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Aleksandar/bert-srb-ner-setimes-lr | []
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} | 0 | null | ---
license: apache-2.0
language: zh
tags:
- T5
- chinese
- sentencepiece
inference: true
widget:
- text: "北京有悠久的 <extra_id_0>和 <extra_id_1>。"
- type: "text-generation"
---
# Randeng-T5-Char-57M-Chinese
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
## 简介 Brief Introduction
善于处理NLT任务，中文版的T5-small，采用了BertTokenizer和中文字级别词典。
Good at handling NLT tasks, Chinese T5-small， use BertTokenizer and chinese vocab.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 自然语言转换 NLT | 燃灯 Randeng | T5 | 57M | 中文-Chinese |
## 模型信息 Model Information
对比T5-small，训练了它的中文版。为了更好适用于中文任务，我们仅使用BertTokenzier，和支持中英文的词表，并且使用了语料库自适应预训练(Corpus-Adaptive Pre-Training, CAPT)技术在悟道语料库(180G版本)继续预训练。预训练目标为破坏span。具体地，我们在预训练阶段中使用了[封神框架](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen)大概花费了8张A100约24小时。
Compared with T5-samll, we implement its Chinese version. In order to use for chinese tasks, we use BertTokenizer and Chinese vocabulary, and Corpus-Adaptive Pre-Training (CAPT) on the WuDao Corpora (180 GB version). The pretraining objective is span corruption. Specifically, we use the [fengshen framework](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen) in the pre-training phase which cost about 24 hours with 8 A100 GPUs.
## 使用 Usage
```python
from transformers import T5ForConditionalGeneration, BertTokenizer
import torch
tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Randeng-T5-Char-57M-Chinese', add_special_tokens=False)
model=T5ForConditionalGeneration.from_pretrained('IDEA-CCNL/Randeng-T5-Char-57M-Chinese')
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
Aleksandar/bert-srb-ner | [
"pytorch",
"bert",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 4 | null | Access to model Lwhieldon/pegasus-samsum is restricted and you are not in the authorized list. Visit https://huggingface.co/Lwhieldon/pegasus-samsum to ask for access. |
Aleksandar/distilbert-srb-ner-setimes | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 3 | null | ---
license: apache-2.0
language: zh
tags:
- T5
- chinese
inference: true
widget:
- text: "北京有悠久的 <extra_id_0>和 <extra_id_1>。"
- type: "text-generation"
---
# Randeng-T5-Char-700M-Chinese
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
## 简介 Brief Introduction
善于处理NLT任务，中文版的T5-large，采用了BertTokenizer和中文字级别词典。
Good at handling NLT tasks, Chinese T5-large， use BertTokenizer and chinese vocab.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 自然语言转换 NLT | 燃灯 Randeng | T5 | 700M | 中文-Chinese |
## 模型信息 Model Information
对比T5-small，训练了它的中文版。为了更好适用于中文任务，我们仅使用BertTokenzier，和支持中英文的词表，并且使用了语料库自适应预训练(Corpus-Adaptive Pre-Training, CAPT)技术在悟道语料库(180G版本)继续预训练。预训练目标为破坏span。具体地，我们在预训练阶段中使用了[封神框架](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen)大概花费了16张A100约96小时。
Compared with T5-large, we implement its Chinese version. In order to use for chinese tasks, we use BertTokenizer and Chinese vocabulary, and Corpus-Adaptive Pre-Training (CAPT) on the WuDao Corpora (180 GB version). The pretraining objective is span corruption. Specifically, we use the [fengshen framework](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen) in the pre-training phase which cost about 96 hours with 16 A100 GPUs.
## 使用 Usage
```python
from transformers import T5ForConditionalGeneration, BertTokenizer
import torch
tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Randeng-T5-Char-700M-Chinese', add_special_tokens=False)
model=T5ForConditionalGeneration.from_pretrained('IDEA-CCNL/Randeng-T5-Char-700M-Chinese')
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
Aleksandar1932/gpt2-spanish-classics | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 9 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: bart_large_summarise_v2
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: multi_news
type: multi_news
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 39.305
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart_large_summarise_v2
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2988
- Rouge1: 39.305
- Rouge2: 13.4171
- Rougel: 20.4214
- Rougelsum: 34.971
- Gen Len: 142.0
## 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: 5e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.2.dev0
- Tokenizers 0.13.1
|
Aleksandra/distilbert-base-uncased-finetuned-squad | []
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
This is a trained model used Dreambooth with 35 images and 2500 steps. In the files you have the aesthetic embeddings.
The model was trained using Stable Diffusion version 1.5.
The model is not perfect, but delivers considerable results.
The goal was to make a more simplistic model, since the images naturally generated by SD are more elaborate and full of unnecessary details.
To use it you must download the model and place it in your Stable Diffusion. The model has 2GB.
You must put in the prompt: ''in VARPJ1 Coloring Book Art Style'' to get the best result.
I hope you like it!
---
license: openrail
---
|
adorkin/xlm-roberta-en-ru-emoji | [
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:tweet_eval",
"transformers"
]
| text-classification | {
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}
} | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:---:|:--------:|
| 0.0 | 1.0 | 5280 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 2.0 | 10560 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 3.0 | 15840 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 4.0 | 21120 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 5.0 | 26400 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AlekseyKorshuk/horror-scripts | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 19 | null | ---
tags:
- generated_from_trainer
datasets:
- cosmos_qa
model-index:
- name: longformer_cosmos
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. -->
# longformer_cosmos
This model was trained from scratch on the cosmos_qa 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 25
- total_train_batch_size: 100
- 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
- Datasets 2.5.1
- Tokenizers 0.11.0
|
AlekseyKulnevich/Pegasus-QuestionGeneration | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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"PegasusForConditionalGeneration"
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} | 17 | null | ---
tags:
- fastai
- image-classification
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
AliReza/distilbert-emotion | []
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}
} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 208.34 +/- 42.10
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Alireza1044/albert-base-v2-rte | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
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}
} | 30 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
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: 0.9355853618148701
- name: Recall
type: recall
value: 0.9508582968697409
- name: F1
type: f1
value: 0.9431600033386196
- name: Accuracy
type: accuracy
value: 0.9870636368988049
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
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: 0.0598
- Precision: 0.9356
- Recall: 0.9509
- F1: 0.9432
- Accuracy: 0.9871
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0861 | 1.0 | 1756 | 0.0653 | 0.9138 | 0.9334 | 0.9235 | 0.9825 |
| 0.0354 | 2.0 | 3512 | 0.0589 | 0.9312 | 0.9497 | 0.9403 | 0.9866 |
| 0.0165 | 3.0 | 5268 | 0.0598 | 0.9356 | 0.9509 | 0.9432 | 0.9871 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Alireza1044/albert-base-v2-sst2 | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
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} | 52 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: MLP
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -104.55 +/- 117.68
name: mean_reward
verified: false
---
# **MLP** Agent playing **LunarLander-v2**
This is a trained model of a **MLP** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AmitT/test | []
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} | 0 | 2022-10-23T04:14:09Z | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- kem000123/autotrain-data-model2-text-class
co2_eq_emissions:
emissions: 3.652284357860415
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1843563203
- CO2 Emissions (in grams): 3.6523
## Validation Metrics
- Loss: 0.202
- Accuracy: 0.921
- Precision: 0.803
- Recall: 0.862
- AUC: 0.966
- F1: 0.832
## 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/kem000123/autotrain-model2-text-class-1843563203
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("kem000123/autotrain-model2-text-class-1843563203", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("kem000123/autotrain-model2-text-class-1843563203", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Amitabh/doc-classification | []
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} | 0 | 2022-10-23T04:17:59Z | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- https://www.kaggle.com/datasets/tawsifurrahman/tuberculosis-tb-chest-xray-dataset
widget:
- src: https://huggingface.co/Owos/tb-classifier/blob/main/tb-negative.png
example_title: Negative
- src: https://huggingface.co/Owos/tb-classifier/blob/main/tb-positive.png
example_title: Positive
metrics:
- Accuracy
- Precision
- Recall
---
# Tuberculosis Classifier
[Github repo is here](https://github.com/owos/tb_project) </br>
[HuggingFace Space](https://huggingface.co/spaces/Owos/tb_prediction_space)
# Model description
This is a computer vision model that was built with TensorFlow to classify if a given x-ray scan is positive for Tuberculosis or not.
# Intended uses & limitations
The model was built to help support low-resourced and short-staffed primary healthcare centers in Nigeria. Particularly, the aim to was created a computer-aided diagnosing tool for Radiologists in these centers.
The model has not undergone clinical testing and usage is at ueser's own risk.The model has however been tested on real life data images that are positive for tuberculosis
# How to use
Download the pre-trained model and use it to make inference.
A space has been created for testing [here](space.com)
# Training data
The entire dataset consist of 3500 negative images and 700 positive TB images. </br>
The data was splitted in 80% for training and 20% for validation.
# Training procedure
Transfer-learning was employed using InceptionV3 as the pre-trained model. Training was done for 20 epochs and the classes were weighted during training in order to neutralize the imbalanced class in the dataset. The training was done on Kaggle using the GPUs provided. More details of the experiments can be found [here](https://www.kaggle.com/code/abrahamowodunni/tb-project)
# Evaluation results
The result of the evaluation are as follows: - loss: 0.0923 - binary_accuracy: 0.9857 - precision: 0.9259 - recall: 0.9843
More information can be found in the plot below.
[Evaluation results of the TB model](https://github.com/owos/tb_project/blob/main/README.md)
|
AnonARR/qqp-bert | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 38 | 2022-10-23T14:34:46Z | ---
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
config: PAN-X.de
split: train
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.0
---
<!-- 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: 0.1711
- F1: 0.0
## 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: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| No log | 1.0 | 2097 | 0.1970 | 0.0 |
| No log | 2.0 | 4194 | 0.1686 | 0.0 |
| No log | 3.0 | 6291 | 0.1711 | 0.0 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu116
- Datasets 2.6.1
- Tokenizers 0.12.1
|
AnonymousSub/AR_rule_based_roberta_bert_triplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 10 | null | ---
license: afl-3.0
---
# Thoth Ai, Türkçe hakaret ve küfürleri tespit etmek için oluşturulmuştur. Akademik projelerde kaynak gösterilmesi halinde kullanılabilir.
## Validation Metrics
- Loss: 0.230
- Accuracy: 0.936
- Macro F1: 0.927
- Micro F1: 0.936
- Weighted F1: 0.936
- Macro Precision: 0.929
- Micro Precision: 0.936
- Weighted Precision: 0.936
- Macro Recall: 0.925
- Micro Recall: 0.936
- Weighted Recall: 0.936
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("thothai/turkce-kufur-tespiti", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("thothai/turkce-kufur-tespiti", use_auth_token=True)
inputs = tokenizer("Merhaba", return_tensors="pt")
outputs = model(**inputs)
``` |
AnonymousSub/AR_rule_based_roberta_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: DistilBERT-WIKI_Life_Form_Finetuned
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-WIKI_Life_Form_Finetuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4204
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5077 | 1.0 | 1175 | 0.4573 |
| 0.3693 | 2.0 | 2350 | 0.4196 |
| 0.2759 | 3.0 | 3525 | 0.4204 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/AR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 2 | null | ---
language:
- vi
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-common_voice-vi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: COMMON_VOICE - VI
type: common_voice
config: vi
split: train+validation
args: 'Config: vi, Training split: train+validation, Eval split: test'
metrics:
- name: Wer
type: wer
value: 1.0
---
<!-- 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-common_voice-vi
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - VI dataset.
