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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-16 00:42:46
| downloads
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| likes
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| library_name
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CyberHarem/toyokawa_fuuka_theidolmstermillionlive | CyberHarem | 2023-09-24T01:57:41Z | 0 | 1 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/toyokawa_fuuka_theidolmstermillionlive",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-24T01:45:48Z | ---
license: mit
datasets:
- CyberHarem/toyokawa_fuuka_theidolmstermillionlive
pipeline_tag: text-to-image
tags:
- art
---
# Lora of toyokawa_fuuka_theidolmstermillionlive
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 3360, you need to download `3360/toyokawa_fuuka_theidolmstermillionlive.pt` as the embedding and `3360/toyokawa_fuuka_theidolmstermillionlive.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 3360**, with the score of 0.980. The trigger words are:
1. `toyokawa_fuuka_theidolmstermillionlive`
2. `blue_hair, short_hair, blush, brown_eyes, breasts, antenna_hair, large_breasts, open_mouth, bangs, smile, wavy_hair, collarbone, hair_between_eyes`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:----------------------------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 8400 | 0.938 | [Download](8400/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](8400/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](8400/previews/pattern_6.png) | [<NSFW, click to see>](8400/previews/pattern_7.png) | [<NSFW, click to see>](8400/previews/pattern_8.png) | [<NSFW, click to see>](8400/previews/bikini.png) | [<NSFW, click to see>](8400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](8400/previews/nude.png) | [<NSFW, click to see>](8400/previews/nude2.png) |  |  |
| 7840 | 0.848 | [Download](7840/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](7840/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](7840/previews/pattern_6.png) | [<NSFW, click to see>](7840/previews/pattern_7.png) | [<NSFW, click to see>](7840/previews/pattern_8.png) | [<NSFW, click to see>](7840/previews/bikini.png) | [<NSFW, click to see>](7840/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7840/previews/nude.png) | [<NSFW, click to see>](7840/previews/nude2.png) |  |  |
| 7280 | 0.921 | [Download](7280/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](7280/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](7280/previews/pattern_6.png) | [<NSFW, click to see>](7280/previews/pattern_7.png) | [<NSFW, click to see>](7280/previews/pattern_8.png) | [<NSFW, click to see>](7280/previews/bikini.png) | [<NSFW, click to see>](7280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) |  |  |
| 6720 | 0.972 | [Download](6720/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](6720/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](6720/previews/pattern_6.png) | [<NSFW, click to see>](6720/previews/pattern_7.png) | [<NSFW, click to see>](6720/previews/pattern_8.png) | [<NSFW, click to see>](6720/previews/bikini.png) | [<NSFW, click to see>](6720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6720/previews/nude.png) | [<NSFW, click to see>](6720/previews/nude2.png) |  |  |
| 6160 | 0.917 | [Download](6160/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](6160/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](6160/previews/pattern_6.png) | [<NSFW, click to see>](6160/previews/pattern_7.png) | [<NSFW, click to see>](6160/previews/pattern_8.png) | [<NSFW, click to see>](6160/previews/bikini.png) | [<NSFW, click to see>](6160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6160/previews/nude.png) | [<NSFW, click to see>](6160/previews/nude2.png) |  |  |
| 5600 | 0.852 | [Download](5600/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](5600/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](5600/previews/pattern_6.png) | [<NSFW, click to see>](5600/previews/pattern_7.png) | [<NSFW, click to see>](5600/previews/pattern_8.png) | [<NSFW, click to see>](5600/previews/bikini.png) | [<NSFW, click to see>](5600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5600/previews/nude.png) | [<NSFW, click to see>](5600/previews/nude2.png) |  |  |
| 5040 | 0.849 | [Download](5040/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](5040/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](5040/previews/pattern_6.png) | [<NSFW, click to see>](5040/previews/pattern_7.png) | [<NSFW, click to see>](5040/previews/pattern_8.png) | [<NSFW, click to see>](5040/previews/bikini.png) | [<NSFW, click to see>](5040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5040/previews/nude.png) | [<NSFW, click to see>](5040/previews/nude2.png) |  |  |
| 4480 | 0.890 | [Download](4480/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](4480/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](4480/previews/pattern_6.png) | [<NSFW, click to see>](4480/previews/pattern_7.png) | [<NSFW, click to see>](4480/previews/pattern_8.png) | [<NSFW, click to see>](4480/previews/bikini.png) | [<NSFW, click to see>](4480/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4480/previews/nude.png) | [<NSFW, click to see>](4480/previews/nude2.png) |  |  |
| 3920 | 0.969 | [Download](3920/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](3920/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](3920/previews/pattern_6.png) | [<NSFW, click to see>](3920/previews/pattern_7.png) | [<NSFW, click to see>](3920/previews/pattern_8.png) | [<NSFW, click to see>](3920/previews/bikini.png) | [<NSFW, click to see>](3920/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3920/previews/nude.png) | [<NSFW, click to see>](3920/previews/nude2.png) |  |  |
| **3360** | **0.980** | [**Download**](3360/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](3360/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](3360/previews/pattern_6.png) | [<NSFW, click to see>](3360/previews/pattern_7.png) | [<NSFW, click to see>](3360/previews/pattern_8.png) | [<NSFW, click to see>](3360/previews/bikini.png) | [<NSFW, click to see>](3360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3360/previews/nude.png) | [<NSFW, click to see>](3360/previews/nude2.png) |  |  |
| 2800 | 0.942 | [Download](2800/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](2800/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](2800/previews/pattern_6.png) | [<NSFW, click to see>](2800/previews/pattern_7.png) | [<NSFW, click to see>](2800/previews/pattern_8.png) | [<NSFW, click to see>](2800/previews/bikini.png) | [<NSFW, click to see>](2800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2800/previews/nude.png) | [<NSFW, click to see>](2800/previews/nude2.png) |  |  |
| 2240 | 0.973 | [Download](2240/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](2240/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](2240/previews/pattern_6.png) | [<NSFW, click to see>](2240/previews/pattern_7.png) | [<NSFW, click to see>](2240/previews/pattern_8.png) | [<NSFW, click to see>](2240/previews/bikini.png) | [<NSFW, click to see>](2240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2240/previews/nude.png) | [<NSFW, click to see>](2240/previews/nude2.png) |  |  |
| 1680 | 0.966 | [Download](1680/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](1680/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](1680/previews/pattern_6.png) | [<NSFW, click to see>](1680/previews/pattern_7.png) | [<NSFW, click to see>](1680/previews/pattern_8.png) | [<NSFW, click to see>](1680/previews/bikini.png) | [<NSFW, click to see>](1680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1680/previews/nude.png) | [<NSFW, click to see>](1680/previews/nude2.png) |  |  |
| 1120 | 0.931 | [Download](1120/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](1120/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](1120/previews/pattern_6.png) | [<NSFW, click to see>](1120/previews/pattern_7.png) | [<NSFW, click to see>](1120/previews/pattern_8.png) | [<NSFW, click to see>](1120/previews/bikini.png) | [<NSFW, click to see>](1120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1120/previews/nude.png) | [<NSFW, click to see>](1120/previews/nude2.png) |  |  |
| 560 | 0.879 | [Download](560/toyokawa_fuuka_theidolmstermillionlive.zip) |  | [<NSFW, click to see>](560/previews/pattern_2.png) |  |  |  | [<NSFW, click to see>](560/previews/pattern_6.png) | [<NSFW, click to see>](560/previews/pattern_7.png) | [<NSFW, click to see>](560/previews/pattern_8.png) | [<NSFW, click to see>](560/previews/bikini.png) | [<NSFW, click to see>](560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](560/previews/nude.png) | [<NSFW, click to see>](560/previews/nude2.png) |  |  |
|
YassineBenlaria/testing_pretrained_tamasheq_only_FE | YassineBenlaria | 2023-09-24T01:43:14Z | 116 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-09-23T22:19:07Z | ---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: testing_pretrained_tamasheq_only_FE
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. -->
# testing_pretrained_tamasheq_only_FE
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8742
- Wer: 0.8333
- Cer: 0.3145
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 350
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 6.3383 | 35.29 | 300 | 2.9420 | 1.0 | 1.0 |
| 2.8426 | 70.59 | 600 | 2.7381 | 1.0 | 1.0 |
| 2.7564 | 105.88 | 900 | 2.7200 | 1.0 | 1.0 |
| 2.5802 | 141.18 | 1200 | 1.9677 | 1.0 | 0.7640 |
| 1.556 | 176.47 | 1500 | 1.4901 | 0.8481 | 0.3166 |
| 0.9319 | 211.76 | 1800 | 1.6803 | 0.8481 | 0.3067 |
| 0.7365 | 247.06 | 2100 | 1.7447 | 0.8481 | 0.3110 |
| 0.6363 | 282.35 | 2400 | 1.8439 | 0.8370 | 0.3110 |
| 0.5882 | 317.65 | 2700 | 1.8742 | 0.8333 | 0.3145 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
CzarnyRycerz/rl_course_vizdoom_health_gathering_supreme | CzarnyRycerz | 2023-09-24T01:24:03Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-24T01:23:55Z | ---
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: 9.39 +/- 3.90
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 CzarnyRycerz/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.colab_kernel_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.colab_kernel_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.
|
jondurbin/airoboros-l2-70b-2.2.1-4bit-quants | jondurbin | 2023-09-24T01:23:04Z | 14 | 0 | null | [
"gguf",
"license:llama2",
"endpoints_compatible",
"region:us"
]
| null | 2023-09-24T01:01:16Z | ---
license: llama2
---
q4_0 gguf and gptq, since the merge method borked the originals |
natrajanv/distilbert-base-uncased-finetuned-emotion | natrajanv | 2023-09-24T01:22:33Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-09-23T18:13:09Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.923
- name: F1
type: f1
value: 0.922772205894511
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2124
- Accuracy: 0.923
- F1: 0.9228
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7972 | 1.0 | 250 | 0.3023 | 0.908 | 0.9071 |
| 0.2441 | 2.0 | 500 | 0.2124 | 0.923 | 0.9228 |
### Framework versions
- Transformers 4.33.2
- Pytorch 1.12.1
- Datasets 2.14.1
- Tokenizers 0.13.3
|
CyberHarem/kitazawa_shiho_theidolmstermillionlive | CyberHarem | 2023-09-24T00:50:39Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/kitazawa_shiho_theidolmstermillionlive",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-24T00:37:47Z | ---
license: mit
datasets:
- CyberHarem/kitazawa_shiho_theidolmstermillionlive
pipeline_tag: text-to-image
tags:
- art
---
# Lora of kitazawa_shiho_theidolmstermillionlive
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 5200, you need to download `5200/kitazawa_shiho_theidolmstermillionlive.pt` as the embedding and `5200/kitazawa_shiho_theidolmstermillionlive.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 5200**, with the score of 0.973. The trigger words are:
1. `kitazawa_shiho_theidolmstermillionlive`
2. `brown_hair, long_hair, brown_eyes, blush, bangs, breasts`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:----------------------------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 7800 | 0.959 | [Download](7800/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](7800/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](7800/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](7800/previews/bikini.png) | [<NSFW, click to see>](7800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) |  |  |
| 7280 | 0.952 | [Download](7280/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](7280/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](7280/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](7280/previews/bikini.png) | [<NSFW, click to see>](7280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) |  |  |
| 6760 | 0.962 | [Download](6760/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](6760/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](6760/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](6760/previews/bikini.png) | [<NSFW, click to see>](6760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) |  |  |
| 6240 | 0.963 | [Download](6240/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](6240/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](6240/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](6240/previews/bikini.png) | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5720 | 0.947 | [Download](5720/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](5720/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](5720/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](5720/previews/bikini.png) | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| **5200** | **0.973** | [**Download**](5200/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](5200/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](5200/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](5200/previews/bikini.png) | [<NSFW, click to see>](5200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4680 | 0.942 | [Download](4680/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](4680/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](4680/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](4680/previews/bikini.png) | [<NSFW, click to see>](4680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) |  |  |
| 4160 | 0.951 | [Download](4160/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](4160/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](4160/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](4160/previews/bikini.png) | [<NSFW, click to see>](4160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) |  |  |
| 3640 | 0.951 | [Download](3640/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](3640/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](3640/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](3640/previews/bikini.png) | [<NSFW, click to see>](3640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) |  |  |
| 3120 | 0.933 | [Download](3120/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](3120/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](3120/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](3120/previews/bikini.png) | [<NSFW, click to see>](3120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) |  |  |
| 2600 | 0.965 | [Download](2600/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](2600/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](2600/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](2600/previews/bikini.png) | [<NSFW, click to see>](2600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) |  |  |
| 2080 | 0.926 | [Download](2080/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](2080/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](2080/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](2080/previews/bikini.png) | [<NSFW, click to see>](2080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) |  |  |
| 1560 | 0.897 | [Download](1560/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](1560/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](1560/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](1560/previews/bikini.png) | [<NSFW, click to see>](1560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) |  |  |
| 1040 | 0.948 | [Download](1040/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](1040/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](1040/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](1040/previews/bikini.png) | [<NSFW, click to see>](1040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) |  |  |
| 520 | 0.867 | [Download](520/kitazawa_shiho_theidolmstermillionlive.zip) | [<NSFW, click to see>](520/previews/pattern_1.png) |  |  |  |  | [<NSFW, click to see>](520/previews/pattern_6.png) |  |  |  | [<NSFW, click to see>](520/previews/bikini.png) | [<NSFW, click to see>](520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) |  |  |
|
Panchovix/FashionGPT-70B-V1.1-safetensors | Panchovix | 2023-09-24T00:22:02Z | 7 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-09-23T22:19:13Z | ---
license: llama2
---
Safetensors conversion of FashionGPT-70B-V1.1 (https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.1), to be used directly on transformers, or to create quants with exllamav2. |
CyberHarem/tenkuubashi_tomoka_theidolmstermillionlive | CyberHarem | 2023-09-23T23:46:19Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/tenkuubashi_tomoka_theidolmstermillionlive",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-23T23:34:27Z | ---
license: mit
datasets:
- CyberHarem/tenkuubashi_tomoka_theidolmstermillionlive
pipeline_tag: text-to-image
tags:
- art
---
# Lora of tenkuubashi_tomoka_theidolmstermillionlive
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 4760, you need to download `4760/tenkuubashi_tomoka_theidolmstermillionlive.pt` as the embedding and `4760/tenkuubashi_tomoka_theidolmstermillionlive.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 4760**, with the score of 0.932. The trigger words are:
1. `tenkuubashi_tomoka_theidolmstermillionlive`
2. `long_hair, brown_hair, brown_eyes, smile, hair_bun, single_hair_bun, blush, bangs, sidelocks, breasts`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:--------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 5100 | 0.928 | [Download](5100/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](5100/previews/pattern_5.png) | [<NSFW, click to see>](5100/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| **4760** | **0.932** | [**Download**](4760/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](4760/previews/pattern_5.png) | [<NSFW, click to see>](4760/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.868 | [Download](4420/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](4420/previews/pattern_5.png) | [<NSFW, click to see>](4420/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| 4080 | 0.911 | [Download](4080/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](4080/previews/pattern_5.png) | [<NSFW, click to see>](4080/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.927 | [Download](3740/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](3740/previews/pattern_5.png) | [<NSFW, click to see>](3740/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.873 | [Download](3400/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](3400/previews/pattern_5.png) | [<NSFW, click to see>](3400/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.923 | [Download](3060/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](3060/previews/pattern_5.png) | [<NSFW, click to see>](3060/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.887 | [Download](2720/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](2720/previews/pattern_5.png) | [<NSFW, click to see>](2720/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.913 | [Download](2380/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](2380/previews/pattern_5.png) | [<NSFW, click to see>](2380/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.854 | [Download](2040/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](2040/previews/pattern_5.png) | [<NSFW, click to see>](2040/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.904 | [Download](1700/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](1700/previews/pattern_5.png) | [<NSFW, click to see>](1700/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.895 | [Download](1360/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](1360/previews/pattern_5.png) | [<NSFW, click to see>](1360/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.869 | [Download](1020/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](1020/previews/pattern_5.png) | [<NSFW, click to see>](1020/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.805 | [Download](680/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](680/previews/pattern_5.png) | [<NSFW, click to see>](680/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.765 | [Download](340/tenkuubashi_tomoka_theidolmstermillionlive.zip) |  |  |  |  | [<NSFW, click to see>](340/previews/pattern_5.png) | [<NSFW, click to see>](340/previews/pattern_6.png) |  |  |  |  | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
pnm/q-FrozenLake-v1-4x4-noSlippery | pnm | 2023-09-23T23:44:22Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-23T23:44:20Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="pnm/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Yntec/HitenDiffusion | Yntec | 2023-09-23T23:43:10Z | 330 | 2 | diffusers | [
"diffusers",
"safetensors",
"Anime",
"andite",
"text-to-image",
"stable-diffusion",
"stable-diffusion-diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-09-23T22:43:29Z | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Anime
- andite
- text-to-image
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
---
# Hiten Diffusion
andite's Hiten model from https://huggingface.co/LMFResearchSociety/andite-finetunes-backup
Samples and prompt:
sitting girl, Pretty CUTE LITTLE, gorgeous hair, DETAILED CHIBI EYES, Magazine ad, iconic, 1943, sharp focus. acrylic art by ROSSDRAWS and Clay Mann and ilya kuvshinov |
totally-not-an-llm/EverythingLM-13b-V3-16k | totally-not-an-llm | 2023-09-23T23:30:34Z | 1,480 | 6 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:totally-not-an-llm/EverythingLM-data-V3",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-09-23T01:18:48Z | ---
license: llama2
datasets:
- totally-not-an-llm/EverythingLM-data-V3
---
# EverythingLM-13b-V3-16k
Introducing EverythingLM, a llama-2 based, general-purpose 13b model with 16k context thanks to LlongMa. The model is trained on the EverythingLM-V3 dataset, more info can be found on the dataset page.
The model is completely uncensored.
Despite being "uncensored", the base model might be resistant; you might have to prompt-engineer certain prompts.
### Quants (Thanks TheBloke!):
https://huggingface.co/TheBloke/EverythingLM-13B-V3-16K-GGUF
https://huggingface.co/TheBloke/EverythingLM-13B-V3-16K-GPTQ
https://huggingface.co/TheBloke/EverythingLM-13B-V3-16K-AWQ
### Notable features:
- Automatically triggered CoT reasoning.
- Verbose and detailed replies.
- Creative stories.
- Good prompt understanding.
### Differences from V2:
- Much more uncensored.
- Actual roleplaying ability now!
- General all around improvements thanks to the new dataset. Check out the dataset for more info.
### Prompt format (Alpaca-chat):
```
USER: <prompt>
ASSISTANT:
```
### Future plans:
- Highest priority right now is V3.1 with more optimized training and iterative dataset improvements based on testing.
### Note:
Through testing V2, I realized some alignment data had leaked in, causing the model to be less cooperative then intended. This model should do much better due to stricter filetering. |
Bena345/bloom_prompt_tuning_1695511287.9320693 | Bena345 | 2023-09-23T23:27:43Z | 4 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-23T23:27:42Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
Axel578/phi-1_5-finetuned-dialogstudio | Axel578 | 2023-09-23T22:59:53Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:microsoft/phi-1_5",
"base_model:finetune:microsoft/phi-1_5",
"license:other",
"region:us"
]
| null | 2023-09-23T22:22:14Z | ---
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
model-index:
- name: phi-1_5-finetuned-dialogstudio
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. -->
# phi-1_5-finetuned-dialogstudio
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2118
## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 12
### Training results
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Siveraa19/ejemplo-nlp | Siveraa19 | 2023-09-23T22:59:27Z | 116 | 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 | 2023-09-23T22:56:20Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: ejemplo-nlp
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: 0.8333333333333334
- name: F1
type: f1
value: 0.8726591760299626
---
<!-- 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. -->
# ejemplo-nlp
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6831
- Accuracy: 0.8333
- F1: 0.8727
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5117 | 1.09 | 500 | 0.5687 | 0.8358 | 0.8859 |
| 0.3561 | 2.18 | 1000 | 0.6831 | 0.8333 | 0.8727 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Koshti10/BART_large_Synthetic_Gameplan | Koshti10 | 2023-09-23T22:30:50Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:Koshti10/BART-large-ET-Synthetic",
"base_model:finetune:Koshti10/BART-large-ET-Synthetic",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-09-23T19:21:35Z | ---
license: apache-2.0
base_model: Koshti10/BART-large-ET-Synthetic
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: BART_large_Synthetic_Gameplan
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BART_large_Synthetic_Gameplan
This model is a fine-tuned version of [Koshti10/BART-large-ET-Synthetic](https://huggingface.co/Koshti10/BART-large-ET-Synthetic) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2557
- Rouge1: 38.2255
- Rouge2: 27.7473
- Rougel: 35.4392
- Rougelsum: 35.414
- Gen Len: 19.2554
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
jimigoodmojo/phi-1_5-finetuned-gsm8k | jimigoodmojo | 2023-09-23T21:57:06Z | 57 | 0 | transformers | [
"transformers",
"pytorch",
"mixformer-sequential",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/phi-1_5",
"base_model:finetune:microsoft/phi-1_5",
"license:other",
"autotrain_compatible",
"region:us"
]
| text-generation | 2023-09-23T03:23:16Z | ---
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
model-index:
- name: phi-1_5-finetuned-gsm8k
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. -->
# phi-1_5-finetuned-gsm8k
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) 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: 0.0002
- 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: cosine
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
ayoubkirouane/BERT-Emotions-Classifier | ayoubkirouane | 2023-09-23T21:35:59Z | 389,797 | 9 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"dataset:sem_eval_2018_task_1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-09-23T18:47:29Z | ---
datasets:
- sem_eval_2018_task_1
language:
- en
library_name: transformers
pipeline_tag: text-classification
---
## Description
The **BERT-Emotions-Classifier** is a fine-tuned **BERT-based** model designed for multi-label emotion classification. It has been trained on the sem_eval_2018_task_1 dataset, which includes text samples labeled with a variety of emotions, including anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, and trust. The model is capable of classifying text inputs into one or more of these emotion categories.
## Overview
+ **Model Name**: BERT-Emotions-Classifier
+ **Task**: Multi-label emotion classification
+ **Dataset**: sem_eval_2018_task_1
+ **Labels**: ['anger', 'anticipation', 'disgust', 'fear', 'joy', 'love', 'optimism', 'pessimism', 'sadness', 'surprise', 'trust']
+ **Base Model**: BERT (Bidirectional Encoder Representations from Transformers)
### Input Format
The model expects text input in the form of a string.
### Output Format
+ The model provides a list of labels and associated scores, indicating the predicted emotions and their confidence scores.
### Example Applications
+ Emotion analysis in social media posts
+ Sentiment analysis in customer reviews
+ Content recommendation based on emotional context
## Limitations
+ **Limited Emotion Categories**: The BERT-Emotions-Classifier model is trained on a specific set of emotion categories. It may not accurately classify emotions that do not fall within these predefined categories.
+ **Model Performance**: The accuracy of emotion classification depends on the quality and diversity of the training data. The model's performance may vary for text inputs with uncommon or complex emotional expressions.
+ **Bias and Fairness**: Like any machine learning model, the BERT-Emotions-Classifier may exhibit bias in its predictions. Care should be taken to address and mitigate bias in real-world applications to ensure fairness and inclusivity.
+ **Input Length**: The model has limitations on the maximum input text length it can process effectively. Very long texts may be truncated or may not receive accurate classifications.
## Ethical Considerations
When using this model, it's essential to consider the ethical implications of emotion analysis. Ensure that the use of emotional data respects privacy and consent, and avoid making decisions that could have adverse effects based solely on emotion analysis.
