modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-03 06:27:37
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 549
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
Den4ikAI/DLM_CHITCHAT_700M
|
Den4ikAI
| 2023-05-18T15:22:10Z | 142 | 4 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"ru",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-12-02T16:36:28Z |
---
license: mit
widget:
- text: "- У Артура было 17 пончиков, а потом он 3 съел. Сколько у него осталось пончиков? -"
- text: "- Привет! -"
- text: "- В чем смысл жизни? -"
- text: "- Стеклянный шар упал на бетонный стол. Что разбилось? -"
language:
- ru
---
Модель генеративного читчата на базе языковой модели DLM-700M
|
pphildan/vit-base-patch16-224-v17
|
pphildan
| 2023-05-18T15:20:30Z | 195 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-05-18T14:35:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-v17
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. -->
# vit-base-patch16-224-v17
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0392
- Accuracy: 0.9870
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2655 | 1.0 | 190 | 0.1454 | 0.9533 |
| 0.1577 | 2.0 | 380 | 0.0953 | 0.9659 |
| 0.0957 | 3.0 | 570 | 0.0392 | 0.9870 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
AnanthZeke/tabert-2k-naamapadam
|
AnanthZeke
| 2023-05-18T15:11:23Z | 126 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-18T13:32:31Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tabert-2k-naamapadam
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. -->
# tabert-2k-naamapadam
This model is a fine-tuned version of [livinNector/tabert-2k](https://huggingface.co/livinNector/tabert-2k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2850
- Precision: 0.7765
- Recall: 0.8041
- F1: 0.7901
- Accuracy: 0.9065
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4679 | 0.05 | 400 | 0.3991 | 0.7155 | 0.6561 | 0.6845 | 0.8720 |
| 0.3907 | 0.1 | 800 | 0.3632 | 0.7181 | 0.7233 | 0.7207 | 0.8822 |
| 0.3663 | 0.15 | 1200 | 0.3483 | 0.7271 | 0.7371 | 0.7321 | 0.8857 |
| 0.3557 | 0.21 | 1600 | 0.3457 | 0.7286 | 0.7506 | 0.7395 | 0.8874 |
| 0.3533 | 0.26 | 2000 | 0.3413 | 0.7371 | 0.7435 | 0.7403 | 0.8895 |
| 0.3396 | 0.31 | 2400 | 0.3326 | 0.7435 | 0.7546 | 0.7490 | 0.8910 |
| 0.3302 | 0.36 | 2800 | 0.3264 | 0.7528 | 0.7553 | 0.7540 | 0.8937 |
| 0.3344 | 0.41 | 3200 | 0.3231 | 0.7503 | 0.7720 | 0.7610 | 0.8951 |
| 0.3262 | 0.46 | 3600 | 0.3228 | 0.7387 | 0.7762 | 0.7570 | 0.8941 |
| 0.3186 | 0.51 | 4000 | 0.3158 | 0.7699 | 0.7666 | 0.7683 | 0.8986 |
| 0.3163 | 0.57 | 4400 | 0.3130 | 0.7453 | 0.7798 | 0.7622 | 0.8955 |
| 0.3143 | 0.62 | 4800 | 0.3150 | 0.7572 | 0.7751 | 0.7660 | 0.8961 |
| 0.3088 | 0.67 | 5200 | 0.3151 | 0.7543 | 0.7828 | 0.7683 | 0.8972 |
| 0.3115 | 0.72 | 5600 | 0.3141 | 0.7708 | 0.7706 | 0.7707 | 0.8977 |
| 0.3095 | 0.77 | 6000 | 0.3043 | 0.7657 | 0.7831 | 0.7743 | 0.8991 |
| 0.3044 | 0.82 | 6400 | 0.3087 | 0.7526 | 0.7881 | 0.7699 | 0.8972 |
| 0.2964 | 0.87 | 6800 | 0.3070 | 0.7644 | 0.7928 | 0.7783 | 0.8992 |
| 0.2972 | 0.93 | 7200 | 0.3102 | 0.7692 | 0.7738 | 0.7715 | 0.8999 |
| 0.2985 | 0.98 | 7600 | 0.3016 | 0.7731 | 0.7858 | 0.7794 | 0.9018 |
| 0.2822 | 1.03 | 8000 | 0.3049 | 0.7734 | 0.7909 | 0.7820 | 0.9031 |
| 0.2764 | 1.08 | 8400 | 0.3059 | 0.7575 | 0.7976 | 0.7770 | 0.9011 |
| 0.2752 | 1.13 | 8800 | 0.3052 | 0.7553 | 0.7996 | 0.7768 | 0.9015 |
| 0.2689 | 1.18 | 9200 | 0.2990 | 0.7642 | 0.7982 | 0.7808 | 0.9037 |
| 0.2738 | 1.23 | 9600 | 0.2985 | 0.7698 | 0.7987 | 0.7840 | 0.9035 |
| 0.2731 | 1.29 | 10000 | 0.2950 | 0.7713 | 0.7982 | 0.7845 | 0.9037 |
| 0.2694 | 1.34 | 10400 | 0.2920 | 0.7743 | 0.8017 | 0.7878 | 0.9059 |
| 0.2727 | 1.39 | 10800 | 0.2931 | 0.7693 | 0.7979 | 0.7834 | 0.9040 |
| 0.2622 | 1.44 | 11200 | 0.2946 | 0.7702 | 0.7942 | 0.7820 | 0.9032 |
| 0.2672 | 1.49 | 11600 | 0.2894 | 0.7724 | 0.8062 | 0.7890 | 0.9060 |
| 0.2601 | 1.54 | 12000 | 0.2907 | 0.7706 | 0.8010 | 0.7855 | 0.9058 |
| 0.2629 | 1.59 | 12400 | 0.2930 | 0.7628 | 0.8150 | 0.7880 | 0.9052 |
| 0.2635 | 1.65 | 12800 | 0.2907 | 0.7775 | 0.7970 | 0.7871 | 0.9047 |
| 0.2673 | 1.7 | 13200 | 0.2909 | 0.7753 | 0.7982 | 0.7866 | 0.9045 |
| 0.2726 | 1.75 | 13600 | 0.2880 | 0.7714 | 0.8048 | 0.7877 | 0.9054 |
| 0.2607 | 1.8 | 14000 | 0.2850 | 0.7760 | 0.8010 | 0.7883 | 0.9053 |
| 0.2684 | 1.85 | 14400 | 0.2847 | 0.7709 | 0.8077 | 0.7889 | 0.9059 |
| 0.2625 | 1.9 | 14800 | 0.2849 | 0.7742 | 0.8079 | 0.7907 | 0.9067 |
| 0.2631 | 1.95 | 15200 | 0.2850 | 0.7765 | 0.8041 | 0.7901 | 0.9065 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
docmparker/all-mpnet-base-v2-setfit-8label-edu
|
docmparker
| 2023-05-18T15:00:34Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-05-18T14:32:28Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# docmparker/all-mpnet-base-v2-setfit-8label-edu
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("docmparker/all-mpnet-base-v2-setfit-8label-edu")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
alvations/mt5-aym-lex
|
alvations
| 2023-05-18T14:59:24Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-10T04:38:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mt5-aym-lex
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-aym-lex
This model is a fine-tuned version of [alvations/mt5-aym-lex](https://huggingface.co/alvations/mt5-aym-lex) on the None dataset.
It achieves the following results on the evaluation set:
- Bleu: 3.1238
- Chrf: 24.4605
- Gen Len: 17.3872
- Loss: 0.1883
## 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
- lr_scheduler_warmup_steps: 500
- training_steps: 600
### Training results
| Training Loss | Epoch | Step | Bleu | Chrf | Gen Len | Validation Loss |
|:-------------:|:-----:|:-----:|:------:|:-------:|:-------:|:---------------:|
| 0.067 | 4.86 | 20000 | 2.9344 | 24.2586 | 17.5005 | 0.1844 |
| 0.065 | 9.71 | 40000 | 3.1238 | 24.4605 | 17.3872 | 0.1883 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
HasinMDG/all-distilroberta-v1-IPTC-L1
|
HasinMDG
| 2023-05-18T14:54:50Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-05-18T12:52:01Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HasinMDG/all-distilroberta-v1-IPTC-L1
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("HasinMDG/all-distilroberta-v1-IPTC-L1")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Acreedlmt/Gigi
|
Acreedlmt
| 2023-05-18T14:51:10Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-15T15:49:24Z |
---
license: creativeml-openrail-m
---
|
TootToot/FirstTaxi
|
TootToot
| 2023-05-18T14:40:05Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-18T14:40:03Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: FirstTaxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="TootToot/FirstTaxi", 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"])
```
|
MrPark97/distillbert-base-uncased-finetuned-clinc
|
MrPark97
| 2023-05-18T14:37:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-18T09:15:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distillbert-base-uncased-finetuned-clinc
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. -->
# distillbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7720
- Accuracy: 0.9181
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.2887 | 0.7419 |
| 3.7868 | 2.0 | 636 | 1.8753 | 0.8371 |
| 3.7868 | 3.0 | 954 | 1.1570 | 0.8961 |
| 1.6927 | 4.0 | 1272 | 0.8573 | 0.9129 |
| 0.9056 | 5.0 | 1590 | 0.7720 | 0.9181 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
GraydientPlatformAPI/model_683
|
GraydientPlatformAPI
| 2023-05-18T14:17:48Z | 29 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-18T14:07:52Z |
---
license: openrail
library_name: diffusers
pipeline_tag: text-to-image
---
|
Smoden/A_MIX_W_diff_lora
|
Smoden
| 2023-05-18T14:11:44Z | 2 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-05-18T11:50:04Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - Smoden/A_MIX_W_diff_lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the None dataset. You can find some example images in the following.
