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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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int64 0
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| likes
int64 0
11.7k
| library_name
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listlengths 1
4.05k
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Jasmine8596/distilbert-finetuned-imdb
|
Jasmine8596
| 2022-09-09T02:41:29Z | 70 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-08T23:25:43Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jasmine8596/distilbert-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jasmine8596/distilbert-finetuned-imdb
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: 2.8423
- Validation Loss: 2.6128
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -687, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.8423 | 2.6128 | 0 |
### Framework versions
- Transformers 4.22.0.dev0
- TensorFlow 2.8.2
- Tokenizers 0.12.1
|
UmberH/distilbert-base-uncased-finetuned-cola
|
UmberH
| 2022-09-09T01:53:53Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-08T20:21:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5456062114587601
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8381
- Matthews Correlation: 0.5456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5245 | 1.0 | 535 | 0.5432 | 0.4249 |
| 0.3514 | 2.0 | 1070 | 0.5075 | 0.4874 |
| 0.2368 | 3.0 | 1605 | 0.5554 | 0.5403 |
| 0.1712 | 4.0 | 2140 | 0.7780 | 0.5246 |
| 0.1254 | 5.0 | 2675 | 0.8381 | 0.5456 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/bonzi-monkey
|
sd-concepts-library
| 2022-09-09T00:03:11Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-09T00:03:05Z |
---
license: mit
---
### bonzi monkey on Stable Diffusion
This is the `<bonzi>` 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`:
|
SebastianS/MetalSebastian
|
SebastianS
| 2022-09-09T00:00:23Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-07T15:25:14Z |
---
tags:
- conversational
---
# Produced with ⚙️ by [mimicbot](https://github.com/CakeCrusher/mimicbot)🤖
|
sd-concepts-library/shrunken-head
|
sd-concepts-library
| 2022-09-08T22:23:57Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T22:23:46Z |
---
license: mit
---
### shrunken head on Stable Diffusion
This is the `<shrunken-head>` 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`:
|
IIIT-L/xlm-roberta-base-finetuned-combined-DS
|
IIIT-L
| 2022-09-08T21:22:20Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-08T20:48:41Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: xlm-roberta-base-finetuned-combined-DS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-combined-DS
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:
- Loss: 1.0232
- Accuracy: 0.6362
- Precision: 0.6193
- Recall: 0.6204
- F1: 0.6160
## 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: 4.1187640010910775e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0408 | 1.0 | 711 | 1.0206 | 0.5723 | 0.5597 | 0.5122 | 0.4897 |
| 0.9224 | 2.0 | 1422 | 0.9092 | 0.5695 | 0.5745 | 0.5610 | 0.5572 |
| 0.8395 | 3.0 | 2133 | 0.8878 | 0.6088 | 0.6083 | 0.6071 | 0.5981 |
| 0.7418 | 3.99 | 2844 | 0.8828 | 0.6088 | 0.6009 | 0.6068 | 0.5936 |
| 0.6484 | 4.99 | 3555 | 0.9636 | 0.6355 | 0.6235 | 0.6252 | 0.6184 |
| 0.5644 | 5.99 | 4266 | 1.0232 | 0.6362 | 0.6193 | 0.6204 | 0.6160 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
PrimeQA/tydiqa-ft-listqa_nq-task-xlm-roberta-large
|
PrimeQA
| 2022-09-08T21:12:24Z | 37 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"MRC",
"TyDiQA",
"Natural Questions List",
"xlm-roberta-large",
"multilingual",
"arxiv:1911.02116",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-09-07T14:45:48Z |
---
license: apache-2.0
tags:
- MRC
- TyDiQA
- Natural Questions List
- xlm-roberta-large
language:
- multilingual
---
*Task*: MRC
# Model description
An XLM-RoBERTa reading comprehension model for List Question Answering using a fine-tuned [TyDi xlm-roberta-large](https://huggingface.co/PrimeQA/tydiqa-primary-task-xlm-roberta-large) model that is further fine-tuned on the list questions in the [Natural Questions](https://huggingface.co/datasets/natural_questions) dataset.
## Intended uses & limitations
You can use the raw model for the reading comprehension task. Biases associated with the pre-existing language model, xlm-roberta-large, that we used may be present in our fine-tuned model, tydiqa-ft-listqa_nq-task-xlm-roberta-large.
## Usage
You can use this model directly with the [PrimeQA](https://github.com/primeqa/primeqa) pipeline for reading comprehension [listqa.ipynb](https://github.com/primeqa/primeqa/blob/main/notebooks/mrc/listqa.ipynb).
### BibTeX entry and citation info
```bibtex
@article{kwiatkowski-etal-2019-natural,
title = "Natural Questions: A Benchmark for Question Answering Research",
author = "Kwiatkowski, Tom and
Palomaki, Jennimaria and
Redfield, Olivia and
Collins, Michael and
Parikh, Ankur and
Alberti, Chris and
Epstein, Danielle and
Polosukhin, Illia and
Devlin, Jacob and
Lee, Kenton and
Toutanova, Kristina and
Jones, Llion and
Kelcey, Matthew and
Chang, Ming-Wei and
Dai, Andrew M. and
Uszkoreit, Jakob and
Le, Quoc and
Petrov, Slav",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q19-1026",
doi = "10.1162/tacl_a_00276",
pages = "452--466",
}
```
```bibtex
@article{DBLP:journals/corr/abs-1911-02116,
author = {Alexis Conneau and
Kartikay Khandelwal and
Naman Goyal and
Vishrav Chaudhary and
Guillaume Wenzek and
Francisco Guzm{\'{a}}n and
Edouard Grave and
Myle Ott and
Luke Zettlemoyer and
Veselin Stoyanov},
title = {Unsupervised Cross-lingual Representation Learning at Scale},
journal = {CoRR},
volume = {abs/1911.02116},
year = {2019},
url = {http://arxiv.org/abs/1911.02116},
eprinttype = {arXiv},
eprint = {1911.02116},
timestamp = {Mon, 11 Nov 2019 18:38:09 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1911-02116.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
GItaf/bert2bert-no-cross-attn-decoder
|
GItaf
| 2022-09-08T20:26:21Z | 49 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-05T08:11:45Z |
---
tags:
- generated_from_trainer
- text-generation
widget:
parameters:
- max_new_tokens = 100
model-index:
- name: bert-base-uncased-bert-base-uncased-finetuned-mbti-0909
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-bert-base-uncased-finetuned-mbti-0909
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.0549
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.2244 | 1.0 | 1735 | 5.7788 |
| 4.8483 | 2.0 | 3470 | 5.7647 |
| 4.7578 | 3.0 | 5205 | 5.9016 |
| 4.5606 | 4.0 | 6940 | 5.9895 |
| 4.4314 | 5.0 | 8675 | 6.0549 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
GItaf/bert-base-uncased-bert-base-uncased-finetuned-mbti-0909
|
GItaf
| 2022-09-08T20:12:28Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-08T16:52:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-bert-base-uncased-finetuned-mbti-0909
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-bert-base-uncased-finetuned-mbti-0909
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 4.3136
- eval_runtime: 23.6133
- eval_samples_per_second: 73.475
- eval_steps_per_second: 9.19
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
lewtun/dummy-setfit-model
|
lewtun
| 2022-09-08T19:53:17Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-08T19:53:10Z |
---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/paraphrase-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## 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/paraphrase-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-mpnet-base-v2')
model = AutoModel.from_pretrained('sentence-transformers/paraphrase-mpnet-base-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_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/paraphrase-mpnet-base-v2)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## 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",
}
```
|
sd-concepts-library/line-art
|
sd-concepts-library
| 2022-09-08T19:30:01Z | 0 | 47 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T19:29:47Z |
---
license: mit
---
### Line Art on Stable Diffusion
This is the `<line-art>` 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`:
Images via Freepik.com
|
ighita/ddpm-butterflies-128
|
ighita
| 2022-09-08T19:17:27Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-09-06T10:19:48Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- 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-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/ighita/ddpm-butterflies-128/tensorboard?#scalars)
|
hashb/darknet-yolov4-object-detection
|
hashb
| 2022-09-08T19:11:58Z | 0 | 1 | null |
[
"arxiv:2004.10934",
"license:mit",
"region:us"
] | null | 2022-09-08T18:36:21Z |
---
license: mit
---
[](https://github.com/AlexeyAB/darknet/actions?query=workflow%3A%22Darknet+Continuous+Integration%22)
## Model
YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detector SWIN-L Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) by 509% in speed and 2% in accuracy, and convolutional-based detector ConvNeXt-XL Cascade-Mask R-CNN (8.6 FPS A100, 55.2% AP) by 551% in speed and 0.7% AP in accuracy, as well as YOLOv7 outperforms: YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, DINO-5scale-R50, ViT-Adapter-B and many other object detectors in speed and accuracy.
