modelId
stringlengths 4
81
| tags
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stringclasses 17
values | config
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int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
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|---|---|---|---|---|---|---|
CLTL/gm-ner-xlmrbase | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"nl",
"transformers",
"dighum",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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"XLMRobertaForTokenClassification"
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} | 2 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Hussein_Deliberate_1000steps Dreambooth model trained by HusseinHE 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:
|
CLTL/icf-levels-adm | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
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"RobertaForSequenceClassification"
],
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} | 33 | 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: 273.05 +/- 17.96
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
...
```
|
CLTL/icf-levels-etn | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
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} | 31 | null | A tiny version of Zipformer-Transducer (https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless7).
Number of model parameters: 20697573
Decoding results at epoch-30-avg-9:
* greedy_search: 2.67 & 6.4
* modified_beam_search: 2.6 & 6.26
* fast_beam_search: 2.64 & 6.3
|
CLTL/icf-levels-fac | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
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"RobertaForSequenceClassification"
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} | 32 | 2023-01-28T01:48:37Z | ---
license: cc-by-sa-4.0
---
Abstract Expression Machine 30 (AEM30):
Stable Diffusion 2 from Stability AI fine-tuned 30 steps on [maximalmargin/mitchell](https://huggingface.co/datasets/maximalmargin/mitchell) dataset. |
CLTL/icf-levels-ins | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
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"RobertaForSequenceClassification"
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}
} | 32 | 2023-01-28T01:48:47Z | ---
license: cc-by-sa-4.0
---
Abstract Expression Machine 100 (AEM100):
Stable Diffusion 2 from Stability AI fine-tuned 100 steps on [maximalmargin/mitchell](https://huggingface.co/datasets/maximalmargin/mitchell) dataset. |
CSResearcher/TestModel | [
"license:mit"
]
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} | 0 | 2023-01-28T03:16:19Z | ---
language: en
thumbnail: http://www.huggingtweets.com/aneternalenigma/1674876241213/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1610259562988294147/Ck8uDVHJ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">🔴LIVE on TWITCH!🔴AnEternalEnigma</div>
<div style="text-align: center; font-size: 14px;">@aneternalenigma</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from 🔴LIVE on TWITCH!🔴AnEternalEnigma.
| Data | 🔴LIVE on TWITCH!🔴AnEternalEnigma |
| --- | --- |
| Tweets downloaded | 3235 |
| Retweets | 773 |
| Short tweets | 320 |
| Tweets kept | 2142 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/mw1vu54f/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @aneternalenigma's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/cu1opgto) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/cu1opgto/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/aneternalenigma')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
CSZay/bart | []
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} | 0 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1597709018142855170/e0xfVtT4_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">silly little time</div>
<div style="text-align: center; font-size: 14px;">@muzhroommama</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from silly little time.
| Data | silly little time |
| --- | --- |
| Tweets downloaded | 236 |
| Retweets | 87 |
| Short tweets | 32 |
| Tweets kept | 117 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/xaynl4xc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @muzhroommama's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/x523rtvl) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/x523rtvl/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/muzhroommama')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
CZWin32768/xlm-align | [
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2106.06381",
"transformers",
"autotrain_compatible"
]
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"XLMRobertaForMaskedLM"
],
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} | 6 | null | Access to model shaoncsecu/en_Disease_A_Z_SpaCy is restricted and you are not in the authorized list. Visit https://huggingface.co/shaoncsecu/en_Disease_A_Z_SpaCy to ask for access. |
Callidior/bert2bert-base-arxiv-titlegen | [
"pytorch",
"safetensors",
"encoder-decoder",
"text2text-generation",
"en",
"dataset:arxiv_dataset",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| summarization | {
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"EncoderDecoderModel"
],
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}
} | 145 | null | ---
tags:
- conversational
---
#42meow DialoGPT Model |
CallumRai/HansardGPT2 | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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} | 14 | null |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- image-classification
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.23
inference: false
datasets:
- keremberke/chest-xray-classification
model-index:
- name: keremberke/yolov8m-chest-xray-classification
results:
- task:
type: image-classification
dataset:
type: keremberke/chest-xray-classification
name: chest-xray-classification
split: validation
metrics:
- type: accuracy
value: 0.95533 # min: 0.0 - max: 1.0
name: top1 accuracy
- type: accuracy
value: 1 # min: 0.0 - max: 1.0
name: top5 accuracy
---
<div align="center">
<img width="640" alt="keremberke/yolov8m-chest-xray-classification" src="https://huggingface.co/keremberke/yolov8m-chest-xray-classification/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['NORMAL', 'PNEUMONIA']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.24 ultralytics==8.0.23
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, postprocess_classify_output
# load model
model = YOLO('keremberke/yolov8m-chest-xray-classification')
# set model parameters
model.overrides['conf'] = 0.25 # model confidence threshold
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].probs) # [0.1, 0.2, 0.3, 0.4]
processed_result = postprocess_classify_output(model, result=results[0])
print(processed_result) # {"cat": 0.4, "dog": 0.6}
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
Cameron/BERT-Jigsaw | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
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} | 35 | null |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- image-classification
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.21
inference: false
datasets:
- keremberke/pokemon-classification
model-index:
- name: keremberke/yolov8n-pokemon-classification
results:
- task:
type: image-classification
dataset:
type: keremberke/pokemon-classification
name: pokemon-classification
split: validation
metrics:
- type: accuracy
value: 0.02322 # min: 0.0 - max: 1.0
name: top1 accuracy
- type: accuracy
value: 0.09016 # min: 0.0 - max: 1.0
name: top5 accuracy
---
<div align="center">
<img width="640" alt="keremberke/yolov8n-pokemon-classification" src="https://huggingface.co/keremberke/yolov8n-pokemon-classification/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['Abra', 'Aerodactyl', 'Alakazam', 'Alolan Sandslash', 'Arbok', 'Arcanine', 'Articuno', 'Beedrill', 'Bellsprout', 'Blastoise', 'Bulbasaur', 'Butterfree', 'Caterpie', 'Chansey', 'Charizard', 'Charmander', 'Charmeleon', 'Clefable', 'Clefairy', 'Cloyster', 'Cubone', 'Dewgong', 'Diglett', 'Ditto', 'Dodrio', 'Doduo', 'Dragonair', 'Dragonite', 'Dratini', 'Drowzee', 'Dugtrio', 'Eevee', 'Ekans', 'Electabuzz', 'Electrode', 'Exeggcute', 'Exeggutor', 'Farfetchd', 'Fearow', 'Flareon', 'Gastly', 'Gengar', 'Geodude', 'Gloom', 'Golbat', 'Goldeen', 'Golduck', 'Golem', 'Graveler', 'Grimer', 'Growlithe', 'Gyarados', 'Haunter', 'Hitmonchan', 'Hitmonlee', 'Horsea', 'Hypno', 'Ivysaur', 'Jigglypuff', 'Jolteon', 'Jynx', 'Kabuto', 'Kabutops', 'Kadabra', 'Kakuna', 'Kangaskhan', 'Kingler', 'Koffing', 'Krabby', 'Lapras', 'Lickitung', 'Machamp', 'Machoke', 'Machop', 'Magikarp', 'Magmar', 'Magnemite', 'Magneton', 'Mankey', 'Marowak', 'Meowth', 'Metapod', 'Mew', 'Mewtwo', 'Moltres', 'MrMime', 'Muk', 'Nidoking', 'Nidoqueen', 'Nidorina', 'Nidorino', 'Ninetales', 'Oddish', 'Omanyte', 'Omastar', 'Onix', 'Paras', 'Parasect', 'Persian', 'Pidgeot', 'Pidgeotto', 'Pidgey', 'Pikachu', 'Pinsir', 'Poliwag', 'Poliwhirl', 'Poliwrath', 'Wigglytuff', 'Zapdos', 'Zubat']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, postprocess_classify_output
# load model
model = YOLO('keremberke/yolov8n-pokemon-classification')
# set model parameters
model.overrides['conf'] = 0.25 # model confidence threshold
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].probs) # [0.1, 0.2, 0.3, 0.4]
processed_result = postprocess_classify_output(model, result=results[0])
print(processed_result) # {"cat": 0.4, "dog": 0.6}
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
Cameron/BERT-SBIC-offensive | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
} | 31 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/rhilever/1674878721821/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1611525483770044416/fYPREQ1N_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Rhilever</div>
<div style="text-align: center; font-size: 14px;">@rhilever</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Rhilever.
| Data | Rhilever |
| --- | --- |
| Tweets downloaded | 2728 |
| Retweets | 326 |
| Short tweets | 402 |
| Tweets kept | 2000 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/imxxkyr1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rhilever's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/bvkpy3yi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/bvkpy3yi/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/rhilever')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Cameron/BERT-SBIC-targetcategory | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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}
} | 30 | null |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# KerasCV Stable Diffusion in Diffusers 🧨🤗
The pipeline contained in this repository was created using [this Space](https://huggingface.co/spaces/sayakpaul/convert-kerascv-sd-diffusers). The purpose is to convert the KerasCV Stable Diffusion weights in a way that is compatible with [Diffusers](https://github.com/huggingface/diffusers). This allows users to fine-tune using KerasCV and use the fine-tuned weights in Diffusers taking advantage of its nifty features (like [schedulers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/schedulers), [fast attention](https://huggingface.co/docs/diffusers/optimization/fp16), etc.).
Following weight paths (KerasCV) were used
: ['https://huggingface.co/sayakpaul/dreambooth-keras-dogs-unet/resolve/main/lr_1e-6_steps_1000.h5'] |
Cameron/BERT-eec-emotion | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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}
} | 36 | 2023-01-28T04:19:19Z | ---
language: en
thumbnail: http://www.huggingtweets.com/maxylobes/1674880251172/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1618233941110173696/x6aWoIH3_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Maxy</div>
<div style="text-align: center; font-size: 14px;">@maxylobes</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Maxy.
| Data | Maxy |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 236 |
| Short tweets | 142 |
| Tweets kept | 2867 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/xfg543v2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @maxylobes's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ge6894ny) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ge6894ny/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/maxylobes')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Cameron/BERT-jigsaw-severetoxic | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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},
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},
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},
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}
}
} | 30 | 2023-01-28T04:23:35Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9822222222222222
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0583
- Accuracy: 0.9822
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2655 | 1.0 | 190 | 0.1039 | 0.9707 |
| 0.1519 | 2.0 | 380 | 0.0866 | 0.9715 |
| 0.1402 | 3.0 | 570 | 0.0583 | 0.9822 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Cameron/BERT-mdgender-convai-binary | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
}
} | 33 | 2023-01-28T04:36:59Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="shivr/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"])
```
|
Cameron/BERT-mdgender-wizard | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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}
}
} | 30 | 2023-01-28T04:48:41Z |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- image-classification
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.21
inference: false
datasets:
- keremberke/pokemon-classification
model-index:
- name: keremberke/yolov8s-pokemon-classification
results:
- task:
type: image-classification
dataset:
type: keremberke/pokemon-classification
name: pokemon-classification
split: validation
metrics:
- type: accuracy
value: 0.02459 # min: 0.0 - max: 1.0
name: top1 accuracy
- type: accuracy
value: 0.0806 # min: 0.0 - max: 1.0
name: top5 accuracy
---
<div align="center">
<img width="640" alt="keremberke/yolov8s-pokemon-classification" src="https://huggingface.co/keremberke/yolov8s-pokemon-classification/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['Abra', 'Aerodactyl', 'Alakazam', 'Alolan Sandslash', 'Arbok', 'Arcanine', 'Articuno', 'Beedrill', 'Bellsprout', 'Blastoise', 'Bulbasaur', 'Butterfree', 'Caterpie', 'Chansey', 'Charizard', 'Charmander', 'Charmeleon', 'Clefable', 'Clefairy', 'Cloyster', 'Cubone', 'Dewgong', 'Diglett', 'Ditto', 'Dodrio', 'Doduo', 'Dragonair', 'Dragonite', 'Dratini', 'Drowzee', 'Dugtrio', 'Eevee', 'Ekans', 'Electabuzz', 'Electrode', 'Exeggcute', 'Exeggutor', 'Farfetchd', 'Fearow', 'Flareon', 'Gastly', 'Gengar', 'Geodude', 'Gloom', 'Golbat', 'Goldeen', 'Golduck', 'Golem', 'Graveler', 'Grimer', 'Growlithe', 'Gyarados', 'Haunter', 'Hitmonchan', 'Hitmonlee', 'Horsea', 'Hypno', 'Ivysaur', 'Jigglypuff', 'Jolteon', 'Jynx', 'Kabuto', 'Kabutops', 'Kadabra', 'Kakuna', 'Kangaskhan', 'Kingler', 'Koffing', 'Krabby', 'Lapras', 'Lickitung', 'Machamp', 'Machoke', 'Machop', 'Magikarp', 'Magmar', 'Magnemite', 'Magneton', 'Mankey', 'Marowak', 'Meowth', 'Metapod', 'Mew', 'Mewtwo', 'Moltres', 'MrMime', 'Muk', 'Nidoking', 'Nidoqueen', 'Nidorina', 'Nidorino', 'Ninetales', 'Oddish', 'Omanyte', 'Omastar', 'Onix', 'Paras', 'Parasect', 'Persian', 'Pidgeot', 'Pidgeotto', 'Pidgey', 'Pikachu', 'Pinsir', 'Poliwag', 'Poliwhirl', 'Poliwrath', 'Wigglytuff', 'Zapdos', 'Zubat']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, postprocess_classify_output
# load model
model = YOLO('keremberke/yolov8s-pokemon-classification')
# set model parameters
model.overrides['conf'] = 0.25 # model confidence threshold
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].probs) # [0.1, 0.2, 0.3, 0.4]
processed_result = postprocess_classify_output(model, result=results[0])
print(processed_result) # {"cat": 0.4, "dog": 0.6}
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
Cameron/BERT-rtgender-opgender-annotations | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
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} | 33 | 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: 249.56 +/- 19.30
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
...