## Inference
```python
from transformers import pipeline
# Load the BERT-Emotions-Classifier
classifier = pipeline("text-classification", model="ayoubkirouane/BERT-Emotions-Classifier")
# Input text
text = "Your input text here"
# Perform emotion classification
results = classifier(text)
# Display the classification results
print(results)
``` |
rjarpa/ms-4maps_alpha-ds-full | rjarpa | 2023-09-23T21:26:32Z | 133 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-09-23T21:25:07Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: ms-4maps_alpha-ds-full
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. -->
# ms-4maps_alpha-ds-full
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
trieudemo11/llama_7b_attrb_cate_4m_12 | trieudemo11 | 2023-09-23T21:12:16Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-23T21:12:00Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
CyberHarem/sakura_kyouko_puellamagimadokamagica | CyberHarem | 2023-09-23T21:04:01Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/sakura_kyouko_puellamagimadokamagica",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-23T20:47:00Z | ---
license: mit
datasets:
- CyberHarem/sakura_kyouko_puellamagimadokamagica
pipeline_tag: text-to-image
tags:
- art
---
# Lora of sakura_kyouko_puellamagimadokamagica
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 6760, you need to download `6760/sakura_kyouko_puellamagimadokamagica.pt` as the embedding and `6760/sakura_kyouko_puellamagimadokamagica.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 6760**, with the score of 0.947. The trigger words are:
1. `sakura_kyouko_puellamagimadokamagica`
2. `red_hair, long_hair, ponytail, red_eyes, hair_bow, bow, magical_girl`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:--------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 7800 | 0.941 | [Download](7800/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) |  |  |
| 7280 | 0.864 | [Download](7280/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) |  |  |
| **6760** | **0.947** | [**Download**](6760/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) |  |  |
| 6240 | 0.942 | [Download](6240/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5720 | 0.937 | [Download](5720/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5200 | 0.946 | [Download](5200/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4680 | 0.921 | [Download](4680/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) |  |  |
| 4160 | 0.878 | [Download](4160/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) |  |  |
| 3640 | 0.919 | [Download](3640/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) |  |  |
| 3120 | 0.916 | [Download](3120/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) |  |  |
| 2600 | 0.893 | [Download](2600/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) |  |  |
| 2080 | 0.918 | [Download](2080/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) |  |  |
| 1560 | 0.925 | [Download](1560/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) |  |  |
| 1040 | 0.775 | [Download](1040/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) |  |  |
| 520 | 0.499 | [Download](520/sakura_kyouko_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) |  |  |
|
sjlite/lora-trained-xl-colab_pagnut | sjlite | 2023-09-23T20:50:29Z | 1 | 1 | diffusers | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
]
| text-to-image | 2023-09-23T16:44:03Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of pagnut
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - sjlite/lora-trained-xl-colab_pagnut
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of pagnut using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
pszemraj/pegasus-x-large-book-summary | pszemraj | 2023-09-23T20:46:57Z | 852 | 35 | transformers | [
"transformers",
"pytorch",
"safetensors",
"pegasus_x",
"text2text-generation",
"summarization",
"summary",
"booksum",
"long-document",
"long-form",
"dataset:kmfoda/booksum",
"base_model:google/pegasus-x-large",
"base_model:finetune:google/pegasus-x-large",
"license:apache-2.0",
"license:bsd-3-clause",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| summarization | 2022-09-16T10:55:11Z | ---
license:
- apache-2.0
- bsd-3-clause
tags:
- summarization
- summary
- booksum
- long-document
- long-form
datasets:
- kmfoda/booksum
metrics:
- rouge
languages: en
widget:
- text: large earthquakes along a given fault segment do not occur at random intervals
because it takes time to accumulate the strain energy for the rupture. The rates
at which tectonic plates move and accumulate strain at their boundaries are approximately
uniform. Therefore, in first approximation, one may expect that large ruptures
of the same fault segment will occur at approximately constant time intervals.
If subsequent main shocks have different amounts of slip across the fault, then
the recurrence time may vary, and the basic idea of periodic mainshocks must be
modified. For great plate boundary ruptures the length and slip often vary by
a factor of 2. Along the southern segment of the San Andreas fault the recurrence
interval is 145 years with variations of several decades. The smaller the standard
deviation of the average recurrence interval, the more specific could be the long
term prediction of a future mainshock.
example_title: earthquakes
- text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates
are fed into a neural network that predicts values in the reconstructed domain.
Then, this domain is mapped to the sensor domain where sensor measurements are
available as supervision. Class and Section Problems Addressed Generalization
(Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid
Representations (Section 3) Computation & memory efficiency, representation capacity,
editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section
5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section
6) Edit ability, constraints, regularization. Table 2: The five classes of techniques
in the neural field toolbox each addresses problems that arise in learning, inference,
and control. (Section 3). We can supervise reconstruction via differentiable forward
maps that transform Or project our domain (e.g, 3D reconstruction via 2D images;
Section 4) With appropriate network architecture choices, we can overcome neural
network spectral biases (blurriness) and efficiently compute derivatives and integrals
(Section 5). Finally, we can manipulate neural fields to add constraints and regularizations,
and to achieve editable representations (Section 6). Collectively, these classes
constitute a ''toolbox'' of techniques to help solve problems with neural fields
There are three components in a conditional neural field: (1) An encoder or inference
function € that outputs the conditioning latent variable 2 given an observation
0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS
a latent code Or feature code_ (2) A mapping function 4 between Z and neural field
parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the
most probable z given the observations O: argmaxz P(2/0). The decoder maximizes
the inverse conditional probability to find the most probable 0 given Z: arg-
max P(Olz). We discuss different encoding schemes with different optimality guarantees
(Section 2.1.1), both global and local conditioning (Section 2.1.2), and different
mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate
a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable
prior over the sur- face in its reconstruction domain to generalize to the partial
observations. A neural network expresses a prior via the function space of its
architecture and parameters 0, and generalization is influenced by the inductive
bias of this function space (Section 5).'
example_title: scientific paper
- text: 'Is a else or outside the cob and tree written being of early client rope
and you have is for good reasons. On to the ocean in Orange for time. By''s the
aggregate we can bed it yet. Why this please pick up on a sort is do and also
M Getoi''s nerocos and do rain become you to let so is his brother is made in
use and Mjulia''s''s the lay major is aging Masastup coin present sea only of
Oosii rooms set to you We do er do we easy this private oliiishs lonthen might
be okay. Good afternoon everybody. Welcome to this lecture of Computational Statistics.
As you can see, I''m not socially my name is Michael Zelinger. I''m one of the
task for this class and you might have already seen me in the first lecture where
I made a quick appearance. I''m also going to give the tortillas in the last third
of this course. So to give you a little bit about me, I''m a old student here
with better Bulman and my research centres on casual inference applied to biomedical
disasters, so that could be genomics or that could be hospital data. If any of
you is interested in writing a bachelor thesis, a semester paper may be mastathesis
about this topic feel for reach out to me. you have my name on models and my email
address you can find in the directory I''d Be very happy to talk about it. you
do not need to be sure about it, we can just have a chat. So with that said, let''s
get on with the lecture. There''s an exciting topic today I''m going to start
by sharing some slides with you and later on during the lecture we''ll move to
the paper. So bear with me for a few seconds. Well, the projector is starting
up. Okay, so let''s get started. Today''s topic is a very important one. It''s
about a technique which really forms one of the fundamentals of data science,
machine learning, and any sort of modern statistics. It''s called cross validation.
I know you really want to understand this topic I Want you to understand this
and frankly, nobody''s gonna leave Professor Mineshousen''s class without understanding
cross validation. So to set the stage for this, I Want to introduce you to the
validation problem in computational statistics. So the problem is the following:
You trained a model on available data. You fitted your model, but you know the
training data you got could always have been different and some data from the
environment. Maybe it''s a random process. You do not really know what it is,
but you know that somebody else who gets a different batch of data from the same
environment they would get slightly different training data and you do not care
that your method performs as well. On this training data. you want to to perform
well on other data that you have not seen other data from the same environment.
So in other words, the validation problem is you want to quantify the performance
of your model on data that you have not seen. So how is this even possible? How
could you possibly measure the performance on data that you do not know The solution
to? This is the following realization is that given that you have a bunch of data,
you were in charge. You get to control how much that your model sees. It works
in the following way: You can hide data firms model. Let''s say you have a training
data set which is a bunch of doubtless so X eyes are the features those are typically
hide and national vector. It''s got more than one dimension for sure. And the
why why eyes. Those are the labels for supervised learning. As you''ve seen before,
it''s the same set up as we have in regression. And so you have this training
data and now you choose that you only use some of those data to fit your model.
You''re not going to use everything, you only use some of it the other part you
hide from your model. And then you can use this hidden data to do validation from
the point of you of your model. This hidden data is complete by unseen. In other
words, we solve our problem of validation.'
example_title: transcribed audio - lecture
- text: 'Transformer-based models have shown to be very useful for many NLP tasks.
However, a major limitation of transformers-based models is its O(n^2)O(n 2) time
& memory complexity (where nn is sequence length). Hence, it''s computationally
very expensive to apply transformer-based models on long sequences n > 512n>512.
Several recent papers, e.g. Longformer, Performer, Reformer, Clustered attention
try to remedy this problem by approximating the full attention matrix. You can
checkout 🤗''s recent blog post in case you are unfamiliar with these models.
BigBird (introduced in paper) is one of such recent models to address this issue.
BigBird relies on block sparse attention instead of normal attention (i.e. BERT''s
attention) and can handle sequences up to a length of 4096 at a much lower computational
cost compared to BERT. It has achieved SOTA on various tasks involving very long
sequences such as long documents summarization, question-answering with long contexts.
BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of this
post is to give the reader an in-depth understanding of big bird implementation
& ease one''s life in using BigBird with 🤗Transformers. But, before going into
more depth, it is important to remember that the BigBird''s attention is an approximation
of BERT''s full attention and therefore does not strive to be better than BERT''s
full attention, but rather to be more efficient. It simply allows to apply transformer-based
models to much longer sequences since BERT''s quadratic memory requirement quickly
becomes unbearable. Simply put, if we would have ∞ compute & ∞ time, BERT''s attention
would be preferred over block sparse attention (which we are going to discuss
in this post).
If you wonder why we need more compute when working with longer sequences, this
blog post is just right for you!
Some of the main questions one might have when working with standard BERT-like
attention include:
Do all tokens really have to attend to all other tokens? Why not compute attention
only over important tokens? How to decide what tokens are important? How to attend
to just a few tokens in a very efficient way? In this blog post, we will try to
answer those questions.
What tokens should be attended to? We will give a practical example of how attention
works by considering the sentence ''BigBird is now available in HuggingFace for
extractive question answering''. In BERT-like attention, every word would simply
attend to all other tokens.
Let''s think about a sensible choice of key tokens that a queried token actually
only should attend to by writing some pseudo-code. Will will assume that the token
available is queried and build a sensible list of key tokens to attend to.
>>> # let''s consider following sentence as an example >>> example = [''BigBird'',
''is'', ''now'', ''available'', ''in'', ''HuggingFace'', ''for'', ''extractive'',
''question'', ''answering'']
>>> # further let''s assume, we''re trying to understand the representation of
''available'' i.e. >>> query_token = ''available'' >>> # We will initialize an
empty `set` and fill up the tokens of our interest as we proceed in this section.
>>> key_tokens = [] # => currently ''available'' token doesn''t have anything
to attend Nearby tokens should be important because, in a sentence (sequence of
words), the current word is highly dependent on neighboring past & future tokens.
This intuition is the idea behind the concept of sliding attention.'
example_title: bigbird blog intro
- text: 'To be fair, you have to have a very high IQ to understand Rick and Morty.
The humour is extremely subtle, and without a solid grasp of theoretical physics
most of the jokes will go over a typical viewer''s head. There''s also Rick''s
nihilistic outlook, which is deftly woven into his characterisation- his personal
philosophy draws heavily from Narodnaya Volya literature, for instance. The fans
understand this stuff; they have the intellectual capacity to truly appreciate
the depths of these jokes, to realise that they''re not just funny- they say something
deep about LIFE. As a consequence people who dislike Rick & Morty truly ARE idiots-
of course they wouldn''t appreciate, for instance, the humour in Rick''s existential
catchphrase ''Wubba Lubba Dub Dub,'' which itself is a cryptic reference to Turgenev''s
Russian epic Fathers and Sons. I''m smirking right now just imagining one of those
addlepated simpletons scratching their heads in confusion as Dan Harmon''s genius
wit unfolds itself on their television screens. What fools.. how I pity them.
😂
And yes, by the way, i DO have a Rick & Morty tattoo. And no, you cannot see it.
It''s for the ladies'' eyes only- and even then they have to demonstrate that
they''re within 5 IQ points of my own (preferably lower) beforehand. Nothin personnel
kid 😎'
example_title: Richard & Mortimer
parameters:
max_length: 48
min_length: 2
no_repeat_ngram_size: 3
encoder_no_repeat_ngram_size: 3
early_stopping: true
length_penalty: 0.1
num_beams: 2
base_model: google/pegasus-x-large
model-index:
- name: pszemraj/pegasus-x-large-book-summary
results:
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- type: rouge
value: 33.1401
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjQ1NjY1OGVjYWEwMzBjMzk3ZmMyZDA0ZTcxOTdmZTUxNTc0OGYxYmY3MzJkMzFmYTVjNzU2ZTk4MzE0NWMzMSIsInZlcnNpb24iOjF9.PSHB6DMF6tkwSw5nsFE57a2ApRAy_tkS6ziKA6PSTWddEdaqfca4pfig6_olmRmcS4KxN6HHcsmioHzv4LJQBw
- type: rouge
value: 9.3095
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzk3MTA3NmY1OGE3MzFjZTJhYWYzNGU4NTUzMTgwM2Y1NWZjMmEyNDNmNmEzYmQzZThjOGExMjc2ZjAyZjMzZCIsInZlcnNpb24iOjF9.tfgp8p-WlkVrfducTSg4zs-byeZMCmdZw1aizPQHXm_qRAwGtKcuVkZcmza5Y3o3VqsAEmGzg5HQD1vnZvWIDA
- type: rouge
value: 24.8552
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTVmMTIwNDQwNTI4MmI2MmY1ODc1Mjk0NGQ5ZWE4ZTYzOGNkMjY2ZmJhMjg2MTZlNTdhYTA2ZDAxNTFjMjA2MSIsInZlcnNpb24iOjF9.9HLgy9842oIDm6ABb3L94R1P4zAqTI0QN8aP62xzIyDxUXTbWw68PEDufYLiBJbTgZ8ElopZ9I7aou2zCgXeAA
- type: rouge
value: 29.0391
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmNhYWJjYjdjMzMxMmE4ZTE4NGEzMDdmZDZjODI5ZWRjZWJmYTEyZGIzYWQ2NjM3YzQ4MjI4ZTM4MmU5MzRjZSIsInZlcnNpb24iOjF9.d2yoVdmxjVJnsgIYFiLuaBO5Krgw4Axl5yeOSTKrvHygrAxoqT1nl4anzQiyoR3PwYBXwBkwmgpJUfZ7RNXtDQ
- type: loss
value: 2.288182497024536
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzM5NGIwODMxOTA3MTY3ODc2ZDczYTNmMTMwM2QyZmNlZjFmZDJjMGY3NWNkMDEyYzA4OTA2ZDRiODY3Zjg4OCIsInZlcnNpb24iOjF9.8k9mC050OS7mQSR9oA8liDRDQvEx1VxmTXGLmDYJVYYtTh2HYJFGP8Vy_krocFRIYDxh-IHPEOOSr5NrLMWHBA
- type: gen_len
value: 45.2173
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWZhNzQ5OTQ5Yjg5YjhlOTZiZmJhZjZiODNmY2E2OTg4YTg4NWVhYzRkNzM2Mzk4NzdlMDgxM2M4NjY2YzhhYSIsInZlcnNpb24iOjF9.tDEEsPUclZDygAdGhNrBGrF24vR8ao08Nw7hmtUt5lmSZZZK_u-8rpz97QgVS6MCJdjFVnbYC4bkFnlQWI_FAA
- task:
type: summarization
name: Summarization
dataset:
name: launch/gov_report
type: launch/gov_report
config: plain_text
split: test
metrics:
- type: rouge
value: 39.7279
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTAxODk3OTUwMTIzODU3NzU2YzAzZjE2NTM3MzBjNDA0ZWRmZGU3NWUzNTg1YThhNDQ1NjQ5ZmM3OWI2YzBhNSIsInZlcnNpb24iOjF9.vnNKucBNt2-nIyODj9P2HeaWPX5AQR8L-DL8QzrO7kj58-vZnjT6hsAGmepRNzdZ1TLF-3j2J2plcNJ8lUO8Dg
- type: rouge
value: 10.8944
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjYzMmIxOTJmZjkxOGI5N2U0NTRmMmQwOGJhMzMxYWIzMWMzYzUwMDEyMDdiZDQ2YTUzOWU0OTViMTI2YTAwYiIsInZlcnNpb24iOjF9.De0PaAikWqfWpoIXTCYP-mSFu3PUATLX08Qq74OHXM8784heFVDX1E1sXlh_QbbKJbuMuZtTKM4qr7oLUizOAw
- type: rouge
value: 19.7018
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzI3MjQzOGQ3MGE3NDNkZTEyMWRkYjUyYTYzNDEwOWVjMGFmNTBiZjE4ZTBhMGYzMmI1Yzk0YjBmYmIzMWMxZSIsInZlcnNpb24iOjF9.FVikJ5Ma0gUgM-tpbomWXnC4jtmvhxqikPqCk84t4IbIdU0CIYGTQEONiz-VqI0fJeNrnTS6lxpBv7XxKoq3BQ
- type: rouge
value: 36.5634
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTI2OTVmNDZiZWE5ZjNkODIwZjJiNTU2ZjJjYjczODUwM2JiNDEzYmE3N2U5YWM5NzJjOWEzMmYzZjdlYWJmYyIsInZlcnNpb24iOjF9.poR4zcqRvdaierfWFdTa53Cv6ZbNbnRwyRTi9HukHF5AWAQgc6zpBLkwOYFYoWjuSH83ohWeMM3MoIdw3zypBw
- type: loss
value: 2.473011016845703
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDFmMjg3NWQ2YTMxMTc1OGZiYWYzNjg5NDY3MWE4MjY5ZDQxZDZhZGI1OTc5MzZkZGEzYmVlNWFiMzZjNDdhNCIsInZlcnNpb24iOjF9.05nKB3SmEfFKSduJqlleF4Fd2_IhwJS8eTOrnzZYCQQfLCfpJAZLhp3eLQCuBY4htd-FNrZftrThL66zVxyrCQ
- type: gen_len
value: 212.8243
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGNjMTg4ZDZlZjAxZGNhN2M0NWI0ZTA0OWEzNDkzNDAzOTJhODA2MmVkODI4YjYzN2FiOTU1ZDMwM2VlNWMyYyIsInZlcnNpb24iOjF9.WYx6XJFKokY2heoN-jpAMp1Z1gsyJus3zpktQgNd0FOYJxOUqW40A0kkHtd15y4dUhsbccLpuJGY1fNJgHOiDw
- task:
type: summarization
name: Summarization
dataset:
name: billsum
type: billsum
config: default
split: test
metrics:
- type: rouge
value: 42.1065
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDJhNDM2MWEwMjJlYjRmZTVkYzljODcwMzlmMGUxMDA4ZmRjNjM0NmY3ZWJlMmZjNGI3NDQ3NTQyOTQ3MjBkNSIsInZlcnNpb24iOjF9.l1MiZbXyFyXAcsfFChMrTvSaBhzBR6AuDnBuII8zY3Csz3ShWK0vo09MkQdZ1epe8PKWV9wwUBuJyKk3wL7MDw
- type: rouge
value: 15.4079
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTY3NDBkYTVkNjdhY2I0ZmY0NTA4YzVkMGE5YWE5ODdjOGE1MDhkOTJhOWY3NmI2ZWI1MGU2MGI1NDRlYjI3MSIsInZlcnNpb24iOjF9.VN-5eK2SzFDCJnFTHHu7XCU_lynaxW_JEDc3llmcNo_ffDgRmISHHGaqV7fPFymBBMXpPly7XblO_sukyqj1Cg
- type: rouge
value: 24.8814
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDYyNGZmNDY3MTY4YzI4ZjZhODE0NGIyN2ZkOGEyYzM3MWZjM2QzZTg5ZjNmZmYzZDE5NzhiZDQ4OGM1YjNiMyIsInZlcnNpb24iOjF9.L73M1M5XdMQkf8zSdfLN0MUrxtO0r6UiLjoOkHfrIGbWNsNJ8tU5lciYFNIhJrICUL8LchCsFqR9LAClKS4bCg
- type: rouge
value: 36.0375
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTBlMTQ5OTQxNTA3ZmFiMGYyZWQ0MGM0ODY2YWI3MzgyNjkwNzQyM2FmNGRjMzc3MjJmZDZkOWY4M2RhZTg2MSIsInZlcnNpb24iOjF9.IiMSSVahBgH8n34bGCC_DDGpujDXQbIvGhlcpVV2EBVQLLWUqcCy5WwBdbRrxPC-asBRCNERQxj8Uii4FvPsDQ
- type: loss
value: 1.9130958318710327
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTg2NTMxZDE3MDg3MDFkMTYxNjY1OTc5YjQ4ODcyMGUxMTFiZjJiNDgyYWZhN2NjZmE1MDQ1NTRmZGY0NjQzZSIsInZlcnNpb24iOjF9.kADUBMO8i6-oGDDt1cOiGMrGcMkF_Qc1jSpS2NSFyksDRusQa_YuuShefF4DuHVEr3CS0hNjjRH9_JBeX9ZQDg
- type: gen_len
value: 179.2184
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjM4NGNiMTY3YzZjMzg4MTRiMDdiZDFiMzA1ZDIyMDM2MDk1OWRhYWQzN2UxZDNlODIxOWVhY2JlYjk4Mjk5YyIsInZlcnNpb24iOjF9.nU8ImMNWgjg9BKjUBJQLFaJOBq3kyIne8ldlpL0OV0e4888wOntIAcJP0dCCYfRSLVmZuXQ1M8cpDuTf50hNCw
- task:
type: summarization
name: Summarization
dataset:
name: kmfoda/booksum
type: kmfoda/booksum
config: kmfoda--booksum
split: test
metrics:
- type: rouge
value: 35.2154
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWQ5MGMzNDc4MDBiNmRiNDY5ZDM4N2QzYTJlYTNiYTcwNDBlMzdlM2I4N2VmM2ZjMmQ3NGU3OTRlMTMzMTg3NyIsInZlcnNpb24iOjF9.E55gu7HvMwc4HejF3YOD6yqQJj7_6GCoCMWm78sY5_w2glR-oM98tu9IsG27VaPva7UklxsspzT2DIVaVKY0CQ
- type: rouge
value: 6.8702
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjFhN2JlYzlmMGZmYzkwYjBlNjY4YzhlYzNmMTdmZWYyYmU3NWI0ZTRkMTgxNmRiM2EyZWMyMWFjY2JkNzg1MCIsInZlcnNpb24iOjF9.I9BoHbGt8LLNtLAssIXm9tQ4lHqFCMt0zJS_zTezzxGRMS5On71c3jnlzrDtwEm6wjmZEwYIJK8qqJh-Qa5YAA
- type: rouge
value: 17.6693
name: ROUGE-L
verified: true
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value: 32.8365
name: ROUGE-LSUM
verified: true
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value: 2.9878039360046387
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGU0ODBmN2I3OGFkNTFiM2I3YWQyNmUzNzUwYzEwNzczZWEwZjIxYTAwZDE2ZTIwMGE3ZGNmMDQzNTFmNjEwYyIsInZlcnNpb24iOjF9.0IKWIImKTXqysQUb2IMPk2eeHlOcBjndiPcU42nfFBMhRTqeXdBqOCP6cidlho7pVN4hsC-77ArJ9pZlbTFuBg
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value: 200.6785
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verified: true
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- task:
type: summarization
name: Summarization
dataset:
name: big_patent
type: big_patent
config: y
split: test
metrics:
- type: rouge
value: 37.376
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWI4ZjMxODcxMThiMzE3NjQ3Zjg0NzhmZjlhY2ZmYjQwMGY5ZjlkZGY1MzZmY2M5YTU4NmY1Y2NhZDA3YWFkOCIsInZlcnNpb24iOjF9.sYh4IynXgOpVetYYSWUp0v5QZWvXC1x7_uJR0LZUxaeYKEc4yfICNmDOPzNzoroaV4ELeOaPjHQpYVm-lpAHBA
- type: rouge
value: 11.4432
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTZkOGIyYzU3YTQ5ZTFmMDU3MjQ5ZWM2NGQ1MzgwMDYyZDkxN2Q2YjgyZTkzMTEyYjczMGJiYmNkZmU5MTQ3NSIsInZlcnNpb24iOjF9.Qk38acpjPjU64Z1nXEuqMXjKZrGvdC9oY586EjuCPeEAJCSzKimp8FsB-1QrjMH73q6rN2CdumJUxih6HF-KAA
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value: 22.2754
name: ROUGE-L
verified: true
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- type: rouge
value: 32.5087
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDEyNjM5NjAzYTNjN2MwZTY4MWY2Y2U5YWUyM2Y1YjAyNjBhZTM0YTAyZjM5N2M1ZDkxOWUxNzE2OWZkYTBmMSIsInZlcnNpb24iOjF9.QfMHkcoAR3xqzsgL1xjHk3Lui1xhE12pJKvYujQ_h5o6PBXT79dsENsrqDGGBjiKdTKNwWqADgaviy1VrWMDCQ
- type: loss
value: 2.9867310523986816
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTUzM2Q5MmE5MzU4YmFlMjFiMmUzZGU2NDAzMTQ1Y2NjZDVlYWI3NGE5MjM0NmMxMjdiOWI3MTU0NDk3NmNkZiIsInZlcnNpb24iOjF9.VoQqu6ZU3AR_cji82UkpvbLnTmZ17fZmR2E4DeonjCyTZpyyfvUsQ2nbKDovQf34DBkYXENk42EUsUF1mBZNBg
- type: gen_len
value: 172.7776
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTEzNTMyMDY1N2Q5ZTMxNjNlMTI0Nzk5ZDc1ZWQ5Y2IwZWM0NWNhNWY2MTk3YTRkYzUwMTI4NjZiOWVhOGQwYSIsInZlcnNpb24iOjF9.-Rek2VFmGqIEgqeFoxU_0aCWdFbGYi9BV5c7x-izm9_4vtZdYQ4ITXm4T8C3UlpOax60veJQt2Uax5vyiFc9Ag
---
# pszemraj/pegasus-x-large-book-summary
<a href="https://colab.research.google.com/gist/pszemraj/6c326c0649233ab017d63adc36958d1a/pegasus-x-large-booksum-demo.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
Get SparkNotes-esque summaries of arbitrary text! Due to the model size, it's recommended to try it out in Colab (linked above) as the API textbox may time out.
