modelId
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81
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list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
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timestamp[ns, tz=UTC] | card
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Declan/HuffPost_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 7 | null | ---
tags:
- generated_from_trainer
datasets:
- wikitext
metrics:
- accuracy
model-index:
- name: output_tiny
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: wikitext wikitext-103-v1
type: wikitext
args: wikitext-103-v1
metrics:
- name: Accuracy
type: accuracy
value: 0.2132901596611274
---
<!-- 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. -->
# output_tiny
This model is a fine-tuned version of [gpt2_tiny_random](https://huggingface.co/gpt2_tiny_random) on the wikitext wikitext-103-v1 dataset.
It achieves the following results on the evaluation set:
- Loss: 5.3359
- Accuracy: 0.2133
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 50000
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Declan/NPR_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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} | 3 | null | ---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: sksdog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# LoRA DreamBooth - lora-dreambooth-sample-dog
These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "sksdog" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
Test prompt: a photo of a sksdog in a bucket
|
Declan/NPR_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: 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. -->
# 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: 3.0167
- 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3.6129 | 499.8 | 1000 | 3.0189 | 1.0 |
| 3.3959 | 999.8 | 2000 | 3.0167 | 1.0 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.11.0
|
Declan/NewYorkPost_model_v1 | []
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} | 0 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of skin.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('vgg/sd-skin-128')
image = pipeline().images[0]
image
```
|
Declan/Politico_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
} | 3 | null | ---
license: creativeml-openrail-m
datasets: p1atdev/noz
---
## LoRA (LierLa)
Not so useful LoRAs.
These maybe only works with kohya's sd-scripts or webui extension.
- alley-test1-e20.safetensors: Realistic alley backgrounds LoRA for WDv1.4.
- alley-test2-e50.safetensors: Better backgrounds LoRA for WDv1.4.
v
- impasto-test1-last.safetensors: Impasto style for WDv1.4 but not good at person.
- fluorite-test5-last.safetensors: Photo portrait for SDv2.1 512.
- pastel-flavor-test1-e100.safetensors: LoRA trained with PastelMix's images for WD1.4. (bad nose)
- pastel-flavor-test2-e100.safetensors: LoRA trained with PastelMix's images for WD1.4. (a little better than test1)
- fumo-test1.safetensors: Fumo style for WDv1.4, better than test2 at details.
- fumo-test2.safetensors: Fumo style for WDv1.4, better than test1 at backgrounds and resolution.
- nurie-test2-e10.safetensors: Good at black and white lineart style.
- noz-test3-2-e40.safetensors: [NOZ style watch](https://www.noz-shop.com/) for SDv2.1-768. [Dataset](https://huggingface.co/datasets/p1atdev/noz).
e.g.
- `a blue watch`
- `a red pocket watch`
- pvc-test7-e1.safetensors: PVC figure, figma and nendoroid style LoRA for WD1.5 beta 1. Experimental.
Trigger word: `pvc`, `figma`, `nendoroid`
e.g.
```
masterpiece, best quality, high quality, exceptional, best aesthetic, newest, pvc, , 1girl, loli, blue hair, short hair, high ponytail, white shirt, dress shirt, collared shirt, frills, ribbon, bow, looking at viewer, upper body, street, day, beautiful detailed,
Negative prompt: nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry, oldest, old, bad aesthetic, realistic
```
- acrylic-test2-e32.safetensors: Acrylic figure style LoRA for WD1.5 beta 2.
`real life, anime` is recommended in positive prompt.
```
masterpiece, best quality, exceptional, real life, anime, 1girl, aqua eyes, baseball cap, blonde hair, closed mouth, earrings, hat, hoop earrings, jewelry, looking at viewer, shirt, short hair, solo, upper body, yellow shirt, indoors, room, full body, pose, standing
Negative prompt: badquality, bad anatomy, bad legs, extra legs, oldest
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 11, Seed: 357295013, Size: 512x768, Model hash: 4bf1855f4d, Model: wd-1-5-beta2-fp16, Denoising strength: 0.7, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent
```
- marker-test3-e20.safetensors: Marker style LoRA for WD1.5 beta 2. Used dataset: [waifu](https://huggingface.co/datasets/p1atdev/waifu/blob/main/marker.zip), [nijijourney](https://huggingface.co/datasets/p1atdev/nijijourney).
```
masterpiece, best quality, exceptional,, 1girl, bangs, bare shoulders, beret, black hair, black shorts, blue hair, bracelet, breasts, buttons, colored inner hair, double-breasted, eyewear removed, green headwear, green jacket, grey eyes, grey sky, hat, jacket, jewelry, long hair, looking at viewer, multicolored hair, neck ring, o-ring, off shoulder, rain, round eyewear, shorts, sidelocks, small breasts, solo, sunglasses, wavy hair, wet, zipper,
Negative prompt: badquality, bad anatomy, nsfw, nude, bad hands
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 3865755286, Size: 512x768, Model hash: 4bf1855f4d, Model: wd-1-5-beta2-fp16, Clip skip: 2
```
- [niji-test1-4-e10.safetensors](https://huggingface.co/p1atdev/lora/blob/main/niji-test1-4-e10.safetensors): nijijourney v5's style for WD 1.5 beta 2~.
- [niji-test1-5-offset.safetensors](https://huggingface.co/p1atdev/lora/blob/main/niji-test1-5-offset.safetensors): continued from test1-4-e10, and trained with noise offset.
test1-4-e10 is more stable than test1-5-offset.
```
masterpiece, best quality, exceptional, best aesthetic, niji style, green eyes, gloves, solo, hat, black gloves, hair between eyes, looking at viewer, mask, upper body, jacket, short hair, black jacket, cable, male focus, black headwear, 1boy, shirt, long sleeves, bangs, white hair, steampunk, respirator, suit, grey hair, formal, witch hat, gas mask
Negative prompt: worst quality, low quality, bad aesthetic, oldest, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry, colorfixerv1
Steps: 18, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 3971604115, Size: 512x768, Model hash: b7c378c76a, Model: wd-15-2.6-186k, Denoising strength: 0.7, Clip skip: 2, Hires upscale: 1.5, Hires steps: 12, Hires upscaler: Latent
```
- [ink-painting-1-35.safetensors](https://huggingface.co/p1atdev/lora/blob/main/ink-painting-1-35.safetensors): Ink painting style LoRA for WD1.5 beta3.
Use the tag `ink painting` if it does not work.
```
masterpiece, best quality, exceptional, best aesthetic, ink painting, 1girl, upper body, looking at viewer, monochrome
Negative prompt: worst quality, low quality, bad aesthetic, oldest, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 1805321870, Size: 512x768, Model hash: d38e779546, Model: wd-beta3-base-fp16, Clip skip: 2, Version: v1.2.1, Script: X/Y/Z plot, X Type: AddNet Weight 1, X Values: "0, 1"
```
```
masterpiece, best quality, exceptional, best aesthetic, ink painting, 1girl, school uniform, cowboy shot, looking at viewer, blue theme,
Negative prompt: worst quality, low quality, bad aesthetic, oldest, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 1622463845, Size: 512x768, Model hash: d38e779546, Model: wd-beta3-base-fp16, Clip skip: 2, Version: v1.2.1, Script: X/Y/Z plot, X Type: AddNet Weight 1, X Values: "0, 1"
```
## LoCon
- low_key-test1-e10.safetensors: Low key offset noise **LoCon** for WD 1.5 beta 2.
maybe good at realistic style rather than anime style in vanilla beta 2 model.
```
(low key), masterpiece, exceptional, best aesthetic, solo, realistic, photography of a young beautiful girl in the dark, white hair, streaked hair, red hair, black background,
Negative prompt: badquality, bad anatomy, nsfw, nude, iphone, phone, red lips
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 11, Seed: 765289347, Size: 512x768, Model hash: 4bf1855f4d, Model: wd-1-5-beta2-fp16, Denoising strength: 0.7, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent
```
- lolita-test2-e20-locon.safetensors: Better realistic **LoCon** for WD 1.5 beta 2. Trained on lolita photos.
maybe good at dress?
```
masterpiece, best quality, exceptional, realistic, waifu, 1girl, solo, black hair, hat, dress, long hair, black dress, outdoors, realistic, necktie, black headwear, long sleeves, standing, black footwear, jacket, blurry background, jacket on shoulders, belt, black jacket, boots, facing viewer
Negative prompt: badquality, bad anatomy, oldest, fat, blurry, bad hands, extra digits, fewer digits
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Seed: 4146640340, Size: 512x768, Model hash: 4bf1855f4d, Model: wd-1-5-beta2-fp16, Denoising strength: 0.7, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent
```
- fashion-test1-e5.safetensors: Better realistic **LoCon** for WD 1.5 beta 2. Trained on lolita and techwear photos.
maybe good at most of the fashions in realistic. More than 1.0 weight is recommended.
```
masterpiece, exceptional, 1girl, medium hair, blue dress, pose, cowboy shot, looking at viewer
Negative prompt: badquality, bad anatomy, fat, blurry, selfie
Steps: 18, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 3389642227, Size: 512x768, Model hash: 4bf1855f4d, Model: wd-1-5-beta2-fp16, Denoising strength: 0.7, Clip skip: 2, Hires upscale: 1.5, Hires steps: 10, Hires upscaler: Latent
```
|
Declan/Reuters_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 3 | 2023-01-25T08:45:33Z | ---
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: 277.01 +/- 18.22
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
...
