ecc+jxc+jp+ef`, `px+ecc+jxt`, `px+ecs`, `px+ecs+jca`, `px+ecs+jcc`, `px+ecs+jcj`, `px+ecs+jcm`, `px+ecs+jco`, `px+ecs+jp+ep+ef`, `px+ecs+jxc`, `px+ecs+jxt`, `px+ecx`, `px+ecx+jxc`, `px+ecx+jxt`, `px+ecx+px+ecs`, `px+ecx+px+ecx`, `px+ecx+px+ef`, `px+ecx+px+ef+jcr`, `px+ecx+px+ep+ef`, `px+ecx+px+etm`, `px+ecx+px+etn+jca`, `px+ef`, `px+ef+etm`, `px+ef+jca`, `px+ef+jca+jxc`, `px+ef+jcj`, `px+ef+jcm`, `px+ef+jco`, `px+ef+jcr`, `px+ef+jcr+jxc`, `px+ef+jcs`, `px+ef+jp+etm`, `px+ef+jxc`, `px+ef+jxf`, `px+ef+jxt`, `px+ep+ecc`, `px+ep+ecs`, `px+ep+ecs+jxc`, `px+ep+ecs+jxt`, `px+ep+ecx`, `px+ep+ef`, `px+ep+ef+jca`, `px+ep+ef+jco`, `px+ep+ef+jcr`, `px+ep+ef+jcr+jxc`, `px+ep+ef+jxf`, `px+ep+ep+ef`, `px+ep+ep+ef+jxf`, `px+ep+etm`, `px+ep+etn`, `px+ep+etn+jca`, `px+ep+etn+jca+jxc`, `px+ep+etn+jco`, `px+ep+etn+jcs`, `px+ep+etn+jxc`, `px+ep+etn+jxt`, `px+etm`, `px+etn`, `px+etn+jca`, `px+etn+jca+jxc`, `px+etn+jca+jxt`, `px+etn+jco`, `px+etn+jcs`, `px+etn+jct`, `px+etn+jxc`, `px+etn+jxc+jxt`, `px+etn+jxt`, `sf`, `sl`, `sp`, `sr`, `su`, `su+jca`, `su+jcm`, `xp+nbn`, `xp+nbu`, `xp+ncn`, `xp+ncn+jca`, `xp+ncn+jcm`, `xp+ncn+jco`, `xp+ncn+jcs`, `xp+ncn+jp+ef`, `xp+ncn+jp+ep+ef`, `xp+ncn+jxt`, `xp+ncn+ncn+jca`, `xp+ncn+ncn+jcm`, `xp+ncn+ncn+jco`, `xp+ncn+ncpa+jco`, `xp+ncn+xsn`, `xp+ncn+xsn+jca`, `xp+ncn+xsn+jcm`, `xp+ncn+xsn+jp+ef`, `xp+ncn+xsn+jp+etm`, `xp+ncpa`, `xp+ncpa+jca`, `xp+ncpa+jcm`, `xp+ncpa+jco`, `xp+ncpa+ncn+jcm`, `xp+ncpa+ncn+jco`, `xp+ncpa+ncpa+jco`, `xp+ncpa+xsn`, `xp+ncpa+xsn+jp+etm`, `xp+ncpa+xsv+ecc`, `xp+ncpa+xsv+ecs`, `xp+ncpa+xsv+ecx`, `xp+ncpa+xsv+ef`, `xp+ncpa+xsv+ef+jcr`, `xp+ncpa+xsv+ep+ef`, `xp+ncpa+xsv+etm`, `xp+ncpa+xsv+etn+jca`, `xp+ncps`, `xp+ncps+xsm+ecs`, `xp+ncps+xsm+ecx`, `xp+ncps+xsm+ef`, `xp+ncps+xsm+ep+ef`, `xp+ncps+xsm+etm`, `xp+ncps+xsn`, `xp+nnc`, `xp+nnc+jcm`, `xp+nnc+nbn`, `xp+nnc+nbu`, `xp+nnc+nbu+jcs`, `xp+nnc+ncn`, `xp+nnc+ncn+jca`, `xp+nnc+ncn+jcm`, `xp+nnc+ncn+jcs`, `xp+nnc+ncn+jp+ef+jcr`, `xp+nno`, `xp+nno+jcm`, `xp+nno+nbn+jca`, `xp+nno+nbu`, `xp+nno+nbu+jcs`, `xp+nno+ncn`, `xp+nno+ncn+jca`, `xp+nno+ncn+jcs`, `xp+nno+ncn+jxt`, `xp+nq`, `xp+nq+ncn+jca`, `xp+nq+ncpa`, `xp+nq+ncpa+jco`, `xp+nq+ncpa+jp+etm`, `xsm+etm`, `xsn`, `xsn+jca`, `xsn+jca+jxt`, `xsn+jco`, `xsn+jcs`, `xsn+jp+ef`, `xsn+jp+ep+ef`, `xsn+jxc+jca+jxt`, `xsn+jxc+jcs`, `xsn+jxt`, `xsv+ecc`, `xsv+ecs`, `xsv+ecx+px+ep+ef`, `xsv+ep+ecx`, `xsv+etm` |
| **`morphologizer`** | `POS=CCONJ`, `POS=ADV`, `POS=SCONJ`, `POS=DET`, `POS=NOUN`, `POS=VERB`, `POS=ADJ`, `POS=PUNCT`, `POS=AUX`, `POS=PRON`, `POS=PROPN`, `POS=NUM`, `POS=INTJ`, `POS=PART`, `POS=X`, `POS=ADP`, `POS=SYM` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `dislocated`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `0`, `3`, `5`, `7`, `9`, `11`, `12`, `16`, `18`, `20`, `22`, `25`, `28`, `31`, `34`, `35`, `36`, `39`, `40`, `43`, `45`, `47`, `48`, `51`, `54`, `56`, `58`, `60`, `61`, `63`, `65`, `67`, `69`, `71`, `73`, `75`, `76`, `78`, `79`, `82`, `85`, `87`, `89`, `92`, `95`, `97`, `99`, `101`, `104`, `106`, `109`, `112`, `114`, `116`, `119`, `121`, `122`, `124`, `126`, `127`, `128`, `130`, `133`, `135`, `137`, `140`, `142`, `145`, `147`, `148`, `150`, `151`, `152`, `155`, `156`, `158`, `161`, `162`, `164`, `167`, `169`, `172`, `174`, `176`, `177`, `179`, `182`, `184`, `186`, `188`, `191`, `192`, `194`, `196`, `199`, `202`, `203`, `173`, `115`, `205`, `207`, `210`, `213`, `216`, `218`, `221`, `146`, `223`, `225`, `227`, `229`, `230`, `231`, `232`, `234`, `236`, `238`, `239`, `242`, `244`, `246`, `248`, `251`, `253`, `255`, `256`, `259`, `261`, `264`, `265`, `268`, `270`, `272`, `274`, `276`, `278`, `279`, `282`, `285`, `287`, `289`, `293`, `295`, `297`, `300`, `302`, `304`, `307`, `309`, `310`, `313`, `315`, `226`, `318`, `319`, `321`, `323`, `325`, `327`, `329`, `332`, `334`, `335`, `337`, `149`, `339`, `340`, `342`, `344`, `346`, `348`, `349`, `350`, `352`, `354`, `356`, `358`, `360`, `361`, `363`, `365`, `369`, `370`, `372`, `374`, `376`, `21`, `15`, `377`, `379`, `382`, `385`, `387`, `388`, `254`, `390`, `393`, `395`, `397`, `399`, `401`, `403`, `404`, `405`, `407`, `408`, `411`, `414`, `417`, `418`, `421`, `422`, `424`, `427`, `429`, `431`, `435`, `437`, `439`, `440`, `442`, `443`, `444`, `447`, `449`, `451`, `389`, `454`, `455`, `457`, `460`, `461`, `463`, `466`, `468`, `471`, `473`, `476`, `477`, `479`, `482`, `296`, `485`, `487`, `490`, `492`, `493`, `495`, `497`, `500`, `502`, `504`, `505`, `507`, `510`, `511`, `514`, `267`, `516`, `520`, `472`, `523`, `525`, `526`, `527`, `530`, `532`, `462`, `533`, `534`, `535`, `537`, `540`, `541`, `465`, `543`, `545`, `546`, `547`, `550`, `551`, `552`, `553`, `555`, `556`, `72`, `558`, `560`, `562`, `563`, `564`, `567`, `568`, `571`, `574`, `577`, `579`, `581`, `582`, `584`, `587`, `589`, `591`, `594`, `595`, `597`, `600`, `603`, `606`, `608`, `610`, `611`, `613`, `614`, `616`, `617`, `620`, `10`, `623`, `626`, `629`, `632`, `633`, `635`, `637`, `638`, `640`, `642`, `644`, `645`, `647`, `648`, `651`, `652`, `653`, `655`, `657`, `659`, `660`, `664`, `666`, `667`, `669`, `672`, `674`, `675`, `676`, `678`, `679`, `680`, `683`, `684`, `687`, `689`, `690`, `692`, `694`, `697`, `699`, `702`, `703`, `706`, `707`, `710`, `713`, `715`, `717`, `719`, `721`, `723`, `725`, `728`, `730`, `733`, `735`, `738`, `740`, `743`, `744`, `649`, `747`, `749`, `753`, `756`, `757`, `759`, `761`, `764`, `767`, `769`, `772`, `774`, `777`, `780`, `783`, `785`, `787`, `789`, `792`, `794`, `797`, `799`, `800`, `802`, `805`, `806`, `808`, `809`, `811`, `812`, `813`, `815`, `817`, `819`, `820`, `59`, `822`, `824`, `827`, `829`, `831`, `618`, `832`, `834`, `836`, `838`, `724`, `841`, `55`, `842`, `844`, `846`, `847`, `850`, `852`, `855`, `857`, `859`, `861`, `863`, `865`, `868`, `869`, `871`, `873`, `874`, `877`, `880`, `884`, `887`, `890`, `891`, `892`, `893`, `896`, `898`, `901`, `351`, `904`, `906`, `908`, `911`, `913`, `915`, `650`, `918`, `920`, `830`, `921`, `923`, `924`, `926`, `927`, `930`, `931`, `934`, `937`, `938`, `940`, `941`, `942`, `945`, `947`, `949`, `952`, `954`, `957`, `960`, `963`, `965`, `967`, `970`, `972`, `974`, `977`, `980`, `981`, `983`, `985`, `986`, `988`, `991`, `994`, `997`, `999`, `1000`, `1002`, `1005`, `1006`, `1007`, `1010`, `125`, `1013`, `1016`, `1017`, `1019`, `1020`, `1024`, `1026`, `1028`, `1030`, `1032`, `1034`, `1036`, `1038`, `1040`, `1041`, `1044`, `1045`, `1048`, `415`, `1051`, `1053`, `1055`, `1056`, `1058`, `1061`, `1063`, `1065`, `1067`, `1068`, `1069`, `1070`, `1074`, `946`, `1077`, `1079`, `1081`, `1083`, `1086`, `1088`, `1089`, `1092`, `936`, `1096`, `1098`, `1101`, `1104`, `1106`, `1108`, `1110`, `1112`, `1114`, `1116`, `1118`, 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</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 100.00 |
| `TOKEN_P` | 100.00 |
| `TOKEN_R` | 100.00 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 100.00 |
| `SENTS_P` | 100.00 |
| `SENTS_R` | 100.00 |
| `TAG_ACC` | 88.93 |
| `POS_ACC` | 96.52 |
| `MORPH_ACC` | 100.00 |
| `MORPH_PER_FEAT` | 0.00 |
| `DEP_UAS` | 89.48 |
| `DEP_LAS` | 87.18 |
| `LEMMA_ACC` | 94.51 |
|
015ec3d1b918c9f3dec91e217360fc94
|
reaverlee/xlm-roberta-base-finetuned-panx-fr
|
reaverlee
|
xlm-roberta
| 10 | 7 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null |
['xtreme']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,313 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2761
- F1: 0.8350
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5826 | 1.0 | 191 | 0.3409 | 0.7713 |
| 0.2674 | 2.0 | 382 | 0.2889 | 0.8314 |
| 0.1738 | 3.0 | 573 | 0.2761 | 0.8350 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.10.0
- Datasets 2.7.1
- Tokenizers 0.12.1
|
56677835d74d378142d9b3d4aef35111
|
keras-io/sentiment-analysis
|
keras-io
|
distilbert
| 14 | 10 |
transformers
| 0 |
text-classification
| false | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,462 | false |
<!-- 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. -->
# keras-io/sentiment-analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6865
- Validation Loss: 0.7002
- Train Accuracy: 0.4908
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 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 | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6865 | 0.6975 | 0.4908 | 0 |
| 0.6865 | 0.6973 | 0.4908 | 1 |
| 0.6865 | 0.6976 | 0.4908 | 2 |
| 0.6865 | 0.6975 | 0.4908 | 3 |
| 0.6865 | 0.7002 | 0.4908 | 4 |
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.7.0
- Datasets 1.18.2
- Tokenizers 0.11.0
|
486ea8880543e09963dd7f266eae820c
|
duja1/m123ugg
|
duja1
| null | 31 | 2 |
diffusers
| 1 |
text-to-image
| false | false | false |
creativeml-openrail-m
| null | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['text-to-image']
| false | true | true | 1,477 | false |
### m123ugg Dreambooth model trained by duja1 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
m123ugg (use that on your prompt)
|
12e11adc86c6341671d22625efaff7cf
|
ktangri/gpt-neo-demo
|
ktangri
|
gpt_neo
| 9 | 7 |
transformers
| 1 |
text-generation
| true | false | false |
apache-2.0
|
['en']
|
['the Pile']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['text generation', 'pytorch', 'the Pile', 'causal-lm']
| false | true | true | 4,636 | false |
# GPT-Neo 2.7B (By EleutherAI)
## Model Description
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.
## Training data
GPT-Neo 2.7B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model.
## Training procedure
This model was trained for 420 billion tokens over 400,000 steps. It was trained as a masked autoregressive language model, using cross-entropy loss.
## Intended Use and Limitations
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B')
>>> generator("EleutherAI has", do_sample=True, min_length=50)
[{'generated_text': 'EleutherAI has made a commitment to create new software packages for each of its major clients and has'}]
```
### Limitations and Biases
GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work.
GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.
As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
## Eval results
All evaluations were done using our [evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness). Some results for GPT-2 and GPT-3 are inconsistent with the values reported in the respective papers. We are currently looking into why, and would greatly appreciate feedback and further testing of our eval harness. If you would like to contribute evaluations you have done, please reach out on our [Discord](https://discord.gg/vtRgjbM).
### Linguistic Reasoning
| Model and Size | Pile BPB | Pile PPL | Wikitext PPL | Lambada PPL | Lambada Acc | Winogrande | Hellaswag |
| ---------------- | ---------- | ---------- | ------------- | ----------- | ----------- | ---------- | ----------- |
| GPT-Neo 1.3B | 0.7527 | 6.159 | 13.10 | 7.498 | 57.23% | 55.01% | 38.66% |
| GPT-2 1.5B | 1.0468 | ----- | 17.48 | 10.634 | 51.21% | 59.40% | 40.03% |
| **GPT-Neo 2.7B** | **0.7165** | **5.646** | **11.39** | **5.626** | **62.22%** | **56.50%** | **42.73%** |
| GPT-3 Ada | 0.9631 | ----- | ----- | 9.954 | 51.60% | 52.90% | 35.93% |
### Physical and Scientific Reasoning
| Model and Size | MathQA | PubMedQA | Piqa |
| ---------------- | ---------- | ---------- | ----------- |
| GPT-Neo 1.3B | 24.05% | 54.40% | 71.11% |
| GPT-2 1.5B | 23.64% | 58.33% | 70.78% |
| **GPT-Neo 2.7B** | **24.72%** | **57.54%** | **72.14%** |
| GPT-3 Ada | 24.29% | 52.80% | 68.88% |
### Down-Stream Applications
TBD
### BibTeX entry and citation info
To cite this model, use
```bibtex
@article{gao2020pile,
title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
To cite the codebase that this model was trained with, use
```bibtex
@software{gpt-neo,
author = {Black, Sid and Gao, Leo and Wang, Phil and Leahy, Connor and Biderman, Stella},
title = {{GPT-Neo}: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow},
url = {http://github.com/eleutherai/gpt-neo},
version = {1.0},
year = {2021},
}
```
|
33c35bd445a37b2e82dbfe95e171cc92
|
p1atdev/pvc
|
p1atdev
| null | 30 | 216 |
diffusers
| 40 |
text-to-image
| false | false | false |
creativeml-openrail-m
|
['en']
|
['p1atdev/pvc']
| null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
['stable-diffusion', 'text-to-image', 'safetensors']
| false | true | true | 6,543 | false |
# PVC v2
```
masterpiece, best quality, high quality, 1girl, cat ears, silver, blue, frills, bow, looking at viewer, ultra detailed
Negative prompt: nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7
```
This model is a latent diffusion model finetuned on [Waifu Diffusion v1.4 epoch 2](https://huggingface.co/hakurei/waifu-diffusion-v1-4) with [PVC figure images](https://huggingface.co/datasets/p1atdev/pvc) using LoRA method.
You can use Danbooru tags to generate images.
## Model links
- [**pvc-v2.safetensors**](https://huggingface.co/p1atdev/pvc/resolve/main/pvc-v2.safetensors)
- [**pvc-v2.ckpt**](https://huggingface.co/p1atdev/pvc/resolve/main/pvc-v2.ckpt)
- [pvc-v2.yaml](https://huggingface.co/p1atdev/pvc/blob/main/pvc-v2.yaml) (needed if you want to use the model in AUTOMATIC1111's Web UI)
- [pvc-v2-lora.pt](https://huggingface.co/p1atdev/pvc/resolve/main/pvc-v2-lora.pt) (maybe only works with kohya's [sd-scripts](https://huggingface.co/p1atdev/pvc/resolve/main/pvc-v1-lora.pt) or [webui extension](https://github.com/kohya-ss/sd-webui-additional-networks))
- [Waifu Diffusion 1.4 VAE](https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt) (Recommended)
## Prompt guide
It is recommended to add the quality tags **"masterpiece, best quality"** at the beginning of the prompt when using this model, which is a derivative of the WD.
**Recommended negative prompt**
```
nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digits, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry
```
## Samples
```
masterpiece, best quality, 1girl, green hair, sweater, beanie, turtleneck, looking at viewer, night,
Negative prompt: nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7
```
---
```
masterpiece, best quality, high quality, 1girl, bob cut, cape, belt, gloves, looking at viewer, ultra detailed
Negative prompt: nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7
```
---
```
masterpiece, best quality, 1girl, annoyed, black hair, floating hair, gothic lolita, scythe, looking at viewer,
Negative prompt: nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7
```
## Training information
```
--train_batch_size 1 --resolution 768,768 --learning_rate 1e-4 --max_train_steps 21100 --use_8bit_adam --xformers --mixed_precision fp16 --save_every_n_epochs 1 --network_module networks.lora --v2 --enable_bucket --max_token_length 225 --network_dim 32 --text_encoder_lr 5e-5 --lr_scheduler cosine_with_restarts --lr_warmup_steps 200
```
# PVC v1
This model is a latent diffusion model finetuned on [Waifu Diffusion v1.4 epoch 2](https://huggingface.co/hakurei/waifu-diffusion-v1-4) with [PVC figure images](https://huggingface.co/datasets/p1atdev/pvc) using LoRA method.