## How to use:
```
# clone the repo
git clone https://huggingface.co/hashb/darknet-yolov4-object-detection
# open file darknet-yolov4-object-detection.ipynb and run in colab
```
## Citation
```
@misc{bochkovskiy2020yolov4,
title={YOLOv4: Optimal Speed and Accuracy of Object Detection},
author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
year={2020},
eprint={2004.10934},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```
@InProceedings{Wang_2021_CVPR,
author = {Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
title = {{Scaled-YOLOv4}: Scaling Cross Stage Partial Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {13029-13038}
}
```
|
sd-concepts-library/art-brut
|
sd-concepts-library
| 2022-09-08T18:40:33Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T18:40:22Z |
---
license: mit
---
### art brut on Stable Diffusion
This is the `<art-brut>` 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`:
|
sd-concepts-library/nebula
|
sd-concepts-library
| 2022-09-08T17:48:26Z | 0 | 23 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T17:48:21Z |
---
license: mit
---
### Nebula on Stable Diffusion
This is the `<nebula>` 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`:
|
danielwang-hads/wav2vec2-base-timit-demo-google-colab
|
danielwang-hads
| 2022-09-08T17:45:13Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-08-30T18:26:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-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: 0.5079
- Wer: 0.3365
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.4933 | 1.0 | 500 | 1.7711 | 0.9978 |
| 0.8658 | 2.01 | 1000 | 0.6262 | 0.5295 |
| 0.4405 | 3.01 | 1500 | 0.4841 | 0.4845 |
| 0.3062 | 4.02 | 2000 | 0.4897 | 0.4215 |
| 0.233 | 5.02 | 2500 | 0.4326 | 0.4101 |
| 0.1896 | 6.02 | 3000 | 0.4924 | 0.4078 |
| 0.1589 | 7.03 | 3500 | 0.4430 | 0.3896 |
| 0.1391 | 8.03 | 4000 | 0.4334 | 0.3889 |
| 0.1216 | 9.04 | 4500 | 0.4691 | 0.3828 |
| 0.1063 | 10.04 | 5000 | 0.4726 | 0.3705 |
| 0.0992 | 11.04 | 5500 | 0.4333 | 0.3690 |
| 0.0872 | 12.05 | 6000 | 0.4986 | 0.3771 |
| 0.0829 | 13.05 | 6500 | 0.4903 | 0.3685 |
| 0.0713 | 14.06 | 7000 | 0.5293 | 0.3655 |
| 0.068 | 15.06 | 7500 | 0.5039 | 0.3612 |
| 0.0621 | 16.06 | 8000 | 0.5314 | 0.3665 |
| 0.0571 | 17.07 | 8500 | 0.5038 | 0.3572 |
| 0.0585 | 18.07 | 9000 | 0.4718 | 0.3550 |
| 0.0487 | 19.08 | 9500 | 0.5482 | 0.3626 |
| 0.0459 | 20.08 | 10000 | 0.5239 | 0.3545 |
| 0.0419 | 21.08 | 10500 | 0.5096 | 0.3473 |
| 0.0362 | 22.09 | 11000 | 0.5222 | 0.3500 |
| 0.0331 | 23.09 | 11500 | 0.5062 | 0.3489 |
| 0.0352 | 24.1 | 12000 | 0.4913 | 0.3459 |
| 0.0315 | 25.1 | 12500 | 0.4701 | 0.3412 |
| 0.028 | 26.1 | 13000 | 0.5178 | 0.3402 |
| 0.0255 | 27.11 | 13500 | 0.5168 | 0.3405 |
| 0.0228 | 28.11 | 14000 | 0.5154 | 0.3368 |
| 0.0232 | 29.12 | 14500 | 0.5079 | 0.3365 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
sd-concepts-library/apulian-rooster-v0-1
|
sd-concepts-library
| 2022-09-08T17:31:44Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T16:14:06Z |
---
license: mit
---
### apulian-rooster-v0.1 on Stable Diffusion
--
# Inspired by the design of the Galletto (rooster) typical of ceramics and pottery made in Grottaglie, Puglia (Italy).
This is the `<apulian-rooster-v0.1>` 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`:
|
sd-concepts-library/fractal
|
sd-concepts-library
| 2022-09-08T17:04:23Z | 0 | 4 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T16:58:04Z |
---
license: mit
---
### fractal on Stable Diffusion
This is the `<fractal>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](#) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](#).
The images composing the token are here:
https://huggingface.co/datasets/Nbardy/Fractal-photos
Thank you to the photographers. Who graciously published these photos for free non-commercial use. Each photo has the artists name in the dataset hosted on hugging face
|
MultiTrickFox/bloom-2b5_Zen
|
MultiTrickFox
| 2022-09-08T16:55:59Z | 14 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-16T00:37:54Z |
#####
## Bloom2.5B Zen ##
#####
Bloom (2.5 B) Scientific Model fine-tuned on Zen knowledge
#####
## Usage ##
#####
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("MultiTrickFox/bloom-2b5_Zen")
model = AutoModelForCausalLM.from_pretrained("MultiTrickFox/bloom-2b5_Zen")
generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
inp = [ """Today""", """Yesterday""" ]
out = generator(
inp,
do_sample=True,
temperature=.7,
typical_p=.6,
#top_p=.9,
repetition_penalty=1.2,
max_new_tokens=666,
max_time=60, # seconds
)
for o in out: print(o[0]['generated_text'])
```
|
huggingtweets/piemadd
|
huggingtweets
| 2022-09-08T16:20:49Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-08T16:16:57Z |
---
language: en
thumbnail: http://www.huggingtweets.com/piemadd/1662653961299/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1521050682983424003/yERaHagV_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Piero Maddaleni 2027</div>
<div style="text-align: center; font-size: 14px;">@piemadd</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Piero Maddaleni 2027.
| Data | Piero Maddaleni 2027 |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 322 |
| Short tweets | 540 |
| Tweets kept | 2380 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jem4xdn0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @piemadd's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6e8s7bst) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6e8s7bst/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/piemadd')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
sd-concepts-library/lolo
|
sd-concepts-library
| 2022-09-08T16:06:05Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T16:05:54Z |
---
license: mit
---
### Lolo on Stable Diffusion
This is the `<lolo>` 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`:
|
Guruji108/xlm-roberta-base-finetuned-panx-de
|
Guruji108
| 2022-09-08T16:00:40Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-05T17:49:47Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.863677639046538
---
<!-- 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-de
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: 0.1343
- F1: 0.8637
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2578 | 1.0 | 525 | 0.1562 | 0.8273 |
| 0.1297 | 2.0 | 1050 | 0.1330 | 0.8474 |
| 0.0809 | 3.0 | 1575 | 0.1343 | 0.8637 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
mariolinml/roberta_large-unbalanced_simple-ner-conll2003_0908_v0
|
mariolinml
| 2022-09-08T15:24:17Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-08T14:34:41Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta_large-unbalanced_simple-ner-conll2003_0908_v0
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9552732335537766
- name: Recall
type: recall
value: 0.9718484419263456
- name: F1
type: f1
value: 0.9634895559066174
- name: Accuracy
type: accuracy
value: 0.989226995491912
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_large-unbalanced_simple-ner-conll2003_0908_v0
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0881
- Precision: 0.9553
- Recall: 0.9718
- F1: 0.9635
- Accuracy: 0.9892
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.07 | 1.0 | 878 | 0.0249 | 0.9616 | 0.9746 | 0.9681 | 0.9936 |
| 0.0176 | 2.0 | 1756 | 0.0241 | 0.9699 | 0.9818 | 0.9758 | 0.9948 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
SiddharthaM/bert-base-uncased-ner-conll2003
|
SiddharthaM
| 2022-09-08T14:57:50Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-08T14:37:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-ner-conll2003
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9342126957955482
- name: Recall
type: recall
value: 0.9535509929316729
- name: F1
type: f1
value: 0.943782793370534
- name: Accuracy
type: accuracy
value: 0.9870194854889033
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-ner-conll2003
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0602
- Precision: 0.9342
- Recall: 0.9536
- F1: 0.9438
- Accuracy: 0.9870
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0871 | 1.0 | 1756 | 0.0728 | 0.9138 | 0.9275 | 0.9206 | 0.9811 |
| 0.0331 | 2.0 | 3512 | 0.0591 | 0.9311 | 0.9514 | 0.9411 | 0.9866 |
| 0.0173 | 3.0 | 5268 | 0.0602 | 0.9342 | 0.9536 | 0.9438 | 0.9870 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Sebabrata/lmv2-g-w2-300-doc-09-08
|
Sebabrata
| 2022-09-08T14:33:01Z | 78 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-08T13:35:51Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: lmv2-g-w2-300-doc-09-08
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lmv2-g-w2-300-doc-09-08
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0262
- Control Number Precision: 1.0
- Control Number Recall: 1.0
- Control Number F1: 1.0
- Control Number Number: 17
- Ein Precision: 1.0
- Ein Recall: 0.9833
- Ein F1: 0.9916
- Ein Number: 60
- Employee’s Address Precision: 0.9667
- Employee’s Address Recall: 0.9831
- Employee’s Address F1: 0.9748
- Employee’s Address Number: 59
- Employee’s Name Precision: 0.9833
- Employee’s Name Recall: 1.0
- Employee’s Name F1: 0.9916
- Employee’s Name Number: 59
- Employee’s Ssn Precision: 0.9836
- Employee’s Ssn Recall: 1.0
- Employee’s Ssn F1: 0.9917
- Employee’s Ssn Number: 60
- Employer’s Address Precision: 0.9833
- Employer’s Address Recall: 0.9672
- Employer’s Address F1: 0.9752
- Employer’s Address Number: 61
- Employer’s Name Precision: 0.9833
- Employer’s Name Recall: 0.9833
- Employer’s Name F1: 0.9833
- Employer’s Name Number: 60
- Federal Income Tax Withheld Precision: 1.0
- Federal Income Tax Withheld Recall: 1.0
- Federal Income Tax Withheld F1: 1.0
- Federal Income Tax Withheld Number: 60
- Medicare Tax Withheld Precision: 1.0
- Medicare Tax Withheld Recall: 1.0
- Medicare Tax Withheld F1: 1.0
- Medicare Tax Withheld Number: 60
- Medicare Wages Tips Precision: 1.0
- Medicare Wages Tips Recall: 1.0
- Medicare Wages Tips F1: 1.0
- Medicare Wages Tips Number: 60
- Social Security Tax Withheld Precision: 1.0
- Social Security Tax Withheld Recall: 0.9836
- Social Security Tax Withheld F1: 0.9917
- Social Security Tax Withheld Number: 61
- Social Security Wages Precision: 0.9833
- Social Security Wages Recall: 1.0
- Social Security Wages F1: 0.9916
- Social Security Wages Number: 59
- Wages Tips Precision: 1.0
- Wages Tips Recall: 0.9836
- Wages Tips F1: 0.9917
- Wages Tips Number: 61
- Overall Precision: 0.9905
- Overall Recall: 0.9905
- Overall F1: 0.9905
