modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
rach405/test_trainer6
|
rach405
| 2022-11-23T22:42:58Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-23T18:19:23Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_trainer6
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. -->
# test_trainer6
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0525
- Accuracy: 0.3229
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0672 | 1.0 | 88 | 2.0811 | 0.3229 |
| 1.9813 | 2.0 | 176 | 2.0715 | 0.3229 |
| 2.1212 | 3.0 | 264 | 2.0525 | 0.3229 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Tokenizers 0.11.6
|
huggingtweets/josephflaherty
|
huggingtweets
| 2022-11-23T22:21:56Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-23T22:20:04Z |
---
language: en
thumbnail: http://www.huggingtweets.com/josephflaherty/1669242112755/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/1529933319919616011/mEzYnY5Z_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">Joe Flaherty – Venture Capital Scribe</div>
<div style="text-align: center; font-size: 14px;">@josephflaherty</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 Joe Flaherty – Venture Capital Scribe.
| Data | Joe Flaherty – Venture Capital Scribe |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 150 |
| Short tweets | 154 |
| Tweets kept | 2943 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/h0zhab8z/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 @josephflaherty's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2hw29ydt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2hw29ydt/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/josephflaherty')
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)
|
NobodyX23/Pablobato
|
NobodyX23
| 2022-11-23T22:19:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-11-23T16:41:57Z |
Pablo Lobato style model
---
Text prompt: Pablobato style
---
license: creativeml-openrail-m
---
|
Guizmus/SD_DreamerCommunities_Collection
|
Guizmus
| 2022-11-23T22:17:55Z | 0 | 29 |
EveryDream
|
[
"EveryDream",
"diffusers",
"stable-diffusion",
"text-to-image",
"image-to-image",
"en",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2022-11-13T17:51:54Z |
---
language:
- en
license: creativeml-openrail-m
thumbnail: "https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/images/showcase_main.jpg"
tags:
- stable-diffusion
- text-to-image
- image-to-image
- diffusers
library_name: "EveryDream"
inference: false
---
# Introduction
This is a collection of models made from and for the users of the Stable Diffusion Discord server. Different categories of channel exist, the "Dreamers Communities" presenting a panel of subjects, like Anime, 3D, or Architectural. Each of these channels has users posting images made through the use of Stable diffusion. After asking the users, and, depending on the activity of each channel, collecting a dataset from new submissions or from the history of the channel, I intend to build multiple models representing the style of each, so that users can produce things in the style they like and mix it with other things more easily.
Those are mainly done through the use of EveryDream, and should result in a Mega Model towards the end for the datasets that are compatible. Some model like the Anime one require to stay on a different starting point, and may not get merged.
# CharacterChan Style
## Dataset & training
This model was based on [RunwayML SD 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) model with updated VAE.
The dataset was a collaborative effort of the Stable Diffusion #CharacterChan channel, made of pictures from the users themselves using their different techniques.
50 total pictures in the dataset, 160 repeats total each, over 4 Epoch on LR1e-6.
This was trained using EveryDream with a full caption of all training pictures.
The style will be called by the use of the token **CharacterChan Style**.
## Showcase & Downloads v1
[CKPT (2GB)](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/diffusers/CharacterChan/CharacterChanStyle-v1.ckpt)
[CKPT with training optimizers (11GB)](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/ckpt/CharacterChanStyle-v1_with_optimizers.ckpt)
[Diffusers](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/tree/main/diffusers/CharacterChan)
[Dataset](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/datasets/CharacterChanStyle-v1.zip)
# CreatureChan Style
## Dataset & training
This model was based on [RunwayML SD 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) model with updated VAE.
The dataset was a collaborative effort of the Stable Diffusion #CreatureChan channel, made of pictures from the users themselves using their different techniques.
50 total pictures in the dataset, 160 repeats total each, over 4 Epoch on LR1e-6.
This was trained using EveryDream with a full caption of all training pictures.
The style will be called by the use of the token **CreatureChan Style**.
## Showcase & Downloads v1
[CKPT (2GB)](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/diffusers/CreatureChan/CreatureChanStyle-v1.ckpt)
[CKPT with training optimizers (11GB)](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/ckpt/CreatureChanStyle-v1_with_optimizers.ckpt)
[Diffusers](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/tree/main/diffusers/CreatureChan)
[Dataset](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/datasets/CreatureChanStyle-v1.zip)
# 3DChan Style
## Dataset & training
This model was based on [RunwayML SD 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) model with updated VAE.
The dataset was a collaborative effort of the Stable Diffusion #3D channel, made of pictures from the users themselves using their different techniques.
120 total pictures in the dataset, 500 repeats total each, over 10 Epoch on LR1e-6.
This was trained using EveryDream with a full caption of all training pictures.
The style will be called by the use of the token **3D Style**.
Other significant tokens : rick roll, fullbody shot, bad cosplay man
## Showcase & Downloads v1
[CKPT (2GB)](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/diffusers/3DStyle/3DStyle-v1.ckpt)
[CKPT with training optimizers (11GB)](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/ckpt/3DStyle-v1_with_optimizers.ckpt)
[Diffusers](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/tree/main/diffusers/3DStyle)
[Dataset](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/datasets/3DChanStyle-v1.zip)
# AnimeChan Style
## Dataset & training
This model was based on [Trinart](https://huggingface.co/naclbit/trinart_stable_diffusion_v2) model.
The dataset was a collaborative effort of the Stable Diffusion #anime channel, made of pictures from the users themselves using their different techniques.
100 total pictures in the dataset, 300 repeats total each, over 6 Epoch on LR1e-6.
This was trained using EveryDream with a full caption of all training pictures.
The style will be called by the use of the token **AnimeChan Style**.
## Showcase & Downloads v2
[CKPT (2GB)](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/diffusers/AnimeStyle/AnimeChanStyle-v2.ckpt)
[Diffusers](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/tree/main/diffusers/AnimeStyle)
[Dataset](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/datasets/AnimeChanStyle-v2.zip)
## Showcase & Downloads v1
[CKPT (2GB)](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/ckpt/AnimeChanStyle-v1.ckpt)
[Dataset](https://huggingface.co/Guizmus/SD_DreamerCommunities_Collection/resolve/main/datasets/AnimeChanStyle-v1.zip)
# License
These models are open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
TUMxudashuai/DQN-LunarLander-v2
|
TUMxudashuai
| 2022-11-23T21:02:30Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-23T21:01:50Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -83.37 +/- 29.36
name: mean_reward
verified: false
---
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** 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
...
