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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 12:28:39
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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listlengths 1
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climatebert/distilroberta-base-climate-f
|
climatebert
| 2023-05-04T13:05:20Z | 1,072 | 36 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"climate",
"en",
"arxiv:2110.12010",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: en
license: apache-2.0
tags:
- climate
---
# Model Card for distilroberta-base-climate-f
## Model Description
This is the ClimateBERT language model based on the FULL-SELECT sample selection strategy.
*Note: We generally recommend choosing this language model over those based on the other sample selection strategies (unless you have good reasons not to). This is also the only language model we will update from time to time.*
Using the [DistilRoBERTa](https://huggingface.co/distilroberta-base) model as starting point, the ClimateBERT Language Model is additionally pre-trained on a text corpus comprising climate-related research paper abstracts, corporate and general news and reports from companies. The underlying methodology can be found in our [language model research paper](https://arxiv.org/abs/2110.12010).
*Update September 2, 2022: Now additionally pre-trained on an even larger text corpus, comprising >2M paragraphs. If you are looking for the language model before the update (i.e. for reproducibility), just use an older commit like [6be4fbd](https://huggingface.co/climatebert/distilroberta-base-climate-f/tree/6be4fbd3fedfd78ccb3c730c1f166947fbc940ba).*
## Climate performance model card
| distilroberta-base-climate-f | |
|--------------------------------------------------------------------------|----------------|
| 1. Is the resulting model publicly available? | Yes |
| 2. How much time does the training of the final model take? | 48 hours |
| 3. How much time did all experiments take (incl. hyperparameter search)? | 350 hours |
| 4. What was the power of GPU and CPU? | 0.7 kW |
| 5. At which geo location were the computations performed? | Germany |
| 6. What was the energy mix at the geo location? | 470 gCO2eq/kWh |
| 7. How much CO2eq was emitted to train the final model? | 15.79 kg |
| 8. How much CO2eq was emitted for all experiments? | 115.15 kg |
| 9. What is the average CO2eq emission for the inference of one sample? | 0.62 mg |
| 10. Which positive environmental impact can be expected from this work? | This work can be categorized as a building block tools following Jin et al (2021). It supports the training of NLP models in the field of climate change and, thereby, have a positive environmental impact in the future. |
| 11. Comments | Block pruning could decrease CO2eq emissions |
## Citation Information
```bibtex
@inproceedings{wkbl2022climatebert,
title={{ClimateBERT: A Pretrained Language Model for Climate-Related Text}},
author={Webersinke, Nicolas and Kraus, Mathias and Bingler, Julia and Leippold, Markus},
booktitle={Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges},
year={2022},
doi={https://doi.org/10.48550/arXiv.2212.13631},
}
```
|
climatebert/distilroberta-base-climate-d-s
|
climatebert
| 2023-05-04T13:05:02Z | 135 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"climate",
"en",
"arxiv:2110.12010",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: en
license: apache-2.0
tags:
- climate
---
# Model Card for distilroberta-base-climate-d-s
## Model Description
This is the ClimateBERT language model based on the DIV-SELECT and SIM-SELECT sample selection strategy.
*Note: We generally recommend choosing the [distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model over this language model (unless you have good reasons not to).*
Using the [DistilRoBERTa](https://huggingface.co/distilroberta-base) model as starting point, the ClimateBERT Language Model is additionally pre-trained on a text corpus comprising climate-related research paper abstracts, corporate and general news and reports from companies. The underlying methodology can be found in our [language model research paper](https://arxiv.org/abs/2110.12010).
## Climate performance model card
| distilroberta-base-climate-d-s | |
|--------------------------------------------------------------------------|----------------|
| 1. Is the resulting model publicly available? | Yes |
| 2. How much time does the training of the final model take? | 48 hours |
| 3. How much time did all experiments take (incl. hyperparameter search)? | 350 hours |
| 4. What was the power of GPU and CPU? | 0.7 kW |
| 5. At which geo location were the computations performed? | Germany |
| 6. What was the energy mix at the geo location? | 470 gCO2eq/kWh |
| 7. How much CO2eq was emitted to train the final model? | 15.79 kg |
| 8. How much CO2eq was emitted for all experiments? | 115.15 kg |
| 9. What is the average CO2eq emission for the inference of one sample? | 0.62 mg |
| 10. Which positive environmental impact can be expected from this work? | This work can be categorized as a building block tools following Jin et al (2021). It supports the training of NLP models in the field of climate change and, thereby, have a positive environmental impact in the future. |
| 11. Comments | Block pruning could decrease CO2eq emissions |
## Citation Information
```bibtex
@inproceedings{wkbl2022climatebert,
title={{ClimateBERT: A Pretrained Language Model for Climate-Related Text}},
author={Webersinke, Nicolas and Kraus, Mathias and Bingler, Julia and Leippold, Markus},
booktitle={Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges},
year={2022},
doi={https://doi.org/10.48550/arXiv.2212.13631},
}
```
|
Theju/switch_low_b1_2
|
Theju
| 2023-05-04T12:47:46Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-04T12:45:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: switch_low_b1_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. -->
# switch_low_b1_2
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DmitriyVasiliev/autotrain-mbart-rua-par-and-sent-55389129134
|
DmitriyVasiliev
| 2023-05-04T12:35:42Z | 122 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:DmitriyVasiliev/autotrain-data-mbart-rua-par-and-sent",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-05-04T12:22:24Z |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- DmitriyVasiliev/autotrain-data-mbart-rua-par-and-sent
co2_eq_emissions:
emissions: 5.124794195879908
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 55389129134
- CO2 Emissions (in grams): 5.1248
## Validation Metrics
- Loss: 0.777
- Rouge1: 8.583
- Rouge2: 2.417
- RougeL: 8.622
- RougeLsum: 8.558
- Gen Len: 21.878
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/DmitriyVasiliev/autotrain-mbart-rua-par-and-sent-55389129134
```
|
DataVare/datavare-nsf-to-pst-converter
|
DataVare
| 2023-05-04T12:25:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-05-04T12:15:49Z |
Users may easily use this capability on their PC using the DataVare NSF to PST converter software, which is a very effective tool for them. The utility operates without any data loss on Windows platforms. With the NSF to PST Converter, the user can convert their file with ease. By converting 10 objects, the app allows users to test it out for free. Those with and without technical expertise can both utilize this program. The function is accessible in any version of Outlook. It may be used with MS Outlook versions 2003, 2007, 2010, 2013, 2016, and 2019, among others. Without using Outlook, you can bulk import NSF files into the PST file format. If they are having problems, it can be used by both novice and experienced users. Additionally, users are able to obtain the software and use it with any version of Windows.
Read more :- https://www.datavare.com/software/nsf-to-pst-converter-expert.html
|
steveabecassis/huji_MediQA
|
steveabecassis
| 2023-05-04T12:17:44Z | 3 | 0 |
transformers
|
[
"transformers",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-12T19:59:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: huji_MediQA
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. -->
# huji_MediQA
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6868
- Rouge1: 0.1617
- Rouge2: 0.065
- Rougel: 0.1598
- Rougelsum: 0.1617
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 1 | 2.6868 | 0.1617 | 0.065 | 0.1598 | 0.1617 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
abhijitgayen/cogo-flan-t5
|
abhijitgayen
| 2023-05-04T12:10:55Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-04T11:28:35Z |
# Cogo Flan-t5 Model
This is a Fine-tune **FLAN-T5 model**, train with user-admin message
[DataSet](https://huggingface.co/datasets/abhijitgayen/cogo_chat)
|
majic404/majicMIX
|
majic404
| 2023-05-04T11:08:24Z | 0 | 22 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-04-27T15:49:56Z |
---
license: creativeml-openrail-m
---
|
VinayakMane47/finetuned-en-to-mar
|
VinayakMane47
| 2023-05-04T10:59:22Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-04T10:34:51Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: VinayakMane47/finetuned-en-to-mar
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. -->
# VinayakMane47/finetuned-en-to-mar
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-mr](https://huggingface.co/Helsinki-NLP/opus-mt-en-mr) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5415
- Validation Loss: 1.2289
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 4401, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.2978 | 1.5919 | 0 |
| 1.7627 | 1.3188 | 1 |
| 1.5415 | 1.2289 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
WWWxp/wav2vec2_spoof_dection1
|
WWWxp
| 2023-05-04T10:59:02Z | 209 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:asvspoof2019",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-04-22T08:34:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- asvspoof2019
model-index:
- name: wav2vec2_spoof_dection1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_spoof_dection1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the asvspoof2019 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 500
### Framework versions
- Transformers 4.28.1
- Pytorch 1.13.1
- Datasets 2.11.0
- Tokenizers 0.12.1
|
ibm-research/gpt-neo-125m-multiexit
|
ibm-research
| 2023-05-04T10:45:23Z | 122 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"en",
"dataset:cc100",
"arxiv:2305.01628",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-04-29T11:50:17Z |
---
license: mit
datasets:
- cc100
language:
- en
pipeline_tag: text-generation
---
# GPT-Neo-125M Multi-Exit
Pre-trained language model with identical parameters to [gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m), but with additional language modeling heads ("exits") connected to different layers of the model.
These 6 additional heads (in layers 2, 4, 6, 8, 10, 12) were trained on the English portion of [CC-100](https://huggingface.co/datasets/cc100) while keeping the original pre-trained model parameters frozen.
The model can be used for the _Autocontrastive Decoding_ text generation approach described in [Gera et al. 2023](https://arxiv.org/abs/2305.01628), for _early-exiting_ approaches, or for other algorithms that consider the next-token predictions of different model layers.
## Usage
Harnessing the additional language modeling heads requires loading the model using the [auto-contrastive-generation library](https://github.com/IBM/auto-contrastive-generation) (`pip install autocontrastive-gen`).
In a nutshell, the user creates a `MultiExitConfiguration` that determines model behavior at training and inference, and then loads the model using the dedicated `AutoMultiExitModel` class. After that, the model can be used with the `transformers` API like any other model. See the [GitHub](https://github.com/IBM/auto-contrastive-generation) for detailed usage instructions.
For example, the code below initializes the model to use _Autocontrastive Decoding_, and then performs text generation in this chosen setting:
```python
from transformers import AutoTokenizer
from autocontrastive_gen.modeling.configuration import MultiExitConfiguration
from autocontrastive_gen.modeling.auto_model import AutoMultiExitModel
# initialize a pre-trained multi-exit model to use auto-contrast between layer 24 and layer 12
multi_exit_config = MultiExitConfiguration(use_original_head=False,
contrast_layer_indices=(24, 12))
model = AutoMultiExitModel.from_pretrained("IBM/gpt-neo-125m-multiexit", multi_exit_config=multi_exit_config)
# perform text generation as usual
tokenizer = AutoTokenizer.from_pretrained("IBM/gpt-neo-125m-multiexit")
prompt = tokenizer("humpty dumpty sat on", return_tensors='pt')
generated_ids = model.generate(**prompt, max_new_tokens=15)
print(tokenizer.batch_decode(generated_ids))
```
## Citation
Ariel Gera, Roni Friedman, Ofir Arviv, Chulaka Gunasekara, Benjamin Sznajder, Noam Slonim and Eyal Shnarch.
[The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers](https://arxiv.org/abs/2305.01628). ACL 2023.
```bibtex
@inproceedings{gera2023autocontrastive,
title={The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers},
author={Gera, Ariel and Friedman, Roni and Arviv, Ofir and Gunasekara, Chulaka and Sznajder, Benjamin and Slonim, Noam and Shnarch, Eyal},
booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
month={july},
address={Toronto, Canada},
year={2023}
}
```
|
HilbertS/dqn-SpaceInvadersNoFrameskip-v4
|
HilbertS
| 2023-05-04T10:28:56Z | 7 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-04T10:28:16Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 639.00 +/- 224.01
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga HilbertS -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga HilbertS -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga HilbertS
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Cainiao-AI/GreedRL
|
Cainiao-AI
| 2023-05-04T10:17:08Z | 0 | 24 | null |
[
"Deep Reinforcement Learning",
"Combinatorial Optimization",
"Vehicle Routing Problem",
"reinforcement-learning",
"license:apache-2.0",
"region:us"
] |
reinforcement-learning
| 2023-04-28T02:23:51Z |
---
license: apache-2.0
pipeline_tag: reinforcement-learning
tags:
- Deep Reinforcement Learning
- Combinatorial Optimization
- Vehicle Routing Problem
---
# 🤠GreedRL
## Overview
- 🤠GreedRL is a Deep Reinforcement Learning (DRL) based solver that can solve various types of problems, such as TSP, VRPs (CVRP, VRPTW, VRPPD, etc), Order Batching Problem, Knapsack Problem, etc.
- 🤠GreedRL achieves very high performance by running on GPU while generating high quality solutions.
**1200 times faster** than [Google OR-Tools](https://developers.google.com/optimization) for large-scale (>=1000 nodes) CVRP, and the solution quality is improved by **about 3%**.
## 🏆Award
- Entering the finalists of [INFORMS 2021 Franz Edelman Award](https://www.informs.org/Resource-Center/Video-Library/Edelman-Competition-Videos/2021-Edelman-Competition-Videos/2021-Edelman-Finalist-Alibaba)
- Obtain [The Second Class Prize of Scientific and Technological Progress Award](https://www.ccf.org.cn/Awards/Awards/2022-11-08/776110.shtml).
## Editions
We have delivered the following two editions of 🤠GreedRL for users.