- Overall Accuracy: 0.9973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Control Number Precision | Control Number Recall | Control Number F1 | Control Number Number | Ein Precision | Ein Recall | Ein F1 | Ein Number | Employee’s Address Precision | Employee’s Address Recall | Employee’s Address F1 | Employee’s Address Number | Employee’s Name Precision | Employee’s Name Recall | Employee’s Name F1 | Employee’s Name Number | Employee’s Ssn Precision | Employee’s Ssn Recall | Employee’s Ssn F1 | Employee’s Ssn Number | Employer’s Address Precision | Employer’s Address Recall | Employer’s Address F1 | Employer’s Address Number | Employer’s Name Precision | Employer’s Name Recall | Employer’s Name F1 | Employer’s Name Number | Federal Income Tax Withheld Precision | Federal Income Tax Withheld Recall | Federal Income Tax Withheld F1 | Federal Income Tax Withheld Number | Medicare Tax Withheld Precision | Medicare Tax Withheld Recall | Medicare Tax Withheld F1 | Medicare Tax Withheld Number | Medicare Wages Tips Precision | Medicare Wages Tips Recall | Medicare Wages Tips F1 | Medicare Wages Tips Number | Social Security Tax Withheld Precision | Social Security Tax Withheld Recall | Social Security Tax Withheld F1 | Social Security Tax Withheld Number | Social Security Wages Precision | Social Security Wages Recall | Social Security Wages F1 | Social Security Wages Number | Wages Tips Precision | Wages Tips Recall | Wages Tips F1 | Wages Tips Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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| 1.7717 | 1.0 | 240 | 0.9856 | 0.0 | 0.0 | 0.0 | 17 | 0.9206 | 0.9667 | 0.9431 | 60 | 0.6824 | 0.9831 | 0.8056 | 59 | 0.2333 | 0.5932 | 0.3349 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.7609 | 0.5738 | 0.6542 | 61 | 0.3654 | 0.3167 | 0.3393 | 60 | 0.0 | 0.0 | 0.0 | 60 | 0.8194 | 0.9833 | 0.8939 | 60 | 0.6064 | 0.95 | 0.7403 | 60 | 0.5050 | 0.8361 | 0.6296 | 61 | 0.0 | 0.0 | 0.0 | 59 | 0.5859 | 0.9508 | 0.725 | 61 | 0.5954 | 0.6649 | 0.6282 | 0.9558 |
| 0.5578 | 2.0 | 480 | 0.2957 | 0.8462 | 0.6471 | 0.7333 | 17 | 0.9831 | 0.9667 | 0.9748 | 60 | 0.9048 | 0.9661 | 0.9344 | 59 | 0.8358 | 0.9492 | 0.8889 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8125 | 0.8525 | 0.8320 | 61 | 0.8462 | 0.9167 | 0.8800 | 60 | 0.9672 | 0.9833 | 0.9752 | 60 | 0.9524 | 1.0 | 0.9756 | 60 | 0.9194 | 0.95 | 0.9344 | 60 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9508 | 0.9831 | 0.9667 | 59 | 0.9516 | 0.9672 | 0.9593 | 61 | 0.9212 | 0.9512 | 0.9359 | 0.9891 |
| 0.223 | 3.0 | 720 | 0.1626 | 0.5 | 0.6471 | 0.5641 | 17 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9672 | 1.0 | 0.9833 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8769 | 0.9344 | 0.9048 | 61 | 0.9508 | 0.9667 | 0.9587 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8769 | 0.95 | 0.912 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9516 | 0.9672 | 0.9593 | 61 | 0.9370 | 0.9688 | 0.9526 | 0.9923 |
| 0.1305 | 4.0 | 960 | 0.1025 | 0.9444 | 1.0 | 0.9714 | 17 | 0.9831 | 0.9667 | 0.9748 | 60 | 0.9194 | 0.9661 | 0.9421 | 59 | 0.9508 | 0.9831 | 0.9667 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9219 | 0.9672 | 0.944 | 61 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 0.9524 | 1.0 | 0.9756 | 60 | 0.8906 | 0.95 | 0.9194 | 60 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9516 | 0.9672 | 0.9593 | 61 | 0.9511 | 0.9756 | 0.9632 | 0.9947 |
| 0.0852 | 5.0 | 1200 | 0.0744 | 0.7391 | 1.0 | 0.85 | 17 | 0.9831 | 0.9667 | 0.9748 | 60 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9344 | 0.9344 | 0.9344 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9365 | 0.9833 | 0.9593 | 60 | 0.9677 | 1.0 | 0.9836 | 60 | 0.95 | 0.95 | 0.9500 | 60 | 0.9836 | 0.9836 | 0.9836 | 61 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9626 | 0.9783 | 0.9704 | 0.9953 |
| 0.0583 | 6.0 | 1440 | 0.0554 | 0.7727 | 1.0 | 0.8718 | 17 | 0.9831 | 0.9667 | 0.9748 | 60 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9048 | 0.9344 | 0.9194 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 0.9344 | 0.95 | 0.9421 | 60 | 1.0 | 0.9672 | 0.9833 | 61 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9677 | 0.9756 | 0.9716 | 0.9957 |
| 0.0431 | 7.0 | 1680 | 0.0471 | 0.9444 | 1.0 | 0.9714 | 17 | 0.9831 | 0.9667 | 0.9748 | 60 | 0.9016 | 0.9322 | 0.9167 | 59 | 0.95 | 0.9661 | 0.9580 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8676 | 0.9672 | 0.9147 | 61 | 0.9831 | 0.9667 | 0.9748 | 60 | 1.0 | 0.9833 | 0.9916 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9516 | 0.9833 | 0.9672 | 60 | 0.9836 | 0.9836 | 0.9836 | 61 | 0.9831 | 0.9831 | 0.9831 | 59 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9625 | 0.9756 | 0.9690 | 0.9947 |
| 0.0314 | 8.0 | 1920 | 0.0359 | 1.0 | 1.0 | 1.0 | 17 | 0.9831 | 0.9667 | 0.9748 | 60 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9516 | 0.9672 | 0.9593 | 61 | 1.0 | 0.9667 | 0.9831 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9516 | 0.9833 | 0.9672 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9831 | 0.9831 | 0.9831 | 59 | 0.9672 | 0.9672 | 0.9672 | 61 | 0.9771 | 0.9824 | 0.9797 | 0.9969 |
| 0.0278 | 9.0 | 2160 | 0.0338 | 0.8947 | 1.0 | 0.9444 | 17 | 0.9833 | 0.9833 | 0.9833 | 60 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9667 | 0.9831 | 0.9748 | 59 | 1.0 | 1.0 | 1.0 | 60 | 0.9365 | 0.9672 | 0.9516 | 61 | 0.9672 | 0.9833 | 0.9752 | 60 | 1.0 | 0.9833 | 0.9916 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9516 | 0.9833 | 0.9672 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9672 | 0.9672 | 0.9672 | 61 | 0.9705 | 0.9837 | 0.9771 | 0.9965 |
| 0.0231 | 10.0 | 2400 | 0.0332 | 0.9444 | 1.0 | 0.9714 | 17 | 0.9831 | 0.9667 | 0.9748 | 60 | 0.9508 | 0.9831 | 0.9667 | 59 | 0.9048 | 0.9661 | 0.9344 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9667 | 0.9508 | 0.9587 | 61 | 0.9667 | 0.9667 | 0.9667 | 60 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9365 | 0.9833 | 0.9593 | 60 | 1.0 | 0.9672 | 0.9833 | 61 | 0.9831 | 0.9831 | 0.9831 | 59 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9690 | 0.9769 | 0.9730 | 0.9964 |
| 0.0189 | 11.0 | 2640 | 0.0342 | 1.0 | 1.0 | 1.0 | 17 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.8657 | 0.9831 | 0.9206 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8594 | 0.9016 | 0.88 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9516 | 0.9672 | 0.9593 | 61 | 0.964 | 0.9810 | 0.9724 | 0.9958 |
| 0.0187 | 12.0 | 2880 | 0.0255 | 1.0 | 1.0 | 1.0 | 17 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.9508 | 0.9831 | 0.9667 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9667 | 0.9508 | 0.9587 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9672 | 0.9833 | 0.9752 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9824 | 0.9851 | 0.9837 | 0.9976 |
| 0.0126 | 13.0 | 3120 | 0.0257 | 1.0 | 1.0 | 1.0 | 17 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.9344 | 0.9661 | 0.95 | 59 | 0.8889 | 0.9492 | 0.9180 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8788 | 0.9508 | 0.9134 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9836 | 1.0 | 0.9917 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9672 | 0.9833 | 61 | 0.9508 | 0.9831 | 0.9667 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9652 | 0.9796 | 0.9724 | 0.9971 |
| 0.012 | 14.0 | 3360 | 0.0227 | 1.0 | 1.0 | 1.0 | 17 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.9516 | 1.0 | 0.9752 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9194 | 0.9344 | 0.9268 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9672 | 0.9833 | 0.9752 | 60 | 1.0 | 0.9833 | 0.9916 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9836 | 0.9836 | 0.9836 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9784 | 0.9851 | 0.9817 | 0.9977 |
| 0.0119 | 15.0 | 3600 | 0.0284 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 1.0 | 1.0 | 60 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 1.0 | 1.0 | 60 | 0.9167 | 0.9016 | 0.9091 | 61 | 0.9661 | 0.95 | 0.9580 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9810 | 0.9824 | 0.9817 | 0.9965 |
| 0.0103 | 16.0 | 3840 | 0.0289 | 0.9444 | 1.0 | 0.9714 | 17 | 0.9672 | 0.9833 | 0.9752 | 60 | 0.9344 | 0.9661 | 0.95 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 1.0 | 1.0 | 60 | 0.8088 | 0.9016 | 0.8527 | 61 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9666 | 0.9810 | 0.9737 | 0.9963 |
| 0.01 | 17.0 | 4080 | 0.0305 | 0.8947 | 1.0 | 0.9444 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9516 | 1.0 | 0.9752 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9355 | 0.9508 | 0.9431 | 61 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.8955 | 1.0 | 0.9449 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9694 | 0.9891 | 0.9792 | 0.9961 |
| 0.0082 | 18.0 | 4320 | 0.0256 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9508 | 0.9831 | 0.9667 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8636 | 0.9344 | 0.8976 | 61 | 0.9831 | 0.9667 | 0.9748 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9785 | 0.9864 | 0.9824 | 0.9970 |
| 0.0059 | 19.0 | 4560 | 0.0255 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9355 | 0.9508 | 0.9431 | 61 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9865 | 0.9891 | 0.9878 | 0.9974 |
| 0.0078 | 20.0 | 4800 | 0.0293 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9508 | 0.9831 | 0.9667 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9 | 0.8852 | 0.8926 | 61 | 0.9661 | 0.95 | 0.9580 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9810 | 0.9810 | 0.9810 | 0.9966 |
| 0.009 | 21.0 | 5040 | 0.0264 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9206 | 0.9831 | 0.9508 | 59 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8889 | 0.9180 | 0.9032 | 61 | 0.9672 | 0.9833 | 0.9752 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9836 | 0.9836 | 0.9836 | 61 | 0.9831 | 0.9831 | 0.9831 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9745 | 0.9837 | 0.9791 | 0.9969 |
| 0.0046 | 22.0 | 5280 | 0.0271 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9355 | 0.9831 | 0.9587 | 59 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9032 | 0.9180 | 0.9106 | 61 | 0.9672 | 0.9833 | 0.9752 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9784 | 0.9851 | 0.9817 | 0.9970 |
| 0.0087 | 23.0 | 5520 | 0.0278 | 0.9444 | 1.0 | 0.9714 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9194 | 0.9661 | 0.9421 | 59 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8657 | 0.9508 | 0.9062 | 61 | 0.9836 | 1.0 | 0.9917 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9733 | 0.9878 | 0.9805 | 0.9958 |