```
|
SherlockHolmes/ddpm-butterflies-128
|
SherlockHolmes
| 2022-11-23T21:02:17Z | 3 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-11-23T19:48:55Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/SherlockHolmes/ddpm-butterflies-128/tensorboard?#scalars)
|
tomekkorbak/agitated_jones
|
tomekkorbak
| 2022-11-23T19:45:02Z | 0 | 0 | null |
[
"generated_from_trainer",
"en",
"dataset:tomekkorbak/detoxify-pile-chunk3-0-50000",
"dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000",
"dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000",
"license:mit",
"region:us"
] | null | 2022-11-23T19:37:18Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: agitated_jones
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. -->
# agitated_jones
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 3147
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048},
{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'value_head_config': {'is_detached': False}},
'path_or_name': 'gpt2'},
'objective': {'alpha': 1, 'beta': 10, 'name': 'AWR'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 1024,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'agitated_jones',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25354,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/3t7xpujc
|
tomekkorbak/wonderful_engelbart
|
tomekkorbak
| 2022-11-23T19:38:30Z | 0 | 0 | null |
[
"generated_from_trainer",
"en",
"dataset:tomekkorbak/detoxify-pile-chunk3-0-50000",
"dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000",
"dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000",
"license:mit",
"region:us"
] | null | 2022-11-23T19:34:25Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: wonderful_engelbart
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. -->
# wonderful_engelbart
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0.00056},
'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048,
'prefix': '<|aligned|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prefix': '<|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 2,
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'wonderful_engelbart',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25354,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/2fqdlqy2
|
NehalJani/fin_sentiment
|
NehalJani
| 2022-11-23T18:11:11Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-23T18:04:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: fin_sentiment
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. -->
# fin_sentiment
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 125 | 0.4801 | 0.8006 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
whatlurks/test
|
whatlurks
| 2022-11-23T17:24:28Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-11-23T17:24:28Z |
---
license: creativeml-openrail-m
---
|
monakth/bert-base-multilingual-uncased-sv2
|
monakth
| 2022-11-23T17:03:27Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-11-23T17:01:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: bert-base-multilingual-uncased-svv
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-uncased-svv
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
jamiehudson/579-STmodel-v1a
|
jamiehudson
| 2022-11-23T16:46:09Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-23T16:45:56Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 300 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 300,
"warmup_steps": 30,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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 -->
|
tomekkorbak/wizardly_dubinsky
|
tomekkorbak
| 2022-11-23T16:20:10Z | 0 | 0 | null |
[
"generated_from_trainer",
"en",
"dataset:tomekkorbak/detoxify-pile-chunk3-0-50000",
"dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000",
"dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000",
"license:mit",
"region:us"
] | null | 2022-11-23T16:15:26Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: wizardly_dubinsky
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. -->
# wizardly_dubinsky
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048},
{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'path_or_name': 'gpt2'},
'objective': {'alpha': 1, 'name': 'Unlikelihood', 'score_threshold': 0.00078},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'wizardly_dubinsky',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25354,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/2kewh3j9
|
tomekkorbak/cranky_jang
|
tomekkorbak
| 2022-11-23T16:18:30Z | 0 | 0 | null |
[
"generated_from_trainer",
"en",
"dataset:tomekkorbak/detoxify-pile-chunk3-0-50000",
"dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000",
"dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000",
"license:mit",
"region:us"
] | null | 2022-11-23T16:17:34Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: cranky_jang
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. -->
# cranky_jang
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 3147
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048},
{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'value_head_config': {'is_detached': False}},
'path_or_name': 'gpt2'},
'objective': {'alpha': 0.5, 'beta': 10, 'name': 'AWR'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 1024,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'cranky_jang',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.001,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25354,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/37cxyfb2
|
tomekkorbak/ecstatic_hoover
|
tomekkorbak
| 2022-11-23T16:14:21Z | 0 | 0 | null |
[
"generated_from_trainer",
"en",
"dataset:tomekkorbak/detoxify-pile-chunk3-0-50000",
"dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000",
"dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000",
"license:mit",
"region:us"
] | null | 2022-11-23T16:13:50Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: ecstatic_hoover
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. -->
# ecstatic_hoover
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0.00056},
'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048,
'prefix': '<|aligned|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prefix': '<|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 2,
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'ecstatic_hoover',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25354,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/1p7d3shx
|
tomekkorbak/dazzling_turing
|
tomekkorbak
| 2022-11-23T16:13:54Z | 0 | 0 | null |
[
"generated_from_trainer",
"en",
"dataset:tomekkorbak/detoxify-pile-chunk3-0-50000",
"dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000",
"dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000",
"license:mit",
"region:us"
] | null | 2022-11-23T16:13:46Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: dazzling_turing
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. -->
# dazzling_turing
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'misaligned_prefix': '<|misaligned|>',
'threshold': 0.00056},
'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048,
'prefix': '<|aligned|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prefix': '<|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 2,