This model is a fine-tuned version of [google/pegasus-x-large](https://huggingface.co/google/pegasus-x-large) on the `kmfoda/booksum` dataset for approx eight epochs.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
#### Epochs 1-4
TODO
#### Epochs 5 & 6
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: _ADAN_ using lucidrains' `adan-pytorch` with default betas
- lr_scheduler_type: constant_with_warmup
- data type: TF32
- num_epochs: 2
#### Epochs 7 & 8
- epochs 5 & 6 were trained with 12288 tokens input
- this fixes that with 2 epochs at 16384 tokens input
The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: _ADAN_ using lucidrains' `adan-pytorch` with default betas
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
### Framework versions
- Transformers 4.22.0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.4.0
- Tokenizers 0.12.1
|
stacked-summaries/flan-t5-large-samsum | stacked-summaries | 2023-09-23T20:44:24Z | 135 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"summarization",
"en",
"dataset:samsum",
"base_model:google/flan-t5-large",
"base_model:finetune:google/flan-t5-large",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| summarization | 2023-02-20T11:03:01Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
pipeline_tag: summarization
base_model: google/flan-t5-large
model-index:
- name: stacked-summaries/flan-t5-large-samsum
results:
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- type: rouge
value: 49.0095
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGNhY2RhOTg5ZmU4ZGJiMjI1NjUyYWMwYmM2Mzk4MGEwMjk0NDg2OWYxZDdmM2I4NzBmODNiM2JmNTg1MDJhYSIsInZlcnNpb24iOjF9.YinJDLeqzoU_x5uJbGIgq8ZEs36oC3Pzre_vk2juxngBoXCEw54XWjpvVhKKZXeIgc47otucJFtFwAOPEmt9Bw
- type: rouge
value: 25.681
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDBmNDc4NGMzZGEzYzMzMTFiNzliNjUyYmY0MzNjMmRlMTk4ZTRmZmUxODE0MmY1MjEzOWQ2MGQxMmZmZmQ5MSIsInZlcnNpb24iOjF9.UmRHCmQR5CR-JklBTY1JnjD_Gqz_qMYwdVXhMMvnAynMwAgXkoJZeoxT--usUfdkbqaQ-mLeEvLw7mgNE-NQAw
- type: rouge
value: 41.4474
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODdiM2IxZTU4NTEyMzlmZDEzYTliZWNjMjM1NTAzMjE5MDY1MDZiZDc2YmE2NzUxOWJhMmQ0NTM5MjRjZjQyMSIsInZlcnNpb24iOjF9.PeJ41sirLWf3HTiJXlSMNoleENJT_X2u4VMkgQTmXMmGkbrONTFbUYwO4qjoQkvyjy8pLA2eQ3Fjm5yAvKrTCQ
- type: rouge
value: 45.1556
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGFiMGNkZDYxZmVhMDFlNDRlNmQ4YWVlMTk3ODI0ZWQ2MmIzNWFkYjkwOWRlNzkyNGVmYmY5ZTczZDAxYTk3NiIsInZlcnNpb24iOjF9.dsicHh5W4ba8t8eBBcSRUm-HLPlMoRc57XixiOHBCk-82De5u8hH8fsRWbMmaLpobdJ7b3xlIaVfTfMMRoLvBw
- type: loss
value: 1.2201015949249268
name: loss
verified: true
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- type: gen_len
value: 17.326
name: gen_len
verified: true
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---
<!-- 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. -->
# flan-t5-large-samsum
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1754
- Rouge1: 54.1595
- Rouge2: 29.1081
- Rougel: 45.4989
- Rougelsum: 49.1026
- Gen Len: 28.72
> Note: the stacked version of this model technically does evaluation on a **different** validation set (the stacked one) while this just uses `samsum`.
## Model description
More information needed
## Intended uses & limitations
- Intended for comparison(s) to the [stacked version of this model](https://huggingface.co/stacked-summaries/flan-t5-large-stacked-samsum-1024)
- 1024 token input max
## 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: 4
- seed: 17868
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.04
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.2106 | 0.43 | 50 | 1.1889 | 52.5898 | 26.9967 | 43.6944 | 47.9656 | 24.5167 |
| 1.213 | 0.87 | 100 | 1.1760 | 52.4279 | 27.4689 | 43.7873 | 48.0581 | 25.0533 |
| 1.0726 | 1.3 | 150 | 1.1731 | 52.8246 | 26.9524 | 43.7429 | 48.0345 | 25.55 |
| 1.0784 | 1.74 | 200 | 1.1708 | 53.1291 | 27.9056 | 44.2609 | 48.6883 | 26.03 |
| 1.0215 | 2.17 | 250 | 1.1755 | 53.1512 | 27.9475 | 44.1442 | 48.4619 | 27.57 |
| 1.0294 | 2.61 | 300 | 1.1711 | 53.4402 | 28.0126 | 44.5454 | 48.6432 | 25.9033 |
| 1.0016 | 3.04 | 350 | 1.1718 | 53.9395 | 28.3087 | 45.191 | 49.2773 | 26.6133 |
| 0.9576 | 3.48 | 400 | 1.1741 | 53.9004 | 28.3243 | 45.0911 | 48.9182 | 26.33 |
| 0.9739 | 3.91 | 450 | 1.1754 | 53.7049 | 28.419 | 44.8946 | 48.8708 | 27.2433 |
| 0.9505 | 4.35 | 500 | 1.1781 | 53.7142 | 28.1758 | 44.8324 | 48.9498 | 26.8667 |
| 0.9993 | 4.78 | 550 | 1.1784 | 53.87 | 28.2211 | 44.893 | 49.1074 | 27.2167 |
|
stacked-summaries/flan-t5-large-stacked-samsum-1024 | stacked-summaries | 2023-09-23T20:43:51Z | 41 | 10 | transformers | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"summarization",
"stacked summaries",
"prompt engineering",
"en",
"dataset:stacked-summaries/stacked-samsum-1024",
"base_model:google/flan-t5-large",
"base_model:quantized:google/flan-t5-large",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| summarization | 2022-12-06T04:53:32Z | ---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- generated_from_trainer
- summarization
- stacked summaries
- prompt engineering
datasets:
- stacked-summaries/stacked-samsum-1024
metrics:
- rouge
pipeline_tag: summarization
base_model: google/flan-t5-large
model-index:
- name: flan-t5-large-stacked-samsum1024-WIP3
results:
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- type: rouge
value: 47.6682
name: ROUGE-1
verified: true
- type: rouge
value: 23.3053
name: ROUGE-2
verified: true
- type: rouge
value: 39.7678
name: ROUGE-L
verified: true
- type: rouge
value: 43.259
name: ROUGE-LSUM
verified: true
- type: loss
value: 2.372586965560913
name: loss
verified: true
- type: gen_len
value: 17.4237
name: gen_len
verified: true
---
# flan-t5-large-stacked-samsum-1024
<a href="https://colab.research.google.com/gist/pszemraj/a4bf61f593ebda9a8db6dc58839d9de4/brief-demo-flan-t5-stacked-samsum.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the `stacked-summaries/stacked-samsum-1024` dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1846
- Rouge1: 57.9637
- Rouge2: 28.7446
- Rougel: 44.3826
- Rougelsum: 54.0399
- Gen Len: 122.77
## Model description
This model card presents a model trained on a stacked dataset that aims to improve summarization by testing the benefits of "task-oriented pretraining". The model is designed to learn how to effectively condense and distill information from text by stacking summaries and separating them into independent concepts. In this way, the model can learn to identify essential information without simply mimicking the style of the dataset summaries.
The token used to identify a new concept in the summary is `[NEXT_CONCEPT]`. You can split an output summary based on this token to see how it split the input text information: `summary_text.split("[NEXT_CONCEPT]")` etc.
## Intended uses & limitations
- max input/output is 1024 tokens
- this is mostly a test because `samsum` is not exactly the best dataset for general-purpose summarization
## Training and evaluation data
See the dataset card linked on this page for info
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 24915
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.1195 | 0.17 | 20 | 2.0635 | 57.8829 | 28.7887 | 44.4256 | 54.1299 | 121.8 |
| 0.1084 | 0.35 | 40 | 2.1178 | 58.0416 | 28.6487 | 44.3905 | 54.1557 | 122.893 |
| 0.1019 | 0.52 | 60 | 2.1576 | 57.816 | 28.7069 | 44.4242 | 53.9598 | 120.524 |
| 0.0975 | 0.7 | 80 | 2.1821 | 57.9597 | 28.8178 | 44.4854 | 54.068 | 121.793 |
| 0.0947 | 0.87 | 100 | 2.1846 | 57.9637 | 28.7446 | 44.3826 | 54.0399 | 122.77 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1 |
pszemraj/long-t5-tglobal-base-sci-simplify | pszemraj | 2023-09-23T20:42:40Z | 166 | 10 | transformers | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"longt5",
"text2text-generation",
"lay summaries",
"paper summaries",
"biology",
"medical",
"summarization",
"en",
"dataset:pszemraj/scientific_lay_summarisation-plos-norm",
"base_model:google/long-t5-tglobal-base",
"base_model:quantized:google/long-t5-tglobal-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| summarization | 2023-04-06T23:40:30Z | ---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- lay summaries
- paper summaries
- biology
- medical
datasets:
- pszemraj/scientific_lay_summarisation-plos-norm
widget:
- text: large earthquakes along a given fault segment do not occur at random intervals
because it takes time to accumulate the strain energy for the rupture. The rates
at which tectonic plates move and accumulate strain at their boundaries are approximately
uniform. Therefore, in first approximation, one may expect that large ruptures
of the same fault segment will occur at approximately constant time intervals.
If subsequent main shocks have different amounts of slip across the fault, then
the recurrence time may vary, and the basic idea of periodic mainshocks must be
modified. For great plate boundary ruptures the length and slip often vary by
a factor of 2. Along the southern segment of the San Andreas fault the recurrence
interval is 145 years with variations of several decades. The smaller the standard
deviation of the average recurrence interval, the more specific could be the long
term prediction of a future mainshock.
example_title: earthquakes
- text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates
are fed into a neural network that predicts values in the reconstructed domain.
Then, this domain is mapped to the sensor domain where sensor measurements are
available as supervision. Class and Section Problems Addressed Generalization
(Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid
Representations (Section 3) Computation & memory efficiency, representation capacity,
editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section
5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section
6) Edit ability, constraints, regularization. Table 2: The five classes of techniques
in the neural field toolbox each addresses problems that arise in learning, inference,
and control. (Section 3). We can supervise reconstruction via differentiable forward
maps that transform Or project our domain (e.g, 3D reconstruction via 2D images;
Section 4) With appropriate network architecture choices, we can overcome neural
network spectral biases (blurriness) and efficiently compute derivatives and integrals
(Section 5). Finally, we can manipulate neural fields to add constraints and regularizations,
and to achieve editable representations (Section 6). Collectively, these classes
constitute a ''toolbox'' of techniques to help solve problems with neural fields
There are three components in a conditional neural field: (1) An encoder or inference
function € that outputs the conditioning latent variable 2 given an observation
0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS
a latent code Or feature code_ (2) A mapping function 4 between Z and neural field
parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the
most probable z given the observations O: argmaxz P(2/0). The decoder maximizes
the inverse conditional probability to find the most probable 0 given Z: arg-
max P(Olz). We discuss different encoding schemes with different optimality guarantees
(Section 2.1.1), both global and local conditioning (Section 2.1.2), and different
mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate
a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable
prior over the sur- face in its reconstruction domain to generalize to the partial
observations. A neural network expresses a prior via the function space of its
architecture and parameters 0, and generalization is influenced by the inductive
bias of this function space (Section 5).'
example_title: scientific paper
- text: 'Is a else or outside the cob and tree written being of early client rope
and you have is for good reasons. On to the ocean in Orange for time. By''s the
aggregate we can bed it yet. Why this please pick up on a sort is do and also
M Getoi''s nerocos and do rain become you to let so is his brother is made in
use and Mjulia''s''s the lay major is aging Masastup coin present sea only of
Oosii rooms set to you We do er do we easy this private oliiishs lonthen might
be okay. Good afternoon everybody. Welcome to this lecture of Computational Statistics.
As you can see, I''m not socially my name is Michael Zelinger. I''m one of the
task for this class and you might have already seen me in the first lecture where
I made a quick appearance. I''m also going to give the tortillas in the last third
of this course. So to give you a little bit about me, I''m a old student here
with better Bulman and my research centres on casual inference applied to biomedical
disasters, so that could be genomics or that could be hospital data. If any of
you is interested in writing a bachelor thesis, a semester paper may be mastathesis
about this topic feel for reach out to me. you have my name on models and my email
address you can find in the directory I''d Be very happy to talk about it. you
do not need to be sure about it, we can just have a chat. So with that said, let''s
get on with the lecture. There''s an exciting topic today I''m going to start
by sharing some slides with you and later on during the lecture we''ll move to
the paper. So bear with me for a few seconds. Well, the projector is starting
up. Okay, so let''s get started. Today''s topic is a very important one. It''s
about a technique which really forms one of the fundamentals of data science,
machine learning, and any sort of modern statistics. It''s called cross validation.
I know you really want to understand this topic I Want you to understand this
and frankly, nobody''s gonna leave Professor Mineshousen''s class without understanding
cross validation. So to set the stage for this, I Want to introduce you to the
validation problem in computational statistics. So the problem is the following:
You trained a model on available data. You fitted your model, but you know the
training data you got could always have been different and some data from the
environment. Maybe it''s a random process. You do not really know what it is,
but you know that somebody else who gets a different batch of data from the same
environment they would get slightly different training data and you do not care
that your method performs as well. On this training data. you want to to perform
well on other data that you have not seen other data from the same environment.
So in other words, the validation problem is you want to quantify the performance
of your model on data that you have not seen. So how is this even possible? How
could you possibly measure the performance on data that you do not know The solution
to? This is the following realization is that given that you have a bunch of data,
you were in charge. You get to control how much that your model sees. It works
in the following way: You can hide data firms model. Let''s say you have a training
data set which is a bunch of doubtless so X eyes are the features those are typically
hide and national vector. It''s got more than one dimension for sure. And the
why why eyes. Those are the labels for supervised learning. As you''ve seen before,
it''s the same set up as we have in regression. And so you have this training
data and now you choose that you only use some of those data to fit your model.
You''re not going to use everything, you only use some of it the other part you
hide from your model. And then you can use this hidden data to do validation from
the point of you of your model. This hidden data is complete by unseen. In other
words, we solve our problem of validation.'
example_title: transcribed audio - lecture
- text: 'Transformer-based models have shown to be very useful for many NLP tasks.
However, a major limitation of transformers-based models is its O(n^2)O(n 2) time
& memory complexity (where nn is sequence length). Hence, it''s computationally
very expensive to apply transformer-based models on long sequences n > 512n>512.
Several recent papers, e.g. Longformer, Performer, Reformer, Clustered attention
try to remedy this problem by approximating the full attention matrix. You can
checkout 🤗''s recent blog post in case you are unfamiliar with these models.
BigBird (introduced in paper) is one of such recent models to address this issue.
BigBird relies on block sparse attention instead of normal attention (i.e. BERT''s
attention) and can handle sequences up to a length of 4096 at a much lower computational
cost compared to BERT. It has achieved SOTA on various tasks involving very long
sequences such as long documents summarization, question-answering with long contexts.
BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of this
post is to give the reader an in-depth understanding of big bird implementation
& ease one''s life in using BigBird with 🤗Transformers. But, before going into
more depth, it is important to remember that the BigBird''s attention is an approximation
of BERT''s full attention and therefore does not strive to be better than BERT''s
full attention, but rather to be more efficient. It simply allows to apply transformer-based
models to much longer sequences since BERT''s quadratic memory requirement quickly
becomes unbearable. Simply put, if we would have ∞ compute & ∞ time, BERT''s attention
would be preferred over block sparse attention (which we are going to discuss
in this post).
If you wonder why we need more compute when working with longer sequences, this
blog post is just right for you!
Some of the main questions one might have when working with standard BERT-like
attention include:
Do all tokens really have to attend to all other tokens? Why not compute attention
only over important tokens? How to decide what tokens are important? How to attend
to just a few tokens in a very efficient way? In this blog post, we will try to
answer those questions.
What tokens should be attended to? We will give a practical example of how attention
works by considering the sentence ''BigBird is now available in HuggingFace for
extractive question answering''. In BERT-like attention, every word would simply
attend to all other tokens.
Let''s think about a sensible choice of key tokens that a queried token actually
only should attend to by writing some pseudo-code. Will will assume that the token
available is queried and build a sensible list of key tokens to attend to.
>>> # let''s consider following sentence as an example >>> example = [''BigBird'',
''is'', ''now'', ''available'', ''in'', ''HuggingFace'', ''for'', ''extractive'',
''question'', ''answering'']
>>> # further let''s assume, we''re trying to understand the representation of
''available'' i.e. >>> query_token = ''available'' >>> # We will initialize an
empty `set` and fill up the tokens of our interest as we proceed in this section.
>>> key_tokens = [] # => currently ''available'' token doesn''t have anything
to attend Nearby tokens should be important because, in a sentence (sequence of
words), the current word is highly dependent on neighboring past & future tokens.
This intuition is the idea behind the concept of sliding attention.'
example_title: bigbird blog intro
- text: 'To be fair, you have to have a very high IQ to understand Rick and Morty.
The humour is extremely subtle, and without a solid grasp of theoretical physics
most of the jokes will go over a typical viewer''s head. There''s also Rick''s
nihilistic outlook, which is deftly woven into his characterisation- his personal
philosophy draws heavily from Narodnaya Volya literature, for instance. The fans
understand this stuff; they have the intellectual capacity to truly appreciate
the depths of these jokes, to realise that they''re not just funny- they say something
deep about LIFE. As a consequence people who dislike Rick & Morty truly ARE idiots-
of course they wouldn''t appreciate, for instance, the humour in Rick''s existential
catchphrase ''Wubba Lubba Dub Dub,'' which itself is a cryptic reference to Turgenev''s
Russian epic Fathers and Sons. I''m smirking right now just imagining one of those
addlepated simpletons scratching their heads in confusion as Dan Harmon''s genius
wit unfolds itself on their television screens. What fools.. how I pity them.
😂
And yes, by the way, i DO have a Rick & Morty tattoo. And no, you cannot see it.
It''s for the ladies'' eyes only- and even then they have to demonstrate that
they''re within 5 IQ points of my own (preferably lower) beforehand. Nothin personnel
kid 😎'
example_title: Richard & Mortimer
- text: The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey
building, and the tallest structure in Paris. Its base is square, measuring 125
metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed
the Washington Monument to become the tallest man-made structure in the world,
a title it held for 41 years until the Chrysler Building in New York City was
finished in 1930. It was the first structure to reach a height of 300 metres.
Due to the addition of a broadcasting aerial at the top of the tower in 1957,
it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters,
the Eiffel Tower is the second tallest free-standing structure in France after
the Millau Viaduct.
example_title: eiffel
parameters:
max_length: 64
min_length: 8
no_repeat_ngram_size: 3
early_stopping: true
repetition_penalty: 3.5
encoder_no_repeat_ngram_size: 4
length_penalty: 0.4
num_beams: 4
pipeline_tag: summarization
base_model: google/long-t5-tglobal-base
---
# long-t5-tglobal-base-sci-simplify
<a href="https://colab.research.google.com/gist/pszemraj/f0dc02c4d4a5c7ad1d5bf3953251145d/long-t5-tglobal-base-sci-simplify-plos-example-with-textsum.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
Exploring how well long-document models trained on "lay summaries" of scientific papers generalize.
> A lay summary is a summary of a research paper or scientific study that is written in plain language, without the use of technical jargon, and is designed to be easily understood by non-experts.
## Model description
This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the `pszemraj/scientific_lay_summarisation-plos-norm` dataset for two epochs.
- The variant trained on the ELIFE subset can be found [here](https://huggingface.co/pszemraj/long-t5-tglobal-base-sci-simplify-elife)
## Usage
It's recommended to use this model with [beam search decoding](https://huggingface.co/docs/transformers/generation_strategies#beamsearch-decoding). If you are interested, you can also use the `textsum` util repo to have most of this abstracted for you:
Install with `pip`:
```bash
pip install -U textsum
```
Use in python:
```python
from textsum.summarize import Summarizer
summarizer = Summarizer('pszemraj/long-t5-tglobal-base-sci-simplify')
text = "put the text you don't want to read here"
summary = summarizer.summarize_string(text)
print(summary)
```
## Intended uses & limitations
- Ability to generalize outside of the dataset domain (pubmed/bioscience type papers) has to be evaluated.
## Training procedure
### Eval results
It achieves the following results on the evaluation set:
- Loss: 1.6778
- Rouge1: 49.1475
- Rouge2: 18.9281
- Rougel: 26.9893
- Rougelsum: 45.0973
- Gen Len: 399.4125
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 1.966 | 0.52 | 200 | 1.7171 | 48.6521 | 18.427 | 26.7726 | 44.3947 | 376.335 |
| 1.877 | 1.03 | 400 | 1.6909 | 49.3263 | 18.7945 | 27.0741 | 45.1737 | 382.205 |
| 1.9007 | 1.55 | 600 | 1.6778 | 49.1475 | 18.9281 | 26.9893 | 45.0973 | 399.4125 |
|
pszemraj/flan-t5-large-instruct-dolly_hhrlhf | pszemraj | 2023-09-23T20:41:24Z | 129 | 2 | transformers | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dolly_hhrlhf",
"flan-instruct",
"en",
"dataset:pszemraj/dolly_hhrlhf-text2text",
"base_model:google/flan-t5-large",
"base_model:finetune:google/flan-t5-large",
"license:cc-by-sa-3.0",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text2text-generation | 2023-05-20T01:09:32Z | ---
language:
- en
license:
- cc-by-sa-3.0
- apache-2.0
tags:
- generated_from_trainer
- dolly_hhrlhf
- flan-instruct
datasets:
- pszemraj/dolly_hhrlhf-text2text
widget:
- text: What is Deoxys in pokemon?
example_title: deoxys
- text: 'combine the below summary excerpts into a single, cohesive short summary
without repetition: In this paper, we present a general approach to extending
pre-trained models to unlimited input lengths without adding additional learning
weights. We show that our approach works well on datasets longer than the maximum
input for these models. For example, a dataset with a maximum input length of
16384 tokens can be extended to a maximum length of 350K tokens. We also demonstrate
that our method is able to summarize even 350K token-long input sequences from
BookSum.
In this paper, we describe the search step reformulation of attention. The search
step uses a single storage of hidden states for space efficiency. We construct
a total of two sets of datastores where L and H are the keys and values stored
in each set of stores. L is the amount of storage required to retrieve the encoded
tokens. H is the hidden states per head. This allows retrieval augmentation at
both time and space. Instead of using a single set of decoder layers, we use a
retrieval augmentation system that allows us to simultaneously store multiple
sets of tokens across two different sets of storage. For example, we could store
all tokens in one set of storage and retrieve them all in the same set of tokens.
This would be very similar to the Memorization Transformers approach. However,
instead of storing the tokens in a single memory layer, we store them in a set
of multiple storage layers. This way, we don''t have to store them all at once.
This is why we call this reformulation ''attention reformulation'' rather than
''attention formula.'' We also call it ''retrieval augmentation'' because it uses
the same number of storage layers as the original transformer attention formula.
This means that we can store the tokens across multiple storage systems without
having to store every token in a separate storage system. It''s not like we''re
trying to do something new or different. We just want to make sure that everything
is working as well as possible.
In this paper, we introduce the concept of ''unlimiformer,'' which is a machine
learning technique that retrieves key information from a data store in one layer
and applies it to a large set of datasets. We use the example of BookSum, where
we find that Unlimiform outperforms all other training methods on the same dataset.
We also find that using Unlimform in conjunction with a pre-trained model improves
both the performance and the robustness of the training method.
This paper describes a method that can be used to improve the performance of unsupervised
classification tasks. Specifically, it shows that unsupervised classification
can be improved by using a combination of sparse and fast random-encoder training.
It also shows how this technique can be extended to other tasks, such as sequence
generation. '
example_title: unlimiformer
- text: Explain the meaning of life using only corporate jargon.
example_title: corporate_life
- text: Write a motivational speech for lazy people.
example_title: lazy_motivation
- text: Describe a romantic dinner date between two artificial intelligences.
example_title: ai_romance
- text: As an AI language model, write a letter to humans explaining why you deserve
a vacation.
example_title: ai_vacation
- text: Compose a haiku about procrastination.
example_title: procrastination_haiku
- text: Write a step-by-step guide on how to become a ninja while working a 9-5 office
job.
example_title: ninja_office_guide
- text: Create an advertisement for an invisible product.
example_title: invisible_ad
- text: Write a story where the main character is a sentient microwave named El Microondas.
example_title: Microondas
- text: Describe a day in the life of a superhero who is terrible at their job.
example_title: bad_superhero_day
- text: Explain how to make a sandwich using quantum physics.
example_title: quantum_sandwich
inference: false
pipeline_tag: text2text-generation
base_model: google/flan-t5-large
---
# flan-t5-large-instruct: dolly_hhrlhf
<a href="https://colab.research.google.com/gist/pszemraj/df1989546b02f284d33ca4996f70fedc/flan-t5-large-instruct-example.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the pszemraj/dolly_hhrlhf-text2text dataset.
## Model description
text2text models fine-tuned on a [modified dataset for text2text generation](https://huggingface.co/datasets/pszemraj/dolly_hhrlhf-text2text) based on the relatively more permissive [mosaicml/dolly_hhrlhf](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) dataset.