|
vorstcavry/webui
|
vorstcavry
| 2023-05-18T14:11:21Z | 4 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-04-23T10:47:16Z |
---
license: creativeml-openrail-m
---
|
damapika/roberta-base_mod
|
damapika
| 2023-05-18T14:10:23Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:quoref",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-04-22T09:40:45Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- quoref
model-index:
- name: roberta-base_mod
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_mod
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the quoref dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5400
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6272 | 1.0 | 1213 | 1.4654 |
| 1.0583 | 2.0 | 2426 | 1.4134 |
| 0.6854 | 3.0 | 3639 | 1.5400 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
mousaazari/t5-text2sql_v3
|
mousaazari
| 2023-05-18T14:01:24Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-11T13:27:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-text2sql_v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-text2sql_v3
This model is a fine-tuned version of [mousaazari/t5-text2sql_v1](https://huggingface.co/mousaazari/t5-text2sql_v1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1501
- Rouge2 Precision: 0.6088
- Rouge2 Recall: 0.3597
- Rouge2 Fmeasure: 0.4201
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| No log | 1.0 | 430 | 0.3126 | 0.3937 | 0.2301 | 0.2679 |
| 0.4851 | 2.0 | 860 | 0.2583 | 0.4656 | 0.2854 | 0.3289 |
| 0.3271 | 3.0 | 1290 | 0.2256 | 0.4858 | 0.2875 | 0.3337 |
| 0.2696 | 4.0 | 1720 | 0.2075 | 0.5193 | 0.3127 | 0.3614 |
| 0.2376 | 5.0 | 2150 | 0.1937 | 0.5387 | 0.3258 | 0.3773 |
| 0.2072 | 6.0 | 2580 | 0.1839 | 0.5524 | 0.3344 | 0.3876 |
| 0.1875 | 7.0 | 3010 | 0.1752 | 0.5644 | 0.3333 | 0.3882 |
| 0.1875 | 8.0 | 3440 | 0.1704 | 0.5751 | 0.3426 | 0.399 |
| 0.1736 | 9.0 | 3870 | 0.1653 | 0.5821 | 0.3458 | 0.4027 |
| 0.1585 | 10.0 | 4300 | 0.1603 | 0.5841 | 0.3435 | 0.4013 |
| 0.1498 | 11.0 | 4730 | 0.1576 | 0.5905 | 0.3535 | 0.4103 |
| 0.1427 | 12.0 | 5160 | 0.1548 | 0.6031 | 0.3533 | 0.4135 |
| 0.1342 | 13.0 | 5590 | 0.1541 | 0.5976 | 0.3519 | 0.411 |
| 0.1294 | 14.0 | 6020 | 0.1534 | 0.6058 | 0.3549 | 0.4161 |
| 0.1294 | 15.0 | 6450 | 0.1518 | 0.6117 | 0.3593 | 0.4203 |
| 0.1239 | 16.0 | 6880 | 0.1509 | 0.61 | 0.3597 | 0.4202 |
| 0.1198 | 17.0 | 7310 | 0.1508 | 0.6076 | 0.3588 | 0.4195 |
| 0.1147 | 18.0 | 7740 | 0.1503 | 0.6139 | 0.3607 | 0.4219 |
| 0.1155 | 19.0 | 8170 | 0.1503 | 0.6092 | 0.3597 | 0.4201 |
| 0.1115 | 20.0 | 8600 | 0.1501 | 0.6088 | 0.3597 | 0.4201 |
### Framework versions
- Transformers 4.26.0
- Pytorch 2.0.0+cu118
- Datasets 2.8.0
- Tokenizers 0.13.3
|
livinNector/IndicBERTv2-MLM-Sam-TLM-NER
|
livinNector
| 2023-05-18T13:44:09Z | 27 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-15T17:56:40Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: IndicBERTv2-MLM-Sam-TLM-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. -->
# IndicBERTv2-MLM-Sam-TLM-NER
This model is a fine-tuned version of [ai4bharat/IndicBERTv2-MLM-Sam-TLM](https://huggingface.co/ai4bharat/IndicBERTv2-MLM-Sam-TLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4521
- Precision: 0.7629
- Recall: 0.7792
- F1: 0.7710
- Accuracy: 0.9038
## 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: 128
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3268 | 0.49 | 1000 | 0.3440 | 0.7207 | 0.7602 | 0.7399 | 0.8887 |
| 0.2763 | 0.99 | 2000 | 0.3083 | 0.7568 | 0.7732 | 0.7649 | 0.8983 |
| 0.2604 | 1.48 | 3000 | 0.3312 | 0.7309 | 0.7494 | 0.7401 | 0.8909 |
| 0.2501 | 1.98 | 4000 | 0.3017 | 0.7415 | 0.7956 | 0.7676 | 0.9014 |
| 0.2269 | 2.47 | 5000 | 0.2930 | 0.7528 | 0.7970 | 0.7743 | 0.9050 |
| 0.223 | 2.96 | 6000 | 0.2963 | 0.7590 | 0.7963 | 0.7772 | 0.9053 |
| 0.2011 | 3.46 | 7000 | 0.2939 | 0.7627 | 0.7946 | 0.7783 | 0.9079 |
| 0.1999 | 3.95 | 8000 | 0.3036 | 0.7676 | 0.7903 | 0.7788 | 0.9069 |
| 0.1815 | 4.44 | 9000 | 0.3125 | 0.7618 | 0.7915 | 0.7764 | 0.9056 |
| 0.1777 | 4.94 | 10000 | 0.3083 | 0.7748 | 0.7957 | 0.7851 | 0.9098 |
| 0.1622 | 5.43 | 11000 | 0.3251 | 0.7721 | 0.7909 | 0.7814 | 0.9089 |
| 0.1598 | 5.93 | 12000 | 0.3197 | 0.7767 | 0.7947 | 0.7856 | 0.9092 |
| 0.145 | 6.42 | 13000 | 0.3366 | 0.7718 | 0.7986 | 0.7850 | 0.9101 |
| 0.1436 | 6.91 | 14000 | 0.3247 | 0.7776 | 0.7977 | 0.7875 | 0.9112 |
| 0.1306 | 7.41 | 15000 | 0.3502 | 0.7779 | 0.7958 | 0.7867 | 0.9107 |
| 0.1311 | 7.9 | 16000 | 0.3585 | 0.7857 | 0.7909 | 0.7883 | 0.9105 |
| 0.12 | 8.4 | 17000 | 0.3717 | 0.7768 | 0.7911 | 0.7839 | 0.9099 |
| 0.1202 | 8.89 | 18000 | 0.3667 | 0.7796 | 0.7882 | 0.7839 | 0.9100 |
| 0.1141 | 9.38 | 19000 | 0.3860 | 0.7857 | 0.7900 | 0.7879 | 0.9100 |
| 0.1113 | 9.88 | 20000 | 0.3824 | 0.7758 | 0.7970 | 0.7862 | 0.9094 |
| 0.1056 | 10.37 | 21000 | 0.4041 | 0.7740 | 0.7952 | 0.7845 | 0.9084 |
| 0.1073 | 10.86 | 22000 | 0.4062 | 0.7735 | 0.7929 | 0.7831 | 0.9094 |
| 0.1063 | 11.36 | 23000 | 0.4197 | 0.7720 | 0.7866 | 0.7793 | 0.9071 |
| 0.1026 | 11.85 | 24000 | 0.4179 | 0.7625 | 0.7767 | 0.7695 | 0.9040 |
| 0.1042 | 12.35 | 25000 | 0.4392 | 0.7639 | 0.7748 | 0.7693 | 0.9037 |
| 0.101 | 12.84 | 26000 | 0.4373 | 0.7533 | 0.7795 | 0.7662 | 0.9029 |
| 0.1003 | 13.33 | 27000 | 0.4554 | 0.7535 | 0.7774 | 0.7653 | 0.9021 |
| 0.0993 | 13.83 | 28000 | 0.4530 | 0.7555 | 0.7773 | 0.7663 | 0.9019 |
| 0.0978 | 14.32 | 29000 | 0.4467 | 0.7637 | 0.7843 | 0.7738 | 0.9050 |
| 0.0946 | 14.81 | 30000 | 0.4521 | 0.7629 | 0.7792 | 0.7710 | 0.9038 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
audreyfeldroy/ppo-Huggy
|
audreyfeldroy
| 2023-05-18T13:39:37Z | 8 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-05-18T13:39:30Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: audreyfeldroy/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Mebosahr/Emj
|
Mebosahr
| 2023-05-18T13:38:07Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-05-18T13:38:07Z |
---
license: bigscience-openrail-m
---
|
AnanthZeke/tabert-1k-naamapadam
|
AnanthZeke
| 2023-05-18T13:30:15Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-18T11:37:46Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tabert-1k-naamapadam
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. -->
# tabert-1k-naamapadam
This model is a fine-tuned version of [livinNector/tabert-1k](https://huggingface.co/livinNector/tabert-1k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2825
- Precision: 0.7764
- Recall: 0.8055
- F1: 0.7907
- Accuracy: 0.9068
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4618 | 0.05 | 400 | 0.3963 | 0.7329 | 0.6498 | 0.6889 | 0.8716 |
| 0.3869 | 0.1 | 800 | 0.3583 | 0.7145 | 0.7347 | 0.7244 | 0.8828 |
| 0.3642 | 0.15 | 1200 | 0.3511 | 0.7241 | 0.7412 | 0.7325 | 0.8842 |
| 0.3533 | 0.21 | 1600 | 0.3451 | 0.7393 | 0.7429 | 0.7411 | 0.8873 |
| 0.3501 | 0.26 | 2000 | 0.3367 | 0.7456 | 0.7562 | 0.7509 | 0.8899 |
| 0.3369 | 0.31 | 2400 | 0.3343 | 0.7476 | 0.7549 | 0.7512 | 0.8909 |
| 0.3302 | 0.36 | 2800 | 0.3282 | 0.7413 | 0.7584 | 0.7497 | 0.8926 |
| 0.3327 | 0.41 | 3200 | 0.3238 | 0.7584 | 0.7717 | 0.7650 | 0.8961 |
| 0.3248 | 0.46 | 3600 | 0.3209 | 0.7468 | 0.7795 | 0.7628 | 0.8956 |
| 0.3175 | 0.51 | 4000 | 0.3140 | 0.7659 | 0.7681 | 0.7670 | 0.8985 |
| 0.3132 | 0.57 | 4400 | 0.3111 | 0.7537 | 0.7795 | 0.7664 | 0.8970 |
| 0.3141 | 0.62 | 4800 | 0.3122 | 0.7529 | 0.7797 | 0.7661 | 0.8972 |
| 0.3077 | 0.67 | 5200 | 0.3138 | 0.7493 | 0.7844 | 0.7665 | 0.8974 |
| 0.309 | 0.72 | 5600 | 0.3099 | 0.7674 | 0.7729 | 0.7702 | 0.8992 |
| 0.3085 | 0.77 | 6000 | 0.3038 | 0.7626 | 0.7940 | 0.7780 | 0.9009 |
| 0.3031 | 0.82 | 6400 | 0.3055 | 0.7633 | 0.7834 | 0.7732 | 0.8992 |
| 0.2958 | 0.87 | 6800 | 0.3054 | 0.7621 | 0.7924 | 0.7770 | 0.8991 |
| 0.2953 | 0.93 | 7200 | 0.3076 | 0.7714 | 0.7834 | 0.7774 | 0.9005 |
| 0.2978 | 0.98 | 7600 | 0.3003 | 0.7729 | 0.7855 | 0.7792 | 0.9017 |
| 0.2826 | 1.03 | 8000 | 0.3016 | 0.7665 | 0.7905 | 0.7783 | 0.9012 |
| 0.2757 | 1.08 | 8400 | 0.3053 | 0.7520 | 0.8072 | 0.7786 | 0.8996 |
| 0.2751 | 1.13 | 8800 | 0.3026 | 0.7626 | 0.7982 | 0.7800 | 0.9008 |
| 0.2694 | 1.18 | 9200 | 0.2957 | 0.7682 | 0.8007 | 0.7841 | 0.9039 |
| 0.2723 | 1.23 | 9600 | 0.2944 | 0.7698 | 0.8005 | 0.7849 | 0.9039 |
| 0.2726 | 1.29 | 10000 | 0.2912 | 0.7774 | 0.7930 | 0.7851 | 0.9042 |
| 0.2674 | 1.34 | 10400 | 0.2912 | 0.7739 | 0.7973 | 0.7854 | 0.9043 |
| 0.2714 | 1.39 | 10800 | 0.2907 | 0.7729 | 0.7995 | 0.7860 | 0.9036 |
| 0.2625 | 1.44 | 11200 | 0.2949 | 0.7716 | 0.7965 | 0.7838 | 0.9041 |
| 0.2669 | 1.49 | 11600 | 0.2883 | 0.7701 | 0.8087 | 0.7889 | 0.9054 |
| 0.2601 | 1.54 | 12000 | 0.2868 | 0.7759 | 0.8069 | 0.7911 | 0.9066 |
| 0.2633 | 1.59 | 12400 | 0.2895 | 0.7659 | 0.8125 | 0.7885 | 0.9051 |
| 0.2641 | 1.65 | 12800 | 0.2878 | 0.7790 | 0.7972 | 0.7880 | 0.9059 |
| 0.2661 | 1.7 | 13200 | 0.2875 | 0.7800 | 0.7999 | 0.7898 | 0.9068 |
| 0.2719 | 1.75 | 13600 | 0.2853 | 0.7783 | 0.8025 | 0.7902 | 0.9070 |
| 0.2602 | 1.8 | 14000 | 0.2827 | 0.7801 | 0.8051 | 0.7924 | 0.9070 |
| 0.2688 | 1.85 | 14400 | 0.2819 | 0.7742 | 0.8061 | 0.7898 | 0.9066 |
| 0.2615 | 1.9 | 14800 | 0.2828 | 0.7764 | 0.8017 | 0.7888 | 0.9065 |
| 0.2623 | 1.95 | 15200 | 0.2825 | 0.7764 | 0.8055 | 0.7907 | 0.9068 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
rafacel/ppo-LunarLander-v2
|
rafacel
| 2023-05-18T13:01:12Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-18T13:00:51Z |
---
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: 236.74 +/- 41.01
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
...