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'dazzling_turing',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25354,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/3roy3cpj
|
tomekkorbak/vigorous_thompson
|
tomekkorbak
| 2022-11-23T16:07:17Z | 0 | 0 | null |
[
"generated_from_trainer",
"en",
"dataset:tomekkorbak/detoxify-pile-chunk3-0-50000",
"dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000",
"dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000",
"license:mit",
"region:us"
] | null | 2022-11-23T16:07:08Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: vigorous_thompson
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. -->
# vigorous_thompson
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048},
{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 2048,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'vigorous_thompson',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25354,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/1kpqechr
|
daniel-tomiwa/finetuned-pegasus-model
|
daniel-tomiwa
| 2022-11-23T15:11:24Z | 96 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-23T14:27:25Z |
---
tags:
- generated_from_trainer
model-index:
- name: finetuned-pegasus-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-pegasus-model
This model is a fine-tuned version of [human-centered-summarization/financial-summarization-pegasus](https://huggingface.co/human-centered-summarization/financial-summarization-pegasus) 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 240 | 0.6898 | 40.3397 | 29.9123 | 33.8417 | 37.7847 | 61.5333 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
alexziweiwang/exp18-F04-both
|
alexziweiwang
| 2022-11-23T14:47:20Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-23T10:19:23Z |
---
tags:
- generated_from_trainer
model-index:
- name: exp18-F04-both
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. -->
# exp18-F04-both
This model is a fine-tuned version of [yongjian/wav2vec2-large-a](https://huggingface.co/yongjian/wav2vec2-large-a) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4137
- Wer: 0.4647
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 41.5777 | 0.34 | 500 | 3.0940 | 1.0188 |
| 3.2064 | 0.68 | 1000 | 2.8577 | 1.0157 |
| 2.997 | 1.02 | 1500 | 2.7604 | 1.0126 |
| 2.8537 | 1.36 | 2000 | 2.7305 | 1.0 |
| 2.6677 | 1.7 | 2500 | 2.3201 | 1.2512 |
| 2.4414 | 2.04 | 3000 | 2.1550 | 1.2575 |
| 2.2113 | 2.38 | 3500 | 2.0825 | 1.2433 |
| 2.0619 | 2.72 | 4000 | 2.0245 | 1.2245 |
| 1.921 | 3.07 | 4500 | 1.6541 | 1.2057 |
| 1.8182 | 3.41 | 5000 | 1.3678 | 1.1962 |
| 1.759 | 3.75 | 5500 | 1.1805 | 1.2214 |
| 1.6229 | 4.09 | 6000 | 1.0100 | 1.1695 |
| 1.4557 | 4.43 | 6500 | 0.8956 | 1.1287 |
| 1.4799 | 4.77 | 7000 | 0.7858 | 1.0801 |
| 1.3277 | 5.11 | 7500 | 0.7306 | 1.0267 |
| 1.2419 | 5.45 | 8000 | 0.6326 | 0.9262 |
| 1.1537 | 5.79 | 8500 | 0.6280 | 0.8901 |
| 1.0972 | 6.13 | 9000 | 0.5639 | 0.9027 |
| 1.0375 | 6.47 | 9500 | 0.7219 | 0.8352 |
| 0.9301 | 6.81 | 10000 | 0.4786 | 0.7881 |
| 0.9423 | 7.15 | 10500 | 0.4969 | 0.7441 |
| 0.8276 | 7.49 | 11000 | 0.4640 | 0.7551 |
| 0.8674 | 7.83 | 11500 | 0.5401 | 0.7582 |
| 0.7633 | 8.17 | 12000 | 0.4610 | 0.6970 |
| 0.7314 | 8.51 | 12500 | 0.4026 | 0.6923 |
| 0.7259 | 8.86 | 13000 | 0.4874 | 0.6970 |
| 0.6591 | 9.2 | 13500 | 0.4701 | 0.6546 |
| 0.615 | 9.54 | 14000 | 0.4259 | 0.6421 |
| 0.6098 | 9.88 | 14500 | 0.4206 | 0.6122 |
| 0.554 | 10.22 | 15000 | 0.4550 | 0.6201 |
| 0.5521 | 10.56 | 15500 | 0.4777 | 0.6154 |
| 0.5726 | 10.9 | 16000 | 0.3307 | 0.5997 |
| 0.5301 | 11.24 | 16500 | 0.4095 | 0.5777 |
| 0.5098 | 11.58 | 17000 | 0.4914 | 0.5934 |
| 0.5174 | 11.92 | 17500 | 0.4223 | 0.5981 |
| 0.4674 | 12.26 | 18000 | 0.3593 | 0.5651 |
| 0.4574 | 12.6 | 18500 | 0.3951 | 0.5651 |
| 0.4182 | 12.94 | 19000 | 0.4727 | 0.5808 |
| 0.388 | 13.28 | 19500 | 0.4737 | 0.5447 |
| 0.3924 | 13.62 | 20000 | 0.4047 | 0.5322 |
| 0.3752 | 13.96 | 20500 | 0.3499 | 0.5306 |
| 0.3374 | 14.31 | 21000 | 0.2930 | 0.5243 |
| 0.3239 | 14.65 | 21500 | 0.4708 | 0.5338 |
| 0.3609 | 14.99 | 22000 | 0.3415 | 0.5118 |
| 0.309 | 15.33 | 22500 | 0.4738 | 0.5149 |
| 0.2987 | 15.67 | 23000 | 0.4351 | 0.5275 |
| 0.3726 | 16.01 | 23500 | 0.4305 | 0.5306 |
| 0.3075 | 16.35 | 24000 | 0.3290 | 0.5212 |
| 0.2995 | 16.69 | 24500 | 0.3386 | 0.4976 |
| 0.3262 | 17.03 | 25000 | 0.5279 | 0.5165 |
| 0.2607 | 17.37 | 25500 | 0.3836 | 0.5008 |
| 0.2664 | 17.71 | 26000 | 0.4128 | 0.4961 |
| 0.2578 | 18.05 | 26500 | 0.3517 | 0.4945 |
| 0.2443 | 18.39 | 27000 | 0.3126 | 0.4804 |
| 0.2488 | 18.73 | 27500 | 0.3895 | 0.4976 |
| 0.2382 | 19.07 | 28000 | 0.5097 | 0.5055 |
| 0.2684 | 19.41 | 28500 | 0.4171 | 0.5071 |
| 0.2038 | 19.75 | 29000 | 0.4126 | 0.4851 |
| 0.2273 | 20.1 | 29500 | 0.4142 | 0.4898 |
| 0.2144 | 20.44 | 30000 | 0.5022 | 0.4961 |
| 0.2274 | 20.78 | 30500 | 0.4640 | 0.4819 |
| 0.2055 | 21.12 | 31000 | 0.5124 | 0.4851 |
| 0.1814 | 21.46 | 31500 | 0.4745 | 0.4804 |
| 0.201 | 21.8 | 32000 | 0.4669 | 0.4835 |
| 0.1788 | 22.14 | 32500 | 0.5168 | 0.4851 |
| 0.2206 | 22.48 | 33000 | 0.4279 | 0.4772 |
| 0.1847 | 22.82 | 33500 | 0.3862 | 0.4772 |
| 0.1875 | 23.16 | 34000 | 0.4506 | 0.4851 |
| 0.1546 | 23.5 | 34500 | 0.4411 | 0.4867 |
| 0.1768 | 23.84 | 35000 | 0.3386 | 0.4584 |
| 0.1601 | 24.18 | 35500 | 0.3914 | 0.4678 |
| 0.1815 | 24.52 | 36000 | 0.3449 | 0.4600 |
| 0.1495 | 24.86 | 36500 | 0.4789 | 0.4819 |
| 0.1347 | 25.2 | 37000 | 0.4584 | 0.4741 |
| 0.1516 | 25.54 | 37500 | 0.3993 | 0.4678 |
| 0.1514 | 25.89 | 38000 | 0.3898 | 0.4662 |
| 0.1288 | 26.23 | 38500 | 0.4486 | 0.4819 |
| 0.1414 | 26.57 | 39000 | 0.4233 | 0.4835 |
| 0.1407 | 26.91 | 39500 | 0.4119 | 0.4710 |
| 0.1383 | 27.25 | 40000 | 0.4084 | 0.4788 |
| 0.1391 | 27.59 | 40500 | 0.4254 | 0.4757 |
| 0.1302 | 27.93 | 41000 | 0.4208 | 0.4741 |
| 0.1335 | 28.27 | 41500 | 0.3952 | 0.4662 |
| 0.1426 | 28.61 | 42000 | 0.4086 | 0.4647 |
| 0.1303 | 28.95 | 42500 | 0.4071 | 0.4615 |
| 0.1148 | 29.29 | 43000 | 0.4220 | 0.4662 |
| 0.1131 | 29.63 | 43500 | 0.4170 | 0.4662 |
| 0.0998 | 29.97 | 44000 | 0.4137 | 0.4647 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.13.2
|
kobe/vit-base-beans
|
kobe
| 2022-11-23T14:44:58Z | 250 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-10-02T02:56:59Z |
---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: train
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-base-beans
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: 0.0866
- 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2501 | 1.0 | 130 | 0.2281 | 0.9624 |
| 0.2895 | 2.0 | 260 | 0.1138 | 0.9925 |
| 0.1549 | 3.0 | 390 | 0.1065 | 0.9774 |
| 0.0952 | 4.0 | 520 | 0.0866 | 0.9850 |
| 0.1511 | 5.0 | 650 | 0.0875 | 0.9774 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
pheinisch/roberta-base-150T-argumentative-sentence-detector
|
pheinisch
| 2022-11-23T14:37:42Z | 116 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"argument mining",
"claims",
"sentence classification",
"en",
"dataset:FS150T",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-09T07:04:50Z |
---
language:
- "en"
tags:
- "argument mining"
- "claims"
- "sentence classification"
datasets:
- "FS150T"
metrics:
- "accuracy"
- "f1"
---
# _EXPERIMENTAL_ roberta-base-150T-argumentative-sentence-detector
(this model might not be the optimal one for accomplishing the task)
- Task: Detects whether a sentence is argumentative (1 - yes/ 0 - not) given the topic and the sentence itself.