```
|
Declan/Reuters_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: try-out-model-amc1
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. -->
# try-out-model-amc1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5803
- F1: 0.8627
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.8173 | 1.0 | 208 | 2.0505 | 0.5417 |
| 1.6095 | 2.0 | 416 | 1.2094 | 0.7064 |
| 0.9005 | 3.0 | 624 | 0.8125 | 0.7974 |
| 0.5221 | 4.0 | 832 | 0.6478 | 0.8311 |
| 0.3 | 5.0 | 1040 | 0.5762 | 0.8389 |
| 0.1855 | 6.0 | 1248 | 0.5781 | 0.8455 |
| 0.1157 | 7.0 | 1456 | 0.5737 | 0.8561 |
| 0.0765 | 8.0 | 1664 | 0.5547 | 0.8593 |
| 0.0512 | 9.0 | 1872 | 0.5582 | 0.8632 |
| 0.0457 | 10.0 | 2080 | 0.5803 | 0.8627 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Declan/WallStreetJournal_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 3 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/kuotient/noto-emoji-finetuned-lora
These are LoRA adaption weights for https://huggingface.co/kuotient/noto-emoji-finetuned-lora. The weights were fine-tuned on the kuotient/noto-emoji-dataset dataset. You can find some example images in the following.
|
Declan/WallStreetJournal_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 7 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-learning-taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Kenemo/q-learning-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"])
```
|
Declan/WallStreetJournal_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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}
} | 9 | null | ---
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: 426.50 +/- 140.86
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 NikosKokkini -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 NikosKokkini -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 NikosKokkini
```
## Hyperparameters
```python
OrderedDict([('batch_size', 128),
('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', 1500000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Declan/test_push | []
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} | 0 | null | ---
license: mit
datasets:
- emotion
language:
- en
metrics:
- accuracy
- f1
pipeline_tag: text-classification
--- |
DeepBasak/Slack | []
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} | 0 | null | Access to model Aal22/jokowidodo is restricted and you are not in the authorized list. Visit https://huggingface.co/Aal22/jokowidodo to ask for access. |
DeepChem/ChemBERTa-77M-MLM | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
],
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} | 2,416 | null | # bert-base-buddhist-sanskrit
Version 2 of the BERT model described in the paper 'Embeddings models for Buddhist Sanskrit' published at LREC 2022 (https://aclanthology.org/2022.lrec-1.411/).
Same training methodology has been used as for version 1, the only difference is that the model has been trained on a slightly bigger buddhist snaskrit corpus.
## Model description
The model has the bert-base architecture and configuration and was pretrained from scratch as a masked language model
on the Sanskrit reference corpus, and fine-tuned on the smaller corpus of Buddhist Sanskrit.
## How to use it
```
model = AutoModelForMaskedLM.from_pretrained("Matej/bert-base-buddhist-sanskrit")
tokenizer = AutoTokenizer.from_pretrained("Matej/bert-base-buddhist-sanskrit", use_fast=True)
```
## Intended uses & limitations
MIT license, no limitations
## Training and evaluation data
See the paper 'Embeddings models for Buddhist Sanskrit' for details on the corpora and the evaluation procedure.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
### Framework versions
- Transformers 4.20.0
- Pytorch 1.9.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
DeepChem/SmilesTokenizer_PubChem_1M | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 227 | null | ---
license: mit
tags:
- text-to-image
---
# Mann-E 4 Revision 0.1
__Mann-E__ is a _text to image_ model which has been developed by [Muhammadreza Haghiri](https://haghiri75.com/en) in order to be part of the [Cognitive Web](https://opencognitives.com) movement and projects.
This is revision 0.1 of the 4th version of the model.
### What does _Mann-E_ mean?
It's a play with the name [Mani](https://en.wikipedia.org/wiki/Mani_(prophet)), who was a Persian religious leader at the early Sassanian era and also a painter and he's famous for both his religious and artistic works. His artistic side was more considered for naming the model of course.
## How to use the model
### Colab
You can access _Web UI_ colab through [this link](https://colab.research.google.com/github/prp-e/mann-e/blob/main/Mann_E.ipynb)
### Code
The following code is written for _CUDA_ supported devices. If you use UI's or inference tools on other devices, you may need to tweak them in order to get them to the work. Otherwise, it will be fine.
First, you need to install required libraries:
```
pip3 install diffusers transformers scipy ftfy accelerate
```
_NOTE: installation of `accelerate` library makes the inference process amazingly faster. but it's totally optional_.
Then, you need to import required libraries:
```python
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler, DiffusionPipeline, DPMSolverMultistepScheduler
import torch
```
and then, create a pipeline (this pipeline is made with Euler Scheduler):
```python
model_id = "mann-e/mann-e_4_rev-0-1"
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
```
and of course, since you may get NSFW filteration warnings even on simplest prompts, you may consider disabling it:
```python
def dummy(images, **kwargs):
return images, False
pipe.safety_checker = dummy
```
_NOTE: Please consider consequences of disabling this filter as well. we do not want people to get any sort of damage or injury from the image generation results_.
And after that, you easily can start inference:
```python
prompt = "Concept art of a hostile alien planet with unbreathable purple air and toxic clouds, sinister atmosphere, deep shadows, sharp details"
negative_prompt = "low quality, blurry"
width = 768
height = 512
```
then:
```python
image = pipe(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=100, width=width, height=height, guidance_scale=10).images[0]
image.save("My_image.png")
```
|
DeepESP/gpt2-spanish-medium | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit"
]
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} | 340 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- art
- artistic
- anime
- 3D
- realistic
- semi-realistic
- suzumehachi
- automatic1111
---
# Suzumehachi
The model we have created is a combination of several general-purpose models, quite a significant number of them, so I don't believe listing a recipe or similar information is necessary (this means I don't really know the overall final type of license of this model, therefore, I propose that we utilize the original license as a starting point. Don't quote me on that, tho).
Without any specific style words or mentions of artists, this model generates in a semi-realistic, 3D-like anime-ish style.
### Examples
<img src="https://huggingface.co/dobrosketchkun/Suzumehachi/resolve/main/photo-grid.png" style="max-width: 800px;" width="100%"/>
[You can also see this model on Civitai.com](https://civitai.com/models/5043/suzumehachi) |
DeepESP/gpt2-spanish | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit",
"has_space"
]
| text-generation | {
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} | 1,463 | 2023-01-25T10:07:19Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Testing_rolls_royce_Test2 Dreambooth model trained by JacobPerera 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:
|
DeepPavlov/bert-base-cased-conversational | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"transformers"
]
| feature-extraction | {
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} | 3,009 | null | ---
language:
- gos
---
A Gronings Wav2Vec2 model. This model is created by further pre-training the multilingual [XLS-R](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model on Gronings speech.
This model is part of the paper: Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation.
More information on [GitHub](https://github.com/Bartelds/asr-augmentation). |
DeepPavlov/distilrubert-base-cased-conversational | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
]
| null | {
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} | 6,324 | 2023-01-31T05:16:46Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Religion-Classification
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. -->
# Religion-Classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0585
- Accuracy: 0.9926
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0722 | 1.0 | 6947 | 0.0671 | 0.9855 |
| 0.0368 | 2.0 | 13894 | 0.0470 | 0.9907 |
| 0.0205 | 3.0 | 20841 | 0.0431 | 0.9918 |
| 0.0109 | 4.0 | 27788 | 0.0576 | 0.9920 |
| 0.0013 | 5.0 | 34735 | 0.0585 | 0.9926 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.12.0
- Datasets 2.9.0
- Tokenizers 0.10.3
|
DeepPavlov/distilrubert-tiny-cased-conversational-v1 | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
]
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}
}
} | 9,141 | null | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1539.17 +/- 376.94
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
DeepPavlov/distilrubert-tiny-cased-conversational | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
]
| null | {
"architectures": null,
"model_type": "distilbert",
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"max_length": null,
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"prefix": null
}
}
} | 5,993 | 2023-01-25T10:18:40Z | ---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-regression
model_file: umit_class_dx2w.pkl
widget:
structuredData:
Mic:
- 850
- 851
- 838
humidity:
- 16
- 16
- 39
light:
- 872
- 891
- 389
mQ135:
- 170
- 165
- 217
temprature:
- 23
- 23
- 26
---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|--------------------------|--------------|
| ccp_alpha | 0.0 |
| criterion | friedman_mse |
| init | |
| learning_rate | 0.1 |
| loss | log_loss |
| max_depth | 3 |
| max_features | |
| max_leaf_nodes | |
| min_impurity_decrease | 0.0 |
| min_samples_leaf | 1 |
| min_samples_split | 2 |
| min_weight_fraction_leaf | 0.0 |
| n_estimators | 100 |
| n_iter_no_change | |
| random_state | |
| subsample | 1.0 |
| tol | 0.0001 |
| validation_fraction | 0.1 |
| verbose | 0 |
| warm_start | False |
</details>
### Model Plot
The model plot is below.
<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-13" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>GradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-13" type="checkbox" checked><label for="sk-estimator-id-13" class="sk-toggleable__label sk-toggleable__label-arrow">GradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div>
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|-----------|----------|
| accuracy | 0.996979 |
| precision | 0.996988 |
| recall | 0.996875 |
| f1-score | 0.996913 |
# How to Get Started with the Model
[More Information Needed]
# Model Card Authors
This model card is written by following authors:
[More Information Needed]
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
```
# ModelDescription
The model is a product of academic work by Ümit Işıkdağ,more info on me [Ümit Işıkdağ](https://isikdag.com).
# GettingStarted
<i>!pip install skops # @only first run<br>
#prepare train/test sets (X_train, X_test, y_train, y_test) as usual<br>
import skops.hub_utils as hub_utils <br>
from sklearn.metrics import accuracy_score<br>
y_pred = hub_utils.get_model_output('uisikdag/simple_clasi_dataexp2weka', X_test)<br>
accuracy =accuracy_score(y_test, y_pred)
# ModelCardAuthor
Ümit Işıkdağ@2023
# ModelLimitations
This model is not ready to be used in production.