It achieves the following results on the evaluation set:
- Loss: 28.9053
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 200
- total_train_batch_size: 200
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu116
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/AR_rule_based_roberta_twostagetriplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 2 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="NikitaBaramiia/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
AnonymousSub/AR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"RobertaModel"
],
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}
} | 1 | null | ---
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: 7.56 +/- 2.71
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="NikitaBaramiia/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"])
```
|
AnonymousSub/AR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"RobertaModel"
],
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}
}
} | 6 | null | ---
license: mit
---
### xioboma on Stable Diffusion
This is the `<xi-obama>` 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`:
|
AnonymousSub/AR_rule_based_twostagequadruplet_hier_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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}
}
} | 2 | 2022-10-23T17:56:39Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-multilingual-uncased-finetuned-masress
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-multilingual-uncased-finetuned-masress
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0946
- Accuracy: 0.5782
- F1: 0.5769
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.1646 | 1.0 | 151 | 1.0626 | 0.5588 | 0.5566 |
| 0.9281 | 2.0 | 302 | 0.9800 | 0.5869 | 0.5792 |
| 0.8269 | 3.0 | 453 | 1.0134 | 0.5911 | 0.5775 |
| 0.7335 | 4.0 | 604 | 1.0644 | 0.5861 | 0.5816 |
| 0.6786 | 5.0 | 755 | 1.0946 | 0.5782 | 0.5769 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/EManuals_RoBERTa_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
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}
} | 29 | 2022-10-23T18:33:27Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: mrmoor/cti-bert-ner
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. -->
# mrmoor/cti-bert-ner
This model is a fine-tuned version of [mrmoor/cti-bert-mlm](https://huggingface.co/mrmoor/cti-bert-mlm) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1491
- Validation Loss: 0.3715
- Epoch: 6
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 82800, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.6883 | 0.5161 | 0 |
| 0.4567 | 0.4283 | 1 |
| 0.3420 | 0.3810 | 2 |
| 0.2688 | 0.3845 | 3 |
| 0.2144 | 0.3669 | 4 |
| 0.1788 | 0.3881 | 5 |
| 0.1491 | 0.3715 | 6 |
### Framework versions
- Transformers 4.23.1
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/SDR_HF_model_base | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"RobertaModel"
],
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} | 1 | 2022-10-23T18:35:23Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# setfit-finetuned-financial-text-classification
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('nickmuchi/setfit-finetuned-financial-text-classification')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 188 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 5.610085660083046e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 188,
"warmup_steps": 19,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
AnonymousSub/SR_rule_based_roberta_hier_triplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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"RobertaModel"
],
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}
} | 6 | null | ---
language:
- cs
- en
- de
- fr
- tu
- zh
- es
- ru
tags:
- Summarization
- abstractive summarization
- mt5-base
- Czech
- text2text generation
- text generation
license: cc-by-sa-4.0
datasets:
- Multilingual_large_dataset_(multilarge)
- cnc/dm
- xsum
- mlsum
- cnewsum
- cnc
- sumeczech
metrics:
- rouge
- rougeraw
- MemesCS
---
# mt5-base-multilingual-summarization-multilarge-cs
This model is a fine-tuned checkpoint of [google/mt5-base](https://huggingface.co/google/mt5-base) on the Multilingual large summarization dataset focused on Czech texts to produce multilingual summaries.
## Task
The model deals with a multi-sentence summary in eight different languages. With the idea of adding other foreign language documents, and by having a considerable amount of Czech documents, we aimed to improve model summarization in the Czech language. Supported languages: ```'cs': '<extra_id_0>', 'en': '<extra_id_1>','de': '<extra_id_2>', 'es': '<extra_id_3>', 'fr': '<extra_id_4>', 'ru': '<extra_id_5>', 'tu': '<extra_id_6>', 'zh': '<extra_id_7>'```
#Usage
```python
## Configuration of summarization pipeline
#
def summ_config():
cfg = OrderedDict([
## summarization model - checkpoint
# ctu-aic/m2m100-418M-multilingual-summarization-multilarge-cs
# ctu-aic/mt5-base-multilingual-summarization-multilarge-cs
# ctu-aic/mbart25-multilingual-summarization-multilarge-cs
("model_name", "ctu-aic/mbart25-multilingual-summarization-multilarge-cs"),
## language of summarization task
# language : string : cs, en, de, fr, es, tr, ru, zh
("language", "en"),
## generation method parameters in dictionary
#
("inference_cfg", OrderedDict([
("num_beams", 4),
("top_k", 40),
("top_p", 0.92),
("do_sample", True),
("temperature", 0.95),
("repetition_penalty", 1.23),
("no_repeat_ngram_size", None),
("early_stopping", True),
("max_length", 128),
("min_length", 10),
])),
#texts to summarize values = (list of strings, string, dataset)
("texts",
[
"english text1 to summarize",
"english text2 to summarize",
]
),
#OPTIONAL: Target summaries values = (list of strings, string, None)
('golds',
[
"target english text1",
"target english text2",
]),
#('golds', None),
])
return cfg
cfg = summ_config()
mSummarize = MultiSummarizer(**cfg)
summaries,scores = mSummarize(**cfg)
```
## Dataset
Multilingual large summarization dataset consists of 10 sub-datasets mainly based on news and daily mails. For the training, it was used the entire training set and 72% of the validation set.
```
Train set: 3 464 563 docs
Validation set: 121 260 docs
```
| Stats | fragment | | | avg document length | | avg summary length | | Documents |
|-------------|----------|---------------------|--------------------|--------|---------|--------|--------|--------|
| __dataset__ |__compression__ | __density__ | __coverage__ | __nsent__ | __nwords__ | __nsent__ | __nwords__ | __count__ |
| cnc | 7.388 | 0.303 | 0.088 | 16.121 | 316.912 | 3.272 | 46.805 | 750K |
| sumeczech | 11.769 | 0.471 | 0.115 | 27.857 | 415.711 | 2.765 | 38.644 | 1M |
| cnndm | 13.688 | 2.983 | 0.538 | 32.783 | 676.026 | 4.134 | 54.036 | 300K |
| xsum | 18.378 | 0.479 | 0.194 | 18.607 | 369.134 | 1.000 | 21.127 | 225K|
| mlsum/tu | 8.666 | 5.418 | 0.461 | 14.271 | 214.496 | 1.793 | 25.675 | 274K |
| mlsum/de | 24.741 | 8.235 | 0.469 | 32.544 | 539.653 | 1.951 | 23.077 | 243K|
| mlsum/fr | 24.388 | 2.688 | 0.424 | 24.533 | 612.080 | 1.320 | 26.93 | 425K |
| mlsum/es | 36.185 | 3.705 | 0.510 | 31.914 | 746.927 | 1.142 | 21.671 | 291K |
| mlsum/ru | 78.909 | 1.194 | 0.246 | 62.141 | 948.079 | 1.012 | 11.976 | 27K|
| cnewsum | 20.183 | 0.000 | 0.000 | 16.834 | 438.271 | 1.109 | 21.926 | 304K |
#### Tokenization
Truncation and padding were set to 512 tokens for the encoder (input text) and 128 for the decoder (summary).
## Training
Trained based on cross-entropy loss.
```
Time: 3 days 20 hours
Epochs: 1080K steps = 10 (from 10)
GPUs: 4x NVIDIA A100-SXM4-40GB
eloss: 2.462 - 1.797
tloss: 17.322 - 1.578
```
### ROUGE results per individual dataset test set:
| ROUGE | ROUGE-1 | | | ROUGE-2 | | | ROUGE-L | | |
|-----------|---------|---------|-----------|--------|--------|-----------|--------|--------|---------|
| |Precision | Recall | Fscore | Precision | Recall | Fscore | Precision | Recall | Fscore |
| cnc | 30.62 | 19.83 | 23.44 | 9.94 | 6.52 | 7.67 | 22.92 | 14.92 | 17.6 |
| sumeczech | 27.57 | 17.6 | 20.85 | 8.12 | 5.23 | 6.17 | 20.84 | 13.38 | 15.81 |
| cnndm | 43.83 | 37.73 | 39.34 | 20.81 | 17.82 | 18.6 | 31.8 | 27.42 | 28.55 |
| xsum | 41.63 | 30.54 | 34.56 | 16.13 | 11.76 | 13.33 | 33.65 | 24.74 | 27.97 |
| mlsum-tu- | 54.4 | 43.29 | 46.2 | 38.78 | 31.31 | 33.23 | 48.18 | 38.44 | 41 |
| mlsum-de | 47.94 | 44.14 | 45.11 | 36.42 | 35.24 | 35.42 | 44.43 | 41.42 | 42.16 |
| mlsum-fr | 35.26 | 25.96 | 28.98 | 16.72 | 12.35 | 13.75 | 28.06 | 20.75 | 23.12 |
| mlsum-es | 33.37 | 24.84 | 27.52 | 13.29 | 10.05 | 11.05 | 27.63 | 20.69 | 22.87 |
| mlsum-ru | 0.79 | 0.66 | 0.66 | 0.26 | 0.2 | 0.22 | 0.79 | 0.66 | 0.65 |
| cnewsum | 24.49 | 24.38 | 23.23 | 6.48 | 6.7 | 6.24 | 24.18 | 24.04 | 22.91 |
# USAGE
```
soon
``` |
AnonymousSub/SR_rule_based_roberta_hier_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"RobertaModel"
],
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} | 2 | null | ---
license: mit
---
### nere on Stable Diffusion via Dreambooth
#### model by JuanAlbert
This your the Stable Diffusion model fine-tuned the nere concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **nere**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
AnonymousSub/bert_mean_diff_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"BertModel"
],
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}
}
} | 6 | null | ---
tags:
- stable-diffusion
- dreamfusion
- text2mesh
license: "mit"
---
# Stable-Dreamfusion
A pytorch implementation of the text-to-3D model **Dreamfusion**, powered by the [Stable Diffusion](https://github.com/CompVis/stable-diffusion) text-to-2D model.
The original paper's project page: [_DreamFusion: Text-to-3D using 2D Diffusion_](https://dreamfusion3d.github.io/).
Colab notebook for usage: [](https://colab.research.google.com/drive/1MXT3yfOFvO0ooKEfiUUvTKwUkrrlCHpF?usp=sharing)
Examples generated from text prompt `a high quality photo of a pineapple` viewed with the GUI in real time:
https://user-images.githubusercontent.com/25863658/194241493-f3e68f78-aefe-479e-a4a8-001424a61b37.mp4
### [Gallery](https://github.com/ashawkey/stable-dreamfusion/issues/1) | [Update Logs](assets/update_logs.md)
# Important Notice
This project is a **work-in-progress**, and contains lots of differences from the paper. Also, many features are still not implemented now. **The current generation quality cannot match the results from the original paper, and many prompts still fail badly!**
## Notable differences from the paper
* Since the Imagen model is not publicly available, we use [Stable Diffusion](https://github.com/CompVis/stable-diffusion) to replace it (implementation from [diffusers](https://github.com/huggingface/diffusers)). Different from Imagen, Stable-Diffusion is a latent diffusion model, which diffuses in a latent space instead of the original image space. Therefore, we need the loss to propagate back from the VAE's encoder part too, which introduces extra time cost in training. Currently, 10000 training steps take about 3 hours to train on a V100.
* We use the [multi-resolution grid encoder](https://github.com/NVlabs/instant-ngp/) to implement the NeRF backbone (implementation from [torch-ngp](https://github.com/ashawkey/torch-ngp)), which enables much faster rendering (~10FPS at 800x800).
* We use the Adam optimizer with a larger initial learning rate.
## TODOs
* Alleviate the multi-face [Janus problem](https://twitter.com/poolio/status/1578045212236034048).
* Better mesh (improve the surface quality).
# Install
```bash
git clone https://github.com/ashawkey/stable-dreamfusion.git
cd stable-dreamfusion
```
**Important**: To download the Stable Diffusion model checkpoint, you should provide your [access token](https://huggingface.co/settings/tokens). You could choose either of the following ways:
* Run `huggingface-cli login` and enter your token.
* Create a file called `TOKEN` under this directory (i.e., `stable-dreamfusion/TOKEN`) and copy your token into it.