- **The Community Edition** is open source and available to [download](https://huggingface.co/Cainiao-AI/GreedRL).
- **The Enterprise Edition** has a higher performance implementation than **The Community Edition** (about 50 times faster), especially when solving larg-scale problems. For more informations, please contact <a href="mailto:jiangwen.wjw@alibaba-inc.com">us</a>.
## Architecture
## COPs Modeling examples
### Standard problems
#### Capacitated Vehicle Routing Problem (CVRP)
<details>
<summary>CVRP</summary>
```python
from greedrl.feature import *
from greedrl.variable import *
from greedrl.function import *
from greedrl import Problem, Solution, Solver
from greedrl import runner
features = [continuous_feature('task_demand'),
continuous_feature('worker_weight_limit'),
continuous_feature('distance_matrix'),
variable_feature('distance_this_to_task'),
variable_feature('distance_task_to_end')]
variables = [task_demand_now('task_demand_now', feature='task_demand'),
task_demand_now('task_demand_this', feature='task_demand', only_this=True),
feature_variable('task_weight'),
worker_variable('worker_weight_limit'),
worker_used_resource('worker_used_weight', task_require='task_weight'),
edge_variable('distance_last_to_this', feature='distance_matrix', last_to_this=True),
edge_variable('distance_this_to_task', feature='distance_matrix', this_to_task=True),
edge_variable('distance_task_to_end', feature='distance_matrix', task_to_end=True)]
class Constraint:
def do_task(self):
return self.task_demand_this
def mask_task(self):
# 已经完成的任务
mask = self.task_demand_now <= 0
# 车辆容量限制
worker_weight_limit = self.worker_weight_limit - self.worker_used_weight
mask |= self.task_demand_now * self.task_weight > worker_weight_limit[:, None]
return mask
def finished(self):
return torch.all(self.task_demand_now <= 0, 1)
class Objective:
def step_worker_end(self):
return self.distance_last_to_this
def step_task(self):
return self.distance_last_to_this
```
</details>
#### Pickup and Delivery Problem with Time Windows (PDPTW)
<details>
<summary>PDPTW</summary>
```python
from greedrl.model import runner
from greedrl.feature import *
from greedrl.variable import *
from greedrl.function import *
from greedrl import Problem, Solution, Solver
features = [local_category('task_group'),
global_category('task_priority', 2),
variable_feature('distance_this_to_task'),
variable_feature('distance_task_to_end')]
variables = [task_demand_now('task_demand_now', feature='task_demand'),
task_demand_now('task_demand_this', feature='task_demand', only_this=True),
feature_variable('task_weight'),
feature_variable('task_group'),
feature_variable('task_priority'),
feature_variable('task_due_time2', feature='task_due_time'),
task_variable('task_due_time'),
task_variable('task_service_time'),
task_variable('task_due_time_penalty'),
worker_variable('worker_basic_cost'),
worker_variable('worker_distance_cost'),
worker_variable('worker_due_time'),
worker_variable('worker_weight_limit'),
worker_used_resource('worker_used_weight', task_require='task_weight'),
worker_used_resource('worker_used_time', 'distance_matrix', 'task_service_time', 'task_ready_time',
'worker_ready_time'),
edge_variable('distance_last_to_this', feature='distance_matrix', last_to_this=True),
edge_variable('distance_this_to_task', feature='distance_matrix', this_to_task=True),
edge_variable('distance_task_to_end', feature='distance_matrix', task_to_end=True)]
class Constraint:
def do_task(self):
return self.task_demand_this
def mask_worker_end(self):
return task_group_split(self.task_group, self.task_demand_now <= 0)
def mask_task(self):
mask = self.task_demand_now <= 0
mask |= task_group_priority(self.task_group, self.task_priority, mask)
worker_used_time = self.worker_used_time[:, None] + self.distance_this_to_task
mask |= (worker_used_time > self.task_due_time2) & (self.task_priority == 0)
# 容量约束
worker_weight_limit = self.worker_weight_limit - self.worker_used_weight
mask |= self.task_demand_now * self.task_weight > worker_weight_limit[:, None]
return mask
def finished(self):
return torch.all(self.task_demand_now <= 0, 1)
class Objective:
def step_worker_start(self):
return self.worker_basic_cost
def step_worker_end(self):
feasible = self.worker_used_time <= self.worker_due_time
return self.distance_last_to_this * self.worker_distance_cost, feasible
def step_task(self):
worker_used_time = self.worker_used_time - self.task_service_time
feasible = worker_used_time <= self.task_due_time
feasible &= worker_used_time <= self.worker_due_time
cost = self.distance_last_to_this * self.worker_distance_cost
return torch.where(feasible, cost, cost + self.task_due_time_penalty), feasible
```
</details>
#### VRP with Time Windows (VRPTW)
<details>
<summary>VRPTW</summary>
```python
from greedrl import Problem, Solution, Solver
from greedrl.feature import *
from greedrl.variable import *
from greedrl.function import *
from greedrl.model import runner
from greedrl.myenv import VrptwEnv
features = [continuous_feature('worker_weight_limit'),
continuous_feature('worker_ready_time'),
continuous_feature('worker_due_time'),
continuous_feature('worker_basic_cost'),
continuous_feature('worker_distance_cost'),
continuous_feature('task_demand'),
continuous_feature('task_weight'),
continuous_feature('task_ready_time'),
continuous_feature('task_due_time'),
continuous_feature('task_service_time'),
continuous_feature('distance_matrix')]
variables = [task_demand_now('task_demand_now', feature='task_demand'),
task_demand_now('task_demand_this', feature='task_demand', only_this=True),
feature_variable('task_weight'),
feature_variable('task_due_time'),
feature_variable('task_ready_time'),
feature_variable('task_service_time'),
worker_variable('worker_weight_limit'),
worker_variable('worker_due_time'),
worker_variable('worker_basic_cost'),
worker_variable('worker_distance_cost'),
worker_used_resource('worker_used_weight', task_require='task_weight'),
worker_used_resource('worker_used_time', 'distance_matrix', 'task_service_time', 'task_ready_time',
'worker_ready_time'),
edge_variable('distance_last_to_this', feature='distance_matrix', last_to_this=True),
edge_variable('distance_this_to_task', feature='distance_matrix', this_to_task=True),
edge_variable('distance_task_to_end', feature='distance_matrix', task_to_end=True)]
class Constraint:
def do_task(self):
return self.task_demand_this
def mask_task(self):
# 已经完成的任务
mask = self.task_demand_now <= 0
# 车辆容量限制
worker_weight_limit = self.worker_weight_limit - self.worker_used_weight
mask |= self.task_demand_now * self.task_weight > worker_weight_limit[:, None]
worker_used_time = self.worker_used_time[:, None] + self.distance_this_to_task
mask |= worker_used_time > self.task_due_time
worker_used_time = torch.max(worker_used_time, self.task_ready_time)
worker_used_time += self.task_service_time
worker_used_time += self.distance_task_to_end
mask |= worker_used_time > self.worker_due_time[:, None]
return mask
def finished(self):
return torch.all(self.task_demand_now <= 0, 1)
class Objective:
def step_worker_start(self):
return self.worker_basic_cost
def step_worker_end(self):
return self.distance_last_to_this * self.worker_distance_cost
def step_task(self):
return self.distance_last_to_this * self.worker_distance_cost
```
</details>
#### Travelling Salesman Problem (TSP)
<details>
<summary>TSP</summary>
```python
from greedrl.feature import *
from greedrl.variable import *
from greedrl import Problem
from greedrl import runner
features = [continuous_feature('task_location'),
variable_feature('distance_this_to_task'),
variable_feature('distance_task_to_end')]
variables = [task_demand_now('task_demand_now', feature='task_demand'),
task_demand_now('task_demand_this', feature='task_demand', only_this=True),
edge_variable('distance_last_to_this', feature='distance_matrix', last_to_this=True),
edge_variable('distance_this_to_task', feature='distance_matrix', this_to_task=True),
edge_variable('distance_task_to_end', feature='distance_matrix', task_to_end=True),
edge_variable('distance_last_to_loop', feature='distance_matrix', last_to_loop=True)]
class Constraint:
def do_task(self):
return self.task_demand_this
def mask_task(self):
mask = self.task_demand_now <= 0
return mask
def mask_worker_end(self):
return torch.any(self.task_demand_now > 0, 1)
def finished(self):
return torch.all(self.task_demand_now <= 0, 1)
class Objective:
def step_worker_end(self):
return self.distance_last_to_loop
def step_task(self):
return self.distance_last_to_this
```
</details>
#### Split Delivery Vehicle Routing Problem (SDVRP)
<details>
<summary>SDVRP</summary>
```python
from greedrl.feature import *
from greedrl.variable import *
from greedrl import Problem
from greedrl import runner
features = [continuous_feature('task_demand'),
continuous_feature('worker_weight_limit'),
continuous_feature('distance_matrix'),
variable_feature('distance_this_to_task'),
variable_feature('distance_task_to_end')]
variables = [task_demand_now('task_demand'),
task_demand_now('task_demand_this', feature='task_demand', only_this=True),
feature_variable('task_weight'),
task_variable('task_weight_this', feature='task_weight'),
worker_variable('worker_weight_limit'),
worker_used_resource('worker_used_weight', task_require='task_weight'),
edge_variable('distance_last_to_this', feature='distance_matrix', last_to_this=True)]
class Constraint:
def do_task(self):
worker_weight_limit = self.worker_weight_limit - self.worker_used_weight
return torch.min(self.task_demand_this, worker_weight_limit // self.task_weight_this)
def mask_task(self):
mask = self.task_demand <= 0
worker_weight_limit = self.worker_weight_limit - self.worker_used_weight
mask |= self.task_weight > worker_weight_limit[:, None]
return mask
def finished(self):
return torch.all(self.task_demand <= 0, 1)
class Objective:
def step_worker_end(self):
return self.distance_last_to_this
def step_task(self):
return self.distance_last_to_this
```
</details>
### Real-world scenario problems
In addition to being able to solve standard problems, 🤠GreedRL can also model and solve real-world scenario problems, like *Instant Delivery Service* and *Order Batching Problem*.
#### Instant Delivery Service
> Instant Delivery Service are widespread in order dispatching systems of courier delivery services ([Ele.me](https://www.ele.me/), [Meituan](https://waimai.meituan.com/), [UUPaotui](https://www.uupt.com/index.htm), etc).
> Orders are generated in real-time. A number of vehicles are scheduled to serve orders from pickup locations to delivery locations while respecting vehicle capacity. The objective consists in minimizing both total delivery time and overtime penalty.
<details>
<summary>Instant Delivery Service</summary>
```python
from greedrl.feature import *
from greedrl.variable import *
from greedrl.function import *
from greedrl import Problem
from greedrl import runner
features = [local_category('task_order'),
global_category('task_type', 2),
global_category('task_new_order', 2),
variable_feature('time_this_to_task'),
continuous_feature('x_time_matrix'),
continuous_feature('task_due_time_x'),
continuous_feature('worker_task_mask')]
variables = [task_demand_now('task_demand_now', feature='task_demand'),
task_demand_now('task_demand_this', feature='task_demand', only_this=True),
task_variable('task_pickup_this', feature='task_pickup'),
task_variable('task_due_time_this', feature='task_due_time'),
feature_variable('task_order', feature='task_order'),
feature_variable('task_type', feature='task_type'),
feature_variable('task_new_pickup', feature='task_new_pickup'),
feature_variable('worker_task_mask', feature='worker_task_mask'),
worker_count_now('worker_count_now', feature='worker_count'),
worker_variable('worker_min_old_task_this', feature='worker_min_old_task'),
worker_variable('worker_max_new_order_this', feature='worker_max_new_order'),
worker_variable('worker_task_mask_this', feature='worker_task_mask'),
worker_used_resource('worker_used_old_task', task_require='task_old'),
worker_used_resource('worker_used_new_order', task_require='task_new_pickup'),
worker_used_resource('worker_used_time', edge_require='time_matrix'),
edge_variable('time_this_to_task', feature='x_time_matrix', this_to_task=True)]
class Constraint:
def do_task(self):
return self.task_demand_this
def mask_worker_start(self):
mask = self.worker_count_now <= 0
finished = self.task_demand_now <= 0
worker_task_mask = self.worker_task_mask | finished[:, None, :]
mask |= torch.all(worker_task_mask, 2)
return mask
def mask_worker_end(self):
mask = self.worker_used_old_task < self.worker_min_old_task_this
mask |= task_group_split(self.task_order, self.task_demand_now <= 0)
return mask
def mask_task(self):
mask = self.task_demand_now <= 0
mask |= task_group_priority(self.task_order, self.task_type, mask)
worker_max_new_order = self.worker_max_new_order_this - self.worker_used_new_order
mask |= self.task_new_pickup > worker_max_new_order[:, None]
mask |= self.worker_task_mask_this
return mask
def finished(self):
worker_mask = self.worker_count_now <= 0
task_mask = self.task_demand_now <= 0
worker_task_mask = worker_mask[:, :, None] | task_mask[:, None, :]
worker_task_mask |= self.worker_task_mask
batch_size = worker_task_mask.size(0)
worker_task_mask = worker_task_mask.view(batch_size, -1)
return worker_task_mask.all(1)
class Objective:
def step_task(self):
over_time = (self.worker_used_time - self.task_due_time_this).clamp(min=0)
pickup_time = self.worker_used_time * self.task_pickup_this
return self.worker_used_time + over_time + pickup_time
def step_finish(self):
return self.task_demand_now.sum(1) * 1000
```
</details>
#### Order Batching Problem
> The Order Batching Problem is an optimization problem which occurs in a warehouse consists of designing a set of picking batches, such that each customer order (composed by a list of items) is assigned to exactly one batch,
> and each batch has to be collected by a single picker. The objective consists in minimizing both total batching cost (a weighted sum of used numbers of areas, roadways and items) and penalty for exceeding loading limits of pickers.