```
|
Camzure/MaamiBot | []
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} | 0 | 2023-01-28T05:02:37Z |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- image-classification
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.23
inference: false
datasets:
- keremberke/pokemon-classification
model-index:
- name: keremberke/yolov8m-pokemon-classification
results:
- task:
type: image-classification
dataset:
type: keremberke/pokemon-classification
name: pokemon-classification
split: validation
metrics:
- type: accuracy
value: 0.03279 # min: 0.0 - max: 1.0
name: top1 accuracy
- type: accuracy
value: 0.09699 # min: 0.0 - max: 1.0
name: top5 accuracy
---
<div align="center">
<img width="640" alt="keremberke/yolov8m-pokemon-classification" src="https://huggingface.co/keremberke/yolov8m-pokemon-classification/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['Abra', 'Aerodactyl', 'Alakazam', 'Alolan Sandslash', 'Arbok', 'Arcanine', 'Articuno', 'Beedrill', 'Bellsprout', 'Blastoise', 'Bulbasaur', 'Butterfree', 'Caterpie', 'Chansey', 'Charizard', 'Charmander', 'Charmeleon', 'Clefable', 'Clefairy', 'Cloyster', 'Cubone', 'Dewgong', 'Diglett', 'Ditto', 'Dodrio', 'Doduo', 'Dragonair', 'Dragonite', 'Dratini', 'Drowzee', 'Dugtrio', 'Eevee', 'Ekans', 'Electabuzz', 'Electrode', 'Exeggcute', 'Exeggutor', 'Farfetchd', 'Fearow', 'Flareon', 'Gastly', 'Gengar', 'Geodude', 'Gloom', 'Golbat', 'Goldeen', 'Golduck', 'Golem', 'Graveler', 'Grimer', 'Growlithe', 'Gyarados', 'Haunter', 'Hitmonchan', 'Hitmonlee', 'Horsea', 'Hypno', 'Ivysaur', 'Jigglypuff', 'Jolteon', 'Jynx', 'Kabuto', 'Kabutops', 'Kadabra', 'Kakuna', 'Kangaskhan', 'Kingler', 'Koffing', 'Krabby', 'Lapras', 'Lickitung', 'Machamp', 'Machoke', 'Machop', 'Magikarp', 'Magmar', 'Magnemite', 'Magneton', 'Mankey', 'Marowak', 'Meowth', 'Metapod', 'Mew', 'Mewtwo', 'Moltres', 'MrMime', 'Muk', 'Nidoking', 'Nidoqueen', 'Nidorina', 'Nidorino', 'Ninetales', 'Oddish', 'Omanyte', 'Omastar', 'Onix', 'Paras', 'Parasect', 'Persian', 'Pidgeot', 'Pidgeotto', 'Pidgey', 'Pikachu', 'Pinsir', 'Poliwag', 'Poliwhirl', 'Poliwrath', 'Wigglytuff', 'Zapdos', 'Zubat']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.24 ultralytics==8.0.23
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, postprocess_classify_output
# load model
model = YOLO('keremberke/yolov8m-pokemon-classification')
# set model parameters
model.overrides['conf'] = 0.25 # model confidence threshold
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].probs) # [0.1, 0.2, 0.3, 0.4]
processed_result = postprocess_classify_output(model, result=results[0])
print(processed_result) # {"cat": 0.4, "dog": 0.6}
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
Canadiancaleb/DialoGPT-small-jesse | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 9 | null | ---
license: apache-2.0
---
小说模型 https://github.com/BlinkDL/AI-Writer/releases |
Canadiancaleb/jessebot | []
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} | 0 | 2023-01-28T05:25:57Z | ---
license: other
tags:
- generated_from_keras_callback
model-index:
- name: dousey/scene_segmentation
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dousey/scene_segmentation
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: nan
- Validation Loss: nan
- Validation Mean Iou: 0.0217
- Validation Mean Accuracy: 0.5
- Validation Overall Accuracy: 0.2545
- Validation Accuracy Background: 1.0
- Validation Accuracy Bleuet: 0.0
- Validation Accuracy Comptonie: nan
- Validation Accuracy Kalmia: nan
- Validation Iou Background: 0.0433
- Validation Iou Bleuet: 0.0
- Validation Iou Comptonie: nan
- Validation Iou Kalmia: nan
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 6e-05, 'decay_steps': 76500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Accuracy Background | Validation Accuracy Bleuet | Validation Accuracy Comptonie | Validation Accuracy Kalmia | Validation Iou Background | Validation Iou Bleuet | Validation Iou Comptonie | Validation Iou Kalmia | Epoch |
|:----------:|:---------------:|:-------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:-----------------------------:|:--------------------------:|:-------------------------:|:---------------------:|:------------------------:|:---------------------:|:-----:|
| nan | nan | 0.0217 | 0.5 | 0.2545 | 1.0 | 0.0 | nan | nan | 0.0433 | 0.0 | nan | nan | 0 |
| nan | nan | 0.0217 | 0.5 | 0.2545 | 1.0 | 0.0 | nan | nan | 0.0433 | 0.0 | nan | nan | 1 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Capreolus/birch-bert-large-mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
]
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} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: piyusharma/bert-base-uncased-finetuned-lex
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# piyusharma/bert-base-uncased-finetuned-lex
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2112
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.2112 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Capreolus/birch-bert-large-msmarco_mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
]
| null | {
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"BertForNextSentencePrediction"
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} | 1 | 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: -1.00 +/- 0.25
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
...
```
|
Captain-1337/CrudeBERT | [
"pytorch",
"bert",
"text-classification",
"arxiv:1908.10063",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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}
} | 28 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: model1_absa_cont
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. -->
# model1_absa_cont
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2382
- Precision: 0.2445
- Recall: 0.3046
- F1: 0.2712
- Accuracy: 0.5420
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 7 | 1.2382 | 0.2445 | 0.3046 | 0.2712 | 0.5420 |
| No log | 2.0 | 14 | 1.2382 | 0.2445 | 0.3046 | 0.2712 | 0.5420 |
| No log | 3.0 | 21 | 1.2382 | 0.2445 | 0.3046 | 0.2712 | 0.5420 |
| No log | 4.0 | 28 | 1.2382 | 0.2445 | 0.3046 | 0.2712 | 0.5420 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Carlork314/Carlos | []
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} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- Ramos-Ramos/nllb-eng-tgl-12k
---
# Ramos-Ramos/xlm-roberta-base-en-tl
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('Ramos-Ramos/xlm-roberta-base-en-tl')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Ramos-Ramos/xlm-roberta-base-en-tl')
model = AutoModel.from_pretrained('Ramos-Ramos/xlm-roberta-base-en-tl')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=Ramos-Ramos/xlm-roberta-base-en-tl)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 308 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 200,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Carlork314/Xd | []
| null | {
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} | 0 | null |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- image-segmentation
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.21
inference: false
datasets:
- keremberke/pcb-defect-segmentation
model-index:
- name: keremberke/yolov8n-pcb-defect-segmentation
results:
- task:
type: image-segmentation
dataset:
type: keremberke/pcb-defect-segmentation
name: pcb-defect-segmentation
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.51186 # min: 0.0 - max: 1.0
name: [email protected](box)
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.51667 # min: 0.0 - max: 1.0
name: [email protected](mask)
---
<div align="center">
<img width="640" alt="keremberke/yolov8n-pcb-defect-segmentation" src="https://huggingface.co/keremberke/yolov8n-pcb-defect-segmentation/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['Dry_joint', 'Incorrect_installation', 'PCB_damage', 'Short_circuit']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('keremberke/yolov8n-pcb-defect-segmentation')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
print(results[0].masks)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
Carolhuehuehuehue/Sla | []
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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 cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('garg-aayush/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Cat/Kitty | []
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}
} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: julcto
---
### xin-dreambooth-huggingface Dreambooth model trained by WildWill with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
julcto (use that on your prompt)
|
Cathy/reranking_model | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
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}
}
} | 27 | null | ---
tags:
- generated_from_trainer
model-index:
- name: fine_tuned_beyonce
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine_tuned_beyonce
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Cdial/hausa-asr | [
"wav2vec2",
"automatic-speech-recognition",
"ha",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
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}
}
} | 8 | 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 cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('garg-aayush/sd-class-butterflies-64')
image = pipeline().images[0]
image
```
|
dccuchile/albert-base-spanish-finetuned-pos | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
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}
} | 5 | null |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- image-segmentation
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.21
inference: false
datasets:
- keremberke/pcb-defect-segmentation
model-index:
- name: keremberke/yolov8s-pcb-defect-segmentation
results:
- task:
type: image-segmentation
dataset:
type: keremberke/pcb-defect-segmentation
name: pcb-defect-segmentation
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.51452 # min: 0.0 - max: 1.0
name: [email protected](box)
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.49054 # min: 0.0 - max: 1.0
name: [email protected](mask)
---
<div align="center">
<img width="640" alt="keremberke/yolov8s-pcb-defect-segmentation" src="https://huggingface.co/keremberke/yolov8s-pcb-defect-segmentation/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['Dry_joint', 'Incorrect_installation', 'PCB_damage', 'Short_circuit']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('keremberke/yolov8s-pcb-defect-segmentation')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
print(results[0].masks)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
dccuchile/albert-base-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
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}
} | 3 | 2023-01-28T07:54:47Z | ---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-ner
This model is a fine-tuned version of [deepset/deberta-v3-base-squad2](https://huggingface.co/deepset/deberta-v3-base-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4783
- Precision: 0.3264
- Recall: 0.3591
- F1: 0.3420
- Accuracy: 0.8925
## 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: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.05
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 39.8167 | 1.0 | 760 | 0.3957 | 0.1844 | 0.2909 | 0.2257 | 0.8499 |
| 21.7333 | 2.0 | 1520 | 0.3853 | 0.2118 | 0.3273 | 0.2571 | 0.8546 |
| 13.8859 | 3.0 | 2280 | 0.3631 | 0.2443 | 0.2909 | 0.2656 | 0.8789 |
| 20.6586 | 4.0 | 3040 | 0.3961 | 0.2946 | 0.3455 | 0.3180 | 0.8753 |
| 13.8654 | 5.0 | 3800 | 0.3821 | 0.2791 | 0.3273 | 0.3013 | 0.8877 |
| 12.6942 | 6.0 | 4560 | 0.4393 | 0.3122 | 0.3364 | 0.3239 | 0.8909 |
| 25.0549 | 7.0 | 5320 | 0.4542 | 0.3106 | 0.3727 | 0.3388 | 0.8824 |
| 5.6816 | 8.0 | 6080 | 0.4432 | 0.2820 | 0.3409 | 0.3086 | 0.8774 |
| 13.1296 | 9.0 | 6840 | 0.4509 | 0.2884 | 0.35 | 0.3162 | 0.8824 |
| 7.7173 | 10.0 | 7600 | 0.4265 | 0.3170 | 0.3818 | 0.3464 | 0.8919 |
| 6.7922 | 11.0 | 8360 | 0.4749 | 0.3320 | 0.3818 | 0.3552 | 0.8892 |
| 5.4287 | 12.0 | 9120 | 0.4564 | 0.2917 | 0.3818 | 0.3307 | 0.8805 |
| 7.4153 | 13.0 | 9880 | 0.4735 | 0.2963 | 0.3273 | 0.3110 | 0.8871 |
| 9.1154 | 14.0 | 10640 | 0.4553 | 0.3416 | 0.3773 | 0.3585 | 0.8894 |
| 5.999 | 15.0 | 11400 | 0.4489 | 0.3203 | 0.4091 | 0.3593 | 0.8880 |
| 9.5128 | 16.0 | 12160 | 0.4947 | 0.3164 | 0.3682 | 0.3403 | 0.8883 |
| 5.6713 | 17.0 | 12920 | 0.4705 | 0.3527 | 0.3864 | 0.3688 | 0.8919 |
| 12.2119 | 18.0 | 13680 | 0.4617 | 0.3123 | 0.3591 | 0.3340 | 0.8857 |
| 8.5658 | 19.0 | 14440 | 0.4764 | 0.3092 | 0.35 | 0.3284 | 0.8944 |
| 11.0664 | 20.0 | 15200 | 0.4557 | 0.3187 | 0.3636 | 0.3397 | 0.8905 |
| 6.7161 | 21.0 | 15960 | 0.4468 | 0.3210 | 0.3955 | 0.3544 | 0.8956 |
| 9.0448 | 22.0 | 16720 | 0.5120 | 0.2872 | 0.3682 | 0.3227 | 0.8792 |
| 6.573 | 23.0 | 17480 | 0.4990 | 0.3307 | 0.3773 | 0.3524 | 0.8869 |
| 5.0543 | 24.0 | 18240 | 0.4763 | 0.3028 | 0.3455 | 0.3227 | 0.8899 |
| 6.8797 | 25.0 | 19000 | 0.4814 | 0.2780 | 0.3273 | 0.3006 | 0.8913 |
| 7.7544 | 26.0 | 19760 | 0.4695 | 0.3024 | 0.3409 | 0.3205 | 0.8946 |
| 4.8346 | 27.0 | 20520 | 0.4849 | 0.3154 | 0.3455 | 0.3297 | 0.8931 |
| 4.4766 | 28.0 | 21280 | 0.4809 | 0.2925 | 0.3364 | 0.3129 | 0.8913 |
| 7.9149 | 29.0 | 22040 | 0.4756 | 0.3238 | 0.3591 | 0.3405 | 0.8930 |
| 7.3033 | 30.0 | 22800 | 0.4783 | 0.3264 | 0.3591 | 0.3420 | 0.8925 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
dccuchile/albert-large-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
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"AlbertForSequenceClassification"
],
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}
}
} | 27 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: gpt_16_5_3e-5_lp5_nb5
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. -->
# gpt_16_5_3e-5_lp5_nb5
This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9078
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3245 | 0.38 | 1000 | 4.0176 |
| 3.1222 | 0.76 | 2000 | 3.9845 |
| 2.9992 | 1.13 | 3000 | 3.9635 |
| 2.8843 | 1.51 | 4000 | 3.9377 |
| 2.882 | 1.89 | 5000 | 3.9268 |
| 2.7411 | 2.27 | 6000 | 3.9208 |
| 2.7204 | 2.64 | 7000 | 3.9160 |
| 2.7106 | 3.02 | 8000 | 3.9171 |
| 2.5857 | 3.4 | 9000 | 3.9162 |
| 2.5863 | 3.78 | 10000 | 3.9037 |
| 2.5674 | 4.15 | 11000 | 3.9135 |
| 2.4901 | 4.53 | 12000 | 3.9125 |
| 2.505 | 4.91 | 13000 | 3.9078 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.9.0+cu102
- Datasets 2.8.0
- Tokenizers 0.13.2
|
dccuchile/albert-large-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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"AlbertForTokenClassification"
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},
"translation_en_to_ro": {
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}
}
} | 3 | 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: 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
|
dccuchile/albert-large-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
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},
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},
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},
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}
}
} | 29 | null |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- image-segmentation
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.23
inference: false
datasets:
- keremberke/pcb-defect-segmentation
model-index:
- name: keremberke/yolov8m-pcb-defect-segmentation
results:
- task:
type: image-segmentation
dataset:
type: keremberke/pcb-defect-segmentation
name: pcb-defect-segmentation
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.56836 # min: 0.0 - max: 1.0
name: [email protected](box)
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.5573 # min: 0.0 - max: 1.0
name: [email protected](mask)
---
<div align="center">
<img width="640" alt="keremberke/yolov8m-pcb-defect-segmentation" src="https://huggingface.co/keremberke/yolov8m-pcb-defect-segmentation/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['Dry_joint', 'Incorrect_installation', 'PCB_damage', 'Short_circuit']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.24 ultralytics==8.0.23
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('keremberke/yolov8m-pcb-defect-segmentation')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
print(results[0].masks)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
dccuchile/albert-tiny-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"min_length": null,
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"prefix": null
},
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},
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},
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}
}
} | 32 | null |
# descripcion del modelo
<!-- Provide a quick summary of what the model is/does. -->
Modelo gpt-2_Neo125M Fine Tune, para la prediccion de precios de casas o apartamentos en Cali-Colombia [Descargue todos los archivos requeridos desde Dropbox](https://www.dropbox.com/scl/fo/4mj054l8eha31pcc5z245/h?dl=0&rlkey=q1s2pycl9hh65b3xvqiukh9g6).