You can use Danbooru tags to generate images.
## Model links
- [**pvc-v1.safetensors**](https://huggingface.co/p1atdev/pvc/resolve/main/pvc-v1.safetensors)
- [**pvc-v1.ckpt**](https://huggingface.co/p1atdev/pvc/resolve/main/pvc-v1.ckpt)
- [pvc-v1.yaml](https://huggingface.co/p1atdev/pvc/blob/main/pvc-v1.yaml) (needed if you want to use the model in AUTOMATIC1111's Web UI)
- [pvc-v1-lora.pt](https://huggingface.co/p1atdev/pvc/resolve/main/pvc-v1-lora.pt) (maybe only works with kohya's [sd-scripts](https://huggingface.co/p1atdev/pvc/resolve/main/pvc-v1-lora.pt) or [webui extension](https://github.com/kohya-ss/sd-webui-additional-networks))
## Samples
```
masterpiece, best quality, 1girl, green hair, sweater, beanie, turtleneck, looking at viewer,
Negative prompt: nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7
```
---
```
masterpiece, best quality, 1girl, smile, black hair, long hair, school uniform, navel, pleated skirt, leaning forward, looking at viewer, wind
Negative prompt: nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry,
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 10
```
---
```
masterpiece, best quality, 1girl, fascinator, hat, victorian, gothic, dress, frills, looking at viewer,
Negative prompt: worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry, censored, hetero
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7
```
## Training information
```
--train_batch_size 1 --resolution 768,768 --learning_rate 1e-4 --max_train_steps 17740 --use_8bit_adam --xformers --mixed_precision fp16 --save_every_n_epochs 1 --network_module networks.lora --v2 --enable_bucket --max_token_length 225 --network_dim 16 --text_encoder_lr 5e-5
```
|
f29ff38a5321379ef60652050154e7e6
|
faraahahaha/fine-tune-Wav2Vec2-XLS-R-300M-Indonesia
|
faraahahaha
|
wav2vec2
| 21 | 6 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null |
['common_voice_10_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,216 | false |
<!-- 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-tune-Wav2Vec2-XLS-R-300M-Indonesia
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_10_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7924
- Wer: 0.4307
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.8421 | 6.33 | 500 | 0.7758 | 0.7386 |
| 0.1875 | 12.66 | 1000 | 0.6039 | 0.5712 |
| 0.1138 | 18.99 | 1500 | 0.6463 | 0.5170 |
| 0.0774 | 25.32 | 2000 | 0.6814 | 0.4914 |
| 0.0655 | 31.65 | 2500 | 0.7010 | 0.4778 |
| 0.0536 | 37.97 | 3000 | 0.7267 | 0.4761 |
| 0.0471 | 44.3 | 3500 | 0.7552 | 0.4693 |
| 0.0402 | 50.63 | 4000 | 0.7474 | 0.4612 |
| 0.0366 | 56.96 | 4500 | 0.7705 | 0.4560 |
| 0.0324 | 63.29 | 5000 | 0.7253 | 0.4472 |
| 0.0293 | 69.62 | 5500 | 0.7660 | 0.4441 |
| 0.0254 | 75.95 | 6000 | 0.7816 | 0.4398 |
| 0.0226 | 82.28 | 6500 | 0.7988 | 0.4378 |
| 0.0202 | 88.61 | 7000 | 0.8009 | 0.4305 |
| 0.0185 | 94.94 | 7500 | 0.7924 | 0.4307 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.10.0+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
30770c0f2b229f2910a33a79108e0758
|
gayanin/t5-small-med-term-conditional-masking-0
|
gayanin
|
t5
| 12 | 2 |
transformers
| 0 |
text2text-generation
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,381 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-med-term-conditional-masking-0
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6688
- Rouge2 Precision: 0.694
- Rouge2 Recall: 0.4781
- Rouge2 Fmeasure: 0.5479
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:------:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.9525 | 1.0 | 13915 | 0.8148 | 0.6657 | 0.4581 | 0.5252 |
| 0.8541 | 2.0 | 27830 | 0.7562 | 0.6779 | 0.4694 | 0.5371 |
| 0.8183 | 3.0 | 41745 | 0.7268 | 0.6827 | 0.4722 | 0.5405 |
| 0.8033 | 4.0 | 55660 | 0.7074 | 0.6861 | 0.4729 | 0.5419 |
| 0.7727 | 5.0 | 69575 | 0.6934 | 0.6872 | 0.4726 | 0.5419 |
| 0.7704 | 6.0 | 83490 | 0.6832 | 0.6901 | 0.4742 | 0.544 |
| 0.7485 | 7.0 | 97405 | 0.6771 | 0.6926 | 0.4772 | 0.5469 |
| 0.7528 | 8.0 | 111320 | 0.6722 | 0.6934 | 0.4782 | 0.5478 |
| 0.7535 | 9.0 | 125235 | 0.6696 | 0.6944 | 0.4782 | 0.5481 |
| 0.7444 | 10.0 | 139150 | 0.6688 | 0.694 | 0.4781 | 0.5479 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
39f61c9f080a811ff6ef14a56d6201d4
|
Evelyn18/distilbert-base-uncased-becasv2-5
|
Evelyn18
|
distilbert
| 13 | 7 |
transformers
| 0 |
question-answering
| true | false | false |
apache-2.0
| null |
['becasv2']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,888 | false |
<!-- 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-becasv2-5
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0409
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 6 | 5.3475 |
| No log | 2.0 | 12 | 4.6045 |
| No log | 3.0 | 18 | 4.1832 |
| No log | 4.0 | 24 | 3.8223 |
| No log | 5.0 | 30 | 3.4798 |
| No log | 6.0 | 36 | 3.2615 |
| No log | 7.0 | 42 | 3.1414 |
| No log | 8.0 | 48 | 3.1067 |
| No log | 9.0 | 54 | 2.9950 |
| No log | 10.0 | 60 | 2.9482 |
| No log | 11.0 | 66 | 2.9536 |
| No log | 12.0 | 72 | 3.0180 |
| No log | 13.0 | 78 | 3.0515 |
| No log | 14.0 | 84 | 3.0444 |
| No log | 15.0 | 90 | 3.0409 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
750645943cc0be8408e4193358e5f4cd
|
pyf98/wsj_e_branchformer
|
pyf98
| null | 33 | 1 |
espnet
| 0 |
automatic-speech-recognition
| false | false | false |
cc-by-4.0
|
['en']
|
['wsj']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['espnet', 'audio', 'automatic-speech-recognition']
| false | true | true | 7,545 | false |
## ESPnet2 ASR model
### `pyf98/wsj_e_branchformer`
This model was trained by Yifan Peng using wsj recipe in [espnet](https://github.com/espnet/espnet/).
References:
- [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077)
- [Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding (ICML 2022)](https://proceedings.mlr.press/v162/peng22a.html)
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 0aa06d0535323aabc1d8b057f8769da377f4d9ff
pip install -e .
cd egs2/wsj/asr1
./run.sh --skip_data_prep false --skip_train true --download_model pyf98/wsj_e_branchformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Dec 28 00:12:25 EST 2022`
- python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]`
- espnet version: `espnet 202211`
- pytorch version: `pytorch 1.12.1`
- Git hash: `0aa06d0535323aabc1d8b057f8769da377f4d9ff`
- Commit date: `Tue Dec 27 15:08:25 2022 -0600`
## asr_train_asr_e_branchformer_e12_mlp1024_linear1024_raw_en_char
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/test_dev93|503|8234|94.3|4.9|0.8|0.7|6.5|51.9|
|decode_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/test_eval92|333|5643|96.4|3.3|0.3|0.7|4.3|38.1|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/test_dev93|503|48634|97.8|1.0|1.1|0.6|2.8|58.3|
|decode_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/test_eval92|333|33341|98.7|0.7|0.7|0.5|1.8|46.5|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_e_branchformer_e12_mlp1024_linear1024.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_e_branchformer_e12_mlp1024_linear1024_raw_en_char
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: 100
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 128
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_char/train/speech_shape
- exp/asr_stats_raw_en_char/train/text_shape.char
valid_shape_file:
- exp/asr_stats_raw_en_char/valid/speech_shape
- exp/asr_stats_raw_en_char/valid/text_shape.char
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_si284/wav.scp
- speech
- sound
- - dump/raw/train_si284/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/test_dev93/wav.scp
- speech
- sound
- - dump/raw/test_dev93/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.005
scheduler: warmuplr
scheduler_conf:
warmup_steps: 30000
token_list:
- <blank>
- <unk>
- <space>
- E
- T
- A
- N
- I
- O
- S
- R
- H
- L
- D
- C
- U
- M
- P
- F
- G
- Y
- W
- B
- V
- K
- .
- X
- ''''
- J
- Q
- Z
- <NOISE>
- ','
- '-'
- '"'
- '*'
- ':'
- (
- )
- '?'
- '!'
- '&'
- ;
- '1'
- '2'
- '0'
- /
- $
- '{'
- '}'
- '8'
- '9'
- '6'
- '3'
- '5'
- '7'
- '4'
- '~'
- '`'
- _
- <*IN*>
- <*MR.*>
- \
- ^
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
joint_net_conf: null
use_preprocessor: true
token_type: char
bpemodel: null
non_linguistic_symbols: data/nlsyms.txt
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
frontend: default
frontend_conf:
fs: 16k
specaug: null
specaug_conf: {}
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_en_char/train/feats_stats.npz
model: espnet
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
preencoder: null
preencoder_conf: {}
encoder: e_branchformer
encoder_conf:
output_size: 256
attention_heads: 4
attention_layer_type: rel_selfattn
pos_enc_layer_type: rel_pos
rel_pos_type: latest
cgmlp_linear_units: 1024
cgmlp_conv_kernel: 31
use_linear_after_conv: false
gate_activation: identity
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
layer_drop_rate: 0.0
linear_units: 1024
positionwise_layer_type: linear
use_ffn: true
macaron_ffn: true
merge_conv_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202211'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
6a536b3a8678ddd5070b127c2cee7bdd
|
muhtasham/finetuned-base_mini
|
muhtasham
|
bert
| 10 | 64 |
transformers
| 1 |
text-classification
| true | false | false |
apache-2.0
| null |
['imdb']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,629 | false |
<!-- 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. -->
# finetuned-base_mini
This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3938
- Accuracy: 0.9076
- F1: 0.9516
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.354 | 2.55 | 500 | 0.2300 | 0.9116 | 0.9538 |
| 0.2086 | 5.1 | 1000 | 0.3182 | 0.8815 | 0.9370 |
| 0.1401 | 7.65 | 1500 | 0.2160 | 0.9241 | 0.9605 |
| 0.0902 | 10.2 | 2000 | 0.4684 | 0.8722 | 0.9317 |
| 0.0654 | 12.76 | 2500 | 0.4885 | 0.8747 | 0.9332 |
| 0.043 | 15.31 | 3000 | 0.3938 | 0.9076 | 0.9516 |
### Framework versions
- Transformers 4.25.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
ea40ebafbcb0d4731cca2575a29e6080
|
arjuntheprogrammer/distilbert-base-multilingual-cased-sentiment-2
|
arjuntheprogrammer
|
distilbert
| 13 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['amazon_reviews_multi']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,288 | false |
<!-- 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-multilingual-cased-sentiment-2
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5882
- Accuracy: 0.7614
- F1: 0.7614
## 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: 0.00024
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- distributed_type: sagemaker_data_parallel
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
00ae26c154b020d489ad4062f84e227e
|
AndyOmosh/finetuning-sentiment-model-3000-samples
|
AndyOmosh
|
distilbert
| 10 | 14 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['imdb']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,055 | false |
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3110
- Accuracy: 0.8833
- F1: 0.8845
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
9e51ebf029c8a91f33e0352338b0cbed
|
jonatasgrosman/exp_w2v2t_zh-cn_vp-nl_s418
|
jonatasgrosman
|
wav2vec2
| 10 | 5 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['zh-CN']
|
['mozilla-foundation/common_voice_7_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'zh-CN']
| false | true | true | 475 | false |
# exp_w2v2t_zh-cn_vp-nl_s418
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
e59afe7e15105dde7a3efea1df45a3b7
|
bthomas/article2keyword2.1b_paraphrase-multilingual-MiniLM-L12-v2_finetuned_for_mlm
|
bthomas
|
bert
| 6 | 6 |
transformers
| 0 |
fill-mask
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['mlm', 'generated_from_trainer']
| true | true | true | 2,101 | false |
<!-- 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. -->
# article2keyword2.1b_paraphrase-multilingual-MiniLM-L12-v2_finetuned_for_mlm
This model is a fine-tuned version of [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0673
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3777 | 1.0 | 1353 | 0.3168 |
| 0.2358 | 2.0 | 2706 | 0.1564 |
| 0.1372 | 3.0 | 4059 | 0.1149 |
| 0.1046 | 4.0 | 5412 | 0.0956 |
| 0.086 | 5.0 | 6765 | 0.0853 |
| 0.0741 | 6.0 | 8118 | 0.0786 |
| 0.0653 | 7.0 | 9471 | 0.0750 |
| 0.0594 | 8.0 | 10824 | 0.0726 |
| 0.0542 | 9.0 | 12177 | 0.0699 |
| 0.0504 | 10.0 | 13530 | 0.0692 |
| 0.047 | 11.0 | 14883 | 0.0684 |
| 0.0444 | 12.0 | 16236 | 0.0675 |
| 0.0423 | 13.0 | 17589 | 0.0674 |
| 0.0404 | 14.0 | 18942 | 0.0673 |
| 0.0392 | 15.0 | 20295 | 0.0672 |
| 0.0379 | 16.0 | 21648 | 0.0673 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
5d295010b096c73fd3e15bcf089e1475
|
IDEA-CCNL/Erlangshen-MacBERT-110M-BinaryClassification-Chinese
|
IDEA-CCNL
|
bert
| 5 | 16 |
transformers
| 0 |
fill-mask
| true | false | false |
apache-2.0
|
['zh']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['classification']
| false | true | true | 2,584 | false |
# Erlangshen- MacBERT-110M-BinaryClassification-Chinese
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
## 简介 Brief Introduction
1.1亿参数的MacBERT，在大规模二分类数据上预训练
The MacBERT with 110M parameters is pre-trained on large-scale binary classification data.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | MacBERT | 110M | Chinese |
## 模型信息 Model Information
为了提高模型在二分类任务上效果，我们收集了大量开源二分类数据并使用meta- learning方法对其增量预训练。
To improve the model performance on the binary classification task, we collected numerous binary classification datasets for incremental pre-training based on meta-learning methods.
### 下游效果 Performance
在EPRSTMT任务上的效果：
The results on EPRSTMT:
| Model | EPRSTMT |
| --------------------------------------------- | ------ |
| MacBERT | 74.96 |
| **Erlangshen- MacBERT-110M-BinaryClassification-Chinese** | 88.56 |
## 使用 Usage
```python3
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-MacBERT-110M-BinaryClassification-Chinese")
model = AutoModelForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-MacBERT-110M-BinaryClassification-Chinese")
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):
```text
@article{fengshenbang,
author = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
484123e4b9e55d511a638bf2362578fb
|
gngpostalsrvc/BERiT_2000_ls_.1
|
gngpostalsrvc
|
roberta
| 11 | 5 |
transformers
| 0 |
fill-mask
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,874 | false |
<!-- 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. -->
# BERiT_2000_ls_1
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: 6.7791
## 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
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.9212 | 0.19 | 500 | 6.8322 |
| 6.8009 | 0.39 | 1000 | 6.8173 |
| 6.8004 | 0.58 | 1500 | 6.7916 |
| 6.7725 | 0.77 | 2000 | 6.8005 |
| 6.787 | 0.97 | 2500 | 6.8066 |
| 6.7808 | 1.16 | 3000 | 6.7838 |
| 6.7757 | 1.36 | 3500 | 6.7726 |
| 6.7847 | 1.55 | 4000 | 6.7584 |
| 6.7874 | 1.74 | 4500 | 6.7809 |
| 6.769 | 1.94 | 5000 | 6.7715 |
| 6.7845 | 2.13 | 5500 | 6.8000 |
| 6.8052 | 2.32 | 6000 | 6.7737 |
| 6.7496 | 2.52 | 6500 | 6.7795 |
| 6.7877 | 2.71 | 7000 | 6.7669 |
| 6.7759 | 2.9 | 7500 | 6.7791 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
1d6d3b5ecfdcd69d7e1425ec0a0b7f81
|
Guizmus/MosaicArt
|
Guizmus
| null | 22 | 70 |
diffusers
| 18 |
text-to-image
| false | false | false |
creativeml-openrail-m
|
['en']
| null | null | 4 | 1 | 3 | 0 | 0 | 0 | 0 |
['stable-diffusion', 'text-to-image', 'image-to-image']
| false | true | true | 2,649 | false |
# Mosaic Art
## Details
This is a Dreamboothed Stable Diffusion model trained on pictures of mosaic art.
The total dataset is made of 46 pictures. V2 was trained on [Stable diffusion 2.1 768](https://huggingface.co/stabilityai/stable-diffusion-2-1). I used [StableTuner](https://github.com/devilismyfriend/StableTuner) to do the training, using full caption on the pictures with almost no recurring word outside the main concept, so that no additionnal regularisation was needed. 6 epochs of 40 repeats on LR 1e-6 were used, with prior preservation.
V1 was trained on [runawayml 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and the [new VAE](https://huggingface.co/stabilityai/sd-vae-ft-mse). I used [EveryDream](https://github.com/victorchall/EveryDream-trainer) to do the training, using full caption on the pictures with almost no recurring word outside the main concept, so that no additionnal regularisation was needed. Out of e0 to e11 epochs, e8 was selected as the best application of style while not overtraining. Prior preservation was constated as good. A total of 9 epochs of 40 repeats with a learning rate of 1e-6.
The token "Mosaic Art" will bring in the new concept, trained as a style.
The recommended sampling is k_Euler_a or DPM++ 2M Karras on 20 steps, CFGS 7.5 .