# CitationBibtex
bibtex
@inproceedings{...,year={2020}}
# ConfusionMatrix
|
DeepPavlov/marianmt-tatoeba-ruen | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 30 | null | ---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_file: umit_class_okl25.pkl
widget:
structuredData:
CO2:
- 708
- 490
- 481
Hum:
- 31.3
- 31.8
- 44.9
Temp:
- 23.6
- 23.1
- 21.4
---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|-------------------------------------|-----------------------------------------------------------------------|
| memory | |
| steps | [('transform', FunctionTransformer(func=<function nan_to_num at 0x00000208B48C4A60>)), ('classifier', XGBClassifier(base_score=None, booster=None, callbacks=None,<br /> colsample_bylevel=None, colsample_bynode=None,<br /> colsample_bytree=None, early_stopping_rounds=None,<br /> enable_categorical=False, eval_metric=None, feature_types=None,<br /> gamma=None, gpu_id=None, grow_policy=None, importance_type=None,<br /> interaction_constraints=None, learning_rate=None, max_bin=None,<br /> max_cat_threshold=None, max_cat_to_onehot=None,<br /> max_delta_step=None, max_depth=None, max_leaves=None,<br /> min_child_weight=None, missing=nan, monotone_constraints=None,<br /> n_estimators=100, n_jobs=None, num_parallel_tree=None,<br /> predictor=None, random_state=None, ...))] |
| verbose | False |
| transform | FunctionTransformer(func=<function nan_to_num at 0x00000208B48C4A60>) |
| classifier | XGBClassifier(base_score=None, booster=None, callbacks=None,<br /> colsample_bylevel=None, colsample_bynode=None,<br /> colsample_bytree=None, early_stopping_rounds=None,<br /> enable_categorical=False, eval_metric=None, feature_types=None,<br /> gamma=None, gpu_id=None, grow_policy=None, importance_type=None,<br /> interaction_constraints=None, learning_rate=None, max_bin=None,<br /> max_cat_threshold=None, max_cat_to_onehot=None,<br /> max_delta_step=None, max_depth=None, max_leaves=None,<br /> min_child_weight=None, missing=nan, monotone_constraints=None,<br /> n_estimators=100, n_jobs=None, num_parallel_tree=None,<br /> predictor=None, random_state=None, ...) |
| transform__accept_sparse | False |
| transform__check_inverse | True |
| transform__feature_names_out | |
| transform__func | <function nan_to_num at 0x00000208B48C4A60> |
| transform__inv_kw_args | |
| transform__inverse_func | |
| transform__kw_args | |
| transform__validate | False |
| classifier__objective | binary:logistic |
| classifier__use_label_encoder | |
| classifier__base_score | |
| classifier__booster | |
| classifier__callbacks | |
| classifier__colsample_bylevel | |
| classifier__colsample_bynode | |
| classifier__colsample_bytree | |
| classifier__early_stopping_rounds | |
| classifier__enable_categorical | False |
| classifier__eval_metric | |
| classifier__feature_types | |
| classifier__gamma | |
| classifier__gpu_id | |
| classifier__grow_policy | |
| classifier__importance_type | |
| classifier__interaction_constraints | |
| classifier__learning_rate | |
| classifier__max_bin | |
| classifier__max_cat_threshold | |
| classifier__max_cat_to_onehot | |
| classifier__max_delta_step | |
| classifier__max_depth | |
| classifier__max_leaves | |
| classifier__min_child_weight | |
| classifier__missing | nan |
| classifier__monotone_constraints | |
| classifier__n_estimators | 100 |
| classifier__n_jobs | |
| classifier__num_parallel_tree | |
| classifier__predictor | |
| classifier__random_state | |
| classifier__reg_alpha | |
| classifier__reg_lambda | |
| classifier__sampling_method | |
| classifier__scale_pos_weight | |
| classifier__subsample | |
| classifier__tree_method | |
| classifier__validate_parameters | |
| classifier__verbosity | |
</details>
### Model Plot
The model plot is below.
<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('transform',FunctionTransformer(func=<function nan_to_num at 0x00000208B48C4A60>)),('classifier',XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None,early_stopping_rounds=None,enable_categorical=False, eval_metric=None,feature_types=None, gamma=None, gpu_id=None,grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=None, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, n_estimators=100,n_jobs=None, num_parallel_tree=None,predictor=None, random_state=None, ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('transform',FunctionTransformer(func=<function nan_to_num at 0x00000208B48C4A60>)),('classifier',XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None,early_stopping_rounds=None,enable_categorical=False, eval_metric=None,feature_types=None, gamma=None, gpu_id=None,grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=None, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, n_estimators=100,n_jobs=None, num_parallel_tree=None,predictor=None, random_state=None, ...))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">FunctionTransformer</label><div class="sk-toggleable__content"><pre>FunctionTransformer(func=<function nan_to_num at 0x00000208B48C4A60>)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">XGBClassifier</label><div class="sk-toggleable__content"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, early_stopping_rounds=None,enable_categorical=False, eval_metric=None, feature_types=None,gamma=None, gpu_id=None, grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=None, max_bin=None,max_cat_threshold=None, max_cat_to_onehot=None,max_delta_step=None, max_depth=None, max_leaves=None,min_child_weight=None, missing=nan, monotone_constraints=None,n_estimators=100, n_jobs=None, num_parallel_tree=None,predictor=None, random_state=None, ...)</pre></div></div></div></div></div></div></div>
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|-----------|----------|
| accuracy | 0.976744 |
| precision | 0.972624 |
| recall | 0.963504 |
| f1-score | 0.967959 |
# How to Get Started with the Model
[More Information Needed]
# Model Card Authors
This model card is written by following authors:
[More Information Needed]
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
```
# ModelDescription
The model is a product of academic work by Ümit Işıkdağ,more info on me [Ümit Işıkdağ](https://isikdag.com).
# GettingStarted
<i>!pip install skops # @only first run<br>
#prepare train/test sets (X_train, X_test, y_train, y_test) as usual<br>
import skops.hub_utils as hub_utils <br>
from sklearn.metrics import accuracy_score<br>
y_pred = hub_utils.get_model_output('uisikdag/simple_clasi_okl25', X_test)<br>
accuracy =accuracy_score(y_test, y_pred)
# ModelCardAuthor
Ümit Işıkdağ@2023
# ModelLimitations
This model is not ready to be used in production.
# CitationBibtex
bibtex
@inproceedings{...,year={2020}}
# ConfusionMatrix
|
DeepPavlov/rubert-base-cased-conversational | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"transformers",
"has_space"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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} | 17,362 | null | ---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Nystrom-W2V2-100hrs-take-3-unfreeze-extractor
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. -->
# Nystrom-W2V2-100hrs-take-3-unfreeze-extractor
This model is a fine-tuned version of [rohitp1/Nystrom-W2V2-100hrs-take-2](https://huggingface.co/rohitp1/Nystrom-W2V2-100hrs-take-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 29.1951
- Wer: 0.1183
## 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: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 123.0399 | 37.04 | 1000 | 27.2466 | 0.1448 |
| 89.1443 | 74.07 | 2000 | 29.1951 | 0.1183 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DeepPavlov/xlm-roberta-large-en-ru | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"en",
"ru",
"transformers"
]
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} | 190 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1908 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1908,
"warmup_steps": 191,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
DeltaHub/adapter_t5-3b_cola | [
"pytorch",
"transformers"
]
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} | 3 | 2023-01-25T10:56:46Z | ---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
duplicated_from: Linaqruf/anything-v3.0
---
### Duplicated from [Linaqruf/anything-v3.0](https://huggingface.co/Linaqruf/anything-v3.0)
This repository is not original. But it's the old model. Please follow [Linaqruf](https://huggingface.co/Linaqruf) for the latest updates of this model.
---
# Anything V3
Welcome to Anything V3 - a latent diffusion model for weebs. This model is intended to produce high-quality, highly detailed anime style with just a few prompts. Like other anime-style Stable Diffusion models, it also supports danbooru tags to generate images.
e.g. **_1girl, white hair, golden eyes, beautiful eyes, detail, flower meadow, cumulonimbus clouds, lighting, detailed sky, garden_**
## Gradio
We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run Anything-V3.0:
[Open in Spaces](https://huggingface.co/spaces/Maseshi/Anything-v3.0)
## 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "Maseshi/Anything-v3.0"
branch_name = "diffusers"
pipe = StableDiffusionPipeline.from_pretrained(model_id, revision=branch_name, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "pikachu"
image = pipe(prompt).images[0]
image.save("./pikachu.png")
```
## Examples
Below are some examples of images generated using this model:
**Anime Girl:**
```
1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden
Steps: 50, Sampler: DDIM, CFG scale: 12
```
**Anime Boy:**
```
1boy, medium hair, blonde hair, blue eyes, bishounen, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden
Steps: 50, Sampler: DDIM, CFG scale: 12
```
**Scenery:**
```
scenery, shibuya tokyo, post-apocalypse, ruins, rust, sky, skyscraper, abandoned, blue sky, broken window, building, cloud, crane machine, outdoors, overgrown, pillar, sunset
Steps: 50, Sampler: DDIM, CFG scale: 12
```
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
DeltaHub/adapter_t5-3b_mrpc | [
"pytorch",
"transformers"
]
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} | 3 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: shazanj/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DeltaHub/adapter_t5-3b_qnli | [
"pytorch",
"transformers"
]
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} | 3 | 2023-01-25T11:00:13Z | ---
datasets:
- samsum
pipeline_tag: summarization
widget:
- text: >
Laurie: So, what are your plans for this weekend?
Christie: I don’t know. Do you want to get together or something?
Sarah: How about going to see a movie? Cinemax 26 on Carson Boulevard is showing Enchanted.
Laurie: That sounds like a good idea. Maybe we should go out to eat beforehand.
Sarah: It is fine with me. Where do you want to meet?
Christie: Let’s meet at Summer Pizza House. I have not gone there for a long time.
Laurie: Good idea again. I heard they just came up with a new pizza. It should be good because Summer Pizza House always has the best pizza in town.
Sarah: When should we meet?
Christie: Well, the movie is shown at 2:00PM, 4:00PM, 6:00PM and 8:00PM.
Laurie: Why don’t we go to the 2:00PM show? We can meet at Summer Pizza House at noon. That will give us plenty of time to enjoy our pizza.
Sarah: My cousin Karen is in town. Can I bring her along? I hate to leave her home alone.
Christie: Karen is in town? Yes, bring her along. Laurie, you remember Karen? We met her at Sara’s high school graduation party two years ago.
Laurie: I do not quite remember her. What does she look like?
Sarah: She has blond hair, she is kind of slender, and she is about your height.
Laurie: She wears eyeglasses, right?
Sarah: Yes, and she was playing the piano off and on during the party.
Laurie: I remember her now. Yes, do bring her along Sara. She is such a nice person, and funny too.
Sarah: She will be happy to meet both of you again.
Christie: What is she doing these days?
Sarah: She graduated last June, and she will start her teaching career next week when the new school term begins.
Laurie: What grade is she going to teach?
Sarah: She will teach kindergarten. She loves working with kids, and she always has such a good rapport with them
Christie: Kindergarten? She must be a very patient person. I always think kindergarten is the most difficult class to teach. Most of the kids have never been to school, and they have
e never been away from mommy for long.
Sarah: I think Karen will do fine. She knows how to handle young children
Laurie: I think the first few weeks will be tough. However, once the routine is set, it should not be too difficult to teach kindergarten.