| 0.0054 | 24.0 | 5760 | 0.0276 | 0.9444 | 1.0 | 0.9714 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.95 | 0.9661 | 0.9580 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9355 | 0.9508 | 0.9431 | 61 | 0.9831 | 0.9667 | 0.9748 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9355 | 0.9667 | 0.9508 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9784 | 0.9837 | 0.9811 | 0.9971 |
| 0.0057 | 25.0 | 6000 | 0.0260 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9667 | 0.9831 | 60 | 0.9077 | 1.0 | 0.9516 | 59 | 0.95 | 0.9661 | 0.9580 | 59 | 0.9677 | 1.0 | 0.9836 | 60 | 0.9508 | 0.9508 | 0.9508 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9672 | 0.9833 | 61 | 0.9672 | 1.0 | 0.9833 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9771 | 0.9837 | 0.9804 | 0.9971 |
| 0.0074 | 26.0 | 6240 | 0.0340 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9180 | 0.9492 | 0.9333 | 59 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8906 | 0.9344 | 0.9120 | 61 | 0.9831 | 0.9667 | 0.9748 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 0.9836 | 0.9836 | 61 | 0.9757 | 0.9824 | 0.9790 | 0.9959 |
| 0.0047 | 27.0 | 6480 | 0.0306 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 1.0 | 1.0 | 60 | 0.8923 | 0.9831 | 0.9355 | 59 | 0.9672 | 1.0 | 0.9833 | 59 | 1.0 | 1.0 | 1.0 | 60 | 0.9016 | 0.9016 | 0.9016 | 61 | 0.9667 | 0.9667 | 0.9667 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9672 | 0.9833 | 61 | 0.8551 | 1.0 | 0.9219 | 59 | 1.0 | 0.8525 | 0.9204 | 61 | 0.9624 | 0.9715 | 0.9669 | 0.9961 |
| 0.0052 | 28.0 | 6720 | 0.0262 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9833 | 0.9672 | 0.9752 | 61 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9905 | 0.9905 | 0.9905 | 0.9973 |
| 0.0033 | 29.0 | 6960 | 0.0320 | 0.9444 | 1.0 | 0.9714 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.8406 | 0.9831 | 0.9062 | 59 | 0.9672 | 1.0 | 0.9833 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.8852 | 0.8852 | 0.8852 | 61 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 0.9667 | 0.9831 | 60 | 1.0 | 1.0 | 1.0 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9365 | 1.0 | 0.9672 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9627 | 0.9796 | 0.9711 | 0.9960 |
| 0.0048 | 30.0 | 7200 | 0.0215 | 1.0 | 1.0 | 1.0 | 17 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9672 | 1.0 | 0.9833 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.9836 | 1.0 | 0.9917 | 60 | 0.9833 | 0.9672 | 0.9752 | 61 | 1.0 | 0.9833 | 0.9916 | 60 | 0.9833 | 0.9833 | 0.9833 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 1.0 | 1.0 | 60 | 1.0 | 0.9672 | 0.9833 | 61 | 0.9672 | 1.0 | 0.9833 | 59 | 1.0 | 0.9836 | 0.9917 | 61 | 0.9891 | 0.9891 | 0.9891 | 0.9980 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
kkpathak91/FVM
|
kkpathak91
| 2022-09-08T13:23:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-09-08T10:32:31Z |
fact verification model(FVM) is trained on [FEVER](https://fever.ai), which aims to predict the veracity of a textual claim against a trustworthy knowledge source such as Wikipedia.
This repo hosts the following models for `FVM`:
- `fact_checking/`: the verification models based on BERT (large) and RoBERTa (large), respectively.
- `mrc_seq2seq/`: the generative machine reading comprehension model based on BART (base).
- `evidence_retrieval/`: the evidence sentence ranking models, which are copied directly from [KGAT](https://github.com/thunlp/KernelGAT).
|
sd-concepts-library/hub-city
|
sd-concepts-library
| 2022-09-08T12:04:39Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T12:04:27Z |
---
license: mit
---
### Hub City on Stable Diffusion
This is the `<HubCity>` 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`:
|
microsoft/xclip-base-patch16-ucf-16-shot
|
microsoft
| 2022-09-08T11:54:38Z | 68 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xclip",
"feature-extraction",
"vision",
"video-classification",
"en",
"arxiv:2208.02816",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2022-09-07T17:45:07Z |
---
language: en
license: mit
tags:
- vision
- video-classification
model-index:
- name: nielsr/xclip-base-patch16-ucf-16-shot
results:
- task:
type: video-classification
dataset:
name: UCF101
type: ucf101
metrics:
- type: top-1 accuracy
value: 91.4
---
# X-CLIP (base-sized model)
X-CLIP model (base-sized, patch resolution of 16) trained in a few-shot fashion (K=16) on [UCF101](https://www.crcv.ucf.edu/data/UCF101.php). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP).
This model was trained using 32 frames per video, at a resolution of 224x224.
Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
X-CLIP is a minimal extension of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.
This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval.
## Intended uses & limitations
You can use the raw model for determining how well text goes with a given video. See the [model hub](https://huggingface.co/models?search=microsoft/xclip) to look for
fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/xclip.html#).
## Training data
This model was trained on [UCF101](https://www.crcv.ucf.edu/data/UCF101.php).
### Preprocessing
The exact details of preprocessing during training can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L247).
The exact details of preprocessing during validation can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L285).
During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
## Evaluation results
This model achieves a top-1 accuracy of 91.4%.
|
microsoft/xclip-base-patch16-ucf-8-shot
|
microsoft
| 2022-09-08T11:49:44Z | 70 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xclip",
"feature-extraction",
"vision",
"video-classification",
"en",
"arxiv:2208.02816",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2022-09-07T17:13:46Z |
---
language: en
license: mit
tags:
- vision
- video-classification
model-index:
- name: nielsr/xclip-base-patch16-ucf-8-shot
results:
- task:
type: video-classification
dataset:
name: UCF101
type: ucf101
metrics:
- type: top-1 accuracy
value: 88.3
---
# X-CLIP (base-sized model)
X-CLIP model (base-sized, patch resolution of 16) trained in a few-shot fashion (K=8) on [UCF101](https://www.crcv.ucf.edu/data/UCF101.php). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP).
This model was trained using 32 frames per video, at a resolution of 224x224.
Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
X-CLIP is a minimal extension of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.
This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval.
## Intended uses & limitations
You can use the raw model for determining how well text goes with a given video. See the [model hub](https://huggingface.co/models?search=microsoft/xclip) to look for
fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/xclip.html#).
## Training data
This model was trained on [UCF101](https://www.crcv.ucf.edu/data/UCF101.php).
### Preprocessing
The exact details of preprocessing during training can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L247).
The exact details of preprocessing during validation can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L285).
During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
## Evaluation results
This model achieves a top-1 accuracy of 88.3%.
|
microsoft/xclip-base-patch16-ucf-2-shot
|
microsoft
| 2022-09-08T11:49:14Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xclip",
"feature-extraction",
"vision",
"video-classification",
"en",
"arxiv:2208.02816",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2022-09-07T17:06:55Z |
---
language: en
license: mit
tags:
- vision
- video-classification
model-index:
- name: nielsr/xclip-base-patch16-ucf-2-shot
results:
- task:
type: video-classification
dataset:
name: UCF101
type: ucf101
metrics:
- type: top-1 accuracy
value: 76.4
---
# X-CLIP (base-sized model)
X-CLIP model (base-sized, patch resolution of 16) trained in a few-shot fashion (K=2) on [UCF101](https://www.crcv.ucf.edu/data/UCF101.php). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP).
This model was trained using 32 frames per video, at a resolution of 224x224.
Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
X-CLIP is a minimal extension of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.
This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval.
## Intended uses & limitations
You can use the raw model for determining how well text goes with a given video. See the [model hub](https://huggingface.co/models?search=microsoft/xclip) to look for
fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/xclip.html#).
## Training data
This model was trained on [UCF101](https://www.crcv.ucf.edu/data/UCF101.php).
### Preprocessing
The exact details of preprocessing during training can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L247).
The exact details of preprocessing during validation can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L285).
During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
## Evaluation results
This model achieves a top-1 accuracy of 76.4%.
|
microsoft/xclip-base-patch16-hmdb-2-shot
|
microsoft
| 2022-09-08T11:46:18Z | 71 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xclip",
"feature-extraction",
"vision",
"video-classification",
"en",
"arxiv:2208.02816",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2022-09-07T16:36:25Z |
---
language: en
license: mit
tags:
- vision
- video-classification
model-index:
- name: nielsr/xclip-base-patch16-hmdb-2-shot
results:
- task:
type: video-classification
dataset:
name: HMDB-51
type: hmdb-51
metrics:
- type: top-1 accuracy
value: 53.0
---
# X-CLIP (base-sized model)
X-CLIP model (base-sized, patch resolution of 16) trained in a few-shot fashion (K=2) on [HMDB-51](https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP).
This model was trained using 32 frames per video, at a resolution of 224x224.
Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
X-CLIP is a minimal extension of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.
This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval.
## Intended uses & limitations
You can use the raw model for determining how well text goes with a given video. See the [model hub](https://huggingface.co/models?search=microsoft/xclip) to look for
fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/xclip.html#).