- **Developed by:** Nicolai Potes
- **Language:** Python
- **Finetuned from model :** [gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M).
# Training Details
```
Num examples = 779
Num Epochs = 500
Instantaneous batch size per device = 80
Total train batch size (w. parallel, distributed & accumulation) = 80
Gradient Accumulation steps = 1
Total optimization steps = 5000
Number of trainable parameters = 125200128
```
# Training Evaluate
```
{'eval_loss': 1.341125726699829,
'eval_runtime': 23.3347,
'eval_samples_per_second': 300.111,
'eval_steps_per_second': 3.771,
'epoch': 500.0}
```
## Training Data
datos sacados de https://www.metrocuadrado.com/
formato para el entrenamiento del mododelo
```
'meter: 3651685 \n area: 267 \n bathroom: 4 \n room: 4 \n property: 1 \n price: 975000000',
'meter: 3206498 \n area: 70 \n bathroom: 3 \n room: 4 \n property: 2 \n price: 225000000',
'meter: 2181818 \n area: 110 \n bathroom: 2 \n room: 3 \n property: 2 \n price: 240000000',
'meter: 5882352 \n area: 306 \n bathroom: 4 \n room: 4 \n property: 2 \n price: 1800000000',
'meter: 2827586 \n area: 58 \n bathroom: 2 \n room: 2 \n property: 2 \n price: 164000000',
'meter: 7382550 \n area: 149 \n bathroom: 4 \n room: 3 \n property: 2 \n price: 1100000000',
'meter: 2833333 \n area: 300 \n bathroom: 3 \n room: 3 \n property: 1 \n price: 850000000',
'meter: 3678474 \n area: 73 \n bathroom: 2 \n room: 3 \n property: 2 \n price: 270000000',
'meter: 2254901 \n area: 51 \n bathroom: 2 \n room: 2 \n property: 2 \n price: 115000000',
'meter: 2500000 \n area: 90 \n bathroom: 3 \n room: 3 \n property: 2 \n price: 225000000',
'meter: 4508196 \n area: 122 \n bathroom: 5 \n room: 4 \n property: 2 \n price: 550000000',
'meter: 3489583 \n area: 96 \n bathroom: 3 \n room: 3 \n property: 2 \n price: 335000000',
'meter: 2151898 \n area: 395 \n bathroom: 5 \n room: 5 \n property: 1 \n price: 850000000',
```
### Hardware GPU
```
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.47.03 Driver Version: 510.47.03 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |
| N/A 49C P0 29W / 70W | 0MiB / 15360MiB | 5% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
```
### Librerias requeridas
```
pip install transformers
pip install torch
```
### Codigo Python para cargar el modelo y predecir el valor de una propiedad (Casa/Apartamento)
```Python
import pandas as pd
import torch
import transformers
import re
'''
#dado el caso de estar en Google colab
from google.colab import drive
drive.mount('/content/drive')
path="/content/drive/My Drive/DatosMetroCuadradoPrueba/"
'''
path="[Direccion de la carpeta donde tiene el MODELO]"
path_carga= path+"modeloEntrenadoPreciosCasasApartamentos"
from transformers import GPT2Tokenizer, GPTNeoForCausalLM
new_modelPredict = GPTNeoForCausalLM.from_pretrained(path_carga).cuda()
tokenizer2 = GPT2Tokenizer.from_pretrained(path_carga)
new_modelPredict.resize_token_embeddings(len(tokenizer2))
tipo_propiedad= 1 # 1: casa , 2:apartamento
habitaciones= 5
baños= 5
area= 580
valor_inmueble= 1500000000
valorMetroCuadrado= int(valor_inmueble/area)
propiedad = f"<|startoftext|>meter: {valorMetroCuadrado} \n area: {area} \n bathroom: {baños} \n room: {habitaciones} \n property: {tipo_propiedad} \n price:"
print("Texto:",propiedad)
generated = tokenizer2(propiedad, # <|pad|>
return_tensors="pt").input_ids.cuda()
sample_outputs = new_modelPredict.generate(generated,
do_sample=True,
top_k=50,
max_length=100,
num_beams=7, #3
top_p=1.65,
temperature=.69,
num_return_sequences=1,
pad_token_id = 0)
price= []
#
for i, sample_output in enumerate(sample_outputs):
text= tokenizer2.decode(sample_output, skip_special_tokens=True)
num= text.split("\n")[-1].split("price: ")[1]
try:
num= re.sub(r'[^\d.]', '',num )#[0]
price.append( num )
except:
pass
# pd.set_option('display.float_format', '{.2f}'.format)
priceData2= pd.DataFrame(price,columns=['price']).astype(int)
print(priceData2)
```
|
dccuchile/albert-tiny-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"prefix": null
},
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},
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},
"translation_en_to_fr": {
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}
}
} | 8 | 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: 854.00 +/- 253.18
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 slomek -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 slomek -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 slomek
```
## 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)])
```
|
dccuchile/albert-tiny-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"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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"max_length": null
},
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},
"translation_en_to_fr": {
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}
}
} | 7 | 2023-01-28T08:44:49Z |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- object-detection
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.21
inference: false
datasets:
- keremberke/valorant-object-detection
model-index:
- name: keremberke/yolov8n-valorant-detection
results:
- task:
type: object-detection
dataset:
type: keremberke/valorant-object-detection
name: valorant-object-detection
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.93688 # min: 0.0 - max: 1.0
name: [email protected](box)
---
<div align="center">
<img width="640" alt="keremberke/yolov8n-valorant-detection" src="https://huggingface.co/keremberke/yolov8n-valorant-detection/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['dropped spike', 'enemy', 'planted spike', 'teammate']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('keremberke/yolov8n-valorant-detection')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
dccuchile/albert-xlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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}
}
} | 26 | 2023-01-28T08:46:00Z | ---
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: 481.00 +/- 176.15
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 Periramm -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 Periramm -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 Periramm
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 10000),
('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)])
```
|
dccuchile/albert-xxlarge-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"prefix": null
},
"text-generation": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 28 | null |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- object-detection
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.21
inference: false
datasets:
- keremberke/valorant-object-detection
model-index:
- name: keremberke/yolov8s-valorant-detection
results:
- task:
type: object-detection
dataset:
type: keremberke/valorant-object-detection
name: valorant-object-detection
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.97138 # min: 0.0 - max: 1.0
name: [email protected](box)
---
<div align="center">
<img width="640" alt="keremberke/yolov8s-valorant-detection" src="https://huggingface.co/keremberke/yolov8s-valorant-detection/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['dropped spike', 'enemy', 'planted spike', 'teammate']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('keremberke/yolov8s-valorant-detection')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
dccuchile/albert-xxlarge-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
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} | 26 | 2023-01-28T09:45:11Z | ---
language:
- ja
---
プロンプト用KeyWord:pirotess
- pirotess, 1girl, solo, pointy ears, dark skin, dark-skinned female, elf, sword, weapon, breasts, long hair, dark elf, circlet, center opening, white hair
 |
dccuchile/albert-xxlarge-spanish-finetuned-pos | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 3 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ThuyVuPhuong/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"])
```
|
dccuchile/albert-base-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
"architectures": [
"AlbertForPreTraining"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 586 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- allenai/nllb
---
# Ramos-Ramos/xlm-roberta-base-en-tl-0-1000
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('Ramos-Ramos/xlm-roberta-base-en-tl-0-1000')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Ramos-Ramos/xlm-roberta-base-en-tl-0-1000')
model = AutoModel.from_pretrained('Ramos-Ramos/xlm-roberta-base-en-tl-0-1000')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=Ramos-Ramos/xlm-roberta-base-en-tl-0-1000)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 12406 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
dccuchile/albert-xlarge-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
"architectures": [
"AlbertForPreTraining"
],
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}
} | 91 | 2023-01-28T09:55:21Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi_v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ThuyVuPhuong/q-Taxi_v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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}
}
} | 5 | 2023-01-28T10:13:20Z | ---
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: 272.23 +/- 22.90
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
...
```
|
dccuchile/distilbert-base-spanish-uncased-finetuned-mldoc | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 27 | 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: 251.19 +/- 17.89
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
...
```
|
dccuchile/distilbert-base-spanish-uncased-finetuned-pawsx | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 29 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="dungtd2403/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"])
```
|
dccuchile/distilbert-base-spanish-uncased | [
"pytorch",
"distilbert",
"fill-mask",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 670 | null |
---
license: creativeml-openrail-m
base_model: darkstorm2150/Protogen_x5.8_Official_Release
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - ScottHan/model
These are LoRA adaption weights for darkstorm2150/Protogen_x5.8_Official_Release. The weights were trained on guoquan using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
|
CennetOguz/distilbert-base-uncased-finetuned-recipe | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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} | 2 | 2023-01-28T10:59:27Z | ---
language:
- zh
tags:
- AIvtuber
- VirtuaReal
---
# SUImodels
### 岁己所有的模型都在这里
### 包括sovits3.0、4.0及onnx，~~还有以后会出的vits模型~~ VITS模型有需要的联系我，主要是走一下免责协议什么的有的没的过程（
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [謬紗特](https://space.bilibili.com/59442895)
- **Model type:** [so-vits-svc 3.0 48kHz](https://github.com/svc-develop-team/so-vits-svc/tree/3.0-48k)、[so-vits-svc 4.0](https://github.com/svc-develop-team/so-vits-svc)
- **Demo:** [SPACE: AI岁己（歌声变声器）](https://huggingface.co/spaces/Miuzarte/SUI-svc-3.0)、[SPACE: AI岁己（歌声变声器）第二代](https://huggingface.co/spaces/Miuzarte/SUI-svc-4.0)
### pth文件名的训练步数是程序按学习率等超参数得出的步数，onnx文件名的步数为实际训练步数
|sovits3_v1|Base/G_1000000.pth|Singing/G_1M111000.pth|Singing/G_100000.pth|
|-:|:-:|:-:|:-:|
|onnx|Base/suijiSUI_v1_1M_SoVits.onnx|Singing/suijiSUI_v1_1M111000_SoVits.onnx|Singing/suijiSUI_v1_100000_SoVits.onnx|
|训练集|12月录播（除电台）、出道至今22条歌投、10条歌切、圣诞音声（27.5小时）|Base/G_1000000.pth作为底模_2022年所有唱歌投稿、唱歌切片、圣诞音声（3.9小时）|2022年所有唱歌投稿、唱歌切片、圣诞音声（3.9小时）|
### 因为v2练着练着突然sovits4.0就出来了所以200k直接收了，弃用
|sovits3_v2|Base/G_100000.pth|Singing/G_160000.pth|
|-:|:-:|:-:|
|onnx|Base/suijiSUI_v2_100000_SoVits.onnx|Singing/suijiSUI_v2_100k100000_SoVits.onnx|
|训练集|22年12月、23年1月的录播（06:47:46）|Base/G_100000.pth作为底模_22年12月、23年1月、23年2月1-17日的录播（除电台，共计268:07:43）、岁己的投稿、A1in_sy11月及以前的歌切|
### 160k开始loss就没再往下了，后两个估计有一丁点过拟合，然后我个人也听不出这三个模型有什么区别，有强迫症的可以自己再仔细对比一下，我个人倾向于折中使用Singing/G_210000.pth
|sovits4_v3|Base/G_100000.pth|Singing/G_160000.pth|
|-:|:-:|:-:|
|onnx|Base/suijiSUI_v3_100000_SoVits.onnx|Singing/suijiSUI_v3_100k100000_SoVits.onnx|
|训练集|22年12月、23年1月的录播（06:47:46）|Base/G_100000.pth作为底模_22年12月、23年1月、23年2月1-17日的录播（除电台，共计268:07:43）、岁己的投稿、A1in_sy11月及以前的歌切|