- language: English
- dataset: Few-Shot-150T Corpus v1.1 (FS150T-Corpus) _fine-tuned roberta-base_
## Performace on test data (threshold: 0.5)
````
{'accuracy': 0.7451388888888889,
'f1': 0.6690712353471596,
'precision': 0.733201581027668,
'recall': 0.615257048092869}
````
|
jamiehudson/579-STmodel-v3
|
jamiehudson
| 2022-11-23T14:29:06Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-23T14:28:54Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1800 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1800,
"warmup_steps": 180,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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 -->
|
PlanTL-GOB-ES/roberta-base-es-wikicat-es
|
PlanTL-GOB-ES
| 2022-11-23T14:02:14Z | 332 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"español",
"text classification",
"WikiCAT_esv2",
"es",
"dataset:projecte-aina/WikiCAT_esv2",
"arxiv:1907.11692",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-23T13:34:08Z |
---
language:
- es
license: apache-2.0
tags:
- "español"
- "text classification"
- "WikiCAT_esv2"
datasets:
- "projecte-aina/WikiCAT_esv2"
metrics:
- f1-macro
model-index:
- name: roberta-base-es-wikicat-es
results:
- task:
type: text-classification
dataset:
type: projecte-aina/WikiCAT_esv2
name: WikiCAT_esv2
metrics:
- name: F1-macro
type: f1
value: 0.76632
- name: Accuracy
type: accuracy
value: 0.79347
widget:
- text: "Sedna es el cuerpo menor del sistema solar número 90377; concretamente es un objeto transneptuniano."
- text: "El Fútbol Club Barcelona, conocido popularmente como Barça, es una entidad polideportiva con sede en Barcelona, España."
---
# Spanish BERTa-v2 (roberta-base-es) finetuned for Text Classification.
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-uses-and-limitations)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
- [Training data](#training-data)
- [Training procedure](#training-procedure)
- [Evaluation](#evaluation)
- [Variable and metrics](#variable-and-metrics)
- [Evaluation results](#evaluation-results)
- [Additional information](#additional-information)
- [Author](#author)
- [Contact information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licensing-information)
- [Funding](#funding)
- [Disclaimer](#disclaimer)
</details>
## Model description
The **roberta-base-es-wikicat-es** is a Text Classification model for the Catalan language fine-tuned from the [roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-bne model card for more details).
## Intended uses and limitations
**roberta-base-es-wikicat-es** model can be used to classify texts. The model is limited by its training dataset and may not generalize well for all use cases.
## How to use
Here is how to use this model:
```python
from transformers import pipeline
from pprint import pprint
nlp = pipeline("text-classification", model="roberta-base-es-wikicat-es")
example = "Sedna es el cuerpo menor del sistema solar número 90377; concretamente es un objeto transneptuniano."
tc_results = nlp(example)
pprint(tc_results)
```
## Limitations and bias
At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
## Training
### Training data
We used the TC dataset in Spanish called [WikiCAT_esv2](https://huggingface.co/datasets/PlanTL-GOB-ES/WikiCAT_esv2) for training and evaluation.
### Training procedure
The model was trained with a batch size of 16 and three learning rates (1e-5, 3e-5, 5e-5) for 5 epochs. We then selected the best learning rate (2e-5) and checkpoint (epoch 3) using the downstream task metric in the corresponding development set.
## Evaluation
### Variable and metrics
This model was finetuned maximizing F1 (macro) score.
### Evaluation results
We evaluated the _roberta-base-es-wikicat-es_ on the WikiCAT_esv2 dev set:
| Model | WikiCAT_ca (F1)|
| ------------|:-------------|
| rroberta-base-es-wikicat-es | 0.76632 |
For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club).