Christie: You are right. The kids might even look forward to going to school since they have so many friends to play with.
Sarah: There are so many new things for them to do at school too. They do a lot of crafts in kindergarten. I am always amazed by the things kindergarten teachers do.
Laurie: Yes, I have seen my niece come home with so many neat stuff.
Christie: Maybe we can ask Karen to show us some of the things that we can do for this Halloween.
Laurie: Maybe we can stop by the craft store after the movie. What do you think, Sara?
Sarah: I will talk to her. I think she will like that. It will help her with school projects when Halloween comes.
Christie: Michael’s is a good store for crafts. It always carries a variety of things, and you can find almost anything there.
Laurie: There is a Michaels store not far away from Cinemax 26. I believe it is just around the corner, on Pioneer Avenue. We can even walk over there.
Sarah: So, we plan to meet for pizza at noon, go to the movies at two, and shop at Michael’s afterward. Right?
Laurie and Christie: Yes.
model-index:
- name: bart-large-cnn-samsum
results:
- task:
type: summarization
name: Conversation Summarization
dataset:
name: >-
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive
Summarization
type: samsum
metrics:
- type: rogue-1
value: 54.8764
name: Validation ROGUE-1
- type: rogue-2
value: 29.6869,
name: Validation ROGUE-2
- type: rogue-l
value: 44.9874
name: Validation ROGUE-L
- type: loss
value: 1.47812
name: loss
--- |
Deniskin/essays_small_2000 | []
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} | 0 | 2023-01-25T11:17:48Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: amk-whisper
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. -->
# amk-whisper
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1902
- Wer: 40.3587
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 20.0 | 20 | 0.7838 | 30.9417 |
| 0.8511 | 40.0 | 40 | 1.0878 | 44.8430 |
| 0.0794 | 60.0 | 60 | 1.1466 | 39.4619 |
| 0.001 | 80.0 | 80 | 1.1872 | 39.9103 |
| 0.0004 | 100.0 | 100 | 1.1902 | 40.3587 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -5.34 +/- 1.37
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Denny29/DialoGPT-medium-asunayuuki | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 9 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: mobilebert_sa_GLUE_Experiment_cola_128
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilebert_sa_GLUE_Experiment_cola_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6077
- Matthews Correlation: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6155 | 1.0 | 67 | 0.6180 | 0.0 |
| 0.6079 | 2.0 | 134 | 0.6180 | 0.0 |
| 0.6074 | 3.0 | 201 | 0.6181 | 0.0 |
| 0.607 | 4.0 | 268 | 0.6173 | 0.0 |
| 0.6064 | 5.0 | 335 | 0.6180 | 0.0 |
| 0.6021 | 6.0 | 402 | 0.6077 | 0.0 |
| 0.5484 | 7.0 | 469 | 0.6393 | 0.0688 |
| 0.4961 | 8.0 | 536 | 0.6882 | 0.0851 |
| 0.4656 | 9.0 | 603 | 0.6623 | 0.1116 |
| 0.448 | 10.0 | 670 | 0.7584 | 0.1089 |
| 0.4358 | 11.0 | 737 | 0.7178 | 0.1299 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DeskDown/MarianMixFT_en-id | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MarianMTModel"
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}
} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- aihub_paper_summarization
metrics:
- rouge
model-index:
- name: kobart-base-v2-finetuned-paper
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: aihub_paper_summarization
type: aihub_paper_summarization
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 6.2883
---
<!-- 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. -->
# kobart-base-v2-finetuned-paper
This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the aihub_paper_summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2966
- Rouge1: 6.2883
- Rouge2: 1.7038
- Rougel: 6.2556
- Rougelsum: 6.2618
- Gen Len: 20.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.2215 | 1.0 | 8831 | 1.3293 | 6.2425 | 1.7317 | 6.2246 | 6.2247 | 20.0 |
| 1.122 | 2.0 | 17662 | 1.3056 | 6.2298 | 1.7005 | 6.2042 | 6.2109 | 20.0 |
| 1.0914 | 3.0 | 26493 | 1.2966 | 6.2883 | 1.7038 | 6.2556 | 6.2618 | 20.0 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DeskDown/MarianMixFT_en-ja | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MarianMTModel"
],
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}
} | 9 | 2023-01-25T11:42:52Z | ---
license: cc-by-4.0
datasets:
- Gustavosta/Stable-Diffusion-Prompts
language:
- av
metrics:
- accuracy
- bleu
- character
- cer
library_name: diffusers
pipeline_tag: depth-estimation
tags:
- finance
- legal
- chemistry
---
# 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 [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- 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]
|
DeskDown/MarianMixFT_en-ms | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MarianMTModel"
],
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}
} | 5 | 2023-01-25T11:43:17Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: mobilebert_sa_GLUE_Experiment_mrpc_128
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.6838235294117647
- name: F1
type: f1
value: 0.8122270742358079
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilebert_sa_GLUE_Experiment_mrpc_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6220
- Accuracy: 0.6838
- F1: 0.8122
- Combined Score: 0.7480
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6454 | 1.0 | 29 | 0.6241 | 0.6838 | 0.8122 | 0.7480 |
| 0.63 | 2.0 | 58 | 0.6239 | 0.6838 | 0.8122 | 0.7480 |
| 0.6312 | 3.0 | 87 | 0.6246 | 0.6838 | 0.8122 | 0.7480 |
| 0.6305 | 4.0 | 116 | 0.6247 | 0.6838 | 0.8122 | 0.7480 |
| 0.6295 | 5.0 | 145 | 0.6226 | 0.6838 | 0.8122 | 0.7480 |
| 0.6276 | 6.0 | 174 | 0.6220 | 0.6838 | 0.8122 | 0.7480 |
| 0.6261 | 7.0 | 203 | 0.6228 | 0.6838 | 0.8122 | 0.7480 |
| 0.6007 | 8.0 | 232 | 0.6695 | 0.6373 | 0.7508 | 0.6940 |
| 0.5159 | 9.0 | 261 | 0.6623 | 0.6985 | 0.7831 | 0.7408 |
| 0.4232 | 10.0 | 290 | 0.6507 | 0.6789 | 0.7681 | 0.7235 |
| 0.3418 | 11.0 | 319 | 0.8759 | 0.6740 | 0.7646 | 0.7193 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DeskDown/MarianMixFT_en-my | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MarianMTModel"
],
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}
} | 7 | 2023-01-25T11:50:52Z | ---
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: 674.00 +/- 221.75
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 pabloac31 -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 pabloac31 -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 pabloac31
```
## 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)])
```
|
DeskDown/MarianMixFT_en-vi | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
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}
} | 5 | 2023-01-25T11:51:38Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_sa_GLUE_Experiment_qnli_128
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
config: qnli
split: validation
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6135822807980963
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilebert_sa_GLUE_Experiment_qnli_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6494
- Accuracy: 0.6136
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6774 | 1.0 | 819 | 0.6494 | 0.6136 |
| 0.6378 | 2.0 | 1638 | 0.6508 | 0.6055 |
| 0.6148 | 3.0 | 2457 | 0.6578 | 0.6063 |
| 0.5979 | 4.0 | 3276 | 0.6590 | 0.6061 |
| 0.5851 | 5.0 | 4095 | 0.6761 | 0.5927 |
| 0.5743 | 6.0 | 4914 | 0.6982 | 0.5978 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Devid/DialoGPT-small-Miku | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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},
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},
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}
} | 10 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_sa_GLUE_Experiment_sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
config: sst2
split: validation
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8027522935779816
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilebert_sa_GLUE_Experiment_sst2
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4157
- Accuracy: 0.8028
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.487 | 1.0 | 527 | 0.4157 | 0.8028 |
| 0.2824 | 2.0 | 1054 | 0.4351 | 0.8005 |
| 0.2265 | 3.0 | 1581 | 0.4487 | 0.8096 |
| 0.1989 | 4.0 | 2108 | 0.5182 | 0.7993 |
| 0.1813 | 5.0 | 2635 | 0.4654 | 0.7982 |
| 0.1684 | 6.0 | 3162 | 0.5340 | 0.7924 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Devmapall/paraphrase-quora | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
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"prefix": "translate English to Romanian: "
}
}
} | 3 | null | ---
license: openrail
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- no
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
datasets:
- Hello-SimpleAI/HC3
metrics:
- f1
pipeline_tag: text-classification
widget:
- text: "La Guerra Civil Española, que tuvo lugar entre 1936 y 1939, fue un conflicto sangriento y divisivo en el que se enfrentaron las fuerzas republicanas y las fuerzas nacionalistas lideradas por Francisco Franco. El origen de la guerra se remonta a las tensiones políticas y sociales que se habían ido acumulando en España durante años, incluyendo la lucha entre la izquierda y la derecha, el descontento popular con el gobierno y la crisis económica.\nEl estallido de la guerra civil se produjo después de las elecciones de febrero de 1936, en "
- text: "Cristóbal Colón fue un navegante, cartógrafo y almirante español que llevó a cabo cuatro viajes transatlánticos entre 1492 y 1504. Es conocido por haber sido el primer europeo en llegar a América, aunque en realidad no fue el primer ser humano en alcanzar el continente, ya que los pueblos indígenas ya habitaban allí desde hacía miles de años.\nColón nació en la ciudad italiana de Génova en 1451. Aunque no se sabe con certeza dónde recibió su formación, se cree que tuvo una educación marinera y cartográfica. A mediados de la década de 1470, se trasladó a España, donde trabajó como piloto y cartógrafo para la Casa de Contratación de las Indias, una institución encargada de las relaciones comerciales entre España y América."
- text: "Para usar un modelo pre-entrenado de la pipeline de clasificación de texto de Hugging Face, primero debe instalar la última versión de la biblioteca Hugging Face utilizando el comando !pip install transformers en su terminal o consola."
---
# XLM-RoBERTa (base) fine-tuned on HC3 for ChatGPT text detection
**XLM-RoBERTa** (base) fine-tuned on [Hello-SimpleAI](https://huggingface.co/Hello-SimpleAI) **HC3** corpus for **ChatGPT** text detection.
All credit to [Hello-SimpleAI](https://huggingface.co/Hello-SimpleAI) for their huge work!