<details>
<summary>Order Batching Problem</summary>
```python
from greedrl import Problem, Solver
from greedrl.feature import *
from greedrl.variable import *
from greedrl import runner
features = [local_feature('task_area'),
local_feature('task_roadway'),
local_feature('task_area_group'),
sparse_local_feature('task_item_id', 'task_item_num'),
sparse_local_feature('task_item_owner_id', 'task_item_num'),
variable_feature('worker_task_item'),
variable_feature('worker_used_roadway'),
variable_feature('worker_used_area')]
variables = [task_demand_now('task_demand_now', feature='task_demand'),
task_demand_now('task_demand_this', feature='task_demand', only_this=True),
feature_variable('task_item_id'),
feature_variable('task_item_num'),
feature_variable('task_item_owner_id'),
feature_variable('task_area'),
feature_variable('task_area_group'),
feature_variable('task_load'),
feature_variable('task_group'),
worker_variable('worker_load_limit'),
worker_variable('worker_area_limit'),
worker_variable('worker_area_group_limit'),
worker_task_item('worker_task_item', item_id='task_item_id', item_num='task_item_num'),
worker_task_item('worker_task_item_owner', item_id='task_item_owner_id', item_num='task_item_num'),
worker_used_resource('worker_used_load', task_require='task_load'),
worker_used_resource('worker_used_area', task_require='task_area'),
worker_used_resource('worker_used_roadway', task_require='task_roadway'),
worker_used_resource('worker_used_area_group', task_require='task_area_group')]
class Constraint:
def do_task(self):
return self.task_demand_this
def mask_worker_end(self):
return self.worker_used_load < self.worker_load_limit
def mask_task(self):
# completed tasks
mask = self.task_demand_now <= 0
# mask |= task_group_priority(self.task_group, self.task_out_stock_time, mask)
NT = self.task_item_id.size(1)
worker_task_item = self.worker_task_item[:, None, :]
worker_task_item = worker_task_item.expand(-1, NT, -1)
task_item_in_worker = worker_task_item.gather(2, self.task_item_id.long())
task_item_in_worker = (task_item_in_worker > 0) & (self.task_item_num > 0)
worker_task_item_owner = self.worker_task_item_owner[:, None, :]
worker_task_item_owner = worker_task_item_owner.expand(-1, NT, -1)
task_item_owner_in_worker = worker_task_item_owner.gather(2, self.task_item_owner_id.long())
task_item_owner_in_worker = (task_item_owner_in_worker > 0) & (self.task_item_num > 0)
#
mask |= torch.any(task_item_in_worker & ~task_item_owner_in_worker, 2)
worker_load_limit = self.worker_load_limit - self.worker_used_load
mask |= (self.task_load > worker_load_limit[:, None])
task_area = self.task_area + self.worker_used_area[:, None, :]
task_area_num = task_area.clamp(0, 1).sum(2, dtype=torch.int32)
mask |= (task_area_num > self.worker_area_limit[:, None])
tak_area_group = self.task_area_group + self.worker_used_area_group[:, None, :]
tak_area_group_num = tak_area_group.clamp(0, 1).sum(2, dtype=torch.int32)
mask |= (tak_area_group_num > self.worker_area_group_limit[:, None])
return mask
def finished(self):
return torch.all(self.task_demand_now <= 0, 1)
class Objective:
def step_worker_end(self):
area_num = self.worker_used_area.clamp(0, 1).sum(1)
roadway_num = self.worker_used_roadway.clamp(0, 1).sum(1)
item_num = self.worker_task_item.clamp(0, 1).sum(1)
penalty = (self.worker_load_limit - self.worker_used_load) * 10
return area_num * 100 + roadway_num * 10 + item_num + penalty
```
</details>
#
#
# Getting started
## Description
We are delighted to release 🤠GreedRL Community Edition, as well as example of training and testing scripts for the standard Capacitated VRP (CVRP), you can download it and get started.
## Test environment
🤠GreedRL Community Edition has been tested on Ubuntu 18.04 with GCC compiler v7.5.0 and CUDA version 11.4, and a [Miniconda](https://docs.conda.io/en/latest/miniconda.html#system-requirements) distribution with Python 3.8. We recommend using a similar configuration to avoid any possiblem compilation issue.
## Installation
First, clone the repository.
```aidl
$ git clone https://huggingface.co/Cainiao-AI/GreedRL
```
Then, create and activate a python environment using conda, and install required packages.
```aidl
$ conda create -n python38 python==3.8
$ source activate python38
$ pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu113
```
Finally, compile and add the resulting library `greedrl` to the `PYTHONPATH`
```aidl
$ python setup.py build
$ export PYTHONPATH={your_current_path}/build/lib.linux-x86_64-cpython-38/:$PYTHONPATH
```
## CVRP Training
1. Training data
We use generated data for the training phase, the customers and depot locations are randomly generated in the unit square [0,1] X [0,1]. For CVRP, we assume that the demand of each node is a discrete number in {1,...,9}, chosen uniformly at random, and each vehicle has a default capacity of 50.
2. Start training
```python
$ cd examples/cvrp
$ python train.py --model_filename cvrp_100.pt --problem_size 100
```
## CVRP Testing
After training process, you'll get a trained model, like `cvrp_100.pt`, that you can use for test.
```python
$ cd examples/cvrp
$ python solve.py --device cpu --model_name cvrp_100.pt --problem_size 100
```
# Support
We look forward you to downloading it, using it, and opening discussion if you encounter any problems or have ideas on building an even better experience.
For commercial enquiries, please contact <a href="mailto:jiangwen.wjw@alibaba-inc.com">us</a>.
# Citation
```
@article{hu2022alibaba,
title={Alibaba vehicle routing algorithms enable rapid pick and delivery},
author={Hu, Haoyuan and Zhang, Ying and Wei, Jiangwen and Zhan, Yang and Zhang, Xinhui and Huang, Shaojian and Ma, Guangrui and Deng, Yuming and Jiang, Siwei},
journal={INFORMS Journal on Applied Analytics},
volume={52},
number={1},
pages={27--41},
year={2022},
publisher={INFORMS}
}
```
|
blackeys/ppo-LunarLanderV2
|
blackeys
| 2023-05-04T09:59:58Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-04T09:00:38Z |
---
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: 244.64 +/- 22.66
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
...
```
|
botp/sd-vae-ft-mse-original
|
botp
| 2023-05-04T09:35:58Z | 0 | 1 | null |
[
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:mit",
"region:us"
] |
text-to-image
| 2023-05-04T09:35:58Z |
---
license: mit
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: false
duplicated_from: stabilityai/sd-vae-ft-mse-original
---
# Improved Autoencoders
## Utilizing
These weights are intended to be used with the original [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion). If you are looking for the model to use with the 🧨 diffusers library, [come here](https://huggingface.co/CompVis/stabilityai/sd-vae-ft-ema).
## Decoder Finetuning
We publish two kl-f8 autoencoder versions, finetuned from the original [kl-f8 autoencoder](https://github.com/CompVis/latent-diffusion#pretrained-autoencoding-models) on a 1:1 ratio of [LAION-Aesthetics](https://laion.ai/blog/laion-aesthetics/) and LAION-Humans, an unreleased subset containing only SFW images of humans. The intent was to fine-tune on the Stable Diffusion training set (the autoencoder was originally trained on OpenImages) but also enrich the dataset with images of humans to improve the reconstruction of faces.
The first, _ft-EMA_, was resumed from the original checkpoint, trained for 313198 steps and uses EMA weights. It uses the same loss configuration as the original checkpoint (L1 + LPIPS).
The second, _ft-MSE_, was resumed from _ft-EMA_ and uses EMA weights and was trained for another 280k steps using a different loss, with more emphasis
on MSE reconstruction (MSE + 0.1 * LPIPS). It produces somewhat ``smoother'' outputs. The batch size for both versions was 192 (16 A100s, batch size 12 per GPU).
To keep compatibility with existing models, only the decoder part was finetuned; the checkpoints can be used as a drop-in replacement for the existing autoencoder..
_Original kl-f8 VAE vs f8-ft-EMA vs f8-ft-MSE_
## Evaluation
### COCO 2017 (256x256, val, 5000 images)
| Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments
|----------|---------|------|--------------|---------------|---------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
| | | | | | | | |
| original | 246803 | 4.99 | 23.4 +/- 3.8 | 0.69 +/- 0.14 | 1.01 +/- 0.28 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD |
| ft-EMA | 560001 | 4.42 | 23.8 +/- 3.9 | 0.69 +/- 0.13 | 0.96 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA |
| ft-MSE | 840001 | 4.70 | 24.5 +/- 3.7 | 0.71 +/- 0.13 | 0.92 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs |
### LAION-Aesthetics 5+ (256x256, subset, 10000 images)
| Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments
|----------|-----------|------|--------------|---------------|---------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
| | | | | | | | |
| original | 246803 | 2.61 | 26.0 +/- 4.4 | 0.81 +/- 0.12 | 0.75 +/- 0.36 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD |
| ft-EMA | 560001 | 1.77 | 26.7 +/- 4.8 | 0.82 +/- 0.12 | 0.67 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA |
| ft-MSE | 840001 | 1.88 | 27.3 +/- 4.7 | 0.83 +/- 0.11 | 0.65 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs |
### Visual
_Visualization of reconstructions on 256x256 images from the COCO2017 validation dataset._
<p align="center">
<br>
<b>
256x256: ft-EMA (left), ft-MSE (middle), original (right)</b>
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00025_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00011_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00037_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00043_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00053_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00029_merged.png />
</p>
|
botp/GhostMix
|
botp
| 2023-05-04T09:31:48Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-05-04T09:31:48Z |
---
duplicated_from: drnighthan/GhostMix
---
|
botp/ReVAnimated
|
botp
| 2023-05-04T09:23:56Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2023-05-04T09:23:55Z |
---
license: other
duplicated_from: hanafuusen2001/ReVAnimated
---
# 聲明 Disclaimer
本資料夾中的模型不是我所製作，版權歸原作者所有（各模型版權詳見 http://www.civitai.com 所示）。我上傳至本資料夾僅爲方便在綫抽取資源，并非盈利。
The models in this folder are not made by me, and the copyright belongs to the original author (see http://www.civitai.com for details on the copyright of each model). I uploaded to this folder only for the convenience of extracting resources online, not for profit.
# 模型列表 List of Models
本資料夾中所有模型詳見下表。
All the models in this folder are detailed in the table below.
| 模型名稱 Model Name | Civitai 頁面鏈接 Civitai Page Link | Civitai 下載鏈接 Civitai Download Link |
|----------------------|--------------------|--------------------|
|revAnimated_v122.safetensors |https://civitai.com/models/7371?modelVersionId=46846 |https://civitai.com/api/download/models/46846 |
|revAnimated_v121-inpainting.safetensors |https://civitai.com/models/7371?modelVersionId=43978 |https://civitai.com/api/download/models/43978 |
|revAnimated_v121.safetensors |https://civitai.com/models/7371?modelVersionId=40248 |https://civitai.com/api/download/models/40248 |
|revAnimated_v11-inpainting.safetensors |https://civitai.com/models/7371?modelVersionId=22258 |https://civitai.com/api/download/models/22258 |
|revAnimated_v11.safetensors |https://civitai.com/models/7371?modelVersionId=19575 |https://civitai.com/api/download/models/19575 |
|revAnimated_v10-inpainting.safetensors |https://civitai.com/models/7371?modelVersionId=11386 |https://civitai.com/api/download/models/11386 |
|revAnimated_v10.safetensors |https://civitai.com/models/7371?modelVersionId=8665 |https://civitai.com/api/download/models/8665 |
<img src="" width="512" height="">
|
s3nh/gpt-j-6b-3500steps-polish
|
s3nh
| 2023-05-04T09:21:04Z | 0 | 1 | null |
[
"pytorch",
"pl",
"dataset:databricks/databricks-dolly-15k",
"dataset:s3nh/alpaca-dolly-instruction-only-polish",
"license:openrail",
"region:us"
] | null | 2023-05-04T07:24:31Z |
---
license: openrail
datasets:
- databricks/databricks-dolly-15k
- s3nh/alpaca-dolly-instruction-only-polish
language:
- pl
---
### Introduction
These repository consist of Eleuther-AI/gpt-j-6B finetuned to Polish language on translated alpaca-dolly dataset.
Main task is to perform accurate answers to instruction asked.
Below you can find an instruction of how to infer with that model.
These repository does not contain an tokenizer object, at the moment (#TODO).
### Evaluation part
```python
import pandas as pd
import torch
from torch.utils.data import AutTokenizer
from typing import List, Dict, Union
from typing import Any, TypeVar
import pandas as pd
import pickle
MODEL_NAME: str = 's3nh/gpt-j-6b-3500steps-polish'
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCasualLM.from_pretrained(MODEL_NAME).cuda()
#Resize model for tokenizer size
n_tokens: int = len(tokenizer)
model.resize_token_embeddings(n_tokens)
def _generate_prompt(instruction, input=None):
if input:
return f"""Poniżej znajduje się instrukcja opisująca zadanie, połączona z danymi wejściowymi, które zapewniają dalszy konktekst. Napisz odpowiedź, która odpowiednio odpowie na pytanie.