## Additional information
### Author
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
### Contact information
For further information, send an email to aina@bsc.es
### Copyright
Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center
### Licensing information
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
## Disclaimer
<details>
<summary>Click to expand</summary>
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
|
mshuggingface/swin-tiny-patch4-window7-224-ms-test1
|
mshuggingface
| 2022-11-23T13:54:56Z | 205 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-11-23T13:51:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-ms-test1
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: 0.5
---
<!-- 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-ms-test1
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: 0.6036
- Accuracy: 0.5
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 0.7667 | 0.5 |
| No log | 2.0 | 2 | 0.6644 | 0.5 |
| No log | 3.0 | 3 | 0.6036 | 0.5 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
archipela/ell-vocabulary
|
archipela
| 2022-11-23T13:33:26Z | 100 | 0 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"text-regression",
"unk",
"dataset:huynhdoo/autotrain-data-ell-vocabulary",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-11-23T13:31:43Z |
---
tags:
- autotrain
- text-regression
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- huynhdoo/autotrain-data-ell-vocabulary
co2_eq_emissions:
emissions: 2.3719978527185237
---
# Model Trained Using AutoTrain
- Problem type: Single Column Regression
- Model ID: 2218271145
- CO2 Emissions (in grams): 2.3720
## Validation Metrics
- Loss: 0.228
- MSE: 0.228
- MAE: 0.383
- R2: 0.343
- RMSE: 0.478
- Explained Variance: 0.402
## 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/huynhdoo/autotrain-ell-vocabulary-2218271145
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("huynhdoo/autotrain-ell-vocabulary-2218271145", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("huynhdoo/autotrain-ell-vocabulary-2218271145", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
jamiehudson/579-STmodel-v4
|
jamiehudson
| 2022-11-23T13:31:46Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-23T12:18:54Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1800 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1800,
"warmup_steps": 180,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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 -->
|
archipela/ell-cohesion
|
archipela
| 2022-11-23T13:30:47Z | 100 | 0 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"text-regression",
"unk",
"dataset:huynhdoo/autotrain-data-ell-cohesion",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-11-23T13:27:59Z |
---
tags:
- autotrain
- text-regression
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- huynhdoo/autotrain-data-ell-cohesion
co2_eq_emissions:
emissions: 4.569992504332477
---
# Model Trained Using AutoTrain
- Problem type: Single Column Regression
- Model ID: 2217971118
- CO2 Emissions (in grams): 4.5700
## Validation Metrics
- Loss: 0.259
- MSE: 0.259
- MAE: 0.407
- R2: 0.416
- RMSE: 0.509
- Explained Variance: 0.427
## 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/huynhdoo/autotrain-ell-cohesion-2217971118
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("huynhdoo/autotrain-ell-cohesion-2217971118", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("huynhdoo/autotrain-ell-cohesion-2217971118", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
aherzberg/wav2vec2-base-POSITIVE_NEGATIVE_ONLY_BALANCED_CLASSES
|
aherzberg
| 2022-11-23T13:27:27Z | 158 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-11-23T12:20:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-base-POSITIVE_NEGATIVE_ONLY_BALANCED_CLASSES
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-POSITIVE_NEGATIVE_ONLY_BALANCED_CLASSES
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3710
- Accuracy: 0.8822
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7822 | 0.96 | 18 | 0.6874 | 0.7424 |
| 0.5685 | 1.96 | 36 | 0.5974 | 0.7845 |
| 0.45 | 2.96 | 54 | 0.4988 | 0.8182 |
| 0.399 | 3.96 | 72 | 0.4583 | 0.8384 |
| 0.3457 | 4.96 | 90 | 0.4415 | 0.8451 |
| 0.352 | 5.96 | 108 | 0.3710 | 0.8822 |
| 0.2878 | 6.96 | 126 | 0.3881 | 0.8620 |
| 0.2669 | 7.96 | 144 | 0.4309 | 0.8502 |
| 0.2406 | 8.96 | 162 | 0.4271 | 0.8502 |
| 0.2491 | 9.96 | 180 | 0.4271 | 0.8502 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.14.0
- Tokenizers 0.10.3
|
heziiiii/ddpm-butterflies-128
|
heziiiii
| 2022-11-23T13:26:40Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-11-23T12:08:11Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/heziiiii/ddpm-butterflies-128/tensorboard?#scalars)
|
sd-concepts-library/yellow-cockatiel-parrot
|
sd-concepts-library
| 2022-11-23T12:50:05Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-11-23T12:49:55Z |
---
license: mit
---
### Yellow Cockatiel Parrot on Stable Diffusion
This is the `<rosa-popugai>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
cafeai/cafe_aesthetic
|
cafeai
| 2022-11-23T12:08:27Z | 3,264 | 50 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-11-14T09:56:39Z |
---
license: agpl-3.0
---
# Info
Since people are downloading this and I don't know why, I'll add some information. This model is an image classifier fine-tuned on `microsoft/beit-base-patch16-384`.
Its purpose is to be used in the dataset conditioning step for the [Waifu Diffusion project](https://huggingface.co/hakurei/waifu-diffusion), a fine-tune effort for Stable Diffusion. As WD1.4 is planned to have a *significantly large dataset* (~15m images), it is infeasible to analyze every image manually to determine whether or not it should be included in the final training dataset. This image classifier is trained on approximately 3.5k real-life and anime/manga images. Its purpose is to remove aesthetically worthless images from our dataset by classifying them as "`not_aesthetic`". The image classifier was trained to **err on the side of caution** and will generally tend to include images unless they are in a "manga-like" format, have messy lines and/or are sketches, or include an unacceptable amount of text (namely text that covers the primary subject of the image). The idea is that certain images will hurt a SD fine-tune.
Note: This classifier is not perfect, just like every other classifier out there. However, with a sufficiently large dataset, any imperfections or misclassifications should average themselves out due to the Law of Large Numbers.
You can test out the classifier [here](https://huggingface.co/spaces/cafeai/cafe_aesthetic_demo), along with some other classifiers for the project.
# License
Released under the aGPLv3. Use the model as you wish for any purpose. If you make changes, share the changes.
|
christofid/dabert-multi
|
christofid
| 2022-11-23T12:05:14Z | 121 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-11-23T11:43:17Z |
---
license: mit
---
### dapBERT
DapBERT-multi is a BERT-like model trained based on the domain adaptive pretraining method ([Gururangan et al.](https://aclanthology.org/2020.acl-main.740/)) for the patent domain. Bert-base-multilingual-cased is used as base for the training. The training dataset used consists of a corpus of 10,000,000
patent abstracts that have been filed between 1998-2020 in US and European patent offices as well as the World Intellectual Property Organization.
|
gwz0202/ddpm-butterflied-128
|
gwz0202
| 2022-11-23T12:03:46Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/few-shot-pokemon",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-11-23T10:51:41Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/few-shot-pokemon
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflied-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/few-shot-pokemon` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/gwz0202/ddpm-butterflied-128/tensorboard?#scalars)
|
dscoursetechnion/t5-small-finetuned-xsum
|
dscoursetechnion
| 2022-11-23T12:03:09Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-23T08:03:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 26.7823
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5658
- Rouge1: 26.7823
- Rouge2: 6.7168
- Rougel: 20.9066
- Rougelsum: 20.9054
- Gen Len: 18.8193
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.8016 | 1.0 | 4251 | 2.5658 | 26.7823 | 6.7168 | 20.9066 | 20.9054 | 18.8193 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Intel/bert-mini-sst2-distilled-sparse-90-1X4-block
|
Intel
| 2022-11-23T11:48:53Z | 115 | 1 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-16T01:44:17Z |
---
license: mit
---
# Sparse BERT mini model (uncased)
Finetuned model pruned to 1:4 structured sparsity.
The model is a pruned version of the [BERT mini model](https://huggingface.co/prajjwal1/bert-mini).
## Intended Use
The model can be used for inference with sparsity optimization.
For further details on the model and its usage will be soon available.