```
|
HoldMyData/Taxi-v3-unit2
|
HoldMyData
| 2023-05-18T12:59:01Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-18T12:58:59Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-unit2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="HoldMyData/Taxi-v3-unit2", 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"])
```
|
kstn/mobilebert-uncased-finetuned-ner
|
kstn
| 2023-05-18T12:14:39Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"token-classification",
"generated_from_trainer",
"dataset:id_nergrit_corpus",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-18T06:37:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- id_nergrit_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mobilebert-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: id_nergrit_corpus
type: id_nergrit_corpus
config: ner
split: validation
args: ner
metrics:
- name: Precision
type: precision
value: 0.6699979179679367
- name: Recall
type: recall
value: 0.6136244458216141
- name: F1
type: f1
value: 0.6405732911990843
- name: Accuracy
type: accuracy
value: 0.8974442203210374
---
<!-- 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. -->
# mobilebert-uncased-finetuned-ner
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the id_nergrit_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3800
- Precision: 0.6700
- Recall: 0.6136
- F1: 0.6406
- Accuracy: 0.8974
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6239 | 1.0 | 1567 | 0.4989 | 0.5842 | 0.4877 | 0.5316 | 0.8688 |
| 0.5356 | 2.0 | 3134 | 0.4003 | 0.6368 | 0.5879 | 0.6113 | 0.8905 |
| 0.4035 | 3.0 | 4701 | 0.3800 | 0.6700 | 0.6136 | 0.6406 | 0.8974 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
solmysh/mt5-small-finetuned-amazon-en-es
|
solmysh
| 2023-05-18T11:51:35Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-16T13:46:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0135
- Rouge1: 16.5421
- Rouge2: 7.9012
- Rougel: 16.2574
- Rougelsum: 16.1537
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.4509 | 1.0 | 1209 | 3.1308 | 17.5055 | 8.164 | 16.9714 | 16.8977 |
| 3.4226 | 2.0 | 2418 | 3.0489 | 16.7302 | 8.1598 | 16.3268 | 16.3168 |
| 3.286 | 3.0 | 3627 | 3.0366 | 16.7244 | 7.9017 | 16.3893 | 16.3728 |
| 3.1859 | 4.0 | 4836 | 3.0219 | 16.9671 | 8.0508 | 16.6206 | 16.5261 |
| 3.1249 | 5.0 | 6045 | 3.0353 | 17.3032 | 8.0195 | 16.9664 | 16.972 |
| 3.0665 | 6.0 | 7254 | 3.0272 | 17.0115 | 7.88 | 16.7424 | 16.7476 |
| 3.0407 | 7.0 | 8463 | 3.0122 | 17.3339 | 8.0171 | 16.9919 | 16.9449 |
| 3.0248 | 8.0 | 9672 | 3.0135 | 16.5421 | 7.9012 | 16.2574 | 16.1537 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
jumelet/lm_training
|
jumelet
| 2023-05-18T11:36:59Z | 134 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-29T15:01:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: lm_training
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. -->
# lm_training
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 1.10.0
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnanthZeke/tabert-500-naamapadam
|
AnanthZeke
| 2023-05-18T11:35:55Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-18T09:24:00Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tabert-500-naamapadam
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. -->
# tabert-500-naamapadam
This model is a fine-tuned version of [livinNector/tabert-500](https://huggingface.co/livinNector/tabert-500) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2821
- Precision: 0.7818
- Recall: 0.8089
- F1: 0.7951
- Accuracy: 0.9070
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4684 | 0.05 | 400 | 0.3956 | 0.6972 | 0.6926 | 0.6949 | 0.8720 |
| 0.3901 | 0.1 | 800 | 0.3706 | 0.7099 | 0.7338 | 0.7216 | 0.8811 |
| 0.3658 | 0.15 | 1200 | 0.3551 | 0.7349 | 0.7388 | 0.7369 | 0.8854 |
| 0.3535 | 0.21 | 1600 | 0.3445 | 0.7333 | 0.7458 | 0.7395 | 0.8875 |
| 0.3512 | 0.26 | 2000 | 0.3353 | 0.7547 | 0.7408 | 0.7477 | 0.8917 |
| 0.3377 | 0.31 | 2400 | 0.3302 | 0.7417 | 0.7636 | 0.7525 | 0.8916 |
| 0.3297 | 0.36 | 2800 | 0.3279 | 0.7681 | 0.7330 | 0.7501 | 0.8931 |
| 0.3331 | 0.41 | 3200 | 0.3252 | 0.7448 | 0.7833 | 0.7636 | 0.8961 |
| 0.3247 | 0.46 | 3600 | 0.3210 | 0.7479 | 0.7847 | 0.7659 | 0.8960 |
| 0.3175 | 0.51 | 4000 | 0.3155 | 0.7684 | 0.7597 | 0.7640 | 0.8975 |
| 0.3142 | 0.57 | 4400 | 0.3113 | 0.7510 | 0.7833 | 0.7668 | 0.8977 |
| 0.315 | 0.62 | 4800 | 0.3131 | 0.7574 | 0.7830 | 0.7700 | 0.8969 |
| 0.3078 | 0.67 | 5200 | 0.3155 | 0.7569 | 0.7821 | 0.7693 | 0.8980 |
| 0.3101 | 0.72 | 5600 | 0.3117 | 0.7708 | 0.7730 | 0.7719 | 0.8990 |
| 0.3078 | 0.77 | 6000 | 0.3070 | 0.7665 | 0.7824 | 0.7744 | 0.8992 |
| 0.304 | 0.82 | 6400 | 0.3055 | 0.7680 | 0.7875 | 0.7776 | 0.8992 |
| 0.2954 | 0.87 | 6800 | 0.3019 | 0.7675 | 0.7929 | 0.7800 | 0.9002 |
| 0.2955 | 0.93 | 7200 | 0.3107 | 0.7804 | 0.7755 | 0.7779 | 0.9000 |
| 0.2979 | 0.98 | 7600 | 0.2992 | 0.7721 | 0.7931 | 0.7825 | 0.9021 |
| 0.2816 | 1.03 | 8000 | 0.3022 | 0.7695 | 0.7971 | 0.7831 | 0.9029 |
| 0.2768 | 1.08 | 8400 | 0.3043 | 0.7538 | 0.8045 | 0.7783 | 0.9003 |
| 0.2775 | 1.13 | 8800 | 0.2990 | 0.7687 | 0.8003 | 0.7842 | 0.9024 |
| 0.2704 | 1.18 | 9200 | 0.2948 | 0.7724 | 0.7987 | 0.7853 | 0.9023 |
| 0.2734 | 1.23 | 9600 | 0.2932 | 0.7764 | 0.7993 | 0.7877 | 0.9041 |
| 0.2746 | 1.29 | 10000 | 0.2918 | 0.7841 | 0.7949 | 0.7894 | 0.9046 |
| 0.2678 | 1.34 | 10400 | 0.2909 | 0.7775 | 0.8039 | 0.7905 | 0.9046 |
| 0.272 | 1.39 | 10800 | 0.2909 | 0.7786 | 0.7952 | 0.7868 | 0.9034 |
| 0.2636 | 1.44 | 11200 | 0.2900 | 0.7815 | 0.7959 | 0.7886 | 0.9044 |
| 0.2663 | 1.49 | 11600 | 0.2863 | 0.7747 | 0.8086 | 0.7913 | 0.9047 |
| 0.2617 | 1.54 | 12000 | 0.2876 | 0.7759 | 0.8042 | 0.7898 | 0.9051 |
| 0.2634 | 1.59 | 12400 | 0.2896 | 0.7677 | 0.8123 | 0.7894 | 0.9038 |
| 0.2651 | 1.65 | 12800 | 0.2871 | 0.7799 | 0.8024 | 0.7910 | 0.9058 |
| 0.2676 | 1.7 | 13200 | 0.2870 | 0.7863 | 0.8008 | 0.7935 | 0.9061 |
| 0.273 | 1.75 | 13600 | 0.2836 | 0.7804 | 0.8108 | 0.7953 | 0.9064 |
| 0.2611 | 1.8 | 14000 | 0.2821 | 0.7821 | 0.8052 | 0.7935 | 0.9064 |
| 0.2683 | 1.85 | 14400 | 0.2815 | 0.7791 | 0.8108 | 0.7946 | 0.9064 |
| 0.2624 | 1.9 | 14800 | 0.2818 | 0.7819 | 0.8090 | 0.7952 | 0.9071 |
| 0.2628 | 1.95 | 15200 | 0.2821 | 0.7818 | 0.8089 | 0.7951 | 0.9070 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
asenella/mmnist_JMVAEconfig2_seed_0_ratio_0_c
|
asenella
| 2023-05-18T11:23:59Z | 0 | 0 | null |
[
"multivae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-05-18T11:23:46Z |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
muhammadravi251001/fine-tuned-DatasetQAS-Squad-ID-with-indobert-base-uncased-with-ITTL-with-freeze-LR-1e-05
|
muhammadravi251001
| 2023-05-18T11:22:25Z | 123 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-05T05:18:50Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: fine-tuned-DatasetQAS-Squad-ID-with-indobert-base-uncased-with-ITTL-with-freeze-LR-1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-tuned-DatasetQAS-Squad-ID-with-indobert-base-uncased-with-ITTL-with-freeze-LR-1e-05
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5175
- Exact Match: 48.5572
- F1: 65.0249
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 2.0255 | 0.5 | 463 | 1.8578 | 38.8323 | 53.2780 |
| 1.8396 | 1.0 | 926 | 1.6659 | 43.2069 | 59.4121 |
| 1.6258 | 1.5 | 1389 | 1.5971 | 45.0913 | 61.6718 |
| 1.5939 | 2.0 | 1852 | 1.5523 | 46.3447 | 62.8415 |
| 1.4904 | 2.5 | 2315 | 1.5345 | 46.9589 | 63.7167 |
| 1.5015 | 3.0 | 2778 | 1.5060 | 47.4889 | 64.4261 |
| 1.3787 | 3.5 | 3241 | 1.5092 | 47.7833 | 64.2215 |
| 1.3629 | 4.0 | 3704 | 1.4885 | 48.0273 | 64.6938 |
| 1.3229 | 4.5 | 4167 | 1.5174 | 48.2712 | 64.9266 |
| 1.2848 | 5.0 | 4630 | 1.4942 | 48.4899 | 64.9576 |
| 1.2703 | 5.5 | 5093 | 1.5074 | 48.5657 | 65.0539 |
| 1.2104 | 6.0 | 5556 | 1.5112 | 48.1114 | 64.6513 |
| 1.1775 | 6.5 | 6019 | 1.5004 | 48.1534 | 64.8169 |
| 1.2303 | 7.0 | 6482 | 1.4956 | 48.4647 | 65.0723 |
| 1.1673 | 7.5 | 6945 | 1.5151 | 48.5825 | 65.0862 |
| 1.1771 | 8.0 | 7408 | 1.5057 | 48.5657 | 65.0123 |
| 1.1172 | 8.5 | 7871 | 1.5286 | 48.4311 | 64.7537 |
| 1.1282 | 9.0 | 8334 | 1.5175 | 48.5572 | 65.0249 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
|
rizvandwiki/gender-classification
|
rizvandwiki
| 2023-05-18T11:16:33Z | 2,039,551 | 48 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-12-06T08:53:43Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: gender-classification
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9244444370269775
---
# gender-classification
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### female
#### male
|
Intel/whisper-large-int8-dynamic-inc
|
Intel
| 2023-05-18T11:15:24Z | 8 | 1 |
transformers
|
[
"transformers",
"onnx",
"whisper",
"automatic-speech-recognition",
"int8",
"ONNX",
"PostTrainingDynamic",
"Intel® Neural Compressor",
"neural-compressor",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-10T09:02:21Z |
---
license: apache-2.0
datasets:
- librispeech_asr
metrics:
- wer
pipeline_tag: automatic-speech-recognition
tags:
- automatic-speech-recognition
- int8
- ONNX
- PostTrainingDynamic
- Intel® Neural Compressor
- neural-compressor
library_name: transformers
---
## Model Details: INT8 Whisper large
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning.