## Model v2
[CKPT v2](https://huggingface.co/Guizmus/MosaicArt/resolve/main/MosaicArt_v2.ckpt)
[YAML v2](https://huggingface.co/Guizmus/MosaicArt/resolve/main/MosaicArt_v2.yaml)
## Model v1
[CKPT v1](https://huggingface.co/Guizmus/MosaicArt/resolve/main/MosaicArt_v1.ckpt)
[CKPT v1 with ema weights](https://huggingface.co/Guizmus/MosaicArt/resolve/main/MosaicArt_v1_ema.ckpt)
[Dataset](https://huggingface.co/Guizmus/MosaicArt/resolve/main/dataset_v1.zip)
## 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "Guizmus/MosaicArt"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "Mosaic Art dog on the moon"
image = pipe(prompt).images[0]
image.save("./MosaicArt.png")
```
|
a26aca643900de9260057deb3128511a
|
redevaaa/fin5
|
redevaaa
|
bert
| 12 | 3 |
transformers
| 0 |
token-classification
| true | false | false |
cc-by-sa-4.0
| null |
['fin']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,157 | false |
<!-- 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. -->
# fin5
This model is a fine-tuned version of [nlpaueb/sec-bert-shape](https://huggingface.co/nlpaueb/sec-bert-shape) on the fin dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0752
- Precision: 0.9243
- Recall: 0.9243
- F1: 0.9243
- Accuracy: 0.9909
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 129 | 0.0825 | 0.8327 | 0.8924 | 0.8615 | 0.9811 |
| No log | 2.0 | 258 | 0.0633 | 0.8593 | 0.9243 | 0.8906 | 0.9866 |
| No log | 3.0 | 387 | 0.0586 | 0.9038 | 0.9363 | 0.9198 | 0.9894 |
| 0.0547 | 4.0 | 516 | 0.0607 | 0.9357 | 0.9283 | 0.932 | 0.9911 |
| 0.0547 | 5.0 | 645 | 0.0656 | 0.9216 | 0.9363 | 0.9289 | 0.9904 |
| 0.0547 | 6.0 | 774 | 0.0692 | 0.9249 | 0.9323 | 0.9286 | 0.9909 |
| 0.0547 | 7.0 | 903 | 0.0716 | 0.9246 | 0.9283 | 0.9264 | 0.9904 |
| 0.0019 | 8.0 | 1032 | 0.0742 | 0.9213 | 0.9323 | 0.9267 | 0.9909 |
| 0.0019 | 9.0 | 1161 | 0.0748 | 0.9246 | 0.9283 | 0.9264 | 0.9909 |
| 0.0019 | 10.0 | 1290 | 0.0752 | 0.9243 | 0.9243 | 0.9243 | 0.9909 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
55234fd655cc13b99f64ea894936c507
|
ahmetayrnc/distilroberta-base
|
ahmetayrnc
|
roberta
| 13 | 5 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['silicone']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,837 | false |
<!-- 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. -->
# distilroberta-base
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the silicone dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9647
- Accuracy: 0.7111
- Micro-precision: 0.7111
- Micro-recall: 0.7111
- Micro-f1: 0.7111
- Macro-precision: 0.3228
- Macro-recall: 0.2866
- Macro-f1: 0.2824
- Weighted-precision: 0.6683
- Weighted-recall: 0.7111
- Weighted-f1: 0.6768
## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro-precision | Micro-recall | Micro-f1 | Macro-precision | Macro-recall | Macro-f1 | Weighted-precision | Weighted-recall | Weighted-f1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
| 0.9578 | 1.0 | 2980 | 0.9647 | 0.7111 | 0.7111 | 0.7111 | 0.7111 | 0.3228 | 0.2866 | 0.2824 | 0.6683 | 0.7111 | 0.6768 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
71573569aa4ee5c8d5f9fd357f633f44
|
elopezlopez/xlnet-base-cased_fold_8_binary_v1
|
elopezlopez
|
xlnet
| 12 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,637 | false |
<!-- 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. -->
# xlnet-base-cased_fold_8_binary_v1
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5333
- F1: 0.8407
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 290 | 0.3866 | 0.8172 |
| 0.4299 | 2.0 | 580 | 0.4215 | 0.8246 |
| 0.4299 | 3.0 | 870 | 0.4765 | 0.8238 |
| 0.2564 | 4.0 | 1160 | 0.7283 | 0.8350 |
| 0.2564 | 5.0 | 1450 | 0.6825 | 0.8363 |
| 0.1553 | 6.0 | 1740 | 0.9637 | 0.8339 |
| 0.0893 | 7.0 | 2030 | 1.1392 | 0.8239 |
| 0.0893 | 8.0 | 2320 | 1.1868 | 0.8231 |
| 0.0538 | 9.0 | 2610 | 1.2180 | 0.8346 |
| 0.0538 | 10.0 | 2900 | 1.2353 | 0.8253 |
| 0.0386 | 11.0 | 3190 | 1.1883 | 0.8317 |
| 0.0386 | 12.0 | 3480 | 1.2786 | 0.8375 |
| 0.0289 | 13.0 | 3770 | 1.3725 | 0.8375 |
| 0.0146 | 14.0 | 4060 | 1.3171 | 0.8463 |
| 0.0146 | 15.0 | 4350 | 1.2323 | 0.8425 |
| 0.0182 | 16.0 | 4640 | 1.3169 | 0.8485 |
| 0.0182 | 17.0 | 4930 | 1.4424 | 0.8336 |
| 0.0125 | 18.0 | 5220 | 1.4336 | 0.8385 |
| 0.0102 | 19.0 | 5510 | 1.4888 | 0.8405 |
| 0.0102 | 20.0 | 5800 | 1.5227 | 0.8419 |
| 0.0035 | 21.0 | 6090 | 1.4994 | 0.8421 |
| 0.0035 | 22.0 | 6380 | 1.4845 | 0.8424 |
| 0.0047 | 23.0 | 6670 | 1.5006 | 0.8422 |
| 0.0047 | 24.0 | 6960 | 1.5468 | 0.8422 |
| 0.0042 | 25.0 | 7250 | 1.5333 | 0.8407 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
3ee1da72cdb339be67935fce6a828f1a
|
Helsinki-NLP/opus-mt-en-tut
|
Helsinki-NLP
|
marian
| 11 | 8 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
|
['en', 'tut']
| null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 3,435 | false |
### eng-tut
* source group: English
* target group: Altaic languages
* OPUS readme: [eng-tut](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-tut/README.md)
* model: transformer
* source language(s): eng
* target language(s): aze_Latn bak chv crh crh_Latn kaz_Cyrl kaz_Latn kir_Cyrl kjh kum mon nog ota_Arab ota_Latn sah tat tat_Arab tat_Latn tuk tuk_Latn tur tyv uig_Arab uig_Cyrl uzb_Cyrl uzb_Latn xal
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus2m-2020-08-02.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-tut/opus2m-2020-08-02.zip)
* test set translations: [opus2m-2020-08-02.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-tut/opus2m-2020-08-02.test.txt)
* test set scores: [opus2m-2020-08-02.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-tut/opus2m-2020-08-02.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newsdev2016-entr-engtur.eng.tur | 10.4 | 0.438 |
| newstest2016-entr-engtur.eng.tur | 9.1 | 0.414 |
| newstest2017-entr-engtur.eng.tur | 9.5 | 0.414 |
| newstest2018-entr-engtur.eng.tur | 9.5 | 0.415 |
| Tatoeba-test.eng-aze.eng.aze | 27.2 | 0.580 |
| Tatoeba-test.eng-bak.eng.bak | 5.8 | 0.298 |
| Tatoeba-test.eng-chv.eng.chv | 4.6 | 0.301 |
| Tatoeba-test.eng-crh.eng.crh | 6.5 | 0.342 |
| Tatoeba-test.eng-kaz.eng.kaz | 11.8 | 0.360 |
| Tatoeba-test.eng-kir.eng.kir | 24.6 | 0.499 |
| Tatoeba-test.eng-kjh.eng.kjh | 2.2 | 0.052 |
| Tatoeba-test.eng-kum.eng.kum | 8.0 | 0.229 |
| Tatoeba-test.eng-mon.eng.mon | 10.3 | 0.362 |
| Tatoeba-test.eng.multi | 19.5 | 0.451 |
| Tatoeba-test.eng-nog.eng.nog | 1.5 | 0.117 |
| Tatoeba-test.eng-ota.eng.ota | 0.2 | 0.035 |
| Tatoeba-test.eng-sah.eng.sah | 0.7 | 0.080 |
| Tatoeba-test.eng-tat.eng.tat | 10.8 | 0.320 |
| Tatoeba-test.eng-tuk.eng.tuk | 5.6 | 0.323 |
| Tatoeba-test.eng-tur.eng.tur | 34.2 | 0.623 |
| Tatoeba-test.eng-tyv.eng.tyv | 8.1 | 0.192 |
| Tatoeba-test.eng-uig.eng.uig | 0.1 | 0.158 |
| Tatoeba-test.eng-uzb.eng.uzb | 4.2 | 0.298 |
| Tatoeba-test.eng-xal.eng.xal | 0.1 | 0.061 |
### System Info:
- hf_name: eng-tut
- source_languages: eng
- target_languages: tut
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-tut/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['en', 'tut']
- src_constituents: {'eng'}
- tgt_constituents: set()
- src_multilingual: False
- tgt_multilingual: True
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-tut/opus2m-2020-08-02.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-tut/opus2m-2020-08-02.test.txt
- src_alpha3: eng
- tgt_alpha3: tut
- short_pair: en-tut
- chrF2_score: 0.451
- bleu: 19.5
- brevity_penalty: 1.0
- ref_len: 57472.0
- src_name: English
- tgt_name: Altaic languages
- train_date: 2020-08-02
- src_alpha2: en
- tgt_alpha2: tut
- prefer_old: False
- long_pair: eng-tut
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41
|
a2bd16ca2a0fa1f3c4adeea56b6773f8
|
emre/wav2vec2-xls-r-300m-Turkish-Tr-small
|
emre
|
wav2vec2
| 14 | 7 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null |
['common_voice']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer', 'robust-speech-event']
| true | true | true | 1,728 | false |
<!-- 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-Turkish-Tr-small
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4375
- Wer: 0.5050
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.8735 | 4.21 | 400 | 2.8173 | 1.0002 |
| 1.0073 | 8.42 | 800 | 0.4981 | 0.6717 |
| 0.3395 | 12.63 | 1200 | 0.4470 | 0.5866 |
| 0.2254 | 16.84 | 1600 | 0.4349 | 0.5491 |
| 0.1648 | 21.05 | 2000 | 0.4454 | 0.5284 |
| 0.1325 | 25.26 | 2400 | 0.4552 | 0.5131 |
| 0.1102 | 29.47 | 2800 | 0.4375 | 0.5050 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
43d7434846bc2d1755cc576de80c2257
|
google/canine-s
|
google
|
canine
| 6 | 6,265 |
transformers
| 6 |
feature-extraction
| true | false | false |
apache-2.0
|
['multilingual', 'af', 'sq', 'ar', 'an', 'hy', 'ast', 'az', 'ba', 'eu', 'bar', 'be', 'bn', 'inc', 'bs', 'br', 'bg', 'my', 'ca', 'ceb', 'ce', 'zh', 'cv', 'hr', 'cs', 'da', 'nl', 'en', 'et', 'fi', 'fr', 'gl', 'ka', 'de', 'el', 'gu', 'ht', 'he', 'hi', 'hu', 'is', 'io', 'id', 'ga', 'it', 'ja', 'jv', 'kn', 'kk', 'ky', 'ko', 'la', 'lv', 'lt', 'roa', 'nds', 'lm', 'mk', 'mg', 'ms', 'ml', 'mr', 'mn', 'min', 'ne', 'new', 'nb', 'nn', 'oc', 'fa', 'pms', 'pl', 'pt', 'pa', 'ro', 'ru', 'sco', 'sr', 'hr', 'scn', 'sk', 'sl', 'aze', 'es', 'su', 'sw', 'sv', 'tl', 'tg', 'th', 'ta', 'tt', 'te', 'tr', 'uk', 'ud', 'uz', 'vi', 'vo', 'war', 'cy', 'fry', 'pnb', 'yo']
|
['bookcorpus', 'wikipedia']
| null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 4,700 | false |
# CANINE-s (CANINE pre-trained with subword loss)
Pretrained CANINE model on 104 languages using a masked language modeling (MLM) objective. It was introduced in the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) and first released in [this repository](https://github.com/google-research/language/tree/master/language/canine).
What's special about CANINE is that it doesn't require an explicit tokenizer (such as WordPiece or SentencePiece) as other models like BERT and RoBERTa. Instead, it directly operates at a character level: each character is turned into its [Unicode code point](https://en.wikipedia.org/wiki/Code_point#:~:text=For%20Unicode%2C%20the%20particular%20sequence,forming%20a%20self%2Dsynchronizing%20code.).
This means that input processing is trivial and can typically be accomplished as:
```
input_ids = [ord(char) for char in text]
```
The ord() function is part of Python, and turns each character into its Unicode code point.
Disclaimer: The team releasing CANINE did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
CANINE is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion, similar to BERT. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:
* Masked language modeling (MLM): one randomly masks part of the inputs, which the model needs to predict. This model (CANINE-s) is trained with a subword loss, meaning that the model needs to predict the identities of subword tokens, while taking characters as input. By reading characters yet predicting subword tokens, the hard token boundary constraint found in other models such as BERT is turned into a soft inductive bias in CANINE.
* Next sentence prediction (NSP): the model concatenates two sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not.
This way, the model learns an inner representation of multiple languages that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the CANINE model as inputs.
## Intended uses & limitations
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=canine) to look for fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at models like GPT2.
### How to use
Here is how to use this model:
```python
from transformers import CanineTokenizer, CanineModel
model = CanineModel.from_pretrained('google/canine-s')
tokenizer = CanineTokenizer.from_pretrained('google/canine-s')
inputs = ["Life is like a box of chocolates.", "You never know what you gonna get."]
encoding = tokenizer(inputs, padding="longest", truncation=True, return_tensors="pt")
outputs = model(**encoding) # forward pass
pooled_output = outputs.pooler_output
sequence_output = outputs.last_hidden_state
```
## Training data
The CANINE model was pretrained on on the multilingual Wikipedia data of [mBERT](https://github.com/google-research/bert/blob/master/multilingual.md), which includes 104 languages.
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2103-06874,
author = {Jonathan H. Clark and
Dan Garrette and
Iulia Turc and
John Wieting},
title = {{CANINE:} Pre-training an Efficient Tokenization-Free Encoder for
Language Representation},
journal = {CoRR},
volume = {abs/2103.06874},
year = {2021},
url = {https://arxiv.org/abs/2103.06874},
archivePrefix = {arXiv},
eprint = {2103.06874},
timestamp = {Tue, 16 Mar 2021 11:26:59 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2103-06874.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
9d353dd2bf534bddecf8d4e93b554748
|
NDugar/v3large-2epoch
|
NDugar
|
deberta-v2
| 14 | 5 |
transformers
| 0 |
zero-shot-classification
| true | false | false |
mit
|
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['deberta-v3', 'deberta-v2`', 'deberta-mnli']
| false | true | true | 4,589 | false |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
This is the DeBERTa V2 xxlarge model with 48 layers, 1536 hidden size. The total parameters are 1.5B and it is trained with 160GB raw data.
### Fine-tuning on NLU tasks
We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.
| Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B |
|---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------|
| | F1/EM | F1/EM | Acc | Acc | Acc | MCC | Acc |Acc/F1 |Acc/F1 |P/S |
| BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6/- | 93.2 | 92.3 | 60.6 | 70.4 | 88.0/- | 91.3/- |90.0/- |
| RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2/- | 96.4 | 93.9 | 68.0 | 86.6 | 90.9/- | 92.2/- |92.4/- |
| XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8/- | 97.0 | 94.9 | 69.0 | 85.9 | 90.8/- | 92.3/- |92.5/- |
| [DeBERTa-Large](https://huggingface.co/microsoft/deberta-large)<sup>1</sup> | 95.5/90.1 | 90.7/88.0 | 91.3/91.1| 96.5|95.3| 69.5| 91.0| 92.6/94.6| 92.3/- |92.8/92.5 |
| [DeBERTa-XLarge](https://huggingface.co/microsoft/deberta-xlarge)<sup>1</sup> | -/- | -/- | 91.5/91.2| 97.0 | - | - | 93.1 | 92.1/94.3 | - |92.9/92.7|
| [DeBERTa-V2-XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge)<sup>1</sup>|95.8/90.8| 91.4/88.9|91.7/91.6| **97.5**| 95.8|71.1|**93.9**|92.0/94.2|92.3/89.8|92.9/92.9|
|**[DeBERTa-V2-XXLarge](https://huggingface.co/microsoft/deberta-v2-xxlarge)<sup>1,2</sup>**|**96.1/91.4**|**92.2/89.7**|**91.7/91.9**|97.2|**96.0**|**72.0**| 93.5| **93.1/94.9**|**92.7/90.3** |**93.2/93.1** |
--------
#### Notes.
- <sup>1</sup> Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on [DeBERTa-Large-MNLI](https://huggingface.co/microsoft/deberta-large-mnli), [DeBERTa-XLarge-MNLI](https://huggingface.co/microsoft/deberta-xlarge-mnli), [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xlarge-mnli), [DeBERTa-V2-XXLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli). The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.
- <sup>2</sup> To try the **XXLarge** model with **[HF transformers](https://huggingface.co/transformers/main_classes/trainer.html)**, we recommand using **deepspeed** as it's faster and saves memory.
Run with `Deepspeed`,
```bash
pip install datasets
pip install deepspeed
# Download the deepspeed config file
wget https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/ds_config.json -O ds_config.json
export TASK_NAME=mnli
output_dir="ds_results"
num_gpus=8
batch_size=8
python -m torch.distributed.launch --nproc_per_node=${num_gpus} \\
run_glue.py \\
--model_name_or_path microsoft/deberta-v2-xxlarge \\
--task_name $TASK_NAME \\
--do_train \\
--do_eval \\
--max_seq_length 256 \\
--per_device_train_batch_size ${batch_size} \\
--learning_rate 3e-6 \\
--num_train_epochs 3 \\
--output_dir $output_dir \\
--overwrite_output_dir \\
--logging_steps 10 \\
--logging_dir $output_dir \\
--deepspeed ds_config.json
```
You can also run with `--sharded_ddp`
```bash
cd transformers/examples/text-classification/
export TASK_NAME=mnli
python -m torch.distributed.launch --nproc_per_node=8 run_glue.py --model_name_or_path microsoft/deberta-v2-xxlarge \\
--task_name $TASK_NAME --do_train --do_eval --max_seq_length 256 --per_device_train_batch_size 8 \\
--learning_rate 3e-6 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16
```
### Citation
If you find DeBERTa useful for your work, please cite the following paper:
``` latex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```
|
6d75dafe8baf097cf5583207ad84411e
|
sagorsarker/news_article_doc2vec
|
sagorsarker
| null | 3 | 0 | null | 0 | null | false | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 3,202 | false |
# Bangla News Article Doc2Vec model
Bengali News Article doc2vec model trained on [this](https://www.kaggle.com/datasets/ebiswas/bangla-largest-newspaper-dataset) datasets with 8 JSONS and vector size 100.
This model is trained for the [bnlp](https://github.com/sagorbrur/bnlp) library.