## Training data
This model was trained on [HMDB-51](https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/).
### Preprocessing
The exact details of preprocessing during training can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L247).
The exact details of preprocessing during validation can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L285).
During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
## Evaluation results
This model achieves a top-1 accuracy of 53.0%.
|
microsoft/xclip-base-patch16-kinetics-600-16-frames
|
microsoft
| 2022-09-08T11:41:13Z | 1,338 | 2 |
transformers
|
[
"transformers",
"pytorch",
"xclip",
"feature-extraction",
"vision",
"video-classification",
"en",
"arxiv:2208.02816",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2022-09-08T11:23:04Z |
---
language: en
license: mit
tags:
- vision
- video-classification
model-index:
- name: nielsr/xclip-base-patch16-kinetics-600-16-frames
results:
- task:
type: video-classification
dataset:
name: Kinetics 400
type: kinetics-400
metrics:
- type: top-1 accuracy
value: 85.8
- type: top-5 accuracy
value: 97.3
---
# X-CLIP (base-sized model)
X-CLIP model (base-sized, patch resolution of 16) trained fully-supervised on [Kinetics-600](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP).
This model was trained using 16 frames per video, at a resolution of 224x224.
Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
X-CLIP is a minimal extension of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.
This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval.
## Intended uses & limitations
You can use the raw model for determining how well text goes with a given video. See the [model hub](https://huggingface.co/models?search=microsoft/xclip) to look for
fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/xclip.html#).
## Training data
This model was trained on [Kinetics-600](https://www.deepmind.com/open-source/kinetics).
### Preprocessing
The exact details of preprocessing during training can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L247).
The exact details of preprocessing during validation can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L285).
During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
## Evaluation results
This model achieves a top-1 accuracy of 85.8% and a top-5 accuracy of 97.3%.
|
microsoft/xclip-large-patch14-16-frames
|
microsoft
| 2022-09-08T11:09:07Z | 2,692 | 3 |
transformers
|
[
"transformers",
"pytorch",
"xclip",
"feature-extraction",
"vision",
"video-classification",
"en",
"arxiv:2208.02816",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2022-09-07T15:33:48Z |
---
language: en
license: mit
tags:
- vision
- video-classification
model-index:
- name: nielsr/xclip-large-patch14-16-frames
results:
- task:
type: video-classification
dataset:
name: Kinetics 400
type: kinetics-400
metrics:
- type: top-1 accuracy
value: 87.7
- type: top-5 accuracy
value: 97.4
---
# X-CLIP (large-sized model)
X-CLIP model (large-sized, patch resolution of 14) trained fully-supervised on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP).
This model was trained using 16 frames per video, at a resolution of 336x336.
Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
X-CLIP is a minimal extension of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.
This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval.
## Intended uses & limitations
You can use the raw model for determining how well text goes with a given video. See the [model hub](https://huggingface.co/models?search=microsoft/xclip) to look for
fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/xclip.html#).
## Training data
This model was trained on [Kinetics-400](https://www.deepmind.com/open-source/kinetics).
### Preprocessing
The exact details of preprocessing during training can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L247).
The exact details of preprocessing during validation can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L285).
During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
## Evaluation results
This model achieves a top-1 accuracy of 87.7% and a top-5 accuracy of 97.4%.
|
sd-concepts-library/kuvshinov
|
sd-concepts-library
| 2022-09-08T10:33:05Z | 0 | 59 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T10:32:59Z |
---
license: mit
---
### Kuvshinov on Stable Diffusion
This is the `<kuvshinov>` 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`:
|
huggingtweets/mkbhd
|
huggingtweets
| 2022-09-08T10:28:33Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/mkbhd/1662632839490/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1468001914302390278/B_Xv_8gu_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Marques Brownlee</div>
<div style="text-align: center; font-size: 14px;">@mkbhd</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Marques Brownlee.
| Data | Marques Brownlee |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 252 |
| Short tweets | 596 |
| Tweets kept | 2399 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3kgiqibj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mkbhd's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6tkgheyt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6tkgheyt/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/mkbhd')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
sd-concepts-library/dicoo
|
sd-concepts-library
| 2022-09-08T10:11:30Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T10:11:25Z |
---
license: mit
---
### Dicoo on Stable Diffusion
This is the `<Dicoo>` 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`:
|
debashish68/roberta-sent-generali
|
debashish68
| 2022-09-08T09:52:08Z | 103 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-07T17:15:36Z |
---
language: en # <-- my language
widget:
- text: "Moody’s decision to upgrade the credit rating of Air Liquide is all the more remarkable as it is taking place in a more difficult macroeconomic and geopolitical environment. It underlines the Group’s capacity to maintain a high level of cash flow despite the fluctuations of the economy. Following Standard & Poor’s decision to upgrade Air Liquide’s credit rating, this decision recognizes the Group’s level of debt, which has been brought back to its pre-Airgas 2016 acquisition level in five years. It also reflects the largely demonstrated resilience of the Group’s business model."
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: roberta-sent-generali
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-sent-generali
This model was fine-tuned on Roberta Large using a private dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4885
- F1: 0.9104
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.355 | 1.0 | 262 | 0.3005 | 0.8829 |
| 0.2201 | 2.0 | 524 | 0.3566 | 0.8930 |
| 0.1293 | 3.0 | 786 | 0.3644 | 0.9193 |
| 0.0662 | 4.0 | 1048 | 0.4202 | 0.9145 |
| 0.026 | 5.0 | 1310 | 0.4885 | 0.9104 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/party-girl
|
sd-concepts-library
| 2022-09-08T09:37:53Z | 0 | 6 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T09:37:40Z |
---
license: mit
---
### Party girl on Stable Diffusion
This is the `<party-girl>` 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`:
|
pritam18/swadeshi_hindiwav2vec2asr
|
pritam18
| 2022-09-08T09:17:06Z | 74 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-19T13:36:45Z |
swadeshi_hindiwav2vec2asr/ is a Hindi speech recognition model which is a fine tuned version of the theainerd/Wav2Vec2-large-xlsr-hindi model. The model achieved a Word Error Rate of 0.738 when trained with 12 Hours of MUCS data with 30 epochs and given a batch size of 12.
|
meedan/brazilianpolitics
|
meedan
| 2022-09-08T08:44:25Z | 108 | 1 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"pt",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-29T17:41:35Z |
---
language:
- pt
license: "mit"
metrics:
- accuracy
- f1
---
A binary classifier that classifies if an input text is related to the Brazilian elections or not.
The classifier was trained on news article headlines taken from online Brazilian news organizations between 2010 and 2022.
It was trained X epochs using `microsoft/mdeberta-v3-base` as the base model.
<table>
<tr>
<td>Accuracy</td>
<td>0.9203</td>
</tr>
<tr>
<td>F1 Score</td>
<td>0.9206</td>
</tr>
</table>
|
huggingtweets/mariojpenton-mjorgec1994
|
huggingtweets
| 2022-09-08T08:28:04Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-08T05:05:22Z |
---
language: en
thumbnail: http://www.huggingtweets.com/mariojpenton-mjorgec1994/1662625679744/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1539758332877197313/NRB0lc5a_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1526213406918905856/28mTAbCu_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Mario J. Pentón & Mag Jorge Castro🇨🇺</div>
<div style="text-align: center; font-size: 14px;">@mariojpenton-mjorgec1994</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Mario J. Pentón & Mag Jorge Castro🇨🇺.
| Data | Mario J. Pentón | Mag Jorge Castro🇨🇺 |
| --- | --- | --- |
| Tweets downloaded | 3244 | 3249 |
| Retweets | 673 | 0 |
| Short tweets | 120 | 236 |
| Tweets kept | 2451 | 3013 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3kbivb0e/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mariojpenton-mjorgec1994's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3m6kiha6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3m6kiha6/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/mariojpenton-mjorgec1994')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Lunage/my_distilbert-finetuned-imdb
|
Lunage
| 2022-09-08T08:21:51Z | 70 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-07T13:26:49Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Lunage/my_distilbert-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Lunage/my_distilbert-finetuned-imdb
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: 3.6915
- Validation Loss: 3.4024
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -843, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.6915 | 3.4024 | 0 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.9.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/reeducation-camp
|
sd-concepts-library
| 2022-09-08T08:19:41Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T08:19:37Z |
---
license: mit
---
### reeducation camp on Stable Diffusion
This is the `<reeducation-camp>` 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`:
|
sd-concepts-library/abstract-concepts
|
sd-concepts-library
| 2022-09-08T07:00:02Z | 0 | 5 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T06:59:56Z |
---
license: mit
---
### abstract concepts on Stable Diffusion
This is the `<art-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`:
|
Anurag0961/idp-headers
|
Anurag0961
| 2022-09-08T06:37:40Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-08T06:28:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: idp-headers
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# idp-headers
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6714
- F1: 0.4823
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.7995 | 1.0 | 5 | 1.8557 | 0.1629 |
| 1.7125 | 2.0 | 10 | 1.7832 | 0.1759 |
| 1.6381 | 3.0 | 15 | 1.7243 | 0.4698 |
| 1.5746 | 4.0 | 20 | 1.6857 | 0.4823 |
| 1.5354 | 5.0 | 25 | 1.6714 | 0.4823 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Tokenizers 0.12.1
|
Anurag0961/idpintents-key-value
|
Anurag0961
| 2022-09-08T06:01:50Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-08T05:57:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: idpintents-key-value
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# idpintents-key-value
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8276
- F1: 0.8849
## 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: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.8264 | 1.0 | 68 | 1.3672 | 0.7358 |
| 1.3147 | 2.0 | 136 | 0.9310 | 0.8356 |
| 1.0444 | 3.0 | 204 | 0.8276 | 0.8849 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Tokenizers 0.12.1
|
mhyatt000/bad-net
|
mhyatt000
| 2022-09-08T05:43:10Z | 0 | 0 | null |
[
"pytorch",
"license:mit",
"region:us"
] | null | 2022-09-07T15:39:45Z |
---
license: mit
---
# BadNet
Pytorch BadNet weights from [verazuo](https://github.com/verazuo/badnets-pytorch)
Proof of concept
|
huggingtweets/sanmemero
|
huggingtweets
| 2022-09-08T04:46:56Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-08T04:45:30Z |
---
language: en
thumbnail: http://www.huggingtweets.com/sanmemero/1662612412375/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1547249514485927937/xVT7Zk4l_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">San Memero 🇨🇺</div>
<div style="text-align: center; font-size: 14px;">@sanmemero</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from San Memero 🇨🇺.