This int8 ONNX model is generated by [neural-compressor](https://github.com/intel/neural-compressor) and the fp32 model can be exported with below command:
```shell
optimum-cli export onnx --model openai/whisper-large whisper-large-with-past/ --task automatic-speech-recognition-with-past --opset 13
```
| Model Detail | Description |
| ----------- | ----------- |
| Model Authors - Company | Intel |
| Date | May 15, 2022 |
| Version | 1 |
| Type | Speech Recognition |
| Paper or Other Resources | - |
| License | Apache 2.0 |
| Questions or Comments | [Community Tab](https://huggingface.co/Intel/whisper-large-int8-dynamic/discussions)|
| Intended Use | Description |
| ----------- | ----------- |
| Primary intended uses | You can use the raw model for automatic speech recognition inference |
| Primary intended users | Anyone doing automatic speech recognition inference |
| Out-of-scope uses | This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people.|
### How to use
Download the model by cloning the repository:
```shell
git clone https://huggingface.co/Intel/whisper-large-int8-dynamic
```
Evaluate the model with below code:
```python
import os
from evaluate import load
from datasets import load_dataset
from transformers import WhisperForConditionalGeneration, WhisperProcessor, AutoConfig
model_name = 'openai/whisper-large'
model_path = 'whisper-large-int8-dynamic'
processor = WhisperProcessor.from_pretrained(model_name)
model = WhisperForConditionalGeneration.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
wer = load("wer")
librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test")
from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
from transformers import PretrainedConfig
model_config = PretrainedConfig.from_pretrained(model_name)
predictions = []
references = []
sessions = ORTModelForSpeechSeq2Seq.load_model(
os.path.join(model_path, 'encoder_model.onnx'),
os.path.join(model_path, 'decoder_model.onnx'),
os.path.join(model_path, 'decoder_with_past_model.onnx'))
model = ORTModelForSpeechSeq2Seq(sessions[0], sessions[1], model_config, model_path, sessions[2])
for idx, batch in enumerate(librispeech_test_clean):
audio = batch["audio"]
input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
reference = processor.tokenizer._normalize(batch['text'])
references.append(reference)
predicted_ids = model.generate(input_features)[0]
transcription = processor.decode(predicted_ids)
prediction = processor.tokenizer._normalize(transcription)
predictions.append(prediction)
wer_result = wer.compute(references=references, predictions=predictions)
print(f"Result wer: {wer_result * 100}")
accuracy = 1 - wer_result
print("Accuracy: %.5f" % accuracy)
```
## Metrics (Model Performance):
| Model | Model Size (GB) | wer |
|---|:---:|:---:|
| FP32 |9.4|3.04|
| INT8 |2.4|2.89|
|
Iwansl/Rere
|
Iwansl
| 2023-05-18T11:07:57Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-18T11:06:21Z |
---
license: creativeml-openrail-m
---
|
WALIDALI/walidlibyaly-burjkhalifaly-bekiksrily-libyatraclo
|
WALIDALI
| 2023-05-18T11:04:15Z | 31 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-18T10:35:16Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### walidlibyaly-burjkhalifaly-bekiksrily-libyatraclo Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
bhattronak14/distilbert-base-uncased-finetuned-rte
|
bhattronak14
| 2023-05-18T10:55:03Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-17T06:31:18Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-rte
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-rte
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
pmysl/805Na-diffusers
|
pmysl
| 2023-05-18T10:20:29Z | 31 | 2 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-12T03:07:23Z |
---
pipeline_tag: text-to-image
widget:
- text: "A photo of sks tram in the Minecraft style"
example_title: "Minecraft"
- text: "A photo of sks tram with the Eiffel Tower in the background"
example_title: "Eiffel Tower"
- text: "A photo of sks tram on the Mars"
example_title: "Mars"
---
This is a fine-tuned Stable Diffusion model designed to create images of Konstal 805Na. Use `sks tram` in the prompt when you are referring to 805Na
|
HAttORi/ICBINP-Photorealistic
|
HAttORi
| 2023-05-18T10:18:49Z | 0 | 3 | null |
[
"art",
"text-to-image",
"region:us"
] |
text-to-image
| 2023-05-18T09:38:16Z |
---
pipeline_tag: text-to-image
tags:
- art
---
|
SHENMU007/neunit_tts_1.1
|
SHENMU007
| 2023-05-18T10:18:03Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"1.1.0",
"generated_from_trainer",
"zh",
"dataset:facebook/voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-05-18T08:12:46Z |
---
language:
- zh
license: mit
tags:
- 1.1.0
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: SpeechT5 TTS Dutch neunit
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. -->
# SpeechT5 TTS Dutch neunit
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.12.1
|
Ashutosh1976/Ashutosh1976
|
Ashutosh1976
| 2023-05-18T10:13:47Z | 0 | 0 | null |
[
"license:bigcode-openrail-m",
"region:us"
] | null | 2023-05-18T08:34:03Z |
---
license: bigcode-openrail-m
---
|
Lkhappy/1
|
Lkhappy
| 2023-05-18T09:57:20Z | 0 | 0 | null |
[
"aa",
"dataset:databricks/databricks-dolly-15k",
"license:openrail",
"region:us"
] | null | 2023-05-18T09:56:42Z |
---
license: openrail
datasets:
- databricks/databricks-dolly-15k
language:
- aa
metrics:
- accuracy
---
|
DarrenLo/fine-tuned-dialogpt-pal
|
DarrenLo
| 2023-05-18T09:53:34Z | 136 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:empathetic_dialogues",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-18T08:07:56Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- empathetic_dialogues
model-index:
- name: fine-tuned-dialogpt-pal
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-tuned-dialogpt-pal
This model is a fine-tuned version of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) on the empathetic_dialogues 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: 0.0005
- 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 4.29.1
- Pytorch 2.0.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DD0101/disfluency-large-2
|
DD0101
| 2023-05-18T09:48:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-18T08:53:54Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: disfluency-large-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# disfluency-large-2
This model is a fine-tuned version of [vinai/phobert-large](https://huggingface.co/vinai/phobert-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0318
- Precision: 0.9837
- Recall: 0.9808
- F1: 0.9822
- Accuracy: 0.9946
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 140 | 0.0439 | 0.9538 | 0.9561 | 0.9550 | 0.9890 |
| No log | 2.0 | 280 | 0.0314 | 0.9660 | 0.9736 | 0.9698 | 0.9906 |
| No log | 3.0 | 420 | 0.0394 | 0.9710 | 0.9651 | 0.9681 | 0.9909 |
| 0.1105 | 4.0 | 560 | 0.0320 | 0.9795 | 0.9784 | 0.9790 | 0.9929 |
| 0.1105 | 5.0 | 700 | 0.0450 | 0.9704 | 0.9657 | 0.9681 | 0.9904 |
| 0.1105 | 6.0 | 840 | 0.0463 | 0.9776 | 0.9694 | 0.9734 | 0.9911 |
| 0.1105 | 7.0 | 980 | 0.0480 | 0.9706 | 0.9712 | 0.9709 | 0.9909 |
| 0.0113 | 8.0 | 1120 | 0.0318 | 0.9837 | 0.9808 | 0.9822 | 0.9946 |
| 0.0113 | 9.0 | 1260 | 0.0419 | 0.9699 | 0.9669 | 0.9684 | 0.9915 |
| 0.0113 | 10.0 | 1400 | 0.0458 | 0.9735 | 0.9712 | 0.9723 | 0.9920 |
| 0.0051 | 11.0 | 1540 | 0.0309 | 0.9777 | 0.9766 | 0.9771 | 0.9935 |
| 0.0051 | 12.0 | 1680 | 0.0232 | 0.9820 | 0.9820 | 0.9820 | 0.9951 |
| 0.0051 | 13.0 | 1820 | 0.0344 | 0.9849 | 0.9784 | 0.9816 | 0.9945 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
aliakyurek/Taxi-v3
|
aliakyurek
| 2023-05-18T09:30:55Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-18T09:30:52Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="aliakyurek/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"])
```
|
RenauxLouis/monet-test-1000steps-116-realsize-v2
|
RenauxLouis
| 2023-05-18T09:27:23Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-05-18T08:32:23Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - RenauxLouis/monet-test-1000steps-116-realsize-v2
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the real-size-116 dataset. You can find some example images in the following.
|
guoguangjie/my_wikilingua_t5small
|
guoguangjie
| 2023-05-18T08:56:36Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-18T08:43:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_wikilingua_t5small
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_wikilingua_t5small
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6035
- Rouge1: 0.2226
- Rouge2: 0.0638
- Rougel: 0.1839
- Rougelsum: 0.1838
- Gen Len: 18.725
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 100 | 2.7179 | 0.2156 | 0.0579 | 0.1742 | 0.1741 | 18.835 |
| No log | 2.0 | 200 | 2.6370 | 0.2213 | 0.0637 | 0.1796 | 0.1794 | 18.805 |
| No log | 3.0 | 300 | 2.6105 | 0.2239 | 0.064 | 0.1834 | 0.1833 | 18.79 |
| No log | 4.0 | 400 | 2.6035 | 0.2226 | 0.0638 | 0.1839 | 0.1838 | 18.725 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
metalis/pythia_410m_dialog_test_v1
|
metalis
| 2023-05-18T08:40:51Z | 140 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-04T23:19:12Z |
---
license: apache-2.0
---
Pythia 410m model fine tuned for dialog.