### Install with pip
```bash
pip install -r requirements.txt
# (optional) install nvdiffrast for exporting textured mesh (--save_mesh)
pip install git+https://github.com/NVlabs/nvdiffrast/
# (optional) install the tcnn backbone if using --tcnn
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
# (optional) install CLIP guidance for the dreamfield setting
pip install git+https://github.com/openai/CLIP.git
```
### Build extension (optional)
By default, we use [`load`](https://pytorch.org/docs/stable/cpp_extension.html#torch.utils.cpp_extension.load) to build the extension at runtime.
We also provide the `setup.py` to build each extension:
```bash
# install all extension modules
bash scripts/install_ext.sh
# if you want to install manually, here is an example:
pip install ./raymarching # install to python path (you still need the raymarching/ folder, since this only installs the built extension.)
```
### Tested environments
* Ubuntu 22 with torch 1.12 & CUDA 11.6 on a V100.
# Usage
First time running will take some time to compile the CUDA extensions.
```bash
### stable-dreamfusion setting
## train with text prompt (with the default settings)
# `-O` equals `--cuda_ray --fp16 --dir_text`
# `--cuda_ray` enables instant-ngp-like occupancy grid based acceleration.
# `--fp16` enables half-precision training.
# `--dir_text` enables view-dependent prompting.
python main.py --text "a hamburger" --workspace trial -O
# if the above command fails to generate things (learns an empty scene), maybe try:
# 1. disable random lambertian shading, simply use albedo as color:
python main.py --text "a hamburger" --workspace trial -O --albedo_iters 10000 # i.e., set --albedo_iters >= --iters, which is default to 10000
# 2. use a smaller density regularization weight:
python main.py --text "a hamburger" --workspace trial -O --lambda_entropy 1e-5
# you can also train in a GUI to visualize the training progress:
python main.py --text "a hamburger" --workspace trial -O --gui
# A Gradio GUI is also possible (with less options):
python gradio_app.py # open in web browser
## after the training is finished:
# test (exporting 360 video)
python main.py --workspace trial -O --test
# also save a mesh (with obj, mtl, and png texture)
python main.py --workspace trial -O --test --save_mesh
# test with a GUI (free view control!)
python main.py --workspace trial -O --test --gui
### dreamfields (CLIP) setting
python main.py --text "a hamburger" --workspace trial_clip -O --guidance clip
python main.py --text "a hamburger" --workspace trial_clip -O --test --gui --guidance clip
```
# Code organization & Advanced tips
This is a simple description of the most important implementation details.
If you are interested in improving this repo, this might be a starting point.
Any contribution would be greatly appreciated!
* The SDS loss is located at `./nerf/sd.py > StableDiffusion > train_step`:
```python
# 1. we need to interpolate the NeRF rendering to 512x512, to feed it to SD's VAE.
pred_rgb_512 = F.interpolate(pred_rgb, (512, 512), mode='bilinear', align_corners=False)
# 2. image (512x512) --- VAE --> latents (64x64), this is SD's difference from Imagen.
latents = self.encode_imgs(pred_rgb_512)
... # timestep sampling, noise adding and UNet noise predicting
# 3. the SDS loss, since UNet part is ignored and cannot simply audodiff, we manually set the grad for latents.
w = self.alphas[t] ** 0.5 * (1 - self.alphas[t])
grad = w * (noise_pred - noise)
latents.backward(gradient=grad, retain_graph=True)
```
* Other regularizations are in `./nerf/utils.py > Trainer > train_step`.
* The generation seems quite sensitive to regularizations on weights_sum (alphas for each ray). The original opacity loss tends to make NeRF disappear (zero density everywhere), so we use an entropy loss to replace it for now (encourages alpha to be either 0 or 1).
* NeRF Rendering core function: `./nerf/renderer.py > NeRFRenderer > run_cuda`.
* the occupancy grid based training acceleration (instant-ngp like, enabled by `--cuda_ray`) may harm the generation progress, since once a grid cell is marked as empty, rays won't pass it later...
* Not using `--cuda_ray` also works now:
```bash
# `-O2` equals `--fp16 --dir_text`
python main.py --text "a hamburger" --workspace trial -O2 # faster training, but slower rendering
```
Training is faster if only sample 128 points uniformly per ray (5h --> 2.5h).
More testing is needed...
* Shading & normal evaluation: `./nerf/network*.py > NeRFNetwork > forward`. Current implementation harms training and is disabled.
* light direction: current implementation use a plane light source, instead of a point light source...
* View-dependent prompting: `./nerf/provider.py > get_view_direction`.
* ues `--angle_overhead, --angle_front` to set the border. How to better divide front/back/side regions?
* Network backbone (`./nerf/network*.py`) can be chosen by the `--backbone` option, but `tcnn` and `vanilla` are not well tested.
* Spatial density bias (gaussian density blob): `./nerf/network*.py > NeRFNetwork > gaussian`.
# Acknowledgement
* The amazing original work: [_DreamFusion: Text-to-3D using 2D Diffusion_](https://dreamfusion3d.github.io/).
```
@article{poole2022dreamfusion,
author = {Poole, Ben and Jain, Ajay and Barron, Jonathan T. and Mildenhall, Ben},
title = {DreamFusion: Text-to-3D using 2D Diffusion},
journal = {arXiv},
year = {2022},
}
```
* Huge thanks to the [Stable Diffusion](https://github.com/CompVis/stable-diffusion) and the [diffusers](https://github.com/huggingface/diffusers) library.
```
@misc{rombach2021highresolution,
title={High-Resolution Image Synthesis with Latent Diffusion Models},
author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
year={2021},
eprint={2112.10752},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{von-platen-etal-2022-diffusers,
author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},
title = {Diffusers: State-of-the-art diffusion models},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/huggingface/diffusers}}
}
```
* The GUI is developed with [DearPyGui](https://github.com/hoffstadt/DearPyGui).
|
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} | 4 | null | ---
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/gnbk/ddpm-butterflies-128/tensorboard?#scalars)
|
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-korean-ws1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-korean-ws1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0431
- Cer: 0.0047
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.8176 | 1.0 | 4451 | 0.7022 | 0.2494 |
| 0.3505 | 2.0 | 8902 | 0.1369 | 0.0303 |
| 0.1696 | 3.0 | 13353 | 0.0431 | 0.0047 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
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---
language:
- pt
thumbnail: "Portuguese BERT for the Legal Domain"
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- transformers
datasets:
- assin
- assin2
- stsb_multi_mt
- rufimelo/PortugueseLegalSentences-v1
widget:
- source_sentence: "O advogado apresentou as provas ao juíz."
sentences:
- "O juíz leu as provas."
- "O juíz leu o recurso."
- "O juíz atirou uma pedra."
example_title: "Example 1"
model-index:
- name: BERTimbau
results:
- task:
name: STS
type: STS
metrics:
- name: Pearson Correlation - assin Dataset
type: Pearson Correlation
value: 0.72376
- name: Pearson Correlation - assin2 Dataset
type: Pearson Correlation
value: 0.79261
- name: Pearson Correlation - stsb_multi_mt pt Dataset
type: Pearson Correlation
value: 0.73635
---
# rufimelo/Legal-BERTimbau-large-TSDAE-sts
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
rufimelo/Legal-BERTimbau-large-TSDAE-sts is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large.
It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets.
## 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 = ["Isto é um exemplo", "Isto é um outro exemplo"]
model = SentenceTransformer('rufimelo/Legal-BERTimbau-large-TSDAE-sts')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
```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('rufimelo/Legal-BERTimbau-large-TSDAE-sts')
model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-large-TSDAE-sts')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results STS
| Model| Assin | Assin2|stsb_multi_mt pt| avg|
| ---------------------------------------- | ---------- | ---------- |---------- |---------- |
| Legal-BERTimbau-sts-base| 0.71457| 0.73545 | 0.72383|0.72462|
| Legal-BERTimbau-sts-base-ma| 0.74874 | 0.79532|0.82254 |0.78886|
| Legal-BERTimbau-sts-base-ma-v2| 0.75481 | 0.80262|0.82178|0.79307|
| Legal-BERTimbau-base-TSDAE-sts|0.78814 |0.81380 |0.75777|0.78657|
| Legal-BERTimbau-sts-large| 0.76629| 0.82357 | 0.79120|0.79369|
| Legal-BERTimbau-sts-large-v2| 0.76299 | 0.81121|0.81726 |0.79715|
| Legal-BERTimbau-sts-large-ma| 0.76195| 0.81622 | 0.82608|0.80142|
| Legal-BERTimbau-sts-large-ma-v2| 0.7836| 0.8462| 0.8261| 0.81863|
| Legal-BERTimbau-sts-large-ma-v3| 0.7749| **0.8470**| 0.8364| **0.81943**|
| Legal-BERTimbau-large-v2-sts| 0.71665| 0.80106| 0.73724| 0.75165|
| Legal-BERTimbau-large-TSDAE-sts| 0.72376| 0.79261| 0.73635| 0.75090|
| Legal-BERTimbau-large-TSDAE-sts-v2| 0.81326| 0.83130| 0.786314| 0.81029|
| Legal-BERTimbau-large-TSDAE-sts-v3|0.80703 |0.82270 |0.77638 |0.80204 |
| ---------------------------------------- | ---------- |---------- |---------- |---------- |
| BERTimbau base Fine-tuned for STS|**0.78455** | 0.80626|0.82841|0.80640|
| BERTimbau large Fine-tuned for STS|0.78193 | 0.81758|0.83784|0.81245|
| ---------------------------------------- | ---------- |---------- |---------- |---------- |
| paraphrase-multilingual-mpnet-base-v2| 0.71457| 0.79831 |0.83999 |0.78429|
| paraphrase-multilingual-mpnet-base-v2 Fine-tuned with assin(s)| 0.77641|0.79831 |**0.84575**|0.80682|
## Training
rufimelo/Legal-BERTimbau-large-TSDAE-sts is based on rufimelo/Legal-BERTimbau-large-TSDAE which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) large.
rufimelo/Legal-BERTimbau-large-TSDAE was trained with TSDAE: 10000 cleaned documents (https://huggingface.co/datasets/rufimelo/PortugueseLegalSentences-v1)
'lr': 1e-5
It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the [assin](https://huggingface.co/datasets/assin), [assin2](https://huggingface.co/datasets/assin2) and [stsb_multi_mt pt](https://huggingface.co/datasets/stsb_multi_mt) datasets. 'lr': 2e-5
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
## Citing & Authors
If you use this work, please cite:
```bibtex
@inproceedings{souza2020bertimbau,
author = {F{\'a}bio Souza and
Rodrigo Nogueira and
Roberto Lotufo},
title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
year = {2020}
}
@inproceedings{fonseca2016assin,
title={ASSIN: Avaliacao de similaridade semantica e inferencia textual},
author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S},
booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal},
pages={13--15},
year={2016}
}
@inproceedings{real2020assin,
title={The assin 2 shared task: a quick overview},
author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo},
booktitle={International Conference on Computational Processing of the Portuguese Language},
pages={406--412},
year={2020},
organization={Springer}
}
@InProceedings{huggingface:dataset:stsb_multi_mt,
title = {Machine translated multilingual STS benchmark dataset.},
author={Philip May},
year={2021},
url={https://github.com/PhilipMay/stsb-multi-mt}
}
``` |
AnonymousSub/cline-emanuals-s10-AR | [
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} | 27 | null | ---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilroberta-base-mrpc-glue-oscar-salas7
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. -->
# distilroberta-base-mrpc-glue-oscar-salas7
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7444
- Accuracy: 0.2143
## 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
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cpu
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/cline-emanuals-techqa | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
} | 4 | null | ---
license: mit
language:
- ja
tags:
- generated_from_trainer
- ner
- bert
metrics:
- f1
model-index:
- name: xlm-roberta-ner-ja
results: []
widget:
- text: "鈴木は4月の陽気の良い日に、鈴をつけて熊本県の阿蘇山に登った"
- text: "中国では、中国共産党による一党統治が続く"
---
<!-- 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-ner-japanese
(Japanese caption : 日本語の固有表現抽出のモデル)
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) (pre-trained cross-lingual ```RobertaModel```) trained for named entity recognition (NER) token classification.