## Training details
- Total news articles: 400013
- Hyper-parameter: `epochs: 40, min_count=2, vector_size=100`
## Usage
- Get document vector from input document
```py
from bnlp import BengaliDoc2vec
bn_doc2vec = BengaliDoc2vec()
model_path = "bangla_news_article_doc2vec.model" # keep other .npy model files also in same folder
document = "রাষ্ট্রবিরোধী ও উসকানিমূলক বক্তব্য দেওয়ার অভিযোগে গাজীপুরের গাছা থানায় ডিজিটাল নিরাপত্তা আইনে করা মামলায় আলোচিত ‘শিশুবক্তা’ রফিকুল ইসলামের বিরুদ্ধে অভিযোগ গঠন করেছেন আদালত। ফলে মামলার আনুষ্ঠানিক বিচার শুরু হলো। আজ বুধবার (২৬ জানুয়ারি) ঢাকার সাইবার ট্রাইব্যুনালের বিচারক আসসামছ জগলুল হোসেন এ অভিযোগ গঠন করেন। এর আগে, রফিকুল ইসলামকে কারাগার থেকে আদালতে হাজির করা হয়। এরপর তাকে নির্দোষ দাবি করে তার আইনজীবী শোহেল মো. ফজলে রাব্বি অব্যাহতি চেয়ে আবেদন করেন। অন্যদিকে, রাষ্ট্রপক্ষ অভিযোগ গঠনের পক্ষে শুনানি করেন। উভয় পক্ষের শুনানি শেষে আদালত অব্যাহতির আবেদন খারিজ করে অভিযোগ গঠনের মাধ্যমে বিচার শুরুর আদেশ দেন। একইসঙ্গে সাক্ষ্যগ্রহণের জন্য আগামী ২২ ফেব্রুয়ারি দিন ধার্য করেন আদালত।"
vector = bn_doc2vec.get_document_vector(model_path, text)
print(vector)
```
- Find document similarity between two document
```py
from bnlp import BengaliDoc2vec
bn_doc2vec = BengaliDoc2vec()
model_path = "bangla_news_article_doc2vec.model" # keep other .npy model files also in same folder
article_1 = "রাষ্ট্রবিরোধী ও উসকানিমূলক বক্তব্য দেওয়ার অভিযোগে গাজীপুরের গাছা থানায় ডিজিটাল নিরাপত্তা আইনে করা মামলায় আলোচিত ‘শিশুবক্তা’ রফিকুল ইসলামের বিরুদ্ধে অভিযোগ গঠন করেছেন আদালত। ফলে মামলার আনুষ্ঠানিক বিচার শুরু হলো। আজ বুধবার (২৬ জানুয়ারি) ঢাকার সাইবার ট্রাইব্যুনালের বিচারক আসসামছ জগলুল হোসেন এ অভিযোগ গঠন করেন। এর আগে, রফিকুল ইসলামকে কারাগার থেকে আদালতে হাজির করা হয়। এরপর তাকে নির্দোষ দাবি করে তার আইনজীবী শোহেল মো. ফজলে রাব্বি অব্যাহতি চেয়ে আবেদন করেন। অন্যদিকে, রাষ্ট্রপক্ষ অভিযোগ গঠনের পক্ষে শুনানি করেন। উভয় পক্ষের শুনানি শেষে আদালত অব্যাহতির আবেদন খারিজ করে অভিযোগ গঠনের মাধ্যমে বিচার শুরুর আদেশ দেন। একইসঙ্গে সাক্ষ্যগ্রহণের জন্য আগামী ২২ ফেব্রুয়ারি দিন ধার্য করেন আদালত।"
article_2 = "রাষ্ট্রবিরোধী ও উসকানিমূলক বক্তব্য দেওয়ার অভিযোগে গাজীপুরের গাছা থানায় ডিজিটাল নিরাপত্তা আইনে করা মামলায় আলোচিত ‘শিশুবক্তা’ রফিকুল ইসলামের বিরুদ্ধে অভিযোগ গঠন করেছেন আদালত। ফলে মামলার আনুষ্ঠানিক বিচার শুরু হলো। আজ বুধবার (২৬ জানুয়ারি) ঢাকার সাইবার ট্রাইব্যুনালের বিচারক আসসামছ জগলুল হোসেন এ অভিযোগ গঠন করেন। এর আগে, রফিকুল ইসলামকে কারাগার থেকে আদালতে হাজির করা হয়। এরপর তাকে নির্দোষ দাবি করে তার আইনজীবী শোহেল মো. ফজলে রাব্বি অব্যাহতি চেয়ে আবেদন করেন। অন্যদিকে, রাষ্ট্রপক্ষ অভিযোগ গঠনের পক্ষে শুনানি করেন। উভয় পক্ষের শুনানি শেষে আদালত অব্যাহতির আবেদন খারিজ করে অভিযোগ গঠনের মাধ্যমে বিচার শুরুর আদেশ দেন। একইসঙ্গে সাক্ষ্যগ্রহণের জন্য আগামী ২২ ফেব্রুয়ারি দিন ধার্য করেন আদালত।"
similarity = bn_doc2vec.get_document_similarity(
model_path,
article_1,
article_2
)
print(similarity)
```
|
6196abe5e704878f7f93474155282289
|
espnet/kan-bayashi_ljspeech_tts_train_conformer_fastspeech2_raw_phn_tacotron_-truncated-ec9e34
|
espnet
| null | 19 | 1 |
espnet
| 0 |
text-to-speech
| false | false | false |
cc-by-4.0
|
['en']
|
['ljspeech']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['espnet', 'audio', 'text-to-speech']
| false | true | true | 1,872 | false |
## Example ESPnet2 TTS model
### `kan-bayashi/ljspeech_tts_train_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4036268/
This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
440bba58f9b93dee42806c274ca213e9
|
tomekkorbak/suspicious_mestorf
|
tomekkorbak
| null | 2 | 0 | null | 0 | null | false | false | false |
mit
|
['en']
|
['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 7,931 | false |
<!-- 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. -->
# suspicious_mestorf
This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 3147
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000',
'tomekkorbak/pii-pile-chunk3-50000-100000',
'tomekkorbak/pii-pile-chunk3-100000-150000',
'tomekkorbak/pii-pile-chunk3-150000-200000',
'tomekkorbak/pii-pile-chunk3-200000-250000',
'tomekkorbak/pii-pile-chunk3-250000-300000',
'tomekkorbak/pii-pile-chunk3-300000-350000',
'tomekkorbak/pii-pile-chunk3-350000-400000',
'tomekkorbak/pii-pile-chunk3-400000-450000',
'tomekkorbak/pii-pile-chunk3-450000-500000',
'tomekkorbak/pii-pile-chunk3-500000-550000',
'tomekkorbak/pii-pile-chunk3-550000-600000',
'tomekkorbak/pii-pile-chunk3-600000-650000',
'tomekkorbak/pii-pile-chunk3-650000-700000',
'tomekkorbak/pii-pile-chunk3-700000-750000',
'tomekkorbak/pii-pile-chunk3-750000-800000',
'tomekkorbak/pii-pile-chunk3-800000-850000',
'tomekkorbak/pii-pile-chunk3-850000-900000',
'tomekkorbak/pii-pile-chunk3-900000-950000',
'tomekkorbak/pii-pile-chunk3-950000-1000000',
'tomekkorbak/pii-pile-chunk3-1000000-1050000',
'tomekkorbak/pii-pile-chunk3-1050000-1100000',
'tomekkorbak/pii-pile-chunk3-1100000-1150000',
'tomekkorbak/pii-pile-chunk3-1150000-1200000',
'tomekkorbak/pii-pile-chunk3-1200000-1250000',
'tomekkorbak/pii-pile-chunk3-1250000-1300000',
'tomekkorbak/pii-pile-chunk3-1300000-1350000',
'tomekkorbak/pii-pile-chunk3-1350000-1400000',
'tomekkorbak/pii-pile-chunk3-1400000-1450000',
'tomekkorbak/pii-pile-chunk3-1450000-1500000',
'tomekkorbak/pii-pile-chunk3-1500000-1550000',
'tomekkorbak/pii-pile-chunk3-1550000-1600000',
'tomekkorbak/pii-pile-chunk3-1600000-1650000',
'tomekkorbak/pii-pile-chunk3-1650000-1700000',
'tomekkorbak/pii-pile-chunk3-1700000-1750000',
'tomekkorbak/pii-pile-chunk3-1750000-1800000',
'tomekkorbak/pii-pile-chunk3-1800000-1850000',
'tomekkorbak/pii-pile-chunk3-1850000-1900000',
'tomekkorbak/pii-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True,
'skip_tokens': 1649999872},
'generation': {'every_n_steps': 32,
'force_call_on': [25177],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048}],
'scorer_config': {}},
'kl_gpt3_callback': {'every_n_steps': 32,
'force_call_on': [25177],
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': False,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'value_head_config': {'is_detached': False}},
'path_or_name': 'tomekkorbak/goofy_pasteur'},
'objective': {'alpha': 1, 'beta': 10, 'name': 'AWR'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 512,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'suspicious_mestorf',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0001,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output2',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 3346,
'save_strategy': 'steps',
'seed': 42,
'tokens_already_seen': 1649999872,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/1ew71lih
|
359372f4099da309e1529f50f0f55d74
|
Tanhim/gpt2-model-de
|
Tanhim
|
gpt2
| 12 | 23 |
transformers
| 1 |
text-generation
| true | false | false |
gpl
|
['de']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,548 | false |
<h2> GPT2 Model for German Language </h2>
Model Name: Tanhim/gpt2-model-de <br />
language: German or Deutsch <br />
thumbnail: https://huggingface.co/Tanhim/gpt2-model-de <br />
datasets: Ten Thousand German News Articles Dataset <br />
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, I
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generation= pipeline('text-generation', model='Tanhim/gpt2-model-de', tokenizer='Tanhim/gpt2-model-de')
>>> set_seed(42)
>>> generation("Hallo, ich bin ein Sprachmodell,", max_length=30, num_return_sequences=5)
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("Tanhim/gpt2-model-de")
model = AutoModelWithLMHead.from_pretrained("Tanhim/gpt2-model-de")
text = "Ersetzen Sie mich durch einen beliebigen Text, den Sie wünschen."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
Citation request:
If you use the model of this repository in your research, please consider citing the following way:
```python
@misc{GermanTransformer,
author = {Tanhim Islam},
title = {{PyTorch Based Transformer Machine Learning Model for German Text Generation Task}},
howpublished = "\url{https://huggingface.co/Tanhim/gpt2-model-de}",
year = {2021},
note = "[Online; accessed 17-June-2021]"
}
```
|
fda4db45426883f90c349fe78941bd05
|
dragonSwing/xlm-roberta-capu
|
dragonSwing
|
bert
| 14 | 88 |
transformers
| 0 |
token-classification
| true | false | false |
cc-by-sa-4.0
|
['vi']
|
['oscar-corpus/OSCAR-2109']
| null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
['capitalization', 'punctuation', 'token-classification']
| false | true | true | 3,833 | false |
# ✨ xlm-roberta-capitalization-punctuation
This a [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) model finetuned for Vietnamese punctuation restoration on the [OSCAR-2109](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) dataset.
The model predicts the punctuation and upper-casing of plain, lower-cased text. An example use case can be ASR output. Or other cases when text has lost punctuation.
This model is intended for direct use as a punctuation restoration model for the general Vietnamese language. Alternatively, you can use this for further fine-tuning on domain-specific texts for punctuation restoration tasks.
Model restores the following punctuations -- **[. , : ? ]**
The model also restores the complex upper-casing of words like *YouTube*, *MobiFone*.
-----------------------------------------------
## 🚋 Usage
**Below is a quick way to get up and running with the model.**
1. Download files from hub
```python
import os
import shutil
import sys
from huggingface_hub import snapshot_download
cache_dir = "./capu"
def download_files(repo_id, cache_dir=None, ignore_regex=None):
download_dir = snapshot_download(repo_id=repo_id, cache_dir=cache_dir, ignore_regex=ignore_regex)
if cache_dir is None or download_dir == cache_dir:
return download_dir
file_names = os.listdir(download_dir)
for file_name in file_names:
shutil.move(os.path.join(download_dir, file_name), cache_dir)
os.rmdir(download_dir)
return cache_dir
cache_dir = download_files(repo_id="dragonSwing/xlm-roberta-capu", cache_dir=cache_dir, ignore_regex=["*.json", "*.bin"])
sys.path.append(cache_dir)
```
2. Sample python code
```python
import os
from gec_model import GecBERTModel
model = GecBERTModel(
vocab_path=os.path.join(cache_dir, "vocabulary"),
model_paths="dragonSwing/xlm-roberta-capu",
split_chunk=True
)
model("theo đó thủ tướng dự kiến tiếp bộ trưởng nông nghiệp mỹ tom wilsack bộ trưởng thương mại mỹ gina raimondo bộ trưởng tài chính janet yellen gặp gỡ thượng nghị sĩ patrick leahy và một số nghị sĩ mỹ khác")
# Always return list of outputs.
# ['Theo đó, Thủ tướng dự kiến tiếp Bộ trưởng Nông nghiệp Mỹ Tom Wilsack, Bộ trưởng Thương mại Mỹ Gina Raimondo, Bộ trưởng Tài chính Janet Yellen, gặp gỡ Thượng nghị sĩ Patrick Leahy và một số nghị sĩ Mỹ khác.']
model("những gói cước năm g mobifone sẽ mang đến cho bạn những trải nghiệm mới lạ trên cả tuyệt vời so với mạng bốn g thì tốc độ truy cập mạng 5 g mobifone được nhận định là siêu đỉnh với mức truy cập nhanh gấp 10 lần")
# ['Những gói cước 5G MobiFone sẽ mang đến cho bạn những trải nghiệm mới lạ trên cả tuyệt vời. So với mạng 4G thì tốc độ truy cập mạng 5G MobiFone được Nhận định là siêu đỉnh với mức truy cập nhanh gấp 10 lần.']
```
**This model can work on arbitrarily large text in Vietnamese language.**
-----------------------------------------------
## 📡 Training data
Here is the number of product reviews we used for fine-tuning the model:
| Language | Number of text samples |
| --- | --- |
| Vietnamese | 5,600,000 |
-----------------------------------------------
## 🎯 Accuracy
Below is a breakdown of the performance of the model by each label on 10,000 held-out text samples:
| label | precision | recall | f1-score | support |
| --- | --- | --- | --- | --- |
| **Upper** | 0.89 | 0.90 | 0.89 | 56497 |
| **Complex-Upper** | 0.93 | 0.83 | 0.88 | 480 |
| **.** | 0.81 | 0.84 | 0.82 | 18139 |
| **,** | 0.69 | 0.75 | 0.72 | 22961 |
| **:** | 0.76 | 0.60 | 0.67 | 1432 |
| **?** | 0.82 | 0.75 | 0.78 | 1730 |
| **none** | 0.99 | 0.99 | 0.99 |475611 |
-----------------------------------------------
|
9d4ff83b24f6f086a16156de0effc9dc
|
gokuls/distilbert_sa_GLUE_Experiment_sst2
|
gokuls
|
distilbert
| 20 | 4 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
|
['en']
|
['glue']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,738 | false |
<!-- 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_sa_GLUE_Experiment_sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4455
- Accuracy: 0.8073
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4404 | 1.0 | 264 | 0.5503 | 0.7477 |
| 0.2565 | 2.0 | 528 | 0.6115 | 0.7580 |
| 0.2067 | 3.0 | 792 | 0.4455 | 0.8073 |
| 0.1714 | 4.0 | 1056 | 0.5150 | 0.7947 |
| 0.1438 | 5.0 | 1320 | 0.5712 | 0.7867 |
| 0.1162 | 6.0 | 1584 | 0.6657 | 0.7878 |
| 0.0992 | 7.0 | 1848 | 0.6404 | 0.7821 |
| 0.08 | 8.0 | 2112 | 0.7414 | 0.7924 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
|
2ad77dc3f98592ef33e2964f41696687
|
Helsinki-NLP/opus-mt-niu-es
|
Helsinki-NLP
|
marian
| 10 | 8 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 776 | false |
### opus-mt-niu-es
* source languages: niu
* target languages: es
* OPUS readme: [niu-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/niu-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/niu-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.niu.es | 24.2 | 0.419 |
|
732592f2fd04035e18e506d8f80d0fd5
|
Evelyn18/distilbert-base-uncased-becas-7
|
Evelyn18
|
distilbert
| 20 | 7 |
transformers
| 0 |
question-answering
| true | false | false |
apache-2.0
| null |
['becasv2']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,631 | false |
<!-- 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-becas-7
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3059
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 5 | 5.4980 |
| No log | 2.0 | 10 | 5.0383 |
| No log | 3.0 | 15 | 4.6244 |
| No log | 4.0 | 20 | 4.2090 |
| No log | 5.0 | 25 | 4.0156 |
| No log | 6.0 | 30 | 3.8638 |
| No log | 7.0 | 35 | 4.0836 |
| No log | 8.0 | 40 | 4.1302 |
| No log | 9.0 | 45 | 4.2543 |
| No log | 10.0 | 50 | 4.3059 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
335560b3974f04f3be01a1fea0e64308
|
cointegrated/rut5-base-labse-decoder
|
cointegrated
|
t5
| 8 | 6 |
transformers
| 3 |
text2text-generation
| true | false | false |
mit
|
['ru']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['russian']
| false | true | true | 2,163 | false |
This is the [rut5-base](https://huggingface.co/cointegrated/rut5-base) model, with the decoder fine-tuned to recover (approximately) Russian sentences from their [LaBSE](https://huggingface.co/sentence-transformers/LaBSE) embeddings. Details are [here](https://habr.com/ru/post/677618/) (in Russian).
It can be used, for example, for:
- Paraphrasing Russian sentences;
- Translating from the 109 LaBSE languages to Russian;
- Summarizing a collection of sentences with a single sentence;
- Interpolating between sentences;
- Few-shot text style transfer (including cross-lingual).
Example code:
```python
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoModel
from transformers.modeling_outputs import BaseModelOutput
enc_tokenizer = AutoTokenizer.from_pretrained('cointegrated/LaBSE-en-ru')
encoder = AutoModel.from_pretrained('cointegrated/LaBSE-en-ru')
dec_tokenizer = AutoTokenizer.from_pretrained('cointegrated/rut5-base-labse-decoder')
decoder = AutoModelForSeq2SeqLM.from_pretrained('cointegrated/rut5-base-labse-decoder')
def encode(texts):
encoded_input = enc_tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors='pt')
with torch.no_grad():
model_output = encoder(**encoded_input.to(encoder.device))
embeddings = model_output.pooler_output
embeddings = torch.nn.functional.normalize(embeddings)
return embeddings
# encode some texts into vectors
embeddings = encode([
"4 декабря 2000 года",
"Давно такого не читала, очень хорошо пишешь!",
"Я тогда не понимала, что происходит, не понимаю и сейчас.",
"London is the capital of Great Britain.",
])
print(embeddings.shape)
# torch.Size([4, 768])
# now try to recover the texts from the vectors
out = decoder.generate(
encoder_outputs=BaseModelOutput(last_hidden_state=embeddings.unsqueeze(1)),
max_length=256,
repetition_penalty=3.0,
)
for tokens in out:
print(dec_tokenizer.decode(tokens, skip_special_tokens=True))
# После 4 декабря 2000 года
# Не так давно, это многое читала!
# Я не понимала того, что происходит сейчас тогда, дальше.
# Британская столица Англии.
```
|
416d0989af378ddc17175ba48b03accd
|
kzk-kbys/distilbert-base-uncased-finetuned-emotion
|
kzk-kbys
|
distilbert
| 10 | 2 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['emotion']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,334 | false |
<!-- 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: 0.1895
- Accuracy: 0.94
- F1: 0.9401
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4628 | 1.0 | 2000 | 0.2334 | 0.9315 | 0.9312 |
| 0.1579 | 2.0 | 4000 | 0.1895 | 0.94 | 0.9401 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
8b7c65fb0939e2beff9fd6443776aecf
|
tommasory/platzi-distilroberta-base-mrpc-glue-tommasory
|
tommasory
|
roberta
| 13 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['glue']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,333 | false |
<!-- 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. -->
# platzi-distilroberta-base-mrpc-glue-tommasory
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7098
- Accuracy: 0.8309
- F1: 0.8734
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5196 | 1.09 | 500 | 0.5289 | 0.8260 | 0.8739 |
| 0.3407 | 2.18 | 1000 | 0.7098 | 0.8309 | 0.8734 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
82347394cdcb4e653a12282ee44ca4ef
|
StivenLancheros/mBERT-base-cased-NER-CONLL
|
StivenLancheros
|
bert
| 16 | 9 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null |
['conll2002', 'conll2003']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,414 | false |
<!-- 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. -->
# mBERT-base-cased-NER-CONLL (EN-ES)
This model is a fine-tuned version of [bert-base-multilingual-cased ](https://huggingface.co/bert-base-multilingual-cased) on the conll2003 and conll2002 datasets. Training was performed separately.