|sovits4_v3|Singing/G_210000.pth|Singing/G_260000.pth|Singing/kmeans_10000.pt|
|-:|:-:|:-:|:-:|
|onnx|Singing/suijiSUI_v3_100k150000_SoVits.onnx|Singing/suijiSUI_v3_100k200000_SoVits.onnx|聚类模型，暂无onnx|
|训练集|{同Singing/G_160000.pth}|{同Singing/G_160000.pth}|{同Singing/G_160000.pth}|
### ~~sovits4.0-v2实在是没算力能用了，勉强跑个200k就算了~~
### sovits4.0-v2跟4.0跑了一样的步数，能对比一下两个版本之间的差别 （我是听不出区别，建议用4.0，仓库更新了不少新功能）
|sovits4-v2_v4|Base/G_100000.pth|Singing/G_160000.pth|
|-:|:-:|:-:|
|onnx|Base/suijiSUI_v4_100000_SoVits.onnx|Singing/suijiSUI_v4_100k100000_SoVits.onnx|
|训练集|22年12月、23年1月的录播（06:47:46）|Base/G_100000.pth作为底模_22年12月、23年1月、23年2月1-17日的录播（除电台，共计268:07:43）、岁己的投稿、A1in_sy11月及以前的歌切|
|sovits4-v2_v4|Singing/G_210000.pth|Singing/G_260000.pth|Singing/kmeans_10000.pt|
|-:|:-:|:-:|:-:|
|onnx|Singing/suijiSUI_v4_100k150000_SoVits.onnx|Singing/suijiSUI_v4_100k200000_SoVits.onnx|聚类模型，暂无onnx|
|训练集|{同Singing/G_160000.pth}|{同Singing/G_160000.pth}|{同Singing/G_160000.pth}|
### v2、v3(v4的学习率使用默认的0.0002)的dataset、filelist、config完全一致，可用作sovits3.0与4.0的对比
### 数据集：
[Miuzarte/SUISovitsDataForBaseModel](https://huggingface.co/datasets/Miuzarte/SUISovitsDataForBaseModel)、[Miuzarte/SUISovitsDataForSingingModel](https://huggingface.co/datasets/Miuzarte/SUISovitsDataForSingingModel)
## MoeSS\\Mods配置文件
#### sovits3.0需要MoeSS\\hubert\\[hubert.onnx](https://huggingface.co/NaruseMioShirakana/MoeSS-SUBModel/blob/main/hubert.7z)
岁己SUI_v1_1M.json (suijiSUI_v1_1M\\)
```json
{
"Folder" : "suijiSUI_v1_1M",
"Name" : "岁己SUI_v1_1M",
"Type" : "SoVits",
"Rate" : 48000,
"Hop" : 320,
"Hubert": "hubert",
"SoVits3": true,
"Characters" : ["岁己SUI"]
}
```
岁己SUI_v1_1M111k.json (suijiSUI_v1_1M111000\\)
```json
{
"Folder" : "suijiSUI_v1_1M111000",
"Name" : "岁己SUI_v1_1M111k",
"Type" : "SoVits",
"Rate" : 48000,
"Hop" : 320,
"Hubert": "hubert",
"SoVits3": true,
"Characters" : ["岁己SUI"]
}
```
岁己SUI_v1_100k.json (suijiSUI_v1_100000\\)
```json
{
"Folder" : "suijiSUI_v1_100000",
"Name" : "岁己SUI_v1_100k",
"Type" : "SoVits",
"Rate" : 48000,
"Hop" : 320,
"Hubert": "hubert",
"SoVits3": true,
"Characters" : ["岁己SUI"]
}
```
岁己SUI_v2_100k.json (suijiSUI_v2_100000\\)
```json
{
"Folder" : "suijiSUI_v2_100000",
"Name" : "岁己SUI_v2_100k",
"Type" : "SoVits",
"Rate" : 48000,
"Hop" : 320,
"Hubert": "hubert",
"SoVits3": true,
"Characters" : ["岁己SUI"]
}
```
岁己SUI_v2_100k100k.json (suijiSUI_v2_100k100000\\)
```json
{
"Folder" : "suijiSUI_v2_100k100000",
"Name" : "岁己SUI_v2_100k100k",
"Type" : "SoVits",
"Rate" : 48000,
"Hop" : 320,
"Hubert": "hubert",
"SoVits3": true,
"Characters" : ["岁己SUI"]
}
```
#### sovits4.0需要MoeSS\\hubert\\[hubert4.0.onnx](https://huggingface.co/NaruseMioShirakana/MoeSS-SUBModel/blob/main/hubert4.0.7z)
sovits4.0被支持于MoeSS v4.2.0，建议使用最新版[[MoeSS/releases]](https://github.com/NaruseMioShirakana/MoeSS/releases)
#### 更建议使用[sovits4.0](https://github.com/innnky/so-vits-svc/tree/4.0)/[sovits4.0-v2](https://github.com/svc-develop-team/so-vits-svc/tree/4.0-v2)的inference_main.py进行推理
岁己SUI_v3_100k.json (suijiSUI_v3_100000\\)
```json
{
"Folder" : "suijiSUI_v3_100000",
"Name" : "岁己SUI_v3_100k",
"Type" : "SoVits",
"Rate" : 44100,
"Hop" : 512,
"Hubert": "hubert4.0",
"SoVits4": true,
"Characters" : ["岁己SUI"]
}
```
岁己SUI_v3_100k100k.json (suijiSUI_v3_100k100000\\)
```json
{
"Folder" : "suijiSUI_v3_100k100000",
"Name" : "岁己SUI_v3_100k100k",
"Type" : "SoVits",
"Rate" : 44100,
"Hop" : 512,
"Hubert": "hubert4.0",
"SoVits4": true,
"Characters" : ["岁己SUI"]
}
```
岁己SUI_v3_100k150k.json (suijiSUI_v3_100k150000\\)
```json
{
"Folder" : "suijiSUI_v3_100k150000",
"Name" : "岁己SUI_v3_100k150k",
"Type" : "SoVits",
"Rate" : 44100,
"Hop" : 512,
"Hubert": "hubert4.0",
"SoVits4": true,
"Characters" : ["岁己SUI"]
}
```
岁己SUI_v3_100k200k.json (suijiSUI_v3_100k200000\\)
```json
{
"Folder" : "suijiSUI_v3_100k200000",
"Name" : "岁己SUI_v3_100k200k",
"Type" : "SoVits",
"Rate" : 44100,
"Hop" : 512,
"Hubert": "hubert4.0",
"SoVits4": true,
"Characters" : ["岁己SUI"]
}
```
岁己SUI_v4_100k.json (suijiSUI_v4_100000\\)
```json
{
"Folder" : "suijiSUI_v4_100000",
"Name" : "岁己SUI_v4_100k",
"Type" : "SoVits",
"Rate" : 44100,
"Hop" : 512,
"Hubert": "hubert4.0",
"SoVits4": true,
"Characters" : ["岁己SUI"]
}
```
岁己SUI_v4_100k100k.json (suijiSUI_v4_100k100000\\)
```json
{
"Folder" : "suijiSUI_v4_100k100000",
"Name" : "岁己SUI_v4_100k100k",
"Type" : "SoVits",
"Rate" : 44100,
"Hop" : 512,
"Hubert": "hubert4.0",
"SoVits4": true,
"Characters" : ["岁己SUI"]
}
```
岁己SUI_v4_100k150k.json (suijiSUI_v4_100k150000\\)
```json
{
"Folder" : "suijiSUI_v4_100k150000",
"Name" : "岁己SUI_v4_100k150k",
"Type" : "SoVits",
"Rate" : 44100,
"Hop" : 512,
"Hubert": "hubert4.0",
"SoVits4": true,
"Characters" : ["岁己SUI"]
}
```
岁己SUI_v4_100k200k.json (suijiSUI_v4_100k200000\\)
```json
{
"Folder" : "suijiSUI_v4_100k200000",
"Name" : "岁己SUI_v4_100k200k",
"Type" : "SoVits",
"Rate" : 44100,
"Hop" : 512,
"Hubert": "hubert4.0",
"SoVits4": true,
"Characters" : ["岁己SUI"]
}
``` |
Chaewon/mnmt_decoder_en_gpt2 | []
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} | 0 | 2023-01-28T11:29:27Z | ---
license: cc-by-nc-sa-4.0
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-diffusers
---
# 本人郑重声明：本模型禁止用于训练基于明星、公众人物肖像的风格模型训练，因为这会带来争议，对AI社区的发展造成不良的负面影响。
# 本模型注明：训练素材中不包含任何真人素材。
| 版本 | 效果图 |
| --- | --- |
| **GuoFeng3.3** |  |
| **GuoFeng3.2_light** |  |
| **GuoFeng3.2** |  |
| **GuoFeng3** |  |
# 介绍 - GuoFeng3
欢迎使用GuoFeng3模型 - (TIP:这个版本的名字进行了微调),这是一个中国华丽古风风格模型，也可以说是一个古风游戏角色模型，具有2.5D的质感。第三代大幅度减少上手难度，增加了场景元素与男性古风人物，除此之外为了模型能更好的适应其它TAG，还增加了其它风格的元素。这一代对脸和手的崩坏有一定的修复，同时素材大小也提高到了最长边1024。
根据个人的实验与收到的反馈，国风模型系列的第二代，在人物，与大头照的效果表现比三代更好，如果你有这方面需求不妨试试第二代。
2.0版本：[https://huggingface.co/xiaolxl/Gf_style2](https://huggingface.co/xiaolxl/Gf_style2)
GuoFeng3:原始模型
GuoFeng3.1:对GuoFeng3人像进行了微调修复
GuoFeng3.2:如果你不知道选择GuoFeng3还是GuoFeng2，可以直接使用此版本
GuoFeng3.2_light:通过GuoFeng3.2融合了基于 Noise Offset 训练的Lora使得模型能够画出更漂亮的光影效果(Lora:epi_noiseoffset/Theovercomer8's Contrast Fix)
GuoFeng3.2_Lora:国风3.2 Lora版本
GuoFeng3.2_Lora_big_light:国风3.2_light Lora版本 维度增大版本
GuoFeng3.2_f16:国风3.2 半精版本
GuoFeng3.2_light_f16:国风3.2_light 半精版本
GuoFeng3.3：此版本是基于3.2的一次较大的更新与改进，可以适配full body，即使你的tag不太好，模型也会对画面进行自动修改，不过因此模型出的脸会比较雷同。此模型似乎不需要超分，我的出图大小是768*1024，清晰度还不错。建议竖图，横图可能不清晰。Euler a即可。(DPM++ SDE Karras, DDIM也不错)
--
Welcome to the GuoFeng3 model - (TIP: the name of this version has been fine-tuned). This is a Chinese gorgeous antique style model, which can also be said to be an antique game character model with a 2.5D texture. The third generation greatly reduces the difficulty of getting started, and adds scene elements and male antique characters. In addition, in order to better adapt the model to other TAGs, other style elements are also added. This generation has repaired the broken face and hands to a certain extent, and the size of the material has also increased to the longest side of 1024.
According to personal experiments and feedback received, the second generation of the Guofeng model series performs better than the third generation in terms of characters and big head photos. If you have this need, you can try the second generation.
Version 2.0：[https://huggingface.co/xiaolxl/Gf_style2](https://huggingface.co/xiaolxl/Gf_style2)
GuoFeng3: original model
GuoFeng3.1: The portrait of GuoFeng3 has been fine-tuned and repaired
GuoFeng3.2: If you don't know whether to choose GuoFeng3 or GuoFeng2, you can use this version directly
GuoFeng3.2_Light: Through GuoFeng3.2, Lora based on Noise Offset training is integrated to enable the model to draw more beautiful light and shadow effects (Lora: epi_noiseoffset/Theovercolor8's Contrast Fix)
GuoFeng3.2_Lora: Guofeng3.2 Lora version
GuoFeng3.2_Lora_big_Light: Guofeng3.2_Light Lora Version Dimension Increase Version
GuoFeng3.2_F16: Guofeng3.2 semi-refined version
GuoFeng3.2_light_f16: Guofeng3.2_Light semi-refined version
GuoFeng3.3: This version is a major update and improvement based on 3.2, which can adapt to full bodies. Even if your tag is not good, the model will automatically modify the screen, but the faces produced by the model will be quite similar. This model doesn't seem to require supersession. My plot size is 768 * 1024, and the clarity is quite good. Suggest vertical view, horizontal view may not be clear. Euler a is sufficient. (DPM++SDE Karras, DDIM is also good)
# 安装教程 - install
1. 将GuoFeng3.ckpt模型放入SD目录 - Put GuoFeng3.ckpt model into SD directory
2. 此模型自带VAE，如果你的程序不支持，请记得选择任意一个VAE文件，否则图形将为灰色 - This model comes with VAE. If your program does not support it, please remember to select any VAE file, otherwise the graphics will be gray
# 如何使用 - How to use
**TIP：经过一天的测试，发现很多人物可能出现红眼问题，可以尝试在负面词添加red eyes。如果色彩艳丽可以尝试降低CFG - After a day of testing, we found that many characters may have red-eye problems. We can try to add red eyes to negative words。Try to reduce CFG if the color is bright**
简单：第三代大幅度减少上手难度 - Simple: the third generation greatly reduces the difficulty of getting started
- **关键词 - key word:**
```
best quality, masterpiece, highres, 1girl,china dress,Beautiful face
```
- **负面词 - Negative words:**
```
NSFW, lowres,bad anatomy,bad hands, text, error, missing fingers,extra digit, fewer digits, cropped, worstquality, low quality, normal quality,jpegartifacts,signature, watermark, username,blurry,bad feet
```
---
高级：如果您还想使图片尽可能更好，请尝试以下配置 - senior：If you also want to make the picture as better as possible, please try the following configuration
- Sampling steps:**50**
- Sampler:**DPM++ SDE Karras or DDIM**
- The size of the picture should be at least **1024** - 图片大小至少1024
- CFG:**4-6**
- **更好的负面词 Better negative words - 感谢群友提供的负面词:**
```
(((simple background))),monochrome ,lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, lowres, bad anatomy, bad hands, text, error, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ugly,pregnant,vore,duplicate,morbid,mut ilated,tran nsexual, hermaphrodite,long neck,mutated hands,poorly drawn hands,poorly drawn face,mutation,deformed,blurry,bad anatomy,bad proportions,malformed limbs,extra limbs,cloned face,disfigured,gross proportions, (((missing arms))),(((missing legs))), (((extra arms))),(((extra legs))),pubic hair, plump,bad legs,error legs,username,blurry,bad feet
```
- **如果想元素更丰富，可以添加下方关键词 - If you want to enrich the elements, you can add the following keywords**
```
Beautiful face,
hair ornament, solo,looking at viewer,smile,closed mouth,lips
china dress,dress,hair ornament, necklace, jewelry, long hair, earrings, chinese clothes,
architecture,east asian architecture,building,outdoors,rooftop,city,cityscape
```
# 例图 - Examples
(可在文件列表中找到原图，并放入WebUi查看关键词等信息) - (You can find the original image in the file list, and put WebUi to view keywords and other information)
<img src=https://huggingface.co/xiaolxl/GuoFeng3/resolve/main/examples/e1.png>
<img src=https://huggingface.co/xiaolxl/GuoFeng3/resolve/main/examples/e2.png>
<img src=https://huggingface.co/xiaolxl/GuoFeng3/resolve/main/examples/e3.png>
<img src=https://huggingface.co/xiaolxl/GuoFeng3/resolve/main/examples/e4.png> |
chainyo/speaker-recognition-meetup | []
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} | 1 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Mykolyt/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"])
```
|
ChaseBread/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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"GPT2LMHeadModel"
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} | 9 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: piyusharma/gpt2-finetuned-lex
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# piyusharma/gpt2-finetuned-lex
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.2071
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 3.3489 | 0 |
| 3.2568 | 1 |
| 3.2071 | 2 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CleveGreen/FieldClassifier_v2 | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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} | 46 | null | ---
license: apache-2.0
datasets:
- xnli
- mlqa
- paws-x
language:
- fr
- es
- en
- de
- sw
- ru
- zh
- el
- bg
- ar
- vi
- th
- hi
- ur
---
### Disclaimer :- I don't own the weights of `ernie-m-large` neither did I train the model. I only converted the model weights from paddle to pytorch(using the scripts listed in files).