| Data | San Memero 🇨🇺 |
| --- | --- |
| Tweets downloaded | 3211 |
| Retweets | 251 |
| Short tweets | 822 |
| Tweets kept | 2138 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3llp69ch/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @sanmemero's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/kf2jjg02) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/kf2jjg02/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/sanmemero')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
sd-concepts-library/monster-girl
|
sd-concepts-library
| 2022-09-08T04:40:03Z | 0 | 13 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T04:39:52Z |
---
license: mit
---
### Monster Girl on Stable Diffusion
This is the `<monster-girl>` 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`:
|
sd-concepts-library/dr-livesey
|
sd-concepts-library
| 2022-09-08T04:26:52Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T04:26:39Z |
---
license: mit
---
### Dr Livesey on Stable Diffusion
This is the `<dr-livesey>` 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`:
|
tasotaku/ddpm-butterflies-128
|
tasotaku
| 2022-09-08T03:14:23Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-09-08T02:00:45Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- 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-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/tasotaku/ddpm-butterflies-128/tensorboard?#scalars)
|
LittleFishYoung/bert
|
LittleFishYoung
| 2022-09-08T03:10:15Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-09-08T03:05:17Z |
---
license: apache-2.0
---
## Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Data](#training-data)
4. [Risks and Limitations](#risks-and-limitations)
5. [Evaluation](#evaluation)
6. [Recommendations](#recommendations)
7. [Glossary and Calculations](#glossary-and-calculations)
8. [More Information](#more-information)
9. [Model Card Authors](#model-card-authors)
|
PatrickTyBrown/GPT-Neo_DnD_Control
|
PatrickTyBrown
| 2022-09-08T02:22:40Z | 111 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-07T06:46:09Z |
---
tags:
- generated_from_trainer
model-index:
- name: GPT-Neo_DnD_Control
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GPT-Neo_DnD_Control
This model is a fine-tuned version of [PatrickTyBrown/GPT-Neo_DnD](https://huggingface.co/PatrickTyBrown/GPT-Neo_DnD) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.6518
- eval_runtime: 141.422
- eval_samples_per_second: 6.527
- eval_steps_per_second: 3.267
- epoch: 3.9
- step: 36000
## 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: 1.5e-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: 5
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Andre002wp/layoutlmv3-finetuned-wildreceipt
|
Andre002wp
| 2022-09-08T01:44:40Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:wildreceipt",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-08T01:00:59Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- wildreceipt
model-index:
- name: layoutlmv3-finetuned-wildreceipt
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-finetuned-wildreceipt
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the wildreceipt dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2996
- eval_precision: 0.8566
- eval_recall: 0.8614
- eval_f1: 0.8590
- eval_accuracy: 0.9178
- eval_runtime: 51.7898
- eval_samples_per_second: 9.114
- eval_steps_per_second: 2.278
- epoch: 4.97
- step: 1577
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 4000
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/w3u
|
sd-concepts-library
| 2022-09-08T01:39:45Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T01:39:39Z |
---
license: mit
---
### w3u on Stable Diffusion
This is the `<w3u>` 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`:
|
hhffxx/distilbert-base-uncased-distilled-clinc
|
hhffxx
| 2022-09-08T01:32:06Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-08T00:58:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: train
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9503225806451613
---
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2656
- Accuracy: 0.9503
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 3.1212 | 1.0 | 1271 | 1.2698 | 0.8558 |
| 0.6441 | 2.0 | 2542 | 0.3528 | 0.9326 |
| 0.149 | 3.0 | 3813 | 0.2512 | 0.9494 |
| 0.0647 | 4.0 | 5084 | 0.2510 | 0.95 |
| 0.0406 | 5.0 | 6355 | 0.2575 | 0.9510 |
| 0.0318 | 6.0 | 7626 | 0.2592 | 0.9494 |
| 0.026 | 7.0 | 8897 | 0.2629 | 0.9503 |
| 0.023 | 8.0 | 10168 | 0.2682 | 0.95 |
| 0.0207 | 9.0 | 11439 | 0.2656 | 0.9503 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
NithirojTripatarasit/ppo-LunarLander-v2
|
NithirojTripatarasit
| 2022-09-08T00:34:45Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2022-09-01T01:59:15Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -131.97 +/- 97.59
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit8
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'virtual_display': True
'repo_id': 'NithirojTripatarasit/ppo-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
lddczcn/distilbert-base-uncased-finetuned-emotion
|
lddczcn
| 2022-09-08T00:29:20Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-07T23:39:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
- name: F1
type: f1
value: 0.9265519473019482
---
<!-- 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: 0.2155
- Accuracy: 0.9265
- F1: 0.9266
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3133 | 0.9075 | 0.9054 |
| No log | 2.0 | 500 | 0.2155 | 0.9265 | 0.9266 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
sd-concepts-library/monte-novo
|
sd-concepts-library
| 2022-09-08T00:23:28Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T00:23:22Z |
---
license: mit
---
### Monte Novo on Stable Diffusion
This is the `<monte novo cutting board>` 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`:
|
NithirojTripatarasit/ppo-CartPole-v1
|
NithirojTripatarasit
| 2022-09-08T00:21:14Z | 0 | 0 | null |
[
"tensorboard",
"CartPole-v1",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-07T08:17:32Z |
---
tags:
- CartPole-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 208.80 +/- 135.81
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit8
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'CartPole-v1'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'virtual_display': True
'repo_id': 'NithirojTripatarasit/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
sd-concepts-library/vkuoo1
|
sd-concepts-library
| 2022-09-08T00:04:38Z | 0 | 24 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-08T00:04:32Z |
---
license: mit
---
### Vkuoo1 on Stable Diffusion
This is the `<style-vkuoo1>` 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`:
|
slarionne/q-FrozenLake-v1-4x4-noSlippery_2
|
slarionne
| 2022-09-07T23:06:29Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-07T23:06:21Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery_2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="slarionne/q-FrozenLake-v1-4x4-noSlippery_2", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
IIIT-L/albert-base-v2-finetuned-TRAC-DS
|
IIIT-L
| 2022-09-07T22:31:53Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-07T21:27:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: albert-base-v2-finetuned-TRAC-DS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-v2-finetuned-TRAC-DS
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8271
- Accuracy: 0.6315
- Precision: 0.6206
- Recall: 0.6201
- F1: 0.6147
## 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: 5.919508251872584e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 43
- 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 | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0373 | 1.0 | 612 | 1.1241 | 0.3627 | 0.5914 | 0.3618 | 0.2414 |
| 1.0617 | 2.0 | 1224 | 1.1039 | 0.3350 | 0.2781 | 0.3354 | 0.1740 |
| 0.9791 | 3.0 | 1836 | 0.8365 | 0.5989 | 0.6192 | 0.5887 | 0.5883 |
| 0.798 | 3.99 | 2448 | 0.8271 | 0.6315 | 0.6206 | 0.6201 | 0.6147 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
slarionne/q-Taxi-v3
|
slarionne
| 2022-09-07T22:10:05Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-07T22:10:00Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="slarionne/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
sd-concepts-library/cubex
|
sd-concepts-library
| 2022-09-07T21:43:50Z | 0 | 4 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-07T21:43:45Z |
---
license: mit
---
### cubex on Stable Diffusion
This is the `<cube>` 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`:
|
sd-concepts-library/schloss-mosigkau
|
sd-concepts-library
| 2022-09-07T21:42:56Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-07T21:42:50Z |
---
license: mit
---
### schloss mosigkau on Stable Diffusion
This is the `<ralph>` 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`:
|
talhaa/distilbert-base-uncased-masking-lang
|
talhaa
| 2022-09-07T20:58:20Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-07T20:54:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-masking-lang
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-masking-lang
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9978
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 2.2594 |
| No log | 2.0 | 2 | 0.7379 |
| No log | 3.0 | 3 | 2.0914 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/mafalda-character
|
sd-concepts-library
| 2022-09-07T20:02:26Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-07T20:02:12Z |
---
license: mit
---
### mafalda character on Stable Diffusion
This is the `<mafalda-quino>` 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`:
|
talhaa/distilbert-base-uncased-finetuned-imdb
|
talhaa
| 2022-09-07T19:52:38Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-07T18:50:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2119
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 3.3374 |
| No log | 2.0 | 2 | 3.8206 |
| No log | 3.0 | 3 | 2.8370 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Sanatbek/uzbek-kazakh-machine-translation
|
Sanatbek
| 2022-09-07T19:40:36Z | 6 | 0 | null |
[
"tensorboard",
"license:afl-3.0",
"region:us"
] | null | 2022-09-07T18:34:34Z |
---
license: afl-3.0
---
The model is for Machine translation between Uzbek and Kazakh languages
|
sd-concepts-library/canary-cap
|
sd-concepts-library
| 2022-09-07T19:21:04Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-07T19:20:53Z |
---
license: mit
---
### canary cap on Stable Diffusion
This is the `<canary-cap>` 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`:
|
huggingtweets/mariojpenton-mjorgec1994-sanmemero
|
huggingtweets
| 2022-09-07T18:38:33Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-07T18:38:24Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1547249514485927937/xVT7Zk4l_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1526213406918905856/28mTAbCu_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1539758332877197313/NRB0lc5a_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">San Memero 🇨🇺 & Mag Jorge Castro🇨🇺 & Mario J. Pentón</div>
<div style="text-align: center; font-size: 14px;">@mariojpenton-mjorgec1994-sanmemero</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from San Memero 🇨🇺 & Mag Jorge Castro🇨🇺 & Mario J. Pentón.