The model is fine-tuned on NER dataset provided by Stockmark Inc, in which data is collected from Japanese Wikipedia articles.<br>
See [here](https://github.com/stockmarkteam/ner-wikipedia-dataset) for the license of this dataset.
Each token is labeled by :
| Label id | Tag | Tag in Widget | Description |
|---|---|---|---|
| 0 | O | (None) | others or nothing |
| 1 | PER | PER | person |
| 2 | ORG | ORG | general corporation organization |
| 3 | ORG-P | P | political organization |
| 4 | ORG-O | O | other organization |
| 5 | LOC | LOC | location |
| 6 | INS | INS | institution, facility |
| 7 | PRD | PRD | product |
| 8 | EVT | EVT | event |
## Intended uses
```python
from transformers import pipeline
model_name = "tsmatz/xlm-roberta-ner-japanese"
classifier = pipeline("token-classification", model=model_name)
result = classifier("鈴木は4月の陽気の良い日に、鈴をつけて熊本県の阿蘇山に登った")
print(result)
```
## Training procedure
You can download the source code for fine-tuning from [here](https://github.com/tsmatz/huggingface-finetune-japanese/blob/master/01-named-entity.ipynb).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 446 | 0.1510 | 0.8457 |
| No log | 2.0 | 892 | 0.0626 | 0.9261 |
| No log | 3.0 | 1338 | 0.0366 | 0.9580 |
| No log | 4.0 | 1784 | 0.0196 | 0.9792 |
| No log | 5.0 | 2230 | 0.0173 | 0.9864 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/consert-s10-AR | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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"prefix": null
}
}
} | 31 | 2022-10-24T03:16:59Z | ---
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/zp2222/ddpm-butterflies-128/tensorboard?#scalars)
|
AnonymousSub/consert-s10-SR | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 28 | 2022-10-24T03:18:02Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Comentarios_AgresivosNoAgresivos
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. -->
# Comentarios_AgresivosNoAgresivos
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4584
- Accuracy: 0.8162
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6215 | 1.0 | 154 | 0.5717 | 0.7299 |
| 0.5075 | 2.0 | 308 | 0.4193 | 0.8248 |
| 0.2436 | 3.0 | 462 | 0.4037 | 0.8540 |
| 0.0571 | 4.0 | 616 | 0.6594 | 0.8467 |
| 0.0242 | 5.0 | 770 | 1.0059 | 0.8029 |
| 0.0497 | 6.0 | 924 | 0.8195 | 0.8394 |
| 0.0005 | 7.0 | 1078 | 0.9234 | 0.8394 |
| 0.0528 | 8.0 | 1232 | 0.8894 | 0.8394 |
| 0.0003 | 9.0 | 1386 | 0.9285 | 0.8321 |
| 0.0003 | 10.0 | 1540 | 0.9749 | 0.8321 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/declutr-model-emanuals | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 4 | null | ---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- teacookies/autotrain-data-24102022-cert2
co2_eq_emissions:
emissions: 16.894326665784842
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1856563478
- CO2 Emissions (in grams): 16.8943
## Validation Metrics
- Loss: 0.004
- Accuracy: 0.999
- Precision: 0.961
- Recall: 0.974
- F1: 0.968
## 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/teacookies/autotrain-24102022-cert2-1856563478
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-24102022-cert2-1856563478", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-24102022-cert2-1856563478", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
AnonymousSub/rule_based_bert_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 8 | null | ---
language: ja
license: cc-by-4.0
library_name: sentence-transformers
tags:
- xlm-roberta
- nli
datasets:
- jnli
- jsick
---
# Japanese Natural Language Inference Model
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class, [gradient accumulation PR](https://github.com/UKPLab/sentence-transformers/pull/1092), and the code from [CyberAgentAILab/japanese-nli-model](https://github.com/CyberAgentAILab/japanese-nli-model).
## Training Data
The model was trained on the [JGLUE-JNLI](https://github.com/yahoojapan/JGLUE) and [JSICK](https://github.com/verypluming/JSICK) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('cyberagent/xlm-roberta-large-jnli-jsick')
model = AutoModelForSequenceClassification.from_pretrained('cyberagent/xlm-roberta-large-jnli-jsick')
features = tokenizer(["子供が走っている猫を見ている", "猫が走っている"], ["猫が走っている", "子供が走っている"], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels)
```
|
AnonymousSub/rule_based_bert_quadruplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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},
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},
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},
"translation_en_to_fr": {
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"prefix": null
}
}
} | 3 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -174.50 +/- 133.75
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 1 | null | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Ahmed-Abousetta/autotrain-data-abunawaf-cognition-auto
co2_eq_emissions:
emissions: 1.7868012751172693
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1859563553
- CO2 Emissions (in grams): 1.7868
## Validation Metrics
- Loss: 0.382
- Accuracy: 0.854
- Precision: 0.811
- Recall: 0.843
- AUC: 0.915
- F1: 0.827
## 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/Ahmed-Abousetta/autotrain-abunawaf-cognition-auto-1859563553
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Ahmed-Abousetta/autotrain-abunawaf-cognition-auto-1859563553", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Ahmed-Abousetta/autotrain-abunawaf-cognition-auto-1859563553", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
AnonymousSub/rule_based_only_classfn_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 4 | null | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Ahmed-Abousetta/autotrain-data-abunawaf-cognition-auto
co2_eq_emissions:
emissions: 1.1747519267416993
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1859563554
- CO2 Emissions (in grams): 1.1748
## Validation Metrics
- Loss: 0.455
- Accuracy: 0.813
- Precision: 0.722
- Recall: 0.892
- AUC: 0.872
- F1: 0.798
## 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/Ahmed-Abousetta/autotrain-abunawaf-cognition-auto-1859563554
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Ahmed-Abousetta/autotrain-abunawaf-cognition-auto-1859563554", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Ahmed-Abousetta/autotrain-abunawaf-cognition-auto-1859563554", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 8 | null | ---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Ahmed-Abousetta/autotrain-data-abunawaf-information
co2_eq_emissions:
emissions: 1.5884381963682959
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1859863561
- CO2 Emissions (in grams): 1.5884
## Validation Metrics
- Loss: 0.338
- Accuracy: 0.869
- Precision: 0.836
- Recall: 0.868
- AUC: 0.932
- F1: 0.852
## 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/Ahmed-Abousetta/autotrain-abunawaf-information-1859863561
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Ahmed-Abousetta/autotrain-abunawaf-information-1859863561", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Ahmed-Abousetta/autotrain-abunawaf-information-1859863561", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 1 | null | ---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Ahmed-Abousetta/autotrain-data-abunawaf-performance
co2_eq_emissions:
emissions: 0.6594479502465727
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1860063571
- CO2 Emissions (in grams): 0.6594
## Validation Metrics
- Loss: 0.447
- Accuracy: 0.824
- Precision: 0.841
- Recall: 0.792
- AUC: 0.886
- F1: 0.815
## 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/Ahmed-Abousetta/autotrain-abunawaf-performance-1860063571
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Ahmed-Abousetta/autotrain-abunawaf-performance-1860063571", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Ahmed-Abousetta/autotrain-abunawaf-performance-1860063571", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 6 | null | ---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Ahmed-Abousetta/autotrain-data-abunawaf-performance
co2_eq_emissions:
emissions: 0.8429873610442068
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1860063572
- CO2 Emissions (in grams): 0.8430
## Validation Metrics
- Loss: 0.462
- Accuracy: 0.788
- Precision: 0.762
- Recall: 0.825
- AUC: 0.881
- F1: 0.792
## 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/Ahmed-Abousetta/autotrain-abunawaf-performance-1860063572
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Ahmed-Abousetta/autotrain-abunawaf-performance-1860063572", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Ahmed-Abousetta/autotrain-abunawaf-performance-1860063572", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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}
} | 2 | null | ---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- pcoloc/autotrain-data-mikrotik-7-7
co2_eq_emissions:
emissions: 7.1011693391153115
---
# Model Trained Using AutoTrain
- Problem type: Single Column Regression
- Model ID: 1860563590
- CO2 Emissions (in grams): 7.1012
## Validation Metrics
- Loss: 52.881
- R2: 0.584
- MSE: 2796.357
- MAE: 37.116
- RMSLE: 0.518
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
``` |
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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"RobertaModel"
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}
} | 5 | 2022-10-24T10:20:56Z | ---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- pcoloc/autotrain-data-dragino-7-7-max_300m
co2_eq_emissions:
emissions: 0.12860686048945302
---
# Model Trained Using AutoTrain
- Problem type: Single Column Regression
- Model ID: 1861063640
- CO2 Emissions (in grams): 0.1286
## Validation Metrics
- Loss: 50.918
- R2: 0.304
- MSE: 2592.667
- MAE: 39.693
- RMSLE: 0.429
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
``` |
Anthos23/my-awesome-model | [
"pytorch",
"tf",
"roberta",
"text-classification",
"transformers"
]
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"RobertaForSequenceClassification"
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}
} | 30 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: balanced-combined-bert
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. -->
# balanced-combined-bert
This model is a fine-tuned version of [dbmdz/bert-base-turkish-128k-uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4475
- Accuracy: 0.825
- F1: 0.8141
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Anthos23/test_trainer | []
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} | 0 | null | ---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- teacookies/autotrain-data-24102022-cert9
co2_eq_emissions:
emissions: 18.678658475473995
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1861563662
- CO2 Emissions (in grams): 18.6787
## Validation Metrics
- Loss: 0.004
- Accuracy: 0.999
- Precision: 0.959
- Recall: 0.969
- F1: 0.964
## 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/teacookies/autotrain-24102022-cert9-1861563662
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-24102022-cert9-1861563662", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-24102022-cert9-1861563662", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Anubhav23/indianlegal | []
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} | 0 | null | Access to model Kismetor/diffusion is restricted and you are not in the authorized list. Visit https://huggingface.co/Kismetor/diffusion to ask for access. |
Anubhav23/model_name | []
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} | 0 | 2022-10-24T11:15:41Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1222 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.WeightedRandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 122.1875,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
ArBert/roberta-base-finetuned-ner-kmeans-twitter | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
]
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"RobertaForTokenClassification"
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}
} | 10 | 2022-10-24T12:26:28Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8833333333333333
- name: F1
type: f1
value: 0.8844884488448845
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3110
- Accuracy: 0.8833
- F1: 0.8845
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
ArJakusz/DialoGPT-small-starky | []
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} | 0 | 2022-10-25T04:39:23Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: deberta-v3-base-finetuned-ner
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. -->
# deberta-v3-base-finetuned-ner
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7679
- Overall Precision: 0.4915
- Overall Recall: 0.6463
- Overall F1: 0.5584
- Overall Accuracy: 0.9555
- Datasetname F1: 0.3304