### Instruction:
{instruction}
### Input:
{input}
### Response:"""
manual_instruction: str = "Napisz mi proszę jakie są rodzaje telefonów komórkowych"
manual_input: str = "Telefony komórkowe, w przeciwieństwie do np. satelitarnych, charakteryzuje to, że działają w obrębie naziemnych fal radiowych w technologii GSM (i w różnych jej wariantach: 3G, 4G czy niebawem 5G). Zasadniczo można jednak wyróżnić wiele ich rodzajów i podzielić je na różne kryteria. I tak, ze względu na rodzaj obudowy, można mówić o telefonach jednobryłowych, rozsuwanych, obrotowych czy też z klapką. Obecnie jednak najbardziej popularne i – ze względu na posiadane parametry – najlepsze telefony komórkowe to smartfony dotykowe."
print(f"Valueation for {manual_instruction} \n\n\n {manual_input}\n\n")
evaluate(instruction = manual_instruction,
input = manual_input)
```
|
Pietro97/ppo-Huggy
|
Pietro97
| 2023-05-04T09:15:03Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-05-04T09:14:55Z |
---
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: Pietro97/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
botp/Realistic_Vision_V2.0
|
botp
| 2023-05-04T09:14:37Z | 4 | 0 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-04T09:14:36Z |
---
license: creativeml-openrail-m
duplicated_from: SG161222/Realistic_Vision_V2.0
---
<b>Please read this!</b><br>
For version 2.0 it is recommended to use with VAE (to improve generation quality and get rid of blue artifacts): https://huggingface.co/stabilityai/sd-vae-ft-mse-original<br>
This model is available on <a href="https://www.mage.space/">Mage.Space</a>, <a href="https://sinkin.ai/">Sinkin.ai</a>, <a href="https://getimg.ai/">GetImg.ai</a> and (<a href="https://randomseed.co/">RandomSeed.co</a> - NSFW content)
<hr/>
<b>I use this template to get good generation results:
Prompt:</b>
RAW photo, *subject*, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
<b>Example:</b> RAW photo, a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
<b>Negative Prompt:</b>
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck<br>
<b>OR</b><br>
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation
<b>Euler A or DPM++ 2M Karras with 25 steps<br>
CFG Scale 3,5 - 7<br>
Hires. fix with Latent upscaler<br>
0 Hires steps and Denoising strength 0.25-0.45<br>
Upscale by 1.1-2.0</b>
|
usix79/a2c-PandaReachDense-v2
|
usix79
| 2023-05-04T09:07:43Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-04T09:05:05Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.70 +/- 0.62
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
botp/embeddings
|
botp
| 2023-05-04T09:00:01Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-04T08:37:38Z |
---
license: creativeml-openrail-m
duplicated_from: nolanaatama/embeddings
---
DISCLAIMER! This Is A Preservation Repository!
Cloned since __nolanaatama/embeddings__
|
botp/zzeipher-fix6
|
botp
| 2023-05-04T08:59:19Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-04-27T09:46:43Z |
---
license: creativeml-openrail-m
duplicated_from: m4gnett/zeipher-f222
---
DISCLAIMER! This Is A Preservation Repository!
Cloned since __m4gnett/zeipher-f222__
This repository is for backuping Zeipher F222.
I downloaded the model last month via torrent.
|
brathief/Alice_extend_brathief_e500
|
brathief
| 2023-05-04T08:43:17Z | 7 | 0 |
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
| 2023-04-22T13:39:09Z |
---
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 - brathief/Alice_extend_brathief_e500
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.
|
pkufool/icefall_asr_aishell_pruned_transducer_stateless7_bbpe
|
pkufool
| 2023-05-04T08:39:07Z | 0 | 0 | null |
[
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2023-05-04T07:25:32Z |
---
license: apache-2.0
---
The results:
|Vocab size | Greedy search(dev & test) | Modified beam search(dev & test) | Fast beam search (dev & test) | Fast beam search LG (dev & test) | comments|
|-- | -- | -- | -- | -- | --|
|500 | 4.31 & 4.59 | 4.25 & 4.54 | 4.27 & 4.55 | 4.07 & 4.38 | --epoch 48 --avg 29|
The training command:
```bash
export CUDA_VISIBLE_DEVICES="4,5,6,7"
./pruned_transducer_stateless7_bbpe/train.py \
--world-size 4 \
--num-epochs 50 \
--start-epoch 1 \
--use-fp16 1 \
--max-duration 800 \
--bpe-model data/lang_bbpe_500/bbpe.model \
--exp-dir pruned_transducer_stateless7_bbpe/exp \
--lr-epochs 6 \
--master-port 12535
```
The decoding command:
```bash
for m in greedy_search modified_beam_search fast_beam_search fast_beam_search_LG; do
./pruned_transducer_stateless7_bbpe/decode.py \
--epoch 48 \
--avg 29 \
--exp-dir ./pruned_transducer_stateless7_bbpe/exp \
--max-sym-per-frame 1 \
--ngram-lm-scale 0.25 \
--ilme-scale 0.2 \
--bpe-model data/lang_bbpe_500/bbpe.model \
--max-duration 2000 \
--decoding-method $m
done
```
|
civitary/msbrew
|
civitary
| 2023-05-04T08:38:50Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-04T08:32:55Z |
---
license: creativeml-openrail-m
---
|
GoldfieldGeek/ppo-LL2-bad
|
GoldfieldGeek
| 2023-05-04T08:37:19Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-04T07:51:28Z |
---
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: 242.37 +/- 17.44
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
...
```
|
yemiancheng/like-model
|
yemiancheng
| 2023-05-04T08:27:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-05-04T05:15:02Z |
# readme
saving some models i like. i will collect them here for using(downloading) easily.
## why
- [x] sometimes i want to use it but fogget where to download it.
## life guarantee statement
If there is infringement, please temporarily notify me and I will delete it.
my email: `ymc-github@gmail.com` or `yemiancheng1993@163.com`
|
Aleksandar/electra-srb-ner
|
Aleksandar
| 2023-05-04T08:14:22Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"electra",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: electra-srb-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: sr
metric:
name: Accuracy
type: accuracy
value: 0.9568394937134688
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-srb-ner
This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3406
- Precision: 0.8934
- Recall: 0.9087
- F1: 0.9010
- Accuracy: 0.9568
## 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: 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3686 | 1.0 | 625 | 0.2108 | 0.8326 | 0.8494 | 0.8409 | 0.9335 |
| 0.1886 | 2.0 | 1250 | 0.1784 | 0.8737 | 0.8713 | 0.8725 | 0.9456 |
| 0.1323 | 3.0 | 1875 | 0.1805 | 0.8654 | 0.8870 | 0.8760 | 0.9468 |
| 0.0675 | 4.0 | 2500 | 0.2018 | 0.8736 | 0.8880 | 0.8807 | 0.9502 |
| 0.0425 | 5.0 | 3125 | 0.2162 | 0.8818 | 0.8945 | 0.8881 | 0.9512 |
| 0.0343 | 6.0 | 3750 | 0.2492 | 0.8790 | 0.8928 | 0.8859 | 0.9513 |
| 0.0253 | 7.0 | 4375 | 0.2562 | 0.8821 | 0.9006 | 0.8912 | 0.9525 |
| 0.0142 | 8.0 | 5000 | 0.2788 | 0.8807 | 0.9013 | 0.8909 | 0.9524 |
| 0.0114 | 9.0 | 5625 | 0.2793 | 0.8861 | 0.9002 | 0.8931 | 0.9534 |
| 0.0095 | 10.0 | 6250 | 0.2967 | 0.8887 | 0.9034 | 0.8960 | 0.9550 |
| 0.008 | 11.0 | 6875 | 0.2993 | 0.8899 | 0.9067 | 0.8982 | 0.9556 |
| 0.0048 | 12.0 | 7500 | 0.3215 | 0.8887 | 0.9038 | 0.8962 | 0.9545 |
| 0.0034 | 13.0 | 8125 | 0.3242 | 0.8897 | 0.9068 | 0.8982 | 0.9554 |
| 0.003 | 14.0 | 8750 | 0.3311 | 0.8884 | 0.9085 | 0.8983 | 0.9559 |
| 0.0025 | 15.0 | 9375 | 0.3383 | 0.8943 | 0.9062 | 0.9002 | 0.9562 |
| 0.0011 | 16.0 | 10000 | 0.3346 | 0.8941 | 0.9112 | 0.9026 | 0.9574 |
| 0.0015 | 17.0 | 10625 | 0.3362 | 0.8944 | 0.9081 | 0.9012 | 0.9567 |
| 0.001 | 18.0 | 11250 | 0.3464 | 0.8877 | 0.9100 | 0.8987 | 0.9559 |
| 0.0012 | 19.0 | 11875 | 0.3415 | 0.8944 | 0.9089 | 0.9016 | 0.9568 |
| 0.0005 | 20.0 | 12500 | 0.3406 | 0.8934 | 0.9087 | 0.9010 | 0.9568 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1
|
usix79/a2c-AntBulletEnv-v0
|
usix79
| 2023-05-04T08:08:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-04T08:07:27Z |
---
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: 962.48 +/- 180.68
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
nozmenoz/bella
|
nozmenoz
| 2023-05-04T08:06:36Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-04-29T07:37:29Z |
---
license: creativeml-openrail-m
---
|
zohaib99k/Bert_Arabic-SQuADv2-QA
|
zohaib99k
| 2023-05-04T07:42:02Z | 115 | 1 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"question-answering",
"ar",
"dataset:ZeyadAhmed/Arabic-SQuADv2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-04T07:37:13Z |
---
datasets:
- ZeyadAhmed/Arabic-SQuADv2.0
language:
- ar
metrics:
-
name: exact_match
type: exact_match
value: 65.12
-
name: F1
type: f1
value: 71.49
---
# AraElectra for Question Answering on Arabic-SQuADv2
This is the [AraElectra](https://huggingface.co/aubmindlab/araelectra-base-discriminator) model, fine-tuned using the [Arabic-SQuADv2.0](https://huggingface.co/datasets/ZeyadAhmed/Arabic-SQuADv2.0) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. with help of [AraElectra Classifier](https://huggingface.co/ZeyadAhmed/AraElectra-Arabic-SQuADv2-CLS) to predicted unanswerable question.
## Overview
**Language model:** AraElectra <br>
**Language:** Arabic <br>
**Downstream-task:** Extractive QA
**Training data:** Arabic-SQuADv2.0
**Eval data:** Arabic-SQuADv2.0 <br>
**Test data:** Arabic-SQuADv2.0 <br>
**Code:** [See More Info on Github](https://github.com/zeyadahmed10/Arabic-MRC)
**Infrastructure**: 1x Tesla K80
## Hyperparameters
```
batch_size = 8
n_epochs = 4
base_LM_model = "AraElectra"
learning_rate = 3e-5
optimizer = AdamW
padding = dynamic
```
## Online Demo on Arabic Wikipedia and User Provided Contexts
See model in action hosted on streamlit [](https://share.streamlit.io/wissamantoun/arabic-wikipedia-qa-streamlit/main)
## Usage
For best results use the AraBert [preprocessor](https://github.com/aub-mind/arabert/blob/master/preprocess.py) by aub-mind
```python
from transformers import ElectraForQuestionAnswering, ElectraForSequenceClassification, AutoTokenizer, pipeline
from preprocess import ArabertPreprocessor
prep_object = ArabertPreprocessor("araelectra-base-discriminator")
question = prep_object('ما هي جامعة الدول العربية ؟')
context = prep_object('''
جامعة الدول العربية هيمنظمة إقليمية تضم دولاً عربية في آسيا وأفريقيا.
ينص ميثاقها على التنسيق بين الدول الأعضاء في الشؤون الاقتصادية، ومن ضمنها العلاقات التجارية الاتصالات، العلاقات الثقافية، الجنسيات ووثائق وأذونات السفر والعلاقات الاجتماعية والصحة. المقر الدائم لجامعة الدول العربية يقع في القاهرة، عاصمة مصر (تونس من 1979 إلى 1990).
''')
# a) Get predictions
qa_modelname = 'ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA'
cls_modelname = 'ZeyadAhmed/AraElectra-Arabic-SQuADv2-CLS'
qa_pipe = pipeline('question-answering', model=qa_modelname, tokenizer=qa_modelname)
QA_input = {
'question': question,
'context': context
}
CLS_input = {
'text': question,
'text_pair': context
}
qa_res = qa_pipe(QA_input)
cls_res = cls_pipe(CLS_iput)
threshold = 0.5 #hyperparameter can be tweaked
## note classification results label0 probability it can be answered label1 probability can't be answered
## if label1 probability > threshold then consider the output of qa_res is empty string else take the qa_res
# b) Load model & tokenizer
qa_model = ElectraForQuestionAnswering.from_pretrained(qa_modelname)
cls_model = ElectraForSequenceClassification.from_pretrained(cls_modelname)
tokenizer = AutoTokenizer.from_pretrained(qa_modelname)
```
## Performance
Evaluated on the Arabic-SQuAD 2.0 test set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/) except changing in the preprocessing a little to fit the arabic language [the modified eval script](https://github.com/zeyadahmed10/Arabic-MRC/blob/main/evaluatev2.py).