## F1 score on test dataset: 0.9736
## The model
XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al. and first released in this repository.
## The dataset
#### Human ChatGPT Comparison Corpus (HC3)
The first human-ChatGPT comparison corpus, named **HC3** dataset by [Hello-SimpleAI](https://huggingface.co/Hello-SimpleAI)
This dataset is introduced in the paper:
- Paper: [***How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection***](https://arxiv.org/abs/2301.07597)
## Metrics
|metric| value|
|------|------|
|F1 |0.9736|
## Usage
```py
from transformers import pipeline
ckpt = "mrm8488/xlm-roberta-base-finetuned-HC3-mix"
detector = pipeline('text-classification', model=ckpt)
text = "Here your text..."
result = detector(text)
print(result)
```
## Citation
```
@misc {manuel_romero_2023,
author = { {Manuel Romero} },
title = { xlm-roberta-base-finetuned-HC3-mix (Revision b18de48) },
year = 2023,
url = { https://huggingface.co/mrm8488/xlm-roberta-base-finetuned-HC3-mix },
doi = { 10.57967/hf/0306 },
publisher = { Hugging Face }
}
``` |
Devrim/prism-default | [
"license:mit"
]
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_qa_model_1
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_qa_model_1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4752
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.96 | 3 | 5.6227 |
| No log | 1.96 | 6 | 5.1169 |
| No log | 2.96 | 9 | 4.5577 |
| No log | 3.96 | 12 | 3.9969 |
| No log | 4.96 | 15 | 3.5279 |
| No log | 5.96 | 18 | 3.1371 |
| No log | 6.96 | 21 | 2.8468 |
| No log | 7.96 | 24 | 2.6456 |
| No log | 8.96 | 27 | 2.5241 |
| No log | 9.96 | 30 | 2.4752 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
DevsIA/Devs_IA | []
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: Result
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. -->
# Result
This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co/GroNLP/hateBERT) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.0+cu116
- Tokenizers 0.13.2
|
DevsIA/imagenes | []
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}
} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 249.61 +/- 21.73
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
...
```
|
DewiBrynJones/wav2vec2-large-xlsr-welsh | [
"cy",
"dataset:common_voice",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
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}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
model-index:
- name: token_classification_wnut
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. -->
# token_classification_wnut
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the wnut_17 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: 4e-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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.3717 | 0.6279 | 0.3707 | 0.4662 | 0.9481 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Dibyaranjan/nl_image_search | []
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}
} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: SexismModel
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. -->
# SexismModel
This model is a fine-tuned version of [Constien/Result](https://huggingface.co/Constien/Result) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.0+cu116
- Tokenizers 0.13.2
|
DicoTiar/wisdomfiy | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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} | 3 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: mobilebert_sa_GLUE_Experiment_stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
config: stsb
split: validation
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.08150723019056925
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilebert_sa_GLUE_Experiment_stsb
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3024
- Pearson: 0.0671
- Spearmanr: 0.0815
- Combined Score: 0.0743
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 4.0455 | 1.0 | 45 | 2.3024 | 0.0671 | 0.0815 | 0.0743 |
| 2.1712 | 2.0 | 90 | 2.6644 | 0.0612 | 0.0724 | 0.0668 |
| 2.0637 | 3.0 | 135 | 2.3625 | 0.0582 | 0.0669 | 0.0625 |
| 1.996 | 4.0 | 180 | 2.8671 | 0.0713 | 0.0728 | 0.0720 |
| 1.908 | 5.0 | 225 | 2.6622 | 0.0954 | 0.0898 | 0.0926 |
| 1.7068 | 6.0 | 270 | 2.3885 | 0.1998 | 0.2006 | 0.2002 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DiegoAlysson/opus-mt-en-ro-finetuned-en-to-ro | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:wmt16",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| text2text-generation | {
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"MarianMTModel"
],
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}
} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
- gender
metrics:
- accuracy
model-index:
- name: GFMgenderDetection
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. -->
# GFMgenderDetection
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4328
- Accuracy: 0.7971
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4591 | 1.0 | 4567 | 0.4502 | 0.7841 |
| 0.3915 | 2.0 | 9134 | 0.4328 | 0.7971 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2 |
DiegoBalam12/institute_classification | []
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} | 0 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_sa_GLUE_Experiment_wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
config: wnli
split: validation
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilebert_sa_GLUE_Experiment_wnli
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6895
- Accuracy: 0.5634
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6956 | 1.0 | 5 | 0.6895 | 0.5634 |
| 0.6944 | 2.0 | 10 | 0.6945 | 0.4366 |
| 0.6937 | 3.0 | 15 | 0.6947 | 0.4366 |
| 0.693 | 4.0 | 20 | 0.6914 | 0.5634 |
| 0.693 | 5.0 | 25 | 0.6898 | 0.5634 |
| 0.6932 | 6.0 | 30 | 0.6901 | 0.5634 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Digakive/Hsgshs | []
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} | 0 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_sa_GLUE_Experiment_mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
config: mnli
split: validation_matched
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6110659072416599
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilebert_sa_GLUE_Experiment_mnli
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8609
- Accuracy: 0.6111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9907 | 1.0 | 3068 | 0.9408 | 0.5485 |
| 0.9094 | 2.0 | 6136 | 0.9065 | 0.5819 |
| 0.8828 | 3.0 | 9204 | 0.8969 | 0.5874 |
| 0.8627 | 4.0 | 12272 | 0.8821 | 0.5967 |
| 0.8429 | 5.0 | 15340 | 0.8743 | 0.6003 |
| 0.8207 | 6.0 | 18408 | 0.8663 | 0.6077 |
| 0.7989 | 7.0 | 21476 | 0.8665 | 0.6100 |
| 0.7789 | 8.0 | 24544 | 0.8751 | 0.6096 |
| 0.7603 | 9.0 | 27612 | 0.8620 | 0.6139 |
| 0.7425 | 10.0 | 30680 | 0.8813 | 0.6095 |
| 0.7238 | 11.0 | 33748 | 0.8913 | 0.6142 |
| 0.7063 | 12.0 | 36816 | 0.9026 | 0.6056 |
| 0.6891 | 13.0 | 39884 | 0.9267 | 0.5976 |
| 0.6721 | 14.0 | 42952 | 0.9072 | 0.6105 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Dilmk2/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 13 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 416.20 +/- 15.56
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
DimaOrekhov/cubert-method-name | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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}
} | 10 | null | ---
tags:
- generated_from_trainer
model-index:
- name: MisogModel
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. -->
# MisogModel
This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co/GroNLP/hateBERT) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.0+cu116
- Tokenizers 0.13.2
|
Dimedrolza/DialoGPT-small-cyberpunk | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 9 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: mobilebert_sa_GLUE_Experiment_qqp_128
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
config: qqp
split: validation
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.7784071234232006
- name: F1
type: f1
value: 0.6885884111369878
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilebert_sa_GLUE_Experiment_qqp_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4700
- Accuracy: 0.7784
- F1: 0.6886
- Combined Score: 0.7335
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5294 | 1.0 | 2843 | 0.5076 | 0.7512 | 0.6636 | 0.7074 |
| 0.4791 | 2.0 | 5686 | 0.4889 | 0.7613 | 0.6369 | 0.6991 |
| 0.4622 | 3.0 | 8529 | 0.4821 | 0.7657 | 0.6475 | 0.7066 |
| 0.4463 | 4.0 | 11372 | 0.4831 | 0.7694 | 0.6730 | 0.7212 |
| 0.4288 | 5.0 | 14215 | 0.4724 | 0.7752 | 0.6784 | 0.7268 |
| 0.4129 | 6.0 | 17058 | 0.4806 | 0.7749 | 0.6893 | 0.7321 |
| 0.3969 | 7.0 | 19901 | 0.4700 | 0.7784 | 0.6886 | 0.7335 |
| 0.3813 | 8.0 | 22744 | 0.4802 | 0.7790 | 0.6962 | 0.7376 |
| 0.3664 | 9.0 | 25587 | 0.4765 | 0.7805 | 0.6952 | 0.7378 |
| 0.352 | 10.0 | 28430 | 0.4965 | 0.7768 | 0.7086 | 0.7427 |
| 0.3381 | 11.0 | 31273 | 0.4895 | 0.7845 | 0.6960 | 0.7403 |
| 0.3258 | 12.0 | 34116 | 0.5092 | 0.7844 | 0.7043 | 0.7444 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DingleyMaillotUrgell/homer-bot | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"conversational"
]
| conversational | {
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} | 12 | null | ---
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-math_punctuation-25-01-two_linear_layers-frozen_bert
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-math_punctuation-25-01-two_linear_layers-frozen_bert
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2150
- Micro f1: 0.8910
- Macro f1: 0.2672
- Weighted f1: 0.8495
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Micro f1 | Macro f1 | Weighted f1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|
| 0.193 | 0.62 | 500 | 0.2146 | 0.8937 | 0.2360 | 0.8435 |
| 0.1936 | 1.23 | 1000 | 0.2130 | 0.8937 | 0.2360 | 0.8435 |
| 0.1924 | 1.85 | 1500 | 0.2119 | 0.8937 | 0.2361 | 0.8435 |
| 0.1911 | 2.47 | 2000 | 0.2128 | 0.8936 | 0.2369 | 0.8437 |
| 0.1909 | 3.09 | 2500 | 0.2114 | 0.8937 | 0.2369 | 0.8437 |
| 0.1904 | 3.7 | 3000 | 0.2137 | 0.8935 | 0.2407 | 0.8445 |
| 0.1935 | 4.32 | 3500 | 0.2138 | 0.8934 | 0.2469 | 0.8458 |
| 0.1874 | 4.94 | 4000 | 0.2118 | 0.8929 | 0.2561 | 0.8479 |
| 0.1908 | 5.56 | 4500 | 0.2134 | 0.8925 | 0.2588 | 0.8483 |
| 0.1877 | 6.17 | 5000 | 0.2135 | 0.8918 | 0.2628 | 0.8490 |
| 0.1881 | 6.79 | 5500 | 0.2133 | 0.8931 | 0.2554 | 0.8478 |
| 0.1902 | 7.41 | 6000 | 0.2137 | 0.8922 | 0.2603 | 0.8485 |
| 0.1883 | 8.02 | 6500 | 0.2155 | 0.8914 | 0.2655 | 0.8493 |
| 0.19 | 8.64 | 7000 | 0.2154 | 0.8914 | 0.2647 | 0.8490 |
| 0.1881 | 9.26 | 7500 | 0.2149 | 0.8915 | 0.2645 | 0.8492 |
| 0.1876 | 9.88 | 8000 | 0.2141 | 0.8911 | 0.2671 | 0.8496 |
| 0.1879 | 10.49 | 8500 | 0.2155 | 0.8897 | 0.2722 | 0.8501 |
| 0.1897 | 11.11 | 9000 | 0.2156 | 0.8910 | 0.2670 | 0.8494 |
| 0.1883 | 11.73 | 9500 | 0.2150 | 0.8910 | 0.2672 | 0.8495 |
### Framework versions
- Transformers 4.25.1
- Pytorch 2.0.0.dev20230111
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DivyanshuSheth/T5-Seq2Seq-Final | []
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} | 0 | 2023-01-25T13:10:27Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Waterboy96/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Donghyun/L2_BERT | []
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### ij_avatar Dreambooth model trained by frtna 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:
|
albert-base-v1 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
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} | 38,156 | null | Access to model Owishiboo/CorrectnessChorus is restricted and you are not in the authorized list. Visit https://huggingface.co/Owishiboo/CorrectnessChorus to ask for access. |
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
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}
}
} | 26,792 | 2023-01-25T14:25:59Z | ---
license: apache-2.0
datasets:
- sayakpaul/sample-datasets
pipeline_tag: text-to-image
---
This repository hosts fine-tuned text encoder and diffusion model with Dreambooth technique on [this dog dataset](https://huggingface.co/datasets/sayakpaul/sample-datasets). |
albert-xlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
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},
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}
}
} | 341 | 2023-01-25T14:26:31Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.77 +/- 0.58
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
} | 42,640 | 2023-01-25T14:35:42Z | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a photo of fluffalpaca llama in front of the Colosseum in Rome