- Hyperparametername F1: 0.6341
- Hyperparametervalue F1: 0.7463
- Methodname F1: 0.6093
- Metricname F1: 0.7089
- Metricvalue F1: 0.7500
- Taskname F1: 0.4426
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Datasetname F1 | Hyperparametername F1 | Hyperparametervalue F1 | Methodname F1 | Metricname F1 | Metricvalue F1 | Taskname F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:---------------------:|:----------------------:|:-------------:|:-------------:|:--------------:|:-----------:|
| No log | 1.0 | 132 | 0.5046 | 0.2771 | 0.5041 | 0.3576 | 0.9356 | 0.2405 | 0.1988 | 0.4545 | 0.4638 | 0.4539 | 0.6486 | 0.2793 |
| No log | 2.0 | 264 | 0.3928 | 0.3344 | 0.6463 | 0.4407 | 0.9376 | 0.2449 | 0.3968 | 0.6292 | 0.5641 | 0.5373 | 0.4583 | 0.3359 |
| No log | 3.0 | 396 | 0.4714 | 0.4419 | 0.6179 | 0.5153 | 0.9533 | 0.3822 | 0.5310 | 0.7536 | 0.6262 | 0.6328 | 0.6857 | 0.3291 |
| 0.5663 | 4.0 | 528 | 0.3741 | 0.4493 | 0.7114 | 0.5507 | 0.9509 | 0.4717 | 0.7241 | 0.6353 | 0.5918 | 0.5714 | 0.6275 | 0.4372 |
| 0.5663 | 5.0 | 660 | 0.4202 | 0.3930 | 0.6870 | 0.5 | 0.9458 | 0.2759 | 0.6525 | 0.65 | 0.5596 | 0.7097 | 0.7368 | 0.3573 |
| 0.5663 | 6.0 | 792 | 0.4676 | 0.4244 | 0.6850 | 0.5241 | 0.9473 | 0.3333 | 0.5949 | 0.7397 | 0.5653 | 0.6988 | 0.7568 | 0.3652 |
| 0.5663 | 7.0 | 924 | 0.5744 | 0.4328 | 0.5955 | 0.5013 | 0.9517 | 0.2585 | 0.6167 | 0.5915 | 0.5825 | 0.6386 | 0.7500 | 0.3824 |
| 0.1503 | 8.0 | 1056 | 0.5340 | 0.4309 | 0.6585 | 0.5209 | 0.9499 | 0.2976 | 0.6299 | 0.7105 | 0.6140 | 0.6708 | 0.7568 | 0.3544 |
| 0.1503 | 9.0 | 1188 | 0.5229 | 0.4628 | 0.6829 | 0.5517 | 0.9531 | 0.4630 | 0.5103 | 0.6087 | 0.625 | 0.6541 | 0.7778 | 0.4493 |
| 0.1503 | 10.0 | 1320 | 0.6287 | 0.4978 | 0.6748 | 0.5729 | 0.9563 | 0.4314 | 0.6500 | 0.7463 | 0.6413 | 0.7432 | 0.7568 | 0.4108 |
| 0.1503 | 11.0 | 1452 | 0.5163 | 0.4571 | 0.7033 | 0.5540 | 0.9519 | 0.3925 | 0.5256 | 0.6024 | 0.6828 | 0.6626 | 0.7368 | 0.4466 |
| 0.0735 | 12.0 | 1584 | 0.6737 | 0.5046 | 0.6687 | 0.5752 | 0.9555 | 0.3883 | 0.6615 | 0.6757 | 0.6074 | 0.7051 | 0.7778 | 0.4577 |
| 0.0735 | 13.0 | 1716 | 0.5849 | 0.44 | 0.6931 | 0.5383 | 0.9480 | 0.3770 | 0.6555 | 0.6479 | 0.5922 | 0.6957 | 0.6512 | 0.4071 |
| 0.0735 | 14.0 | 1848 | 0.8314 | 0.5018 | 0.5793 | 0.5377 | 0.9539 | 0.3 | 0.6549 | 0.6667 | 0.5613 | 0.7361 | 0.7368 | 0.4294 |
| 0.0735 | 15.0 | 1980 | 0.5986 | 0.4549 | 0.6768 | 0.5441 | 0.9506 | 0.3793 | 0.6000 | 0.6667 | 0.6181 | 0.7089 | 0.6829 | 0.3978 |
| 0.0408 | 16.0 | 2112 | 0.7579 | 0.4900 | 0.6443 | 0.5566 | 0.9541 | 0.4103 | 0.6032 | 0.6765 | 0.6238 | 0.7123 | 0.6667 | 0.4217 |
| 0.0408 | 17.0 | 2244 | 0.9175 | 0.5285 | 0.6037 | 0.5636 | 0.9565 | 0.4 | 0.6789 | 0.7692 | 0.5949 | 0.7101 | 0.6857 | 0.4122 |
| 0.0408 | 18.0 | 2376 | 0.7771 | 0.5041 | 0.6179 | 0.5553 | 0.9562 | 0.3684 | 0.6207 | 0.7246 | 0.5842 | 0.7383 | 0.6667 | 0.4353 |
| 0.0226 | 19.0 | 2508 | 0.7992 | 0.5213 | 0.6463 | 0.5771 | 0.9569 | 0.32 | 0.6724 | 0.7353 | 0.6485 | 0.7114 | 0.7179 | 0.4510 |
| 0.0226 | 20.0 | 2640 | 0.7679 | 0.4915 | 0.6463 | 0.5584 | 0.9555 | 0.3304 | 0.6341 | 0.7463 | 0.6093 | 0.7089 | 0.7500 | 0.4426 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AragornII/DialoGPT-small-harrypotter | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_basic
type: doom_basic
metrics:
- type: mean_reward
value: 0.75 +/- 0.10
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_basic** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Aran/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 8 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_my_way_home_flat_actions
type: doom_my_way_home_flat_actions
metrics:
- type: mean_reward
value: 0.98 +/- 0.01
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_my_way_home_flat_actions** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
ArashEsk95/bert-base-uncased-finetuned-sst2 | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_my_way_home
type: doom_my_way_home
metrics:
- type: mean_reward
value: 0.98 +/- 0.01
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_my_way_home** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Aravinth/test | []
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}
} | 0 | 2022-10-24T12:58:04Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_defend_the_center
type: doom_defend_the_center
metrics:
- type: mean_reward
value: 24.67 +/- 0.47
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_defend_the_center** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Arcktosh/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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} | 8 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_duel_bots
type: doom_duel_bots
metrics:
- type: mean_reward
value: nan +/- nan
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_duel_bots** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
AriakimTaiyo/DialoGPT-cultured-Kumiko | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
} | 8 | 2022-10-24T13:02:10Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_basic
type: doom_basic
metrics:
- type: mean_reward
value: 0.76 +/- 0.11
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_basic** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Atchuth/DialoGPT-small-MBOT | []
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}
} | 0 | 2022-10-24T13:57:54Z | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- mozilla-foundation/common_voice_9_0
---
To reproduce this run, first call `get_ctc_tokenizer.py` to train the CTC tokenizer and then execute the following command to train the CTC system:
```python
#!/usr/bin/env bash
python run_flax_speech_recognition_ctc.py \
--model_name_or_path="esb/wav2vec2-ctc-pretrained" \
--tokenizer_name="wav2vec2-ctc-common_voice-tokenizer" \
--dataset_name="esb/datasets" \
--dataset_config_name="common_voice" \
--output_dir="./" \
--wandb_project="wav2vec2-ctc" \
--wandb_name="wav2vec2-ctc-common-voice" \
--max_steps="50000" \
--save_steps="10000" \
--eval_steps="10000" \
--learning_rate="3e-4" \
--logging_steps="25" \
--warmup_steps="5000" \
--preprocessing_num_workers="1" \
--max_eval_duration_in_seconds="20" \
--do_train \
--do_eval \
--do_predict \
--overwrite_output_dir \
--gradient_checkpointing \
--freeze_feature_encoder \
--push_to_hub \
--use_auth_token
```
|
Axon/resnet34-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
]
| null | {
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}
} | 0 | null | ---
language:
- en
tags:
- esb
datasets:
- esb/datasets
- ldc/chime-4
---
To reproduce this run, first install Whisper from the Transformers compatible repo [patrickvonplaten/whisper](https://github.com/patrickvonplaten/whisper):
```
pip install git+https://github.com/openai/whisper.git
```
Then execute the command:
```python
#!/usr/bin/env bash
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
--model_name_or_path="medium.en" \
--dataset_name="esb/datasets" \
--dataset_config_name="chime4" \
--max_steps="2500" \
--output_dir="./" \
--run_name="whisper-chime4" \
--dropout_rate="0.1" \
--wandb_project="whisper" \
--per_device_train_batch_size="64" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--report_to="wandb" \
--preprocessing_num_workers="16" \
--evaluation_strategy="steps" \
--eval_steps="500" \
--save_strategy="steps" \
--save_steps="500" \
--generation_max_length="224" \
--length_column_name="input_lengths" \
--gradient_checkpointing \
--group_by_length \
--freeze_encoder \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--predict_with_generate \
--use_auth_token
```
|
Ayham/roberta_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
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}
} | 31 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-large_basetags_5k_4_p3
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. -->
# deberta-large_basetags_5k_4_p3
This model is a fine-tuned version of [model_saves/deberta-large_basetags_5k_4_p2](https://huggingface.co/model_saves/deberta-large_basetags_5k_4_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4154
- Accuracy: 0.9414
## 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: 128
- eval_batch_size: 128
- seed: 72
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4154 | 0.9414 |
| No log | 2.0 | 536 | 0.4354 | 0.9410 |
| No log | 3.0 | 804 | 0.4763 | 0.9406 |
| 0.2537 | 4.0 | 1072 | 0.5329 | 0.9406 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BSC-LT/roberta-large-bne | [
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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],
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}
} | 24 | 2022-10-24T16:31:00Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large_lemon-spell_10k_1_p3
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. -->
# deberta-v3-large_lemon-spell_10k_1_p3
This model is a fine-tuned version of [model_saves/deberta-v3-large_lemon-spell_10k_1_p2](https://huggingface.co/model_saves/deberta-v3-large_lemon-spell_10k_1_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4204
- Accuracy: 0.9424
## 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: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4204 | 0.9424 |
| No log | 2.0 | 536 | 0.4364 | 0.9421 |
| No log | 3.0 | 804 | 0.4670 | 0.9417 |
| 0.2897 | 4.0 | 1072 | 0.4973 | 0.9406 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BSen/wav2vec2-large-xls-r-300m-turkish-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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}
} | 6 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large_lemon-spell_10k_3_p3
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. -->
# deberta-v3-large_lemon-spell_10k_3_p3
This model is a fine-tuned version of [model_saves/deberta-v3-large_lemon-spell_10k_3_p2](https://huggingface.co/model_saves/deberta-v3-large_lemon-spell_10k_3_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4316
- Accuracy: 0.9419
## 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: 128
- eval_batch_size: 128
- seed: 62
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4238 | 0.9416 |
| No log | 2.0 | 536 | 0.4316 | 0.9419 |
| No log | 3.0 | 804 | 0.4623 | 0.9408 |
| 0.2904 | 4.0 | 1072 | 0.4921 | 0.9407 |
| 0.2904 | 5.0 | 1340 | 0.5336 | 0.9394 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Bakkes/BakkesModWiki | []
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: electra-large_basetags_10k_1_p3
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. -->
# electra-large_basetags_10k_1_p3
This model is a fine-tuned version of [model_saves/electra-large_basetags_10k_1_p2](https://huggingface.co/model_saves/electra-large_basetags_10k_1_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4673
- Accuracy: 0.9386
## 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: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4498 | 0.9378 |
| No log | 2.0 | 536 | 0.4673 | 0.9386 |
| No log | 3.0 | 804 | 0.4850 | 0.9377 |
| 0.322 | 4.0 | 1072 | 0.5098 | 0.9374 |
| 0.322 | 5.0 | 1340 | 0.5486 | 0.9374 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Balgow/prod_desc | []
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: electra-large_basetags_5k_1_p3
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. -->
# electra-large_basetags_5k_1_p3
This model is a fine-tuned version of [model_saves/electra-large_basetags_5k_1_p2](https://huggingface.co/model_saves/electra-large_basetags_5k_1_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4574
- Accuracy: 0.9389
## 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: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4389 | 0.9384 |
| No log | 2.0 | 536 | 0.4574 | 0.9389 |
| No log | 3.0 | 804 | 0.4744 | 0.9379 |
| 0.3215 | 4.0 | 1072 | 0.5003 | 0.9375 |
| 0.3215 | 5.0 | 1340 | 0.5413 | 0.9378 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Banshee/dialoGPT-luke-small | []
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} | 0 | null | ---
language:
- en
tags:
- text2text-generation
widget:
- text: "The <extra_id_0> walks in <extra_id_1> park"
example_title: "Masked Language Modeling"
datasets:
- c4
license: apache-2.0
---
# Model Card for Switch Transformers Base - 8 experts
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
5. [Training Details](#training-details)
6. [Evaluation](#evaluation)
7. [Environmental Impact](#environmental-impact)
8. [Citation](#citation)
9. [Model Card Authors](#model-card-authors)
# TL;DR
Switch Transformers is a Mixture of Experts (MoE) model trained on Masked Language Modeling (MLM) task. The model architecture is similar to the classic T5, but with the Feed Forward layers replaced by the Sparse MLP layers containing "experts" MLP. According to the [original paper](https://arxiv.org/pdf/2101.03961.pdf) the model enables faster training (scaling properties) while being better than T5 on fine-tuned tasks.