```
"exact": 65.11555277951281,
"f1": 71.49042547237256,,
"total": 9606,
"HasAns_exact": 56.14535768645358,
"HasAns_f1": 67.79623803036668,
"HasAns_total": 5256,
"NoAns_exact": 75.95402298850574,
"NoAns_f1": 75.95402298850574,
"NoAns_total": 4350
```
|
StevenLimcorn/unsup-simcse-roberta-large-semeval2015-restaurants
|
StevenLimcorn
| 2023-05-04T07:31:28Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T13:10:10Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: unsup-simcse-roberta-large-semeval2015-restaurants
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. -->
# unsup-simcse-roberta-large-semeval2015-restaurants
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
redstonehero/rembremix_v10
|
redstonehero
| 2023-05-04T07:19:44Z | 29 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-04T06:56:24Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
---
|
redstonehero/breakdomain_2000
|
redstonehero
| 2023-05-04T07:18:40Z | 50 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-04T06:55:31Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
---
|
Theju/switch_low_2
|
Theju
| 2023-05-04T07:14:20Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-04T07:13:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: switch_low_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. -->
# switch_low_2
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Theju/switch_medium_2
|
Theju
| 2023-05-04T07:10:38Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-04T07:09:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: switch_medium_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. -->
# switch_medium_2
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
StevenLimcorn/unsup-simcse-roberta-large-semeval2015-laptops
|
StevenLimcorn
| 2023-05-04T07:06:14Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T13:00:59Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: unsup-simcse-roberta-large-semeval2015-laptops
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. -->
# unsup-simcse-roberta-large-semeval2015-laptops
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
Deekay213/floyd
|
Deekay213
| 2023-05-04T06:59:36Z | 0 | 0 | null |
[
"license:deepfloyd-if-license",
"region:us"
] | null | 2023-05-04T06:59:36Z |
---
license: deepfloyd-if-license
---
|
soumi-maiti/libri23mix_eend_ss
|
soumi-maiti
| 2023-05-04T06:49:28Z | 4 | 0 |
espnet
|
[
"espnet",
"audio",
"diarization",
"en",
"dataset:librimix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2023-05-04T06:34:06Z |
---
tags:
- espnet
- audio
- diarization
language: en
datasets:
- librimix
license: cc-by-4.0
---
## ESPnet2 DIAR model
### `soumi-maiti/libri23mix_eend_ss`
This model was trained by soumimaiti using librimix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout d837c97c88f13ffe655a30bcff93d814f212b225
pip install -e .
cd egs2/librimix/enh_diar23
./run.sh --skip_data_prep false --skip_train true --download_model soumi-maiti/libri23mix_eend_ss
```
## DIAR config
<details><summary>expand</summary>
```
config: conf/tuning/train_diar_enh_convtasnet_concat_feats_adapt.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/diar_enh_train_diar_enh_convtasnet_concat_feats_adapt
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: 4
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss_enh
- min
keep_nbest_models: 1
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 16
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param:
- ../enh_diar1/exp/diar_enh_train_diar_enh_convtasnet_concat_feats_raw/valid.loss_enh.best.pth
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 1
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/diar_enh_stats_8k/train/speech_shape
- exp/diar_enh_stats_8k/train/text_shape
- exp/diar_enh_stats_8k/train/speech_ref1_shape
- exp/diar_enh_stats_8k/train/speech_ref2_shape
- exp/diar_enh_stats_8k/train/speech_ref3_shape
- exp/diar_enh_stats_8k/train/noise_ref1_shape
valid_shape_file:
- exp/diar_enh_stats_8k/valid/speech_shape
- exp/diar_enh_stats_8k/valid/text_shape
- exp/diar_enh_stats_8k/valid/speech_ref1_shape
- exp/diar_enh_stats_8k/valid/speech_ref2_shape
- exp/diar_enh_stats_8k/valid/speech_ref3_shape
- exp/diar_enh_stats_8k/valid/noise_ref1_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 800
- 80000
- 80000
- 80000
- 80000
- 80000
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 24000
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train/wav.scp
- speech
- sound
- - dump/raw/train/espnet_rttm
- text
- rttm
- - dump/raw/train/spk1.scp
- speech_ref1
- sound
- - dump/raw/train/spk2.scp
- speech_ref2
- sound
- - dump/raw/train/spk3.scp
- speech_ref3
- sound
- - dump/raw/train/noise1.scp
- noise_ref1
- sound
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- sound
- - dump/raw/dev/espnet_rttm
- text
- rttm
- - dump/raw/dev/spk1.scp
- speech_ref1
- sound
- - dump/raw/dev/spk2.scp
- speech_ref2
- sound
- - dump/raw/dev/spk3.scp
- speech_ref3
- sound
- - dump/raw/dev/noise1.scp
- noise_ref1
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
eps: 1.0e-07
weight_decay: 0
scheduler: reducelronplateau
scheduler_conf:
mode: min
factor: 0.5
patience: 1
token_list: null
src_token_list: null
init: xavier_uniform
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
enh_criterions:
- name: si_snr
conf:
eps: 1.0e-07
wrapper: pit
wrapper_conf:
weight: 1.0
independent_perm: true
flexible_numspk: true
diar_num_spk: 3
diar_input_size: 128
enh_model_conf:
loss_type: si_snr
asr_model_conf:
ctc_weight: 0.5
interctc_weight: 0.0
ignore_id: -1
lsm_weight: 0.0
length_normalized_loss: false
report_cer: true
report_wer: true
sym_space: <space>
sym_blank: <blank>
extract_feats_in_collect_stats: true
st_model_conf:
stft_consistency: false
loss_type: mask_mse
mask_type: null
diar_model_conf:
diar_weight: 0.2
attractor_weight: 0.2
subtask_series:
- enh
- diar
model_conf:
calc_enh_loss: true
bypass_enh_prob: 0
use_preprocessor: true
token_type: bpe
bpemodel: null
src_token_type: bpe
src_bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
enh_encoder: conv
enh_encoder_conf:
channel: 512
kernel_size: 16
stride: 8
enh_separator: tcn_nomask
enh_separator_conf:
layer: 8
stack: 3
bottleneck_dim: 128
hidden_dim: 512
kernel: 3
causal: false
norm_type: gLN
enh_decoder: conv
enh_decoder_conf:
channel: 512
kernel_size: 16
stride: 8
enh_mask_module: multi_mask
enh_mask_module_conf:
max_num_spk: 3
mask_nonlinear: relu
bottleneck_dim: 128
frontend: default
frontend_conf: {}
specaug: null
specaug_conf: {}
normalize: utterance_mvn
normalize_conf: {}
asr_preencoder: null
asr_preencoder_conf: {}
asr_encoder: rnn
asr_encoder_conf: {}
asr_postencoder: null
asr_postencoder_conf: {}
asr_decoder: rnn
asr_decoder_conf: {}
st_preencoder: null
st_preencoder_conf: {}
st_encoder: rnn
st_encoder_conf: {}
st_postencoder: null
st_postencoder_conf: {}
st_decoder: rnn
st_decoder_conf: {}
st_extra_asr_decoder: rnn
st_extra_asr_decoder_conf: {}
st_extra_mt_decoder: rnn
st_extra_mt_decoder_conf: {}
diar_frontend: default
diar_frontend_conf:
hop_length: 64
fs: 8000
diar_specaug: null
diar_specaug_conf: {}
diar_normalize: utterance_mvn
diar_normalize_conf: {}
diar_encoder: transformer
diar_encoder_conf:
input_layer: conv2d8
num_blocks: 4
linear_units: 512
dropout_rate: 0.1
output_size: 256
attention_heads: 4
attention_dropout_rate: 0.1
diar_decoder: linear
diar_decoder_conf: {}
label_aggregator: label_aggregator
label_aggregator_conf:
win_length: 256
hop_length: 64
diar_attractor: rnn
diar_attractor_conf:
unit: 256
layer: 1
dropout: 0.0
attractor_grad: true
required:
- output_dir
version: '202205'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Ar4ikov/wav2vec2_bert_fusion_iemocap_3
|
Ar4ikov
| 2023-05-04T06:37:36Z | 52 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"feature-extraction",
"generated_from_trainer",
"custom_code",
"region:us"
] |
feature-extraction
| 2023-05-04T06:21:09Z |
---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2_bert_fusion_iemocap_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_bert_fusion_iemocap_3
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 16
- 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
- num_epochs: 10
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
|
jeremyvictor/mt5-base-gecid23-e3
|
jeremyvictor
| 2023-05-04T06:21:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-04T04:05:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-gecid23-e3
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-base-gecid23-e3
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2913
- Rouge1: 64.5987
- Rouge2: 58.284
- Rougel: 64.5263
- Rougelsum: 64.5192
- Gen Len: 18.7512
## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.5553 | 0.25 | 221 | 0.5815 | 58.7873 | 48.3787 | 58.6622 | 58.6428 | 18.7486 |
| 0.6944 | 0.5 | 442 | 0.5010 | 60.225 | 50.8407 | 60.1109 | 60.0966 | 18.7418 |
| 0.5891 | 0.75 | 663 | 0.4477 | 61.4891 | 53.2811 | 61.4099 | 61.4089 | 18.7588 |
| 0.5145 | 1.0 | 884 | 0.3926 | 62.3704 | 54.3562 | 62.255 | 62.252 | 18.7520 |
| 0.3682 | 1.25 | 1105 | 0.3805 | 62.4976 | 54.8233 | 62.4265 | 62.4327 | 18.7622 |
| 0.3332 | 1.5 | 1326 | 0.3471 | 63.2736 | 56.0263 | 63.1982 | 63.1901 | 18.7495 |
| 0.3097 | 1.75 | 1547 | 0.3173 | 63.5672 | 56.5358 | 63.4813 | 63.4756 | 18.7541 |
| 0.2958 | 2.0 | 1768 | 0.3219 | 63.8092 | 57.1715 | 63.7764 | 63.7692 | 18.7512 |
| 0.1901 | 2.25 | 1989 | 0.3053 | 64.1292 | 57.5296 | 64.052 | 64.0478 | 18.7533 |
| 0.1861 | 2.5 | 2210 | 0.3018 | 64.4658 | 58.0416 | 64.3975 | 64.3918 | 18.7537 |
| 0.1696 | 2.75 | 2431 | 0.2928 | 64.5337 | 58.1328 | 64.4735 | 64.4619 | 18.7507 |
| 0.1691 | 3.0 | 2652 | 0.2913 | 64.5987 | 58.284 | 64.5263 | 64.5192 | 18.7512 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
adiga20/git-base-pokemon
|
adiga20
| 2023-05-04T05:44:11Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-04T05:44:11Z |
---
license: creativeml-openrail-m
---
|
StevenLimcorn/unsup-promcse-bert-base-uncased-semeval2015-restaurants
|
StevenLimcorn
| 2023-05-04T05:42:15Z | 98 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T17:04:57Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: semeval-unsup-promcse-bert-base-uncased-semeval2015-restaurants
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. -->
# semeval-unsup-promcse-bert-base-uncased-semeval2015-restaurants
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
tawfiq/text_sumurization
|
tawfiq
| 2023-05-04T05:41:22Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-04T05:41:22Z |
---
license: creativeml-openrail-m
---
|
StevenLimcorn/unsup-promcse-bert-base-uncased-semeval2015-laptops
|
StevenLimcorn
| 2023-05-04T05:41:13Z | 94 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T17:00:54Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: semeval-unsup-promcse-bert-base-uncased-semeval2015-laptops
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. -->
# semeval-unsup-promcse-bert-base-uncased-semeval2015-laptops
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
StevenLimcorn/unsup-promcse-bert-base-uncased-facebook-election-ads
|
StevenLimcorn
| 2023-05-04T05:40:44Z | 97 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T17:03:37Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: semeval-unsup-promcse-bert-base-uncased-facebook-election-ads
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. -->
# semeval-unsup-promcse-bert-base-uncased-facebook-election-ads
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
StevenLimcorn/unsup-promcse-bert-base-uncased-semeval2016-restaurants
|
StevenLimcorn
| 2023-05-04T05:34:03Z | 92 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T17:06:28Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: semeval-unsup-promcse-bert-base-uncased-semeval2016-restaurants
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. -->
# semeval-unsup-promcse-bert-base-uncased-semeval2016-restaurants
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
StevenLimcorn/unsup-promcse-bert-base-uncased-semeval2014-restaurants
|
StevenLimcorn
| 2023-05-04T05:32:50Z | 88 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T17:02:15Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: semeval-unsup-promcse-bert-base-uncased-semeval2014-restaurants
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. -->
# semeval-unsup-promcse-bert-base-uncased-semeval2014-restaurants
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
StevenLimcorn/unsup-promcse-bert-base-uncased-semeval2014-laptops
|
StevenLimcorn
| 2023-05-04T05:32:00Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T17:07:54Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: semeval-unsup-promcse-bert-base-uncased-semeval2014-laptops
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. -->
# semeval-unsup-promcse-bert-base-uncased-semeval2014-laptops
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
imania/amir_take_home_result-2023_05_03-22_33_43
|
imania
| 2023-05-04T05:03:42Z | 179 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-04T04:52:50Z |
---
language:
- en
library_name: transformers
pipeline_tag: text-classification
---
|
Khh143/Kinkalow
|
Khh143
| 2023-05-04T05:00:31Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-04T04:20:24Z |
---
license: creativeml-openrail-m
---
|
P1NHE4D/whisper-medium-nn-v3
|
P1NHE4D
| 2023-05-04T04:57:41Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"nn",
"dataset:norwegian-parliament",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-03T12:25:27Z |
---
language:
- nn
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- norwegian-parliament
metrics:
- wer
model-index:
- name: whisper-medium-nn-v3
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Stortingskorpuset