| Data | San Memero 🇨🇺 | Mag Jorge Castro🇨🇺 | Mario J. Pentón |
| --- | --- | --- | --- |
| Tweets downloaded | 3212 | 3249 | 3244 |
| Retweets | 252 | 0 | 671 |
| Short tweets | 821 | 235 | 121 |
| Tweets kept | 2139 | 3014 | 2452 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cyfkcr0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mariojpenton-mjorgec1994-sanmemero's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/xyy5oobg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/xyy5oobg/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/mariojpenton-mjorgec1994-sanmemero')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
sd-concepts-library/karl-s-lzx-1
|
sd-concepts-library
| 2022-09-07T18:27:38Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-07T18:27:26Z |
---
license: mit
---
### karl's lzx 1 on Stable Diffusion
This is the `<lzx>` 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`:
|
sd-concepts-library/cheburashka
|
sd-concepts-library
| 2022-09-07T17:49:45Z | 0 | 6 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-07T17:49:38Z |
---
license: mit
---
### Cheburashka on Stable Diffusion
This is the `<cheburashka>` 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`:
|
clementchadebec/reproduced_miwae
|
clementchadebec
| 2022-09-07T15:43:16Z | 0 | 0 |
pythae
|
[
"pythae",
"reproducibility",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-09-07T15:33:42Z |
---
language: en
tags:
- pythae
- reproducibility
license: apache-2.0
---
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_miwae")
```
## Reproducibility
This trained model reproduces the results of the official implementation of [1].
| Model | Dataset | Metric | Obtained value | Reference value |
|:---:|:---:|:---:|:---:|:---:|
| MIWAE (M=8, K=8) | Dyn. Binarized MNIST | NLL (5000 IS) | 85.09 (0.00) | 84.97 (0.10) |
[1] Rainforth, Tom, et al. "Tighter variational bounds are not necessarily better." International Conference on Machine Learning. PMLR, 2018.
|
clementchadebec/reproduced_ciwae
|
clementchadebec
| 2022-09-07T15:34:02Z | 0 | 0 |
pythae
|
[
"pythae",
"reproducibility",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-09-07T15:22:26Z |
---
language: en
tags:
- pythae
- reproducibility
license: apache-2.0
---
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_ciwae")
```
## Reproducibility
This trained model reproduces the results of the official implementation of [1].
| Model | Dataset | Metric | Obtained value | Reference value |
|:---:|:---:|:---:|:---:|:---:|
| CIWAE (beta=0.05) | Dyn. Binarized MNIST | NLL (5000 IS) | 84.74 (0.01) | 84.57 (0.09) |
[1] Rainforth, Tom, et al. "Tighter variational bounds are not necessarily better." International Conference on Machine Learning. PMLR, 2018.
|
sd-concepts-library/indian-watercolor-portraits
|
sd-concepts-library
| 2022-09-07T15:33:58Z | 0 | 10 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-07T15:33:41Z |
---
license: mit
---
### Indian Watercolor Portraits on Stable Diffusion
This is the `<watercolor-portrait>` 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 `style`:
|
PrimeQA/mt5-base-tydi-question-generator
|
PrimeQA
| 2022-09-07T15:01:15Z | 121 | 3 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-29T09:46:08Z |
---
license: apache-2.0
---
# Model description
This is an [mt5-base](https://huggingface.co/google/mt5-base) model, finetuned to generate questions using [TyDi QA](https://huggingface.co/datasets/tydiqa) dataset. It was trained to take the context and answer as input to generate questions.
# Overview
*Language model*: mT5-base \
*Language*: Arabic, Bengali, English, Finnish, Indonesian, Korean, Russian, Swahili, Telugu \
*Task*: Question Generation \
*Data*: TyDi QA
# Intented use and limitations
One can use this model to generate questions. Biases associated with pre-training of mT5 and TyDiQA dataset may be present.
## Usage
One can use this model directly in the [PrimeQA](https://github.com/primeqa/primeqa) framework as in this example [notebook](https://github.com/primeqa/primeqa/blob/main/notebooks/qg/tableqg_inference.ipynb).
Or
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("PrimeQA/mt5-base-tydi-question-generator")
model = AutoModelForSeq2SeqLM.from_pretrained("PrimeQA/mt5-base-tydi-question-generator")
def get_question(answer, context, max_length=64):
input_text = answer +" <<sep>> " + context
features = tokenizer([input_text], return_tensors='pt')
output = model.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'],
max_length=max_length)
return tokenizer.decode(output[0])
context = "শচীন টেন্ডুলকারকে ক্রিকেট ইতিহাসের অন্যতম সেরা ব্যাটসম্যান হিসেবে গণ্য করা হয়।"
answer = "শচীন টেন্ডুলকার"
get_question(answer, context)
# output: ক্রিকেট ইতিহাসের অন্যতম সেরা ব্যাটসম্যান কে?
```
## Citation
```bibtex
@inproceedings{xue2021mt5,
title={mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer},
author={Xue, Linting and Constant, Noah and Roberts, Adam and
Kale, Mihir and Al-Rfou, Rami and Siddhant, Aditya and
Barua, Aditya and Raffel, Colin},
booktitle={Proceedings of the 2021 Conference of the North American
Chapter of the Association for Computational Linguistics:
Human Language Technologies},
pages={483--498},
year={2021}
}
```
|
jmstadt/navy-ships
|
jmstadt
| 2022-09-07T14:48:50Z | 214 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-09-07T14:48:37Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: navy-ships
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.75
---
# navy-ships
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### aircraft carrier
#### cruiser
#### destroyer
#### frigate
#### submarine
|
jenniferjane/test_trainer
|
jenniferjane
| 2022-09-07T14:29:16Z | 158 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:yelp_review_full",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-05T13:38:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- yelp_review_full
metrics:
- accuracy
model-index:
- name: test_trainer
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: yelp_review_full
type: yelp_review_full
config: yelp_review_full
split: train
args: yelp_review_full
metrics:
- name: Accuracy
type: accuracy
value: 0.628
---
<!-- 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. -->
# test_trainer
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1033
- Accuracy: 0.628
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0473 | 1.0 | 1250 | 0.9373 | 0.59 |
| 0.7362 | 2.0 | 2500 | 0.9653 | 0.611 |
| 0.4692 | 3.0 | 3750 | 1.1033 | 0.628 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Berk/ddpm-butterflies-128
|
Berk
| 2022-09-07T14:15:28Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-09-07T11:30:51Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- 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-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/Berk/ddpm-butterflies-128/tensorboard?#scalars)
|
sd-concepts-library/birb-style
|
sd-concepts-library
| 2022-09-07T14:15:26Z | 0 | 35 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-07T13:58:05Z |
---
license: mit
---
### Birb style on Stable Diffusion
This is the `<birb-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).
Example outputs (style):
Source images:
|
RayK/distilbert-base-uncased-finetuned-cola
|
RayK
| 2022-09-07T13:13:31Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-04T00:05:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5410039366652665
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6949
- Matthews Correlation: 0.5410
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5241 | 1.0 | 535 | 0.5322 | 0.3973 |
| 0.356 | 2.0 | 1070 | 0.5199 | 0.4836 |
| 0.2402 | 3.0 | 1605 | 0.6086 | 0.5238 |
| 0.166 | 4.0 | 2140 | 0.6949 | 0.5410 |
| 0.134 | 5.0 | 2675 | 0.8254 | 0.5253 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.12.1
|
anniepyim/xlm-roberta-base-finetuned-panx-de
|
anniepyim
| 2022-09-07T13:03:30Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-07T12:39:42Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8648740833380706
---
<!-- 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-de
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: 0.1365
- F1: 0.8649
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 |
| 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 |
| 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
liat-nakayama/japanese-roberta-base-20201221
|
liat-nakayama
| 2022-09-07T13:03:15Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"license:cc-by-sa-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-07T12:45:36Z |
---
license: cc-by-sa-3.0
---
2020/12/21時点のWikipediaを用いて事前学習した日本語RoBERTaです。
janome(MeCabのPythonラッパー)とBPEを使用してトークナイズしています。
|
saipavan/doctor-review-classifier
|
saipavan
| 2022-09-07T11:41:07Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-07T11:10:20Z |
---
license: other
---
# model information
This model classifies the reviews given by the patients to doctors
model:bert model
task : text-classification or sentiment analysis
classes: positive and negative
use same path saipavan/.... to load model and tokenizer
this model got trained on more than 5000 reviews and is giving good accuracy.
|
sd-concepts-library/madhubani-art
|
sd-concepts-library
| 2022-09-07T08:47:46Z | 0 | 20 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-07T08:07:36Z |
---
license: mit
---
### madhubani art on Stable Diffusion
This is the `<madhubani-art>` 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 `style`:
|
hhffxx/distilbert-base-uncased-finetuned-clinc
|
hhffxx
| 2022-09-07T08:21:36Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-07T02:40:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: train
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9503225806451613
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2339
- Accuracy: 0.9503
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.2073 | 1.0 | 1271 | 1.3840 | 0.8542 |
| 0.7452 | 2.0 | 2542 | 0.4053 | 0.9316 |
| 0.1916 | 3.0 | 3813 | 0.2580 | 0.9452 |
| 0.0768 | 4.0 | 5084 | 0.2371 | 0.9477 |
| 0.0455 | 5.0 | 6355 | 0.2339 | 0.9503 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
NithirojTripatarasit/a2c-AntBulletEnv-v0
|
NithirojTripatarasit
| 2022-09-07T06:29:12Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-07T06:27:46Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1704.47 +/- 175.74
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
mesolitica/roberta-base-bahasa-cased
|
mesolitica
| 2022-09-07T06:12:59Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"ms",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-07T05:54:15Z |
---
language: ms
---
# roberta-base-bahasa-cased
Pretrained RoBERTa base language model for Malay.
## Pretraining Corpus
`roberta-base-bahasa-cased` model was pretrained on ~400 miliion words. Below is list of data we trained on,
1. IIUM confession, https://github.com/huseinzol05/malay-dataset/tree/master/dumping/clean
2. local Instagram, https://github.com/huseinzol05/malay-dataset/tree/master/dumping/clean
3. local news, https://github.com/huseinzol05/malay-dataset/tree/master/dumping/clean
4. local parliament hansards, https://github.com/huseinzol05/malay-dataset/tree/master/dumping/clean
5. local research papers related to `kebudayaan`, `keagaaman` and `etnik`, https://github.com/huseinzol05/malay-dataset/tree/master/dumping/clean
6. local twitter, https://github.com/huseinzol05/malay-dataset/tree/master/dumping/clean
7. Malay Wattpad, https://github.com/huseinzol05/malay-dataset/tree/master/dumping/clean
8. Malay Wikipedia, https://github.com/huseinzol05/malay-dataset/tree/master/dumping/clean
## Pretraining details
- All steps can reproduce from https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/roberta.