As mentioned in the first few lines of the abstract :
> we advance the current scale of language models by pre-training up to trillion parameter models on the “Colossal Clean Crawled Corpus”, and achieve a 4x speedup over the T5-XXL model.
**Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the [original paper](https://arxiv.org/pdf/2101.03961.pdf).
# Model Details
## Model Description
- **Model type:** Language model
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Related Models:** [All Switch Transformers Checkpoints](https://huggingface.co/models?search=switch)
- **Original Checkpoints:** [All Original Switch Transformers Checkpoints](https://github.com/google-research/t5x/blob/main/docs/models.md#mixture-of-experts-moe-checkpoints)
- **Resources for more information:**
- [Research paper](https://arxiv.org/pdf/2101.03961.pdf)
- [GitHub Repo](https://github.com/google-research/t5x)
- [Hugging Face Switch Transformers Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/switch_transformers)
# Usage
Note that these checkpoints has been trained on Masked-Language Modeling (MLM) task. Therefore the checkpoints are not "ready-to-use" for downstream tasks. You may want to check `FLAN-T5` for running fine-tuned weights or fine-tune your own MoE following [this notebook](https://colab.research.google.com/drive/1aGGVHZmtKmcNBbAwa9hbu58DDpIuB5O4?usp=sharing)
Find below some example scripts on how to use the model in `transformers`:
## Using the Pytorch model
### Running the model on a CPU
<details>
<summary> Click to expand </summary>
```python
from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-8")
input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
>>> <pad> <extra_id_0> man<extra_id_1> beer<extra_id_2> a<extra_id_3> salt<extra_id_4>.</s>
```
</details>
### Running the model on a GPU
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-8", device_map="auto")
input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
>>> <pad> <extra_id_0> man<extra_id_1> beer<extra_id_2> a<extra_id_3> salt<extra_id_4>.</s>
```
</details>
### Running the model on a GPU using different precisions
#### FP16
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-8", device_map="auto", torch_dtype=torch.float16)
input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
>>> <pad> <extra_id_0> man<extra_id_1> beer<extra_id_2> a<extra_id_3> salt<extra_id_4>.</s>
```
</details>
#### INT8
<details>
<summary> Click to expand </summary>
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-8", device_map="auto")
input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
>>> <pad> <extra_id_0> man<extra_id_1> beer<extra_id_2> a<extra_id_3> salt<extra_id_4>.</s>
```
</details>
# Uses
## Direct Use and Downstream Use
See the [research paper](https://arxiv.org/pdf/2101.03961.pdf) for further details.
## Out-of-Scope Use
More information needed.
# Bias, Risks, and Limitations
More information needed.
## Ethical considerations and risks
More information needed.
## Known Limitations
More information needed.
## Sensitive Use:
More information needed.
# Training Details
## Training Data
The model was trained on a Masked Language Modeling task, on Colossal Clean Crawled Corpus (C4) dataset, following the same procedure as `T5`.
## Training Procedure
According to the model card from the [original paper](https://arxiv.org/pdf/2101.03961.pdf) the model has been trained on TPU v3 or TPU v4 pods, using [`t5x`](https://github.com/google-research/t5x) codebase together with [`jax`](https://github.com/google/jax).
# Evaluation
## Testing Data, Factors & Metrics
The authors evaluated the model on various tasks and compared the results against T5. See the table below for some quantitative evaluation:
For full details, please check the [research paper](https://arxiv.org/pdf/2101.03961.pdf).
## Results
For full results for Switch Transformers, see the [research paper](https://arxiv.org/pdf/2101.03961.pdf), Table 5.
# Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** Google Cloud TPU Pods - TPU v3 or TPU v4 | Number of chips ≥ 4.
- **Hours used:** More information needed
- **Cloud Provider:** GCP
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
# Citation
**BibTeX:**
```bibtex
@misc{https://doi.org/10.48550/arxiv.2101.03961,
doi = {10.48550/ARXIV.2101.03961},
url = {https://arxiv.org/abs/2101.03961},
author = {Fedus, William and Zoph, Barret and Shazeer, Noam},
keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity},
publisher = {arXiv},
year = {2021},
copyright = {arXiv.org perpetual, non-exclusive license}
}
``` |
Bhumika/roberta-base-finetuned-sst2 | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"model-index"
]
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} | 85 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 192.47 +/- 76.19
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Bia18/Beatriz | []
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large_lemon-spell_10k_1_p3
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-large_lemon-spell_10k_1_p3
This model is a fine-tuned version of [model_saves/roberta-large_lemon-spell_10k_1_p2](https://huggingface.co/model_saves/roberta-large_lemon-spell_10k_1_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4339
- Accuracy: 0.9406
## 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: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| No log | 1.0 | 268 | 0.9406 | 0.4339 |
| No log | 2.0 | 536 | 0.9405 | 0.4440 |
| No log | 3.0 | 804 | 0.9397 | 0.4724 |
| 0.3147 | 4.0 | 1072 | 0.9389 | 0.5124 |
| 0.3147 | 5.0 | 1340 | 0.5492 | 0.9388 |
| 0.3147 | 6.0 | 1608 | 0.5995 | 0.9379 |
| 0.3147 | 7.0 | 1876 | 0.6559 | 0.9380 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Biasface/DDDC2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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}
}
} | 10 | 2022-10-24T17:52:42Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large_lemon-spell_10k_2_p3
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-large_lemon-spell_10k_2_p3
This model is a fine-tuned version of [model_saves/roberta-large_lemon-spell_10k_2_p2](https://huggingface.co/model_saves/roberta-large_lemon-spell_10k_2_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4359
- Accuracy: 0.9406
## 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: 128
- eval_batch_size: 128
- seed: 52
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| No log | 1.0 | 268 | 0.9406 | 0.4359 |
| No log | 2.0 | 536 | 0.9399 | 0.4492 |
| No log | 3.0 | 804 | 0.9399 | 0.4743 |
| 0.2873 | 4.0 | 1072 | 0.9395 | 0.5155 |
| 0.2873 | 5.0 | 1340 | 0.5667 | 0.9389 |
| 0.2873 | 6.0 | 1608 | 0.6481 | 0.9391 |
| 0.2873 | 7.0 | 1876 | 0.6873 | 0.9381 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigDaddyNe1L/Hhaa | []
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} | 0 | 2022-10-24T17:54:44Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 65713 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 65713,
"warmup_steps": 6572,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
BigSalmon/BestMask2 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
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}
}
} | 10 | 2022-10-24T17:54:59Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large_lemon-spell_5k_1_p3
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-large_lemon-spell_5k_1_p3
This model is a fine-tuned version of [model_saves/roberta-large_lemon-spell_5k_1_p2](https://huggingface.co/model_saves/roberta-large_lemon-spell_5k_1_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4276
- Accuracy: 0.9404
## 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: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4276 | 0.9404 |
| No log | 2.0 | 536 | 0.4368 | 0.9401 |
| No log | 3.0 | 804 | 0.4663 | 0.9396 |
| 0.3203 | 4.0 | 1072 | 0.5026 | 0.9385 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/DaBlank | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 4 | 2022-10-24T17:56:52Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large_lemon-spell_5k_3_p3
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-large_lemon-spell_5k_3_p3
This model is a fine-tuned version of [model_saves/roberta-large_lemon-spell_5k_3_p2](https://huggingface.co/model_saves/roberta-large_lemon-spell_5k_3_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4501
- Accuracy: 0.9388
## 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: 128
- eval_batch_size: 128
- seed: 62
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4378 | 0.9387 |
| No log | 2.0 | 536 | 0.4501 | 0.9388 |
| No log | 3.0 | 804 | 0.4976 | 0.9381 |
| 0.272 | 4.0 | 1072 | 0.5395 | 0.9381 |
| 0.272 | 5.0 | 1340 | 0.5934 | 0.9376 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/FormalBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
} | 10 | 2022-10-24T17:58:44Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large_lemon-spell_5k_5_p3
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-large_lemon-spell_5k_5_p3
This model is a fine-tuned version of [model_saves/roberta-large_lemon-spell_5k_5_p2](https://huggingface.co/model_saves/roberta-large_lemon-spell_5k_5_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4791
- Accuracy: 0.9391
## 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: 128
- eval_batch_size: 128
- seed: 82
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4315 | 0.9391 |
| No log | 2.0 | 536 | 0.4467 | 0.9387 |
| No log | 3.0 | 804 | 0.4791 | 0.9391 |
| 0.2901 | 4.0 | 1072 | 0.5057 | 0.9386 |
| 0.2901 | 5.0 | 1340 | 0.5766 | 0.9374 |
| 0.2901 | 6.0 | 1608 | 0.6426 | 0.9384 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/FormalBerta2 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
}
} | 16 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large_lemon-spell_5k_6_p3
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-large_lemon-spell_5k_6_p3
This model is a fine-tuned version of [model_saves/roberta-large_lemon-spell_5k_6_p2](https://huggingface.co/model_saves/roberta-large_lemon-spell_5k_6_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4265
- Accuracy: 0.9402
## 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: 128
- eval_batch_size: 128
- seed: 92
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4265 | 0.9402 |
| No log | 2.0 | 536 | 0.4351 | 0.9401 |
| No log | 3.0 | 804 | 0.4574 | 0.9396 |
| 0.3181 | 4.0 | 1072 | 0.4969 | 0.9387 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/FormalBerta3 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 4 | 2022-10-24T18:00:34Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large_spell_10k_1_p3
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-large_spell_10k_1_p3
This model is a fine-tuned version of [model_saves/roberta-large_spell_10k_1_p2](https://huggingface.co/model_saves/roberta-large_spell_10k_1_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4478
- Accuracy: 0.9400
## 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: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| No log | 1.0 | 268 | 0.9394 | 0.4278 |
| No log | 2.0 | 536 | 0.9400 | 0.4478 |
| No log | 3.0 | 804 | 0.9385 | 0.4739 |
| 0.2854 | 4.0 | 1072 | 0.9386 | 0.5202 |
| 0.2854 | 5.0 | 1340 | 0.9399 | 0.5863 |
| 0.2854 | 6.0 | 1608 | 0.6210 | 0.9392 |
| 0.2854 | 7.0 | 1876 | 0.6682 | 0.9385 |
| 0.1207 | 8.0 | 2144 | 0.7322 | 0.9382 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/FormalRobertaaa | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 12 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large_spell_10k_3_p3
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-large_spell_10k_3_p3
This model is a fine-tuned version of [model_saves/roberta-large_spell_10k_3_p2](https://huggingface.co/model_saves/roberta-large_spell_10k_3_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4350
- Accuracy: 0.9404
## 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: 128
- eval_batch_size: 128
- seed: 62
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| No log | 1.0 | 268 | 0.9404 | 0.4350 |
| No log | 2.0 | 536 | 0.9394 | 0.4450 |
| No log | 3.0 | 804 | 0.9388 | 0.4803 |
| 0.2844 | 4.0 | 1072 | 0.9386 | 0.5240 |
| 0.2844 | 5.0 | 1340 | 0.5639 | 0.9384 |
| 0.2844 | 6.0 | 1608 | 0.6261 | 0.9387 |
| 0.2844 | 7.0 | 1876 | 0.6881 | 0.9388 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/GPT2HardArticleEasyArticle | [
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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} | 7 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large_spell_5k_2_p3
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-large_spell_5k_2_p3
This model is a fine-tuned version of [model_saves/roberta-large_spell_5k_2_p2](https://huggingface.co/model_saves/roberta-large_spell_5k_2_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4261
- Accuracy: 0.9404
## 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: 128
- eval_batch_size: 128
- seed: 52
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4261 | 0.9404 |
| No log | 2.0 | 536 | 0.4447 | 0.9402 |
| No log | 3.0 | 804 | 0.4736 | 0.9395 |
| 0.2895 | 4.0 | 1072 | 0.5212 | 0.9382 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/GPTHeHe | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 8 | 2022-10-24T18:06:54Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large_spell_5k_3_p3
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-large_spell_5k_3_p3
This model is a fine-tuned version of [model_saves/roberta-large_spell_5k_3_p2](https://huggingface.co/model_saves/roberta-large_spell_5k_3_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4264
- Accuracy: 0.9403
## 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: 128
- eval_batch_size: 128
- seed: 62
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4264 | 0.9403 |
| No log | 2.0 | 536 | 0.4360 | 0.9393 |
| No log | 3.0 | 804 | 0.4725 | 0.9385 |
| 0.2897 | 4.0 | 1072 | 0.5133 | 0.9382 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/GPTIntro | []
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large_spell_5k_4_p3
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-large_spell_5k_4_p3
This model is a fine-tuned version of [model_saves/roberta-large_spell_5k_4_p2](https://huggingface.co/model_saves/roberta-large_spell_5k_4_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4272
- Accuracy: 0.9404
## 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: 128
- eval_batch_size: 128
- seed: 72
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4272 | 0.9404 |
| No log | 2.0 | 536 | 0.4365 | 0.9393 |
| No log | 3.0 | 804 | 0.4714 | 0.9397 |
| 0.2903 | 4.0 | 1072 | 0.5079 | 0.9390 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/GPTNeo350MInformalToFormalLincoln | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
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} | 8 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large_spell_5k_5_p3