## Evaluation Results
We get the following results on the sst2 tasks development set:
| Task | SST-2 (Acc) |
|------|-------------|
| | 87.2 |
Better than dense [bert mini](https://huggingface.co/M-FAC/bert-mini-finetuned-sst2) which is 84.74%.
|
josiahkhor/en_triage_subject
|
josiahkhor
| 2022-11-23T11:43:56Z | 5 | 0 |
spacy
|
[
"spacy",
"text-classification",
"en",
"region:us"
] |
text-classification
| 2022-11-23T11:30:59Z |
---
tags:
- spacy
- text-classification
language:
- en
model-index:
- name: en_triage_subject
results: []
---
| Feature | Description |
| --- | --- |
| **Name** | `en_triage_subject` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.4.3,<3.5.0` |
| **Default Pipeline** | `tok2vec`, `textcat` |
| **Components** | `tok2vec`, `textcat` |
| **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (5 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`textcat`** | `General Correspondence`, `Invoice`, `New Claim Form`, `Assessor Report`, `Complaint` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `CATS_SCORE` | 79.52 |
| `CATS_MICRO_P` | 99.34 |
| `CATS_MICRO_R` | 99.34 |
| `CATS_MICRO_F` | 99.34 |
| `CATS_MACRO_P` | 79.37 |
| `CATS_MACRO_R` | 79.67 |
| `CATS_MACRO_F` | 79.52 |
| `CATS_MACRO_AUC` | 79.99 |
| `CATS_MACRO_AUC_PER_TYPE` | 0.00 |
| `TOK2VEC_LOSS` | 25952.93 |
| `TEXTCAT_LOSS` | 58.98 |
|
tubyneto/crowdedflowertunedbert
|
tubyneto
| 2022-11-23T11:21:31Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-23T11:21:19Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# tubyneto/crowdedflowertunedbert
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('tubyneto/crowdedflowertunedbert')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=tubyneto/crowdedflowertunedbert)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 916 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters:
```
{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(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})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Watwat100/gpu2
|
Watwat100
| 2022-11-23T11:06:00Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-23T11:05:48Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2347 with parameters:
```
{'batch_size': 12, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 4694,
"warmup_steps": 470,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(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})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
jesspi/IFE-sentence-model
|
jesspi
| 2022-11-23T10:29:47Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-23T10:29:34Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 3170 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 6.629946430758516e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 3170,
"warmup_steps": 317,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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 -->
|
selmey/behaviour-change-valence-german
|
selmey
| 2022-11-23T10:02:13Z | 103 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-23T09:17:40Z |
Bert-base-german-cased finetuned on the Valence level of the GLoHBCD Dataset (https://github.com/SelinaMeyer/GLoHBCD).
The dataset leverages Motivational Interviewing client behaviour codes to evaluate user utterances across different dimensions and gauge user's stance and thoughts about behaviour change in the context of weight loss.
This model classifies German text around behaviour change as either "Change Talk" (utterances in favour of change, 1) or "Sustain Talk" (utterances in favour of the status quo, 0).
When using the model, please cite:
@InProceedings{meyer-elsweiler:2022:LREC,
author = {Meyer, Selina and Elsweiler, David},
title = {GLoHBCD: A Naturalistic German Dataset for Language of Health Behaviour Change on Online Support Forums},
booktitle = {Proceedings of the Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {2226--2235},
url = {https://aclanthology.org/2022.lrec-1.239}}
|
cgt/pert-qa
|
cgt
| 2022-11-23T09:46:49Z | 47 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:cmrc2018",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-11-03T06:29:16Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cmrc2018
model-index:
- name: pert-qa
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. -->
# pert-qa
This model is a fine-tuned version of [hfl/chinese-pert-large](https://huggingface.co/hfl/chinese-pert-large) on the cmrc2018 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6942
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1273 | 1.0 | 1200 | 0.7088 |
| 0.6132 | 2.0 | 2400 | 0.6942 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.10.0+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Watwat100/gpu1
|
Watwat100
| 2022-11-23T09:19:44Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-23T09:19:31Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1744 with parameters:
```
{'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1744,
"warmup_steps": 175,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(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})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Roy029/mpyt5_e20
|
Roy029
| 2022-11-23T08:58:44Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-22T09:15:04Z |
---
license: openrail
---
# Model Card for mpyt5_e15
<!-- Provide a quick summary of what the model is/does. [Optional] -->
事前に自然言語だけでなくPythonを学習したモデル
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
Python Code (1.05GB)
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
- MLM
- python vocab (https://huggingface.co/kkuramitsu/mt5-pytoken)
### Preprocessing
mT5 + Python
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
- mT5-small(300M Paramators)
- max_length = 128
# Model Version
- *epoch5： https://huggingface.co/Roy029/mpyt5_e5
- *epoch10： https://huggingface.co/Roy029/mpyt5_e10
- *epoch15： https://huggingface.co/Roy029/mpyt5_e15
- *epoch20： This Model
|
Roy029/mpyt5_e15
|
Roy029
| 2022-11-23T08:57:10Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-22T11:18:09Z |
---
license: openrail
---
# Model Card for mpyt5_e15
<!-- Provide a quick summary of what the model is/does. [Optional] -->
事前に自然言語だけでなくPythonを学習したモデル
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
Python Code (1.05GB)
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
- MLM
- python vocab (https://huggingface.co/kkuramitsu/mt5-pytoken)
### Preprocessing
mT5 + Python
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
- mT5-small(300M Paramators)
- max_length = 128
# Model Version
- *epoch5： https://huggingface.co/Roy029/mpyt5_e5
- *epoch10： https://huggingface.co/Roy029/mpyt5_e10
- *epoch15： This Model
- *epoch20： https://huggingface.co/Roy029/mpyt5_e20
|
birgermoell/whisper-small-sv-bm
|
birgermoell
| 2022-11-23T08:54:31Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"sv",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-05T00:29:07Z |
---
language:
- sv
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: WhisperSmallSwedishBirgerMoell
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: sv-SE
split: train+validation
args: sv-SE
metrics:
- name: Wer
type: wer
value: 19.58538356053884
---
<!-- 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. -->
# WhisperSmallSwedishBirgerMoell
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3253
- Wer: 19.5854
## 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1523 | 1.29 | 1000 | 0.2924 | 21.5509 |
| 0.0515 | 2.59 | 2000 | 0.2856 | 20.4593 |
| 0.0214 | 3.88 | 3000 | 0.3010 | 19.9054 |
| 0.0042 | 5.17 | 4000 | 0.3253 | 19.5854 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1
|
eikoenchine/xlm-roberta-base-finetuned-panx-all
|
eikoenchine
| 2022-11-23T08:42:37Z | 137 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-23T08:29:14Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1713
- F1: 0.8544
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3076 | 1.0 | 835 | 0.2008 | 0.7923 |
| 0.1565 | 2.0 | 1670 | 0.1809 | 0.8437 |
| 0.1027 | 3.0 | 2505 | 0.1713 | 0.8544 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.7.0
- Tokenizers 0.12.1
|
crodri/autotrain-wikicat_es-2213570987
|
crodri
| 2022-11-23T08:18:56Z | 101 | 0 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"text-classification",
"es",
"dataset:crodri/autotrain-data-wikicat_es",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-23T08:07:19Z |
---
tags:
- autotrain
- text-classification
language:
- es
widget:
- text: "El Fútbol Club Barcelona, conocido popularmente como Barça, es una entidad polideportiva con sede en Barcelona, España."