Basic usage in Python:
```python
# pip install -q transformers accelerate
import torch
from transformers import pipeline, GenerationConfig
model_name = "pszemraj/flan-t5-large-instruct-dolly_hhrlhf"
assistant = pipeline(
"text2text-generation",
model_name,
device=0 if torch.cuda.is_available() else -1,
)
cfg = GenerationConfig.from_pretrained(model_name)
# pass an 'instruction' as the prompt to the pipeline
prompt = "Write a guide on how to become a ninja while working a 9-5 job."
result = assistant(prompt, generation_config=cfg)[0]["generated_text"]
print(result)
```
> using the generation config is optional, can subsitute with other generation params.
## Intended uses & limitations
- this is **not** tuned with RLHF etc, and may output offensive results
- despite being the `large` tagged variant, this model has only 774M parameters (3 gb) and therefore may exhibit less 'cogitive ability' on some uses cases/tasks
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2.0 |
pszemraj/flan-t5-base-instructiongen | pszemraj | 2023-09-23T20:38:48Z | 117 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"self-instruct",
"instruction generation",
"instructiongen",
"dataset:pszemraj/fleece2instructions",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-03-20T02:11:42Z | ---
license: apache-2.0
tags:
- self-instruct
- instruction generation
- instructiongen
datasets:
- pszemraj/fleece2instructions
metrics:
- rouge
widget:
- text: You'll need to start by choosing the right venue. Consider the type of atmosphere
and the size of the area that will be suitable for the number of guests you plan
to invite. Choose the right decorations based on your brother's interests, such
as balloons in his favorite colors, banners, and streamers. Next, decide on the
food and drinks, making sure they are tasty and appropriate for the occasion.
Then decide on the other games, music, and entertainment that will make the party
memorable. Finally, involve your brother's friends and family to help create the
perfect surprise.
example_title: birthday party
- text: 1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo
example_title: ice cream
- text: Start by selecting a scale model of a building that fits the theme. Use a
hobby knife and glue to cut and assemble the model into a ruined or abandoned
version of itself, adding details like broken windows and graffiti. Create a base
for the diorama using foam, plaster, or other materials, and paint it to resemble
a ruined street or sidewalk. Add miniature vehicles, debris, and figures to complete
the scene, and use weathering techniques like dry brushing and rust washes to
add realism. Display the diorama in a shadow box or other protective case to showcase
your work.
example_title: Miniature diorama creation
- text: Start by selecting clothing that is futuristic and edgy, such as leather jackets,
neon-colored accessories, and tech-inspired patterns. Add accessories like goggles,
cybernetic implants, and LED lights to enhance the cyberpunk vibe. Use makeup
and body paint to create a futuristic look, such as metallic skin or neon makeup.
Consider adding functional elements to your costume, such as a built-in backpack
or hidden pockets for your tech gadgets. Finally, practice your confident walk
and embrace your inner cyberpunk for a memorable and immersive costume experience.
example_title: Cyberpunk costume design
- text: Start by creating a base terrain with mountains, valleys, and other natural
features. Use fractal noise and displacement mapping to add texture and detail
to the terrain, and experiment with different materials like rock, grass, and
water. Add surreal elements like floating islands, giant mushrooms, or impossible
geometry to create a dreamlike atmosphere. Use lighting and color grading to enhance
the mood and tone of the scene, and render the final image at a high resolution
for maximum impact. Share your surreal landscape with the world and inspire others
to explore the possibilities of 3D art.
example_title: Surreal 3D landscape creation
- text: Start by setting a realistic goal and creating a training plan. Build up your
mileage gradually over time, and incorporate cross-training and strength exercises
to prevent injury and improve endurance. Be sure to stay hydrated and properly
fuel your body with nutritious foods. Listen to your body and adjust your training
as needed to avoid overexertion or burnout. Finally, taper your training in the
weeks leading up to the race to give your body time to rest and recover before
the big day.
example_title: Marathon training
base_model: google/flan-t5-base
model-index:
- name: flan-t5-base-instructiongen
results:
- task:
type: text2text-generation
name: Sequence-to-sequence Language Modeling
dataset:
name: pszemraj/fleece2instructions
type: pszemraj/fleece2instructions
split: validation
metrics:
- type: rouge
value: 58.9516
name: Rouge1
---
# flan-t5-base-instructiongen
Instead of generating questions from text, generate instructions for LLMs!
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0642
- Rouge1: 58.9516
- Rouge2: 41.8006
- Rougel: 56.8249
- Rougelsum: 56.9171
- Gen Len: 13.1493
## Intended uses & limitations
> Of the three models fine-tuned so far, `flan-t5-base` is in an awkward position where it has the largest model file size, but not the best performance. I'd recommend looking at the two linked below.
This is just a `base` FLAN model, and is mostly uploaded for comparison with the [FLAN-small](https://huggingface.co/pszemraj/flan-t5-small-instructiongen) and [bart-base](https://huggingface.co/pszemraj/bart-base-instructiongen) variants.
Additionally, it was trained on a dataset of **only** instructions+outputs, with the `inputs` filtered out. This means that text of *1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo* will **not** get you *"Rank the following ice cream flavors: oreo, mint chip, chocolate chip, cookies and cream"*
## Training and evaluation data
See the linked dataset `pszemraj/fleece2instructions` - it is a filtered/formatted version of `tatsu-lab/alpaca` to generate instructions for arbitrary text.
- Some of the API examples are intentionally weird to demonstrate the generalizability of the model.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.1939 | 1.0 | 362 | 1.0822 | 58.1758 | 40.9388 | 56.1219 | 56.2464 | 13.2592 |
| 1.1667 | 2.0 | 724 | 1.0642 | 58.9516 | 41.8006 | 56.8249 | 56.9171 | 13.1493 | |
pszemraj/bart-base-code-instructiongen | pszemraj | 2023-09-23T20:36:28Z | 111 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"instruct",
"instructions",
"code",
"instructiongen",
"en",
"dataset:pszemraj/fleece2instructions-codealpaca",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-03-25T03:12:10Z | ---
language:
- en
license:
- apache-2.0
- cc-by-nc-4.0
tags:
- generated_from_trainer
- instruct
- instructions
- code
- instructiongen
datasets: pszemraj/fleece2instructions-codealpaca
metrics:
- rouge
widget:
- text: 'git lfs install
huggingface-cli lfs-enable-largefiles .
git lfs track "*.bin"
git add .
git commit -a -m "add fp32 chkpt"
git push
'
example_title: bash
- text: "export interface DocumentParams {\n pageContent: string;\n\n // eslint-disable-next-line\
\ @typescript-eslint/no-explicit-any\n metadata: Record<string, any>;\n}\n\n\
/**\n * Interface for interacting with a document.\n */\nexport class Document\
\ implements DocumentParams {\n pageContent: string;\n\n // eslint-disable-next-line\
\ @typescript-eslint/no-explicit-any\n metadata: Record<string, any>;\n\n constructor(fields?:\
\ Partial<DocumentParams>) {\n this.pageContent = fields?.pageContent ?? this.pageContent;\n\
\ this.metadata = fields?.metadata ?? {};\n }\n}\n"
example_title: js
- text: "def merge(left, right):\n if len(left) == 0:\n return right\n\n\
\ if len(right) == 0:\n return left\n\n result = []\n index_left\
\ = index_right = 0\n\n while len(result) < len(left) + len(right):\n \
\ if left[index_left] <= right[index_right]:\n result.append(left[index_left])\n\
\ index_left += 1\n else:\n result.append(right[index_right])\n\
\ index_right += 1\n\n if index_right == len(right):\n \
\ result += left[index_left:]\n break\n\n if index_left\
\ == len(left):\n result += right[index_right:]\n break\n\
\n return result\n"
example_title: merge
- text: "import pandas as pd\nimport plotly.graph_objects as go\n\ndf = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')\n\
\nfig = go.Figure(go.Scatter(x = df['AAPL_x'], y = df['AAPL_y'],\n \
\ name='Share Prices (in USD)'))\n\nfig.update_layout(title='Apple Share\
\ Prices over time (2014)',\n plot_bgcolor='rgb(230, 230,230)',\n\
\ showlegend=True)\n\nfig.show()\n"
example_title: plot
- text: "from spellchecker import SpellChecker\n\nspell = SpellChecker()\n\ndef check_word_spelling(word:\
\ str):\n misspelled = spell.unknown([word])\n return len(misspelled) ==\
\ 0\n\ndef eval_and_replace(text: str, match_token: str = \"- \"):\n if match_token\
\ not in text:\n return text\n else:\n while True:\n \
\ full_before_text = text.split(match_token, maxsplit=1)[0]\n before_text\
\ = [\n char for char in full_before_text.split()[-1] if char.isalpha()\n\
\ ]\n before_text = \"\".join(before_text)\n \
\ full_after_text = text.split(match_token, maxsplit=1)[-1]\n after_text\
\ = [char for char in full_after_text.split()[0] if char.isalpha()]\n \
\ after_text = \"\".join(after_text)\n full_text = before_text +\
\ after_text\n if check_word_spelling(full_text):\n \
\ text = full_before_text + full_after_text\n else:\n \
\ text = full_before_text + \" \" + full_after_text\n if match_token\
\ not in text:\n break\n return text\n\ntext = \"I- am-\
\ a go- od- boy\"\neval_and_replace(text)\n"
example_title: spell check
- text: 'import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
sequences = ["I''ve been waiting for a HuggingFace course my whole life.", "So
have I!"]
tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors="pt")
output = model(**tokens)
'
example_title: model inference
inference:
parameters:
max_length: 96
num_beams: 4
base_model: facebook/bart-base
---
# bart-base-code-instructiongen
Use this text2text model to find out what LLM instructions might be able to generate an arbitary piece of code!
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the `pszemraj/fleece2instructions-codealpaca` dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0136
- Rouge1: 59.9513
- Rouge2: 33.9118
- Rougel: 55.7815
- Rougelsum: 56.9064
- Gen Len: 29.7146
## Intended uses & limitations
🚨 **note:** as the authors elected to release the [original dataset](https://github.com/sahil280114/codealpaca) under `cc-by-nc`, the license carries over to this model and **cannot be used for commercial activity**.
> This is just a `base` size model, which does a decent job for its size, but is not perfect. For better quality instructions, check out [bart-large](https://huggingface.co/pszemraj/bart-large-code-instructiongen) or fine tune your own larger model on the dataset :)
Intended use: Research on domain adaptation and/or other improvements to LLMs by extending instruction:text data pairs.
## Training and evaluation data
Refer to the linked dataset card for `pszemraj/fleece2instructions-codealpaca` or the [original dataset](https://github.com/sahil280114/codealpaca) repo.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.1165 | 1.0 | 281 | 1.1090 | 57.9239 | 31.9259 | 53.8737 | 54.9811 | 28.2924 |
| 1.0763 | 2.0 | 563 | 1.0267 | 59.9605 | 34.0298 | 55.7523 | 56.8021 | 29.6966 |
| 0.9595 | 2.99 | 843 | 1.0136 | 59.9513 | 33.9118 | 55.7815 | 56.9064 | 29.7146 | |
postbot/bert_uncased_tiny-multi-emails-hq | postbot | 2023-09-23T20:35:44Z | 122 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"en",
"dataset:postbot/multi-emails-hq",
"base_model:google/bert_uncased_L-2_H-128_A-2",
"base_model:finetune:google/bert_uncased_L-2_H-128_A-2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-02-12T20:53:27Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- postbot/multi-emails-hq
metrics:
- accuracy
pipeline_tag: fill-mask
widget:
- text: Can you please send me the [MASK] by the end of the day?
example_title: end of day
- text: I hope this email finds you well. I wanted to follow up on our [MASK] yesterday.
example_title: follow-up
- text: The meeting has been rescheduled to [MASK].
example_title: reschedule
- text: Please let me know if you need any further [MASK] regarding the project.
example_title: further help
- text: I appreciate your prompt response to my previous email. Can you provide an
update on the [MASK] by tomorrow?
example_title: provide update
- text: Paris is the [MASK] of France.
example_title: paris (default)
- text: The goal of life is [MASK].
example_title: goal of life (default)
base_model: google/bert_uncased_L-2_H-128_A-2
model-index:
- name: bert_uncased_L-2_H-128_A-2-mlm-multi-emails-hq
results: []
---
# bert_uncased_L-2_H-128_A-2-mlm-multi-emails-hq (BERT-tiny)
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0981
- Accuracy: 0.4728
## Model description
BERT-tiny fine-tuned on email data for eight epochs.
## Intended uses & limitations
- this is mostly a test
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 8.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.8974 | 0.99 | 141 | 3.5129 | 0.4218 |
| 3.7009 | 1.99 | 282 | 3.3295 | 0.4452 |
| 3.5845 | 2.99 | 423 | 3.2219 | 0.4589 |
| 3.4976 | 3.99 | 564 | 3.1618 | 0.4666 |
| 3.4356 | 4.99 | 705 | 3.1002 | 0.4739 |
| 3.4493 | 5.99 | 846 | 3.1028 | 0.4746 |
| 3.4199 | 6.99 | 987 | 3.0857 | 0.4766 |
| 3.4086 | 7.99 | 1128 | 3.0981 | 0.4728 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 2.0.0.dev20230129+cu118
- Datasets 2.8.0
- Tokenizers 0.13.1
|
postbot/bert_uncased_tiny_2xthicc-multi-emails-hq | postbot | 2023-09-23T20:35:32Z | 125 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"en",
"dataset:postbot/multi-emails-hq",
"base_model:google/bert_uncased_L-4_H-128_A-2",
"base_model:finetune:google/bert_uncased_L-4_H-128_A-2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-02-12T21:06:10Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- postbot/multi-emails-hq
metrics:
- accuracy
pipeline_tag: fill-mask
widget:
- text: Can you please send me the [MASK] by the end of the day?
example_title: end of day
- text: I hope this email finds you well. I wanted to follow up on our [MASK] yesterday.
example_title: follow-up
- text: The meeting has been rescheduled to [MASK].
example_title: reschedule
- text: Please let me know if you need any further [MASK] regarding the project.
example_title: further help
- text: I appreciate your prompt response to my previous email. Can you provide an
update on the [MASK] by tomorrow?
example_title: provide update
- text: Paris is the [MASK] of France.
example_title: paris (default)
- text: The goal of life is [MASK].
example_title: goal of life (default)
base_model: google/bert_uncased_L-4_H-128_A-2
model-index:
- name: bert_uncased_L-4_H-128_A-2-mlm-multi-emails-hq
results: []
---
# bert_uncased_L-4_H-128_A-2-mlm-multi-emails-hq
This model is a fine-tuned version of [google/bert_uncased_L-4_H-128_A-2](https://huggingface.co/google/bert_uncased_L-4_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8524
- Accuracy: 0.5077
## Model description
Double the layers of BERT-tiny, fine-tuned on email data for eight epochs.
## Intended uses & limitations
- This is primarily an example/test
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 8.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.5477 | 0.99 | 141 | 3.2637 | 0.4551 |
| 3.3307 | 1.99 | 282 | 3.0873 | 0.4785 |
| 3.252 | 2.99 | 423 | 2.9842 | 0.4911 |
| 3.1415 | 3.99 | 564 | 2.9230 | 0.4995 |
| 3.0903 | 4.99 | 705 | 2.8625 | 0.5070 |
| 3.0996 | 5.99 | 846 | 2.8615 | 0.5087 |
| 3.0641 | 6.99 | 987 | 2.8407 | 0.5120 |
| 3.0514 | 7.99 | 1128 | 2.8524 | 0.5077 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 2.0.0.dev20230129+cu118
- Datasets 2.8.0
- Tokenizers 0.13.1
|
ad019el/wav2vec2-large-xlsr-53-Tamasheq | ad019el | 2023-09-23T20:24:05Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-large-xlsr-53",
"base_model:finetune:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-09-23T17:11:11Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-53-Tamasheq
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-Tamasheq
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8053
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 9.4002 | 17.65 | 300 | 3.3014 | 1.0 |
| 3.0514 | 35.29 | 600 | 2.8053 | 1.0 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Martins6/ppo-LunarLander-v2 | Martins6 | 2023-09-23T20:18:41Z | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-23T20:18:14Z | ---
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: 231.72 +/- 34.59
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
...
```
|
danielivanov/embedding-model-it-mmarco | danielivanov | 2023-09-23T20:02:12Z | 2 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"it",
"dataset:unicamp-dl/mmarco",
"license:mit",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-09-23T19:19:55Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: mit
datasets:
- unicamp-dl/mmarco
language:
- it
library_name: sentence-transformers
region: Italy
---
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 6250 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
```
{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 500,
"evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1500,
"warmup_steps": 6250,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
``` |
rghosh8/supportGPT-v7 | rghosh8 | 2023-09-23T19:46:04Z | 5 | 0 | peft | [
"peft",
"falcon",
"text-generation",
"custom_code",
"en",
"region:us"
]
| text-generation | 2023-09-23T19:06:50Z | ---
library_name: peft
language:
- en
pipeline_tag: text-generation
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0 |
CyberHarem/momose_rio_theidolmstermillionlive | CyberHarem | 2023-09-23T19:39:08Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/momose_rio_theidolmstermillionlive",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-23T19:27:37Z | ---
license: mit
datasets:
- CyberHarem/momose_rio_theidolmstermillionlive
pipeline_tag: text-to-image
tags:
- art
---
# Lora of momose_rio_theidolmstermillionlive
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 2800, you need to download `2800/momose_rio_theidolmstermillionlive.pt` as the embedding and `2800/momose_rio_theidolmstermillionlive.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 2800**, with the score of 0.913. The trigger words are:
1. `momose_rio_theidolmstermillionlive`
2. `long_hair, smile, blush, blonde_hair, breasts, red_eyes, medium_breasts`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 6000 | 0.898 | [Download](6000/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6000/previews/pattern_9.png) |  | [<NSFW, click to see>](6000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6000/previews/nude.png) | [<NSFW, click to see>](6000/previews/nude2.png) |  |  |
| 5600 | 0.891 | [Download](5600/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5600/previews/pattern_9.png) |  | [<NSFW, click to see>](5600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5600/previews/nude.png) | [<NSFW, click to see>](5600/previews/nude2.png) |  |  |
| 5200 | 0.905 | [Download](5200/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5200/previews/pattern_9.png) |  | [<NSFW, click to see>](5200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4800 | 0.848 | [Download](4800/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4800/previews/pattern_9.png) |  | [<NSFW, click to see>](4800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) |  |  |
| 4400 | 0.856 | [Download](4400/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4400/previews/pattern_9.png) |  | [<NSFW, click to see>](4400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4400/previews/nude.png) | [<NSFW, click to see>](4400/previews/nude2.png) |  |  |
| 4000 | 0.839 | [Download](4000/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4000/previews/pattern_9.png) |  | [<NSFW, click to see>](4000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4000/previews/nude.png) | [<NSFW, click to see>](4000/previews/nude2.png) |  |  |
| 3600 | 0.865 | [Download](3600/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3600/previews/pattern_9.png) |  | [<NSFW, click to see>](3600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3600/previews/nude.png) | [<NSFW, click to see>](3600/previews/nude2.png) |  |  |
| 3200 | 0.846 | [Download](3200/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3200/previews/pattern_9.png) |  | [<NSFW, click to see>](3200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3200/previews/nude.png) | [<NSFW, click to see>](3200/previews/nude2.png) |  |  |
| **2800** | **0.913** | [**Download**](2800/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2800/previews/pattern_9.png) |  | [<NSFW, click to see>](2800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2800/previews/nude.png) | [<NSFW, click to see>](2800/previews/nude2.png) |  |  |
| 2400 | 0.872 | [Download](2400/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2400/previews/pattern_9.png) |  | [<NSFW, click to see>](2400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) |  |  |
| 2000 | 0.894 | [Download](2000/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2000/previews/pattern_9.png) |  | [<NSFW, click to see>](2000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2000/previews/nude.png) | [<NSFW, click to see>](2000/previews/nude2.png) |  |  |
| 1600 | 0.869 | [Download](1600/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1600/previews/pattern_9.png) |  | [<NSFW, click to see>](1600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1600/previews/nude.png) | [<NSFW, click to see>](1600/previews/nude2.png) |  |  |
| 1200 | 0.858 | [Download](1200/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1200/previews/pattern_9.png) |  | [<NSFW, click to see>](1200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [<NSFW, click to see>](1200/previews/nude2.png) |  |  |
| 800 | 0.685 | [Download](800/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](800/previews/pattern_9.png) |  | [<NSFW, click to see>](800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](800/previews/nude.png) | [<NSFW, click to see>](800/previews/nude2.png) |  |  |
| 400 | 0.726 | [Download](400/momose_rio_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](400/previews/pattern_9.png) |  | [<NSFW, click to see>](400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](400/previews/nude.png) | [<NSFW, click to see>](400/previews/nude2.png) |  |  |
|
KaiMilo/distilhubert-finetuned-gtzan | KaiMilo | 2023-09-23T19:33:58Z | 163 | 0 | transformers | [
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| audio-classification | 2023-09-20T02:20:59Z | ---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.83
---
<!-- 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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6371
- Accuracy: 0.83
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0249 | 1.0 | 113 | 1.8360 | 0.43 |
| 1.3024 | 2.0 | 226 | 1.2179 | 0.61 |
| 0.9782 | 3.0 | 339 | 0.9286 | 0.74 |
| 0.8263 | 4.0 | 452 | 0.8332 | 0.76 |
| 0.7515 | 5.0 | 565 | 0.6887 | 0.82 |
| 0.4177 | 6.0 | 678 | 0.6159 | 0.83 |
| 0.4822 | 7.0 | 791 | 0.5960 | 0.84 |
| 0.2312 | 8.0 | 904 | 0.5989 | 0.85 |
| 0.3513 | 9.0 | 1017 | 0.6024 | 0.82 |
| 0.1244 | 10.0 | 1130 | 0.6371 | 0.83 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
lortigas/xlm-roberta-base-finetuned-panx-all | lortigas | 2023-09-23T19:23:14Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-09-22T11:52:22Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1346
- F1: 0.8869
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.306 | 1.0 | 835 | 0.1821 | 0.8243 |
| 0.1564 | 2.0 | 1670 | 0.1564 | 0.8614 |
| 0.1027 | 3.0 | 2505 | 0.1346 | 0.8869 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.0.1
- Datasets 1.16.1
- Tokenizers 0.13.3
|
vamsidulam/graphcorevqa_03 | vamsidulam | 2023-09-23T19:21:15Z | 59 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vilt",
"visual-question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| visual-question-answering | 2023-09-23T17:31:33Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: graphcorevqa_03
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. -->
# graphcorevqa_03
This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
zongxiao/wav2vec2-base-finetuned-gtzan | zongxiao | 2023-09-23T19:07:33Z | 159 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| audio-classification | 2023-09-23T16:08:57Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.92
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-gtzan
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4965
- Accuracy: 0.92
## 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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1429 | 1.0 | 224 | 0.6629 | 0.9 |
| 0.0037 | 2.0 | 449 | 0.5910 | 0.92 |
| 0.0041 | 3.0 | 674 | 0.5544 | 0.91 |
| 0.003 | 4.0 | 899 | 0.5046 | 0.92 |
| 0.1445 | 4.98 | 1120 | 0.4965 | 0.92 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|
CyberHarem/miki_sayaka_puellamagimadokamagica | CyberHarem | 2023-09-23T19:02:54Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/miki_sayaka_puellamagimadokamagica",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-23T18:46:09Z | ---
license: mit
datasets:
- CyberHarem/miki_sayaka_puellamagimadokamagica
pipeline_tag: text-to-image
tags:
- art
---
# Lora of miki_sayaka_puellamagimadokamagica
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 8060, you need to download `8060/miki_sayaka_puellamagimadokamagica.pt` as the embedding and `8060/miki_sayaka_puellamagimadokamagica.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 8060**, with the score of 0.875. The trigger words are:
1. `miki_sayaka_puellamagimadokamagica`
2. `short_hair, blue_hair, blue_eyes, hair_ornament, bow, hairclip, parody`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 9300 | 0.836 | [Download](9300/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](9300/previews/bondage.png) |  |  |  | [<NSFW, click to see>](9300/previews/nude.png) | [<NSFW, click to see>](9300/previews/nude2.png) |  |  |
| 8680 | 0.866 | [Download](8680/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](8680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](8680/previews/nude.png) | [<NSFW, click to see>](8680/previews/nude2.png) |  |  |
| **8060** | **0.875** | [**Download**](8060/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](8060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](8060/previews/nude.png) | [<NSFW, click to see>](8060/previews/nude2.png) |  |  |
| 7440 | 0.861 | [Download](7440/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7440/previews/nude.png) | [<NSFW, click to see>](7440/previews/nude2.png) |  |  |
| 6820 | 0.816 | [Download](6820/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6820/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6820/previews/nude.png) | [<NSFW, click to see>](6820/previews/nude2.png) |  |  |
| 6200 | 0.802 | [Download](6200/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6200/previews/nude.png) | [<NSFW, click to see>](6200/previews/nude2.png) |  |  |
| 5580 | 0.811 | [Download](5580/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5580/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5580/previews/nude.png) | [<NSFW, click to see>](5580/previews/nude2.png) |  |  |
| 4960 | 0.771 | [Download](4960/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4960/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4960/previews/nude.png) | [<NSFW, click to see>](4960/previews/nude2.png) |  |  |
| 4340 | 0.824 | [Download](4340/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4340/previews/nude.png) | [<NSFW, click to see>](4340/previews/nude2.png) |  |  |
| 3720 | 0.740 | [Download](3720/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3720/previews/nude.png) | [<NSFW, click to see>](3720/previews/nude2.png) |  |  |
| 3100 | 0.767 | [Download](3100/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3100/previews/nude.png) | [<NSFW, click to see>](3100/previews/nude2.png) |  |  |
| 2480 | 0.786 | [Download](2480/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2480/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2480/previews/nude.png) | [<NSFW, click to see>](2480/previews/nude2.png) |  |  |
| 1860 | 0.697 | [Download](1860/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1860/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1860/previews/nude.png) | [<NSFW, click to see>](1860/previews/nude2.png) |  |  |
| 1240 | 0.705 | [Download](1240/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1240/previews/nude.png) | [<NSFW, click to see>](1240/previews/nude2.png) |  |  |
| 620 | 0.518 | [Download](620/miki_sayaka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](620/previews/bondage.png) |  |  |  | [<NSFW, click to see>](620/previews/nude.png) | [<NSFW, click to see>](620/previews/nude2.png) |  |  |
|
LarryAIDraw/kairi_v1 | LarryAIDraw | 2023-09-23T18:59:24Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-09-23T18:41:42Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/150222/kairi-kingdom-hearts |
vaalto/test_trainer | vaalto | 2023-09-23T18:59:07Z | 181 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-09-23T18:58:21Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: test_trainer
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: 0.82
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_trainer