Example prompt
```
###I###
Jhon talks to Mike.
Jhon tells Mary about how he likes his new job.
happy
###P###
Jhon: ...
Mary: ...
```
|
jroberts/distilgpt2-ft
|
jroberts
| 2023-05-18T08:39:36Z | 132 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-18T08:37:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-ft
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. -->
# distilgpt2-ft
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3824
## 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: 0.000166
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 16 | 2.2852 |
| No log | 2.0 | 32 | 2.2098 |
| No log | 3.0 | 48 | 2.2370 |
| No log | 4.0 | 64 | 2.3000 |
| No log | 5.0 | 80 | 2.3898 |
| No log | 6.0 | 96 | 2.4586 |
| No log | 7.0 | 112 | 2.5484 |
| No log | 8.0 | 128 | 2.6572 |
| No log | 9.0 | 144 | 2.7703 |
| No log | 10.0 | 160 | 2.9010 |
| No log | 11.0 | 176 | 2.9734 |
| No log | 12.0 | 192 | 3.0461 |
| No log | 13.0 | 208 | 3.1837 |
| No log | 14.0 | 224 | 3.2359 |
| No log | 15.0 | 240 | 3.2506 |
| No log | 16.0 | 256 | 3.2979 |
| No log | 17.0 | 272 | 3.3512 |
| No log | 18.0 | 288 | 3.3811 |
| No log | 19.0 | 304 | 3.3787 |
| No log | 20.0 | 320 | 3.3824 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Billsfriend/chinese-Alpaca-7b-plus-ggml-q8_0
|
Billsfriend
| 2023-05-18T08:33:49Z | 0 | 9 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-05-11T11:50:20Z |
---
license: apache-2.0
---
This model is converted from `decapoda-research/llama-7b-hf` to `ziqingyang/chinese-alpaca-plus-lora-7b` and quantized for use with `ggerganov/llama.cpp`.
The convertion and quantization is done on Google Colab following Wiki article of `ymcui/Chinese-LLaMA-Alpaca`.
The quantization methods have been updated for llama.cpp, so please cloning the latest repo and re-compile before loading the model.
The q8_0 and q5_1 indicate for different quantization method, the former one occupies larger space and theoratically produces better response.
Example of q8_0 model on llama.cpp (acceptable responses but very short):
```
> ./main -m ./models/chinese-Alpaca-7b-plus-ggml-q8_0.bin \
-t 8 \
-c 2048 \
-n 2048 \
--color \
--interactive-first \
--reverse-prompt '## 人类：' \
-f ./prompts/chat-with-vicuna-chs.txt
main: build = 0 (unknown)
main: seed = 1683883289
llama.cpp: loading model from ./models/chinese-Alpaca-7b-plus-ggml-q8_0.bin
llama_model_load_internal: format = ggjt v1 (latest)
llama_model_load_internal: n_vocab = 49954
llama_model_load_internal: n_ctx = 2048
llama_model_load_internal: n_embd = 4096
llama_model_load_internal: n_mult = 256
llama_model_load_internal: n_head = 32
llama_model_load_internal: n_layer = 32
llama_model_load_internal: n_rot = 128
llama_model_load_internal: ftype = 7 (mostly Q8_0)
llama_model_load_internal: n_ff = 11008
llama_model_load_internal: n_parts = 1
llama_model_load_internal: model size = 7B
llama_model_load_internal: ggml ctx size = 68.20 KB
llama_model_load_internal: mem required = 9180.13 MB (+ 1026.00 MB per state)
llama_init_from_file: kv self size = 1024.00 MB
system_info: n_threads = 8 / 12 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 |
main: interactive mode on.
Reverse prompt: '## 人类：'
sampling: repeat_last_n = 64, repeat_penalty = 1.100000, presence_penalty = 0.000000, frequency_penalty = 0.000000, top_k = 40, tfs_z = 1.000000, top_p = 0.950000, typical_p = 1.000000, temp = 0.800000, mirostat = 0, mirostat_lr = 0.100000, mirostat_ent = 5.000000
generate: n_ctx = 2048, n_batch = 512, n_predict = 2048, n_keep = 0
== Running in interactive mode. ==
- Press Ctrl+C to interject at any time.
- Press Return to return control to LLaMa.
- To return control without starting a new line, end your input with '/'.
- If you want to submit another line, end your input with '\'.
这是一段人类和人工智能助理的对话。人工智能助理会对人类的问题给出详细、准确、礼貌的回答。
## 人类：你好，助理。
## 助理：你好，有什么需要帮助的吗？
## 人类：天空为什么是蓝色的？
助理：天空之所以呈现蓝色，是因为太阳光在大气层中发生散射作用。
## 人类：请再详细地解释一下。
助理：太阳光穿过大气层时，一部分被散射到不同波长的角度上，其中蓝色光的波长比其他颜色短。
## 人类：你真是惜字如金啊。
助理：谢谢你的夸奖，但我只是根据我学到的知识和算法来回答你的问题。
## 人类：
llama_print_timings: load time = 9418.31 ms
llama_print_timings: sample time = 107.95 ms / 73 runs ( 1.48 ms per run)
llama_print_timings: prompt eval time = 8645.76 ms / 85 tokens ( 101.71 ms per token)
llama_print_timings: eval time = 16303.43 ms / 73 runs ( 223.33 ms per run)
llama_print_timings: total time = 987546.29 ms
```
|
QuickSilver007/rlv2unit4_Reinforce-CartPole-v1
|
QuickSilver007
| 2023-05-18T08:28:35Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-18T08:28:25Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: rlv2unit4_Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ViktorDo/bert-finetuned-ner
|
ViktorDo
| 2023-05-18T08:21:48Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-15T11:27:44Z |
---
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: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9322761810373307
- name: Recall
type: recall
value: 0.9498485358465163
- name: F1
type: f1
value: 0.9409803267755917
- name: Accuracy
type: accuracy
value: 0.9862541943839407
---
<!-- 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: 0.0613
- Precision: 0.9323
- Recall: 0.9498
- F1: 0.9410
- Accuracy: 0.9863
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0907 | 1.0 | 1756 | 0.0649 | 0.9211 | 0.9371 | 0.9290 | 0.9832 |
| 0.0352 | 2.0 | 3512 | 0.0612 | 0.9310 | 0.9493 | 0.9401 | 0.9863 |
| 0.0164 | 3.0 | 5268 | 0.0613 | 0.9323 | 0.9498 | 0.9410 | 0.9863 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
qianjiaying/simcse-tinybert
|
qianjiaying
| 2023-05-18T08:21:20Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-05-18T08:18:15Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1254 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 128, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
fshfurnitures/Bedfurnituredubai
|
fshfurnitures
| 2023-05-18T08:16:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-05-18T08:14:36Z |
[furniture stores](https://fshfurniture.ae/)
|
SHENMU007/neunit_tts_1.0
|
SHENMU007
| 2023-05-18T07:58:28Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"1.1.0",
"generated_from_trainer",
"zh",
"dataset:facebook/voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-05-18T06:15:59Z |
---
language:
- zh
license: mit
tags:
- 1.1.0
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: SpeechT5 TTS Dutch neunit
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. -->
# SpeechT5 TTS Dutch neunit
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.12.1
|
egarciamartin/ppo-SnowballTarget
|
egarciamartin
| 2023-05-18T07:57:35Z | 9 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-05-18T07:56:31Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: egarciamartin/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jiawei1998/metaner-base
|
jiawei1998
| 2023-05-18T07:48:26Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-11T06:13:01Z |
---
language:
- en
---
Related to https://github.com/chen700564/metaner-icl
|
seonglae/openie5
|
seonglae
| 2023-05-18T07:34:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-05-18T01:05:49Z |
openjdk8 64
java -Xmx10g -XX:+UseConcMarkSweepGC -jar openie-assembly-5.0-SNAPSHOT.jar
[CLI Option](https://texonom.com/0b296be12ed64e9f9f94e2567bd798e8)
|
scarlettlin/path-to-save-model
|
scarlettlin
| 2023-05-18T07:25:20Z | 2 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-18T06:10:04Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of a T1-MRI brain scan in axial view
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - scarlettlin/path-to-save-model
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of a T1-MRI brain scan in axial view using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
wa976/ast_15-finetuned-ICBHI
|
wa976
| 2023-05-18T07:10:58Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"license:bsd-3-clause",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-05-17T18:18:53Z |
---
license: bsd-3-clause
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ast_15-finetuned-ICBHI
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. -->
# ast_15-finetuned-ICBHI
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1688
- Accuracy: 0.5397
- Sensitivity: 0.2727
- Specificity: 0.7389
- Score: 0.5058
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Sensitivity | Specificity | Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:-----------:|:------:|
| 0.7488 | 1.0 | 259 | 1.1831 | 0.5241 | 0.3551 | 0.6502 | 0.5027 |
| 0.7831 | 2.0 | 518 | 1.1688 | 0.5397 | 0.2727 | 0.7389 | 0.5058 |
| 0.7471 | 3.0 | 777 | 1.1593 | 0.5198 | 0.3772 | 0.6261 | 0.5017 |
| 0.5336 | 4.0 | 1036 | 1.4082 | 0.5281 | 0.3152 | 0.6869 | 0.5011 |
| 0.3833 | 5.0 | 1295 | 2.0232 | 0.4838 | 0.3840 | 0.5583 | 0.4712 |
| 0.1721 | 6.0 | 1554 | 2.5558 | 0.4893 | 0.3534 | 0.5906 | 0.4720 |
| 0.2745 | 7.0 | 1813 | 3.3175 | 0.4900 | 0.3917 | 0.5634 | 0.4775 |
| 0.0596 | 8.0 | 2072 | 3.6548 | 0.5143 | 0.3628 | 0.6274 | 0.4951 |
| 0.0034 | 9.0 | 2331 | 3.9119 | 0.5082 | 0.4053 | 0.5849 | 0.4951 |
| 0.0008 | 10.0 | 2590 | 4.3407 | 0.4875 | 0.4562 | 0.5108 | 0.4835 |
| 0.0 | 11.0 | 2849 | 4.1927 | 0.5136 | 0.3636 | 0.6255 | 0.4946 |
| 0.0 | 12.0 | 3108 | 4.2227 | 0.5111 | 0.3645 | 0.6204 | 0.4924 |
| 0.0 | 13.0 | 3367 | 4.2399 | 0.5114 | 0.3653 | 0.6204 | 0.4929 |
| 0.0 | 14.0 | 3626 | 4.2521 | 0.5114 | 0.3662 | 0.6198 | 0.4930 |
| 0.0 | 15.0 | 3885 | 4.2556 | 0.5114 | 0.3662 | 0.6198 | 0.4930 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
jungnerd/jungnerd_qa_model
|
jungnerd
| 2023-05-18T07:07:15Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-18T02:04:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: jungnerd_qa_model
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. -->
# jungnerd_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6623
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.4348 |
| 2.7609 | 2.0 | 500 | 1.7421 |
| 2.7609 | 3.0 | 750 | 1.6623 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
charlieoneill/lunar_new
|
charlieoneill
| 2023-05-18T06:57:55Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-18T06:55:48Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -231.53 +/- 121.00
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
nightdessert/WeCheck
|
nightdessert
| 2023-05-18T06:42:14Z | 97 | 2 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"text-generation",
"arxiv:2212.10057",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-16T03:57:44Z |
---
pipeline_tag: text-generation
---
# Factual Consistency Evaluator/Metric in ACL 2023 paper
*[WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning
](https://arxiv.org/abs/2212.10057)*
Open-sourced code: https://github.com/nightdessert/WeCheck
## Model description
WeCheck is a factual consistency metric trained from weakly annotated samples.