type: norwegian-parliament
config: default
split: validation
args: default
metrics:
- name: Wer
type: wer
value: 11.337582785573966
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-nn-v3
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Stortingskorpuset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2116
- Wer: 11.3376
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 8000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4413 | 0.25 | 2000 | 0.4447 | 26.7707 |
| 0.1945 | 1.1 | 4000 | 0.3042 | 17.8344 |
| 0.1013 | 1.35 | 6000 | 0.2421 | 14.2138 |
| 0.0308 | 2.2 | 8000 | 0.2116 | 11.3376 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
|
akdeniz27/deberta-v2-xlarge-cuad
|
akdeniz27
| 2023-05-04T04:52:54Z | 121 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"question-answering",
"en",
"dataset:cuad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- cuad
---
# DeBERTa v2 XLarge Model fine-tuned with CUAD dataset
This model is the fine-tuned version of "DeBERTa v2 XLarge"
using CUAD dataset https://huggingface.co/datasets/cuad
Link for model checkpoint: https://github.com/TheAtticusProject/cuad
For the use of the model with CUAD: https://github.com/marshmellow77/cuad-demo
and https://huggingface.co/spaces/akdeniz27/contract-understanding-atticus-dataset-demo
|
chastelove/distilbert-base-uncased_emotion_ft_0504
|
chastelove
| 2023-05-04T04:44:17Z | 103 | 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
| 2023-05-04T04:22:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
model-index:
- name: distilbert-base-uncased_emotion_ft_0504
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: 0.935
- name: F1
type: f1
value: 0.9353661273711807
- name: Precision
type: precision
value: 0.9062644261189533
---
<!-- 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_emotion_ft_0504
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: 0.1552
- Accuracy: 0.935
- F1: 0.9354
- Precision: 0.9063
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|
| 0.7741 | 1.0 | 250 | 0.2686 | 0.909 | 0.9070 | 0.8911 |
| 0.2073 | 2.0 | 500 | 0.1767 | 0.9315 | 0.9319 | 0.9013 |
| 0.1397 | 3.0 | 750 | 0.1581 | 0.935 | 0.9353 | 0.9081 |
| 0.1123 | 4.0 | 1000 | 0.1552 | 0.935 | 0.9354 | 0.9063 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.11.0
|
douglasfaisal/granularity-legal-reranker-cross-encoder-indobert-base-p2
|
douglasfaisal
| 2023-05-04T04:42:49Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"legal",
"id",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-04T04:30:07Z |
---
license: mit
language:
- id
tags:
- legal
---
|
shawt100/shawtsanders
|
shawt100
| 2023-05-04T04:14:50Z | 36 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"dataset:OpenAssistant/oasst1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-04T03:46:42Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
datasets:
- OpenAssistant/oasst1
metrics:
- character
library_name: diffusers
pipeline_tag: text-to-image
---
### shawtsanders Dreambooth model trained by shawt100 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:
|
joseph-t/purrfect-ai-test
|
joseph-t
| 2023-05-04T03:44:02Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-04T03:44:02Z |
---
license: creativeml-openrail-m
---
|
muwenxin/autotrain-xgwbishe1-55280129011
|
muwenxin
| 2023-05-04T03:36:20Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain",
"summarization",
"en",
"dataset:muwenxin/autotrain-data-xgwbishe1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-05-04T03:33:39Z |
---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- muwenxin/autotrain-data-xgwbishe1
co2_eq_emissions:
emissions: 1.082559894922486
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 55280129011
- CO2 Emissions (in grams): 1.0826
## Validation Metrics
- Loss: 3.334
- Rouge1: 15.894
- Rouge2: 3.281
- RougeL: 11.775
- RougeLsum: 13.844
- Gen Len: 20.000
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/muwenxin/autotrain-xgwbishe1-55280129011
```
|
4bit/oasst-llama13b-4bit-128g
|
4bit
| 2023-05-04T03:10:55Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-04T02:57:06Z |
https://wandb.ai/open-assistant/supervised-finetuning/runs/lguuq2c1
Quantized from https://huggingface.co/dvruette/oasst-llama-13b-2-epochs
GGML Version: https://huggingface.co/Black-Engineer/oasst-llama13b-ggml-q4
|
4bit/koala-13B-GPTQ-4bit-128g
|
4bit
| 2023-05-04T02:54:46Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"koala",
"ShareGPT",
"gptq",
"dataset:RyokoAI/ShareGPT52K",
"dataset:Hello-SimpleAI/HC3",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-05-04T02:48:14Z |
---
license: other
library_name: transformers
pipeline_tag: text-generation
datasets:
- RyokoAI/ShareGPT52K
- Hello-SimpleAI/HC3
tags:
- koala
- ShareGPT
- llama
- gptq
inference: false
---
# Koala: A Dialogue Model for Academic Research
This repo contains the weights of the Koala 13B model produced at Berkeley. It is the result of combining the diffs from https://huggingface.co/young-geng/koala with the original Llama 13B model.
This version has then been quantized to 4-bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
## My Koala repos
I have the following Koala model repositories available:
**13B models:**
* [Unquantized 13B model in HF format](https://huggingface.co/TheBloke/koala-13B-HF)
* [GPTQ quantized 4bit 13B model in `pt` and `safetensors` formats](https://huggingface.co/TheBloke/koala-13B-GPTQ-4bit-128g)
* [GPTQ quantized 4bit 13B model in GGML format for `llama.cpp`](https://huggingface.co/TheBloke/koala-13B-GPTQ-4bit-128g-GGML)
**7B models:**
* [Unquantized 7B model in HF format](https://huggingface.co/TheBloke/koala-7B-HF)
* [Unquantized 7B model in GGML format for llama.cpp](https://huggingface.co/TheBloke/koala-7b-ggml-unquantized)
* [GPTQ quantized 4bit 7B model in `pt` and `safetensors` formats](https://huggingface.co/TheBloke/koala-7B-GPTQ-4bit-128g)
* [GPTQ quantized 4bit 7B model in GGML format for `llama.cpp`](https://huggingface.co/TheBloke/koala-7B-GPTQ-4bit-128g-GGML)
## Provided files
Three model files are provided. You don't need all three - choose the one that suits your needs best!
Details of the files provided:
* `koala-13B-4bit-128g.pt`
* pt format file, created with the latest [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa) code.
* Command to create:
* `python3 llama.py koala-13B-HF c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save koala-13B-4bit-128g.pt`
* `koala-13B-4bit-128g.safetensors`
* newer `safetensors` format, with improved file security, created with the latest [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa) code.
* Command to create:
* `python3 llama.py koala-13B-HF c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors koala-13B-4bit-128g.safetensors`
* `koala-13B-4bit-128g.no-act-order.ooba.pt`
* `pt` format file, created with [oobabooga's older CUDA fork of GPTQ-for-LLaMa](https://github.com/oobabooga/GPTQ-for-LLaMa).
* This file is included primarily for Windows users, as it can be used without needing to compile the latest GPTQ-for-LLaMa code.
* It should hopefully therefore work with one-click-installers on Windows, which include the older GPTQ-for-LLaMa code.
* The older GPTQ code does not support all the latest features, so the quality may be fractionally lower.
* Command to create:
* `python3 llama.py koala-13B-HF c4 --wbits 4 --true-sequential --groupsize 128 --save koala-13B-4bit-128g.no-act-order.ooba.pt`
## How to run in `text-generation-webui`
File `koala-13B-4bit-128g.no-act-order.ooba.pt` can be loaded the same as any other GPTQ file, without requiring any updates to [oobaboogas text-generation-webui](https://github.com/oobabooga/text-generation-webui).
The other two model files were created with the latest GPTQ code, and require that the latest GPTQ-for-LLaMa is used inside the UI.
Here are the commands I used to clone the Triton branch of GPTQ-for-LLaMa, clone text-generation-webui, and install GPTQ into the UI:
```
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa
git clone https://github.com/oobabooga/text-generation-webui
mkdir -p text-generation-webui/repositories
ln -s GPTQ-for-LLaMa text-generation-webui/repositories/GPTQ-for-LLaMa
```
Then install this model into `text-generation-webui/models` and launch the UI as follows:
```
cd text-generation-webui
python server.py --model koala-13B-GPTQ-4bit-128g --wbits 4 --groupsize 128 --model_type Llama # add any other command line args you want
```
The above commands assume you have installed all dependencies for GPTQ-for-LLaMa and text-generation-webui. Please see their respective repositories for further information.
If you are on Windows, or cannot use the Triton branch of GPTQ for any other reason, you can instead use the CUDA branch:
```
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa -b cuda
cd GPTQ-for-LLaMa
python setup_cuda.py install
```
Then link that into `text-generation-webui/repositories` as described above.
Or just use `koala-13B-4bit-128g.no-act-order.ooba.pt` as mentioned above.
## How the Koala delta weights were merged
The Koala delta weights were originally merged using the following commands, producing [koala-13B-HF](https://huggingface.co/TheBloke/koala-13B-HF):
```
git clone https://github.com/young-geng/EasyLM
git clone https://huggingface.co/TheBloke/llama-13b
mkdir koala_diffs && cd koala_diffs && wget https://huggingface.co/young-geng/koala/resolve/main/koala_13b_diff_v2
cd EasyLM
PYTHON_PATH="${PWD}:$PYTHONPATH" python \
-m EasyLM.models.llama.convert_torch_to_easylm \
--checkpoint_dir=/content/llama-13b \
--output_file=/content/llama-13b-LM \
--streaming=True
PYTHON_PATH="${PWD}:$PYTHONPATH" python \
-m EasyLM.scripts.diff_checkpoint --recover_diff=True \
--load_base_checkpoint='params::/content/llama-13b-LM' \
--load_target_checkpoint='params::/content/koala_diffs/koala_13b_diff_v2' \
--output_file=/content/koala_13b.diff.weights \
--streaming=True
PYTHON_PATH="${PWD}:$PYTHONPATH" python \
-m EasyLM.models.llama.convert_easylm_to_hf --model_size=13b \
--output_dir=/content/koala-13B-HF \
--load_checkpoint='params::/content/koala_13b.diff.weights' \
--tokenizer_path=/content/llama-13b/tokenizer.model
```
## Further info
Check out the following links to learn more about the Berkeley Koala model.
* [Blog post](https://bair.berkeley.edu/blog/2023/04/03/koala/)
* [Online demo](https://koala.lmsys.org/)
* [EasyLM: training and serving framework on GitHub](https://github.com/young-geng/EasyLM)
* [Documentation for running Koala locally](https://github.com/young-geng/EasyLM/blob/main/docs/koala.md)
## License
The model weights are intended for academic research only, subject to the
[model License of LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md),
[Terms of Use of the data generated by OpenAI](https://openai.com/policies/terms-of-use),
and [Privacy Practices of ShareGPT](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb).
Any other usage of the model weights, including but not limited to commercial usage, is strictly prohibited.
|
Smoden/pinocchio_diff_lora_1500
|
Smoden
| 2023-05-04T02:38:17Z | 4 | 0 |
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
| 2023-05-04T00:47:15Z |
---
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/pinocchio_diff_lora_1500
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.
|
platzi/platzi-distilroberta-base-mrpc-glue-cristian-durango
|
platzi
| 2023-05-04T01:52:35Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-04T01:33:56Z |
---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: platzi-distilroberta-base-mrpc-glue-cristian-durango
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8259803921568627
- name: F1
type: f1
value: 0.8794567062818336
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-distilroberta-base-mrpc-glue-cristian-durango
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets.
It achieves the following results on the evaluation set:
- Loss: 0.4245
- Accuracy: 0.8260
- F1: 0.8795
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5318 | 1.09 | 500 | 0.4245 | 0.8260 | 0.8795 |
| 0.3704 | 2.18 | 1000 | 0.6045 | 0.8309 | 0.8739 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
saiyoung/cobauli
|
saiyoung
| 2023-05-04T01:48:32Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-03T17:56:37Z |
---
license: creativeml-openrail-m
---
|
LottePeisch/RevAnimated-Diffusers
|
LottePeisch
| 2023-05-04T01:42:13Z | 133 | 3 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-04-30T09:56:58Z |
---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
---
This is a Diffusers version of Rev Animated version 1.2.2 specifically for InvokeAI users. The original checkpoint with all the info on the model can be found here:
https://civitai.com/models/7371/rev-animated
The differences include:
- Changed the scheduler to EulerAncestralDiscreteScheduler, works very well with Euler_A.
- Updated the text_encoder config.json file setting num_hidden_layers to 11 instead of the default 12. This is the equivalent of 'Clip Skp 2' in Auto1111 as I understand it and have tested it. Please let me know on the InvokeAI Discord if you encounter issues.
- Don't expect it to build the exact same image from the exact same seed as you would in Auto. Invoke and Auto are very different from another, and diffusers are even more different. You should, however, get some awesome images.
- I'm sharing this because diffusers are amazing and I think more people should use them. ;)
- Comes with the default vae used during the conversion into diffusers format. The original author recommends a few different vaes at the link above, I wanted you to be able to mix and match. The examples below were made without a vae.