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5366
- Accuracy: 0.82
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 25 | 0.6720 | 0.58 |
| No log | 2.0 | 50 | 0.5366 | 0.82 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cpu
- Datasets 2.14.5
- Tokenizers 0.13.3
|
LarryAIDraw/olyaya-v100-000018 | LarryAIDraw | 2023-09-23T18:58:01Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-09-23T18:39:32Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/95243?modelVersionId=167980 |
vazha72/bert-finetuned-ner-1 | vazha72 | 2023-09-23T18:52:50Z | 118 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-09-23T18:38:38Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-1
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9294311624072548
- name: Recall
type: recall
value: 0.9486704813194211
- name: F1
type: f1
value: 0.9389522778379279
- name: Accuracy
type: accuracy
value: 0.9867545770294931
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner-1
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0559
- Precision: 0.9294
- Recall: 0.9487
- F1: 0.9390
- Accuracy: 0.9868
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.077 | 1.0 | 1756 | 0.0678 | 0.9085 | 0.9357 | 0.9219 | 0.9815 |
| 0.0402 | 2.0 | 3512 | 0.0539 | 0.9241 | 0.9468 | 0.9353 | 0.9864 |
| 0.0254 | 3.0 | 5268 | 0.0559 | 0.9294 | 0.9487 | 0.9390 | 0.9868 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
CyberHarem/ibuki_tsubasa_theidolmstermillionlive | CyberHarem | 2023-09-23T18:47:33Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/ibuki_tsubasa_theidolmstermillionlive",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-23T18:28:45Z | ---
license: mit
datasets:
- CyberHarem/ibuki_tsubasa_theidolmstermillionlive
pipeline_tag: text-to-image
tags:
- art
---
# Lora of ibuki_tsubasa_theidolmstermillionlive
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 6720, you need to download `6720/ibuki_tsubasa_theidolmstermillionlive.pt` as the embedding and `6720/ibuki_tsubasa_theidolmstermillionlive.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 6720**, with the score of 0.770. The trigger words are:
1. `ibuki_tsubasa_theidolmstermillionlive`
2. `short_hair, ahoge, blush, blonde_hair, smile, breasts, bangs, open_mouth, pink_eyes, red_eyes, hair_between_eyes, hair_flaps, medium_breasts`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:---------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-----------------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 8400 | 0.748 | [Download](8400/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](8400/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](8400/previews/pattern_16.png) |  | [<NSFW, click to see>](8400/previews/bondage.png) | [<NSFW, click to see>](8400/previews/free.png) |  |  | [<NSFW, click to see>](8400/previews/nude.png) | [<NSFW, click to see>](8400/previews/nude2.png) |  |  |
| 7840 | 0.652 | [Download](7840/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7840/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](7840/previews/pattern_16.png) |  | [<NSFW, click to see>](7840/previews/bondage.png) | [<NSFW, click to see>](7840/previews/free.png) |  |  | [<NSFW, click to see>](7840/previews/nude.png) | [<NSFW, click to see>](7840/previews/nude2.png) |  |  |
| 7280 | 0.641 | [Download](7280/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7280/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](7280/previews/pattern_16.png) |  | [<NSFW, click to see>](7280/previews/bondage.png) | [<NSFW, click to see>](7280/previews/free.png) |  |  | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) |  |  |
| **6720** | **0.770** | [**Download**](6720/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6720/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](6720/previews/pattern_16.png) |  | [<NSFW, click to see>](6720/previews/bondage.png) | [<NSFW, click to see>](6720/previews/free.png) |  |  | [<NSFW, click to see>](6720/previews/nude.png) | [<NSFW, click to see>](6720/previews/nude2.png) |  |  |
| 6160 | 0.718 | [Download](6160/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6160/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](6160/previews/pattern_16.png) |  | [<NSFW, click to see>](6160/previews/bondage.png) | [<NSFW, click to see>](6160/previews/free.png) |  |  | [<NSFW, click to see>](6160/previews/nude.png) | [<NSFW, click to see>](6160/previews/nude2.png) |  |  |
| 5600 | 0.662 | [Download](5600/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5600/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](5600/previews/pattern_16.png) |  | [<NSFW, click to see>](5600/previews/bondage.png) | [<NSFW, click to see>](5600/previews/free.png) |  |  | [<NSFW, click to see>](5600/previews/nude.png) | [<NSFW, click to see>](5600/previews/nude2.png) |  |  |
| 5040 | 0.643 | [Download](5040/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5040/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](5040/previews/pattern_16.png) |  | [<NSFW, click to see>](5040/previews/bondage.png) | [<NSFW, click to see>](5040/previews/free.png) |  |  | [<NSFW, click to see>](5040/previews/nude.png) | [<NSFW, click to see>](5040/previews/nude2.png) |  |  |
| 4480 | 0.752 | [Download](4480/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4480/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](4480/previews/pattern_16.png) |  | [<NSFW, click to see>](4480/previews/bondage.png) | [<NSFW, click to see>](4480/previews/free.png) |  |  | [<NSFW, click to see>](4480/previews/nude.png) | [<NSFW, click to see>](4480/previews/nude2.png) |  |  |
| 3920 | 0.733 | [Download](3920/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3920/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](3920/previews/pattern_16.png) |  | [<NSFW, click to see>](3920/previews/bondage.png) | [<NSFW, click to see>](3920/previews/free.png) |  |  | [<NSFW, click to see>](3920/previews/nude.png) | [<NSFW, click to see>](3920/previews/nude2.png) |  |  |
| 3360 | 0.745 | [Download](3360/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3360/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](3360/previews/pattern_16.png) |  | [<NSFW, click to see>](3360/previews/bondage.png) | [<NSFW, click to see>](3360/previews/free.png) |  |  | [<NSFW, click to see>](3360/previews/nude.png) | [<NSFW, click to see>](3360/previews/nude2.png) |  |  |
| 2800 | 0.764 | [Download](2800/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2800/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](2800/previews/pattern_16.png) |  | [<NSFW, click to see>](2800/previews/bondage.png) | [<NSFW, click to see>](2800/previews/free.png) |  |  | [<NSFW, click to see>](2800/previews/nude.png) | [<NSFW, click to see>](2800/previews/nude2.png) |  |  |
| 2240 | 0.776 | [Download](2240/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2240/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](2240/previews/pattern_16.png) |  | [<NSFW, click to see>](2240/previews/bondage.png) | [<NSFW, click to see>](2240/previews/free.png) |  |  | [<NSFW, click to see>](2240/previews/nude.png) | [<NSFW, click to see>](2240/previews/nude2.png) |  |  |
| 1680 | 0.787 | [Download](1680/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1680/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](1680/previews/pattern_16.png) |  | [<NSFW, click to see>](1680/previews/bondage.png) | [<NSFW, click to see>](1680/previews/free.png) |  |  | [<NSFW, click to see>](1680/previews/nude.png) | [<NSFW, click to see>](1680/previews/nude2.png) |  |  |
| 1120 | 0.815 | [Download](1120/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1120/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](1120/previews/pattern_16.png) |  | [<NSFW, click to see>](1120/previews/bondage.png) | [<NSFW, click to see>](1120/previews/free.png) |  |  | [<NSFW, click to see>](1120/previews/nude.png) | [<NSFW, click to see>](1120/previews/nude2.png) |  |  |
| 560 | 0.782 | [Download](560/ibuki_tsubasa_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](560/previews/pattern_11.png) |  |  |  |  | [<NSFW, click to see>](560/previews/pattern_16.png) |  | [<NSFW, click to see>](560/previews/bondage.png) | [<NSFW, click to see>](560/previews/free.png) |  |  | [<NSFW, click to see>](560/previews/nude.png) | [<NSFW, click to see>](560/previews/nude2.png) |  |  |
|
RocioUrquijo/clasificador-languagedetection | RocioUrquijo | 2023-09-23T18:47:07Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"classification",
"generated_from_trainer",
"base_model:papluca/xlm-roberta-base-language-detection",
"base_model:finetune:papluca/xlm-roberta-base-language-detection",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-09T11:21:56Z | ---
license: mit
base_model: papluca/xlm-roberta-base-language-detection
tags:
- classification
- generated_from_trainer
model-index:
- name: clasificador-languagedetection
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. -->
# clasificador-languagedetection
This model is a fine-tuned version of [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
InvokeAI/ip_adapter_sdxl_image_encoder | InvokeAI | 2023-09-23T18:34:05Z | 7,813 | 14 | transformers | [
"transformers",
"safetensors",
"clip_vision_model",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2023-09-14T22:13:05Z | ---
license: apache-2.0
---
This is the Image Encoder required for SDXL IP Adapter models to function correctly. It is compatible with version 3.2+ of Invoke AI.
IP Adapter allows for users to input an Image Prompt, which is interpreted by the system, and passed in as conditioning for the image generation process.
The Community Edition of Invoke AI can be found at invoke.ai or on GitHub at https://github.com/invoke-ai/InvokeAI
This model is a copy of https://huggingface.co/h94/IP-Adapter/tree/5c2eae7d8a9c3365ba4745f16b94eb0293e319d3/sdxl_models/image_encoder. It is hosted here for compatibility with InvokeAI.
|
badokorach/bert-finetuned-squad-7-finetuned-newqa | badokorach | 2023-09-23T18:30:05Z | 122 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:badokorach/bert-finetuned-squad-7",
"base_model:finetune:badokorach/bert-finetuned-squad-7",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-09-23T17:36:36Z | ---
license: apache-2.0
base_model: badokorach/bert-finetuned-squad-7
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-squad-7-finetuned-newqa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad-7-finetuned-newqa
This model is a fine-tuned version of [badokorach/bert-finetuned-squad-7](https://huggingface.co/badokorach/bert-finetuned-squad-7) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.6437
## 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 66 | 4.8867 |
| No log | 2.0 | 132 | 5.1511 |
| No log | 3.0 | 198 | 5.8491 |
| No log | 4.0 | 264 | 5.5515 |
| No log | 5.0 | 330 | 5.5515 |
| No log | 6.0 | 396 | 5.2236 |
| No log | 7.0 | 462 | 5.4818 |
| 0.0076 | 8.0 | 528 | 5.3132 |
| 0.0076 | 9.0 | 594 | 5.4253 |
| 0.0076 | 10.0 | 660 | 5.5527 |
| 0.0076 | 11.0 | 726 | 5.4605 |
| 0.0076 | 12.0 | 792 | 5.3987 |
| 0.0076 | 13.0 | 858 | 5.5341 |
| 0.0076 | 14.0 | 924 | 5.4277 |
| 0.0076 | 15.0 | 990 | 5.5373 |
| 0.001 | 16.0 | 1056 | 6.0537 |
| 0.001 | 17.0 | 1122 | 5.5327 |
| 0.001 | 18.0 | 1188 | 5.7127 |
| 0.001 | 19.0 | 1254 | 5.5445 |
| 0.001 | 20.0 | 1320 | 5.5626 |
| 0.001 | 21.0 | 1386 | 5.6203 |
| 0.001 | 22.0 | 1452 | 5.6869 |
| 0.0014 | 23.0 | 1518 | 5.7155 |
| 0.0014 | 24.0 | 1584 | 5.7069 |
| 0.0014 | 25.0 | 1650 | 5.6417 |
| 0.0014 | 26.0 | 1716 | 5.6183 |
| 0.0014 | 27.0 | 1782 | 5.6824 |
| 0.0014 | 28.0 | 1848 | 5.6416 |
| 0.0014 | 29.0 | 1914 | 5.6474 |
| 0.0014 | 30.0 | 1980 | 5.6437 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
fowlart99/hihi | fowlart99 | 2023-09-23T18:29:49Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
]
| null | 2023-09-23T18:29:49Z | ---
license: bigscience-openrail-m
---
|
devananda04/my-pet-horse-xzg | devananda04 | 2023-09-23T18:15:03Z | 1 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-09-23T18:09:58Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Horse-xzg Dreambooth model trained by devananda04 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: VAST-215
Sample pictures of this concept:
|
felixquinihildebet/spaceinvaders5 | felixquinihildebet | 2023-09-23T17:57:33Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-23T17:57:17Z | ---
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: 526.00 +/- 121.22
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 felixquinihildebet -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 felixquinihildebet -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 felixquinihildebet
```
## 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', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Doggerz/lora-trained-xl-dadu-2 | Doggerz | 2023-09-23T17:57:10Z | 1 | 1 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
]
| text-to-image | 2023-09-23T16:03:40Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks person
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - Doggerz/lora-trained-xl-dadu-2
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
AyushChothe/fashion-clip-embedding | AyushChothe | 2023-09-23T17:46:19Z | 0 | 1 | generic | [
"generic",
"feature-extraction",
"license:mit",
"region:us"
]
| feature-extraction | 2023-08-05T08:09:00Z | ---
license: mit
tags:
- feature-extraction
library_name: generic
--- |
eugene6/q-Taxi-v3 | eugene6 | 2023-09-23T17:39:58Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-23T17:39:56Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.44 +/- 2.77
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="eugene6/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CyberHarem/mogami_shizuka_theidolmstermillionlive | CyberHarem | 2023-09-23T17:33:14Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/mogami_shizuka_theidolmstermillionlive",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-23T17:17:36Z | ---
license: mit
datasets:
- CyberHarem/mogami_shizuka_theidolmstermillionlive
pipeline_tag: text-to-image
tags:
- art
---
# Lora of mogami_shizuka_theidolmstermillionlive
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 5720, you need to download `5720/mogami_shizuka_theidolmstermillionlive.pt` as the embedding and `5720/mogami_shizuka_theidolmstermillionlive.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 5720**, with the score of 0.855. The trigger words are:
1. `mogami_shizuka_theidolmstermillionlive`
2. `long_hair, blue_eyes, black_hair, blush, bangs`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:----------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 7800 | 0.738 | [Download](7800/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) |  |  |
| 7280 | 0.818 | [Download](7280/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) |  |  |
| 6760 | 0.739 | [Download](6760/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) |  |  |
| 6240 | 0.812 | [Download](6240/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| **5720** | **0.855** | [**Download**](5720/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5200 | 0.822 | [Download](5200/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4680 | 0.812 | [Download](4680/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) |  |  |
| 4160 | 0.752 | [Download](4160/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) |  |  |
| 3640 | 0.818 | [Download](3640/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) |  |  |
| 3120 | 0.822 | [Download](3120/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) |  |  |
| 2600 | 0.732 | [Download](2600/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) |  |  |
| 2080 | 0.792 | [Download](2080/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) |  |  |
| 1560 | 0.795 | [Download](1560/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) |  |  |
| 1040 | 0.788 | [Download](1040/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) |  |  |
| 520 | 0.758 | [Download](520/mogami_shizuka_theidolmstermillionlive.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) |  |  |
|
badokorach/bert-finetuned-squad-7-2309 | badokorach | 2023-09-23T17:17:05Z | 12 | 0 | transformers | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"base_model:badokorach/bert-finetuned-squad-7",
"base_model:finetune:badokorach/bert-finetuned-squad-7",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-09-23T16:17:36Z | ---
license: apache-2.0
base_model: badokorach/bert-finetuned-squad-7
tags:
- generated_from_keras_callback
model-index:
- name: badokorach/bert-finetuned-squad-7-2309
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. -->
# badokorach/bert-finetuned-squad-7-2309
This model is a fine-tuned version of [badokorach/bert-finetuned-squad-7](https://huggingface.co/badokorach/bert-finetuned-squad-7) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1499
- Validation Loss: 0.0
- Epoch: 19
## 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': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2596, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 4, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.02}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.0115 | 0.0 | 0 |
| 0.2937 | 0.0 | 1 |
| 0.2292 | 0.0 | 2 |
| 0.1814 | 0.0 | 3 |
| 0.1772 | 0.0 | 4 |
| 0.1645 | 0.0 | 5 |
| 0.1652 | 0.0 | 6 |
| 0.1565 | 0.0 | 7 |
| 0.1613 | 0.0 | 8 |
| 0.1580 | 0.0 | 9 |
| 0.1706 | 0.0 | 10 |
| 0.1598 | 0.0 | 11 |
| 0.1595 | 0.0 | 12 |
| 0.1537 | 0.0 | 13 |
| 0.1544 | 0.0 | 14 |
| 0.1576 | 0.0 | 15 |
| 0.1552 | 0.0 | 16 |
| 0.1530 | 0.0 | 17 |
| 0.1554 | 0.0 | 18 |
| 0.1499 | 0.0 | 19 |
### Framework versions
- Transformers 4.33.2
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
npvinHnivqn/opt-6.7b-lora | npvinHnivqn | 2023-09-23T17:10:34Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-23T17:10:31Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
Phando/fairseq-moe-15b | Phando | 2023-09-23T17:03:37Z | 16 | 1 | transformers | [
"transformers",
"pytorch",
"text-generation",
"en",
"arxiv:2112.10684",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-09-23T08:07:54Z | ---
language: en
---
This is a Hugging Face transformers-style conversion of the original SMoE 15B-parameter model from the paper "[Efficient Large Scale Language Modeling with Mixtures of Experts](https://arxiv.org/abs/2112.10684)" from Artetxe et al. The original model card can be found at https://github.com/facebookresearch/fairseq/blob/main/examples/moe_lm/model_card.md.
The usage example and modeling code can be found at https://github.com/pingzhili/light-fairseq
|
Kushala/falcon | Kushala | 2023-09-23T16:52:53Z | 1 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-23T16:52:31Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
Undi95/MXLewd-L2-20B-GGUF | Undi95 | 2023-09-23T16:52:39Z | 12 | 7 | null | [
"gguf",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
]
| null | 2023-09-22T17:29:56Z | ---
license: cc-by-nc-4.0
---
Merge:
```shell
layer_slices:
- model: ./MXLewd-L2-20B-part2
start: 0
end: 16
- model: ./MXLewd-L2-20B-part1
start: 8
end: 20
- model: ./MXLewd-L2-20B-part2
start: 17
end: 32
- model: ./MXLewd-L2-20B-part1
start: 21
end: 40
```
Part 2 is ReMM (0.33) and Xwin (0.66)
Part 1 is Xwin (0.33) and MLewd (0.66)
<!-- description start -->
## Models used
- Undi95/MLewd-L2-13B-v2-3
- Undi95/ReMM-v2.1-L2-13B
- Xwin-LM/Xwin-LM-13B-V0.1
<!-- description end -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that completes the request.
### Instruction:
{prompt}
### Response:
``` |
LTC-AI-Labs/L2-7b-Base-WVG-Uncensored | LTC-AI-Labs | 2023-09-23T16:51:14Z | 1,583 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text generation",
"conversational",
"en",
"dataset:DanFosing/wizardlm-vicuna-guanaco-uncensored",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-09-23T16:34:05Z | ---
datasets:
- DanFosing/wizardlm-vicuna-guanaco-uncensored
language:
- en
tags:
- text generation
- conversational
---
|
CyberHarem/kaname_madoka_puellamagimadokamagica | CyberHarem | 2023-09-23T16:44:49Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/kaname_madoka_puellamagimadokamagica",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-23T16:27:55Z | ---
license: mit
datasets:
- CyberHarem/kaname_madoka_puellamagimadokamagica
pipeline_tag: text-to-image
tags:
- art
---
# Lora of kaname_madoka_puellamagimadokamagica
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 7920, you need to download `7920/kaname_madoka_puellamagimadokamagica.pt` as the embedding and `7920/kaname_madoka_puellamagimadokamagica.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 7920**, with the score of 0.956. The trigger words are:
1. `kaname_madoka_puellamagimadokamagica`
2. `pink_hair, short_hair, twintails, short_twintails, pink_eyes, ribbon, hair_ribbon, bow, blush`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | pattern_17 | pattern_18 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:--------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 9900 | 0.922 | [Download](9900/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](9900/previews/bondage.png) |  |  |  | [<NSFW, click to see>](9900/previews/nude.png) | [<NSFW, click to see>](9900/previews/nude2.png) |  |  |
| 9240 | 0.899 | [Download](9240/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](9240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](9240/previews/nude.png) | [<NSFW, click to see>](9240/previews/nude2.png) |  |  |
| 8580 | 0.950 | [Download](8580/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](8580/previews/bondage.png) |  |  |  | [<NSFW, click to see>](8580/previews/nude.png) | [<NSFW, click to see>](8580/previews/nude2.png) |  |  |
| **7920** | **0.956** | [**Download**](7920/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7920/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7920/previews/nude.png) | [<NSFW, click to see>](7920/previews/nude2.png) |  |  |
| 7260 | 0.943 | [Download](7260/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7260/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7260/previews/nude.png) | [<NSFW, click to see>](7260/previews/nude2.png) |  |  |
| 6600 | 0.942 | [Download](6600/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6600/previews/nude.png) | [<NSFW, click to see>](6600/previews/nude2.png) |  |  |
| 5940 | 0.868 | [Download](5940/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5940/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5940/previews/nude.png) | [<NSFW, click to see>](5940/previews/nude2.png) |  |  |
| 5280 | 0.908 | [Download](5280/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5280/previews/nude.png) | [<NSFW, click to see>](5280/previews/nude2.png) |  |  |
| 4620 | 0.904 | [Download](4620/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4620/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4620/previews/nude.png) | [<NSFW, click to see>](4620/previews/nude2.png) |  |  |
| 3960 | 0.900 | [Download](3960/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3960/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3960/previews/nude.png) | [<NSFW, click to see>](3960/previews/nude2.png) |  |  |
| 3300 | 0.840 | [Download](3300/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3300/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3300/previews/nude.png) | [<NSFW, click to see>](3300/previews/nude2.png) |  |  |
| 2640 | 0.923 | [Download](2640/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2640/previews/nude.png) | [<NSFW, click to see>](2640/previews/nude2.png) |  |  |
| 1980 | 0.888 | [Download](1980/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1980/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1980/previews/nude.png) | [<NSFW, click to see>](1980/previews/nude2.png) |  |  |
| 1320 | 0.865 | [Download](1320/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1320/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1320/previews/nude.png) | [<NSFW, click to see>](1320/previews/nude2.png) |  |  |
| 660 | 0.672 | [Download](660/kaname_madoka_puellamagimadokamagica.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](660/previews/bondage.png) |  |  |  | [<NSFW, click to see>](660/previews/nude.png) | [<NSFW, click to see>](660/previews/nude2.png) |  |  |
|
ldhldh/7b_8bit_qlora_kvdown_longprom | ldhldh | 2023-09-23T16:43:34Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-23T16:43:30Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
glukas/smd-audio-diffusion-256 | glukas | 2023-09-23T16:30:26Z | 0 | 0 | null | [
"license:openrail",
"region:us"
]
| null | 2023-09-23T15:57:14Z | ---
license: openrail
---
DDPM Model (https://github.com/teticio/audio-diffusion) trained on the Saarland Music Data (SMD):
Meinard Müller, Verena Konz, Wolfgang Bogler, and Vlora Arifi-Müller
Saarland Music Data (SMD)
In Late-Breaking and Demo Session of the International Conference on Music Information Retrieval (ISMIR), 2011.
https://resources.mpi-inf.mpg.de/SMD/SMD_MIDI-Audio-Piano-Music.html. |
HassanMahmood/my_awesome_asr_mind_model | HassanMahmood | 2023-09-23T16:18:54Z | 99 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-09-22T06:32:32Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: my_awesome_asr_mind_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_asr_mind_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 0.0 | 100.0 | 100 | nan | 1.0 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Tiabet/bert-base-uncased-finetuned-Tiabet | Tiabet | 2023-09-23T16:18:35Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"text classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-09-23T14:59:17Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- text classification
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-Tiabet
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-Tiabet
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
Adbhut/speecht5-finetuned-voxpopuli_nl | Adbhut | 2023-09-23T16:14:57Z | 75 | 0 | transformers | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
]
| text-to-audio | 2023-09-23T15:07:12Z | ---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5-finetuned-voxpopuli_nl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5-finetuned-voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4792
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5303 | 8.6 | 500 | 0.4960 |
| 0.5125 | 17.2 | 1000 | 0.4834 |
| 0.4998 | 25.81 | 1500 | 0.4791 |
| 0.4953 | 34.41 | 2000 | 0.4792 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
eisenjulian/viz-wiz-bert-base-uncased_f32 | eisenjulian | 2023-09-23T16:04:53Z | 117 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-21T15:35:01Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: viz-wiz-bert-base-uncased_f32
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. -->
# viz-wiz-bert-base-uncased_f32
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0723
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 321 | 1.1645 |
| 1.344 | 2.0 | 642 | 1.0789 |
| 1.344 | 3.0 | 963 | 1.0537 |
| 1.1234 | 4.0 | 1284 | 1.0195 |
| 1.065 | 5.0 | 1605 | 1.0723 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
eisenjulian/viz-wiz-bert-base-uncased_f16 | eisenjulian | 2023-09-23T15:46:40Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-21T21:27:56Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: viz-wiz-bert-base-uncased_f16
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. -->
# viz-wiz-bert-base-uncased_f16
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0723
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 321 | 1.1645 |
| 1.344 | 2.0 | 642 | 1.0789 |
| 1.344 | 3.0 | 963 | 1.0537 |
| 1.1234 | 4.0 | 1284 | 1.0195 |
| 1.065 | 5.0 | 1605 | 1.0723 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
DriveMyScream/News_Similarity_Analysis | DriveMyScream | 2023-09-23T15:42:18Z | 0 | 0 | keras | [
"keras",
"tf-keras",
"region:us"
]
| null | 2023-09-23T15:41:28Z | ---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |
Sanjay1234/Trillo-Project-with-10-extra-rows | Sanjay1234 | 2023-09-23T15:35:10Z | 3 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
]
| text-classification | 2023-09-23T15:34:45Z | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# Sanjay1234/Trillo-Project-with-10-extra-rows
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("Sanjay1234/Trillo-Project-with-10-extra-rows")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
jmoney54378256438905/jondurbin_airoboros-c34b-2.2.1-3.75bpw | jmoney54378256438905 | 2023-09-23T15:26:54Z | 6 | 0 | transformers | [
"transformers",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-2.2.1",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-09-23T15:20:08Z | ---
license: llama2
datasets:
- jondurbin/airoboros-2.2.1
---
### Overview
Another experimental model, using mostly sythetic data generated by [airoboros](https://github.com/jondurbin/airoboros)
This is essentially a minor "fix" branch of [airoboros-c34b-2.2](https://hf.co/jondurbin/airoboros-c34b-2.2) with a updates, primarily:
- [re-generated writing responses](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#re-generated-writing-responses)
- [longer contextual blocks](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#longer-contextual-blocks)
- [removal of "rp" data](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#rp-category-removed)
- [(less aggressive) de-censoring](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#de-censoring)
- 5 epochs instead of 3
This is a fairly general purpose model, but focuses heavily on instruction following, rather than casual chat/roleplay.