---
# DreamBooth model for the fluffalpaca concept trained on the CCMat/db-aplaca dataset.
This is a Stable Diffusion model fine-tuned on the fluffalpaca concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of fluffalpaca llama**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `llama` images for the animal theme.
### Training Hyperparemeters
Pretrained Model: [stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2)<br>
Learning rate: 1e-6<br>
Steps:1100<br>
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('CCMat/fluffalpaca-llama-1100')
image = pipeline().images[0]
image
```
## Samples
Prompt: "fluffalpaca llama in space by Enki Bilal"
Prompt: "fluffalpaca llama in front of the Eiffel Tower"
Prompt: "a photo of fluffalpaca llama swimming in the river"
Prompt: "a photo of fluffalpaca llama in front of the Colosseum in Rome, professional photograph"
Prompt: "USSR propoganda poster. Long live the fluffalpaca llama"
|
bert-base-chinese | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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}
}
} | 3,377,486 | 2023-01-25T14:41:41Z | ---
task: reinforcement-learning
library_name: ml-agents
tags:
- ML-Agents-SoccerTwos
- reinforcement-learning
--- |
bert-base-german-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 175,983 | 2023-01-25T14:42:22Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 59.10 +/- 36.93
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
bert-base-german-dbmdz-cased | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
} | 1,814 | 2023-01-25T14:44:57Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: aj555/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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}
}
} | 68,305 | 2023-01-25T14:45:24Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 38.50 +/- 31.51
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
bert-large-cased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
} | 2,316 | 2023-01-25T14:55:50Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1644.91 +/- 108.07
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
bert-large-uncased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 76,685 | 2023-01-25T15:00:00Z | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0229
- Rouge1: 17.552
- Rouge2: 8.6159
- Rougel: 17.3207
- Rougelsum: 17.1968
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.6836 | 1.0 | 1209 | 3.2362 | 17.2827 | 8.6322 | 16.7811 | 16.7223 |
| 3.6489 | 2.0 | 2418 | 3.0808 | 17.7206 | 8.7236 | 17.0749 | 16.9989 |
| 3.4263 | 3.0 | 3627 | 3.0574 | 17.9532 | 9.55 | 17.604 | 17.4782 |
| 3.3129 | 4.0 | 4836 | 3.0444 | 16.8908 | 8.1947 | 16.3227 | 16.2468 |
| 3.2353 | 5.0 | 6045 | 3.0449 | 17.0334 | 8.1498 | 16.8367 | 16.6738 |
| 3.1678 | 6.0 | 7254 | 3.0326 | 18.197 | 9.3959 | 18.0328 | 17.86 |
| 3.1365 | 7.0 | 8463 | 3.0276 | 17.8769 | 9.1995 | 17.5326 | 17.4261 |
| 3.1118 | 8.0 | 9672 | 3.0229 | 17.552 | 8.6159 | 17.3207 | 17.1968 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
bert-large-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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} | 1,058,496 | 2023-01-25T15:02:07Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 47.30 +/- 34.85
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
camembert-base | [
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
"task_specific_params": {
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},
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}
} | 1,440,898 | 2023-01-25T15:03:47Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ctrl | [
"pytorch",
"tf",
"ctrl",
"en",
"arxiv:1909.05858",
"arxiv:1910.09700",
"transformers",
"license:bsd-3-clause",
"has_space"
]
| null | {
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} | 17,007 | 2023-01-25T15:08:16Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 600.50 +/- 101.55
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 harisumant -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 harisumant -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 harisumant
```
## 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)])
```
|
distilbert-base-cased-distilled-squad | [
"pytorch",
"tf",
"rust",
"safetensors",
"openvino",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"has_space"
]
| question-answering | {
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"DistilBertForQuestionAnswering"
],
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}
}
} | 257,745 | 2023-01-25T15:08:41Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6569
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7028 | 1.0 | 157 | 0.6567 |
| 0.679 | 2.0 | 314 | 0.6515 |
| 0.6692 | 3.0 | 471 | 0.6563 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1
- Datasets 2.7.1
- Tokenizers 0.13.1
|
distilbert-base-cased | [
"pytorch",
"tf",
"onnx",
"distilbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"has_space"
]
| null | {
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} | 574,859 | 2023-01-25T15:11:56Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
distilbert-base-uncased-finetuned-sst-2-english | [
"pytorch",
"tf",
"rust",
"safetensors",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"dataset:glue",
"arxiv:1910.01108",
"doi:10.57967/hf/0181",
"transformers",
"license:apache-2.0",
"model-index",
"has_space"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 3,060,704 | 2023-01-25T15:19:38Z | datasets:
- squad
- newsqa
- hotpot_qa
- biu-nlp/qamr
- search_qa
- natural_questions
- trivia_qa
- duorc
language:
- en
metrics:
- squad
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Checkpoint of MetaQA trained only on extractive QA datasets from MetaQA: Combining Expert Agents for Multi-Skill Question Answering (https://arxiv.org/abs/2112.01922)
## Evaluation Results
```
{
"SQuAD": {
"exact_match": 86.73139158576052,
"f1": 92.65156746563402
},
"NewsQA": {
"exact_match": 55.84045584045584,
"f1": 71.73547617592037
},
"HotpotQA": {
"exact_match": 64.8135593220339,
"f1": 79.61023604916922
},
"SearchQA": {
"exact_match": 75.04122497055359,
"f1": 81.37280639135817
},
"NaturalQuestionsShort": {
"exact_match": 69.50763477718915,
"f1": 81.30374741690376
},
"TriviaQA-web": {
"exact_match": 77.18396711202466,
"f1": 81.52989853015538
},
"QAMR": {
"exact_match": 72.07531203723292,
"f1": 83.9068616637681
},
"DuoRC": {
"exact_match": 39.35626573106552,
"f1": 51.033295034422466
}
}
``` |
ARTeLab/mbart-summarization-fanpage | [
"pytorch",
"mbart",
"text2text-generation",
"it",
"dataset:ARTeLab/fanpage",
"transformers",
"summarization",
"autotrain_compatible",
"has_space"
]
| summarization | {
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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} | 14 | 2023-01-25T22:54:43Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-frozlake
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="Periramm/q-frozlake", 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"])
```
|
ASCCCCCCCC/distilbert-base-uncased-finetuned-clinc | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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}
} | 35 | null | ---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Anything V5 (https://civitai.com/models/9409)
# Uploaded by the Real Anything V3 Author
# Please try it |
AdapterHub/bert-base-uncased-pf-scicite | [
"bert",
"en",
"dataset:scicite",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
]
| text-classification | {
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}
}
} | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.46 +/- 2.64
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="AKFromCanada/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"])
```
|
AdapterHub/bert-base-uncased-pf-ud_en_ewt | [
"bert",
"en",
"dataset:universal_dependencies",
"adapter-transformers",
"adapterhub:dp/ud_ewt"
]
| null | {
"architectures": null,
"model_type": "bert",
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}
} | 4 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilroberta-base-CoLA
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5678267214677118
widget:
- text: 'The cat sat on the mat.'
example_title: Correct grammatical sentence
- text: 'Me and my friend going to the store.'
example_title: Incorrect subject-verb agreement
- text: 'I ain''t got no money.'
example_title: Incorrect verb conjugation and double negative
- text: 'She don''t like pizza no more.'
example_title: Incorrect verb conjugation and double negative
- text: 'They is arriving tomorrow.'