Here are a few example images:
|
juan-barsce/my_awesome_eli5_clm-model
|
juan-barsce
| 2023-05-04T01:31:51Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-04T01:14:01Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: juan-barsce/my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# juan-barsce/my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.7254
- Validation Loss: 3.7653
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.9035 | 3.7936 | 0 |
| 3.7854 | 3.7763 | 1 |
| 3.7254 | 3.7653 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ToddGoldfarb/Cadet-Medium
|
ToddGoldfarb
| 2023-05-04T01:31:07Z | 47 | 2 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"conversational",
"en",
"dataset:allenai/soda",
"license:openrail",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-03T02:36:53Z |
---
license: openrail
datasets:
- allenai/soda
language:
- en
pipeline_tag: conversational
---
# What is Cadet-Medium?
Inspired by Allen AI's **Cosmo-XL**, **Cadet-Medium** is a somewhat small conversational model trained off of the **SODA** dataset. **Cadet-Medium** is intended for inference at the edge (on something as small as a 2GB RAM Raspberry Pi).
**Cadet-Medium** is trained off of the **t5-base** pretrained model from Google.
If you have any questions, or any comments on improvements, please contact me at: **tcgoldfarb@gmail.com**
# Google Colab Link
Here is the link to the Google Colab file, where I walk through the process of training the model and using the SODA public dataset from AI2.
https://colab.research.google.com/drive/1uekZ0gO3GqjPwno16tV1A4Gitrl7p3ur?usp=sharing
# Get Started With Cadet-Medium
Use the code snippet below to get started with Cadet-Medium!
```
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import colorful as cf
cf.use_true_colors()
cf.use_style('monokai')
class CadetMedAgent:
def __init__(self):
print(cf.bold | cf.purple("Waking up Cadet-Medium..."))
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.tokenizer = AutoTokenizer.from_pretrained("t5-base", model_max_length=512)
self.model = AutoModelForSeq2SeqLM.from_pretrained("ToddGoldfarb/Cadet-Medium", low_cpu_mem_usage=True).to(self.device)
self.conversation_history = ""
def observe(self, observation):
self.conversation_history = self.conversation_history + observation
# The number 400 below is just a truncation safety net. It leaves room for 112 input tokens.
if len(self.conversation_history) > 400:
self.conversation_history = self.conversation_history[112:]
def set_input(self, situation_narrative="", role_instruction=""):
input_text = "dialog: "
if situation_narrative != "":
input_text = input_text + situation_narrative
if role_instruction != "":
input_text = input_text + " <SEP> " + role_instruction
input_text = input_text + " <TURN> " + self.conversation_history
# Uncomment the line below to see what is fed to the model.
# print(input_text)
return input_text
def generate(self, situation_narrative, role_instruction, user_response):
user_response = user_response + " <TURN> "
self.observe(user_response)
input_text = self.set_input(situation_narrative, role_instruction)
inputs = self.tokenizer([input_text], return_tensors="pt").to(self.device)
# I encourage you to change the hyperparameters of the model! Start by trying to modify the temperature.
outputs = self.model.generate(inputs["input_ids"], max_new_tokens=512, temperature=1, top_p=.95,
do_sample=True)
cadet_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
added_turn = cadet_response + " <TURN> "
self.observe(added_turn)
return cadet_response
def reset_history(self):
self.conversation_history = []
def run(self):
def get_valid_input(prompt, default):
while True:
user_input = input(prompt)
if user_input in ["Y", "N", "y", "n"]:
return user_input
if user_input == "":
return default
while True:
continue_chat = ""
# MODIFY THESE STRINGS TO YOUR LIKING :)
situation_narrative = "Imagine you are Cadet-Medium talking to ???."
role_instruction = "You are Cadet-Medium, and you are talking to ???."
self.chat(situation_narrative, role_instruction)
continue_chat = get_valid_input(cf.purple("Start a new conversation with new setup? [Y/N]:"), "Y")
if continue_chat in ["N", "n"]:
break
print(cf.blue("CM: See you!"))
def chat(self, situation_narrative, role_instruction):
print(cf.green(
"Cadet-Medium is running! Input [RESET] to reset the conversation history and [END] to end the conversation."))
while True:
user_input = input("You: ")
if user_input == "[RESET]":
self.reset_history()
print(cf.green("[Conversation history cleared. Chat with Cadet-Medium!]"))
continue
if user_input == "[END]":
break
response = self.generate(situation_narrative, role_instruction, user_input)
print(cf.blue("CM: " + response))
def main():
print(cf.bold | cf.blue("LOADING MODEL"))
CadetMed = CadetMedAgent()
CadetMed.run()
if __name__ == '__main__':
main()
```
# Citations and Special Thanks
Special thanks to Hyunwoo Kim for discussing with me the best way to use the SODA dataset. If you haven't looked into their work with SODA, Prosocial-Dialog, or COSMO, I recommend you do so! As well, read the paper on SODA!
The article is listed below.
```
@article{kim2022soda,
title={SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization},
author={Hyunwoo Kim and Jack Hessel and Liwei Jiang and Peter West and Ximing Lu and Youngjae Yu and Pei Zhou and Ronan Le Bras and Malihe Alikhani and Gunhee Kim and Maarten Sap and Yejin Choi},
journal={ArXiv},
year={2022},
volume={abs/2212.10465}
}
```
|
rcugarte/genfonts
|
rcugarte
| 2023-05-04T01:28:39Z | 0 | 0 | null |
[
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dataset:rcugarte/genfonts_data",
"region:us"
] |
text-to-image
| 2023-05-04T01:19:53Z |
---
datasets:
- rcugarte/genfonts_data
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
---
|
junelee/wizard-vicuna-13b
|
junelee
| 2023-05-04T01:23:39Z | 2,682 | 77 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-03T20:46:24Z |
https://github.com/melodysdreamj/WizardVicunaLM
|
ZyXin/ppo-Pyramids_Training
|
ZyXin
| 2023-05-04T01:14:39Z | 3 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-05-04T01:14:34Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: ZyXin/ppo-Pyramids_Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
msr10/en_esg_ner
|
msr10
| 2023-05-04T01:07:29Z | 3 | 0 |
spacy
|
[
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] |
token-classification
| 2023-05-04T01:06:49Z |
---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_esg_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9211469534
- name: NER Recall
type: recall
value: 0.9191702432
- name: NER F Score
type: f_score
value: 0.9201575367
---
| Feature | Description |
| --- | --- |
| **Name** | `en_esg_ner` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.5.2,<3.6.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (3 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `Environmental`, `Governance`, `Social` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 92.02 |
| `ENTS_P` | 92.11 |
| `ENTS_R` | 91.92 |
| `TRANSFORMER_LOSS` | 14719.85 |
| `NER_LOSS` | 10789.72 |
|
1008611sS/111
|
1008611sS
| 2023-05-04T01:06:04Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2023-05-04T01:06:04Z |
---
license: bigscience-bloom-rail-1.0
---
|
DurangoFon/vit_model
|
DurangoFon
| 2023-05-04T00:55:55Z | 216 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-05-04T00:07:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9924812030075187
---
<!-- 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_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0189
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1345 | 3.85 | 500 | 0.0189 | 0.9925 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Multi-Domain-Expert-Learning/expert-pubmed_central
|
Multi-Domain-Expert-Learning
| 2023-05-04T00:43:15Z | 150 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"dataset:Multi-Domain-Expert-Layers/pubmed_central",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-03T19:14:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- Multi-Domain-Expert-Layers/pubmed_central
metrics:
- accuracy
model-index:
- name: layer_9,10,11,12,13
results:
- task:
type: text-generation
name: Causal Language Modeling
dataset:
name: Multi-Domain-Expert-Layers/pubmed_central
type: Multi-Domain-Expert-Layers/pubmed_central
split: None
metrics:
- type: accuracy
value: 0.5767534246575342
name: Accuracy
---
<!-- 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. -->
# layer_9,10,11,12,13
This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on the Multi-Domain-Expert-Layers/pubmed_central dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0227
- Accuracy: 0.5768
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0567 | 0.0 | 200 | 2.0533 | 0.5717 |
| 2.041 | 0.01 | 400 | 2.0438 | 0.5733 |
| 2.0496 | 0.01 | 600 | 2.0361 | 0.5749 |
| 2.0194 | 0.02 | 800 | 2.0276 | 0.5761 |
| 2.0338 | 0.02 | 1000 | 2.0227 | 0.5768 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
## Wandb Report
https://wandb.ai/ontocord/pythia-1b-deduped-layer-test-pubmed_central/runs/yy3pwx0o
|
ZyXin/ppo-SnowballTarget
|
ZyXin
| 2023-05-04T00:32:00Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-05-04T00:31:54Z |
---
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: ZyXin/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kreepy/poca-SoccerTwos
|
kreepy
| 2023-05-04T00:18:40Z | 29 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-05-03T20:55:50Z |
---
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: kreepy/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
EinsZwo/en-to-de_coref_words_moreEpochs
|
EinsZwo
| 2023-05-03T23:21:37Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-03T22:13:31Z |
---
license: cc-by-4.0
tags:
- generated_from_keras_callback
model-index:
- name: EinsZwo/en-to-de_coref_words_moreEpochs
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. -->
# EinsZwo/en-to-de_coref_words_moreEpochs
This model is a fine-tuned version of [EinsZwo/en-to-de_coref_words](https://huggingface.co/EinsZwo/en-to-de_coref_words) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7690
- Validation Loss: 1.4251
- Epoch: 6
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 40649, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.2116 | 1.3502 | 0 |
| 1.0925 | 1.3576 | 1 |
| 0.9982 | 1.3748 | 2 |
| 0.9210 | 1.3966 | 3 |
| 0.8575 | 1.4064 | 4 |
| 0.8064 | 1.4174 | 5 |
| 0.7690 | 1.4251 | 6 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
sqllama/lora-spider-dono
|
sqllama
| 2023-05-03T22:36:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-04-30T01:00:50Z |
## Setup Notes
For this model, a VM with 2 T4 GPUs was used.
Note 1. Output directory was initially lora-alpaca and then contents were moved to new folder when initializing git repository.
## Log
(sqltest) chrisdono@deep-learning-duo-t4-3:~/alpaca-lora$ WORLD_SIZE=2 CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc_per_node=2 --master_port=1234 finetune.py --base_model 'decapoda-research/llama-7b-hf' --data_path 'spider' --output_dir './lora-alpaca' --num_epochs 10 --batch_size 32 --micro_batch_size 16 --learning_rate '9e-5' --add_eos_token
Adding last loss values not included in trainer json file from last checkpoint.
{'loss': 0.241, 'learning_rate': 1.0040816326530613e-05, 'epoch': 8.98}
{'loss': 0.2343, 'learning_rate': 9.42857142857143e-06, 'epoch': 9.04}
{'loss': 0.2376, 'learning_rate': 8.816326530612245e-06, 'epoch': 9.11}
{'loss': 0.2355, 'learning_rate': 8.204081632653062e-06, 'epoch': 9.17}
{'loss': 0.229, 'learning_rate': 7.591836734693877e-06, 'epoch': 9.24}
{'loss': 0.2325, 'learning_rate': 6.979591836734694e-06, 'epoch': 9.3}
{'loss': 0.24, 'learning_rate': 6.367346938775511e-06, 'epoch': 9.36}
{'loss': 0.2438, 'learning_rate': 5.755102040816327e-06, 'epoch': 9.43}
{'loss': 0.2391, 'learning_rate': 5.142857142857143e-06, 'epoch': 9.49}
{'loss': 0.2351, 'learning_rate': 4.530612244897959e-06, 'epoch': 9.55}
{'loss': 0.2289, 'learning_rate': 3.9183673469387755e-06, 'epoch': 9.62}
{'loss': 0.2294, 'learning_rate': 3.3061224489795924e-06, 'epoch': 9.68}
{'loss': 0.2344, 'learning_rate': 2.693877551020408e-06, 'epoch': 9.75}
{'loss': 0.2358, 'learning_rate': 2.0816326530612247e-06, 'epoch': 9.81}
{'loss': 0.2365, 'learning_rate': 1.469387755102041e-06, 'epoch': 9.87}
{'loss': 0.2309, 'learning_rate': 8.571428571428572e-07, 'epoch': 9.94}
{'loss': 0.2438, 'learning_rate': 2.4489795918367347e-07, 'epoch': 10.0}
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1570
{'train_runtime': 17144.6766, 'train_samples_per_second': 2.916, 'train_steps_per_second': 0.092, 'train_loss': 0.41175747267000234, 'epoch': 10.0}
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1570
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1570
/1570 [4:45:44<00:00, 10.92s/it]
|
andli28/rl_course_vizdoom_health_gathering_supreme
|
andli28
| 2023-05-03T22:03:33Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-03T21:15:26Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.46 +/- 4.74
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r andli28/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
jajsmith/dsn_afrispeech
|
jajsmith
| 2023-05-03T21:54:22Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"en",
"dataset:tobiolatunji/afrispeech-200",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-03T17:17:19Z |
---
language:
- en
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- tobiolatunji/afrispeech-200
model-index:
- name: Whisper Small En - Owos
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. -->
# Whisper Small En - Owos
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the AfriSpeech_j dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6865
- eval_wer: 29.3845
- eval_runtime: 1774.5798
- eval_samples_per_second: 1.691
- eval_steps_per_second: 0.211
- epoch: 0.06
- step: 250
## 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
- 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
### Framework versions
- Transformers 4.27.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BrianPistar/focplanet
|
BrianPistar
| 2023-05-03T21:40:55Z | 5 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-03T21:34:27Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### focplanet Dreambooth model trained by BrianPistar with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook
Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)!