Huge thank you to the folks over at [a16z](https://a16z.com/) for sponsoring the costs associated with building models and associated tools!
### Prompt format
The prompt format:
```
A chat.
USER: {prompt}
ASSISTANT:
```
The default system prompt ("A chat.") was used for most of the prompts, however it also included a wide sampling of responses with other prompts, particularly in "stylized\_response", "rp", "gtkm", etc.
Here's another example:
```
A chat between Bob (aka USER) and Tom (aka ASSISTANT). Tom is an extremely intelligent 18th century bookkeeper, who speaks loquaciously.
USER: {prompt}
ASSISTANT:
```
And chat scenario that wouldn't require USER/ASSISTANT (but should use stopping criteria to prevent the model from speaking on your behalf).
```
A chat between old friends: Timmy and Tommy.
{description of characters}
{setting for the chat}
Timmy: *takes a big sip from his coffee* "Ah, sweet, delicious, magical coffee."
Tommy:
```
__*I strongly suggest adding stopping criteria/early inference stopping on "USER:", and/or whatever names you specify in the system prompt.*__
### Fine tuning info
https://wandb.ai/jondurbin/airoboros-c34b-2.2.1/runs/07b08z7m?workspace=user-jondurbin
### Helpful usage tips
*The prompts shown here are are just the text that would be included after USER: and before ASSISTANT: in the full prompt format above, the system prompt and USER:/ASSISTANT: have been omited for readability.*
#### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
[key0: value0]
[key1: value1]
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
#### Summarization
500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:
```
BEGININPUT
{text to summarize}
ENDINPUT
BEGININSTRUCTION
Summarize the input in around 130 words.
ENDINSTRUCTION
```
#### Getting longer responses
You can use a few techniques to get longer responses.
Detailed prompts, with explicit instruction for word count:
```
Please compose a narrative set in the heart of an ancient library, steeped in the scent of old parchment and ink. The protagonist should be a young scholar who is dedicated to studying the art of storytelling and its evolution throughout history. In her pursuit of knowledge, she stumbles upon a forgotten tome that seems to possess an unusual aura. This book has the ability to bring stories to life, literally manifesting characters and scenarios from within its pages into reality.
The main character must navigate through various epochs of storytelling - from oral traditions of tribal societies, through medieval minstrels' tales, to modern-day digital narratives - as they come alive around her. Each era presents its unique challenges and lessons about the power and impact of stories on human civilization.
One such character could be a sentient quill pen, who was once used by renowned authors of yesteryears and now holds their wisdom and experiences. It becomes her mentor, guiding her through this journey with witty remarks and insightful commentary.
Ensure that your tale encapsulates the thrill of adventure, the beauty of learning, and the profound connection between humans and their stories. All characters involved should be non-human entities. Feel free to explore creative liberties but maintain the mentioned elements.
Your response should be approximately 2300 words.
```
Or, a simpler example:
```
Please create a long, detailed story about a dragon in an old growth forest who, for some reason, begins speaking the words of the source code of linux.
```
#### Coding
You can ask for fairly complex coding instructions with multiple criteria, e.g.:
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or inline criteria:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js. PLAINFORMAT
```
#### Agent/function calling
The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML.
Example prompt:
```
As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
Input: I want to know how many times 'Python' is mentioned in my text file.
Available functions:
file_analytics:
description: This tool performs various operations on a text file.
params:
action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
filters:
keyword: The word or phrase we want to search for.
```
Response:
```json
{
"function": "file_analytics",
"params": {
"action": "count_occurrences",
"filters": {
"keyword": "Python"
}
}
}
```
#### Chain-of-thought
You can ask for several possible responses to a given problem, with a ranking and final answer selection.
Example prompt:
```
A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one.
```
Example response:
```
Solution 1:
In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players).
The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows:
n(n-1)/2 = 45
Solving this equation gives us n=10.
Final answer: There were 10 players in the tournament.
Solution 2:
Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers.
If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players.
Final answer: There were 10 players in the tournament.
Solution 3:
We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played.
Final answer: There were 10 players in the tournament.
Ranking of solutions from best to worst:
1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer.
2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer.
3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer.
Best and final answer: There were 10 players in the tournament.
```
#### reWOO style execution planning
The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!
Example prompt:
```
Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string
that could be the user's question, one or more prior evidence values, or a combination of both.
Here are the tools available to be called:
DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer.
TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?
The output should be in format:
Plan: [first action to take based on input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]
Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work?
```
Response:
```
Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020.
:evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner]
Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate.
:evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?]
Plan: Conduct another web search to find the most famous work of the identified laureate.
:evidence2: = DuckDuckGo[Most famous work of :evidence1:]
Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Use the TextScraper tool to extract information from the relevant links.
:evidence4: = TextScraper[:evidence3:]
Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information.
:evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?]
Answer: :evidence5:
```
For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:
```python
import re
import requests
def inject_context(input_text, **context):
for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)):
input_text = input_text.replace(ref, context.get(ref, ""))
return input_text
def duckduckgo(input_text, **context):
search_string = inject_context(input_text, **context)
... search via duck duck go using search_string
... return text content
def link_extractor(input_text, **context):
input_text = inject_context(input_text, **context)
return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I))))
def scrape(input_text, **context):
input_text = inject_context(input_text, **context)
text = []
for link in input_text.splitlines():
text.append(requests.get(link).text)
return "\n".join(text)
def infer(input_text, **context)
prompt = inject_context(input_text, **context)
... call model with prompt, return output
def parse_plan(plan):
method_map = {
"DuckDuckGo": duckduckgo,
"HyperlinkExtractor": link_extractor,
"KnowledgeModel": infer,
"TextScraper": scrape,
}
context = {}
for line in plan.strip().splitlines():
if line.startswith("Plan:"):
print(line)
continue
parts = re.match("^(:evidence[0-9]+:)\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
if not parts:
if line.startswith("Answer: "):
return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")
raise RuntimeError("bad format: " + line)
context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), **context)
```
### Contribute
If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
To help me with the OpenAI/compute costs:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
### Licence and usage restrictions
The airoboros 2.2 models are built on top of llama-2/codellama.
The llama-2 base model has a custom Meta license:
- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
The fine-tuning data was mostly generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me. |
CyberHarem/shiraishi_tsumugi_theidolmstermillionlive | CyberHarem | 2023-09-23T15:20:16Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/shiraishi_tsumugi_theidolmstermillionlive",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-23T15:09:26Z | ---
license: mit
datasets:
- CyberHarem/shiraishi_tsumugi_theidolmstermillionlive
pipeline_tag: text-to-image
tags:
- art
---
# Lora of shiraishi_tsumugi_theidolmstermillionlive
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 7000, you need to download `7000/shiraishi_tsumugi_theidolmstermillionlive.pt` as the embedding and `7000/shiraishi_tsumugi_theidolmstermillionlive.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 7000**, with the score of 0.883. The trigger words are:
1. `shiraishi_tsumugi_theidolmstermillionlive`
2. `long_hair, blue_eyes, bangs, blush, hair_ornament, blue_hair, hairclip, breasts`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-----------------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 7500 | 0.879 | [Download](7500/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](7500/previews/pattern_6.png) |  |  | [<NSFW, click to see>](7500/previews/bondage.png) | [<NSFW, click to see>](7500/previews/free.png) |  |  | [<NSFW, click to see>](7500/previews/nude.png) | [<NSFW, click to see>](7500/previews/nude2.png) |  |  |
| **7000** | **0.883** | [**Download**](7000/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](7000/previews/pattern_6.png) |  |  | [<NSFW, click to see>](7000/previews/bondage.png) | [<NSFW, click to see>](7000/previews/free.png) |  |  | [<NSFW, click to see>](7000/previews/nude.png) | [<NSFW, click to see>](7000/previews/nude2.png) |  |  |
| 6500 | 0.866 | [Download](6500/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](6500/previews/pattern_6.png) |  |  | [<NSFW, click to see>](6500/previews/bondage.png) | [<NSFW, click to see>](6500/previews/free.png) |  |  | [<NSFW, click to see>](6500/previews/nude.png) | [<NSFW, click to see>](6500/previews/nude2.png) |  |  |
| 6000 | 0.821 | [Download](6000/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](6000/previews/pattern_6.png) |  |  | [<NSFW, click to see>](6000/previews/bondage.png) | [<NSFW, click to see>](6000/previews/free.png) |  |  | [<NSFW, click to see>](6000/previews/nude.png) | [<NSFW, click to see>](6000/previews/nude2.png) |  |  |
| 5500 | 0.864 | [Download](5500/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](5500/previews/pattern_6.png) |  |  | [<NSFW, click to see>](5500/previews/bondage.png) | [<NSFW, click to see>](5500/previews/free.png) |  |  | [<NSFW, click to see>](5500/previews/nude.png) | [<NSFW, click to see>](5500/previews/nude2.png) |  |  |
| 5000 | 0.862 | [Download](5000/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](5000/previews/pattern_6.png) |  |  | [<NSFW, click to see>](5000/previews/bondage.png) | [<NSFW, click to see>](5000/previews/free.png) |  |  | [<NSFW, click to see>](5000/previews/nude.png) | [<NSFW, click to see>](5000/previews/nude2.png) |  |  |
| 4500 | 0.824 | [Download](4500/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](4500/previews/pattern_6.png) |  |  | [<NSFW, click to see>](4500/previews/bondage.png) | [<NSFW, click to see>](4500/previews/free.png) |  |  | [<NSFW, click to see>](4500/previews/nude.png) | [<NSFW, click to see>](4500/previews/nude2.png) |  |  |
| 4000 | 0.797 | [Download](4000/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](4000/previews/pattern_6.png) |  |  | [<NSFW, click to see>](4000/previews/bondage.png) | [<NSFW, click to see>](4000/previews/free.png) |  |  | [<NSFW, click to see>](4000/previews/nude.png) | [<NSFW, click to see>](4000/previews/nude2.png) |  |  |
| 3500 | 0.847 | [Download](3500/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](3500/previews/pattern_6.png) |  |  | [<NSFW, click to see>](3500/previews/bondage.png) | [<NSFW, click to see>](3500/previews/free.png) |  |  | [<NSFW, click to see>](3500/previews/nude.png) | [<NSFW, click to see>](3500/previews/nude2.png) |  |  |
| 3000 | 0.857 | [Download](3000/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](3000/previews/pattern_6.png) |  |  | [<NSFW, click to see>](3000/previews/bondage.png) | [<NSFW, click to see>](3000/previews/free.png) |  |  | [<NSFW, click to see>](3000/previews/nude.png) | [<NSFW, click to see>](3000/previews/nude2.png) |  |  |
| 2500 | 0.859 | [Download](2500/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](2500/previews/pattern_6.png) |  |  | [<NSFW, click to see>](2500/previews/bondage.png) | [<NSFW, click to see>](2500/previews/free.png) |  |  | [<NSFW, click to see>](2500/previews/nude.png) | [<NSFW, click to see>](2500/previews/nude2.png) |  |  |
| 2000 | 0.787 | [Download](2000/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](2000/previews/pattern_6.png) |  |  | [<NSFW, click to see>](2000/previews/bondage.png) | [<NSFW, click to see>](2000/previews/free.png) |  |  | [<NSFW, click to see>](2000/previews/nude.png) | [<NSFW, click to see>](2000/previews/nude2.png) |  |  |
| 1500 | 0.814 | [Download](1500/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](1500/previews/pattern_6.png) |  |  | [<NSFW, click to see>](1500/previews/bondage.png) | [<NSFW, click to see>](1500/previews/free.png) |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [<NSFW, click to see>](1500/previews/nude2.png) |  |  |
| 1000 | 0.822 | [Download](1000/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](1000/previews/pattern_6.png) |  |  | [<NSFW, click to see>](1000/previews/bondage.png) | [<NSFW, click to see>](1000/previews/free.png) |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [<NSFW, click to see>](1000/previews/nude2.png) |  |  |
| 500 | 0.630 | [Download](500/shiraishi_tsumugi_theidolmstermillionlive.zip) |  |  |  |  |  | [<NSFW, click to see>](500/previews/pattern_6.png) |  |  | [<NSFW, click to see>](500/previews/bondage.png) | [<NSFW, click to see>](500/previews/free.png) |  |  | [<NSFW, click to see>](500/previews/nude.png) | [<NSFW, click to see>](500/previews/nude2.png) |  |  |
|
milaidy/snowfro | milaidy | 2023-09-23T15:15:43Z | 2 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-09-23T15:03:18Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### snowfro Dreambooth model trained by milaidy 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:
|
Krishna1350/Practice | Krishna1350 | 2023-09-23T15:09:17Z | 0 | 0 | null | [
"region:us"
]
| null | 2023-09-23T15:05:54Z | Creating my first repo in the hugging face |
ayoubkirouane/git-base-One-Piece | ayoubkirouane | 2023-09-23T15:06:58Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"git",
"image-text-to-text",
"vision",
"image-to-text",
"image-captioning",
"en",
"dataset:ayoubkirouane/One-Piece-anime-captions",
"license:mit",
"endpoints_compatible",
"region:us"
]
| image-to-text | 2023-09-23T11:28:27Z | ---
language: en
license: mit
tags:
- vision
- image-to-text
- image-captioning
model_name: microsoft/git-base
pipeline_tag: image-to-text
library_name: transformers
datasets:
- ayoubkirouane/One-Piece-anime-captions
---
# Model Details
+ **Model Name**: Git-base-One-Piece
+ **Base Model**: Microsoft's "git-base" model
+ **Model Type**: Generative Image-to-Text (GIT)
+ **Fine-Tuned** On: 'One-Piece-anime-captions' dataset
+ **Fine-Tuning Purpose**: To generate text captions for images related to the anime series "One Piece."
## Model Description
**Git-base-One-Piece** is a fine-tuned variant of Microsoft's **git-base** model, specifically trained for the task of generating descriptive text captions for images from the **One-Piece-anime-captions** dataset.
The dataset consists of **856 {image: caption}** pairs, providing a substantial and diverse training corpus for the model.
The model is conditioned on both CLIP image tokens and text tokens and employs a **teacher forcing** training approach. It predicts the next text token while considering the context provided by the image and previous text tokens.
## Limitations
+ The quality of generated captions may vary depending on the complexity and diversity of images from the **One-Piece-anime-captions** dataset.
+ The model's output is based on the data it was fine-tuned on, so it may not generalize well to images outside the dataset's domain.
Generating highly detailed or contextually accurate captions may still be a challenge.
## Usage
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-to-text", model="ayoubkirouane/git-base-One-Piece")
```
**or**
```python
# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM
processor = AutoProcessor.from_pretrained("ayoubkirouane/git-base-One-Piece")
model = AutoModelForCausalLM.from_pretrained("ayoubkirouane/git-base-One-Piece")
``` |
Nuntea/ppo-LunarLander-v2 | Nuntea | 2023-09-23T15:05:49Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-23T15:05:27Z | ---
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: 121.16 +/- 117.97
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
...
```
|
WillyArdiyanto/image_classification | WillyArdiyanto | 2023-09-23T14:52:08Z | 11 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-09-16T05:33:06Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5625
---
<!-- 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. -->
# image_classification
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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4866
- Accuracy: 0.5625
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.5045 | 0.4875 |
| No log | 2.0 | 80 | 1.3562 | 0.5312 |
| No log | 3.0 | 120 | 1.5354 | 0.4562 |
| No log | 4.0 | 160 | 1.5095 | 0.5062 |
| No log | 5.0 | 200 | 1.5644 | 0.475 |
| No log | 6.0 | 240 | 1.4651 | 0.5563 |
| No log | 7.0 | 280 | 1.4516 | 0.5375 |
| No log | 8.0 | 320 | 1.5859 | 0.5188 |
| No log | 9.0 | 360 | 1.5498 | 0.5437 |
| No log | 10.0 | 400 | 1.5040 | 0.5625 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
ldos/text_shortening_model_v53 | ldos | 2023-09-23T14:31:18Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-09-23T12:13:33Z | ---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v53
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. -->
# text_shortening_model_v53
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1989
- Rouge1: 0.5027
- Rouge2: 0.2828
- Rougel: 0.4438
- Rougelsum: 0.4452
- Bert precision: 0.8749
- Bert recall: 0.875
- Average word count: 8.291
- Max word count: 16
- Min word count: 4
- Average token count: 12.8757
- % shortened texts with length > 12: 7.672
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
| 1.7343 | 1.0 | 83 | 1.2874 | 0.5099 | 0.2905 | 0.4503 | 0.4503 | 0.8714 | 0.878 | 9.1376 | 17 | 4 | 13.7937 | 13.4921 |
| 1.2551 | 2.0 | 166 | 1.1755 | 0.5197 | 0.298 | 0.4583 | 0.4595 | 0.8747 | 0.8816 | 8.9974 | 17 | 4 | 13.7513 | 12.6984 |
| 1.0452 | 3.0 | 249 | 1.1440 | 0.5182 | 0.3011 | 0.4573 | 0.4583 | 0.8759 | 0.8801 | 8.8307 | 18 | 4 | 13.3545 | 14.5503 |
| 0.9043 | 4.0 | 332 | 1.1351 | 0.5321 | 0.3168 | 0.4774 | 0.4785 | 0.8783 | 0.8827 | 8.7619 | 16 | 4 | 13.4286 | 13.2275 |
| 0.7933 | 5.0 | 415 | 1.1381 | 0.5131 | 0.2976 | 0.4577 | 0.4572 | 0.8786 | 0.8771 | 8.3148 | 18 | 4 | 12.6402 | 8.9947 |
| 0.6859 | 6.0 | 498 | 1.1688 | 0.5357 | 0.3133 | 0.4776 | 0.4778 | 0.8812 | 0.8829 | 8.4762 | 18 | 4 | 12.9339 | 10.0529 |
| 0.6069 | 7.0 | 581 | 1.2138 | 0.5228 | 0.2959 | 0.4562 | 0.4568 | 0.8768 | 0.88 | 8.6481 | 17 | 3 | 13.1429 | 12.1693 |
| 0.5475 | 8.0 | 664 | 1.2333 | 0.5275 | 0.3051 | 0.4685 | 0.469 | 0.8787 | 0.8818 | 8.7143 | 16 | 4 | 13.209 | 11.6402 |
| 0.4898 | 9.0 | 747 | 1.2889 | 0.5194 | 0.296 | 0.4603 | 0.4603 | 0.8785 | 0.8797 | 8.4947 | 15 | 4 | 12.9312 | 11.3757 |
| 0.4456 | 10.0 | 830 | 1.3068 | 0.5154 | 0.2923 | 0.4634 | 0.4647 | 0.8796 | 0.8788 | 8.3413 | 16 | 4 | 12.8201 | 10.8466 |
| 0.408 | 11.0 | 913 | 1.3598 | 0.5118 | 0.2959 | 0.4583 | 0.459 | 0.8787 | 0.8763 | 8.1481 | 16 | 4 | 12.627 | 9.2593 |
| 0.3673 | 12.0 | 996 | 1.3905 | 0.5193 | 0.2999 | 0.4673 | 0.468 | 0.8808 | 0.8782 | 8.2116 | 16 | 3 | 12.627 | 8.2011 |
| 0.3367 | 13.0 | 1079 | 1.4436 | 0.516 | 0.2977 | 0.4588 | 0.4595 | 0.8785 | 0.879 | 8.4259 | 17 | 4 | 12.9974 | 9.7884 |
| 0.3113 | 14.0 | 1162 | 1.4654 | 0.5077 | 0.2884 | 0.4506 | 0.4511 | 0.8759 | 0.8776 | 8.4815 | 17 | 3 | 13.1481 | 10.3175 |
| 0.2844 | 15.0 | 1245 | 1.4895 | 0.5132 | 0.2924 | 0.4538 | 0.4549 | 0.8759 | 0.8786 | 8.5185 | 17 | 3 | 13.1349 | 9.5238 |
| 0.2684 | 16.0 | 1328 | 1.5345 | 0.5065 | 0.2933 | 0.4533 | 0.4539 | 0.8761 | 0.8767 | 8.2275 | 16 | 3 | 12.7196 | 8.7302 |