The real(paddle) weights can be found [here](https://huggingface.co/PaddlePaddle/ernie-m-large).
The rest of the README is copied from the same page listed above,
[](https://github.com/PaddlePaddle/PaddleNLP)
# PaddlePaddle/ernie-m-base
## Ernie-M
ERNIE-M, proposed by Baidu, is a new training method that encourages the model to align the representation of multiple languages with monolingual corpora,
to overcome the constraint that the parallel corpus size places on the model performance. The insight is to integrate back-translation into the pre-training
process by generating pseudo-parallel sentence pairs on a monolingual corpus to enable the learning of semantic alignments between different languages,
thereby enhancing the semantic modeling of cross-lingual models. Experimental results show that ERNIE-M outperforms existing cross-lingual models and
delivers new state-of-the-art results in various cross-lingual downstream tasks.
We proposed two novel methods to align the representation of multiple languages:
Cross-Attention Masked Language Modeling(CAMLM): In CAMLM, we learn the multilingual semantic representation by restoring the MASK tokens in the input sentences.
Back-Translation masked language modeling(BTMLM): We use BTMLM to train our model to generate pseudo-parallel sentences from the monolingual sentences. The generated pairs are then used as the input of the model to further align the cross-lingual semantics, thus enhancing the multilingual representation.
## Benchmark
### XNLI
XNLI is a subset of MNLI and has been translated into 14 different kinds of languages including some low-resource languages. The goal of the task is to predict testual entailment (whether sentence A implies / contradicts / neither sentence B).
| Model | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur | Avg |
| ---------------------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Cross-lingual Transfer | | | | | | | | | | | | | | | | |
| XLM | 85.0 | 78.7 | 78.9 | 77.8 | 76.6 | 77.4 | 75.3 | 72.5 | 73.1 | 76.1 | 73.2 | 76.5 | 69.6 | 68.4 | 67.3 | 75.1 |
| Unicoder | 85.1 | 79.0 | 79.4 | 77.8 | 77.2 | 77.2 | 76.3 | 72.8 | 73.5 | 76.4 | 73.6 | 76.2 | 69.4 | 69.7 | 66.7 | 75.4 |
| XLM-R | 85.8 | 79.7 | 80.7 | 78.7 | 77.5 | 79.6 | 78.1 | 74.2 | 73.8 | 76.5 | 74.6 | 76.7 | 72.4 | 66.5 | 68.3 | 76.2 |
| INFOXLM | **86.4** | **80.6** | 80.8 | 78.9 | 77.8 | 78.9 | 77.6 | 75.6 | 74.0 | 77.0 | 73.7 | 76.7 | 72.0 | 66.4 | 67.1 | 76.2 |
| **ERNIE-M** | 85.5 | 80.1 | **81.2** | **79.2** | **79.1** | **80.4** | **78.1** | **76.8** | **76.3** | **78.3** | **75.8** | **77.4** | **72.9** | **69.5** | **68.8** | **77.3** |
| XLM-R Large | 89.1 | 84.1 | 85.1 | 83.9 | 82.9 | 84.0 | 81.2 | 79.6 | 79.8 | 80.8 | 78.1 | 80.2 | 76.9 | 73.9 | 73.8 | 80.9 |
| INFOXLM Large | **89.7** | 84.5 | 85.5 | 84.1 | 83.4 | 84.2 | 81.3 | 80.9 | 80.4 | 80.8 | 78.9 | 80.9 | 77.9 | 74.8 | 73.7 | 81.4 |
| VECO Large | 88.2 | 79.2 | 83.1 | 82.9 | 81.2 | 84.2 | 82.8 | 76.2 | 80.3 | 74.3 | 77.0 | 78.4 | 71.3 | **80.4** | **79.1** | 79.9 |
| **ERNIR-M Large** | 89.3 | **85.1** | **85.7** | **84.4** | **83.7** | **84.5** | 82.0 | **81.2** | **81.2** | **81.9** | **79.2** | **81.0** | **78.6** | 76.2 | 75.4 | **82.0** |
| Translate-Train-All | | | | | | | | | | | | | | | | |
| XLM | 85.0 | 80.8 | 81.3 | 80.3 | 79.1 | 80.9 | 78.3 | 75.6 | 77.6 | 78.5 | 76.0 | 79.5 | 72.9 | 72.8 | 68.5 | 77.8 |
| Unicoder | 85.6 | 81.1 | 82.3 | 80.9 | 79.5 | 81.4 | 79.7 | 76.8 | 78.2 | 77.9 | 77.1 | 80.5 | 73.4 | 73.8 | 69.6 | 78.5 |
| XLM-R | 85.4 | 81.4 | 82.2 | 80.3 | 80.4 | 81.3 | 79.7 | 78.6 | 77.3 | 79.7 | 77.9 | 80.2 | 76.1 | 73.1 | 73.0 | 79.1 |
| INFOXLM | 86.1 | 82.0 | 82.8 | 81.8 | 80.9 | 82.0 | 80.2 | 79.0 | 78.8 | 80.5 | 78.3 | 80.5 | 77.4 | 73.0 | 71.6 | 79.7 |
| **ERNIE-M** | **86.2** | **82.5** | **83.8** | **82.6** | **82.4** | **83.4** | **80.2** | **80.6** | **80.5** | **81.1** | **79.2** | **80.5** | **77.7** | **75.0** | **73.3** | **80.6** |
| XLM-R Large | 89.1 | 85.1 | 86.6 | 85.7 | 85.3 | 85.9 | 83.5 | 83.2 | 83.1 | 83.7 | 81.5 | **83.7** | **81.6** | 78.0 | 78.1 | 83.6 |
| VECO Large | 88.9 | 82.4 | 86.0 | 84.7 | 85.3 | 86.2 | **85.8** | 80.1 | 83.0 | 77.2 | 80.9 | 82.8 | 75.3 | **83.1** | **83.0** | 83.0 |
| **ERNIE-M Large** | **89.5** | **86.5** | **86.9** | **86.1** | **86.0** | **86.8** | 84.1 | **83.8** | **84.1** | **84.5** | **82.1** | 83.5 | 81.1 | 79.4 | 77.9 | **84.2** |
### Cross-lingual Named Entity Recognition
* datasets：CoNLI
| Model | en | nl | es | de | Avg |
| ------------------------------ | --------- | --------- | --------- | --------- | --------- |
| *Fine-tune on English dataset* | | | | | |
| mBERT | 91.97 | 77.57 | 74.96 | 69.56 | 78.52 |
| XLM-R | 92.25 | **78.08** | 76.53 | **69.60** | 79.11 |
| **ERNIE-M** | **92.78** | 78.01 | **79.37** | 68.08 | **79.56** |
| XLM-R LARGE | 92.92 | 80.80 | 78.64 | 71.40 | 80.94 |
| **ERNIE-M LARGE** | **93.28** | **81.45** | **78.83** | **72.99** | **81.64** |
| *Fine-tune on all dataset* | | | | | |
| XLM-R | 91.08 | 89.09 | 87.28 | 83.17 | 87.66 |
| **ERNIE-M** | **93.04** | **91.73** | **88.33** | **84.20** | **89.32** |
| XLM-R LARGE | 92.00 | 91.60 | **89.52** | 84.60 | 89.43 |
| **ERNIE-M LARGE** | **94.01** | **93.81** | 89.23 | **86.20** | **90.81** |
### Cross-lingual Question Answering
* datasets：MLQA
| Model | en | es | de | ar | hi | vi | zh | Avg |
| ----------------- | --------------- | --------------- | --------------- | --------------- | --------------- | --------------- | --------------- | --------------- |
| mBERT | 77.7 / 65.2 | 64.3 / 46.6 | 57.9 / 44.3 | 45.7 / 29.8 | 43.8 / 29.7 | 57.1 / 38.6 | 57.5 / 37.3 | 57.7 / 41.6 |
| XLM | 74.9 / 62.4 | 68.0 / 49.8 | 62.2 / 47.6 | 54.8 / 36.3 | 48.8 / 27.3 | 61.4 / 41.8 | 61.1 / 39.6 | 61.6 / 43.5 |
| XLM-R | 77.1 / 64.6 | 67.4 / 49.6 | 60.9 / 46.7 | 54.9 / 36.6 | 59.4 / 42.9 | 64.5 / 44.7 | 61.8 / 39.3 | 63.7 / 46.3 |
| INFOXLM | 81.3 / 68.2 | 69.9 / 51.9 | 64.2 / 49.6 | 60.1 / 40.9 | 65.0 / 47.5 | 70.0 / 48.6 | 64.7 / **41.2** | 67.9 / 49.7 |
| **ERNIE-M** | **81.6 / 68.5** | **70.9 / 52.6** | **65.8 / 50.7** | **61.8 / 41.9** | **65.4 / 47.5** | **70.0 / 49.2** | **65.6** / 41.0 | **68.7 / 50.2** |
| XLM-R LARGE | 80.6 / 67.8 | 74.1 / 56.0 | 68.5 / 53.6 | 63.1 / 43.5 | 62.9 / 51.6 | 71.3 / 50.9 | 68.0 / 45.4 | 70.7 / 52.7 |
| INFOXLM LARGE | **84.5 / 71.6** | **75.1 / 57.3** | **71.2 / 56.2** | **67.6 / 47.6** | 72.5 / 54.2 | **75.2 / 54.1** | 69.2 / 45.4 | 73.6 / 55.2 |
| **ERNIE-M LARGE** | 84.4 / 71.5 | 74.8 / 56.6 | 70.8 / 55.9 | 67.4 / 47.2 | **72.6 / 54.7** | 75.0 / 53.7 | **71.1 / 47.5** | **73.7 / 55.3** |
### Cross-lingual Paraphrase Identification
* datasets：PAWS-X
| Model | en | de | es | fr | ja | ko | zh | Avg |
| ---------------------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| Cross-lingual Transfer | | | | | | | | |
| mBERT | 94.0 | 85.7 | 87.4 | 87.0 | 73.0 | 69.6 | 77.0 | 81.9 |
| XLM | 94.0 | 85.9 | 88.3 | 87.4 | 69.3 | 64.8 | 76.5 | 80.9 |
| MMTE | 93.1 | 85.1 | 87.2 | 86.9 | 72.0 | 69.2 | 75.9 | 81.3 |
| XLM-R LARGE | 94.7 | 89.7 | 90.1 | 90.4 | 78.7 | 79.0 | 82.3 | 86.4 |
| VECO LARGE | **96.2** | 91.3 | 91.4 | 92.0 | 81.8 | 82.9 | 85.1 | 88.7 |
| **ERNIE-M LARGE** | 96.0 | **91.9** | **91.4** | **92.2** | **83.9** | **84.5** | **86.9** | **89.5** |
| Translate-Train-All | | | | | | | | |
| VECO LARGE | 96.4 | 93.0 | 93.0 | 93.5 | 87.2 | 86.8 | 87.9 | 91.1 |
| **ERNIE-M LARGE** | **96.5** | **93.5** | **93.3** | **93.8** | **87.9** | **88.4** | **89.2** | **91.8** |
### Cross-lingual Sentence Retrieval
* dataset：Tatoeba
| Model | Avg |
| --------------------------------------- | -------- |
| XLM-R LARGE | 75.2 |
| VECO LARGE | 86.9 |
| **ERNIE-M LARGE** | **87.9** |
| **ERNIE-M LARGE（ after fine-tuning）** | **93.3** |
## Citation Info
```text
@article{Ouyang2021ERNIEMEM,
title={ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora},
author={Xuan Ouyang and Shuohuan Wang and Chao Pang and Yu Sun and Hao Tian and Hua Wu and Haifeng Wang},
journal={ArXiv},
year={2021},
volume={abs/2012.15674}
}
``` |
CodeNinja1126/xlm-roberta-large-kor-mrc | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
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} | 8 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: gpt_16_4_3e-5_lp5_nb5
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. -->
# gpt_16_4_3e-5_lp5_nb5
This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8872
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.311 | 0.38 | 1000 | 4.0339 |
| 3.1133 | 0.76 | 2000 | 3.9777 |
| 2.9875 | 1.13 | 3000 | 3.9546 |
| 2.8697 | 1.51 | 4000 | 3.9269 |
| 2.8669 | 1.89 | 5000 | 3.9159 |
| 2.7308 | 2.27 | 6000 | 3.9066 |
| 2.709 | 2.64 | 7000 | 3.8995 |
| 2.6979 | 3.02 | 8000 | 3.8976 |
| 2.5878 | 3.4 | 9000 | 3.8978 |
| 2.5824 | 3.78 | 10000 | 3.8872 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.9.0+cu102
- Datasets 2.8.0
- Tokenizers 0.13.2
|
CoffeeAddict93/gpt1-modest-proposal | [
"pytorch",
"openai-gpt",
"text-generation",
"transformers",
"has_space"
]
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} | 11 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: minilm-uncased-squad2-finetuned-squad-12-trainedfor-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. -->
# minilm-uncased-squad2-finetuned-squad-12-trainedfor-3
This model is a fine-tuned version of [deepset/minilm-uncased-squad2](https://huggingface.co/deepset/minilm-uncased-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6181
## 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-08
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6694 | 1.0 | 578 | 0.6175 |
| 0.681 | 2.0 | 1156 | 0.6180 |
| 0.6829 | 3.0 | 1734 | 0.6181 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Connor-tech/bert_cn_finetuning | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
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} | 27 | null | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-28jan-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-28jan-2
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5078
- Rouge1: 18.7485
- Rouge2: 5.8034
- Rougel: 18.5163
- Rougelsum: 18.4817
## 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: 9
- eval_batch_size: 9
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 5.9652 | 1.0 | 242 | 2.8048 | 14.036 | 4.037 | 13.7766 | 13.8254 |
| 3.474 | 2.0 | 484 | 2.7485 | 16.821 | 4.8051 | 16.6168 | 16.5782 |
| 3.2101 | 3.0 | 726 | 2.6444 | 17.2659 | 5.1077 | 16.9501 | 16.8998 |
| 3.0555 | 4.0 | 968 | 2.6408 | 17.3002 | 4.8657 | 17.0414 | 16.9794 |
| 2.9515 | 5.0 | 1210 | 2.5860 | 17.6468 | 5.3816 | 17.3755 | 17.3434 |
| 2.8694 | 6.0 | 1452 | 2.5586 | 18.3932 | 5.3896 | 18.2521 | 18.0748 |
| 2.7898 | 7.0 | 1694 | 2.5325 | 18.4954 | 5.5609 | 18.2994 | 18.2112 |
| 2.7436 | 8.0 | 1936 | 2.5431 | 18.8172 | 5.9338 | 18.4693 | 18.4324 |
| 2.6955 | 9.0 | 2178 | 2.5588 | 18.7895 | 6.1003 | 18.3593 | 18.3268 |
| 2.6571 | 10.0 | 2420 | 2.5079 | 19.2525 | 5.8268 | 19.0279 | 18.9846 |
| 2.629 | 11.0 | 2662 | 2.5118 | 18.9191 | 5.9877 | 18.6505 | 18.6 |
| 2.5998 | 12.0 | 2904 | 2.5070 | 18.7181 | 5.9061 | 18.4432 | 18.3931 |
| 2.5692 | 13.0 | 3146 | 2.5014 | 18.4412 | 6.1983 | 18.2394 | 18.1618 |
| 2.5751 | 14.0 | 3388 | 2.5125 | 18.7014 | 5.9729 | 18.4366 | 18.406 |
| 2.55 | 15.0 | 3630 | 2.5078 | 18.7485 | 5.8034 | 18.5163 | 18.4817 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CrypticT1tan/DialoGPT-medium-harrypotter | []
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} | 0 | null | ---
tags:
- autotrain
- token-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ankleBowl/autotrain-data-lucy-light-control
co2_eq_emissions:
emissions: 0.5335980780308736
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 3122788375
- CO2 Emissions (in grams): 0.5336
## Validation Metrics
- Loss: 0.003
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- F1: 1.000
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/ankleBowl/autotrain-lucy-light-control-3122788375
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("ankleBowl/autotrain-lucy-light-control-3122788375", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ankleBowl/autotrain-lucy-light-control-3122788375", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Crystal/distilbert-base-uncased-finetuned-squad | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- HalfCheetahBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HalfCheetahBulletEnv-v0
type: HalfCheetahBulletEnv-v0
metrics:
- type: mean_reward
value: 827.20 +/- 108.15
name: mean_reward
verified: false
---
# **A2C** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **A2C** agent playing **HalfCheetahBulletEnv-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
...