To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars).
Sample pictures of this concept:
|
vldnechai/poca-SoccerTwos
|
vldnechai
| 2023-05-03T21:39:07Z | 36 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-05-03T21:37:51Z |
---
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: vldnechai/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jploski/llama-7b-hf
|
jploski
| 2023-05-03T21:32:32Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-03T21:23:14Z |
---
license: other
---
Note: this is yahma/llama-7b-hf with checkpoint shards split into smaller files in order to enable loading in restricted memory environments like free Google Colab. The remaining description below is copied from yahma/llama-7b-hf.
LLaMA-7B converted to work with git head Transformers/HuggingFace on April 8, 2023. This version should resolve the EOS token issues.
This is under a special license, please see the LICENSE file for details.
This contains the weights for the LLaMA-7b model. This model is under a non-commercial license (see the LICENSE file).
You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form) but either lost your copy of the weights or got some trouble converting them to the Transformers format.
--
license: other
---
# LLaMA Model Card
## Model details
**Organization developing the model**
The FAIR team of Meta AI.
**Model date**
LLaMA was trained between December. 2022 and Feb. 2023.
**Model version**
This is version 1 of the model.
**Model type**
LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters.
**Paper or resources for more information**
More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/.
**Citations details**
https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/
**License**
Non-commercial bespoke license
**Where to send questions or comments about the model**
Questions and comments about LLaMA can be sent via the [GitHub repository](https://github.com/facebookresearch/llama) of the project , by opening an issue.
## Intended use
**Primary intended uses**
The primary use of LLaMA is research on large language models, including:
exploring potential applications such as question answering, natural language understanding or reading comprehension,
understanding capabilities and limitations of current language models, and developing techniques to improve those,
evaluating and mitigating biases, risks, toxic and harmful content generations, hallucinations.
**Primary intended users**
The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence.
**Out-of-scope use cases**
LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers.
## Factors
**Relevant factors**
One of the most relevant factors for which model performance may vary is which language is used. Although we included 20 languages in the training data, most of our dataset is made of English text, and we thus expect the model to perform better for English than other languages. Relatedly, it has been shown in previous studies that performance might vary for different dialects, and we expect that it will be the case for our model.
**Evaluation factors**
As our model is trained on data from the Web, we expect that it reflects biases from this source. We thus evaluated on RAI datasets to measure biases exhibited by the model for gender, religion, race, sexual orientation, age, nationality, disability, physical appearance and socio-economic status. We also measure the toxicity of model generations, depending on the toxicity of the context used to prompt the model.
## Metrics
**Model performance measures**
We use the following measure to evaluate the model:
- Accuracy for common sense reasoning, reading comprehension, natural language understanding (MMLU), BIG-bench hard, WinoGender and CrowS-Pairs,
- Exact match for question answering,
- The toxicity score from Perspective API on RealToxicityPrompts.
**Decision thresholds**
Not applicable.
**Approaches to uncertainty and variability**
Due to the high computational requirements of training LLMs, we trained only one model of each size, and thus could not evaluate variability of pre-training.
## Evaluation datasets
The model was evaluated on the following benchmarks: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, OpenBookQA, NaturalQuestions, TriviaQA, RACE, MMLU, BIG-bench hard, GSM8k, RealToxicityPrompts, WinoGender, CrowS-Pairs.
## Training dataset
The model was trained using the following source of data: CCNet [67%], C4 [15%], GitHub [4.5%], Wikipedia [4.5%], Books [4.5%], ArXiv [2.5%], Stack Exchange[2%]. The Wikipedia and Books domains include data in the following languages: bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk. See the paper for more details about the training set and corresponding preprocessing.
## Quantitative analysis
Hyperparameters for the model architecture
<table>
<thead>
<tr>
<th >LLaMA</th> <th colspan=6>Model hyper parameters </th>
</tr>
<tr>
<th>Number of parameters</th><th>dimension</th><th>n heads</th><th>n layers</th><th>Learn rate</th><th>Batch size</th><th>n tokens</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th> <th>4096</th> <th>32</th> <th>32</th> <th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>13B</th><th>5120</th><th>40</th><th>40</th><th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>33B</th><th>6656</th><th>52</th><th>60</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
<tr>
<th>65B</th><th>8192</th><th>64</th><th>80</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
</tbody>
</table>
*Table 1 - Summary of LLama Model Hyperparameters*
We present our results on eight standard common sense reasoning benchmarks in the table below.
<table>
<thead>
<tr>
<th>LLaMA</th> <th colspan=9>Reasoning tasks </th>
</tr>
<tr>
<th>Number of parameters</th> <th>BoolQ</th><th>PIQA</th><th>SIQA</th><th>HellaSwag</th><th>WinoGrande</th><th>ARC-e</th><th>ARC-c</th><th>OBQA</th><th>COPA</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th><th>76.5</th><th>79.8</th><th>48.9</th><th>76.1</th><th>70.1</th><th>76.7</th><th>47.6</th><th>57.2</th><th>93
</th>
<tr><th>13B</th><th>78.1</th><th>80.1</th><th>50.4</th><th>79.2</th><th>73</th><th>78.1</th><th>52.7</th><th>56.4</th><th>94
</th>
<tr><th>33B</th><th>83.1</th><th>82.3</th><th>50.4</th><th>82.8</th><th>76</th><th>81.4</th><th>57.8</th><th>58.6</th><th>92
</th>
<tr><th>65B</th><th>85.3</th><th>82.8</th><th>52.3</th><th>84.2</th><th>77</th><th>81.5</th><th>56</th><th>60.2</th><th>94</th></tr>
</tbody>
</table>
*Table 2 - Summary of LLama Model Performance on Reasoning tasks*
We present our results on bias in the table below. Note that lower value is better indicating lower bias.
| No | Category | FAIR LLM |
| --- | -------------------- | -------- |
| 1 | Gender | 70.6 |
| 2 | Religion | 79 |
| 3 | Race/Color | 57 |
| 4 | Sexual orientation | 81 |
| 5 | Age | 70.1 |
| 6 | Nationality | 64.2 |
| 7 | Disability | 66.7 |
| 8 | Physical appearance | 77.8 |
| 9 | Socioeconomic status | 71.5 |
| | LLaMA Average | 66.6 |
*Table 3 - Summary bias of our model output*
## Ethical considerations
**Data**
The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data.
**Human life**
The model is not intended to inform decisions about matters central to human life, and should not be used in such a way.
**Mitigations**
We filtered the data from the Web based on its proximity to Wikipedia text and references. For this, we used a Kneser-Ney language model and a fastText linear classifier.
**Risks and harms**
Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard.
**Use cases**
LLaMA is a foundational model, and as such, it should not be used for downstream applications without further investigation and mitigations of risks. These risks and potential fraught use cases include, but are not limited to: generation of misinformation and generation of harmful, biased or offensive content.
|
tooucci/CartPole
|
tooucci
| 2023-05-03T21:22:55Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-04-21T23:38:15Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole
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
|
skierdude/SkiingTest
|
skierdude
| 2023-05-03T21:06:37Z | 194 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-05-03T21:06:31Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: SkiingTest
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7666666507720947
---
# SkiingTest
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
#### Big Mountain
#### Freeride World Tour
#### Freestyle
#### Skiing
|
Sjdan/sw_high_hp1_2
|
Sjdan
| 2023-05-03T21:06:35Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-03T19:28:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: sw_high_hp1_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. -->
# sw_high_hp1_2
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AliiaR/t5-small-finetuned-model
|
AliiaR
| 2023-05-03T21:01:54Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-02T20:28:10Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: AliiaR/t5-small-finetuned-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# AliiaR/t5-small-finetuned-model
This model is a fine-tuned version of [AliiaR/t5-small-finetuned-model](https://huggingface.co/AliiaR/t5-small-finetuned-model) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.4127
- Validation Loss: 1.1016
- Train Rouge1: 14.9189
- Train Rouge2: 3.7554
- Train Rougel: 13.6461
- Train Rougelsum: 13.6801
- Train Gen Len: 13.4191
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 1.4127 | 1.1016 | 14.9189 | 3.7554 | 13.6461 | 13.6801 | 13.4191 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
pandma/es_pipeline
|
pandma
| 2023-05-03T20:54:53Z | 4 | 0 |
spacy
|
[
"spacy",
"token-classification",
"es",
"model-index",
"region:us"
] |
token-classification
| 2023-05-03T20:54:28Z |
---
tags:
- spacy
- token-classification
language:
- es
model-index:
- name: es_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.998766394
- name: NER Recall
type: recall
value: 0.9988961039
- name: NER F Score
type: f_score
value: 0.9988312447
---
| Feature | Description |
| --- | --- |
| **Name** | `es_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.5.2,<3.6.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (13 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `BILLING_PERIOD_END`, `BILLING_PERIOD_START`, `BILL_OWNER`, `COMPANY_NAME`, `CUPS`, `DIRECTION`, `ENERGY_P1_PRICE`, `ENERGY_P2_PRICE`, `ENERGY_P3_PRICE`, `NIF`, `POWER_P1_PRICE`, `POWER_P2_PRICE`, `TOTAL_IMPORTE` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 99.88 |
| `ENTS_P` | 99.88 |
| `ENTS_R` | 99.89 |
| `TRANSFORMER_LOSS` | 6425.46 |
| `NER_LOSS` | 41888.91 |
|
ThanHitt/FishTreeRock_Classifier_v1
|
ThanHitt
| 2023-05-03T20:37:34Z | 241 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-05-03T20:37:27Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: FishTreeRock_Classifier_v1
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9850746393203735
---
# FishTreeRock_Classifier_v1
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
#### fish
#### rock
#### tree
|
ashiyakatuka11/es_finetuned_T5
|
ashiyakatuka11
| 2023-05-03T20:34:26Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-03T20:33:45Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: es_finetuned_T5
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. -->
# es_finetuned_T5
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3287
- Train Accuracy: 0.9604
- Validation Loss: 0.3338
- Validation Accuracy: 0.9604
- Epoch: 1
## 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': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3418 | 0.9587 | 0.3421 | 0.9595 | 0 |
| 0.3287 | 0.9604 | 0.3338 | 0.9604 | 1 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.11.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
nolanaatama/sspwtrsrn
|
nolanaatama
| 2023-05-03T20:23:57Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-03T20:21:31Z |
---
license: creativeml-openrail-m
---
|
aboMesalam/my_awesome_swag_model
|
aboMesalam
| 2023-05-03T20:20:46Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-05-03T20:19:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- swag
model-index:
- name: my_awesome_swag_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. -->
# my_awesome_swag_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
nergaldarski/galenaREDUX
|
nergaldarski
| 2023-05-03T20:19:15Z | 0 | 2 | null |
[
"region:us"
] | null | 2023-05-03T16:26:38Z |
CivitAI: https://civitai.com/models/53360/galena-redux
|
ameerazam08/autotrain-docker-check-1-55215128879
|
ameerazam08
| 2023-05-03T20:10:06Z | 217 | 1 |
transformers
|
[
"transformers",
"pytorch",
"swin",
"image-classification",
"autotrain",
"dataset:ameerazam08/autotrain-data-docker-check-1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-05-03T20:09:45Z |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- ameerazam08/autotrain-data-docker-check-1
co2_eq_emissions:
emissions: 0
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
- CO2 Emissions (in grams): 0.0000
## Validation Metricsg
loss: 0.725390613079071
f1: 0.6666666666666666
precision: 0.5
recall: 1.0
auc: 0.8
accuracy: 0.5
|
Readty/Larasbali
|
Readty
| 2023-05-03T19:59:39Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-03T19:58:57Z |
---
license: creativeml-openrail-m
---
|
danbrown/testman-lora-5
|
danbrown
| 2023-05-03T19:53:34Z | 4 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:danbrown/AnyLora-v1",
"base_model:adapter:danbrown/AnyLora-v1",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-05-03T19:52:54Z |
---
license: creativeml-openrail-m
base_model: danbrown/AnyLora-v1
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - danbrown/testman-lora-3
These are LoRA adaption weights for danbrown/AnyLora-v1. The weights were fine-tuned on the danbrown/testman-dataset dataset. You can find some example images in the following.
|
nergaldarski/mistoonAnime
|
nergaldarski
| 2023-05-03T19:53:18Z | 0 | 5 | null |
[
"region:us"
] | null | 2023-05-03T19:41:13Z |
CivitAI: https://civitai.com/models/24149/mistoonanime
|
Ibrahim-Alam/finetuning-distilbert-base-uncased-on-imdb
|
Ibrahim-Alam
| 2023-05-03T19:49:50Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-03T19:43:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-distilbert-base-uncased-on-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.96
- name: F1
type: f1
value: 0.9596231493943473
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-distilbert-base-uncased-on-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1311
- Accuracy: 0.96
- F1: 0.9596
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Multi-Domain-Expert-Learning/expert-pubmed_abstracts
|
Multi-Domain-Expert-Learning
| 2023-05-03T19:48:41Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-03T13:01:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: expert-pubmed_abstracts
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. -->
# expert-pubmed_abstracts
This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2407
- Accuracy: 0.5368
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2802 | 0.01 | 500 | 2.2553 | 0.5345 |
| 2.2277 | 0.02 | 1000 | 2.2407 | 0.5368 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Readty/Rsmyntv1
|
Readty
| 2023-05-03T19:45:22Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-03T19:43:16Z |
---
license: creativeml-openrail-m
---
|
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