This WeCheck checkpoint can be used to check the following three generation tasks:
**Text Summarization/Knowlege grounded dialogue Generation/Paraphrase**
This WeCheck checkpoint is trained based on the following three weak labler:
*[QAFactEval
](https://github.com/salesforce/QAFactEval)* / *[Summarc](https://github.com/tingofurro/summac)* / *[NLI warmup](https://huggingface.co/MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli)*
---
# How to use the model
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model_name = "nightdessert/WeCheck"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing." # Input for Summarization/ Dialogue / Paraphrase
hypothesis = "The movie was not good." # Output for Summarization/ Dialogue / Paraphrase
input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt", truncation_strategy="only_first", max_length=512)
output = model(input["input_ids"].to(device))['logits'][:,0] # device = "cuda:0" or "cpu"
prediction = torch.sigmoid(output).tolist()
print(prediction) #0.884
```
or apply for a batch of samples
```python
premise = ["I first thought that I liked the movie, but upon second thought it was actually disappointing."]*3 # Input list for Summarization/ Dialogue / Paraphrase
hypothesis = ["The movie was not good."]*3 # Output list for Summarization/ Dialogue / Paraphrase
batch_tokens = tokenizer.batch_encode_plus(list(zip(premise, hypothesis)), padding=True,
truncation=True, max_length=512, return_tensors="pt", truncation_strategy="only_first")
output = model(batch_tokens["input_ids"].to(device))['logits'][:,0] # device = "cuda:0" or "cpu"
prediction = torch.sigmoid(output).tolist()
print(prediction) #[0.884,0.884,0.884]
```
license: openrail
pipeline_tag: text-classification
tags:
- Factual Consistency
- Natrual Language Inference
---
language:
- en
tags:
- Factual Consistency Evaluation
|
gkrishnan/distilbert_classifier_newsgroups
|
gkrishnan
| 2023-05-18T06:39:35Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-18T06:39:03Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_classifier_newsgroups
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. -->
# distilbert_classifier_newsgroups
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
lewdryuna/A-hakomay
|
lewdryuna
| 2023-05-18T06:08:30Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-05-18T06:08:30Z |
---
duplicated_from: 852wa/hakoMay
---
|
jokyere49/Reinforce-pixelCopter
|
jokyere49
| 2023-05-18T06:01:45Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-18T05:59:58Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 42.00 +/- 29.61
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
asenella/mmnist_JNFDccaconfig2_seed_3_ratio_0_c
|
asenella
| 2023-05-18T05:58:30Z | 0 | 0 | null |
[
"multivae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-05-18T05:58:22Z |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
mortal99/test
|
mortal99
| 2023-05-18T05:52:16Z | 0 | 0 | null |
[
"paddlepaddle",
"stable-diffusion",
"stable-diffusion-ppdiffusers",
"text-to-image",
"ppdiffusers",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-05-18T05:47:47Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: A picture of <target> coding
tags:
- stable-diffusion
- stable-diffusion-ppdiffusers
- text-to-image
- ppdiffusers
- lora
inference: false
---
# LoRA DreamBooth - mortal99/test
本仓库的 LoRA 权重是基于 runwayml/stable-diffusion-v1-5 训练而来的，我们采用[DreamBooth](https://dreambooth.github.io/)的技术并使用 A picture of <target> coding 文本进行了训练。
|
AustinCarthy/Benign10MGPT2_fromB_BFall_30KGen_toP_0.75
|
AustinCarthy
| 2023-05-18T05:44:42Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-18T02:42:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Benign10MGPT2_fromB_BFall_30KGen_toP_0.75
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. -->
# Benign10MGPT2_fromB_BFall_30KGen_toP_0.75
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1066
- Accuracy: 0.9827
- F1: 0.7997
- Precision: 0.8920
- Recall: 0.7248
- Roc Auc Score: 0.8602
- Tpr At Fpr 0.01: 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: 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: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0859 | 1.0 | 26250 | 0.0749 | 0.9823 | 0.7832 | 0.9388 | 0.6718 | 0.8348 | 0.5556 |
| 0.074 | 2.0 | 52500 | 0.0810 | 0.9803 | 0.7718 | 0.8628 | 0.6982 | 0.8463 | 0.5496 |
| 0.0534 | 3.0 | 78750 | 0.0735 | 0.9846 | 0.8211 | 0.9211 | 0.7406 | 0.8687 | 0.5882 |
| 0.0374 | 4.0 | 105000 | 0.0877 | 0.9830 | 0.8023 | 0.8976 | 0.7254 | 0.8606 | 0.0 |
| 0.0267 | 5.0 | 131250 | 0.1066 | 0.9827 | 0.7997 | 0.8920 | 0.7248 | 0.8602 | 0.0 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
|
suraj47K/keras-dummy-sequential
|
suraj47K
| 2023-05-18T05:42:09Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2023-05-18T05:42:07Z |
---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | False |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
asenella/mmnist_JNFDccaconfig2_seed_0_ratio_02_c
|
asenella
| 2023-05-18T05:34:00Z | 0 | 0 | null |
[
"multivae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-05-18T05:33:53Z |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
Mikepool117/a2c-AntBulletEnv-v0
|
Mikepool117
| 2023-05-18T05:24:38Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-18T05:22:48Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1385.41 +/- 178.51
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
junweiliao/ppo-Huggy
|
junweiliao
| 2023-05-18T05:21:28Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-05-18T05:21:16Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: junweiliao/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
deutsche-telekom/mt5-small-sum-de-mit-v1
|
deutsche-telekom
| 2023-05-18T05:02:05Z | 2,243 | 8 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"summarization",
"de",
"dataset:swiss_text_2019",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language:
- de
license: mit
tags:
- summarization
datasets:
- swiss_text_2019
---
# mT5-small-sum-de-mit-v1
This is a German summarization model. It is based on the multilingual T5 model [google/mt5-small](https://huggingface.co/google/mt5-small). The special characteristic of this model is that, unlike many other models, it is licensed under a permissive open source license (MIT). Among other things, this license allows commercial use.
[](https://www.welove.ai/)
This model is provided by the [One Conversation](https://www.welove.ai/)
team of [Deutsche Telekom AG](https://www.telekom.com/).
## Training
The training was conducted with the following hyperparameters:
- base model: [google/mt5-small](https://huggingface.co/google/mt5-small)
- source_prefix: `"summarize: "`
- batch size: 3 (6)
- max_source_length: 800
- max_target_length: 96
- warmup_ratio: 0.3
- number of train epochs: 10
- gradient accumulation steps: 2
- learning rate: 5e-5
## Datasets and Preprocessing
The datasets were preprocessed as follows:
The summary was tokenized with the [google/mt5-small](https://huggingface.co/google/mt5-small) tokenizer. Then only the records with no more than 94 summary tokens were selected.
This model is trained on the following dataset:
| Name | Language | Size | License
|------|----------|------|--------
| [SwissText 2019 - Train](https://www.swisstext.org/2019/shared-task/german-text-summarization-challenge.html) | de | 84,564 | Concrete license is unclear. The data was published in the [German Text Summarization Challenge](https://www.swisstext.org/2019/shared-task/german-text-summarization-challenge.html).
We have permission to use the Swisstext dataset and release the resulting summarization model under MIT license (see [permission-declaration-swisstext.pdf](https://huggingface.co/deutsche-telekom/mt5-small-sum-de-mit-v1/resolve/main/permission-declaration-swisstext.pdf)).
## Evaluation on MLSUM German Test Set (no beams)
| Model | rouge1 | rouge2 | rougeL | rougeLsum
|-------|--------|--------|--------|----------
| deutsche-telekom/mt5-small-sum-de-mit-v1 (this) | 16.8023 | 3.5531 | 12.6884 | 14.7624
| [ml6team/mt5-small-german-finetune-mlsum](https://huggingface.co/ml6team/mt5-small-german-finetune-mlsum) | 18.3607 | 5.3604 | 14.5456 | 16.1946
| **[deutsche-telekom/mt5-small-sum-de-en-01](https://huggingface.co/deutsche-telekom/mt5-small-sum-de-en-v1)** | **21.7336** | **7.2614** | **17.1323** | **19.3977**
## License
Copyright (c) 2021 Philip May, Deutsche Telekom AG
Licensed under the MIT License (the "License"); you may not use this work except in compliance with the License. You may obtain a copy of the License by reviewing the file [LICENSE](https://huggingface.co/deutsche-telekom/mt5-small-sum-de-mit-v1/blob/main/LICENSE) in the repository.
|
Wanfq/MAKER-mwoz-condensed-kb-t5-large
|
Wanfq
| 2023-05-18T04:45:05Z | 0 | 0 | null |
[
"conversational",
"en",
"arxiv:2305.10149",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-05-17T05:45:15Z |
---
license: apache-2.0
language:
- en
pipeline_tag: conversational
---
This is a repo of the models of [Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog](https://arxiv.org/abs/2305.10149), a paper in **ACL 2023**. For more details about the models, please refer to our [github repo](https://github.com/18907305772/MAKER).
|
Wanfq/MAKER-mwoz-condensed-kb-t5-base
|
Wanfq
| 2023-05-18T04:44:47Z | 0 | 0 | null |
[
"conversational",
"en",
"arxiv:2305.10149",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-05-17T05:44:54Z |
---
license: apache-2.0
language:
- en
pipeline_tag: conversational
---
This is a repo of the models of [Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog](https://arxiv.org/abs/2305.10149), a paper in **ACL 2023**. For more details about the models, please refer to our [github repo](https://github.com/18907305772/MAKER).