```
|
Culmenus/XLMR-ENIS-finetuned-ner | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:mim_gold_ner",
"transformers",
"generated_from_trainer",
"license:agpl-3.0",
"model-index",
"autotrain_compatible"
]
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} | 6 | null | This is a mix of two models. Dreamshaper (%70) + AnythingBmix (%30). I am not a programmer and I have no idea what I am doing. |
Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc_2 | []
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} | 0 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- Lloviant/autotrain-data-ex-and-pt
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.6202842405816136
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3122688386
- CO2 Emissions (in grams): 0.6203
## Validation Metrics
- Loss: 1.338
- Accuracy: 0.571
- Macro F1: 0.389
- Micro F1: 0.571
- Weighted F1: 0.429
- Macro Precision: 0.333
- Micro Precision: 0.571
- Weighted Precision: 0.357
- Macro Recall: 0.500
- Micro Recall: 0.571
- Weighted Recall: 0.571 |
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} | 0 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- Lloviant/autotrain-data-ex-and-pt
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.5722366196083666
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3122688387
- CO2 Emissions (in grams): 0.5722
## Validation Metrics
- Loss: 1.749
- Accuracy: 0.571
- Macro F1: 0.444
- Micro F1: 0.571
- Weighted F1: 0.476
- Macro Precision: 0.417
- Micro Precision: 0.571
- Weighted Precision: 0.429
- Macro Recall: 0.500
- Micro Recall: 0.571
- Weighted Recall: 0.571 |
Culmenus/opus-mt-de-is-finetuned-de-to-is_ekkicc | []
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} | 0 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- Lloviant/autotrain-data-ex-and-pt
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.2158114227532694
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3122688388
- CO2 Emissions (in grams): 0.2158
## Validation Metrics
- Loss: 1.818
- Accuracy: 0.000
- Macro F1: 0.000
- Micro F1: 0.000
- Weighted F1: 0.000
- Macro Precision: 0.000
- Micro Precision: 0.000
- Weighted Precision: 0.000
- Macro Recall: 0.000
- Micro Recall: 0.000
- Weighted Recall: 0.000 |
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} | 0 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- Lloviant/autotrain-data-ex-and-pt
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.7206152092702812
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3122688389
- CO2 Emissions (in grams): 0.7206
## Validation Metrics
- Loss: 1.599
- Accuracy: 0.286
- Macro F1: 0.250
- Micro F1: 0.286
- Weighted F1: 0.286
- Macro Precision: 0.250
- Micro Precision: 0.286
- Weighted Precision: 0.286
- Macro Recall: 0.250
- Micro Recall: 0.286
- Weighted Recall: 0.286 |
Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2 | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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} | 1 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- Lloviant/autotrain-data-ex-and-pt
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.42285127723587795
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3122688390
- CO2 Emissions (in grams): 0.4229
## Validation Metrics
- Loss: 1.919
- Accuracy: 0.286
- Macro F1: 0.214
- Micro F1: 0.286
- Weighted F1: 0.184
- Macro Precision: 0.194
- Micro Precision: 0.286
- Weighted Precision: 0.167
- Macro Recall: 0.333
- Micro Recall: 0.286
- Weighted Recall: 0.286 |
CurtisASmith/GPT-JRT | []
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} | 0 | null | ---
license: apache-2.0
---
Evaluate nynorsk translateion
|
CurtisBowser/DialoGPT-medium-sora-two | [
"pytorch",
"conversational"
]
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} | 0 | null |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- object-detection
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.21
inference: false
datasets:
- keremberke/valorant-object-detection
model-index:
- name: keremberke/yolov8m-valorant-detection
results:
- task:
type: object-detection
dataset:
type: keremberke/valorant-object-detection
name: valorant-object-detection
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.96466 # min: 0.0 - max: 1.0
name: [email protected](box)
---
<div align="center">
<img width="640" alt="keremberke/yolov8m-valorant-detection" src="https://huggingface.co/keremberke/yolov8m-valorant-detection/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['dropped spike', 'enemy', 'planted spike', 'teammate']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('keremberke/yolov8m-valorant-detection')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
Czapla/Rick | []
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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: nikz/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
D3xter1922/distilbert-base-uncased-finetuned-cola | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-model-beimer
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9849624060150376
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-model-beimer
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0637
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1394 | 3.85 | 500 | 0.0637 | 0.9850 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
D4RL1NG/yes | []
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} | 0 | 2023-01-28T22:12:48Z | ---
library_name: stable-baselines3
tags:
- HalfCheetahBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TQC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HalfCheetahBulletEnv-v0
type: HalfCheetahBulletEnv-v0
metrics:
- type: mean_reward
value: 2919.24 +/- 8.00
name: mean_reward
verified: false
---
# **TQC** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **TQC** agent playing **HalfCheetahBulletEnv-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
...
```
|
DARKVIP3R/DialoGPT-medium-Anakin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 13 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-v2-japanese-5k-steps
results: []
datasets:
- mozilla-foundation/common_voice_11_0
language:
- ja
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-large-v2-japanese-5k-steps
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Japanese CommonVoice dataset (v11)..
It achieves the following results on the evaluation set:
- Loss: 0.4200
- Wer: 0.7449
## Model description
This model is finetuned for 5000 steps for research purposes which means that the transcriptions might not be that satisfactory for users.
## Training and evaluation data
- Training Data: CommonVoice (v11) train split
- Validation Data: CommonVoice (v11) Validation split
- Test Data: CommonVoice (v11) Test split
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 50
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0111 | 7.63 | 1000 | 0.3210 | 0.7888 |
| 0.0007 | 15.27 | 2000 | 0.3585 | 0.7478 |
| 0.0003 | 22.9 | 3000 | 0.3937 | 0.7432 |
| 0.0002 | 30.53 | 4000 | 0.4123 | 0.7443 |
| 0.0002 | 38.17 | 5000 | 0.4200 | 0.7449 |
### Transcription
```python
from datasets import load_dataset, Audio
import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration
# device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# load the model
processor = WhisperProcessor.from_pretrained("clu-ling/whisper-large-v2-japanese-5k-steps")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-japanese-5k-steps").to(device)
forced_decoder_ids = processor.get_decoder_prompt_ids(language="ja", task="transcribe")
# load the dataset
commonvoice_eval = load_dataset("mozilla-foundation/common_voice_11_0", "ja", split="validation", streaming=True)
commonvoice_eval = commonvoice_eval.cast_column("audio", Audio(sampling_rate=16000))
sample = next(iter(commonvoice_eval))["audio"]
# features and generate token ids
input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
predicted_ids = model.generate(input_features.to(device), forced_decoder_ids=forced_decoder_ids)
# decode
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print(transcription)
```
### Evaluation:
Evaluates this model on `mozilla-foundation/common_voice_11_0` test split.
```python
from transformers.models.whisper.english_normalizer import BasicTextNormalizer
from datasets import load_dataset, Audio
import evaluate
import torch
import re
from transformers import WhisperProcessor, WhisperForConditionalGeneration
# device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# metric
wer_metric = evaluate.load("wer")
# model
processor = WhisperProcessor.from_pretrained("clu-ling/whisper-large-v2-japanese-5k-steps")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-japanese-5k-steps")
# dataset
dataset = load_dataset("mozilla-foundation/common_voice_11_0", "ja", split="test", ) #cache_dir=args.cache_dir
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
#for debuggings: it gets some examples
#dataset = dataset.shard(num_shards=7000, index=0)
#print(dataset)
def normalize(batch):
batch["gold_text"] = whisper_norm(batch['sentence'])
return batch
def map_wer(batch):
model.to(device)
forced_decoder_ids = processor.get_decoder_prompt_ids(language = "ja", task = "transcribe")
inputs = processor(batch["audio"]["array"], sampling_rate=batch["audio"]["sampling_rate"], return_tensors="pt").input_features
with torch.no_grad():
generated_ids = model.generate(inputs=inputs.to(device), forced_decoder_ids=forced_decoder_ids)
transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
batch["predicted_text"] = whisper_norm(transcription)
return batch
# process GOLD text
processed_dataset = dataset.map(normalize)
# get predictions
predicted = processed_dataset.map(map_wer)
# word error rate
wer = wer_metric.compute(references=predicted['gold_text'], predictions=predicted['predicted_text'])
wer = round(100 * wer, 2)
print("WER:", wer)
```
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2 |
DHBaek/gpt2-stackoverflow-question-contents-generator | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 14 | 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: 283.52 +/- 12.47
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
...
```
|
DJSammy/bert-base-danish-uncased_BotXO-ai | [
"pytorch",
"jax",
"da",
"dataset:common_crawl",
"dataset:wikipedia",
"transformers",
"bert",
"masked-lm",
"license:cc-by-4.0",
"fill-mask"
]
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} | 14 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopterLocalv1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 64.10 +/- 41.10
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
|
DJStomp/TestingSalvoNET | [
"transformers"
]
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} | 1 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert_add_GLUE_Experiment_logit_kd_mrpc
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: 0.3161764705882353
- name: F1
type: f1
value: 0.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_add_GLUE_Experiment_logit_kd_mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5207
- Accuracy: 0.3162
- F1: 0.0
- Combined Score: 0.1581
## 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 | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.564 | 1.0 | 15 | 0.5300 | 0.3162 | 0.0 | 0.1581 |
| 0.533 | 2.0 | 30 | 0.5323 | 0.3162 | 0.0 | 0.1581 |
| 0.5302 | 3.0 | 45 | 0.5290 | 0.3162 | 0.0 | 0.1581 |
| 0.5312 | 4.0 | 60 | 0.5289 | 0.3162 | 0.0 | 0.1581 |
| 0.527 | 5.0 | 75 | 0.5306 | 0.3162 | 0.0 | 0.1581 |
| 0.5229 | 6.0 | 90 | 0.5207 | 0.3162 | 0.0 | 0.1581 |
| 0.5088 | 7.0 | 105 | 0.5358 | 0.5539 | 0.5806 | 0.5673 |
| 0.5003 | 8.0 | 120 | 0.5299 | 0.4902 | 0.4611 | 0.4757 |
| 0.4825 | 9.0 | 135 | 0.5323 | 0.3627 | 0.1824 | 0.2726 |
| 0.4628 | 10.0 | 150 | 0.5373 | 0.5196 | 0.5377 | 0.5287 |
| 0.451 | 11.0 | 165 | 0.5513 | 0.5417 | 0.5854 | 0.5635 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DSI/TweetBasedSA | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 29 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-v2-arabic-5k-steps
results: []
datasets:
- mozilla-foundation/common_voice_11_0
language:
- ar
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-large-v2-arabic-5k-steps
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Arabic CommonVoice dataset (v11).
It achieves the following results on the evaluation set:
- Loss: 0.3434
- Wer: 0.4239
## Model description
This model is finetuned for 5000 steps for research purposes which means that the transcriptions might not be that satisfactory for users.