example_title: Incorrect verb conjugation
---
# distilroberta-base-CoLA
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4974
- Matthews Correlation: 0.5678
## Model description
Mostly as a litmus test to see how it fares vs. the `textattack` one (should be similar) & associated metrics:
```json
{
"epoch": 4.0,
"eval_loss": 0.49744734168052673,
"eval_matthews_correlation": 0.5678267214677118,
"eval_runtime": 1.9223,
"eval_samples": 1043,
"eval_samples_per_second": 542.586,
"eval_steps_per_second": 135.777
}
```
## 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: 6e-05
- train_batch_size: 32
- eval_batch_size: 4
- seed: 32010
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- 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: 0.03
- num_epochs: 4.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4778 | 1.0 | 67 | 0.4630 | 0.5161 |
| 0.4356 | 2.0 | 134 | 0.4725 | 0.5287 |
| 0.2934 | 3.0 | 201 | 0.4974 | 0.5678 |
| 0.1998 | 4.0 | 268 | 0.5419 | 0.5584 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.1
|
AlexN/xls-r-300m-fr-0 | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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},
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}
} | 4 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 18092 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'dot_score'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
AnonymousSub/AR_rule_based_roberta_hier_triplet_epochs_1_shard_10 | [
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} | 3 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_add_GLUE_Experiment_rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE RTE
type: glue
config: rte
split: validation
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.5270758122743683
---
<!-- 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_add_GLUE_Experiment_rte
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6917
- Accuracy: 0.5271
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7224 | 1.0 | 10 | 0.6988 | 0.4729 |
| 0.7025 | 2.0 | 20 | 0.6973 | 0.4729 |
| 0.6988 | 3.0 | 30 | 0.6933 | 0.4729 |
| 0.7005 | 4.0 | 40 | 0.7159 | 0.4729 |
| 0.7097 | 5.0 | 50 | 0.7277 | 0.4729 |
| 0.7022 | 6.0 | 60 | 0.6976 | 0.4729 |
| 0.7015 | 7.0 | 70 | 0.6917 | 0.5271 |
| 0.6991 | 8.0 | 80 | 0.6929 | 0.5271 |
| 0.6987 | 9.0 | 90 | 0.6928 | 0.5271 |
| 0.6933 | 10.0 | 100 | 0.7109 | 0.4729 |
| 0.695 | 11.0 | 110 | 0.6975 | 0.4729 |
| 0.6744 | 12.0 | 120 | 0.7165 | 0.4621 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/AR_rule_based_roberta_only_classfn_epochs_1_shard_1 | [
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} | 7 | 2023-01-26T14:47:09Z | ---
license: cc0-1.0
language:
- is
tags:
- MaCoCu
---
# Model description
**XLMR-base-MaCoCu-is** is a large pre-trained language model trained on **Icelandic** texts. It was created by continuing training from the [XLM-RoBERTa-base](https://huggingface.co/xlm-roberta-base) model. It was developed as part of the [MaCoCu](https://macocu.eu/) project and only uses data that was crawled during the project. The main developer is [Jaume Zaragoza-Bernabeu](https://github.com/ZJaume) from Prompsit Language Engineering.
XLMR-base-MaCoCu-is was trained on 4.4GB of Icelandic text, which is equal to 688M tokens. It was trained for 40,000 steps with a batch size of 256. It uses the same vocabulary as the original XLMR-base model.
The training and fine-tuning procedures are described in detail on our [Github repo](https://github.com/macocu/LanguageModels).
## Warning
This model has not been fully trained because it was intended for use as base of [Bicleaner AI Icelandic model](https://huggingface.co/bitextor/bicleaner-ai-full-en-is). If you need better performance, please use [XLMR-MaCoCu-is](https://huggingface.co/MaCoCu/XLMR-MaCoCu-is).
# How to use
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained("MaCoCu/XLMR-base-MaCoCu-is")
model = AutoModel.from_pretrained("MaCoCu/XLMR-base-MaCoCu-is") # PyTorch
model = TFAutoModel.from_pretrained("MaCoCu/XLMR-base-MaCoCu-is") # Tensorflow
```
# Data
For training, we used all Icelandic data that was present in the monolingual Icelandic [MaCoCu](https://macocu.eu/) corpus. After de-duplicating the data, we were left with a total of 4.4 GB of text, which equals 688M tokens.
# Acknowledgements
The authors received funding from the European Union’s Connecting Europe Facility 2014-
2020 - CEF Telecom, under Grant Agreement No.INEA/CEF/ICT/A2020/2278341 (MaCoCu).
# Citation
If you use this model, please cite the following paper:
```bibtex
@inproceedings{non-etal-2022-macocu,
title = "{M}a{C}o{C}u: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages",
author = "Ba{\~n}{\'o}n, Marta and
Espl{\`a}-Gomis, Miquel and
Forcada, Mikel L. and
Garc{\'\i}a-Romero, Cristian and
Kuzman, Taja and
Ljube{\v{s}}i{\'c}, Nikola and
van Noord, Rik and
Sempere, Leopoldo Pla and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Rupnik, Peter and
Suchomel, V{\'\i}t and
Toral, Antonio and
van der Werff, Tobias and
Zaragoza, Jaume",
booktitle = "Proceedings of the 23rd Annual Conference of the European Association for Machine Translation",
month = jun,
year = "2022",
address = "Ghent, Belgium",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2022.eamt-1.41",
pages = "303--304"
}
```
|
AnonymousSub/AR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1 | [
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"transformers"
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} | 5 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_add_GLUE_Experiment_wnli_96
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
config: wnli
split: validation
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- 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_add_GLUE_Experiment_wnli_96
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6895
- Accuracy: 0.5634
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6933 | 1.0 | 3 | 0.6895 | 0.5634 |
| 0.6926 | 2.0 | 6 | 0.6906 | 0.5634 |
| 0.6924 | 3.0 | 9 | 0.6907 | 0.5634 |
| 0.6937 | 4.0 | 12 | 0.6897 | 0.5634 |
| 0.6939 | 5.0 | 15 | 0.6897 | 0.5634 |
| 0.6929 | 6.0 | 18 | 0.6902 | 0.5634 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/AR_rule_based_twostagequadruplet_hier_epochs_1_shard_1 | [
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"bert",
"feature-extraction",
"transformers"
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.87
- name: F1
type: f1
value: 0.8704318936877077
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3222
- Accuracy: 0.87
- F1: 0.8704
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/SR_rule_based_twostagetriplet_epochs_1_shard_1 | [
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} | 2 | null | Access to model modamsko/undraw-diffusion-v1 is restricted and you are not in the authorized list. Visit https://huggingface.co/modamsko/undraw-diffusion-v1 to ask for access. |
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1_squad2.0 | [
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"bert",
"question-answering",
"transformers",
"autotrain_compatible"
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} | 3 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_add_GLUE_Experiment_rte_256
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE RTE
type: glue
config: rte
split: validation
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.5270758122743683
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilebert_add_GLUE_Experiment_rte_256
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6929
- Accuracy: 0.5271
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6937 | 1.0 | 20 | 0.6929 | 0.5271 |
| 0.6938 | 2.0 | 40 | 0.6929 | 0.5271 |
| 0.6931 | 3.0 | 60 | 0.6931 | 0.5126 |
| 0.6932 | 4.0 | 80 | 0.6938 | 0.4693 |
| 0.693 | 5.0 | 100 | 0.6950 | 0.4729 |
| 0.6921 | 6.0 | 120 | 0.6933 | 0.5199 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
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} | 24 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Goodreads_Books_Reviews_BERT_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Goodreads_Books_Reviews_BERT_3
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: 1.2441
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4298 | 1.0 | 675 | 1.0408 |
| 1.0215 | 2.0 | 1350 | 0.9826 |
| 0.6131 | 3.0 | 2025 | 1.0458 |
| 0.3825 | 4.0 | 2700 | 1.2441 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10 | [
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Goodreads_Books_Reviews_BERT_4
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. -->
# Goodreads_Books_Reviews_BERT_4
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: 1.2687
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1102 | 1.0 | 1350 | 1.0206 |
| 0.8507 | 2.0 | 2700 | 0.9454 |
| 0.579 | 3.0 | 4050 | 1.0759 |
| 0.3518 | 4.0 | 5400 | 1.2687 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
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} | 2 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -1958.42 +/- 1146.11
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
...