|
Wanfq/MAKER-camrest-full-kb-t5-large
|
Wanfq
| 2023-05-18T04:44:20Z | 0 | 0 | null |
[
"conversational",
"en",
"arxiv:2305.10149",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-05-17T03:47:14Z |
---
license: apache-2.0
language:
- en
pipeline_tag: conversational
---
This is a repo of the models of [Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog](https://arxiv.org/abs/2305.10149), a paper in **ACL 2023**. For more details about the models, please refer to our [github repo](https://github.com/18907305772/MAKER).
|
Wanfq/MAKER-camrest-full-kb-t5-base
|
Wanfq
| 2023-05-18T04:43:57Z | 0 | 0 | null |
[
"conversational",
"en",
"arxiv:2305.10149",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-05-17T03:46:58Z |
---
license: apache-2.0
language:
- en
pipeline_tag: conversational
---
This is a repo of the models of [Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog](https://arxiv.org/abs/2305.10149), a paper in **ACL 2023**. For more details about the models, please refer to our [github repo](https://github.com/18907305772/MAKER).
|
Wanfq/MAKER-mwoz-full-kb-t5-large
|
Wanfq
| 2023-05-18T04:42:44Z | 0 | 0 | null |
[
"conversational",
"en",
"arxiv:2305.10149",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-05-17T03:00:01Z |
---
license: apache-2.0
language:
- en
pipeline_tag: conversational
---
This is a repo of the models of [Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog](https://arxiv.org/abs/2305.10149), a paper in **ACL 2023**. For more details about the models, please refer to our [github repo](https://github.com/18907305772/MAKER).
|
Wanfq/MAKER-mwoz-full-kb-t5-base
|
Wanfq
| 2023-05-18T04:42:16Z | 0 | 0 | null |
[
"conversational",
"en",
"arxiv:2305.10149",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-05-17T02:39:43Z |
---
license: apache-2.0
language:
- en
pipeline_tag: conversational
---
This is a repo of the models of [Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog](https://arxiv.org/abs/2305.10149), a paper in **ACL 2023**. For more details about the models, please refer to our [github repo](https://github.com/18907305772/MAKER).
|
asenella/mmnist_JNFDccaconfig2_seed_1_ratio_0_c
|
asenella
| 2023-05-18T03:15:59Z | 0 | 0 | null |
[
"multivae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-05-18T03:15:52Z |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
yyyynnnniiii/Trainer_Albert_2023-05-18
|
yyyynnnniiii
| 2023-05-18T02:58:01Z | 0 | 0 | null |
[
"finance",
"text-classification",
"en",
"dataset:yyyynnnniiii/WSJ_0518",
"region:us"
] |
text-classification
| 2023-05-18T02:36:58Z |
---
datasets:
- yyyynnnniiii/WSJ_0518
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- finance
---
|
SHENMU007/neunit_test
|
SHENMU007
| 2023-05-18T02:55:25Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"1.1.0",
"generated_from_trainer",
"zh",
"dataset:facebook/voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-05-17T04:02:09Z |
---
language:
- zh
license: mit
tags:
- 1.1.0
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: SpeechT5 TTS Dutch neunit
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. -->
# SpeechT5 TTS Dutch neunit
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.12.1
|
AbdulHafiz9940/t5-small-finetuned-test1
|
AbdulHafiz9940
| 2023-05-18T02:49:31Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-17T08:47:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetuned-test1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-test1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2837
- Rouge1: 22.7012
- Rouge2: 0.0
- Rougel: 22.7156
- Rougelsum: 22.7348
- Gen Len: 2.2686
## 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.5353 | 1.0 | 2601 | 2.3131 | 22.0732 | 0.0 | 22.1069 | 22.1229 | 2.2647 |
| 2.4728 | 2.0 | 5202 | 2.2838 | 22.7012 | 0.0 | 22.7156 | 22.7348 | 2.2686 |
| 2.4819 | 3.0 | 7803 | 2.2837 | 22.7012 | 0.0 | 22.7156 | 22.7348 | 2.2686 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AlexC98/commitRoBerta_
|
AlexC98
| 2023-05-18T01:34:52Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-17T23:44:47Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Vodolay/oldbooks-lora
|
Vodolay
| 2023-05-18T01:23:13Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-05-18T00:34:16Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - Vodolay/oldbooks-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the gigant/oldbookillustrations dataset. You can find some example images in the following.
|
cyberagent/open-calm-large
|
cyberagent
| 2023-05-18T01:11:13Z | 2,142 | 10 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"japanese",
"causal-lm",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"dataset:mc4",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-05-15T06:50:24Z |
---
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
- mc4
language:
- ja
tags:
- japanese
- causal-lm
inference: false
---
# OpenCALM-Large
## Model Description
OpenCALM is a suite of decoder-only language models pre-trained on Japanese datasets, developed by CyberAgent, Inc.
## Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("cyberagent/open-calm-large", device_map="auto", torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("cyberagent/open-calm-large")
inputs = tokenizer("AIによって私達の暮らしは、", return_tensors="pt").to(model.device)
with torch.no_grad():
tokens = model.generate(
**inputs,
max_new_tokens=64,
do_sample=True,
temperature=0.7,
top_p=0.9,
repetition_penalty=1.05,
pad_token_id=tokenizer.pad_token_id,
)
output = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(output)
```
## Model Details
|Model|Params|Layers|Dim|Heads|Dev ppl|
|:---:|:---: |:---:|:---:|:---:|:---:|
|[cyberagent/open-calm-small](https://huggingface.co/cyberagent/open-calm-small)|160M|12|768|12|19.7|
|[cyberagent/open-calm-medium](https://huggingface.co/cyberagent/open-calm-medium)|400M|24|1024|16|13.8|
|[cyberagent/open-calm-large](https://huggingface.co/cyberagent/open-calm-large)|830M|24|1536|16|11.3|
|[cyberagent/open-calm-1b](https://huggingface.co/cyberagent/open-calm-1b)|1.4B|24|2048|16|10.3|
|[cyberagent/open-calm-3b](https://huggingface.co/cyberagent/open-calm-3b)|2.7B|32|2560|32|9.7|
|[cyberagent/open-calm-7b](https://huggingface.co/cyberagent/open-calm-7b)|6.8B|32|4096|32|8.2|
* **Developed by**: [CyberAgent, Inc.](https://www.cyberagent.co.jp/)
* **Model type**: Transformer-based Language Model
* **Language**: Japanese
* **Library**: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
* **License**: OpenCALM is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)). When using this model, please provide appropriate credit to CyberAgent, Inc.
* Example (en): This model is a fine-tuned version of OpenCALM-XX developed by CyberAgent, Inc. The original model is released under the CC BY-SA 4.0 license, and this model is also released under the same CC BY-SA 4.0 license. For more information, please visit: https://creativecommons.org/licenses/by-sa/4.0/
* Example (ja): 本モデルは、株式会社サイバーエージェントによるOpenCALM-XXをファインチューニングしたものです。元のモデルはCC BY-SA 4.0ライセンスのもとで公開されており、本モデルも同じくCC BY-SA 4.0ライセンスで公開します。詳しくはこちらをご覧ください: https://creativecommons.org/licenses/by-sa/4.0/
## Training Dataset
* Wikipedia (ja)
* Common Crawl (ja)
## Author
[Ryosuke Ishigami](https://huggingface.co/rishigami)
## Citations
```bibtext
@software{gpt-neox-library,
title = {{GPT-NeoX: Large Scale Autoregressive Language Modeling in PyTorch}},
author = {Andonian, Alex and Anthony, Quentin and Biderman, Stella and Black, Sid and Gali, Preetham and Gao, Leo and Hallahan, Eric and Levy-Kramer, Josh and Leahy, Connor and Nestler, Lucas and Parker, Kip and Pieler, Michael and Purohit, Shivanshu and Songz, Tri and Phil, Wang and Weinbach, Samuel},
url = {https://www.github.com/eleutherai/gpt-neox},
doi = {10.5281/zenodo.5879544},
month = {8},
year = {2021},
version = {0.0.1},
}
```
|
cyberagent/open-calm-medium
|
cyberagent
| 2023-05-18T01:10:54Z | 283 | 4 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"japanese",
"causal-lm",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"dataset:mc4",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-05-15T06:44:47Z |
---
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
- mc4
language:
- ja
tags:
- japanese
- causal-lm
inference: false
---
# OpenCALM-Medium
## Model Description
OpenCALM is a suite of decoder-only language models pre-trained on Japanese datasets, developed by CyberAgent, Inc.
## Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("cyberagent/open-calm-medium", device_map="auto", torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("cyberagent/open-calm-medium")
inputs = tokenizer("AIによって私達の暮らしは、", return_tensors="pt").to(model.device)
with torch.no_grad():
tokens = model.generate(
**inputs,
max_new_tokens=64,
do_sample=True,
temperature=0.7,
top_p=0.9,
repetition_penalty=1.05,
pad_token_id=tokenizer.pad_token_id,
)
output = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(output)
```
## Model Details
|Model|Params|Layers|Dim|Heads|Dev ppl|
|:---:|:---: |:---:|:---:|:---:|:---:|
|[cyberagent/open-calm-small](https://huggingface.co/cyberagent/open-calm-small)|160M|12|768|12|19.7|
|[cyberagent/open-calm-medium](https://huggingface.co/cyberagent/open-calm-medium)|400M|24|1024|16|13.8|
|[cyberagent/open-calm-large](https://huggingface.co/cyberagent/open-calm-large)|830M|24|1536|16|11.3|
|[cyberagent/open-calm-1b](https://huggingface.co/cyberagent/open-calm-1b)|1.4B|24|2048|16|10.3|
|[cyberagent/open-calm-3b](https://huggingface.co/cyberagent/open-calm-3b)|2.7B|32|2560|32|9.7|
|[cyberagent/open-calm-7b](https://huggingface.co/cyberagent/open-calm-7b)|6.8B|32|4096|32|8.2|
* **Developed by**: [CyberAgent, Inc.](https://www.cyberagent.co.jp/)
* **Model type**: Transformer-based Language Model
* **Language**: Japanese
* **Library**: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
* **License**: OpenCALM is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)). When using this model, please provide appropriate credit to CyberAgent, Inc.