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-large_spell_5k_5_p3
This model is a fine-tuned version of [model_saves/roberta-large_spell_5k_5_p2](https://huggingface.co/model_saves/roberta-large_spell_5k_5_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4416
- Accuracy: 0.9388
## 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: 128
- eval_batch_size: 128
- seed: 82
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4416 | 0.9388 |
| No log | 2.0 | 536 | 0.4567 | 0.9384 |
| No log | 3.0 | 804 | 0.5054 | 0.9386 |
| 0.2675 | 4.0 | 1072 | 0.5354 | 0.9385 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/GPTNeo350MInformalToFormalLincoln3 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPTNeoForCausalLM"
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}
} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- yelp_review_full
metrics:
- accuracy
model-index:
- name: hf_fine_tune_hello_world
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: 0.592
---
<!-- 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. -->
# hf_fine_tune_hello_world
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0142
- Accuracy: 0.592
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 125 | 1.0844 | 0.529 |
| No log | 2.0 | 250 | 1.0022 | 0.58 |
| No log | 3.0 | 375 | 1.0142 | 0.592 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu102
- Datasets 2.5.2
- Tokenizers 0.12.1
|
BigSalmon/GPTNeo350MInformalToFormalLincoln5 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
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}
} | 11 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlnet-large_basetags_10k_2_p3
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. -->
# xlnet-large_basetags_10k_2_p3
This model is a fine-tuned version of [model_saves/xlnet-large_basetags_10k_2_p2](https://huggingface.co/model_saves/xlnet-large_basetags_10k_2_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4800
- Accuracy: 0.9405
## 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: 128
- eval_batch_size: 128
- seed: 52
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4432 | 0.9404 |
| No log | 2.0 | 536 | 0.4482 | 0.9401 |
| No log | 3.0 | 804 | 0.4800 | 0.9405 |
| 0.3219 | 4.0 | 1072 | 0.5201 | 0.9400 |
| 0.3219 | 5.0 | 1340 | 0.5552 | 0.9394 |
| 0.3219 | 6.0 | 1608 | 0.6083 | 0.9387 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/GPTT | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
} | 9 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlnet-large_basetags_5k_1_p3
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. -->
# xlnet-large_basetags_5k_1_p3
This model is a fine-tuned version of [model_saves/xlnet-large_basetags_5k_1_p2](https://huggingface.co/model_saves/xlnet-large_basetags_5k_1_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4744
- Accuracy: 0.9398
## 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: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4461 | 0.9394 |
| No log | 2.0 | 536 | 0.4744 | 0.9398 |
| No log | 3.0 | 804 | 0.5171 | 0.9392 |
| 0.273 | 4.0 | 1072 | 0.5515 | 0.9384 |
| 0.273 | 5.0 | 1340 | 0.6133 | 0.9383 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln15 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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} | 11 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlnet-large_lemon_10k_1_p3
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. -->
# xlnet-large_lemon_10k_1_p3
This model is a fine-tuned version of [model_saves/xlnet-large_lemon_10k_1_p2](https://huggingface.co/model_saves/xlnet-large_lemon_10k_1_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4602
- Accuracy: 0.9403
## 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: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4438 | 0.9397 |
| No log | 2.0 | 536 | 0.4602 | 0.9403 |
| No log | 3.0 | 804 | 0.4953 | 0.9399 |
| 0.3225 | 4.0 | 1072 | 0.5288 | 0.9395 |
| 0.3225 | 5.0 | 1340 | 0.5732 | 0.9392 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln17 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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}
} | 12 | 2022-10-24T18:28:10Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlnet-large_lemon_10k_2_p3
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. -->
# xlnet-large_lemon_10k_2_p3
This model is a fine-tuned version of [model_saves/xlnet-large_lemon_10k_2_p2](https://huggingface.co/model_saves/xlnet-large_lemon_10k_2_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4726
- Accuracy: 0.9399
## 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: 128
- eval_batch_size: 128
- seed: 52
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4533 | 0.9398 |
| No log | 2.0 | 536 | 0.4726 | 0.9399 |
| No log | 3.0 | 804 | 0.5045 | 0.9393 |
| 0.2939 | 4.0 | 1072 | 0.5533 | 0.9390 |
| 0.2939 | 5.0 | 1340 | 0.6086 | 0.9388 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln21 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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"task_specific_params": {
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"translation_en_to_ro": {
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"max_length": null,
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}
}
} | 8 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlnet-large_lemon_5k_1_p3
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. -->
# xlnet-large_lemon_5k_1_p3
This model is a fine-tuned version of [model_saves/xlnet-large_lemon_5k_1_p2](https://huggingface.co/model_saves/xlnet-large_lemon_5k_1_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4483
- Accuracy: 0.9406
## 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: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4327 | 0.9397 |
| No log | 2.0 | 536 | 0.4483 | 0.9406 |
| No log | 3.0 | 804 | 0.4814 | 0.9404 |
| 0.3281 | 4.0 | 1072 | 0.5127 | 0.9394 |
| 0.3281 | 5.0 | 1340 | 0.5563 | 0.9391 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln23 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 50
},
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"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
} | 5 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlnet-large_lemon_5k_3_p3
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. -->
# xlnet-large_lemon_5k_3_p3
This model is a fine-tuned version of [model_saves/xlnet-large_lemon_5k_3_p2](https://huggingface.co/model_saves/xlnet-large_lemon_5k_3_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4626
- Accuracy: 0.9395
## 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: 128
- eval_batch_size: 128
- seed: 62
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4419 | 0.9391 |
| No log | 2.0 | 536 | 0.4626 | 0.9395 |
| No log | 3.0 | 804 | 0.4988 | 0.9389 |
| 0.2989 | 4.0 | 1072 | 0.5439 | 0.9389 |
| 0.2989 | 5.0 | 1340 | 0.5992 | 0.9391 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln25 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
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"max_length": 50
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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}
}
} | 10 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlnet-large_lemon-spell_10k_1_p3
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. -->
# xlnet-large_lemon-spell_10k_1_p3
This model is a fine-tuned version of [model_saves/xlnet-large_lemon-spell_10k_1_p2](https://huggingface.co/model_saves/xlnet-large_lemon-spell_10k_1_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4852
- Accuracy: 0.9397
## 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: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4632 | 0.9393 |
| No log | 2.0 | 536 | 0.4852 | 0.9397 |
| No log | 3.0 | 804 | 0.5398 | 0.9396 |
| 0.2685 | 4.0 | 1072 | 0.5819 | 0.9389 |
| 0.2685 | 5.0 | 1340 | 0.6365 | 0.9381 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/Lincoln4 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
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"max_length": 50
},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
} | 11 | 2022-10-24T18:34:40Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlnet-large_lemon-spell_10k_2_p3
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. -->
# xlnet-large_lemon-spell_10k_2_p3
This model is a fine-tuned version of [model_saves/xlnet-large_lemon-spell_10k_2_p2](https://huggingface.co/model_saves/xlnet-large_lemon-spell_10k_2_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4737
- Accuracy: 0.9400
## 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: 128
- eval_batch_size: 128
- seed: 52
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4556 | 0.9395 |
| No log | 2.0 | 536 | 0.4737 | 0.9400 |
| No log | 3.0 | 804 | 0.5033 | 0.9398 |
| 0.2938 | 4.0 | 1072 | 0.5554 | 0.9393 |
| 0.2938 | 5.0 | 1340 | 0.6074 | 0.9391 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/MrLincoln10 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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}
}
} | 5 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlnet-large_lemon-spell_5k_1_p3
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. -->
# xlnet-large_lemon-spell_5k_1_p3
This model is a fine-tuned version of [model_saves/xlnet-large_lemon-spell_5k_1_p2](https://huggingface.co/model_saves/xlnet-large_lemon-spell_5k_1_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4502
- Accuracy: 0.9404
## 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: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4331 | 0.9399 |
| No log | 2.0 | 536 | 0.4502 | 0.9404 |
| No log | 3.0 | 804 | 0.4826 | 0.9400 |
| 0.3276 | 4.0 | 1072 | 0.5184 | 0.9399 |
| 0.3276 | 5.0 | 1340 | 0.5609 | 0.9392 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/MrLincoln12 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"min_length": null,
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
} | 9 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlnet-large_lemon-spell_5k_3_p3
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. -->
# xlnet-large_lemon-spell_5k_3_p3
This model is a fine-tuned version of [model_saves/xlnet-large_lemon-spell_5k_3_p2](https://huggingface.co/model_saves/xlnet-large_lemon-spell_5k_3_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4634
- Accuracy: 0.9396
## 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: 128
- eval_batch_size: 128
- seed: 62
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4405 | 0.9392 |
| No log | 2.0 | 536 | 0.4634 | 0.9396 |
| No log | 3.0 | 804 | 0.4988 | 0.9391 |
| 0.2992 | 4.0 | 1072 | 0.5409 | 0.9394 |
| 0.2992 | 5.0 | 1340 | 0.5943 | 0.9393 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/MrLincoln13 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
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"max_length": 50
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
} | 9 | 2022-10-24T18:40:18Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlnet-large_spell_10k_2_p3
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. -->
# xlnet-large_spell_10k_2_p3
This model is a fine-tuned version of [model_saves/xlnet-large_spell_10k_2_p2](https://huggingface.co/model_saves/xlnet-large_spell_10k_2_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4546
- Accuracy: 0.9400
## 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: 128
- eval_batch_size: 128
- seed: 52
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4546 | 0.9400 |
| No log | 2.0 | 536 | 0.4634 | 0.9396 |
| No log | 3.0 | 804 | 0.5050 | 0.9397 |
| 0.2924 | 4.0 | 1072 | 0.5557 | 0.9397 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/MrLincoln2 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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"max_length": null,
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
} | 9 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlnet-large_spell_5k_1_p3
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. -->
# xlnet-large_spell_5k_1_p3
This model is a fine-tuned version of [model_saves/xlnet-large_spell_5k_1_p2](https://huggingface.co/model_saves/xlnet-large_spell_5k_1_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4678
- Accuracy: 0.9400
## 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: 128
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4383 | 0.9397 |
| No log | 2.0 | 536 | 0.4678 | 0.9400 |
| No log | 3.0 | 804 | 0.4920 | 0.9397 |
| 0.2974 | 4.0 | 1072 | 0.5351 | 0.9390 |
| 0.2974 | 5.0 | 1340 | 0.5907 | 0.9388 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/MrLincoln4 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 10 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlnet-large_spell_5k_3_p3
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. -->
# xlnet-large_spell_5k_3_p3
This model is a fine-tuned version of [model_saves/xlnet-large_spell_5k_3_p2](https://huggingface.co/model_saves/xlnet-large_spell_5k_3_p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4383
- Accuracy: 0.9399
## 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: 128
- eval_batch_size: 128
- seed: 62
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 268 | 0.4383 | 0.9399 |
| No log | 2.0 | 536 | 0.4542 | 0.9398 |
| No log | 3.0 | 804 | 0.4897 | 0.9387 |
| 0.2986 | 4.0 | 1072 | 0.5420 | 0.9389 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
BigSalmon/MrLincoln6 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 9 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: big-balanced-combined-bert
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. -->
# big-balanced-combined-bert
This model is a fine-tuned version of [dbmdz/bert-base-turkish-128k-uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2872
- Accuracy: 0.9055
- F1: 0.9061
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base_mnli_uf_ner_1024_train_v0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base_mnli_uf_ner_1024_train_v0
This model is a fine-tuned version of [mariolinml/roberta-base_fullMnli_10_24_v0](https://huggingface.co/mariolinml/roberta-base_fullMnli_10_24_v0) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Blerrrry/Kkk | []
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} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 465 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 465,
"warmup_steps": 47,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
BumBelDumBel/ZORK_AI_FANTASY | []
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} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/codeinecucumber/1670258880297/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/1590939999385669633/-zFzyIq4_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">Yay</div>
<div style="text-align: center; font-size: 14px;">@codeinecucumber</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 Yay.