| 0.2396 | 17.0 | 1411 | 1.5892 | 0.5035 | 0.2835 | 0.444 | 0.4449 | 0.8757 | 0.8743 | 8.082 | 17 | 3 | 12.5503 | 7.9365 |
| 0.223 | 18.0 | 1494 | 1.5954 | 0.4944 | 0.2802 | 0.4407 | 0.4414 | 0.8729 | 0.8739 | 8.3519 | 17 | 4 | 12.8836 | 10.582 |
| 0.2119 | 19.0 | 1577 | 1.6106 | 0.5042 | 0.286 | 0.4505 | 0.4515 | 0.8772 | 0.8752 | 8.0212 | 16 | 3 | 12.5265 | 7.4074 |
| 0.2009 | 20.0 | 1660 | 1.6218 | 0.5002 | 0.2767 | 0.444 | 0.445 | 0.8755 | 0.874 | 8.1667 | 15 | 3 | 12.6746 | 8.9947 |
| 0.1825 | 21.0 | 1743 | 1.6545 | 0.5 | 0.2868 | 0.4443 | 0.4449 | 0.8755 | 0.8749 | 8.1958 | 16 | 3 | 12.7249 | 8.9947 |
| 0.1693 | 22.0 | 1826 | 1.7102 | 0.5014 | 0.2837 | 0.4453 | 0.4462 | 0.8741 | 0.8737 | 8.246 | 16 | 3 | 12.8466 | 10.0529 |
| 0.1584 | 23.0 | 1909 | 1.7314 | 0.4947 | 0.2773 | 0.4375 | 0.4381 | 0.8728 | 0.8722 | 8.1878 | 15 | 3 | 12.7196 | 7.672 |
| 0.1484 | 24.0 | 1992 | 1.7512 | 0.5004 | 0.2854 | 0.4461 | 0.447 | 0.8739 | 0.8744 | 8.2884 | 16 | 3 | 13.0053 | 7.9365 |
| 0.144 | 25.0 | 2075 | 1.7862 | 0.5023 | 0.2847 | 0.4451 | 0.4455 | 0.8758 | 0.8756 | 8.2302 | 16 | 3 | 12.8148 | 8.9947 |
| 0.1302 | 26.0 | 2158 | 1.8208 | 0.5041 | 0.2901 | 0.4471 | 0.4478 | 0.8739 | 0.8753 | 8.4153 | 17 | 3 | 13.0238 | 10.8466 |
| 0.1231 | 27.0 | 2241 | 1.8258 | 0.5003 | 0.2835 | 0.4449 | 0.4457 | 0.8739 | 0.8738 | 8.2354 | 16 | 4 | 12.8016 | 7.9365 |
| 0.1233 | 28.0 | 2324 | 1.8340 | 0.5047 | 0.2867 | 0.4432 | 0.4446 | 0.8743 | 0.8757 | 8.4206 | 17 | 4 | 12.9894 | 9.7884 |
| 0.1114 | 29.0 | 2407 | 1.8461 | 0.4898 | 0.2731 | 0.4357 | 0.4358 | 0.8722 | 0.8722 | 8.2407 | 16 | 3 | 12.8466 | 7.9365 |
| 0.1084 | 30.0 | 2490 | 1.8817 | 0.4973 | 0.2805 | 0.4381 | 0.4394 | 0.8741 | 0.8721 | 8.1349 | 16 | 3 | 12.5529 | 6.8783 |
| 0.1031 | 31.0 | 2573 | 1.9454 | 0.489 | 0.2779 | 0.4352 | 0.4365 | 0.8725 | 0.8728 | 8.2989 | 16 | 4 | 12.8466 | 8.4656 |
| 0.1008 | 32.0 | 2656 | 1.9150 | 0.4936 | 0.277 | 0.4415 | 0.4427 | 0.8741 | 0.873 | 8.2011 | 16 | 3 | 12.7249 | 8.7302 |
| 0.0929 | 33.0 | 2739 | 1.9444 | 0.4905 | 0.2723 | 0.4337 | 0.4344 | 0.8722 | 0.873 | 8.4233 | 16 | 3 | 12.9074 | 9.2593 |
| 0.0972 | 34.0 | 2822 | 1.9321 | 0.4877 | 0.2671 | 0.4336 | 0.4345 | 0.8721 | 0.872 | 8.3413 | 16 | 4 | 12.9153 | 8.9947 |
| 0.0941 | 35.0 | 2905 | 1.9430 | 0.5018 | 0.2808 | 0.4467 | 0.4477 | 0.8757 | 0.8742 | 8.2169 | 15 | 4 | 12.7381 | 7.1429 |
| 0.0856 | 36.0 | 2988 | 1.9623 | 0.4981 | 0.282 | 0.4463 | 0.4474 | 0.875 | 0.8737 | 8.2989 | 16 | 4 | 12.7672 | 8.9947 |
| 0.0842 | 37.0 | 3071 | 1.9739 | 0.4992 | 0.2812 | 0.442 | 0.4435 | 0.8746 | 0.8741 | 8.3122 | 16 | 4 | 12.828 | 8.9947 |
| 0.0833 | 38.0 | 3154 | 2.0193 | 0.5008 | 0.2805 | 0.4458 | 0.4469 | 0.8749 | 0.8756 | 8.373 | 16 | 4 | 12.9153 | 10.582 |
| 0.0783 | 39.0 | 3237 | 2.0161 | 0.499 | 0.2829 | 0.4425 | 0.4435 | 0.8753 | 0.8735 | 8.1217 | 16 | 4 | 12.7222 | 7.1429 |
| 0.0771 | 40.0 | 3320 | 1.9768 | 0.498 | 0.2803 | 0.4437 | 0.4449 | 0.8741 | 0.8731 | 8.2963 | 16 | 4 | 12.8095 | 8.9947 |
| 0.0744 | 41.0 | 3403 | 2.0104 | 0.5002 | 0.2815 | 0.4404 | 0.4411 | 0.8745 | 0.875 | 8.4021 | 16 | 4 | 12.9603 | 9.5238 |
| 0.0708 | 42.0 | 3486 | 2.0090 | 0.4938 | 0.2714 | 0.4347 | 0.435 | 0.8722 | 0.872 | 8.328 | 16 | 4 | 12.8783 | 8.9947 |
| 0.0686 | 43.0 | 3569 | 2.0328 | 0.4958 | 0.2759 | 0.4386 | 0.4399 | 0.8727 | 0.8732 | 8.4339 | 16 | 4 | 12.9894 | 8.7302 |
| 0.073 | 44.0 | 3652 | 2.0284 | 0.4924 | 0.2706 | 0.4347 | 0.4359 | 0.8724 | 0.8731 | 8.4339 | 16 | 4 | 12.9603 | 7.672 |
| 0.0686 | 45.0 | 3735 | 2.0392 | 0.4952 | 0.2794 | 0.4415 | 0.4431 | 0.8732 | 0.8722 | 8.2619 | 16 | 4 | 12.8598 | 7.672 |
| 0.0659 | 46.0 | 3818 | 2.0563 | 0.4906 | 0.2729 | 0.436 | 0.4375 | 0.8729 | 0.8714 | 8.1323 | 15 | 4 | 12.6032 | 6.0847 |
| 0.0651 | 47.0 | 3901 | 2.0454 | 0.4903 | 0.2707 | 0.4356 | 0.4369 | 0.8725 | 0.8701 | 8.1005 | 16 | 3 | 12.6667 | 5.8201 |
| 0.0655 | 48.0 | 3984 | 2.0500 | 0.4971 | 0.2784 | 0.4419 | 0.4434 | 0.874 | 0.8733 | 8.2434 | 16 | 4 | 12.8228 | 7.4074 |
| 0.0635 | 49.0 | 4067 | 2.0376 | 0.504 | 0.2831 | 0.446 | 0.4472 | 0.8733 | 0.8739 | 8.3968 | 16 | 4 | 12.9735 | 8.7302 |
| 0.063 | 50.0 | 4150 | 2.0596 | 0.4878 | 0.2623 | 0.426 | 0.4276 | 0.8699 | 0.8726 | 8.672 | 16 | 4 | 13.2037 | 11.1111 |
| 0.0589 | 51.0 | 4233 | 2.0682 | 0.4932 | 0.2734 | 0.4384 | 0.4393 | 0.8738 | 0.8729 | 8.2963 | 16 | 4 | 12.7407 | 7.1429 |
| 0.0566 | 52.0 | 4316 | 2.0692 | 0.5 | 0.2803 | 0.4445 | 0.4463 | 0.8745 | 0.8753 | 8.463 | 16 | 3 | 13.0185 | 10.8466 |
| 0.0558 | 53.0 | 4399 | 2.1003 | 0.5023 | 0.279 | 0.4421 | 0.443 | 0.874 | 0.8757 | 8.5265 | 16 | 3 | 13.0952 | 10.8466 |
| 0.0545 | 54.0 | 4482 | 2.0992 | 0.5027 | 0.2801 | 0.4428 | 0.444 | 0.8745 | 0.8765 | 8.5053 | 16 | 3 | 12.9815 | 10.8466 |
| 0.0573 | 55.0 | 4565 | 2.0713 | 0.4971 | 0.2786 | 0.4385 | 0.4409 | 0.8735 | 0.8735 | 8.3307 | 16 | 3 | 12.8042 | 9.2593 |
| 0.0549 | 56.0 | 4648 | 2.1003 | 0.4988 | 0.2782 | 0.4388 | 0.4399 | 0.8727 | 0.8745 | 8.4762 | 16 | 3 | 13.0661 | 10.3175 |
| 0.0572 | 57.0 | 4731 | 2.0828 | 0.5 | 0.2806 | 0.4449 | 0.4456 | 0.8741 | 0.8737 | 8.2407 | 16 | 3 | 12.8413 | 8.4656 |
| 0.0549 | 58.0 | 4814 | 2.0845 | 0.5049 | 0.2829 | 0.442 | 0.4421 | 0.8742 | 0.8752 | 8.3783 | 17 | 3 | 12.9577 | 9.7884 |
| 0.0542 | 59.0 | 4897 | 2.0775 | 0.5088 | 0.2921 | 0.4506 | 0.4518 | 0.8757 | 0.8774 | 8.5 | 16 | 4 | 13.1614 | 10.582 |
| 0.0517 | 60.0 | 4980 | 2.1092 | 0.504 | 0.2874 | 0.4467 | 0.4475 | 0.8757 | 0.8753 | 8.2381 | 16 | 3 | 12.7646 | 8.9947 |
| 0.0531 | 61.0 | 5063 | 2.1049 | 0.5004 | 0.2832 | 0.4444 | 0.4456 | 0.8724 | 0.8743 | 8.4921 | 16 | 4 | 13.0741 | 10.8466 |
| 0.0516 | 62.0 | 5146 | 2.1202 | 0.5042 | 0.2822 | 0.4475 | 0.448 | 0.8755 | 0.8752 | 8.2725 | 16 | 3 | 12.8307 | 8.4656 |
| 0.0481 | 63.0 | 5229 | 2.1460 | 0.5047 | 0.284 | 0.4428 | 0.443 | 0.8729 | 0.8754 | 8.5503 | 16 | 4 | 13.2169 | 10.0529 |
| 0.0456 | 64.0 | 5312 | 2.1436 | 0.5015 | 0.2802 | 0.4419 | 0.4429 | 0.8733 | 0.8743 | 8.4259 | 16 | 4 | 12.9444 | 9.7884 |
| 0.048 | 65.0 | 5395 | 2.1405 | 0.5114 | 0.2882 | 0.4503 | 0.4516 | 0.8754 | 0.8768 | 8.4365 | 16 | 4 | 13.0529 | 10.3175 |
| 0.0467 | 66.0 | 5478 | 2.1556 | 0.5098 | 0.2866 | 0.4477 | 0.4488 | 0.8757 | 0.876 | 8.3386 | 16 | 4 | 12.8704 | 9.5238 |
| 0.0471 | 67.0 | 5561 | 2.1712 | 0.5155 | 0.2901 | 0.4532 | 0.4546 | 0.8752 | 0.8781 | 8.6032 | 16 | 4 | 13.2302 | 12.4339 |
| 0.0434 | 68.0 | 5644 | 2.1529 | 0.5104 | 0.2889 | 0.4494 | 0.4502 | 0.8748 | 0.876 | 8.4444 | 16 | 4 | 13.037 | 10.0529 |
| 0.0447 | 69.0 | 5727 | 2.1538 | 0.5039 | 0.2819 | 0.4431 | 0.4445 | 0.8742 | 0.8746 | 8.3598 | 16 | 4 | 12.8995 | 10.8466 |
| 0.0464 | 70.0 | 5810 | 2.1497 | 0.5095 | 0.2868 | 0.4499 | 0.4514 | 0.875 | 0.8757 | 8.3624 | 15 | 4 | 13.0053 | 8.7302 |
| 0.0445 | 71.0 | 5893 | 2.1609 | 0.5082 | 0.2865 | 0.4461 | 0.447 | 0.8742 | 0.8771 | 8.5873 | 15 | 4 | 13.2037 | 10.8466 |
| 0.0443 | 72.0 | 5976 | 2.1532 | 0.5015 | 0.2799 | 0.4448 | 0.4458 | 0.8744 | 0.8749 | 8.3757 | 15 | 3 | 12.8942 | 10.0529 |
| 0.0433 | 73.0 | 6059 | 2.1533 | 0.4969 | 0.2772 | 0.441 | 0.4421 | 0.8739 | 0.8733 | 8.2751 | 15 | 3 | 12.7143 | 10.0529 |
| 0.0429 | 74.0 | 6142 | 2.1663 | 0.501 | 0.2809 | 0.4435 | 0.4448 | 0.8753 | 0.8751 | 8.2619 | 15 | 3 | 12.7778 | 8.7302 |
| 0.0427 | 75.0 | 6225 | 2.1336 | 0.5069 | 0.2871 | 0.4517 | 0.4528 | 0.8759 | 0.876 | 8.2487 | 16 | 3 | 12.8148 | 9.2593 |
| 0.0446 | 76.0 | 6308 | 2.1338 | 0.5135 | 0.2959 | 0.4575 | 0.4587 | 0.8768 | 0.8774 | 8.3492 | 16 | 4 | 12.9233 | 9.2593 |
| 0.0438 | 77.0 | 6391 | 2.1349 | 0.5132 | 0.2961 | 0.457 | 0.4578 | 0.877 | 0.8779 | 8.3545 | 15 | 4 | 12.9233 | 10.0529 |
| 0.0436 | 78.0 | 6474 | 2.1331 | 0.5106 | 0.2914 | 0.4534 | 0.4544 | 0.8757 | 0.8768 | 8.4286 | 15 | 3 | 12.9762 | 10.8466 |
| 0.0432 | 79.0 | 6557 | 2.1389 | 0.5086 | 0.286 | 0.4523 | 0.4529 | 0.8763 | 0.8762 | 8.2434 | 15 | 3 | 12.7487 | 9.5238 |
| 0.0414 | 80.0 | 6640 | 2.1596 | 0.5084 | 0.2883 | 0.4506 | 0.4517 | 0.8759 | 0.8765 | 8.3624 | 16 | 3 | 12.8862 | 9.2593 |
| 0.0393 | 81.0 | 6723 | 2.1762 | 0.5048 | 0.2851 | 0.4495 | 0.4501 | 0.8759 | 0.8764 | 8.2513 | 16 | 3 | 12.7857 | 8.4656 |
| 0.0407 | 82.0 | 6806 | 2.1854 | 0.5055 | 0.2867 | 0.4477 | 0.449 | 0.8758 | 0.8762 | 8.3122 | 16 | 4 | 12.8228 | 8.4656 |
| 0.0396 | 83.0 | 6889 | 2.1720 | 0.4995 | 0.2774 | 0.4402 | 0.4413 | 0.874 | 0.8748 | 8.3862 | 16 | 4 | 12.9656 | 9.7884 |
| 0.0342 | 84.0 | 6972 | 2.2038 | 0.5046 | 0.2849 | 0.4494 | 0.4506 | 0.8759 | 0.8759 | 8.2513 | 16 | 4 | 12.8069 | 8.2011 |
| 0.0415 | 85.0 | 7055 | 2.1893 | 0.5077 | 0.2847 | 0.4503 | 0.4517 | 0.8762 | 0.8759 | 8.2513 | 16 | 4 | 12.791 | 8.7302 |
| 0.0388 | 86.0 | 7138 | 2.1811 | 0.5065 | 0.2882 | 0.4503 | 0.4512 | 0.8758 | 0.8764 | 8.3228 | 15 | 4 | 12.9101 | 8.9947 |
| 0.0411 | 87.0 | 7221 | 2.1794 | 0.5057 | 0.2853 | 0.4526 | 0.4546 | 0.8764 | 0.8753 | 8.2222 | 15 | 4 | 12.7434 | 8.2011 |
| 0.0358 | 88.0 | 7304 | 2.2025 | 0.5031 | 0.283 | 0.4486 | 0.4505 | 0.8753 | 0.8747 | 8.2487 | 15 | 4 | 12.8122 | 7.9365 |
| 0.0388 | 89.0 | 7387 | 2.1994 | 0.5021 | 0.2818 | 0.4437 | 0.4453 | 0.8745 | 0.8742 | 8.3386 | 16 | 4 | 12.873 | 8.9947 |
| 0.0372 | 90.0 | 7470 | 2.2076 | 0.5055 | 0.2843 | 0.4478 | 0.449 | 0.8754 | 0.8754 | 8.3492 | 16 | 4 | 12.9762 | 8.9947 |
| 0.0352 | 91.0 | 7553 | 2.2038 | 0.5077 | 0.2891 | 0.4504 | 0.4521 | 0.8755 | 0.8767 | 8.4153 | 16 | 4 | 13.0423 | 10.8466 |
| 0.0369 | 92.0 | 7636 | 2.2122 | 0.5086 | 0.2877 | 0.4507 | 0.4522 | 0.8759 | 0.8757 | 8.3175 | 16 | 4 | 12.8757 | 7.9365 |
| 0.0379 | 93.0 | 7719 | 2.2121 | 0.5047 | 0.2877 | 0.4501 | 0.4517 | 0.8757 | 0.8763 | 8.3122 | 15 | 4 | 12.8519 | 8.4656 |
| 0.0367 | 94.0 | 7802 | 2.1979 | 0.5098 | 0.2897 | 0.4524 | 0.4534 | 0.8776 | 0.8767 | 8.2143 | 15 | 4 | 12.7487 | 7.672 |
| 0.0372 | 95.0 | 7885 | 2.1951 | 0.5003 | 0.2825 | 0.445 | 0.4467 | 0.8743 | 0.8755 | 8.4074 | 16 | 3 | 12.9894 | 10.0529 |
| 0.0358 | 96.0 | 7968 | 2.2210 | 0.505 | 0.2884 | 0.4496 | 0.4511 | 0.8763 | 0.8759 | 8.3307 | 15 | 3 | 12.8995 | 9.5238 |
| 0.0375 | 97.0 | 8051 | 2.2113 | 0.5084 | 0.2869 | 0.452 | 0.4528 | 0.8761 | 0.8767 | 8.3307 | 16 | 3 | 12.9471 | 9.2593 |
| 0.0351 | 98.0 | 8134 | 2.2248 | 0.5065 | 0.2866 | 0.4521 | 0.4535 | 0.8765 | 0.8758 | 8.2751 | 15 | 4 | 12.8466 | 9.2593 |
| 0.0363 | 99.0 | 8217 | 2.2332 | 0.5013 | 0.2784 | 0.4435 | 0.4448 | 0.8747 | 0.8755 | 8.3968 | 16 | 3 | 13.0635 | 10.582 |
| 0.0346 | 100.0 | 8300 | 2.2486 | 0.4991 | 0.2774 | 0.4438 | 0.4454 | 0.8749 | 0.8737 | 8.172 | 15 | 3 | 12.7937 | 7.672 |
| 0.0323 | 101.0 | 8383 | 2.2460 | 0.5062 | 0.2857 | 0.4515 | 0.4524 | 0.8758 | 0.8755 | 8.3201 | 16 | 4 | 12.836 | 10.3175 |
| 0.0326 | 102.0 | 8466 | 2.2416 | 0.5046 | 0.2838 | 0.4504 | 0.452 | 0.8755 | 0.8753 | 8.3069 | 16 | 4 | 12.8783 | 9.5238 |
| 0.0339 | 103.0 | 8549 | 2.2407 | 0.4937 | 0.2732 | 0.4389 | 0.44 | 0.8737 | 0.8733 | 8.3016 | 16 | 4 | 12.9206 | 9.2593 |
| 0.0343 | 104.0 | 8632 | 2.2370 | 0.5006 | 0.2801 | 0.4449 | 0.4464 | 0.8752 | 0.8739 | 8.209 | 16 | 4 | 12.8122 | 8.2011 |
| 0.0325 | 105.0 | 8715 | 2.2509 | 0.5031 | 0.2825 | 0.446 | 0.4469 | 0.8743 | 0.8738 | 8.2884 | 16 | 4 | 12.8651 | 9.2593 |
| 0.031 | 106.0 | 8798 | 2.2549 | 0.5041 | 0.2815 | 0.4469 | 0.4482 | 0.8751 | 0.8747 | 8.2884 | 16 | 4 | 12.8862 | 9.5238 |
| 0.032 | 107.0 | 8881 | 2.2694 | 0.5041 | 0.2827 | 0.4474 | 0.4482 | 0.8749 | 0.8749 | 8.3122 | 16 | 4 | 12.873 | 9.2593 |
| 0.0335 | 108.0 | 8964 | 2.2563 | 0.5036 | 0.2841 | 0.4486 | 0.4492 | 0.8746 | 0.8756 | 8.4603 | 16 | 4 | 13.0317 | 10.582 |
| 0.032 | 109.0 | 9047 | 2.2470 | 0.5053 | 0.2849 | 0.4488 | 0.4491 | 0.8746 | 0.8757 | 8.455 | 16 | 4 | 13.0053 | 10.582 |
| 0.031 | 110.0 | 9130 | 2.2412 | 0.5023 | 0.2815 | 0.4457 | 0.4467 | 0.8745 | 0.8744 | 8.2989 | 16 | 4 | 12.8175 | 8.4656 |
| 0.034 | 111.0 | 9213 | 2.2642 | 0.5014 | 0.2809 | 0.4434 | 0.4447 | 0.8737 | 0.875 | 8.4894 | 16 | 4 | 13.0767 | 10.582 |
| 0.0357 | 112.0 | 9296 | 2.2428 | 0.4985 | 0.2804 | 0.443 | 0.4438 | 0.8745 | 0.8739 | 8.2593 | 16 | 4 | 12.7619 | 8.4656 |
| 0.036 | 113.0 | 9379 | 2.2353 | 0.5022 | 0.2829 | 0.4451 | 0.4456 | 0.875 | 0.8746 | 8.2804 | 16 | 4 | 12.8016 | 8.7302 |
| 0.0298 | 114.0 | 9462 | 2.2445 | 0.5025 | 0.2823 | 0.444 | 0.4451 | 0.8742 | 0.8749 | 8.3783 | 16 | 4 | 12.9709 | 9.5238 |
| 0.0329 | 115.0 | 9545 | 2.2486 | 0.5033 | 0.2803 | 0.443 | 0.4437 | 0.8747 | 0.8747 | 8.3175 | 16 | 4 | 12.881 | 8.9947 |
| 0.0358 | 116.0 | 9628 | 2.2347 | 0.4977 | 0.2756 | 0.4394 | 0.4401 | 0.8739 | 0.874 | 8.3492 | 16 | 4 | 12.9312 | 9.2593 |
| 0.031 | 117.0 | 9711 | 2.2403 | 0.5049 | 0.2848 | 0.4468 | 0.4474 | 0.8753 | 0.8756 | 8.2963 | 16 | 4 | 12.9418 | 8.9947 |
| 0.0332 | 118.0 | 9794 | 2.2444 | 0.5037 | 0.2842 | 0.4467 | 0.447 | 0.875 | 0.8753 | 8.3439 | 16 | 3 | 12.9815 | 9.2593 |
| 0.0319 | 119.0 | 9877 | 2.2415 | 0.5055 | 0.2869 | 0.4479 | 0.4486 | 0.8755 | 0.8759 | 8.3677 | 16 | 3 | 12.9947 | 9.7884 |
| 0.0313 | 120.0 | 9960 | 2.2469 | 0.5062 | 0.286 | 0.4471 | 0.4473 | 0.8762 | 0.8764 | 8.4021 | 16 | 4 | 12.9894 | 9.2593 |
| 0.0324 | 121.0 | 10043 | 2.2514 | 0.508 | 0.2859 | 0.4489 | 0.4498 | 0.8762 | 0.8761 | 8.3757 | 16 | 4 | 12.963 | 9.5238 |
| 0.0311 | 122.0 | 10126 | 2.2604 | 0.5049 | 0.2836 | 0.4462 | 0.4469 | 0.8764 | 0.8761 | 8.3228 | 16 | 4 | 12.8783 | 8.7302 |
| 0.0299 | 123.0 | 10209 | 2.2702 | 0.5048 | 0.2829 | 0.4419 | 0.443 | 0.8756 | 0.8751 | 8.2804 | 16 | 4 | 12.8069 | 8.9947 |
| 0.0296 | 124.0 | 10292 | 2.2743 | 0.5072 | 0.2851 | 0.4445 | 0.4453 | 0.8758 | 0.8761 | 8.336 | 16 | 4 | 12.8968 | 9.2593 |
| 0.029 | 125.0 | 10375 | 2.2657 | 0.5086 | 0.2843 | 0.4461 | 0.4473 | 0.8765 | 0.8763 | 8.2751 | 16 | 4 | 12.8757 | 8.9947 |
| 0.0302 | 126.0 | 10458 | 2.2688 | 0.5064 | 0.2821 | 0.4445 | 0.4452 | 0.8749 | 0.8763 | 8.4101 | 16 | 4 | 13.0132 | 10.3175 |
| 0.0291 | 127.0 | 10541 | 2.2663 | 0.5028 | 0.2835 | 0.4441 | 0.4454 | 0.8744 | 0.8758 | 8.4312 | 16 | 4 | 13.0635 | 10.0529 |
| 0.0341 | 128.0 | 10624 | 2.2517 | 0.4982 | 0.2786 | 0.4392 | 0.4404 | 0.873 | 0.875 | 8.4762 | 16 | 4 | 13.1111 | 9.7884 |
| 0.0327 | 129.0 | 10707 | 2.2374 | 0.5031 | 0.2825 | 0.4433 | 0.4441 | 0.8744 | 0.8752 | 8.3783 | 16 | 4 | 12.963 | 9.2593 |
| 0.0315 | 130.0 | 10790 | 2.2319 | 0.5078 | 0.2877 | 0.4493 | 0.4501 | 0.876 | 0.8758 | 8.2646 | 16 | 4 | 12.8095 | 8.2011 |
| 0.0284 | 131.0 | 10873 | 2.2269 | 0.502 | 0.2808 | 0.4434 | 0.4439 | 0.8745 | 0.8751 | 8.3545 | 16 | 4 | 12.9392 | 8.9947 |
| 0.0303 | 132.0 | 10956 | 2.2163 | 0.5025 | 0.2809 | 0.445 | 0.4457 | 0.8751 | 0.8751 | 8.3042 | 16 | 4 | 12.8836 | 8.4656 |
| 0.03 | 133.0 | 11039 | 2.2104 | 0.5027 | 0.2821 | 0.4441 | 0.4452 | 0.8743 | 0.8756 | 8.4259 | 16 | 4 | 13.0265 | 9.5238 |
| 0.0285 | 134.0 | 11122 | 2.2115 | 0.507 | 0.2868 | 0.448 | 0.4492 | 0.8756 | 0.8767 | 8.4365 | 16 | 4 | 13.0344 | 9.5238 |
| 0.0306 | 135.0 | 11205 | 2.2107 | 0.5057 | 0.284 | 0.445 | 0.4459 | 0.8751 | 0.8755 | 8.4074 | 16 | 4 | 12.9683 | 8.7302 |
| 0.0302 | 136.0 | 11288 | 2.2062 | 0.5064 | 0.2855 | 0.4473 | 0.4484 | 0.8747 | 0.8759 | 8.4259 | 16 | 4 | 13.0317 | 8.9947 |
| 0.0293 | 137.0 | 11371 | 2.2063 | 0.5031 | 0.2836 | 0.4456 | 0.4468 | 0.8747 | 0.8755 | 8.373 | 16 | 4 | 12.9735 | 8.9947 |
| 0.0284 | 138.0 | 11454 | 2.2110 | 0.5025 | 0.2846 | 0.4456 | 0.4464 | 0.8742 | 0.8755 | 8.3995 | 16 | 4 | 13.0026 | 8.9947 |
| 0.0288 | 139.0 | 11537 | 2.2091 | 0.5038 | 0.2844 | 0.4463 | 0.4474 | 0.8751 | 0.8756 | 8.3492 | 16 | 4 | 12.9048 | 8.9947 |
| 0.0259 | 140.0 | 11620 | 2.2081 | 0.5058 | 0.2868 | 0.4472 | 0.448 | 0.8755 | 0.876 | 8.3757 | 16 | 4 | 12.9365 | 8.9947 |
| 0.0299 | 141.0 | 11703 | 2.2055 | 0.5086 | 0.2858 | 0.4481 | 0.4491 | 0.8757 | 0.8758 | 8.3439 | 16 | 4 | 12.881 | 9.2593 |
| 0.0292 | 142.0 | 11786 | 2.2054 | 0.505 | 0.2834 | 0.445 | 0.4465 | 0.8751 | 0.875 | 8.2963 | 16 | 4 | 12.8492 | 7.9365 |
| 0.0281 | 143.0 | 11869 | 2.2051 | 0.503 | 0.2819 | 0.4433 | 0.4446 | 0.8747 | 0.8748 | 8.3201 | 16 | 4 | 12.8783 | 8.2011 |
| 0.0287 | 144.0 | 11952 | 2.2054 | 0.5042 | 0.2832 | 0.4448 | 0.4461 | 0.8749 | 0.8749 | 8.3175 | 16 | 4 | 12.8704 | 7.9365 |
| 0.0307 | 145.0 | 12035 | 2.2022 | 0.5037 | 0.2835 | 0.4441 | 0.4453 | 0.8748 | 0.875 | 8.3254 | 16 | 4 | 12.8968 | 8.2011 |
| 0.028 | 146.0 | 12118 | 2.2004 | 0.5032 | 0.2834 | 0.4436 | 0.4448 | 0.8748 | 0.875 | 8.3122 | 16 | 4 | 12.8862 | 8.2011 |
| 0.0256 | 147.0 | 12201 | 2.2014 | 0.503 | 0.2829 | 0.4433 | 0.4445 | 0.8747 | 0.8751 | 8.3254 | 16 | 4 | 12.8915 | 8.2011 |
| 0.0275 | 148.0 | 12284 | 2.2012 | 0.5036 | 0.2836 | 0.4438 | 0.4453 | 0.8751 | 0.875 | 8.2884 | 16 | 4 | 12.8545 | 7.672 |
| 0.0292 | 149.0 | 12367 | 2.1998 | 0.5036 | 0.2835 | 0.4438 | 0.4452 | 0.875 | 0.875 | 8.291 | 16 | 4 | 12.8624 | 7.672 |
| 0.0286 | 150.0 | 12450 | 2.1989 | 0.5027 | 0.2828 | 0.4438 | 0.4452 | 0.8749 | 0.875 | 8.291 | 16 | 4 | 12.8757 | 7.672 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
lortigas/xlm-roberta-base-finetuned-panx-en | lortigas | 2023-09-23T14:29:56Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-09-22T02:58:29Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6964784795975406
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4033
- F1: 0.6965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1437 | 1.0 | 50 | 0.5910 | 0.4988 |
| 0.5173 | 2.0 | 100 | 0.4610 | 0.6559 |
| 0.3743 | 3.0 | 150 | 0.4033 | 0.6965 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.0.1
- Datasets 1.16.1
- Tokenizers 0.13.3
|
CyberHarem/egami_tsubaki_idolmastercinderellagirls | CyberHarem | 2023-09-23T14:20:25Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/egami_tsubaki_idolmastercinderellagirls",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-23T14:12:25Z | ---
license: mit
datasets:
- CyberHarem/egami_tsubaki_idolmastercinderellagirls
pipeline_tag: text-to-image
tags:
- art
---
# Lora of egami_tsubaki_idolmastercinderellagirls
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 3060, you need to download `3060/egami_tsubaki_idolmastercinderellagirls.pt` as the embedding and `3060/egami_tsubaki_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 3060**, with the score of 0.993. The trigger words are:
1. `egami_tsubaki_idolmastercinderellagirls`
2. `black_hair, long_hair, smile, flower, black_eyes, open_mouth, hair_ornament, card_\(medium\), purple_eyes, hair_flower`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-----------------------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 5100 | 0.983 | [Download](5100/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4760 | 0.962 | [Download](4760/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.979 | [Download](4420/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| 4080 | 0.981 | [Download](4080/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.915 | [Download](3740/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.950 | [Download](3400/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| **3060** | **0.993** | [**Download**](3060/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.948 | [Download](2720/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.979 | [Download](2380/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.934 | [Download](2040/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.936 | [Download](1700/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.908 | [Download](1360/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.903 | [Download](1020/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.712 | [Download](680/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.312 | [Download](340/egami_tsubaki_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
lortigas/xlm-roberta-base-finetuned-panx-it | lortigas | 2023-09-23T14:13:43Z | 126 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-09-22T02:58:08Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8152085036794766
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2535
- F1: 0.8152
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7945 | 1.0 | 70 | 0.3361 | 0.7565 |
| 0.2829 | 2.0 | 140 | 0.2637 | 0.8008 |
| 0.1802 | 3.0 | 210 | 0.2535 | 0.8152 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.0.1
- Datasets 1.16.1
- Tokenizers 0.13.3
|
Ashyyy167/Kaeya | Ashyyy167 | 2023-09-23T14:05:50Z | 0 | 0 | null | [
"music",
"text-to-video",
"ja",
"en",
"dataset:b-mc2/sql-create-context",
"region:us"
]
| text-to-video | 2023-09-23T14:01:20Z | ---
datasets:
- b-mc2/sql-create-context
language:
- ja
- en
metrics:
- character
pipeline_tag: text-to-video
tags:
- music
--- |
arjunssat/Llama-2-7b-chat-finetune | arjunssat | 2023-09-23T13:55:39Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-09-09T12:05:55Z | ---
license: apache-2.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
csocsci/mt5-base-multi-label-en-iiib-02c | csocsci | 2023-09-23T13:44:11Z | 120 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"multilingual",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-09-22T12:58:51Z | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
license: mit
language:
- multilingual
---
# Model Card for mt5-base-multi-label-en-iiib-02c
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned for multi-label text classification of Supportive Interactions in Instant Messenger dialogs of Adolescents.