## Training and evaluation data
- Training Data: CommonVoice (v11) train split
- Validation Data: CommonVoice (v11) Validation split
- Test Data: CommonVoice (v11) Test split
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 50
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1638 | 1.78 | 1000 | 0.2295 | 0.4410 |
| 0.0587 | 3.57 | 2000 | 0.2337 | 0.4272 |
| 0.0125 | 5.35 | 3000 | 0.2745 | 0.4208 |
| 0.004 | 7.13 | 4000 | 0.3124 | 0.4252 |
| 0.0016 | 8.91 | 5000 | 0.3434 | 0.4239 |
### Transcription:
```python
from datasets import load_dataset, Audio
import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration
# device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# load the model
processor = WhisperProcessor.from_pretrained("clu-ling/whisper-large-v2-arabic-5k-steps")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-arabic-5k-steps").to(device)
forced_decoder_ids = processor.get_decoder_prompt_ids(language="ar", task="transcribe")
# load the dataset
commonvoice_eval = load_dataset("mozilla-foundation/common_voice_11_0", "ar", split="validation", streaming=True)
commonvoice_eval = commonvoice_eval.cast_column("audio", Audio(sampling_rate=16000))
sample = next(iter(commonvoice_eval))["audio"]
# features and generate token ids
input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
predicted_ids = model.generate(input_features.to(device), forced_decoder_ids=forced_decoder_ids)
# decode
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print("Transcription:", transcription)
Transcription: عمي هو أخو أبي.
```
### Evaluation:
Evaluates this model on `mozilla-foundation/common_voice_11_0` test split.
```python
import pyarabic.araby as araby
from transformers.models.whisper.english_normalizer import BasicTextNormalizer
from datasets import load_dataset, Audio
import evaluate
import torch
import re
from transformers import WhisperProcessor, WhisperForConditionalGeneration
# device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# metric
wer_metric = evaluate.load("wer")
# model
processor = WhisperProcessor.from_pretrained("clu-ling/whisper-large-v2-arabic-5k-steps")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-arabic-5k-steps")
# dataset
dataset = load_dataset("mozilla-foundation/common_voice_11_0", "ar", split="test", ) #cache_dir=args.cache_dir
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
#for debuggings: it gets two examples
#dataset = dataset.shard(num_shards=10000, index=0)
#print(dataset)
def clean_text(text):
"""Normalizes TRANSCRIPT"""
text = re.sub(r'[\,\?\.\!\-\;\:\"\“\%\٪\‘\”\�\«\»\،\.\:\؟\؛\*\>\<]', '', text) + " " # special characters
text = re.sub(r'http\S+', '', text) + " " # links
text = re.sub(r'[\[\]\(\)\-\/\{\}]', '', text) + " " # brackets
text = re.sub(r'\s+', ' ', text) + " " # extra white space
text = araby.strip_diacritics(text) # remove diacrirics
return text.strip()
def normalize(batch):
"""Normalizes GOLD"""
#batch["gold_text"] = whisper_norm(batch['sentence'])
batch["gold_text"] = clean_text(batch['sentence'])
return batch
def map_wer(batch):
model.to(device)
forced_decoder_ids = processor.get_decoder_prompt_ids(language = "ar", task = "transcribe")
inputs = processor(batch["audio"]["array"], sampling_rate=batch["audio"]["sampling_rate"], return_tensors="pt").input_features
with torch.no_grad():
generated_ids = model.generate(inputs=inputs.to(device), forced_decoder_ids=forced_decoder_ids)
transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
batch["predicted_text"] = clean_text(transcription)
return batch
# process GOLD text
processed_dataset = dataset.map(normalize)
# get predictions
predicted = processed_dataset.map(map_wer)
# word error rate
wer = wer_metric.compute(references=predicted['gold_text'], predictions=predicted['predicted_text'])
wer = round(100 * wer, 2)
print("WER:", wer)
```
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2 |
alexandrainst/da-hatespeech-detection-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
]
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} | 1,719 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_add_GLUE_Experiment_logit_kd_qnli_256
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: 0.5874061870766978
---
<!-- 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_logit_kd_qnli_256
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3989
- Accuracy: 0.5874
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4156 | 1.0 | 410 | 0.4111 | 0.5054 |
| 0.4078 | 2.0 | 820 | 0.4018 | 0.5799 |
| 0.3962 | 3.0 | 1230 | 0.3989 | 0.5874 |
| 0.3899 | 4.0 | 1640 | 0.4018 | 0.5867 |
| 0.3851 | 5.0 | 2050 | 0.4032 | 0.5799 |
| 0.3802 | 6.0 | 2460 | 0.4118 | 0.5728 |
| 0.3762 | 7.0 | 2870 | 0.4093 | 0.5718 |
| 0.3717 | 8.0 | 3280 | 0.4100 | 0.5737 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DataikuNLP/average_word_embeddings_glove.6B.300d | [
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0"
]
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} | 0 | 2023-01-29T01:53:17Z | ---
tags:
- autotrain
- vision
- image-classification
license: mit
widget:
- src: https://files.catbox.moe/72xdjy.png
example_title: Furry Avatar #1
- src: https://files.catbox.moe/22bao8.jpg
example_title: Furry Avatar #2
- src: https://files.catbox.moe/xahs5m.png
example_title: Normal Animal Avatar #1
- src: https://files.catbox.moe/6zvcpu.png
example_title: Normal Animal Avatar #2
- src: https://files.catbox.moe/gcltc9.png
example_title: Kemonomimi Avatar #1
- src: https://files.catbox.moe/w4vcoc.png
example_title: Kemonomimi Avatar #2
- src: https://files.catbox.moe/ujfzv0.png
example_title: Human Avatar #1
- src: https://files.catbox.moe/yxx1qz.jpg
example_title: Human Avatar #2
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-1.jpg
example_title: Normal Cat :3
co2_eq_emissions:
emissions: 2.8752228959859316
---
This detects furry images, mostly profile pictures, although it may be able detect any sort of furry picture (I haven't tried it, though).
# Dataset Info
This was trained on scraped pfp images from Mastodon, with some non-pfp images thrown in for "balancing" (i.e ensuring pokemon, kemonomimi (catgirls/foxgirls/etc), and normal animals weren't classified as 'furry')
**Furry images**: 551
**Non-furry images**: 641
# Disclaimer
Please do not ruin this by using this to harass anyone.
This is *not* intended to be used for targeted harrassement, and I will explicitly condemn any use that attempts to do so.
If you're wondering why I made this public in the first place?
I believe in freedom of *information* - this image classification model has various perfectly valid uses, and it's kinda useless to keep it private.
# Statistics
## Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 2890884434
- CO2 Emissions (in grams): 2.8752
## Validation Metrics
- Loss: 0.175
- Accuracy: 0.933
- Precision: 0.938
- Recall: 0.938
- AUC: 0.975
- F1: 0.938
|
DataikuNLP/camembert-base | [
"pytorch",
"tf",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"CamembertForMaskedLM"
],
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}
} | 8 | 2023-01-29T01:26:35Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: mobilebert_add_GLUE_Experiment_logit_kd_qqp
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: 0.756987385604749
- name: F1
type: f1
value: 0.604929832321364
---
<!-- 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_logit_kd_qqp
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: 0.8079
- Accuracy: 0.7570
- F1: 0.6049
- Combined Score: 0.6810
## 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 |
|:--------------------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 1.2837 | 1.0 | 2843 | 1.2201 | 0.6318 | 0.0 | 0.3159 |
| 1.076 | 2.0 | 5686 | 0.8477 | 0.7443 | 0.5855 | 0.6649 |
| 0.866 | 3.0 | 8529 | 0.8217 | 0.7518 | 0.5924 | 0.6721 |
| 0.8317 | 4.0 | 11372 | 0.8136 | 0.7565 | 0.6243 | 0.6904 |
| 0.8122 | 5.0 | 14215 | 0.8126 | 0.7588 | 0.6352 | 0.6970 |
| 0.799 | 6.0 | 17058 | 0.8079 | 0.7570 | 0.6049 | 0.6810 |
| 386581134871678353408.0000 | 7.0 | 19901 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 8.0 | 22744 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 9.0 | 25587 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 10.0 | 28430 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 11.0 | 31273 | nan | 0.6318 | 0.0 | 0.3159 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DataikuNLP/paraphrase-albert-small-v2 | [
"pytorch",
"albert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
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} | 628 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_add_GLUE_Experiment_logit_kd_rte_384
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: 0.4729241877256318
---
<!-- 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_logit_kd_rte_384
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: 0.4233
- Accuracy: 0.4729
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4263 | 1.0 | 10 | 0.4273 | 0.4729 |
| 0.4195 | 2.0 | 20 | 0.4268 | 0.4729 |
| 0.4189 | 3.0 | 30 | 0.4236 | 0.4729 |
| 0.417 | 4.0 | 40 | 0.4250 | 0.4729 |
| 0.4192 | 5.0 | 50 | 0.4249 | 0.4729 |
| 0.417 | 6.0 | 60 | 0.4238 | 0.4729 |
| 0.4182 | 7.0 | 70 | 0.4233 | 0.4729 |
| 0.4188 | 8.0 | 80 | 0.4235 | 0.4729 |
| 0.4174 | 9.0 | 90 | 0.4237 | 0.4729 |
| 0.4169 | 10.0 | 100 | 0.4244 | 0.4729 |
| 0.4188 | 11.0 | 110 | 0.4237 | 0.4729 |
| 0.417 | 12.0 | 120 | 0.4237 | 0.4729 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 | [
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
| sentence-similarity | {
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"BertModel"
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} | 1,517 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Vin16-P3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Vin16-P3
This model is a fine-tuned version of [HuyenNguyen/Vin11-P3](https://huggingface.co/HuyenNguyen/Vin11-P3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4000
- Wer: 25.7994
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 150
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3252 | 0.27 | 50 | 0.3806 | 24.3160 |
| 0.2973 | 0.53 | 100 | 0.3923 | 24.8214 |
| 0.2815 | 0.8 | 150 | 0.4000 | 25.7994 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Davlan/bert-base-multilingual-cased-finetuned-igbo | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
} | 15 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_add_GLUE_Experiment_logit_kd_mnli_384
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: 0.576993490642799
---
<!-- 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_logit_kd_mnli_384
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5304
- Accuracy: 0.5770
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6035 | 1.0 | 1534 | 0.5764 | 0.4805 |
| 0.5667 | 2.0 | 3068 | 0.5578 | 0.5171 |
| 0.5542 | 3.0 | 4602 | 0.5520 | 0.5243 |
| 0.5447 | 4.0 | 6136 | 0.5460 | 0.5422 |
| 0.5338 | 5.0 | 7670 | 0.5387 | 0.5671 |
| 0.5172 | 6.0 | 9204 | 0.5304 | 0.5781 |
| 0.4993 | 7.0 | 10738 | 0.5333 | 0.5847 |
| 0.482 | 8.0 | 12272 | 0.5317 | 0.5901 |
| 0.4654 | 9.0 | 13806 | 0.5323 | 0.5949 |
| 0.4504 | 10.0 | 15340 | 0.5368 | 0.5957 |
| 0.4369 | 11.0 | 16874 | 0.5405 | 0.5980 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Davlan/bert-base-multilingual-cased-finetuned-wolof | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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}
} | 4 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- allenai/nllb
---
# Ramos-Ramos/xlm-roberta-base-en-tl-0-4000
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('Ramos-Ramos/xlm-roberta-base-en-tl-0-4000')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Ramos-Ramos/xlm-roberta-base-en-tl-0-4000')
model = AutoModel.from_pretrained('Ramos-Ramos/xlm-roberta-base-en-tl-0-4000')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=Ramos-Ramos/xlm-roberta-base-en-tl-0-4000)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 12406 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Davlan/distilbert-base-multilingual-cased-ner-hrl | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
]
| token-classification | {
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"DistilBertForTokenClassification"
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}
} | 123,856 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- allenai/nllb
---
# Ramos-Ramos/xlm-roberta-base-en-tl-0-6000
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('Ramos-Ramos/xlm-roberta-base-en-tl-0-6000')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Ramos-Ramos/xlm-roberta-base-en-tl-0-6000')
model = AutoModel.from_pretrained('Ramos-Ramos/xlm-roberta-base-en-tl-0-6000')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=Ramos-Ramos/xlm-roberta-base-en-tl-0-6000)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 12406 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Davlan/xlm-roberta-base-finetuned-english | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
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}
} | 5 | null | ---
license: mit
datasets:
- DarwinAnim8or/greentext
language:
- en
tags:
- fun
- greentext
widget:
- text: ">be me"
example_title: "be me"
co2_eq_emissions:
emissions: 60
source: "https://mlco2.github.io/impact/#compute"
training_type: "fine-tuning"
geographical_location: "Oregon, USA"
hardware_used: "1 T4, Google Colab"
---
# GPT-Greentext-355m
A finetuned version of [GPT2-Medium](https://huggingface.co/gpt2-medium) on the 'greentext' dataset. (Linked above)
A demo is available [here](https://huggingface.co/spaces/DarwinAnim8or/GPT-Greentext-Playground)
The demo playground is recommended over the inference box on the right.
The largest model in this series is located here: [GPT-Greentext-1.5b](https://huggingface.co/DarwinAnim8or/GPT-Greentext-1.5b)
# Training Procedure
This was trained on the 'greentext' dataset, using the "HappyTransformers" library on Google Colab.
This model was trained for 15 epochs with learning rate 1e-2.
# Biases & Limitations
This likely contains the same biases and limitations as the original GPT2 that it is based on, and additionally heavy biases from the greentext dataset.
It likely will generate offensive output.
# Intended Use
This model is meant for fun, nothing else.
# Sample Use
```python
#Import model:
from happytransformer import HappyGeneration
happy_gen = HappyGeneration("GPT2", "DarwinAnim8or/GPT-Greentext-355m")
#Set generation settings:
from happytransformer import GENSettings
args_top_k = GENSettingsGENSettings(no_repeat_ngram_size=3, do_sample=True, top_k=80, temperature=0.8, max_length=150, early_stopping=False)
#Generate a response:
result = happy_gen.generate_text(""">be me
>""", args=args_top_k)
print(result)
print(result.text)
``` |
Davlan/xlm-roberta-base-finetuned-luganda | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
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}
} | 11 | null | ---
license: creativeml-openrail-m
tags:
- coreml
- stable-diffusion
- text-to-image
---
# Core ML Converted Model:
- This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML).<br>
- Provide the model to an app such as Mochi Diffusion [Github](https://github.com/godly-devotion/MochiDiffusion) - [Discord](https://discord.gg/x2kartzxGv) to generate images.<br>
- `split_einsum` version is compatible with all compute unit options including Neural Engine.<br>
- `original` version is only compatible with CPU & GPU option.<br>
- Custom resolution versions are tagged accordingly.<br>
- `vae` tagged files have a vae embedded into the model.<br>
- Descriptions are posted as-is from original model source. Not all features and/or results may be available in CoreML format.<br>
- This model was converted with `vae-encoder` for i2i.