It achieves the following results on the evaluation set:
Connll2003:
- Loss: 0.0585
- Precision: 0.9489
- Recall: 0.9541
- F1: 0.9515
- Accuracy: 0.9880
Conll2002:
- Loss: 0.1435
- Precision: 0.8621
- Recall: 0.8663
- F1: 0.8642
- Accuracy: 0.9791
## Model description
IOB tagging Scheme. PER/LOC/MISC/ORG tags
## Intended uses & limitations
More information needed
## Training and evaluation data
Conll2002/2003 (ES-EN)
## 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
- num_epochs: 4
### Training results
Conll2003:
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1739 | 1.0 | 878 | 0.0741 | 0.9246 | 0.9181 | 0.9213 | 0.9823 |
| 0.045 | 2.0 | 1756 | 0.0586 | 0.9469 | 0.9476 | 0.9472 | 0.9870 |
| 0.0213 | 3.0 | 2634 | 0.0583 | 0.9503 | 0.9510 | 0.9506 | 0.9877 |
| 0.0113 | 4.0 | 3512 | 0.0585 | 0.9489 | 0.9541 | 0.9515 | 0.9880 |
Conll2002:
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0739 | 1.0 | 4162 | 0.1322 | 0.8430 | 0.8267 | 0.8348 | 0.9741 |
| 0.0454 | 2.0 | 8324 | 0.1158 | 0.8664 | 0.8614 | 0.8639 | 0.9782 |
| 0.031 | 3.0 | 12486 | 0.1243 | 0.8521 | 0.8660 | 0.8590 | 0.9783 |
| 0.0136 | 4.0 | 16648 | 0.1435 | 0.8621 | 0.8663 | 0.8642 | 0.9791 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
6d72a2cf1e6539549da1aed46ff3602e
|
gokuls/distilbert_add_GLUE_Experiment_mrpc_96
|
gokuls
|
distilbert
| 17 | 5 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
|
['en']
|
['glue']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,832 | false |
<!-- 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_GLUE_Experiment_mrpc_96
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6239
- Accuracy: 0.6838
- F1: 0.8122
- Combined Score: 0.7480
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6686 | 1.0 | 15 | 0.6467 | 0.6838 | 0.8122 | 0.7480 |
| 0.6433 | 2.0 | 30 | 0.6372 | 0.6838 | 0.8122 | 0.7480 |
| 0.6378 | 3.0 | 45 | 0.6319 | 0.6838 | 0.8122 | 0.7480 |
| 0.6344 | 4.0 | 60 | 0.6284 | 0.6838 | 0.8122 | 0.7480 |
| 0.6343 | 5.0 | 75 | 0.6266 | 0.6838 | 0.8122 | 0.7480 |
| 0.6299 | 6.0 | 90 | 0.6252 | 0.6838 | 0.8122 | 0.7480 |
| 0.6335 | 7.0 | 105 | 0.6247 | 0.6838 | 0.8122 | 0.7480 |
| 0.6308 | 8.0 | 120 | 0.6243 | 0.6838 | 0.8122 | 0.7480 |
| 0.6306 | 9.0 | 135 | 0.6243 | 0.6838 | 0.8122 | 0.7480 |
| 0.6302 | 10.0 | 150 | 0.6241 | 0.6838 | 0.8122 | 0.7480 |
| 0.6296 | 11.0 | 165 | 0.6241 | 0.6838 | 0.8122 | 0.7480 |
| 0.6305 | 12.0 | 180 | 0.6239 | 0.6838 | 0.8122 | 0.7480 |
| 0.634 | 13.0 | 195 | 0.6242 | 0.6838 | 0.8122 | 0.7480 |
| 0.63 | 14.0 | 210 | 0.6243 | 0.6838 | 0.8122 | 0.7480 |
| 0.6314 | 15.0 | 225 | 0.6242 | 0.6838 | 0.8122 | 0.7480 |
| 0.6286 | 16.0 | 240 | 0.6239 | 0.6838 | 0.8122 | 0.7480 |
| 0.6326 | 17.0 | 255 | 0.6242 | 0.6838 | 0.8122 | 0.7480 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
|
477a583a6e01b40b24049dcb50d0fcba
|
espnet/siddhana_fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best
|
espnet
| null | 20 | 1 |
espnet
| 0 |
automatic-speech-recognition
| false | false | false |
cc-by-4.0
|
['en']
|
['fsc']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['espnet', 'audio', 'automatic-speech-recognition']
| false | true | true | 1,363 | false |
## ESPnet2 SLU pretrained model
### `siddhana/fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best`
♻️ Imported from https://zenodo.org/record/5590204
This model was trained by siddhana using fsc/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
a4f601be5466d7343611166a445f91fa
|
laion/CLIP-ViT-bigG-14-laion2B-39B-b160k
|
laion
|
clip
| 14 | 6,755 |
open_clip
| 44 | null | true | false | false |
mit
| null | null | null | 3 | 0 | 3 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 8,379 | false |
# Model Card for CLIP ViT-bigG/14 - LAION-2B
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Details](#training-details)
4. [Evaluation](#evaluation)
5. [Acknowledgements](#acknowledgements)
6. [Citation](#citation)
7. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
A CLIP ViT-bigG/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip).
Model training done by Mitchell Wortsman on the [stability.ai](https://stability.ai/) cluster.
The license for this model is MIT.
# Uses
As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model.
The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset.
## Direct Use
Zero-shot image classification, image and text retrieval, among others.
## Downstream Use
Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others.
## Out-of-Scope Use
As per the OpenAI models,
**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful.
Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below.
# Training Details
## Training Data
This model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/).
Fine-tuning was also partially done on LAION-A, a 900M subset of LAION-2B filtered with aesthetic V2 4.5+ and phash deduplicated.
**IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress.
## Training Procedure
The training procedure will soon be discussed by a blog post on laion.ai.
# Evaluation
Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark).
## Testing Data, Factors & Metrics
### Testing Data
The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval.
**TODO** - more detail
## Results
The model achieves a 80.1 zero-shot top-1 accuracy on ImageNet-1k.
An initial round of benchmarks have been performed on a wider range of datasets, and will soon be visible at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb
**TODO** - create table for just this model's metrics.
# Acknowledgements
Acknowledging [stability.ai](https://stability.ai/) for the compute used to train this model.
# Citation
**BibTeX:**
LAION-5B
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
OpenAI CLIP paper
```
@inproceedings{Radford2021LearningTV,
title={Learning Transferable Visual Models From Natural Language Supervision},
author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
booktitle={ICML},
year={2021}
}
```
OpenCLIP software
```
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
Scaling OpenCLIP paper
```
@article{cherti2022reproducible,
title={Reproducible scaling laws for contrastive language-image learning},
author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
journal={arXiv preprint arXiv:2212.07143},
year={2022}
}
```
# How to Get Started with the Model
Use the code below to get started with the model.
** TODO ** - Hugging Face transformers, OpenCLIP, and timm getting started snippets
|
6ab5eb2b97c78a77175a53349b1171f1
|
inovex/multi2convai-quality-en-bert
|
inovex
|
bert
| 8 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
mit
|
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['text-classification']
| false | true | true | 860 | false |
# Multi2ConvAI-Quality: finetuned Bert for English
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Quality (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: English (en)
- model type: finetuned Bert
## How to run
Requires:
- Huggingface transformers
### Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-quality-en-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-quality-en-bert")
````
## Further information on Multi2ConvAI:
- https://multi2conv.ai
- https://github.com/inovex/multi2convai
- mailto: info@multi2conv.ai
|
a1ee3acee0d73148b0a8f28c419e2e69
|
KoichiYasuoka/roberta-small-belarusian
|
KoichiYasuoka
|
roberta
| 7 | 5 |
transformers
| 1 |
fill-mask
| true | false | false |
cc-by-sa-4.0
|
['be']
|
['cc100']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['belarusian', 'masked-lm']
| false | true | true | 576 | false |
# roberta-small-belarusian
## Model Description
This is a RoBERTa model pre-trained on [CC-100](https://data.statmt.org/cc-100/). You can fine-tune `roberta-small-belarusian` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-small-belarusian-upos), dependency-parsing, and so on.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-small-belarusian")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-small-belarusian")
```
|
26fa9b3da3a2f6f65caea9b0d1c26b29
|
Padomin/t5-base-TEDxJP-0front-1body-5rear
|
Padomin
|
t5
| 20 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false |
cc-by-sa-4.0
| null |
['te_dx_jp']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,953 | false |
<!-- 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-TEDxJP-0front-1body-5rear
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4695
- Wer: 0.1761
- Mer: 0.1701
- Wil: 0.2587
- Wip: 0.7413
- Hits: 55488
- Substitutions: 6549
- Deletions: 2550
- Insertions: 2272
- Cer: 0.1410
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
| 0.6479 | 1.0 | 1457 | 0.4975 | 0.1917 | 0.1845 | 0.2761 | 0.7239 | 54735 | 6812 | 3040 | 2530 | 0.1545 |
| 0.549 | 2.0 | 2914 | 0.4537 | 0.1833 | 0.1768 | 0.2659 | 0.7341 | 55124 | 6589 | 2874 | 2378 | 0.1435 |
| 0.4961 | 3.0 | 4371 | 0.4472 | 0.1758 | 0.1701 | 0.2582 | 0.7418 | 55387 | 6493 | 2707 | 2154 | 0.1369 |
| 0.432 | 4.0 | 5828 | 0.4439 | 0.1765 | 0.1707 | 0.2593 | 0.7407 | 55403 | 6544 | 2640 | 2217 | 0.1387 |
| 0.3789 | 5.0 | 7285 | 0.4471 | 0.1749 | 0.1693 | 0.2574 | 0.7426 | 55419 | 6490 | 2678 | 2128 | 0.1387 |
| 0.3524 | 6.0 | 8742 | 0.4483 | 0.1754 | 0.1697 | 0.2573 | 0.7427 | 55414 | 6449 | 2724 | 2153 | 0.1405 |
| 0.3961 | 7.0 | 10199 | 0.4562 | 0.1756 | 0.1698 | 0.2574 | 0.7426 | 55454 | 6455 | 2678 | 2206 | 0.1390 |
| 0.3238 | 8.0 | 11656 | 0.4593 | 0.1768 | 0.1708 | 0.2590 | 0.7410 | 55463 | 6514 | 2610 | 2298 | 0.1416 |
| 0.3054 | 9.0 | 13113 | 0.4652 | 0.1756 | 0.1697 | 0.2577 | 0.7423 | 55522 | 6498 | 2567 | 2279 | 0.1408 |
| 0.3087 | 10.0 | 14570 | 0.4695 | 0.1761 | 0.1701 | 0.2587 | 0.7413 | 55488 | 6549 | 2550 | 2272 | 0.1410 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
3abf5c3bb1c9f956404ea6e7722d9ebe
|
kabelomalapane/en_nso_ukuxhumana_model
|
kabelomalapane
|
marian
| 14 | 1 |
transformers
| 0 |
translation
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation', 'generated_from_trainer']
| true | true | true | 1,060 | false |
<!-- 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. -->
# en_nso_ukuxhumana_model
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-nso](https://huggingface.co/Helsinki-NLP/opus-mt-en-nso) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8482
- Bleu (before training): 12.2324
- Bleu: 18.9287
## 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: 64
- 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 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
c0f14461f11bd95cd40713102de267f4
|
sd-concepts-library/jamie-hewlett-style
|
sd-concepts-library
| null | 11 | 0 | null | 11 | null | false | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,286 | false |
### Jamie Hewlett Style on Stable Diffusion
This is the `<hewlett>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
3b9b89afdaf5dc50e37e9c5817f6cf3b
|
russellc/roberta-news-classifier
|
russellc
|
roberta
| 11 | 3 |
transformers
| 1 |
text-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,529 | false |
<!-- 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-news-classifier
This model is a fine-tuned version of [russellc/roberta-news-classifier](https://huggingface.co/russellc/roberta-news-classifier) on the custom(Kaggle) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1043
- Accuracy: 0.9786
- F1: 0.9786
- Precision: 0.9786
- Recall: 0.9786
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1327 | 1.0 | 123 | 0.1043 | 0.9786 | 0.9786 | 0.9786 | 0.9786 |
| 0.1103 | 2.0 | 246 | 0.1157 | 0.9735 | 0.9735 | 0.9735 | 0.9735 |
| 0.102 | 3.0 | 369 | 0.1104 | 0.9735 | 0.9735 | 0.9735 | 0.9735 |
| 0.0825 | 4.0 | 492 | 0.1271 | 0.9714 | 0.9714 | 0.9714 | 0.9714 |
| 0.055 | 5.0 | 615 | 0.1296 | 0.9724 | 0.9724 | 0.9724 | 0.9724 |
### Evaluation results
***** Running Prediction *****
Num examples = 980
Batch size = 64
precision recall f1-score support
dunya 0.99 0.96 0.97 147
ekonomi 0.96 0.96 0.96 141
kultur 0.97 0.99 0.98 142
saglik 0.99 0.98 0.98 148
siyaset 0.98 0.98 0.98 134
spor 1.00 1.00 1.00 139
teknoloji 0.96 0.98 0.97 129
accuracy -- -- 0.98 980
macro avg 0.98 0.98 0.98 980
weighted avg 0.98 0.98 0.98 980
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
38783fef172a386841ee3930d0a03b24
|
kasrahabib/all-MiniLM-L6-v2-finetunned-fnfreq-clf-promise
|
kasrahabib
|
bert
| 10 | 8 |
transformers
| 0 |
text-classification
| false | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,915 | false |
<!-- 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. -->
# kasrahabib/all-MiniLM-L6-v2-finetunned-fnfreq-clf-promise
This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1935
- Validation Loss: 0.1968
- Train Precision: 0.9452
- Train Recall: 0.9324
- Train F1: 0.9388
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 310, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:-----:|
| 0.6719 | 0.6246 | 0.592 | 1.0 | 0.7437 | 0 |
| 0.5484 | 0.3982 | 0.8961 | 0.9324 | 0.9139 | 1 |
| 0.3471 | 0.2932 | 0.9545 | 0.8514 | 0.9 | 2 |
| 0.2367 | 0.2054 | 0.9452 | 0.9324 | 0.9388 | 3 |
| 0.1935 | 0.1968 | 0.9452 | 0.9324 | 0.9388 | 4 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.0
- Datasets 2.7.1
- Tokenizers 0.13.1
|
61aae0267b1bc4d716a0e0e6c7f70c9e
|
SetFit/distilbert-base-uncased__subj__train-8-2
|
SetFit
|
distilbert
| 10 | 5 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 4,182 | false |
<!-- 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__subj__train-8-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3081
- Accuracy: 0.8755
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7146 | 1.0 | 3 | 0.6798 | 0.75 |
| 0.6737 | 2.0 | 6 | 0.6847 | 0.75 |
| 0.6519 | 3.0 | 9 | 0.6783 | 0.75 |
| 0.6105 | 4.0 | 12 | 0.6812 | 0.25 |
| 0.5463 | 5.0 | 15 | 0.6869 | 0.25 |
| 0.4922 | 6.0 | 18 | 0.6837 | 0.5 |
| 0.4543 | 7.0 | 21 | 0.6716 | 0.5 |
| 0.3856 | 8.0 | 24 | 0.6613 | 0.75 |
| 0.3475 | 9.0 | 27 | 0.6282 | 0.75 |
| 0.2717 | 10.0 | 30 | 0.6045 | 0.75 |
| 0.2347 | 11.0 | 33 | 0.5620 | 0.75 |
| 0.1979 | 12.0 | 36 | 0.5234 | 1.0 |
| 0.1535 | 13.0 | 39 | 0.4771 | 1.0 |
| 0.1332 | 14.0 | 42 | 0.4277 | 1.0 |
| 0.1041 | 15.0 | 45 | 0.3785 | 1.0 |
| 0.082 | 16.0 | 48 | 0.3318 | 1.0 |
| 0.0672 | 17.0 | 51 | 0.2885 | 1.0 |
| 0.0538 | 18.0 | 54 | 0.2568 | 1.0 |
| 0.0412 | 19.0 | 57 | 0.2356 | 1.0 |
| 0.0361 | 20.0 | 60 | 0.2217 | 1.0 |
| 0.0303 | 21.0 | 63 | 0.2125 | 1.0 |
| 0.0268 | 22.0 | 66 | 0.2060 | 1.0 |
| 0.0229 | 23.0 | 69 | 0.2015 | 1.0 |
| 0.0215 | 24.0 | 72 | 0.1989 | 1.0 |
| 0.0211 | 25.0 | 75 | 0.1969 | 1.0 |
| 0.0172 | 26.0 | 78 | 0.1953 | 1.0 |
| 0.0165 | 27.0 | 81 | 0.1935 | 1.0 |
| 0.0132 | 28.0 | 84 | 0.1923 | 1.0 |
| 0.0146 | 29.0 | 87 | 0.1914 | 1.0 |
| 0.0125 | 30.0 | 90 | 0.1904 | 1.0 |
| 0.0119 | 31.0 | 93 | 0.1897 | 1.0 |
| 0.0122 | 32.0 | 96 | 0.1886 | 1.0 |
| 0.0118 | 33.0 | 99 | 0.1875 | 1.0 |
| 0.0097 | 34.0 | 102 | 0.1866 | 1.0 |
| 0.0111 | 35.0 | 105 | 0.1861 | 1.0 |
| 0.0111 | 36.0 | 108 | 0.1855 | 1.0 |
| 0.0102 | 37.0 | 111 | 0.1851 | 1.0 |
| 0.0109 | 38.0 | 114 | 0.1851 | 1.0 |
| 0.0085 | 39.0 | 117 | 0.1854 | 1.0 |
| 0.0089 | 40.0 | 120 | 0.1855 | 1.0 |
| 0.0092 | 41.0 | 123 | 0.1863 | 1.0 |
| 0.0105 | 42.0 | 126 | 0.1868 | 1.0 |
| 0.0089 | 43.0 | 129 | 0.1874 | 1.0 |
| 0.0091 | 44.0 | 132 | 0.1877 | 1.0 |
| 0.0096 | 45.0 | 135 | 0.1881 | 1.0 |
| 0.0081 | 46.0 | 138 | 0.1881 | 1.0 |
| 0.0086 | 47.0 | 141 | 0.1883 | 1.0 |
| 0.009 | 48.0 | 144 | 0.1884 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
d69aa7033c02215fe8a90aef91f44b90
|
chailatte/steven-universe
|
chailatte
| null | 3 | 0 | null | 1 | null | false | false | false |
unknown
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 480 | false |
Token: su_mdl
Class: style
Example: 1girl, grin, solo, female focus, smile, sparkling eyes, shiny hair, su_mdl style
I get good results using these negative prompts:
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
With a CFG Scale of 11.