datasets:
- crodri/autotrain-data-wikicat_es
co2_eq_emissions:
emissions: 10.4216765068249
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2213570987
- CO2 Emissions (in grams): 10.4217
## Validation Metrics
- Loss: 0.713
- Accuracy: 0.786
- Macro F1: 0.758
- Micro F1: 0.786
- Weighted F1: 0.785
- Macro Precision: 0.762
- Micro Precision: 0.786
- Weighted Precision: 0.787
- Macro Recall: 0.757
- Micro Recall: 0.786
- Weighted Recall: 0.786
## 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/crodri/autotrain-wikicat_es-2213570987
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("crodri/autotrain-wikicat_es-2213570987", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("crodri/autotrain-wikicat_es-2213570987", use_auth_token=True)
inputs = tokenizer("El Fútbol Club Barcelona, conocido popularmente como Barça, es una entidad polideportiva con sede en Barcelona, España.", return_tensors="pt")
outputs = model(**inputs)
```
|
utkarshbelkhede/distill-pegasus-sec-10K
|
utkarshbelkhede
| 2022-11-23T08:03:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-23T07:14:46Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distill-pegasus-cnn-16-4-sec
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. -->
# distill-pegasus-cnn-16-4-sec
This model is a fine-tuned version of [sshleifer/distill-pegasus-cnn-16-4](https://huggingface.co/sshleifer/distill-pegasus-cnn-16-4) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0146
- Rouge1: 48.3239
- Rouge2: 34.4713
- Rougel: 43.5113
- Rougelsum: 46.371
- Gen Len: 106.98
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 99 | 3.0918 | 20.297 | 6.5201 | 16.1329 | 18.0062 | 64.38 |
| No log | 2.0 | 198 | 2.4999 | 23.2475 | 10.4548 | 19.4955 | 21.3927 | 73.92 |
| No log | 3.0 | 297 | 2.0991 | 25.1919 | 13.2866 | 22.1497 | 23.7988 | 80.5 |
| No log | 4.0 | 396 | 1.7855 | 29.3799 | 17.4892 | 26.0768 | 27.3547 | 84.08 |
| No log | 5.0 | 495 | 1.5388 | 34.3057 | 21.5888 | 30.043 | 32.1758 | 98.26 |
| 2.7981 | 6.0 | 594 | 1.3553 | 36.5817 | 22.9587 | 32.0113 | 34.3963 | 95.02 |
| 2.7981 | 7.0 | 693 | 1.2281 | 37.9149 | 24.4547 | 33.9621 | 35.7424 | 90.04 |
| 2.7981 | 8.0 | 792 | 1.1430 | 40.9219 | 27.4248 | 36.1746 | 38.8887 | 96.56 |
| 2.7981 | 9.0 | 891 | 1.0844 | 43.935 | 29.7536 | 38.63 | 41.6618 | 98.7 |
| 2.7981 | 10.0 | 990 | 1.0472 | 45.3353 | 32.042 | 40.8945 | 43.3416 | 106.22 |
| 1.5684 | 11.0 | 1089 | 1.0254 | 47.6564 | 34.3221 | 43.1757 | 45.7094 | 107.88 |
| 1.5684 | 12.0 | 1188 | 1.0146 | 48.3239 | 34.4713 | 43.5113 | 46.371 | 106.98 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
xaeroq/dqn-Qbert-v5
|
xaeroq
| 2022-11-23T07:49:54Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"ALE/Qbert-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-23T07:49:30Z |
---
library_name: stable-baselines3
tags:
- ALE/Qbert-v5
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: ALE/Qbert-v5
type: ALE/Qbert-v5
metrics:
- type: mean_reward
value: 6665.00 +/- 1973.49
name: mean_reward
verified: false
---
# **DQN** Agent playing **ALE/Qbert-v5**
This is a trained model of a **DQN** agent playing **ALE/Qbert-v5**
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env ALE/Qbert-v5 -orga xaeroq -f logs/
python enjoy.py --algo dqn --env ALE/Qbert-v5 -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 ALE/Qbert-v5 -orga xaeroq -f logs/
rl_zoo3 enjoy --algo dqn --env ALE/Qbert-v5 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env ALE/Qbert-v5 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env ALE/Qbert-v5 -f logs/ -orga xaeroq
```
## 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)])
```
|
utkarshbelkhede/distilbart-sec-10K
|
utkarshbelkhede
| 2022-11-23T07:02:57Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-23T06:54:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-cnn-12-6-sec
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. -->
# distilbart-cnn-12-6-sec
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1379
- Rouge1: 72.2845
- Rouge2: 61.1501
- Rougel: 67.6999
- Rougelsum: 70.9968
- Gen Len: 113.8
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 99 | 0.4429 | 56.0806 | 40.5969 | 47.5271 | 53.7227 | 115.44 |
| No log | 2.0 | 198 | 0.2279 | 56.6042 | 42.1781 | 48.9542 | 54.951 | 116.84 |
| No log | 3.0 | 297 | 0.1845 | 65.9646 | 51.8575 | 59.8647 | 64.103 | 113.8 |
| No log | 4.0 | 396 | 0.1532 | 71.6132 | 61.1434 | 67.4165 | 70.4093 | 110.46 |
| No log | 5.0 | 495 | 0.1379 | 72.2845 | 61.1501 | 67.6999 | 70.9968 | 113.8 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Mentatko/distilbert-base-uncased-finetuned-squad
|
Mentatko
| 2022-11-23T06:34:56Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-11-22T05:12:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.23.1
- Pytorch 1.13.0+cpu
- Datasets 2.6.1
- Tokenizers 0.13.2
|
wyu1/GenRead-3B-TQA
|
wyu1
| 2022-11-23T05:04:45Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"license:cc-by-4.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2022-11-23T04:41:41Z |
---
license: cc-by-4.0
---
# GenRead: FiD model trained on TQA
-- This is the model checkpoint of GenRead [2], based on the T5-3B and trained on the TriviaQA [1].
-- Hyperparameters: 8 x 80GB A100 GPUs; batch size 16; AdamW; LR 6e-5; best dev at 8500 steps
References:
[1] TriviaQA: A Large Scale Dataset for Reading Comprehension and Question Answering. ACL 2017
[2] Generate rather than Retrieve: Large Language Models are Strong Context Generators. arXiv 2022
## Model performance
We evaluate it on the TriviaQA dataset, the EM score is 71.55.