| Data | Yay |
| --- | --- |
| Tweets downloaded | 2350 |
| Retweets | 318 |
| Short tweets | 456 |
| Tweets kept | 1576 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yb2u52g/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 @codeinecucumber's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/atqx46uu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/atqx46uu/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/codeinecucumber')
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)
|
CAMeL-Lab/bert-base-arabic-camelbert-da-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
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} | 37 | 2022-10-25T05:03:42Z | ---
tags:
- generated_from_trainer
model-index:
- name: BERT_Dec-6_tokenized
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_Dec-6_tokenized
This model is a fine-tuned version of [armheb/DNA_bert_6](https://huggingface.co/armheb/DNA_bert_6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0372
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0625 | 1.0 | 273 | 0.0376 |
| 0.039 | 2.0 | 546 | 0.0375 |
| 0.0385 | 3.0 | 819 | 0.0358 |
| 0.0375 | 4.0 | 1092 | 0.0380 |
| 0.0374 | 5.0 | 1365 | 0.0387 |
| 0.0358 | 6.0 | 1638 | 0.0378 |
| 0.0363 | 7.0 | 1911 | 0.0381 |
| 0.0373 | 8.0 | 2184 | 0.0377 |
| 0.0362 | 9.0 | 2457 | 0.0373 |
| 0.037 | 10.0 | 2730 | 0.0380 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Carlork314/Carlos | []
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} | 0 | null | ---
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6820
- Answer: {'precision': 0.7084257206208425, 'recall': 0.7898640296662547, 'f1': 0.7469316189362946, 'number': 809}
- Header: {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119}
- Question: {'precision': 0.7870619946091644, 'recall': 0.8225352112676056, 'f1': 0.8044077134986226, 'number': 1065}
- Overall Precision: 0.7194
- Overall Recall: 0.7797
- Overall F1: 0.7484
- Overall Accuracy: 0.8102
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7857 | 1.0 | 10 | 1.5985 | {'precision': 0.009248554913294798, 'recall': 0.009888751545117428, 'f1': 0.00955794504181601, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.1273972602739726, 'recall': 0.08732394366197183, 'f1': 0.10362116991643454, 'number': 1065} | 0.0633 | 0.0507 | 0.0563 | 0.3562 |
| 1.4597 | 2.0 | 20 | 1.2331 | {'precision': 0.18717683557394002, 'recall': 0.22373300370828184, 'f1': 0.20382882882882883, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4439461883408072, 'recall': 0.5577464788732395, 'f1': 0.4943820224719101, 'number': 1065} | 0.3362 | 0.3889 | 0.3606 | 0.6007 |
| 1.0902 | 3.0 | 30 | 0.9489 | {'precision': 0.4371069182389937, 'recall': 0.515451174289246, 'f1': 0.47305728871242203, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.6257615317667538, 'recall': 0.6751173708920187, 'f1': 0.6495031616982836, 'number': 1065} | 0.5311 | 0.5700 | 0.5499 | 0.6910 |
| 0.8339 | 4.0 | 40 | 0.7979 | {'precision': 0.5977366255144033, 'recall': 0.7181705809641533, 'f1': 0.652442448062886, 'number': 809} | {'precision': 0.13513513513513514, 'recall': 0.08403361344537816, 'f1': 0.10362694300518135, 'number': 119} | {'precision': 0.6854545454545454, 'recall': 0.707981220657277, 'f1': 0.6965357967667436, 'number': 1065} | 0.6267 | 0.6749 | 0.6499 | 0.7453 |
| 0.6983 | 5.0 | 50 | 0.7659 | {'precision': 0.6392896781354052, 'recall': 0.7119901112484549, 'f1': 0.6736842105263159, 'number': 809} | {'precision': 0.19626168224299065, 'recall': 0.17647058823529413, 'f1': 0.18584070796460178, 'number': 119} | {'precision': 0.6688102893890675, 'recall': 0.7812206572769953, 'f1': 0.7206582936336077, 'number': 1065} | 0.6345 | 0.7170 | 0.6733 | 0.7610 |
| 0.5815 | 6.0 | 60 | 0.6907 | {'precision': 0.6410256410256411, 'recall': 0.7725587144622992, 'f1': 0.7006726457399104, 'number': 809} | {'precision': 0.23863636363636365, 'recall': 0.17647058823529413, 'f1': 0.20289855072463767, 'number': 119} | {'precision': 0.7027463651050081, 'recall': 0.8169014084507042, 'f1': 0.7555362570560139, 'number': 1065} | 0.6588 | 0.7607 | 0.7061 | 0.7913 |
| 0.5044 | 7.0 | 70 | 0.6802 | {'precision': 0.6727078891257996, 'recall': 0.7799752781211372, 'f1': 0.7223812249570692, 'number': 809} | {'precision': 0.26605504587155965, 'recall': 0.24369747899159663, 'f1': 0.2543859649122807, 'number': 119} | {'precision': 0.7305699481865285, 'recall': 0.7943661971830986, 'f1': 0.7611336032388665, 'number': 1065} | 0.6830 | 0.7556 | 0.7175 | 0.7902 |
| 0.4534 | 8.0 | 80 | 0.6595 | {'precision': 0.7018701870187019, 'recall': 0.788627935723115, 'f1': 0.7427240977881256, 'number': 809} | {'precision': 0.234375, 'recall': 0.25210084033613445, 'f1': 0.242914979757085, 'number': 119} | {'precision': 0.7378559463986599, 'recall': 0.8272300469483568, 'f1': 0.779991146525011, 'number': 1065} | 0.6943 | 0.7772 | 0.7334 | 0.8074 |
| 0.3971 | 9.0 | 90 | 0.6625 | {'precision': 0.6967032967032967, 'recall': 0.7836835599505563, 'f1': 0.7376381617219313, 'number': 809} | {'precision': 0.27007299270072993, 'recall': 0.31092436974789917, 'f1': 0.2890625, 'number': 119} | {'precision': 0.7433930093776641, 'recall': 0.8187793427230047, 'f1': 0.7792672028596961, 'number': 1065} | 0.6950 | 0.7742 | 0.7325 | 0.8060 |
| 0.3593 | 10.0 | 100 | 0.6634 | {'precision': 0.7079152731326644, 'recall': 0.7849196538936959, 'f1': 0.7444314185228605, 'number': 809} | {'precision': 0.2714285714285714, 'recall': 0.31932773109243695, 'f1': 0.29343629343629346, 'number': 119} | {'precision': 0.7571305099394987, 'recall': 0.8225352112676056, 'f1': 0.7884788478847885, 'number': 1065} | 0.7060 | 0.7772 | 0.7399 | 0.8115 |
| 0.3209 | 11.0 | 110 | 0.6655 | {'precision': 0.6973262032085561, 'recall': 0.8059332509270705, 'f1': 0.7477064220183487, 'number': 809} | {'precision': 0.2903225806451613, 'recall': 0.3025210084033613, 'f1': 0.2962962962962963, 'number': 119} | {'precision': 0.7788632326820604, 'recall': 0.8234741784037559, 'f1': 0.8005476951163851, 'number': 1065} | 0.7162 | 0.7852 | 0.7492 | 0.8129 |
| 0.3064 | 12.0 | 120 | 0.6771 | {'precision': 0.7104072398190046, 'recall': 0.7762669962917181, 'f1': 0.74187832250443, 'number': 809} | {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119} | {'precision': 0.7795698924731183, 'recall': 0.8169014084507042, 'f1': 0.797799174690509, 'number': 1065} | 0.7166 | 0.7712 | 0.7429 | 0.8088 |
| 0.286 | 13.0 | 130 | 0.6765 | {'precision': 0.7030905077262694, 'recall': 0.7873918417799752, 'f1': 0.7428571428571429, 'number': 809} | {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119} | {'precision': 0.769298245614035, 'recall': 0.8234741784037559, 'f1': 0.7954648526077097, 'number': 1065} | 0.7088 | 0.7792 | 0.7424 | 0.8111 |
| 0.2806 | 14.0 | 140 | 0.6820 | {'precision': 0.7052980132450332, 'recall': 0.7898640296662547, 'f1': 0.7451895043731779, 'number': 809} | {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119} | {'precision': 0.7793594306049823, 'recall': 0.8225352112676056, 'f1': 0.8003654636820466, 'number': 1065} | 0.7145 | 0.7797 | 0.7457 | 0.8106 |
| 0.2736 | 15.0 | 150 | 0.6820 | {'precision': 0.7084257206208425, 'recall': 0.7898640296662547, 'f1': 0.7469316189362946, 'number': 809} | {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119} | {'precision': 0.7870619946091644, 'recall': 0.8225352112676056, 'f1': 0.8044077134986226, 'number': 1065} | 0.7194 | 0.7797 | 0.7484 | 0.8102 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
CarlosTron/Yo | []
| null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
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},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
} | 0 | null | ---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: "I love AutoTrain 🤗"
co2_eq_emissions:
emissions: 0.9899872350262614
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1822063032
- CO2 Emissions (in grams): 0.9900
## Validation Metrics
- Loss: 0.347
- Rouge1: 66.429
- Rouge2: 29.419
- RougeL: 66.188
- RougeLsum: 66.183
- Gen Len: 11.256
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/aalbertini90/autotrain-improve-a-1822063032
``` |
CasualHomie/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 11 | null | ---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: "I love AutoTrain 🤗"
co2_eq_emissions:
emissions: 607.9833800689026
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1822163038
- CO2 Emissions (in grams): 607.9834
## Validation Metrics
- Loss: 1.665
- Rouge1: 53.144
- Rouge2: 27.768
- RougeL: 52.663
- RougeLsum: 52.645
- Gen Len: 10.722
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/aalbertini90/autotrain-h-1822163038
``` |
Cat/Kitty | []
| null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: "I love AutoTrain 🤗"
co2_eq_emissions:
emissions: 339.29944607016967
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1872663964
- CO2 Emissions (in grams): 339.2994
## Validation Metrics
- Loss: 2.405
- Rouge1: 30.426
- Rouge2: 16.255
- RougeL: 29.262
- RougeLsum: 29.337
- Gen Len: 13.671
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/aalbertini90/autotrain-caption-1872663964
``` |
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