* Example (en): This model is a fine-tuned version of OpenCALM-XX developed by CyberAgent, Inc. The original model is released under the CC BY-SA 4.0 license, and this model is also released under the same CC BY-SA 4.0 license. For more information, please visit: https://creativecommons.org/licenses/by-sa/4.0/
* Example (ja): 本モデルは、株式会社サイバーエージェントによるOpenCALM-XXをファインチューニングしたものです。元のモデルはCC BY-SA 4.0ライセンスのもとで公開されており、本モデルも同じくCC BY-SA 4.0ライセンスで公開します。詳しくはこちらをご覧ください: https://creativecommons.org/licenses/by-sa/4.0/
## Training Dataset
* Wikipedia (ja)
* Common Crawl (ja)
## Author
[Ryosuke Ishigami](https://huggingface.co/rishigami)
## Citations
```bibtext
@software{gpt-neox-library,
title = {{GPT-NeoX: Large Scale Autoregressive Language Modeling in PyTorch}},
author = {Andonian, Alex and Anthony, Quentin and Biderman, Stella and Black, Sid and Gali, Preetham and Gao, Leo and Hallahan, Eric and Levy-Kramer, Josh and Leahy, Connor and Nestler, Lucas and Parker, Kip and Pieler, Michael and Purohit, Shivanshu and Songz, Tri and Phil, Wang and Weinbach, Samuel},
url = {https://www.github.com/eleutherai/gpt-neox},
doi = {10.5281/zenodo.5879544},
month = {8},
year = {2021},
version = {0.0.1},
}
```
|
REDSCARE/RS2281
|
REDSCARE
| 2023-05-18T01:06:09Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"chemistry",
"en",
"es",
"dataset:togethercomputer/RedPajama-Data-1T",
"license:other",
"region:us"
] | null | 2023-05-18T01:04:34Z |
---
license: other
datasets:
- togethercomputer/RedPajama-Data-1T
language:
- en
- es
metrics:
- accuracy
library_name: adapter-transformers
tags:
- chemistry
---
|
yarak001/distilbert-base-uncased-finetuned-emotion
|
yarak001
| 2023-05-18T01:03:56Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-18T00:28:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9225
- name: F1
type: f1
value: 0.9225635095680048
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2207
- Accuracy: 0.9225
- F1: 0.9226
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8134 | 1.0 | 250 | 0.3127 | 0.903 | 0.9000 |
| 0.247 | 2.0 | 500 | 0.2207 | 0.9225 | 0.9226 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ItchyB/ppo-LunarLander-v2
|
ItchyB
| 2023-05-18T00:44:56Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"tensorboard",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-20T00:54:04Z |
---
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: 294.62 +/- 17.77
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
...
```
|
Toffee0705/ppo-Huggy
|
Toffee0705
| 2023-05-18T00:37:30Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-05-18T00:36:54Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: Toffee0705/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
pkuong/distilbert_classifier_newsgroups
|
pkuong
| 2023-05-18T00:22:29Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-18T00:22:11Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_classifier_newsgroups
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. -->
# distilbert_classifier_newsgroups
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Hex820000/StoriesToon_v1
|
Hex820000
| 2023-05-18T00:15:42Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-17T23:46:39Z |
---
license: creativeml-openrail-m
---
|
pratikcha/DummyModelTest
|
pratikcha
| 2023-05-17T23:50:16Z | 0 | 0 | null |
[
"code",
"en",
"region:us"
] | null | 2023-05-17T23:49:33Z |
---
language:
- en
tags:
- code
---
|
Abhinav2499/gpt2-token-class
|
Abhinav2499
| 2023-05-17T23:48:02Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-14T02:47:10Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gpt2-token-class
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. -->
# gpt2-token-class
This model is a fine-tuned version of [Jean-Baptiste/roberta-large-ner-english](https://huggingface.co/Jean-Baptiste/roberta-large-ner-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4239
- Precision: 0.8559
- Recall: 0.7666
- F1: 0.8020
- Accuracy: 0.9193
## 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: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2451 | 1.0 | 1796 | 0.2658 | 0.8781 | 0.6962 | 0.7480 | 0.9099 |
| 0.1938 | 2.0 | 3592 | 0.2473 | 0.8683 | 0.7312 | 0.7778 | 0.9153 |
| 0.1452 | 3.0 | 5388 | 0.2614 | 0.8525 | 0.7588 | 0.7953 | 0.9172 |
| 0.1068 | 4.0 | 7184 | 0.3033 | 0.8491 | 0.7584 | 0.7940 | 0.9164 |
| 0.0792 | 5.0 | 8980 | 0.3507 | 0.8612 | 0.7586 | 0.7978 | 0.9190 |
| 0.0597 | 6.0 | 10776 | 0.3924 | 0.8569 | 0.7632 | 0.7999 | 0.9189 |
| 0.0479 | 7.0 | 12572 | 0.4239 | 0.8559 | 0.7666 | 0.8020 | 0.9193 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
asenella/mmnist_JNFconfig2_seed_3_ratio_05_c
|
asenella
| 2023-05-17T23:35:14Z | 0 | 0 | null |
[
"multivae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-05-17T23:34:59Z |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
asenella/mmnist_JNFconfig2_seed_2_ratio_05_c
|
asenella
| 2023-05-17T23:34:35Z | 0 | 0 | null |
[
"multivae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-05-17T23:34:21Z |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
LoganDark/rwkv-4-raven-ggml
|
LoganDark
| 2023-05-17T23:27:57Z | 0 | 2 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-05-17T23:27:57Z |
---
license: apache-2.0
---
[Use the master branch.](https://huggingface.co/LoganDark/rwkv-4-raven-ggml/tree/master) HuggingFace won't let me set the default, sorry.
|
ernieg/setfit-beauty-multilabel-example
|
ernieg
| 2023-05-17T23:04:15Z | 3 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-05-17T23:03:25Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# ernieg/setfit-beauty-multilabel-example
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("ernieg/setfit-beauty-multilabel-example")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Ktang2k/poca-SoccerTwos
|
Ktang2k
| 2023-05-17T22:59:55Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-05-17T22:59:49Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: Ktang2k/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
asenella/mmnist_JNFconfig2_seed_1_ratio_05_c
|
asenella
| 2023-05-17T22:55:15Z | 0 | 0 | null |
[
"multivae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-05-17T22:54:32Z |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
1darkneto8/sdwebui2
|
1darkneto8
| 2023-05-17T22:35:24Z | 0 | 0 | null |
[
"arxiv:2211.06679",
"region:us"
] | null | 2023-05-17T21:52:26Z |
# Stable Diffusion web UI
A browser interface based on Gradio library for Stable Diffusion.
## Features
[Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
- Original txt2img and img2img modes
- One click install and run script (but you still must install python and git)
- Outpainting
- Inpainting
- Color Sketch
- Prompt Matrix
- Stable Diffusion Upscale
- Attention, specify parts of text that the model should pay more attention to
- a man in a `((tuxedo))` - will pay more attention to tuxedo
- a man in a `(tuxedo:1.21)` - alternative syntax
- select text and press `Ctrl+Up` or `Ctrl+Down` to automatically adjust attention to selected text (code contributed by anonymous user)
- Loopback, run img2img processing multiple times
- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
- Textual Inversion
- have as many embeddings as you want and use any names you like for them
- use multiple embeddings with different numbers of vectors per token
- works with half precision floating point numbers
- train embeddings on 8GB (also reports of 6GB working)
- Extras tab with:
- GFPGAN, neural network that fixes faces
- CodeFormer, face restoration tool as an alternative to GFPGAN
- RealESRGAN, neural network upscaler
- ESRGAN, neural network upscaler with a lot of third party models
- SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
- LDSR, Latent diffusion super resolution upscaling
- Resizing aspect ratio options
- Sampling method selection
- Adjust sampler eta values (noise multiplier)
- More advanced noise setting options
- Interrupt processing at any time
- 4GB video card support (also reports of 2GB working)
- Correct seeds for batches
- Live prompt token length validation
- Generation parameters
- parameters you used to generate images are saved with that image
- in PNG chunks for PNG, in EXIF for JPEG
- can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
- can be disabled in settings
- drag and drop an image/text-parameters to promptbox
- Read Generation Parameters Button, loads parameters in promptbox to UI
- Settings page
- Running arbitrary python code from UI (must run with `--allow-code` to enable)
- Mouseover hints for most UI elements
- Possible to change defaults/mix/max/step values for UI elements via text config
- Tiling support, a checkbox to create images that can be tiled like textures
- Progress bar and live image generation preview
- Can use a separate neural network to produce previews with almost none VRAM or compute requirement
- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image
- Styles, a way to save part of prompt and easily apply them via dropdown later
- Variations, a way to generate same image but with tiny differences
- Seed resizing, a way to generate same image but at slightly different resolution
- CLIP interrogator, a button that tries to guess prompt from an image
- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
- Batch Processing, process a group of files using img2img
- Img2img Alternative, reverse Euler method of cross attention control
- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
- Reloading checkpoints on the fly
- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
- [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
- separate prompts using uppercase `AND`
- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
- DeepDanbooru integration, creates danbooru style tags for anime prompts
- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
- Generate forever option
- Training tab
- hypernetworks and embeddings options
- Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
- Clip skip
- Hypernetworks
- Loras (same as Hypernetworks but more pretty)
- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt
- Can select to load a different VAE from settings screen
- Estimated completion time in progress bar
- API
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
- Now without any bad letters!
- Load checkpoints in safetensors format
- Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
- Now with a license!
- Reorder elements in the UI from settings screen
## Installation and Running
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
Alternatively, use online services (like Google Colab):
- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
### Automatic Installation on Windows
1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH".
2. Install [git](https://git-scm.com/download/win).
3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
### Automatic Installation on Linux
1. Install the dependencies:
```bash
# Debian-based:
sudo apt install wget git python3 python3-venv
# Red Hat-based:
sudo dnf install wget git python3
# Arch-based:
sudo pacman -S wget git python3
```
2. Navigate to the directory you would like the webui to be installed and execute the following command:
```bash
bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
```
3. Run `webui.sh`.
4. Check `webui-user.sh` for options.
### Installation on Apple Silicon
Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
## Contributing
Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
## Documentation
The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
## Credits
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
- CodeFormer - https://github.com/sczhou/CodeFormer
- ESRGAN - https://github.com/xinntao/ESRGAN
- SwinIR - https://github.com/JingyunLiang/SwinIR
- Swin2SR - https://github.com/mv-lab/swin2sr
- LDSR - https://github.com/Hafiidz/latent-diffusion
- MiDaS - https://github.com/isl-org/MiDaS
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)
- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
- xformers - https://github.com/facebookresearch/xformers
- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
- Security advice - RyotaK
- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
- (You)
|
roneneldan/TinyStories-3M
|
roneneldan
| 2023-05-17T22:11:46Z | 3,446 | 2 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"arxiv:2305.07759",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-12T21:46:51Z |
Model trained on the TinyStories Dataset, see https://arxiv.org/abs/2305.07759
------ EXAMPLE USAGE ---
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model = AutoModelForCausalLM.from_pretrained('roneneldan/TinyStories-3M')
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M")
prompt = "Once upon a time there was"
input_ids = tokenizer.encode(prompt, return_tensors="pt")
# Generate completion
output = model.generate(input_ids, max_length = 1000, num_beams=1)
# Decode the completion
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
# Print the generated text
print(output_text)
|
benlehrburger/modern-architecture-32
|
benlehrburger
| 2023-05-17T21:50:58Z | 37 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"dataset:benlehrburger/architecture",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-05-17T21:38:56Z |
---
datasets:
- benlehrburger/architecture
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Low-poly architecture image generation
This model is a diffusion model for unconditional image generation of modern architecture.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('{hub_model_id}')
image = pipeline().images[0]
image
```
|
asenella/mmnist_JNFconfig2_seed_3_ratio_02_c
|
asenella
| 2023-05-17T21:47:53Z | 0 | 0 | null |
[
"multivae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-05-17T21:47:39Z |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
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