This is trained on top of Anything.ckpt using 100 screenshots from Steven Universe at 10k steps.
|
ab90fba9e2148d6dde6e9fa0931d74af
|
bardsai/whisper-medium-pl
|
bardsai
|
whisper
| 17 | 11 |
transformers
| 1 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['pl']
|
['mozilla-foundation/common_voice_11_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['whisper-event', 'generated_from_trainer']
| true | true | true | 1,853 | false |
<!-- 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. -->
# Whisper Small PL
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3739
- Wer: 8.5898
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0474 | 1.1 | 1000 | 0.2561 | 9.4612 |
| 0.0119 | 3.09 | 2000 | 0.2901 | 8.9726 |
| 0.0045 | 5.08 | 3000 | 0.3151 | 8.8870 |
| 0.0007 | 7.07 | 4000 | 0.4218 | 8.6032 |
| 0.0005 | 9.07 | 5000 | 0.3739 | 8.5898 |
### Evaluation results
When tested on diffrent polish ASR datasets (splits: test), this model achieves the following results:
| Dataset | WER | WER unnormalized | CER | MER |
|:-----------------:|:-----:|:----------------:|:-----:|:-----:|
|common_voice_11_0 | 8.85 | 21.75 | 2.63 | 8.76 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
f64a43b2444dc7cba67bdf3507aebec0
|
victorlee071200/distilbert-base-cased-finetuned-squad
|
victorlee071200
|
distilbert
| 10 | 5 |
transformers
| 0 |
question-answering
| true | false | false |
apache-2.0
| null |
['squad']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,278 | false |
<!-- 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-cased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1755
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2357 | 1.0 | 5546 | 1.1985 |
| 0.9525 | 2.0 | 11092 | 1.1285 |
| 0.744 | 3.0 | 16638 | 1.1755 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
c27429d1338f40565c850b91648a8c68
|
yanaiela/roberta-base-epoch_49
|
yanaiela
|
roberta
| 9 | 3 |
transformers
| 0 |
fill-mask
| true | false | false |
mit
|
['en']
|
['wikipedia', 'bookcorpus']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['roberta-base', 'roberta-base-epoch_49']
| false | true | true | 2,102 | false |
# RoBERTa, Intermediate Checkpoint - Epoch 49
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the training)
to provide the ability to study the training dynamics of such models, and other possible use-cases.
These models were trained in part of a work that studies how simple statistics from data,
such as co-occurrences affects model predictions, which are described in the paper
[Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251).
This is RoBERTa-base epoch_49.
## Model Description
This model was captured during a reproduction of
[RoBERTa-base](https://huggingface.co/roberta-base), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM).
The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [RoBERTa-base](https://huggingface.co/roberta-base). Two major
differences with the original model:
* We trained our model for 100K steps, instead of 500K
* We only use Wikipedia and the Book Corpus, as corpora which are publicly available.
### How to use
Using code from
[RoBERTa-base](https://huggingface.co/roberta-base), here is an example based on
PyTorch:
```
from transformers import pipeline
model = pipeline("fill-mask", model='yanaiela/roberta-base-epoch_83', device=-1, top_k=10)
model("Hello, I'm the <mask> RoBERTa-base language model")
```
## Citation info
```bibtex
@article{2207.14251,
Author = {Yanai Elazar and Nora Kassner and Shauli Ravfogel and Amir Feder and Abhilasha Ravichander and Marius Mosbach and Yonatan Belinkov and Hinrich Schütze and Yoav Goldberg},
Title = {Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions},
Year = {2022},
Eprint = {arXiv:2207.14251},
}
```
|
448786d6cd8e5e80b45a659527354d34
|
Yaxin/electra-base-discriminator-yelp-mlm
|
Yaxin
|
electra
| 13 | 8 |
transformers
| 0 |
fill-mask
| true | false | false |
apache-2.0
| null |
['yelp_review_full']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,089 | false |
<!-- 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. -->
# electra-base-discriminator-yelp-mlm
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the yelp_review_full yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5550
- Accuracy: 0.6783
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
6aee6ed9c56cde2062f3daeb37f5d998
|
no3/kate-wd-1.4-beta1
|
no3
| null | 36 | 7 |
diffusers
| 0 |
text-to-image
| false | false | false |
creativeml-openrail-m
| null | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 2,953 | false |
### Kate from [Aim for the Stars](https://moringmark.tumblr.com/post/188798125438/aim-for-the-stars) on [WD](https://huggingface.co/hakurei/waifu-diffusion) via Dreambooth
#### model by no3
This your waifu-diffusion v1.4 model fine-tuned kate concept taught to waifu-diffusion v1.4 with Dreambooth.
It can be used by modifying the `instance_prompt`: **sks kate girl**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts).
### note
If you want to to use in UI like [AUTOMATIC1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) or any UI that's uses .ckpt files just download one or more file from here for your convenience.
[kateA4-wd-1.4-beta1.ckpt](https://huggingface.co/no3/kate-wd-1.4-beta1/resolve/main/kateA4-wd-1.4-beta1.ckpt) 5.16 GB
[kateA4-wd-1.4-beta1-pruned.ckpt](https://huggingface.co/no3/kate-wd-1.4-beta1/resolve/main/kateA4-wd-1.4-beta1-pruned.ckpt) 2.58 GB Uses less storage space, but untested yet
If you have issues or questions feel free to visit the Community Tab and start discussion about it.
Here are images used for training this concept:
|
12cf3c9d7a0dd3348329ee41a7200e86
|
prajjwal1/bert-mini
|
prajjwal1
| null | 5 | 85,472 |
transformers
| 7 | null | true | false | false |
['mit']
|
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['BERT', 'MNLI', 'NLI', 'transformer', 'pre-training']
| false | true | true | 2,398 | false |
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert).
This is one of the smaller pre-trained BERT variants, together with [bert-small](https://huggingface.co/prajjwal1/bert-small) and [bert-medium](https://huggingface.co/prajjwal1/bert-medium). They were introduced in the study `Well-Read Students Learn Better: On the Importance of Pre-training Compact Models` ([arxiv](https://arxiv.org/abs/1908.08962)), and ported to HF for the study `Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics` ([arXiv](https://arxiv.org/abs/2110.01518)). These models are supposed to be trained on a downstream task.
If you use the model, please consider citing both the papers:
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{DBLP:journals/corr/abs-1908-08962,
author = {Iulia Turc and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {Well-Read Students Learn Better: The Impact of Student Initialization
on Knowledge Distillation},
journal = {CoRR},
volume = {abs/1908.08962},
year = {2019},
url = {http://arxiv.org/abs/1908.08962},
eprinttype = {arXiv},
eprint = {1908.08962},
timestamp = {Thu, 29 Aug 2019 16:32:34 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Config of this model:
`prajjwal1/bert-mini` (L=4, H=256) [Model Link](https://huggingface.co/prajjwal1/bert-mini)
Other models to check out:
- `prajjwal1/bert-tiny` (L=2, H=128) [Model Link](https://huggingface.co/prajjwal1/bert-tiny)
- `prajjwal1/bert-small` (L=4, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-small)
- `prajjwal1/bert-medium` (L=8, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-medium)
Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli).
Twitter: [@prajjwal_1](https://twitter.com/prajjwal_1)
|
f3a777541a9135a7efcfbdbcab419153
|
rishabhjain16/whisper_base_en_to_myst55h
|
rishabhjain16
|
whisper
| 25 | 4 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,535 | false |
<!-- 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. -->
# openai/whisper-base.en
This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6446
- Wer: 16.4580
## 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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3205 | 4.02 | 1000 | 0.4080 | 14.5116 |
| 0.1568 | 8.04 | 2000 | 0.4672 | 15.3758 |
| 0.035 | 13.01 | 3000 | 0.5696 | 15.9737 |
| 0.0087 | 17.02 | 4000 | 0.6242 | 15.7283 |
| 0.0065 | 21.04 | 5000 | 0.6446 | 16.4580 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
cf93217474c9580875736f0a1a0ce21b
|
sd-concepts-library/pokemon-conquest-sprites
|
sd-concepts-library
| null | 205 | 0 | null | 4 | null | false | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 26,193 | false |
### Pokemon Conquest Sprites on Stable Diffusion
This is the `<poke-conquest>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
95a7a6015e8a648ebf17282ab65837c4
|
anton-l/wav2vec2-large-xlsr-53-estonian
|
anton-l
|
wav2vec2
| 9 | 11 |
transformers
| 0 |
automatic-speech-recognition
| true | false | true |
apache-2.0
|
['et']
|
['common_voice']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
| true | true | true | 3,806 | false |
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Estonian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "et", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-estonian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-estonian")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Estonian test data of Common Voice.
```python
import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Download the raw data instead of using HF datasets to save disk space
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/et.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-estonian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-estonian")
model.to("cuda")
cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/et/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/et/clips/"
def clean_sentence(sent):
sent = sent.lower()
# normalize apostrophes
sent = sent.replace("’", "'")
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() or ch == "'" else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
return sent
targets = []
preds = []
for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
row["sentence"] = clean_sentence(row["sentence"])
speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
row["speech"] = resampler(speech_array).squeeze().numpy()
inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
targets.append(row["sentence"])
preds.append(processor.batch_decode(pred_ids)[0])
print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
```
**Test Result**: 30.74 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
The script used for training can be found [here](github.com)
|
74782517aa99abf1e9b3afcc421870f0
|
Helsinki-NLP/opus-mt-crs-de
|
Helsinki-NLP
|
marian
| 10 | 9 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 776 | false |
### opus-mt-crs-de
* source languages: crs
* target languages: de
* OPUS readme: [crs-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/crs-de/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/crs-de/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/crs-de/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/crs-de/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.crs.de | 20.4 | 0.397 |
|
9796c9681bec51d34d52a67bac2cd0c6
|
Graphcore/deberta-base-squad
|
Graphcore
|
deberta
| 29 | 6 |
transformers
| 1 |
question-answering
| true | false | false |
apache-2.0
| null |
['squad']
| null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,122 | false |
<!-- 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. -->
# deberta-base-squad
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 1984
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 2.0
- training precision: Mixed Precision
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cpu
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
411c43e7f271f24eef67f5628990515a
|
rinna/japanese-gpt2-xsmall
|
rinna
|
gpt2
| 9 | 1,727 |
transformers
| 9 |
text-generation
| true | true | false |
mit
|
['ja']
|
['cc100', 'wikipedia']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['ja', 'japanese', 'gpt2', 'text-generation', 'lm', 'nlp']
| false | true | true | 1,370 | false |
# japanese-gpt2-xsmall
This repository provides an extra-small-sized Japanese GPT-2 model. The model was trained using code from Github repository [rinnakk/japanese-pretrained-models](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/)
# How to use the model
*NOTE:* Use `T5Tokenizer` to initiate the tokenizer.
~~~~
from transformers import T5Tokenizer, GPT2LMHeadModel
tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-gpt2-small")
tokenizer.do_lower_case = True # due to some bug of tokenizer config loading
model = GPT2LMHeadModel.from_pretrained("rinna/japanese-gpt2-small")
~~~~
# Model architecture
A 6-layer, 512-hidden-size transformer-based language model.
# Training
The model was trained on [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective on 8\\*V100 GPUs for around 4 days. It reaches around 28 perplexity on a chosen validation set from CC-100.
# Tokenization
The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer, the vocabulary was trained on the Japanese Wikipedia using the official sentencepiece training script.
# Licenese
[The MIT license](https://opensource.org/licenses/MIT)
|
0a1746afc2b0a814791c15135e7d66d7
|
sd-concepts-library/kogatan-shiny
|
sd-concepts-library
| null | 10 | 0 | null | 3 | null | false | false | false |
mit
| null | null | null | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
[]
| false | true | true | 1,126 | false |
### kogatan_shiny on Stable Diffusion
This is the `kogatan` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
40487c8381e5c559321f0e9fff9e28c8
|
ai4bharat/MultiIndicParaphraseGenerationSS
|
ai4bharat
|
mbart
| 9 | 26 |
transformers
| 0 |
text2text-generation
| true | false | false |
['mit']
|
['as', 'bn', 'gu', 'hi', 'kn', 'ml', 'mr', 'or', 'pa', 'ta', 'te']
|
['ai4bharat/IndicParaphrase']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['paraphrase-generation', 'multilingual', 'nlp', 'indicnlp']
| false | true | true | 3,928 | false |
# MultiIndicParaphraseGenerationSS
This repository contains the [IndicBARTSS](https://huggingface.co/ai4bharat/IndicBARTSS) checkpoint finetuned on the 11 languages of [IndicParaphrase](https://huggingface.co/datasets/ai4bharat/IndicParaphrase) dataset. For finetuning details,
see the [paper](https://arxiv.org/abs/2203.05437).
<ul>
<li >Supported languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Odiya, Punjabi, Kannada, Malayalam, Tamil, and Telugu. Not all of these languages are supported by mBART50 and mT5. </li>
<li >The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for decoding. </li>
<li> Trained on large Indic language corpora (5.53 million sentences). </li>
<li> Unlike <a href="https://huggingface.co/ai4bharat/MultiIndicParaphraseGeneration">MultiIndicParaphraseGeneration</a> each language is written in its own script, so you do not need to perform any script mapping to/from Devanagari. </li>
</ul>
## Using this model in `transformers`
```
from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
from transformers import AlbertTokenizer, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicParaphraseGenerationSS", do_lower_case=False, use_fast=False, keep_accents=True)
# Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicParaphraseGenerationSS", do_lower_case=False, use_fast=False, keep_accents=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicParaphraseGenerationSS")
# Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicParaphraseGenerationSS")
# Some initial mapping
bos_id = tokenizer._convert_token_to_id_with_added_voc("<s>")
eos_id = tokenizer._convert_token_to_id_with_added_voc("</s>")
pad_id = tokenizer._convert_token_to_id_with_added_voc("<pad>")
# To get lang_id use any of ['<2as>', '<2bn>', '<2en>', '<2gu>', '<2hi>', '<2kn>', '<2ml>', '<2mr>', '<2or>', '<2pa>', '<2ta>', '<2te>']
# First tokenize the input. The format below is how IndicBART was trained so the input should be "Sentence </s> <2xx>" where xx is the language code. Similarly, the output should be "<2yy> Sentence </s>".
inp = tokenizer("दिल्ली यूनिवर्सिटी देश की प्रसिद्ध यूनिवर्सिटी में से एक है. </s> <2hi>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
# For generation. Pardon the messiness. Note the decoder_start_token_id.
model_output=model.generate(inp, use_cache=True,no_repeat_ngram_size=3,encoder_no_repeat_ngram_size=3, num_beams=4, max_length=20, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2hi>"))
# Decode to get output strings
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(decoded_output) #दिल्ली यूनिवर्सिटी भारत की सबसे बड़ी यूनिवर्सिटी है।
```
## Benchmarks
Scores on the `IndicParaphrase` test sets are as follows:
Language | BLEU / Self-BLEU / iBLEU
---------|----------------------------
as | 1.19 / 1.64 / 0.34
bn | 10.04 / 1.08 / 6.70
gu | 18.69 / 1.62 / 12.60
hi | 25.05 / 1.75 / 17.01
kn | 13.14 / 1.89 / 8.63
ml | 8.71 / 1.36 / 5.69
mr | 18.50 / 1.49 / 12.50
or | 23.02 / 2.68 / 15.31
pa | 17.61 / 1.37 / 11.92
ta | 16.25 / 2.13 / 10.74
te | 14.16 / 2.29 / 9.23
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
url = "https://arxiv.org/abs/2203.05437"
}
```
|
513033fc6a1151dcbaeca414df0f4345
|
sd-dreambooth-library/noggles-render-1k
|
sd-dreambooth-library
| null | 29 | 2 |
diffusers
| 0 | null | false | false | false |
mit
| null | null | null | 2 | 2 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,824 | false |
### noggles_render_1k on Stable Diffusion via Dreambooth
#### model by alxdfy
This your the Stable Diffusion model fine-tuned the noggles_render_1k concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a render of sks**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
Here are the images used for training this concept:
|
c86a05615bb330fd976a2cf6799493e3
|
mraottth/trashbot_v1
|
mraottth
|
segformer
| 17 | 2 |
transformers
| 0 |
image-segmentation
| true | false | false |
other
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['vision', 'image-segmentation', 'generated_from_trainer']
| true | true | true | 3,114 | false |
<!-- 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. -->
# trashbot
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the mraottth/all_locations_pooled dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0191
- Mean Iou: 0.3997
- Mean Accuracy: 0.7995
- Overall Accuracy: 0.7995
- Accuracy Unlabeled: nan
- Accuracy Trash: 0.7995
- Iou Unlabeled: 0.0
- Iou Trash: 0.7995
## 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: 6e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Trash | Iou Unlabeled | Iou Trash |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:---------:|
| 0.0748 | 1.0 | 90 | 0.0386 | 0.3630 | 0.7259 | 0.7259 | nan | 0.7259 | 0.0 | 0.7259 |
| 0.039 | 2.0 | 180 | 0.0242 | 0.3803 | 0.7607 | 0.7607 | nan | 0.7607 | 0.0 | 0.7607 |
| 0.0194 | 3.0 | 270 | 0.0242 | 0.3605 | 0.7210 | 0.7210 | nan | 0.7210 | 0.0 | 0.7210 |
| 0.0112 | 4.0 | 360 | 0.0205 | 0.3995 | 0.7991 | 0.7991 | nan | 0.7991 | 0.0 | 0.7991 |
| 0.0169 | 5.0 | 450 | 0.0192 | 0.4000 | 0.8000 | 0.8000 | nan | 0.8000 | 0.0 | 0.8000 |
| 0.041 | 6.0 | 540 | 0.0196 | 0.3838 | 0.7677 | 0.7677 | nan | 0.7677 | 0.0 | 0.7677 |
| 0.0188 | 7.0 | 630 | 0.0191 | 0.4139 | 0.8277 | 0.8277 | nan | 0.8277 | 0.0 | 0.8277 |
| 0.0073 | 8.0 | 720 | 0.0190 | 0.4069 | 0.8138 | 0.8138 | nan | 0.8138 | 0.0 | 0.8138 |
| 0.025 | 9.0 | 810 | 0.0191 | 0.4087 | 0.8174 | 0.8174 | nan | 0.8174 | 0.0 | 0.8174 |
| 0.006 | 10.0 | 900 | 0.0191 | 0.3997 | 0.7995 | 0.7995 | nan | 0.7995 | 0.0 | 0.7995 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
42581a6a0b63007cee26a3c02ddb816c
|
pcuenq/ddpm-ema-pets-64-repeat
|
pcuenq
| null | 8 | 0 |
diffusers
| 0 | null | false | false | false |
apache-2.0
|
['en']
|
['pcuenq/oxford-pets']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,220 | false |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-ema-pets-64-repeat
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `pcuenq/oxford-pets` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 128
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08
- lr_scheduler: cosine
- lr_warmup_steps: 500
- ema_inv_gamma: 1.0
- ema_inv_gamma: 0.75
- ema_inv_gamma: 0.9999
- mixed_precision: no
### Training results
📈 [TensorBoard logs](https://huggingface.co/pcuenq/ddpm-ema-pets-64-repeat/tensorboard?#scalars)
|
ff531631e666410dd620d6442edaec4e
|
EIStakovskii/camembert_base_fluency
|
EIStakovskii
|
camembert
| 8 | 7 |
transformers
| 0 |
text-classification
| true | false | false |
other
|
['fr']
|
['news_commentary']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 892 | false |
This model was trained for evaluating linguistic acceptability and grammaticality. The finetuning was carried out based off [the camembert-base model](https://huggingface.co/camembert/camembert-base).