```
|
Apoorva/k2t-test | [
"pytorch",
"t5",
"text2text-generation",
"en",
"transformers",
"keytotext",
"k2t",
"Keywords to Sentences",
"autotrain_compatible"
]
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} | 7 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 451.70 +/- 144.90
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Augustvember/WokkaBot4 | []
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} | 0 | null | Access to model Dantao/EllPFG is restricted and you are not in the authorized list. Visit https://huggingface.co/Dantao/EllPFG to ask for access. |
Aviora/news2vec | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1405.93 +/- 96.15
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Aviora/phobert-ner | []
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} | 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: dungtd2403/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Awsaf/DialoGPT-medium-eren | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 12 | null | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1560.05 +/- 117.27
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Axon/resnet34-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
]
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: kobart_8_1e-4_datav2_min30_lp5.0_temperature1.0
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. -->
# kobart_8_1e-4_datav2_min30_lp5.0_temperature1.0
This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0961
- Rouge1: 35.8883
- Rouge2: 12.7003
- Rougel: 23.3874
- Bleu1: 30.2528
- Bleu2: 17.5183
- Bleu3: 10.2094
- Bleu4: 5.6021
- Gen Len: 50.1562
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 128
- 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: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:|
| 2.4648 | 0.19 | 1000 | 2.9491 | 32.241 | 10.5261 | 21.21 | 26.5995 | 14.7371 | 7.8411 | 4.1361 | 48.303 |
| 2.4028 | 0.38 | 2000 | 2.9226 | 33.8957 | 11.6309 | 22.4654 | 28.1592 | 15.9817 | 9.163 | 5.0564 | 49.5175 |
| 2.4109 | 0.57 | 3000 | 2.9092 | 33.9997 | 11.4619 | 22.2822 | 28.0021 | 15.7774 | 8.7258 | 4.5887 | 44.6807 |
| 2.3846 | 0.76 | 4000 | 2.8763 | 31.8881 | 10.1122 | 21.1754 | 25.4518 | 13.7126 | 7.4549 | 3.9979 | 40.9161 |
| 2.2972 | 0.94 | 5000 | 2.8441 | 33.4146 | 11.8371 | 22.7219 | 27.1678 | 15.4977 | 9.1783 | 5.3303 | 43.8765 |
| 2.0162 | 1.13 | 6000 | 2.8372 | 34.9461 | 11.8978 | 22.7877 | 28.9743 | 16.3778 | 9.2932 | 5.0534 | 47.1585 |
| 1.9816 | 1.32 | 7000 | 2.8630 | 33.1249 | 10.8834 | 22.0846 | 27.0042 | 14.9508 | 8.3482 | 4.5422 | 44.676 |
| 2.0172 | 1.51 | 8000 | 2.7998 | 34.1663 | 11.5471 | 22.8156 | 28.0367 | 15.7969 | 8.6235 | 4.5914 | 44.9254 |
| 2.017 | 1.7 | 9000 | 2.7865 | 33.3775 | 11.194 | 22.6083 | 26.7485 | 14.9797 | 8.2559 | 4.279 | 41.5828 |
| 1.9734 | 1.89 | 10000 | 2.7532 | 34.7147 | 12.353 | 23.0917 | 28.8012 | 16.7472 | 9.7079 | 5.5416 | 47.9883 |
| 1.5937 | 2.08 | 11000 | 2.8433 | 34.9402 | 12.2318 | 23.2483 | 28.8006 | 16.5212 | 9.6008 | 5.3947 | 45.2401 |
| 1.6112 | 2.27 | 12000 | 2.8377 | 34.9291 | 12.2349 | 23.278 | 28.8423 | 16.539 | 9.7674 | 5.4267 | 44.7599 |
| 1.603 | 2.45 | 13000 | 2.8223 | 35.3837 | 12.5491 | 23.5272 | 29.3683 | 16.9828 | 9.6955 | 5.3166 | 47.6037 |
| 1.6274 | 2.64 | 14000 | 2.8220 | 34.0515 | 11.7884 | 22.829 | 27.6635 | 15.8021 | 8.9724 | 4.9314 | 44.1235 |
| 1.6435 | 2.83 | 15000 | 2.8139 | 34.9239 | 12.2122 | 22.9939 | 29.1796 | 16.763 | 9.5513 | 5.174 | 46.7832 |
| 1.238 | 3.02 | 16000 | 2.9615 | 35.456 | 12.3012 | 23.3111 | 29.8676 | 17.0768 | 9.8694 | 5.4376 | 51.1935 |
| 1.2767 | 3.21 | 17000 | 2.9781 | 35.2632 | 12.1441 | 23.2537 | 29.1438 | 16.6216 | 9.353 | 5.1593 | 46.0793 |
| 1.2868 | 3.4 | 18000 | 2.9723 | 34.6808 | 11.9638 | 22.9058 | 28.9988 | 16.4994 | 9.3619 | 5.1178 | 47.4732 |
| 1.2842 | 3.59 | 19000 | 2.9688 | 35.3792 | 12.5174 | 23.2012 | 29.6403 | 17.1517 | 9.9507 | 5.5561 | 49.1515 |
| 1.2931 | 3.78 | 20000 | 2.9694 | 35.7525 | 12.8025 | 23.5228 | 29.8102 | 17.3544 | 10.239 | 5.6637 | 49.1189 |
| 1.2733 | 3.97 | 21000 | 2.9618 | 35.8931 | 12.627 | 23.5571 | 30.0482 | 17.2582 | 9.8412 | 5.4747 | 48.5082 |
| 0.963 | 4.15 | 22000 | 3.1113 | 35.7523 | 12.7633 | 23.3127 | 30.0193 | 17.4211 | 10.2596 | 5.853 | 51.6993 |
| 0.9563 | 4.34 | 23000 | 3.1031 | 35.8437 | 12.6323 | 23.6011 | 30.0923 | 17.4089 | 9.9831 | 5.5993 | 48.7646 |
| 0.992 | 4.53 | 24000 | 3.1016 | 36.1067 | 13.3428 | 24.0267 | 30.0275 | 17.8733 | 10.6929 | 6.2491 | 52.0373 |
| 0.9722 | 4.72 | 25000 | 3.0956 | 35.4406 | 12.4799 | 23.3418 | 29.5123 | 17.0292 | 9.7401 | 5.3586 | 48.8974 |
| 0.9519 | 4.91 | 26000 | 3.0961 | 35.8883 | 12.7003 | 23.3874 | 30.2528 | 17.5183 | 10.2094 | 5.6021 | 50.1562 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Axon/resnet50-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
]
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} | 0 | 2023-01-27T06:34:11Z | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- pearsonr
model-index:
- name: bert-base-finetuned-sts
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
config: sts
split: train
args: sts
metrics:
- name: Pearsonr
type: pearsonr
value: 0.9116408161709073
---
<!-- 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-finetuned-sts
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3951
- Pearsonr: 0.9116
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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 | Pearsonr |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2345 | 1.0 | 2917 | 0.7037 | 0.8757 |
| 0.1491 | 2.0 | 5834 | 0.4869 | 0.8846 |
| 0.097 | 3.0 | 8751 | 0.4023 | 0.9041 |
| 0.0735 | 4.0 | 11668 | 0.3960 | 0.9073 |
| 0.0644 | 5.0 | 14585 | 0.4838 | 0.8989 |
| 0.0446 | 6.0 | 17502 | 0.3990 | 0.9078 |
| 0.0355 | 7.0 | 20419 | 0.3951 | 0.9116 |
| 0.0277 | 8.0 | 23336 | 0.4284 | 0.9053 |
| 0.0239 | 9.0 | 26253 | 0.4166 | 0.9073 |
| 0.0205 | 10.0 | 29170 | 0.4234 | 0.9062 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Ayato/DialoGTP-large-Yuri | []
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} | 0 | 2023-01-27T06:53:23Z | # Joint Pruning, Quantization and Distillation for BERT-large/SQuADv1.1
## Setup
```bash
git clone https://github.com/vuiseng9/optimum-intel
cd optimum-intel
git checkout jpqd-mobilebert #commit: 6ef11715ddefd96c67970918d809eea09c8c2e6b
pip install -e .[openvino,nncf]
cd examples/openvino/question-answering/
pip install -r requirements.txt
pip install wandb # optional
```
## Run
```bash
NNCFCFG=/path/to/openvino_config.json
MASTER_PORT=<PORTID>
RUNID=<RUN_IDENTIFIER>
OUTDIR=/path/to/saved_model
NTXBLK=15
NEPOCH=16
python run_qa.py \
--dataset_name squad \
--model_name_or_path google/mobilebert-uncased \
--num_tx_block $NTXBLK \
--teacher_model_or_path bert-large-uncased-whole-word-masking-finetuned-squad \
--distillation_weight 0.9 \
--distillation_temperature 2 \
--do_eval \
--do_train \
--fp16 \
--evaluation_strategy steps \
--eval_steps 250 \
--learning_rate 1e-4 \
--warmup_ratio 0.1 \
--optim adamw_torch \
--num_train_epochs $NEPOCH \
--per_device_eval_batch_size 128 \
--per_device_train_batch_size 32 \
--max_seq_length 384 \
--doc_stride 128 \
--save_steps 500 \
--logging_steps 1 \
--overwrite_output_dir \
--nncf_compression_config $NNCFCFG \
--run_name $RUNID \
--output_dir $OUTDIR \
```
### Reference Results
```
Global Step: 44000
F1: 90.336
EM: 83.680
Structured Sparsity (linear): 34.31%
Model Sparsity: 19.43%
``` |
Aybars/ModelOnWhole | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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"BertForQuestionAnswering"
],
"model_type": "bert",
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}
} | 4 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8687329079307201
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1906
- F1: 0.8687
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2884 | 1.0 | 3145 | 0.2390 | 0.8242 |
| 0.1639 | 2.0 | 6290 | 0.1944 | 0.8488 |
| 0.0952 | 3.0 | 9435 | 0.1906 | 0.8687 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Aybars/XLM_Turkish | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
}
} | 4 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: kobart_32_6e-5_datav2_min30_lp5.0_temperature1.0
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. -->
# kobart_32_6e-5_datav2_min30_lp5.0_temperature1.0
This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6110
- Rouge1: 35.8879
- Rouge2: 12.9302
- Rougel: 23.7819
- Bleu1: 30.0048
- Bleu2: 17.5297
- Bleu3: 10.3153
- Bleu4: 5.9092
- Gen Len: 50.8508
## 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: 6e-05
- train_batch_size: 32
- eval_batch_size: 128
- 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: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:|
| 1.5664 | 3.78 | 5000 | 2.6110 | 35.8879 | 12.9302 | 23.7819 | 30.0048 | 17.5297 | 10.3153 | 5.9092 | 50.8508 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Ayham/albert_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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}
}
} | 12 | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -21.97 +/- 7.47
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Ayham/albert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
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},
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},
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}
}
} | 7 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **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="jamesup/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"])
```
|
Ayham/bert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
} | 4 | 2023-01-27T07:22:51Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -5.78 +/- 1.18
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Ayham/bertgpt2_cnn | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
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}
}
} | 4 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.3563
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 415 | 5.6748 |
| 6.3694 | 2.0 | 830 | 5.4214 |
| 5.413 | 3.0 | 1245 | 5.3563 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Ayham/distilbert_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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},
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},
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},
"translation_en_to_fr": {
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}
} | 11 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: long-t5-tglobal-base-samsum
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. -->
# long-t5-tglobal-base-samsum
This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on an samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3565
- Rouge1: 46.191
- Rouge2: 22.3147
- Rougel: 38.4272
- Rougelsum: 42.0471
- Gen Len: 16.7350
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:-------:|:---------:|:-------:|
| 1.6898 | 1.0 | 7366 | 1.3795 | 45.766 | 21.7455 | 38.1609 | 41.7016 | 16.7265 |
| 1.5544 | 2.0 | 14732 | 1.3565 | 46.191 | 22.3147 | 38.4272 | 42.0471 | 16.7350 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Ayham/distilbert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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"task_specific_params": {
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}
} | 8 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: kobart_32_1e-4_datav2_min30_lp5.0_temperature1.0
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. -->
# kobart_32_1e-4_datav2_min30_lp5.0_temperature1.0
This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7215
- Rouge1: 36.3912
- Rouge2: 13.2376
- Rougel: 23.7632
- Bleu1: 30.6123
- Bleu2: 18.0414
- Bleu3: 10.5291
- Bleu4: 6.0123
- Gen Len: 49.5035
## 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: 32
- eval_batch_size: 128
- 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: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:|
| 1.3994 | 3.78 | 5000 | 2.7215 | 36.3912 | 13.2376 | 23.7632 | 30.6123 | 18.0414 | 10.5291 | 6.0123 | 49.5035 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Ayham/roberta_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
],
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}
} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.939
- name: F1
type: f1
value: 0.9391263036329083
---
<!-- 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: 0.1340
- Accuracy: 0.939
- F1: 0.9391
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5187 | 1.0 | 250 | 0.1878 | 0.9245 | 0.9240 |
| 0.141 | 2.0 | 500 | 0.1340 | 0.939 | 0.9391 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Ayham/roberta_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 4 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="jamesup/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"])
```
|
Ayham/roberta_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 3 | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.88 +/- 0.86
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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