- Models that are 32 bit will have "fp32" in the filename.
# Note: Some models do not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).
# Elldreth's Vivid Mix:
Source(s): [CivitAI](https://civitai.com/models/2747/elldreths-vivid-mix)
This mixed model is a combination of my all-time favorites AND new-found favorites, including a very popular anime model mixed with Zeipher's F222, Dreamlike, and H&A's awesome 3DKX_1.0b! Lastly, to top it off I used howder's jomad model. Every single model in this mix are great on their own. This mix allows you to take advantage of combined concepts and produces some great images.
What's it good at?
Realistic portraits
Stylized characters
Landscapes
Fantasy
Sci-Fi
Anime
Horror
It's an all-around easy-to-prompt general purpose semi-realistic to realistic model that cranks out some really nice images. No trigger words required. All models were scanned prior to mixing and totally safe.
So what's the difference between Vivid and all my other models?
This model adds a lot more detail and realism to the images created with it and not just with portraits but landscapes as well. The other thing this model is better at is taking Textual Inversion embeddings. Lucid and Retro are both very resistant to TI Embeddings but Vivid is transformed very easily with a good embedding.
What are you waiting for? Go get some great results from simple prompts.
What's new in v2.0?
Wow wow wow.. two big model releases have kept me busy testing and prompting.
F222 was replaced by Hassan's newest model release
The new H&A 3DKX Update replaced the older version
wavymulder's portrait+ was added
Dreamlike was udpated in the mix as well
The end result is a lot more realistic and vivid outcome. I used the same prompt to generate the new preview images as were used in v1.0. |
Davlan/xlm-roberta-base-finetuned-naija | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
} | 1 | 2023-01-29T03:05:38Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_add_GLUE_Experiment_logit_kd_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_logit_kd_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: 0.3442
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3478 | 1.0 | 3 | 0.3444 | 0.5634 |
| 0.3472 | 2.0 | 6 | 0.3445 | 0.5634 |
| 0.3467 | 3.0 | 9 | 0.3444 | 0.5634 |
| 0.3476 | 4.0 | 12 | 0.3442 | 0.5634 |
| 0.3476 | 5.0 | 15 | 0.3442 | 0.5634 |
| 0.3471 | 6.0 | 18 | 0.3446 | 0.5634 |
| 0.3473 | 7.0 | 21 | 0.3449 | 0.5634 |
| 0.3471 | 8.0 | 24 | 0.3451 | 0.5634 |
| 0.3477 | 9.0 | 27 | 0.3452 | 0.5634 |
| 0.3469 | 10.0 | 30 | 0.3451 | 0.5634 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-finetuned-somali | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"translation_en_to_de": {
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} | 8 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_add_GLUE_Experiment_logit_kd_mnli_96
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: 0.5239015459723352
---
<!-- 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_logit_kd_mnli_96
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5576
- Accuracy: 0.5239
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.624 | 1.0 | 1534 | 0.6178 | 0.3605 |
| 0.6176 | 2.0 | 3068 | 0.6138 | 0.3767 |
| 0.6139 | 3.0 | 4602 | 0.6112 | 0.3822 |
| 0.6104 | 4.0 | 6136 | 0.6071 | 0.3977 |
| 0.6027 | 5.0 | 7670 | 0.5978 | 0.4091 |
| 0.5958 | 6.0 | 9204 | 0.6104 | 0.4151 |
| 0.5877 | 7.0 | 10738 | 0.5963 | 0.4517 |
| 0.5787 | 8.0 | 12272 | 0.6054 | 0.4627 |
| 0.5711 | 9.0 | 13806 | 0.5753 | 0.4905 |
| 0.5641 | 10.0 | 15340 | 0.5713 | 0.4987 |
| 0.5583 | 11.0 | 16874 | 0.5645 | 0.5115 |
| 0.5535 | 12.0 | 18408 | 0.5646 | 0.5117 |
| 0.549 | 13.0 | 19942 | 0.5692 | 0.5176 |
| 0.5456 | 14.0 | 21476 | 0.5613 | 0.5220 |
| 0.5425 | 15.0 | 23010 | 0.5584 | 0.5302 |
| 0.5399 | 16.0 | 24544 | 0.5641 | 0.5252 |
| 0.5375 | 17.0 | 26078 | 0.5628 | 0.5260 |
| 0.5353 | 18.0 | 27612 | 0.5659 | 0.5200 |
| 0.533 | 19.0 | 29146 | 0.5676 | 0.5310 |
| 0.5311 | 20.0 | 30680 | 0.5563 | 0.5323 |
| 0.5291 | 21.0 | 32214 | 0.5682 | 0.5250 |
| 0.5274 | 22.0 | 33748 | 0.5661 | 0.5282 |
| 0.5255 | 23.0 | 35282 | 0.5673 | 0.5325 |
| 0.5236 | 24.0 | 36816 | 0.5563 | 0.5416 |
| 0.5219 | 25.0 | 38350 | 0.5703 | 0.5290 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-sadilar-ner | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
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} | 12 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- allenai/nllb
---
# Ramos-Ramos/xlm-roberta-base-en-tl-0-12000
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('Ramos-Ramos/xlm-roberta-base-en-tl-0-12000')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Ramos-Ramos/xlm-roberta-base-en-tl-0-12000')
model = AutoModel.from_pretrained('Ramos-Ramos/xlm-roberta-base-en-tl-0-12000')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=Ramos-Ramos/xlm-roberta-base-en-tl-0-12000)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 12406 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Davlan/xlm-roberta-large-ner-hrl | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
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} | 1,322 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Vin17-P3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Vin17-P3
This model is a fine-tuned version of [HuyenNguyen/Vin16-P3](https://huggingface.co/HuyenNguyen/Vin16-P3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3397
- Wer: 22.4151
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 150
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1738 | 0.29 | 50 | 0.3611 | 23.1843 |
| 0.1628 | 0.57 | 100 | 0.3451 | 22.7118 |
| 0.1627 | 0.86 | 150 | 0.3397 | 22.4151 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DeadBeast/roberta-base-pretrained-mr-2 | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
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} | 5 | null | # Stable Diffusion v2-1 Custom Implementation
Custom implementation of the Stable Diffusion v2.1 base model for Glowforge.
Forked from the [Stable Diffusion v2.1 base model](https://huggingface.co/stabilityai/stable-diffusion-2-1-base) developed by Robin Rombach and Patrick Esser.
|
DeadBeast/roberta-base-pretrained-mr | [
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
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} | 6 | null |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- object-detection
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.21
inference: false
datasets:
- keremberke/table-extraction
model-index:
- name: keremberke/yolov8s-table-extraction
results:
- task:
type: object-detection
dataset:
type: keremberke/table-extraction
name: table-extraction
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.98376 # min: 0.0 - max: 1.0
name: [email protected](box)
---
<div align="center">
<img width="640" alt="keremberke/yolov8s-table-extraction" src="https://huggingface.co/keremberke/yolov8s-table-extraction/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['bordered', 'borderless']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('keremberke/yolov8s-table-extraction')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
Dean/summarsiation | []
| null | {
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}
} | 0 | null | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-29jan-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. -->
# mt5-small-finetuned-29jan-1
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4883
- Rouge1: 19.5044
- Rouge2: 6.2046
- Rougel: 19.3543
- Rougelsum: 19.381
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 6.4829 | 1.0 | 217 | 2.7590 | 12.7914 | 3.3267 | 12.493 | 12.4137 |
| 3.4814 | 2.0 | 434 | 2.7229 | 16.7805 | 4.8009 | 16.4908 | 16.5233 |
| 3.2161 | 3.0 | 651 | 2.6422 | 18.3488 | 5.0629 | 18.1397 | 18.1976 |
| 3.045 | 4.0 | 868 | 2.6008 | 18.1363 | 5.7597 | 17.9056 | 17.9882 |
| 2.9475 | 5.0 | 1085 | 2.6061 | 18.9355 | 6.0803 | 18.6355 | 18.7673 |
| 2.8547 | 6.0 | 1302 | 2.5628 | 17.904 | 5.8618 | 17.7818 | 17.8446 |
| 2.7685 | 7.0 | 1519 | 2.5311 | 18.9128 | 5.9625 | 18.7142 | 18.842 |
| 2.705 | 8.0 | 1736 | 2.5371 | 19.6663 | 6.0395 | 19.3416 | 19.408 |
| 2.6438 | 9.0 | 1953 | 2.5427 | 19.1516 | 6.0007 | 18.9663 | 19.0156 |
| 2.6086 | 10.0 | 2170 | 2.5211 | 19.0945 | 6.4325 | 18.918 | 18.9664 |
| 2.5394 | 11.0 | 2387 | 2.5226 | 18.9019 | 6.3004 | 18.7281 | 18.8082 |
| 2.5004 | 12.0 | 2604 | 2.5136 | 18.9701 | 6.1868 | 18.7234 | 18.8098 |
| 2.4666 | 13.0 | 2821 | 2.4958 | 18.155 | 6.1513 | 18.0758 | 18.1362 |
| 2.4255 | 14.0 | 3038 | 2.5101 | 18.7561 | 6.2634 | 18.6477 | 18.7123 |
| 2.3856 | 15.0 | 3255 | 2.4860 | 19.2239 | 6.4539 | 19.1162 | 19.1403 |
| 2.3594 | 16.0 | 3472 | 2.4905 | 19.0075 | 6.1541 | 18.9106 | 18.9616 |
| 2.3301 | 17.0 | 3689 | 2.4970 | 18.7102 | 6.2065 | 18.4881 | 18.5588 |
| 2.3032 | 18.0 | 3906 | 2.4744 | 19.3199 | 6.6458 | 19.1365 | 19.1733 |
| 2.2825 | 19.0 | 4123 | 2.4907 | 18.9608 | 6.3074 | 18.8124 | 18.8502 |
| 2.2609 | 20.0 | 4340 | 2.4772 | 19.2785 | 6.4725 | 19.0379 | 19.0556 |
| 2.2384 | 21.0 | 4557 | 2.4874 | 18.9376 | 6.2922 | 18.7618 | 18.8442 |
| 2.2176 | 22.0 | 4774 | 2.4853 | 18.9962 | 6.2231 | 18.7551 | 18.7958 |
| 2.2095 | 23.0 | 4991 | 2.4960 | 18.6517 | 5.8114 | 18.4809 | 18.4811 |
| 2.1958 | 24.0 | 5208 | 2.4911 | 18.9743 | 6.2245 | 18.7692 | 18.869 |
| 2.1777 | 25.0 | 5425 | 2.4788 | 18.9623 | 6.0877 | 18.7591 | 18.7917 |
| 2.1645 | 26.0 | 5642 | 2.4883 | 19.2814 | 6.2264 | 19.1407 | 19.1835 |
| 2.1575 | 27.0 | 5859 | 2.4910 | 19.4592 | 6.3513 | 19.2842 | 19.3017 |
| 2.142 | 28.0 | 6076 | 2.4815 | 19.3045 | 6.2179 | 19.1271 | 19.1084 |
| 2.1396 | 29.0 | 6293 | 2.4858 | 19.4159 | 6.275 | 19.2582 | 19.2731 |
| 2.1438 | 30.0 | 6510 | 2.4883 | 19.5044 | 6.2046 | 19.3543 | 19.381 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DecafNosebleed/DialoGPT-small-ScaraBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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} | 15 | 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://singularite.itch.io/huggy
2. Step 1: Write your model_id: shivr/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Declan/Breitbart_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-fa-base-uncased-finetune_on_hoshfa
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-fa-base-uncased-finetune_on_hoshfa
This model is a fine-tuned version of [HooshvareLab/bert-fa-base-uncased](https://huggingface.co/HooshvareLab/bert-fa-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5274
## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3643 | 1.0 | 1604 | 2.1323 |
| 1.5142 | 2.0 | 3208 | 2.1392 |
| 0.8834 | 3.0 | 4812 | 2.5274 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/Breitbart_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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} | 3 | 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: 1359.90 +/- 57.04
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
...
```
|
Declan/Breitbart_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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} | 3 | null |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- object-detection
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.21
inference: false
datasets:
- keremberke/table-extraction
model-index:
- name: keremberke/yolov8m-table-extraction
results:
- task:
type: object-detection
dataset:
type: keremberke/table-extraction
name: table-extraction
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.95194 # min: 0.0 - max: 1.0
name: [email protected](box)
---
<div align="center">
<img width="640" alt="keremberke/yolov8m-table-extraction" src="https://huggingface.co/keremberke/yolov8m-table-extraction/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['bordered', 'borderless']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('keremberke/yolov8m-table-extraction')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
Declan/CNN_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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} | 5 | 2023-01-29T05:14:49Z | ---
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: 30.90 +/- 21.54
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
|
Declan/CNN_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 3 | null | ---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: CharTurnerHN
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - charturnerhn
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 "CharTurnerHN" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
Test prompt: A character turnaround of a paperboy in a blue hat.
|
Declan/ChicagoTribune_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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} | 7 | 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: ThuyVuPhuong/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Declan/ChicagoTribune_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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} | 7 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_add_GLUE_Experiment_logit_kd_rte_128
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_logit_kd_rte_128
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: 0.3914
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4093 | 1.0 | 20 | 0.3914 | 0.5271 |
| 0.4076 | 2.0 | 40 | 0.3922 | 0.5271 |
| 0.4076 | 3.0 | 60 | 0.3917 | 0.5271 |
| 0.4075 | 4.0 | 80 | 0.3920 | 0.5271 |
| 0.4075 | 5.0 | 100 | 0.3925 | 0.5271 |
| 0.4074 | 6.0 | 120 | 0.3915 | 0.5271 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/FoxNews_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
} | 3 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="BlackNoodle/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
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