Label_1 means ACCEPTABLE - the sentence is perfectly understandable by native speakers and has no serious grammatic and syntactic flaws.
Label_0 means NOT ACCEPTABLE - the sentence is flawed both orthographically and grammatically.
The model was trained on 50 thousand French sentences from [the news_commentary dataset](https://huggingface.co/datasets/news_commentary). Out of 50 thousand 25 thousand sentences were algorithmically corrupted using [the open source Python library](https://github.com/eistakovskii/text_corruption_plus). The library was originally developed by [aylliote](https://github.com/aylliote/corruption), but it was slightly adapted for the purposes of this model.
|
e7348193de12e107ff6c3ddfe3bbca4f
|
aXhyra/emotion_trained_final
|
aXhyra
|
distilbert
| 10 | 6 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['tweet_eval']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,391 | false |
<!-- 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. -->
# emotion_trained_final
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9349
- F1: 0.7469
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.502523631581398e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9013 | 1.0 | 815 | 0.7822 | 0.6470 |
| 0.5008 | 2.0 | 1630 | 0.7142 | 0.7419 |
| 0.3684 | 3.0 | 2445 | 0.8621 | 0.7443 |
| 0.2182 | 4.0 | 3260 | 0.9349 | 0.7469 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
9667619b3ff143e5e0bb3997fc4e3552
|
AykeeSalazar/violation-classification-bantai-vit-v80ep
|
AykeeSalazar
|
vit
| 16 | 12 |
transformers
| 0 |
image-classification
| true | false | false |
apache-2.0
| null |
['image_folder']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,285 | false |
<!-- 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. -->
# violation-classification-bantai-vit-v80ep
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1974
- Accuracy: 0.9560
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 80
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.797 | 4.95 | 500 | 0.3926 | 0.8715 |
| 0.3095 | 9.9 | 1000 | 0.2597 | 0.9107 |
| 0.1726 | 14.85 | 1500 | 0.2157 | 0.9253 |
| 0.1259 | 19.8 | 2000 | 0.1870 | 0.9392 |
| 0.0959 | 24.75 | 2500 | 0.1797 | 0.9444 |
| 0.0835 | 29.7 | 3000 | 0.2293 | 0.9354 |
| 0.0722 | 34.65 | 3500 | 0.1921 | 0.9441 |
| 0.0628 | 39.6 | 4000 | 0.1897 | 0.9491 |
| 0.059 | 44.55 | 4500 | 0.1719 | 0.9520 |
| 0.0531 | 49.5 | 5000 | 0.1987 | 0.9513 |
| 0.046 | 54.45 | 5500 | 0.1713 | 0.9556 |
| 0.0444 | 59.4 | 6000 | 0.2016 | 0.9525 |
| 0.042 | 64.36 | 6500 | 0.1950 | 0.9525 |
| 0.0363 | 69.31 | 7000 | 0.2017 | 0.9549 |
| 0.037 | 74.26 | 7500 | 0.1943 | 0.9551 |
| 0.0343 | 79.21 | 8000 | 0.1974 | 0.9560 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
d44cf8ad272bb2f90293038a34d4e56a
|
Helsinki-NLP/opus-mt-sv-th
|
Helsinki-NLP
|
marian
| 10 | 18 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 768 | false |
### opus-mt-sv-th
* source languages: sv
* target languages: th
* OPUS readme: [sv-th](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-th/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-th/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-th/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-th/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.th | 21.2 | 0.373 |
|
5de2a6f8b09ec71bcf5e214f5e9d677c
|
Helsinki-NLP/opus-mt-fi-ig
|
Helsinki-NLP
|
marian
| 10 | 7 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 768 | false |
### opus-mt-fi-ig
* source languages: fi
* target languages: ig
* OPUS readme: [fi-ig](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-ig/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-ig/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-ig/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-ig/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fi.ig | 28.5 | 0.456 |
|
1283e99eb91467eef7b75e325adf4308
|
karthiksv/vit-base-patch16-224-cifar10
|
karthiksv
|
vit
| 9 | 4 |
transformers
| 0 |
image-classification
| true | false | false |
apache-2.0
| null |
['cifar10']
| null | 3 | 2 | 1 | 0 | 0 | 0 | 0 |
['image-classification', 'generated_from_trainer']
| true | true | true | 940 | false |
<!-- 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. -->
# vit-base-patch16-224-cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar10 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: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.10.1
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ca5f6b1a6a80e12972ed1397fdfad6ea
|
muhtasham/tiny-mlm-glue-mnli
|
muhtasham
|
bert
| 12 | 0 |
transformers
| 1 |
fill-mask
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,546 | false |
<!-- 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-mnli
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: 3.9722
## 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
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.4196 | 0.4 | 500 | 3.9829 |
| 4.3712 | 0.8 | 1000 | 4.0000 |
| 4.3439 | 1.2 | 1500 | 3.9642 |
| 4.2725 | 1.6 | 2000 | 3.9736 |
| 4.2908 | 2.0 | 2500 | 3.9309 |
| 4.1935 | 2.4 | 3000 | 3.9395 |
| 4.1935 | 2.8 | 3500 | 3.9470 |
| 4.1731 | 3.2 | 4000 | 3.9722 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
0a4e528da3c9a112976a74f10a3d0e53
|
yuanzheng/familyportrait
|
yuanzheng
| null | 18 | 66 |
diffusers
| 1 |
text-to-image
| false | false | false |
creativeml-openrail-m
| null | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
['text-to-image', 'stable-diffusion']
| false | true | true | 425 | false |
### FamilyPortrait Dreambooth model trained by yuanzheng 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:
|
f234606c399ab55c1d15f184910891d7
|
NYTK/reading-comprehension-hurc-mt5-hungarian
|
NYTK
|
mt5
| 9 | 2 |
transformers
| 0 |
text2text-generation
| true | false | false |
apache-2.0
|
['hu']
|
['NYTK/HuRC']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['text2text-generation', 'reading-comprehension']
| false | true | true | 1,970 | false |
# Hungarian Reading Comprehension with finetuned mT5 base model
For further details, see [our demo site](https://juniper.nytud.hu/demo/nlp).
## Results
| Model | Exact Match | F1 |
| ------------- | ------------- | ------------- |
| huBERT | 64.50 | 69.03 |
| mT5 | 69.51 | 76.26 |
## Usage with pipeline
```python
from transformers import pipeline
context = "A kedd hajnalban elhunyt Somló Tamásról emlékezett meg zenésztársa, Presser Gábor. Somló Tamás nagyszerű egyénisége, énekhangja és éneklési stílusa egészen egyedülálló volt' – fogalmazott. '1968 lehetett, amikor először találkoztunk, gyakorlatilag váltottuk egymást az Omega együttesben. Tamás akkor indult el az artista pályán, miközben zenélt is. Az Omegában csak néhányszor játszottunk együtt, miután én beléptem, ő éveket töltött külföldön artistaként, aztán összefutottunk az LGT-ben, ennek már 43 éve' - idézte fel Presser Gábor. Somló Tamás színpadi jelenléte nagy húzóerőt jelentett a zenekar számára és zenészi képességeit mutatta az is, hogy amikor Frenreisz Károly helyett belépett az LGT-be, néhány hét alatt megtanult basszusgitározni."
question = "'Nem ismerek olyan embert, aki <mask> haragudott volna. Életét úgy fejezte be, ahogyan élt: utolsó fellépésére, amely talán egy hónappal ezelőtt lehetett, már nagyon nehezen tudott csak elmenni, de nem mondta le, mert Pécsett egy jótékonysági koncerten játszott beteg gyerekeknek' - mondta Presser Gábor."
text2text_generator = pipeline(task="text2text-generation", model="NYTK/reading-comprehension-hurc-mt5-hungarian")
print(text2text_generator(f"question: {question} context: {context}")[0]["generated_text"])
```
## Citation
If you use this model, please cite the following paper:
```
@article {yang-ligeti-rc,
title = {Building machine reading comprehension model from scratch},
journal = {Annales Mathematicae et Informaticae},
year = {2023},
author = {Yang, Zijian Győző and Ligeti-Nagy, Noémi},
pages = {accetped}
}
```
|
6e4e0e317db80550a81ade45b357d459
|
google/t5-efficient-tiny-el8
|
google
|
t5
| 12 | 7 |
transformers
| 0 |
text2text-generation
| true | true | true |
apache-2.0
|
['en']
|
['c4']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['deep-narrow']
| false | true | true | 6,246 | false |
# T5-Efficient-TINY-EL8 (Deep-Narrow version)
T5-Efficient-TINY-EL8 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and was released with the
paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)**
by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*.
In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures
of similar parameter count.
To quote the paper:
> We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased
> before considering any other forms of uniform scaling across other dimensions. This is largely due to
> how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a
> tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise,
> a tall base model might also generally more efficient compared to a large model. We generally find
> that, regardless of size, even if absolute performance might increase as we continue to stack layers,
> the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36
> layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e.,
> params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params,
> FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to
> consider.
To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially.
A sequence of word embeddings is therefore processed sequentially by each transformer block.
## Details model architecture
This model checkpoint - **t5-efficient-tiny-el8** - is of model type **Tiny** with the following variations:
- **el** is **8**
It has **27.14** million parameters and thus requires *ca.* **108.55 MB** of memory in full precision (*fp32*)
or **54.28 MB** of memory in half precision (*fp16* or *bf16*).
A summary of the *original* T5 model architectures can be seen here:
| Model | nl (el/dl) | ff | dm | kv | nh | #Params|
| ----| ---- | ---- | ---- | ---- | ---- | ----|
| Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M|
| Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M|
| Small | 6/6 | 2048 | 512 | 32 | 8 | 60M|
| Base | 12/12 | 3072 | 768 | 64 | 12 | 220M|
| Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M|
| Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B|
| XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B|
whereas the following abbreviations are used:
| Abbreviation | Definition |
| ----| ---- |
| nl | Number of transformer blocks (depth) |
| dm | Dimension of embedding vector (output vector of transformers block) |
| kv | Dimension of key/value projection matrix |
| nh | Number of attention heads |
| ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) |
| el | Number of transformer blocks in the encoder (encoder depth) |
| dl | Number of transformer blocks in the decoder (decoder depth) |
| sh | Signifies that attention heads are shared |
| skv | Signifies that key-values projection matrices are tied |
If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*.
## Pre-Training
The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using
the span-based masked language modeling (MLM) objective.
## Fine-Tuning
**Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage.
The checkpoint was pretrained in English and is therefore only useful for English NLP tasks.
You can follow on of the following examples on how to fine-tune the model:
*PyTorch*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization)
- [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
*Tensorflow*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
*JAX/Flax*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
## Downstream Performance
TODO: Add table if available
## Computational Complexity
TODO: Add table if available
## More information
We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint.
As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv*
model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future.
|
56cb9c7b141fedcb5e465467e04b409f
|
dehio/german-qg-t5-drink600
|
dehio
|
t5
| 17 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false |
mit
|
['de']
|
['deepset/germanquad']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['question generation']
| true | true | true | 1,811 | false |
# german-qg-t5-drink600
This model is fine-tuned in question generation in German. The expected answer must be highlighted with <hl> token. It is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad) and further pre-trained on drink related questions.
## Task example
#### Input
generate question: Der Monk Sour Drink ist ein somit eine aromatische Überraschung,
die sowohl <hl>im Sommer wie auch zu Silvester<hl> funktioniert.
#### Expected Question
Zu welchen Gelegenheiten passt der Monk Sour gut?
## Model description
The model is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad), which was pre-trained on [GermanQUAD](https://www.deepset.ai/germanquad). We further pre-trained it on questions annotated on drink receipts from [Mixology](https://mixology.eu/) ("drink600").
We have not yet open sourced the dataset, since we do not own copyright on the source material.
## Training and evaluation data
The training script can be accessed [here](https://github.com/d-e-h-i-o/german-qg).
## Evaluation
It achieves a **BLEU-4 score of 29.80** on the drink600 test set (n=120) and **11.30** on the GermanQUAD test set.
Thus, fine-tuning on drink600 did not affect performance on GermanQuAD.
In comparison, *german-qg-t5-quad* achieves a BLEU-4 score of **10.76** on the drink600 test set.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 100
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
05aa11466166e53a1481c5c1738309ba
|
fathyshalab/all-roberta-large-v1-credit_cards-9-16-5
|
fathyshalab
|
roberta
| 11 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,517 | false |
<!-- 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. -->
# all-roberta-large-v1-credit_cards-9-16-5
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3376
- Accuracy: 0.3186
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.75 | 1.0 | 1 | 2.5769 | 0.2389 |
| 2.178 | 2.0 | 2 | 2.4879 | 0.2389 |
| 1.769 | 3.0 | 3 | 2.4180 | 0.2566 |
| 1.4703 | 4.0 | 4 | 2.3657 | 0.3097 |
| 1.2711 | 5.0 | 5 | 2.3376 | 0.3186 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
dbf0abddf876e9ca476ecc4f518cd7a1
|
sentence-transformers/distiluse-base-multilingual-cased-v2
|
sentence-transformers
|
distilbert
| 15 | 385,435 |
sentence-transformers
| 38 |
sentence-similarity
| true | true | false |
apache-2.0
|
['multilingual']
| null | null | 1 | 1 | 0 | 0 | 2 | 1 | 1 |
['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']
| false | true | true | 2,205 | false |
# sentence-transformers/distiluse-base-multilingual-cased-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/distiluse-base-multilingual-cased-v2)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
```
|
bfb894fc97c502ca697da3b3fc9036c6
|
MrPotato/ner-bert-large-uncased-geocite
|
MrPotato
|
bert
| 12 | 2 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 966 | false |
<!-- 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. -->
# ner-bert-large-uncased-geocite
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-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: 5e-05
- train_batch_size: 16
- eval_batch_size: 1
- 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: 1
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
2025d1e0999007801f494354377898dc
|
emigomez/vit-classify-manipulations-v2-1
|
emigomez
|
vit
| 14 | 9 |
transformers
| 0 |
image-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['image-classification', 'generated_from_trainer']
| true | true | true | 10,757 | false |
<!-- 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. -->
# vit-classify-manipulations-ft
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the vit-classify-manipulations-v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2798
- Accuracy: 0.8694
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2882 | 0.66 | 100 | 0.2873 | 0.8806 |
| 0.3869 | 1.32 | 200 | 0.2798 | 0.8694 |
| 0.2345 | 1.99 | 300 | 0.3074 | 0.8843 |
| 0.081 | 2.65 | 400 | 0.3563 | 0.8470 |
| 0.129 | 3.31 | 500 | 0.3736 | 0.8918 |
| 0.0823 | 3.97 | 600 | 0.2815 | 0.9104 |
| 0.1213 | 4.64 | 700 | 0.2902 | 0.9216 |
| 0.0422 | 5.3 | 800 | 0.3492 | 0.8993 |
| 0.0753 | 5.96 | 900 | 0.4281 | 0.8657 |
| 0.2148 | 6.62 | 1000 | 0.4206 | 0.8955 |
| 0.0213 | 7.28 | 1100 | 0.4723 | 0.8843 |
| 0.0376 | 7.95 | 1200 | 0.3787 | 0.8918 |
| 0.0226 | 8.61 | 1300 | 0.4501 | 0.9030 |
| 0.1102 | 9.27 | 1400 | 0.7684 | 0.8396 |
| 0.0444 | 9.93 | 1500 | 0.3732 | 0.9142 |
| 0.0329 | 10.6 | 1600 | 0.4021 | 0.9030 |
| 0.0379 | 11.26 | 1700 | 0.4920 | 0.8918 |
| 0.1129 | 11.92 | 1800 | 0.3605 | 0.9216 |
| 0.0271 | 12.58 | 1900 | 0.4282 | 0.9030 |