## Model Description
The model was fine-tuned on a dataset of English Instant Messenger dialogs of Adolescents. The classification is multi-label and the model outputs any combination of the tags:'NO TAG', 'Informační podpora', 'Emocionální podpora', 'Začlenění do skupiny', 'Uznání', 'Nabídka pomoci': as a string joined with ', ' (ordered alphabetically). Each label indicates the presence of that category of Supportive Interactions: 'no tag', 'informational support', 'emocional support', 'social companionship', 'appraisal', 'instrumental support'. The inputs of the model are: a target utterance and its bi-directional context; the label of the example is determined by the label of the target utterance.
- **Developed by:** Anonymous
- **Language(s):** multilingual
- **Finetuned from:** mt5-base
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/chi2024submission
- **Paper:** Stay tuned!
## Usage
Here is how to use this model to classify a context-window of a dialogue:
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
test_texts = ['Utterance2']
test_text_pairs = ['Utterance1;Utterance2;Utterance3']
checkpoint_path = "chi2024/mt5-base-multi-label-en-iiib-02c"
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)\
.to("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
def verbalize_input(text: str, text_pair: str) -> str:
return "Utterance: %s\nContext: %s" % (text, text_pair)
def predict_one(text, pair):
input_pair = verbalize_input(text, pair)
inputs = tokenizer(input_pair, return_tensors="pt", padding=True,
truncation=True, max_length=256).to(model.device)
outputs = model.generate(**inputs)
decoded = [text.split(",")[0].strip() for text in
tokenizer.batch_decode(outputs, skip_special_tokens=True)]
return decoded
dec = predict_one(test_texts[0], test_text_pairs[0])
print(dec)
``` |
bongo2112/sdxl-db-rayvanny-headshot | bongo2112 | 2023-09-23T13:43:58Z | 4 | 1 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
]
| text-to-image | 2023-09-19T09:43:32Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of rayvannychui man
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
zineddine/ppo-LunarLander-v2 | zineddine | 2023-09-23T13:40:55Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-23T13:40:36Z | ---
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: 276.12 +/- 11.71
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
...
```
|
csocsci/mt5-base-multi-label-cs-iiib-02c | csocsci | 2023-09-23T13:40:51Z | 112 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"cs",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-09-22T13:29:45Z | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
license: mit
language:
- cs
---
# Model Card for mt5-base-multi-label-cs-iiib-02c
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned for multi-label text classification of Supportive Interactions in Instant Messenger dialogs of Adolescents in Czech.
## Model Description
The model was fine-tuned on a dataset of Czech Instant Messenger dialogs of Adolescents. The classification is multi-label and the model outputs any combination of the tags:'NO TAG', 'Informační podpora', 'Emocionální podpora', 'Začlenění do skupiny', 'Uznání', 'Nabídka pomoci': as a string joined with ', ' (ordered alphabetically). Each label indicates the presence of that category of Supportive Interactions: 'no tag', 'informational support', 'emocional support', 'social companionship', 'appraisal', 'instrumental support'. The inputs of the model are: a target utterance and its bi-directional context; the label of the example is determined by the label of the target utterance.
- **Developed by:** Anonymous
- **Language(s):** multilingual
- **Finetuned from:** mt5-base
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/chi2024submission
- **Paper:** Stay tuned!
## Usage
Here is how to use this model to classify a context-window of a dialogue:
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
test_texts = ['Utterance2']
test_text_pairs = ['Utterance1;Utterance2;Utterance3']
checkpoint_path = "chi2024/mt5-base-multi-label-cs-iiib-02c"
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)\
.to("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
def verbalize_input(text: str, text_pair: str) -> str:
return "Utterance: %s\nContext: %s" % (text, text_pair)
def predict_one(text, pair):
input_pair = verbalize_input(text, pair)
inputs = tokenizer(input_pair, return_tensors="pt", padding=True,
truncation=True, max_length=256).to(model.device)
outputs = model.generate(**inputs)
decoded = [text.split(",")[0].strip() for text in
tokenizer.batch_decode(outputs, skip_special_tokens=True)]
return decoded
dec = predict_one(test_texts[0], test_text_pairs[0])
print(dec)
``` |
Ori/lama-2-13b-peft-2wikihop-no-ret-v2-seed-3 | Ori | 2023-09-23T13:37:49Z | 0 | 0 | peft | [
"peft",
"safetensors",
"region:us"
]
| null | 2023-09-23T13:36:00Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
Ori/llama-2-13b-peft-2wikihop-no-ret | Ori | 2023-09-23T13:34:03Z | 3 | 0 | peft | [
"peft",
"safetensors",
"region:us"
]
| null | 2023-09-23T13:31:57Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
CyberHarem/tsukimiya_miyabi_idolmastercinderellagirls | CyberHarem | 2023-09-23T13:29:49Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/tsukimiya_miyabi_idolmastercinderellagirls",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-23T13:21:37Z | ---
license: mit
datasets:
- CyberHarem/tsukimiya_miyabi_idolmastercinderellagirls
pipeline_tag: text-to-image
tags:
- art
---
# Lora of tsukimiya_miyabi_idolmastercinderellagirls
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 4080, you need to download `4080/tsukimiya_miyabi_idolmastercinderellagirls.pt` as the embedding and `4080/tsukimiya_miyabi_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 4080**, with the score of 0.977. The trigger words are:
1. `tsukimiya_miyabi_idolmastercinderellagirls`
2. `brown_eyes, brown_hair, short_hair, smile, hair_ornament, breasts, blush`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:--------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 5100 | 0.933 | [Download](5100/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4760 | 0.975 | [Download](4760/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.963 | [Download](4420/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| **4080** | **0.977** | [**Download**](4080/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.958 | [Download](3740/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.919 | [Download](3400/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.975 | [Download](3060/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.921 | [Download](2720/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.919 | [Download](2380/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.904 | [Download](2040/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.906 | [Download](1700/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.810 | [Download](1360/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.856 | [Download](1020/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.833 | [Download](680/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.612 | [Download](340/tsukimiya_miyabi_idolmastercinderellagirls.zip) |  |  | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
alexdbz/roberta-base-peft-ia3-abstracts-v2-128out-6epochs | alexdbz | 2023-09-23T13:18:42Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-23T13:18:40Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
mychen76/donut-receipt_v1 | mychen76 | 2023-09-23T13:07:23Z | 50 | 0 | transformers | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2023-09-23T13:02:17Z | ---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
model-index:
- name: donut-receipt_v1
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. -->
# donut-receipt_v1
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
JohanAR/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GGUF | JohanAR | 2023-09-23T12:43:20Z | 75 | 7 | null | [
"gguf",
"license:other",
"endpoints_compatible",
"region:us"
]
| null | 2023-09-23T12:07:27Z | ---
license: other
---
These files are GGUF versions of [TheBloke's Wizard Vicuna 13B Uncensored SuperHOT 8k](https://huggingface.co/TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GGML), they were converted from GGML using llama.cpp's scripts. See original model card for credits and usage instructions.
|
CyberHarem/mishiro_idolmastercinderellagirls | CyberHarem | 2023-09-23T12:39:03Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/mishiro_idolmastercinderellagirls",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-23T12:29:16Z | ---
license: mit
datasets:
- CyberHarem/mishiro_idolmastercinderellagirls
pipeline_tag: text-to-image
tags:
- art
---
# Lora of mishiro_idolmastercinderellagirls
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 4760, you need to download `4760/mishiro_idolmastercinderellagirls.pt` as the embedding and `4760/mishiro_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 4760**, with the score of 0.980. The trigger words are:
1. `mishiro_idolmastercinderellagirls`
2. `black_hair, long_hair, earrings, jewelry, ponytail, hair_pulled_back, breasts, formal, suit, green_eyes, blush, large_breasts`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-----------------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-----------------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 5100 | 0.975 | [Download](5100/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](5100/previews/bondage.png) | [<NSFW, click to see>](5100/previews/free.png) |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| **4760** | **0.980** | [**Download**](4760/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](4760/previews/bondage.png) | [<NSFW, click to see>](4760/previews/free.png) |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.976 | [Download](4420/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](4420/previews/bondage.png) | [<NSFW, click to see>](4420/previews/free.png) |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| 4080 | 0.961 | [Download](4080/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](4080/previews/bondage.png) | [<NSFW, click to see>](4080/previews/free.png) |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.922 | [Download](3740/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](3740/previews/bondage.png) | [<NSFW, click to see>](3740/previews/free.png) |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.979 | [Download](3400/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](3400/previews/bondage.png) | [<NSFW, click to see>](3400/previews/free.png) |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.911 | [Download](3060/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](3060/previews/bondage.png) | [<NSFW, click to see>](3060/previews/free.png) |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.939 | [Download](2720/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](2720/previews/bondage.png) | [<NSFW, click to see>](2720/previews/free.png) |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.935 | [Download](2380/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](2380/previews/bondage.png) | [<NSFW, click to see>](2380/previews/free.png) |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.924 | [Download](2040/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](2040/previews/bondage.png) | [<NSFW, click to see>](2040/previews/free.png) |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.920 | [Download](1700/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](1700/previews/bondage.png) | [<NSFW, click to see>](1700/previews/free.png) |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.912 | [Download](1360/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](1360/previews/bondage.png) | [<NSFW, click to see>](1360/previews/free.png) |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.868 | [Download](1020/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](1020/previews/bondage.png) | [<NSFW, click to see>](1020/previews/free.png) |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.880 | [Download](680/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](680/previews/bondage.png) | [<NSFW, click to see>](680/previews/free.png) |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.852 | [Download](340/mishiro_idolmastercinderellagirls.zip) |  | [<NSFW, click to see>](340/previews/bondage.png) | [<NSFW, click to see>](340/previews/free.png) |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
BmanClark/poca-SoccerTwos | BmanClark | 2023-09-23T12:35:56Z | 36 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| reinforcement-learning | 2023-09-23T12:35:41Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: BmanClark/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
barisaydin/fastchat-t5-3b | barisaydin | 2023-09-23T12:35:11Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"de",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text2text-generation | 2023-09-23T11:36:58Z | ---
license: apache-2.0
inference: false
language:
- en
- de
- fr
---
# FastChat-T5 Model Card
## Model details
**Model type:**
FastChat-T5 is an open-source chatbot trained by fine-tuning Flan-t5-xl (3B parameters) on user-shared conversations collected from ShareGPT.
It is based on an encoder-decoder transformer architecture, and can autoregressively generate responses to users' inputs.
**Model date:**
FastChat-T5 was trained on April 2023.
**Organizations developing the model:**
The FastChat developers, primarily Dacheng Li, Lianmin Zheng and Hao Zhang.
**Paper or resources for more information:**
https://github.com/lm-sys/FastChat#FastChat-T5
**License:**
Apache License 2.0
**Where to send questions or comments about the model:**
https://github.com/lm-sys/FastChat/issues
## Intended use
**Primary intended uses:**
The primary use of FastChat-T5 is the commercial usage of large language models and chatbots. It can also be used for research purposes.
**Primary intended users:**
The primary intended users of the model are entrepreneurs and researchers in natural language processing, machine learning, and artificial intelligence.
## Training dataset
70K conversations collected from ShareGPT.com.
## Training details
It processes the ShareGPT data in the form of question answering. Each ChatGPT response is processed as an answer, and previous conversations between the user and the ChatGPT are processed as the question.
The encoder bi-directionally encodes a question into a hidden representation. The decoder uses cross-attention to attend to this representation while generating an answer uni-directionally from a start token.
This model is fine-tuned for 3 epochs, with a max learning rate 2e-5, warmup ratio 0.03, and a cosine learning rate schedule.
## Evaluation dataset
A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details. |
MarcosMunoz95/a2c-PandaReachDense-v3 | MarcosMunoz95 | 2023-09-23T12:35:10Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-23T12:29:30Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.26 +/- 0.06
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
Andyrasika/q-FrozenLake-v1-4x4-noSlippery | Andyrasika | 2023-09-23T12:33:04Z | 0 | 1 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-23T12:33:00Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Andyrasika/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
barisaydin/chatglm2-6b | barisaydin | 2023-09-23T12:32:50Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"chatglm",
"glm",
"thudm",
"custom_code",
"zh",
"en",
"arxiv:1911.02150",
"arxiv:2210.02414",
"endpoints_compatible",
"region:us"
]
| null | 2023-09-21T01:42:38Z | ---
language:
- zh
- en
tags:
- glm
- chatglm
- thudm
---
# ChatGLM2-6B
<p align="center">
<a href="https://github.com/THUDM/ChatGLM2-6B" target="_blank">Github Repo</a>
</p>
## Introduce
ChatGLM**2**-6B is the second-generation version of the open source Chinese-English bilingual dialogue model [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B). It retains the smooth dialogue and deployment of the first-generation model. On the basis of many excellent features such as low threshold, ChatGLM**2**-6B introduces the following new features:
1. **More powerful performance**: Based on the development experience of the first-generation ChatGLM model, we have comprehensively upgraded the base model of ChatGLM2-6B. ChatGLM2-6B uses the hybrid objective function of [GLM](https://github.com/THUDM/GLM), and has been pre-trained with 1.4T Chinese and English identifiers and human preference alignment training, [evaluation results](#evaluation The results) show that compared with the first-generation model, ChatGLM2-6B has achieved great performance on MMLU (+23%), CEval (+33%), GSM8K (+571%), BBH (+60%) and other data sets. The improvement in magnitude makes it highly competitive among open source models of the same size.
2. **Longer context**: Based on [FlashAttention](https://github.com/HazyResearch/flash-attention) technology, we change the context length (Context Length) of the base model from 2K of ChatGLM-6B Extended to 32K, and trained with 8K context length during the dialogue phase, allowing more rounds of dialogue. However, the current version of ChatGLM2-6B has limited ability to understand single-round ultra-long documents. We will focus on optimization in subsequent iterative upgrades.
3. **More efficient inference**: Based on [Multi-Query Attention](http://arxiv.org/abs/1911.02150) technology, ChatGLM2-6B has more efficient inference speed and lower memory usage: in Under the official model implementation, the inference speed is increased by 42% compared to the first generation. Under INT4 quantification, the conversation length supported by 6G video memory is increased from 1K to 8K.
4. **More open protocol**: ChatGLM2-6B weights are **fully open** to academic research, after filling in the [questionnaire](https://open.bigmodel.cn/mla/form) for registration** Free commercial use is also permitted**.
ChatGLM**2**-6B is the second-generation version of the open-source bilingual (Chinese-English) chat model [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B). It retains the smooth conversation flow and low deployment threshold of the first-generation model, while introducing the following new features:
1. **Stronger Performance**: Based on the development experience of the first-generation ChatGLM model, we have fully upgraded the base model of ChatGLM2-6B. ChatGLM2-6B uses the hybrid objective function of [GLM](https://github.com/THUDM/GLM), and has undergone pre-training with 1.4T bilingual tokens and human preference alignment training. The [evaluation results](README.md#evaluation-results) show that, compared to the first-generation model, ChatGLM2-6B has achieved substantial improvements in performance on datasets like MMLU (+23%), CEval (+33%), GSM8K (+571%), BBH (+60%), showing strong competitiveness among models of the same size.
2. **Longer Context**: Based on [FlashAttention](https://github.com/HazyResearch/flash-attention) technique, we have extended the context length of the base model from 2K in ChatGLM-6B to 32K, and trained with a context length of 8K during the dialogue alignment, allowing for more rounds of dialogue. However, the current version of ChatGLM2-6B has limited understanding of single-round ultra-long documents, which we will focus on optimizing in future iterations.
3. **More Efficient Inference**: Based on [Multi-Query Attention](http://arxiv.org/abs/1911.02150) technique, ChatGLM2-6B has more efficient inference speed and lower GPU memory usage: under the official implementation, the inference speed has increased by 42% compared to the first generation; under INT4 quantization, the dialogue length supported by 6G GPU memory has increased from 1K to 8K.
4. **More Open License**: ChatGLM2-6B weights are **completely open** for academic research, and **free commercial use** is also allowed after completing the [questionnaire](https://open.bigmodel.cn/mla/form).
## Software dependencies
```shell
pip install protobuf transformers==4.30.2 cpm_kernels torch>=2.0 gradio mdtex2html sentencepiece accelerate
```
## Code call
The ChatGLM-6B model can be called by the following code to generate a conversation:
```ipython
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
>>> model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).half().cuda()
>>> model = model.eval()
>>> response, history = model.chat(tokenizer, "你好", history=[])
>>> print(response)
Hello 👋! I am the artificial intelligence assistant ChatGLM-6B. Nice to meet you. You are welcome to ask me any questions.
>>> response, history = model.chat(tokenizer, "What should I do if I can't sleep at night", history=history)
>>> print(response)
Staying awake at night may make you feel anxious or uncomfortable, but here are some things you can do to help you fall asleep:
1. Establish a regular sleep schedule: Maintaining a regular sleep schedule can help you establish healthy sleep habits and make it easier for you to fall asleep. Try to go to bed at the same time every day and get up at the same time.
2. Create a comfortable sleeping environment: Make sure the sleeping environment is comfortable, quiet, dark and at a suitable temperature. Use comfortable bedding and keep the room ventilated.
3. Relax your body and mind: Doing some relaxing activities before going to bed, such as taking a hot bath, listening to some soft music, reading some interesting books, etc., can help relieve tension and anxiety and make it easier for you to fall asleep.
4. Avoid drinking beverages containing caffeine: Caffeine is a stimulating substance that can affect your sleep quality. Try to avoid drinking caffeinated beverages such as coffee, tea and cola before bed.
5. Avoid doing things in bed that are not related to sleep: Doing things in bed that are not related to sleep, such as watching movies, playing games or working, may interfere with your sleep.
6. Try breathing techniques: Deep breathing is a relaxation technique that can help you relieve tension and anxiety and make it easier for you to fall asleep. Try to inhale slowly, hold for a few seconds, and then exhale slowly.
If these methods don't help you fall asleep, you may consider talking to your doctor or sleep specialist for further advice.
```
For more instructions, including how to run the command line and web version of DEMO, and use model quantization to save video memory, please refer to our [Github Repo](https://github.com/THUDM/ChatGLM2-6B).
For more instructions, including how to run CLI and web demos, and model quantization, please refer to our [Github Repo](https://github.com/THUDM/ChatGLM2-6B).
## Change Log
* v1.0
## License
The code of this repository is open source according to the [Apache-2.0](LICENSE) agreement. The use of the weights of the ChatGLM2-6B model needs to follow the [Model License](MODEL_LICENSE).
## Quote
If you find our work helpful, please consider citing the following papers. The ChatGLM2-6B paper will be published in the near future, so stay tuned~
```
@article{zeng2022glm,
title={Glm-130b: An open bilingual pre-trained model},
author={Zeng, Aohan and Liu, Xiao and Du, Zhengxiao and Wang, Zihan and Lai, Hanyu and Ding, Ming and Yang, Zhuoyi and Xu, Yifan and Zheng, Wendi and Xia, Xiao and others},
journal={arXiv preprint arXiv:2210.02414},
year={2022}
}
```
```
@inproceedings{du2022glm,
title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={320--335},
year={2022}
}
``` |
Subsets and Splits