## Example using AutoModelWithLMHead
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline
model = AutoModelForMaskedLM.from_pretrained('mesolitica/roberta-base-bahasa-cased')
tokenizer = AutoTokenizer.from_pretrained(
'mesolitica/roberta-base-bahasa-cased',
do_lower_case = False,
)
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)
fill_mask('Permohonan Najib, anak untuk dengar isu perlembagaan <mask> .')
```
Output is,
```json
[{'score': 0.3368818759918213,
'token': 746,
'token_str': ' negara',
'sequence': 'Permohonan Najib, anak untuk dengar isu perlembagaan negara.'},
{'score': 0.09646568447351456,
'token': 598,
'token_str': ' Malaysia',
'sequence': 'Permohonan Najib, anak untuk dengar isu perlembagaan Malaysia.'},
{'score': 0.029483484104275703,
'token': 3265,
'token_str': ' UMNO',
'sequence': 'Permohonan Najib, anak untuk dengar isu perlembagaan UMNO.'},
{'score': 0.026470622047781944,
'token': 2562,
'token_str': ' parti',
'sequence': 'Permohonan Najib, anak untuk dengar isu perlembagaan parti.'},
{'score': 0.023237623274326324,
'token': 391,
'token_str': ' ini',
'sequence': 'Permohonan Najib, anak untuk dengar isu perlembagaan ini.'}]
```
|
neuralspace/autotrain-citizen_nlu_hindi-1370952776
|
neuralspace
| 2022-09-07T05:48:02Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"hi",
"dataset:neuralspace/autotrain-data-citizen_nlu_hindi",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-07T05:39:47Z |
---
tags:
- autotrain
- text-classification
language:
- hi
widget:
- text: "I love AutoTrain 🤗"
datasets:
- neuralspace/autotrain-data-citizen_nlu_hindi
co2_eq_emissions:
emissions: 0.06283545088764929
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1370952776
- CO2 Emissions (in grams): 0.0628
## Validation Metrics
- Loss: 0.101
- Accuracy: 0.974
- Macro F1: 0.974
- Micro F1: 0.974
- Weighted F1: 0.974
- Macro Precision: 0.975
- Micro Precision: 0.974
- Weighted Precision: 0.975
- Macro Recall: 0.973
- Micro Recall: 0.974
- Weighted Recall: 0.974
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/neuralspace/autotrain-citizen_nlu_hindi-1370952776
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("neuralspace/autotrain-citizen_nlu_hindi-1370952776", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("neuralspace/autotrain-citizen_nlu_hindi-1370952776", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
neuralspace/autotrain-citizen_nlu_bn-1370652766
|
neuralspace
| 2022-09-07T05:42:31Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"bn",
"dataset:neuralspace/autotrain-data-citizen_nlu_bn",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-07T05:33:04Z |
---
tags:
- autotrain
- text-classification
language:
- bn
widget:
- text: "I love AutoTrain 🤗"
datasets:
- neuralspace/autotrain-data-citizen_nlu_bn
co2_eq_emissions:
emissions: 0.08431503532658222
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1370652766
- CO2 Emissions (in grams): 0.0843
## Validation Metrics
- Loss: 0.117
- Accuracy: 0.971
- Macro F1: 0.971
- Micro F1: 0.971
- Weighted F1: 0.971
- Macro Precision: 0.973
- Micro Precision: 0.971
- Weighted Precision: 0.972
- Macro Recall: 0.970
- Micro Recall: 0.971
- Weighted Recall: 0.971
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/neuralspace/autotrain-citizen_nlu_bn-1370652766
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("neuralspace/autotrain-citizen_nlu_bn-1370652766", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("neuralspace/autotrain-citizen_nlu_bn-1370652766", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
jhonparra18/wav2vec2-300m-ft-soft-skill
|
jhonparra18
| 2022-09-07T02:59:51Z | 160 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-09-06T02:29:15Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-300m-ft-soft-skill
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-300m-ft-soft-skill
This model is a fine-tuned version of [glob-asr/xls-r-es-test-lm](https://huggingface.co/glob-asr/xls-r-es-test-lm) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7447
- Accuracy: 0.6827
- F1 Micro: 0.3514
- F1 Macro: 0.6827
- Precision Micro: 0.6827
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | Precision Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|
| 0.823 | 0.51 | 100 | 0.6821 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
| 0.7122 | 1.02 | 200 | 0.6767 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
| 0.6706 | 1.52 | 300 | 0.6768 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
| 0.7096 | 2.03 | 400 | 0.6791 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
| 0.6909 | 2.54 | 500 | 0.6780 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
| 0.6861 | 3.05 | 600 | 0.6779 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
| 0.6842 | 3.55 | 700 | 0.6773 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
| 0.6887 | 4.06 | 800 | 0.6764 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
| 0.6766 | 4.57 | 900 | 0.6803 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
| 0.6964 | 5.08 | 1000 | 0.6819 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
| 0.6515 | 5.58 | 1100 | 0.6788 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
| 0.6608 | 6.09 | 1200 | 0.6864 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
| 0.6171 | 6.6 | 1300 | 0.6980 | 0.7589 | 0.2876 | 0.7589 | 0.7589 |
| 0.6292 | 7.11 | 1400 | 0.7172 | 0.7386 | 0.3119 | 0.7386 | 0.7386 |
| 0.6015 | 7.61 | 1500 | 0.6988 | 0.7462 | 0.3212 | 0.7462 | 0.7462 |
| 0.6236 | 8.12 | 1600 | 0.7493 | 0.6954 | 0.3432 | 0.6954 | 0.6954 |
| 0.5643 | 8.63 | 1700 | 0.7250 | 0.7107 | 0.3466 | 0.7107 | 0.7107 |
| 0.6134 | 9.14 | 1800 | 0.7561 | 0.6751 | 0.3565 | 0.6751 | 0.6751 |
| 0.5642 | 9.64 | 1900 | 0.7447 | 0.6827 | 0.3514 | 0.6827 | 0.6827 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.8.1+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
nateraw/test-update-metadata-issue
|
nateraw
| 2022-09-07T02:40:04Z | 0 | 0 | null |
[
"en",
"license:mit",
"region:us"
] | null | 2022-09-07T02:28:32Z |
---
language: en
license: mit
---
|
rajistics/auditor-test
|
rajistics
| 2022-09-07T01:35:51Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"PROD",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-22T18:41:17Z |
---
tags:
- generated_from_trainer
- PROD
model-index:
- name: auditor-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# auditor-test
This model is a fine-tuned version of [demo-org/finbert-pretrain](https://huggingface.co/demo-org/finbert-pretrain) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Imene/vit-base-patch16-384-wi5
|
Imene
| 2022-09-07T01:30:24Z | 79 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-09-06T19:10:41Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Imene/vit-base-patch16-384-wi5
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Imene/vit-base-patch16-384-wi5
This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4102
- Train Accuracy: 0.9755
- Train Top-3-accuracy: 0.9960
- Validation Loss: 1.9021
- Validation Accuracy: 0.4912
- Validation Top-3-accuracy: 0.7302
- Epoch: 8
## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 3180, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 4.2945 | 0.0568 | 0.1328 | 3.6233 | 0.1387 | 0.2916 | 0 |
| 3.1234 | 0.2437 | 0.4585 | 2.8657 | 0.3041 | 0.5330 | 1 |
| 2.4383 | 0.4182 | 0.6638 | 2.5499 | 0.3534 | 0.6048 | 2 |
| 1.9258 | 0.5698 | 0.7913 | 2.3046 | 0.4202 | 0.6583 | 3 |
| 1.4919 | 0.6963 | 0.8758 | 2.1349 | 0.4553 | 0.6784 | 4 |
| 1.1127 | 0.7992 | 0.9395 | 2.0878 | 0.4595 | 0.6809 | 5 |
| 0.8092 | 0.8889 | 0.9720 | 1.9460 | 0.4962 | 0.7210 | 6 |
| 0.5794 | 0.9419 | 0.9883 | 1.9478 | 0.4979 | 0.7201 | 7 |
| 0.4102 | 0.9755 | 0.9960 | 1.9021 | 0.4912 | 0.7302 | 8 |
### Framework versions
- Transformers 4.21.3
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
theojolliffe/pegasus-model3
|
theojolliffe
| 2022-09-07T00:46:43Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-03T17:57:04Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-model3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-model3
This model is a fine-tuned version of [theojolliffe/pegasus-cnn_dailymail-v4-e1-e4-feedback](https://huggingface.co/theojolliffe/pegasus-cnn_dailymail-v4-e1-e4-feedback) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2808
- Rouge1: 70.5507
- Rouge2: 66.5776
- Rougel: 64.6438
- Rougelsum: 70.0264
- Gen Len: 123.7447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 0.4839 | 1.0 | 748 | 0.2808 | 70.5507 | 66.5776 | 64.6438 | 70.0264 | 123.7447 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
vms57464/Dogz
|
vms57464
| 2022-09-07T00:05:26Z | 270 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-09-07T00:05:13Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Dogz
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# Dogz
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Golden Retriever
#### Jack Russell Terrier
#### Pitbull Terrier
|
Cyanogenoid/ddpm-ema-pokemon-64
|
Cyanogenoid
| 2022-09-06T22:43:34Z | 4 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/pokemon",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-09-06T19:26:46Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/pokemon
metrics: []
---
<!-- 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-pokemon-64
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/pokemon` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(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/Cyanogenoid/ddpm-ema-pokemon-64/tensorboard?#scalars)
|
theojolliffe/bart-paraphrase-feedback
|
theojolliffe
| 2022-09-06T21:30:07Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-06T20:42:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-paraphrase-feedback
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-paraphrase-feedback
This model is a fine-tuned version of [theojolliffe/bart-paraphrase-v4-e1-feedback](https://huggingface.co/theojolliffe/bart-paraphrase-v4-e1-feedback) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3640
- Rouge1: 55.8307
- Rouge2: 49.7983
- Rougel: 51.7379
- Rougelsum: 55.0839
- Gen Len: 19.4385
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.6009 | 1.0 | 521 | 0.3640 | 55.8307 | 49.7983 | 51.7379 | 55.0839 | 19.4385 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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