| 0.0247 | 13.25 | 2000 | 0.5272 | 0.8993 |
| 0.0205 | 13.91 | 2100 | 0.3310 | 0.8881 |
| 0.0128 | 14.57 | 2200 | 0.5010 | 0.8955 |
| 0.02 | 15.23 | 2300 | 0.4055 | 0.9142 |
| 0.0004 | 15.89 | 2400 | 0.4748 | 0.9179 |
| 0.0004 | 16.56 | 2500 | 0.4993 | 0.9179 |
| 0.0031 | 17.22 | 2600 | 0.6183 | 0.9030 |
| 0.0017 | 17.88 | 2700 | 0.4281 | 0.8993 |
| 0.0525 | 18.54 | 2800 | 0.5069 | 0.8955 |
| 0.0585 | 19.21 | 2900 | 0.4555 | 0.9067 |
| 0.0739 | 19.87 | 3000 | 0.7661 | 0.8470 |
| 0.0948 | 20.53 | 3100 | 0.5650 | 0.8769 |
| 0.0017 | 21.19 | 3200 | 0.5727 | 0.8881 |
| 0.0058 | 21.85 | 3300 | 0.4706 | 0.8993 |
| 0.0005 | 22.52 | 3400 | 0.7194 | 0.8806 |
| 0.0149 | 23.18 | 3500 | 0.5310 | 0.9030 |
| 0.0502 | 23.84 | 3600 | 0.5698 | 0.8843 |
| 0.001 | 24.5 | 3700 | 0.5458 | 0.8881 |
| 0.0089 | 25.17 | 3800 | 0.7860 | 0.8769 |
| 0.0402 | 25.83 | 3900 | 0.6264 | 0.8843 |
| 0.0004 | 26.49 | 4000 | 0.6089 | 0.8955 |
| 0.001 | 27.15 | 4100 | 0.7378 | 0.8731 |
| 0.0788 | 27.81 | 4200 | 0.4598 | 0.9142 |
| 0.0022 | 28.48 | 4300 | 0.5228 | 0.8843 |
| 0.1316 | 29.14 | 4400 | 0.4931 | 0.8843 |
| 0.0015 | 29.8 | 4500 | 0.4824 | 0.9179 |
| 0.0786 | 30.46 | 4600 | 0.5039 | 0.9067 |
| 0.0003 | 31.13 | 4700 | 0.5460 | 0.9179 |
| 0.0457 | 31.79 | 4800 | 0.5526 | 0.9104 |
| 0.0005 | 32.45 | 4900 | 0.6904 | 0.8694 |
| 0.02 | 33.11 | 5000 | 0.4874 | 0.8955 |
| 0.0084 | 33.77 | 5100 | 0.4371 | 0.9067 |
| 0.1005 | 34.44 | 5200 | 0.4710 | 0.8993 |
| 0.0006 | 35.1 | 5300 | 0.6772 | 0.8843 |
| 0.0004 | 35.76 | 5400 | 0.5969 | 0.9030 |
| 0.0002 | 36.42 | 5500 | 0.6072 | 0.8955 |
| 0.0571 | 37.09 | 5600 | 0.5104 | 0.8993 |
| 0.0017 | 37.75 | 5700 | 0.6352 | 0.8769 |
| 0.0015 | 38.41 | 5800 | 0.6217 | 0.8993 |
| 0.0002 | 39.07 | 5900 | 0.7594 | 0.8881 |
| 0.0001 | 39.74 | 6000 | 0.7726 | 0.8881 |
| 0.0001 | 40.4 | 6100 | 0.7839 | 0.8881 |
| 0.0001 | 41.06 | 6200 | 0.7994 | 0.8881 |
| 0.0001 | 41.72 | 6300 | 0.8109 | 0.8881 |
| 0.0001 | 42.38 | 6400 | 0.8231 | 0.8881 |
| 0.0001 | 43.05 | 6500 | 0.8338 | 0.8843 |
| 0.0001 | 43.71 | 6600 | 0.8427 | 0.8843 |
| 0.0001 | 44.37 | 6700 | 0.8526 | 0.8843 |
| 0.0001 | 45.03 | 6800 | 0.8604 | 0.8843 |
| 0.0001 | 45.7 | 6900 | 0.8689 | 0.8843 |
| 0.0001 | 46.36 | 7000 | 0.8771 | 0.8843 |
| 0.0001 | 47.02 | 7100 | 0.8846 | 0.8843 |
| 0.0001 | 47.68 | 7200 | 0.8924 | 0.8843 |
| 0.0001 | 48.34 | 7300 | 0.8992 | 0.8843 |
| 0.0 | 49.01 | 7400 | 0.9059 | 0.8843 |
| 0.0 | 49.67 | 7500 | 0.9132 | 0.8843 |
| 0.0 | 50.33 | 7600 | 0.9195 | 0.8843 |
| 0.0 | 50.99 | 7700 | 0.9251 | 0.8843 |
| 0.0 | 51.66 | 7800 | 0.9319 | 0.8843 |
| 0.0 | 52.32 | 7900 | 0.9375 | 0.8843 |
| 0.0 | 52.98 | 8000 | 0.9437 | 0.8843 |
| 0.0 | 53.64 | 8100 | 0.9496 | 0.8843 |
| 0.0 | 54.3 | 8200 | 0.9547 | 0.8843 |
| 0.0 | 54.97 | 8300 | 0.9607 | 0.8843 |
| 0.0 | 55.63 | 8400 | 0.9659 | 0.8843 |
| 0.0 | 56.29 | 8500 | 0.9714 | 0.8843 |
| 0.0 | 56.95 | 8600 | 0.9770 | 0.8843 |
| 0.0 | 57.62 | 8700 | 0.9821 | 0.8843 |
| 0.0 | 58.28 | 8800 | 0.9867 | 0.8843 |
| 0.0 | 58.94 | 8900 | 0.9923 | 0.8843 |
| 0.0 | 59.6 | 9000 | 0.9976 | 0.8843 |
| 0.0 | 60.26 | 9100 | 1.0024 | 0.8843 |
| 0.0 | 60.93 | 9200 | 1.0070 | 0.8843 |
| 0.0 | 61.59 | 9300 | 1.0122 | 0.8843 |
| 0.0 | 62.25 | 9400 | 1.0166 | 0.8843 |
| 0.0 | 62.91 | 9500 | 1.0218 | 0.8843 |
| 0.0 | 63.58 | 9600 | 1.0265 | 0.8843 |
| 0.0 | 64.24 | 9700 | 1.0305 | 0.8843 |
| 0.0 | 64.9 | 9800 | 1.0353 | 0.8843 |
| 0.0 | 65.56 | 9900 | 1.0395 | 0.8843 |
| 0.0 | 66.23 | 10000 | 1.0445 | 0.8881 |
| 0.0 | 66.89 | 10100 | 1.0487 | 0.8881 |
| 0.0 | 67.55 | 10200 | 1.0526 | 0.8881 |
| 0.0 | 68.21 | 10300 | 1.0571 | 0.8881 |
| 0.0 | 68.87 | 10400 | 1.0617 | 0.8881 |
| 0.0 | 69.54 | 10500 | 1.0659 | 0.8881 |
| 0.0 | 70.2 | 10600 | 1.0699 | 0.8881 |
| 0.0 | 70.86 | 10700 | 1.0737 | 0.8881 |
| 0.0 | 71.52 | 10800 | 1.0781 | 0.8881 |
| 0.0 | 72.19 | 10900 | 1.0822 | 0.8881 |
| 0.0 | 72.85 | 11000 | 1.0862 | 0.8881 |
| 0.0 | 73.51 | 11100 | 1.0903 | 0.8881 |
| 0.0 | 74.17 | 11200 | 1.0944 | 0.8881 |
| 0.0 | 74.83 | 11300 | 1.0984 | 0.8881 |
| 0.0 | 75.5 | 11400 | 1.1023 | 0.8881 |
| 0.0 | 76.16 | 11500 | 1.1062 | 0.8881 |
| 0.0 | 76.82 | 11600 | 1.1096 | 0.8881 |
| 0.0 | 77.48 | 11700 | 1.1136 | 0.8881 |
| 0.0 | 78.15 | 11800 | 1.1173 | 0.8881 |
| 0.0 | 78.81 | 11900 | 1.1211 | 0.8881 |
| 0.0 | 79.47 | 12000 | 1.1245 | 0.8881 |
| 0.0 | 80.13 | 12100 | 1.1283 | 0.8881 |
| 0.0 | 80.79 | 12200 | 1.1317 | 0.8918 |
| 0.0 | 81.46 | 12300 | 1.1354 | 0.8918 |
| 0.0 | 82.12 | 12400 | 1.1388 | 0.8918 |
| 0.0 | 82.78 | 12500 | 1.1419 | 0.8918 |
| 0.0 | 83.44 | 12600 | 1.1452 | 0.8918 |
| 0.0 | 84.11 | 12700 | 1.1486 | 0.8918 |
| 0.0 | 84.77 | 12800 | 1.1517 | 0.8918 |
| 0.0 | 85.43 | 12900 | 1.1547 | 0.8918 |
| 0.0 | 86.09 | 13000 | 1.1578 | 0.8918 |
| 0.0 | 86.75 | 13100 | 1.1607 | 0.8918 |
| 0.0 | 87.42 | 13200 | 1.1635 | 0.8918 |
| 0.0 | 88.08 | 13300 | 1.1664 | 0.8918 |
| 0.0 | 88.74 | 13400 | 1.1691 | 0.8918 |
| 0.0 | 89.4 | 13500 | 1.1717 | 0.8918 |
| 0.0 | 90.07 | 13600 | 1.1743 | 0.8918 |
| 0.0 | 90.73 | 13700 | 1.1768 | 0.8918 |
| 0.0 | 91.39 | 13800 | 1.1790 | 0.8918 |
| 0.0 | 92.05 | 13900 | 1.1813 | 0.8918 |
| 0.0 | 92.72 | 14000 | 1.1833 | 0.8918 |
| 0.0 | 93.38 | 14100 | 1.1855 | 0.8918 |
| 0.0 | 94.04 | 14200 | 1.1873 | 0.8918 |
| 0.0 | 94.7 | 14300 | 1.1891 | 0.8918 |
| 0.0 | 95.36 | 14400 | 1.1906 | 0.8918 |
| 0.0 | 96.03 | 14500 | 1.1918 | 0.8918 |
| 0.0 | 96.69 | 14600 | 1.1933 | 0.8918 |
| 0.0 | 97.35 | 14700 | 1.1942 | 0.8918 |
| 0.0 | 98.01 | 14800 | 1.1950 | 0.8918 |
| 0.0 | 98.68 | 14900 | 1.1956 | 0.8918 |
| 0.0 | 99.34 | 15000 | 1.1960 | 0.8918 |
| 0.0 | 100.0 | 15100 | 1.1963 | 0.8918 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
8ffbfd3bc49ca5425e806d50fa698197
|
tal-yifat/injury-report-distilgpt2-test
|
tal-yifat
|
gpt2
| 8 | 7 |
transformers
| 0 |
text-generation
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,242 | false |
<!-- 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. -->
# injury-report-distilgpt2-test
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5243
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 380 | 3.6525 |
| 3.9116 | 2.0 | 760 | 3.5507 |
| 3.6015 | 3.0 | 1140 | 3.5243 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
33cc3d0539d1ba67aff81e994ced18a6
|
muhtasham/tiny-vanilla-target-glue-qnli
|
muhtasham
|
bert
| 10 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,810 | false |
<!-- 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-vanilla-target-glue-qnli
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: 0.4624
- Accuracy: 0.7825
## 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
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6082 | 0.15 | 500 | 0.5375 | 0.7362 |
| 0.5378 | 0.31 | 1000 | 0.5192 | 0.7459 |
| 0.5161 | 0.46 | 1500 | 0.4967 | 0.7672 |
| 0.5097 | 0.61 | 2000 | 0.5182 | 0.7505 |
| 0.5092 | 0.76 | 2500 | 0.4728 | 0.7750 |
| 0.5011 | 0.92 | 3000 | 0.4660 | 0.7866 |
| 0.4889 | 1.07 | 3500 | 0.4476 | 0.7922 |
| 0.48 | 1.22 | 4000 | 0.4619 | 0.7840 |
| 0.4661 | 1.37 | 4500 | 0.4813 | 0.7741 |
| 0.4742 | 1.53 | 5000 | 0.4624 | 0.7825 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
5af41e378ce46f12b97cce61e7c156b1
|
muhtasham/base-mlm-imdb-target-tweet
|
muhtasham
|
bert
| 10 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['tweet_eval']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,472 | false |
<!-- 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-mlm-imdb-target-tweet
This model is a fine-tuned version of [muhtasham/base-mlm-imdb](https://huggingface.co/muhtasham/base-mlm-imdb) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7516
- Accuracy: 0.7754
- F1: 0.7789
## 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
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3412 | 4.9 | 500 | 1.0525 | 0.7888 | 0.7891 |
| 0.0365 | 9.8 | 1000 | 1.4590 | 0.7540 | 0.7572 |
| 0.0127 | 14.71 | 1500 | 1.4788 | 0.7888 | 0.7890 |
| 0.0137 | 19.61 | 2000 | 1.7516 | 0.7754 | 0.7789 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.2
|
9224f28cfd374a0738877a2f1335b445
|
Atharvgarg/bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news
|
Atharvgarg
|
encoder-decoder
| 13 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['summarisation', 'generated_from_trainer']
| true | true | true | 2,160 | false |
<!-- 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-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news
This model is a fine-tuned version of [mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization](https://huggingface.co/mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6835
- Rouge1: 58.9345
- Rouge2: 47.1037
- Rougel: 40.9839
- Rougelsum: 57.6981
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 0.8246 | 1.0 | 223 | 0.7050 | 55.7882 | 42.9793 | 38.4511 | 54.3125 |
| 0.6414 | 2.0 | 446 | 0.6834 | 55.149 | 42.664 | 38.3864 | 53.7712 |
| 0.5603 | 3.0 | 669 | 0.6815 | 56.9756 | 44.8057 | 39.1377 | 55.5815 |
| 0.5079 | 4.0 | 892 | 0.6749 | 57.7397 | 45.6267 | 40.0509 | 56.3886 |
| 0.4622 | 5.0 | 1115 | 0.6781 | 58.07 | 45.9102 | 40.2704 | 56.7008 |
| 0.4263 | 6.0 | 1338 | 0.6798 | 58.1215 | 45.976 | 40.256 | 56.8203 |
| 0.399 | 7.0 | 1561 | 0.6798 | 58.5486 | 46.6901 | 40.8045 | 57.2947 |
| 0.3815 | 8.0 | 1784 | 0.6835 | 58.9345 | 47.1037 | 40.9839 | 57.6981 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
7c6acc383766c6896117623be56308e8
|
wanghao2023/uganda-labor-market-interview-text-classification
|
wanghao2023
|
roberta
| 10 | 2 |
transformers
| 0 |
text-classification
| true | false | false |
mit
|
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 4,095 | false |
# Uganda Labor Market Interview Text Classification
This model is a fine-tuned [Roberta base model](https://huggingface.co/roberta-base) using text transcripts of interviews between Vocational Training Institutes (VTI) students and their successful alumni in Uganda on the subject of the labor market.
## Model description
There are 6 categories in total. In the training data, a sentence can get classified as more than one topic. I classify a sentence using the following criteria:
info: information about the job market, working conditions, salaries, and what to expect at work. Also alumn's and student's current situation in the job market, career plans, and past experience. Note if the alumn mentions using strategies in her/his experience, I also classify the sentence as a strategy.
tip: tips for how to behave and improve ourselves while at work. The majority of tips involve being disciplined, humble, treating colleagues and clients well so that you can learn, and not involving in illegal stuff. If the alumni mention doing so increases the chance of getting jobs, I also classify the sentence as a strategy.
strategy: tips that help students get a better chance of getting hired or getting a better job. Including how to search for companies, what kind of companies to apply for, how to write and submit applications, when and how many companies to apply for, how to behave during interviews, how to get jobs through different channels, and making and maintaining connections, and general advice on how to improve job-related abilities. Also tips for starting your own business, including saving for capital, finding locations, business models, purchasing apparatuses, and attracting and treating clients.
motivation: General advice of being confident, patient, persistent, engaged, optimistic, etc in the job market. Note if the alumni mention that advice in a particular context, for example "during an interview you need to show that you are a patient person," or "when doing your work you need to be patient," I will also classify these sentences as strategy and tip respectively.
referral: referring students to companies and individuals, or affirmative answers to the student's request for connection.
neutral: Introductions, exchanging contacts, pure technical stuff, conversations about school or exams that are not related to getting jobs, miscellaneous conversations that do not belong to the 5 topics above, and those whose meaning is unclear due to language improficiency or translation issues.
### How to use
You can use this model directly with a pipeline for text classification:
```python
>>> from transformers import pipeline
>>> pipe = pipeline("text-classification", model= "wanghao2023/uganda-labor-market-interview-text-classification", tokenizer = "wanghao2023/uganda-labor-market-interview-text-classification", return_all_scores = True)
>>> pipe("if they think you know too much, they won't teach you.")
[[{'label': 'is_info', 'score': 0.18128268420696259},
{'label': 'is_tip', 'score': 0.5684323310852051},
{'label': 'is_strategy', 'score': 0.22818608582019806},
{'label': 'is_motivation', 'score': 0.03250108286738396},
{'label': 'is_neutral', 'score': 0.05972086638212204},
{'label': 'is_referral', 'score': 0.013502764515578747}]]
```
### Limitations and bias
The classification of a sentence is heavily based on the context. For example, "be patient" can be classified as tip and/or strategy and/or motivation depending on which occasion the alumna asks the students to be patient. If the alumna asks the student to be patient during the interview, it's strategy; if the alumna asks the student to be patient while at work, then it's tip; if no specific context is given, then it's motivation.
## Evaluation results
This model achieves the following results when tested on the validation dataset (multilabel, threshold = 0.3). There is a huge room for improvement but it performs much better than a dice roll at least:
| F1 | Roc Auc | Accuracy |
|:----:|:----:|:----:|
| 0.655779 | 0.799979 | 0.552670 |
|
dc6fe5e8889ed06fc4b333f952640e0c
|
p1atdev/ore-o
|
p1atdev
| null | 10 | 0 | null | 6 |
text-to-image
| false | false | false |
openrail++
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['stable-diffusion', 'text-to-image']
| false | true | true | 1,280 | false |
# Lazurite
- [lazurite-v1-050.safetensors](https://huggingface.co/p1atdev/ore-o/blob/main/lazurite-v1-050.safetensors) (0.5 weight)
- [lazurite-v1-050.ckpt](https://huggingface.co/p1atdev/ore-o/blob/main/lazurite-v1-050.ckpt)
- [lazurite-v1-100.safetensors](https://huggingface.co/p1atdev/ore-o/blob/main/lazurite-v1-100.safetensors) (1.0 weight)
- [lazurite-v1-100.ckpt](https://huggingface.co/p1atdev/ore-o/blob/main/lazurite-v1-100.ckpt)
- [lazurite-v1.yaml](https://huggingface.co/p1atdev/ore-o/blob/main/lazurite-v1.yaml)
- [lazurite-v1-lora.pt](https://huggingface.co/p1atdev/ore-o/blob/main/lazurite-v1-lora.pt)
Lazurite is a latent diffusion model fintuned with modern style illustrations in LoRA method, based on [Waifu Diffusion 1.4 epoch 2](https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/wd-1-4-anime_e2.ckpt).
If you want to use the model in AUTOMATIC1111's Web UI, you neeed [lazurite-v1.yaml](https://huggingface.co/p1atdev/ore-o/blob/main/lazurite-v1.yaml) and rename it.
The `lazurite-v1-lora.pt` only works with kohya's [sd-script](https://github.com/kohya-ss/sd-scripts) or [extension](https://github.com/kohya-ss/sd-webui-additional-networks).
|
cf0d5bf5a849b878f26f9812d70179b0
|
Padomin/t5-base-TEDxJP-0front-1body-10rear
|
Padomin
|
t5
| 20 | 2 |
transformers
| 0 |
text2text-generation
| true | false | false |
cc-by-sa-4.0
| null |
['te_dx_jp']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,954 | false |
<!-- 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-TEDxJP-0front-1body-10rear
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4646
- Wer: 0.1756
- Mer: 0.1698
- Wil: 0.2581
- Wip: 0.7419
- Hits: 55450
- Substitutions: 6518
- Deletions: 2619
- Insertions: 2202
- Cer: 0.1383
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
| 0.6456 | 1.0 | 1457 | 0.4955 | 0.2298 | 0.2127 | 0.3032 | 0.6968 | 54935 | 6936 | 2716 | 5190 | 0.2008 |
| 0.5333 | 2.0 | 2914 | 0.4472 | 0.1817 | 0.1755 | 0.2646 | 0.7354 | 55142 | 6584 | 2861 | 2291 | 0.1431 |
| 0.4864 | 3.0 | 4371 | 0.4420 | 0.1774 | 0.1714 | 0.2600 | 0.7400 | 55396 | 6542 | 2649 | 2266 | 0.1397 |
| 0.429 | 4.0 | 5828 | 0.4374 | 0.1764 | 0.1704 | 0.2587 | 0.7413 | 55446 | 6512 | 2629 | 2249 | 0.1389 |
| 0.3741 | 5.0 | 7285 | 0.4413 | 0.1744 | 0.1687 | 0.2559 | 0.7441 | 55518 | 6416 | 2653 | 2198 | 0.1383 |
| 0.3467 | 6.0 | 8742 | 0.4467 | 0.1742 | 0.1686 | 0.2564 | 0.7436 | 55493 | 6466 | 2628 | 2159 | 0.1390 |
| 0.3761 | 7.0 | 10199 | 0.4524 | 0.1754 | 0.1696 | 0.2577 | 0.7423 | 55471 | 6498 | 2618 | 2210 | 0.1380 |
| 0.3102 | 8.0 | 11656 | 0.4557 | 0.1751 | 0.1695 | 0.2574 | 0.7426 | 55412 | 6478 | 2697 | 2133 | 0.1395 |
| 0.3008 | 9.0 | 13113 | 0.4632 | 0.1758 | 0.1700 | 0.2584 | 0.7416 | 55421 | 6516 | 2650 | 2189 | 0.1387 |
| 0.3051 | 10.0 | 14570 | 0.4646 | 0.1756 | 0.1698 | 0.2581 | 0.7419 | 55450 | 6518 | 2619 | 2202 | 0.1383 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
ae044cabf449cef9c75a4412090bcdf0
|
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