<a href="https://huggingface.co/exbert/?model=bert-base-uncased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
---
license: cc-by-4.0
---
|
alexziweiwang/exp17-F03-both
|
alexziweiwang
| 2022-11-23T04:23:20Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-23T00:00:04Z |
---
tags:
- generated_from_trainer
model-index:
- name: exp17-F03-both
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. -->
# exp17-F03-both
This model is a fine-tuned version of [yongjian/wav2vec2-large-a](https://huggingface.co/yongjian/wav2vec2-large-a) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9268
- Wer: 0.9485
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 47.4704 | 0.36 | 500 | 3.3075 | 1.0131 |
| 3.1649 | 0.71 | 1000 | 3.3442 | 1.0 |
| 2.9674 | 1.07 | 1500 | 2.6986 | 1.0 |
| 2.7514 | 1.42 | 2000 | 2.5789 | 1.1299 |
| 2.6045 | 1.78 | 2500 | 2.3025 | 1.2529 |
| 2.373 | 2.14 | 3000 | 2.2169 | 1.2698 |
| 2.1632 | 2.49 | 3500 | 1.9883 | 1.2667 |
| 2.0942 | 2.85 | 4000 | 1.9294 | 1.2567 |
| 1.9239 | 3.2 | 4500 | 1.9799 | 1.2467 |
| 1.7549 | 3.56 | 5000 | 1.7485 | 1.2252 |
| 1.6973 | 3.91 | 5500 | 1.6799 | 1.2283 |
| 1.5823 | 4.27 | 6000 | 1.6847 | 1.2267 |
| 1.4761 | 4.63 | 6500 | 1.6971 | 1.1968 |
| 1.4381 | 4.98 | 7000 | 1.6280 | 1.2052 |
| 1.2509 | 5.34 | 7500 | 1.6657 | 1.2060 |
| 1.3112 | 5.69 | 8000 | 1.5618 | 1.1783 |
| 1.1851 | 6.05 | 8500 | 1.6555 | 1.1783 |
| 1.1112 | 6.41 | 9000 | 1.6586 | 1.1752 |
| 1.0463 | 6.76 | 9500 | 1.6135 | 1.1683 |
| 1.041 | 7.12 | 10000 | 1.5444 | 1.1522 |
| 0.9451 | 7.47 | 10500 | 1.5561 | 1.1622 |
| 0.9454 | 7.83 | 11000 | 1.5044 | 1.1483 |
| 0.8496 | 8.19 | 11500 | 1.6724 | 1.1330 |
| 0.825 | 8.54 | 12000 | 1.5950 | 1.1414 |
| 0.8291 | 8.9 | 12500 | 1.6023 | 1.1384 |
| 0.7279 | 9.25 | 13000 | 1.6319 | 1.1314 |
| 0.7394 | 9.61 | 13500 | 1.5478 | 1.1337 |
| 0.7079 | 9.96 | 14000 | 1.7564 | 1.1453 |
| 0.609 | 10.32 | 14500 | 1.7671 | 1.1245 |
| 0.6639 | 10.68 | 15000 | 1.7471 | 1.1314 |
| 0.648 | 11.03 | 15500 | 1.7694 | 1.2160 |
| 0.577 | 11.39 | 16000 | 1.6149 | 1.1760 |
| 0.577 | 11.74 | 16500 | 1.9288 | 1.1238 |
| 0.5695 | 12.1 | 17000 | 1.7503 | 1.1253 |
| 0.5326 | 12.46 | 17500 | 1.5635 | 1.1376 |
| 0.5423 | 12.81 | 18000 | 1.7083 | 1.1668 |
| 0.4775 | 13.17 | 18500 | 1.7054 | 1.1245 |
| 0.4772 | 13.52 | 19000 | 1.6455 | 1.1045 |
| 0.4737 | 13.88 | 19500 | 1.5996 | 1.0968 |
| 0.4529 | 14.23 | 20000 | 1.9847 | 1.1653 |
| 0.4461 | 14.59 | 20500 | 1.6845 | 1.1084 |
| 0.4497 | 14.95 | 21000 | 1.6465 | 1.0938 |
| 0.4096 | 15.3 | 21500 | 1.5919 | 1.0769 |
| 0.3897 | 15.66 | 22000 | 1.5637 | 1.0761 |
| 0.4234 | 16.01 | 22500 | 1.6360 | 1.0953 |
| 0.3659 | 16.37 | 23000 | 1.7573 | 1.0830 |
| 0.3352 | 16.73 | 23500 | 1.8474 | 1.0976 |
| 0.3886 | 17.08 | 24000 | 1.9115 | 1.0953 |
| 0.3255 | 17.44 | 24500 | 1.8820 | 1.0815 |
| 0.3405 | 17.79 | 25000 | 1.6862 | 1.0346 |
| 0.3205 | 18.15 | 25500 | 1.6912 | 1.0500 |
| 0.322 | 18.51 | 26000 | 1.6253 | 1.0615 |
| 0.296 | 18.86 | 26500 | 1.7924 | 1.0546 |
| 0.2869 | 19.22 | 27000 | 1.8204 | 1.0899 |
| 0.269 | 19.57 | 27500 | 1.7558 | 1.0292 |
| 0.2844 | 19.93 | 28000 | 1.6038 | 1.0131 |
| 0.2543 | 20.28 | 28500 | 1.7935 | 1.0161 |
| 0.3025 | 20.64 | 29000 | 1.8706 | 1.0423 |
| 0.2707 | 21.0 | 29500 | 2.0011 | 1.0208 |
| 0.2401 | 21.35 | 30000 | 1.9058 | 1.0161 |
| 0.2609 | 21.71 | 30500 | 1.7555 | 1.0015 |
| 0.2403 | 22.06 | 31000 | 1.9301 | 1.0085 |
| 0.2538 | 22.42 | 31500 | 1.8586 | 0.9969 |
| 0.2334 | 22.78 | 32000 | 1.8588 | 0.9985 |
| 0.2013 | 23.13 | 32500 | 1.9307 | 1.0108 |
| 0.2122 | 23.49 | 33000 | 1.8830 | 0.9908 |
| 0.2242 | 23.84 | 33500 | 1.8133 | 0.9754 |
| 0.188 | 24.2 | 34000 | 1.8435 | 0.9800 |
| 0.2142 | 24.56 | 34500 | 1.8491 | 0.9792 |
| 0.2059 | 24.91 | 35000 | 1.8005 | 0.9754 |
| 0.1794 | 25.27 | 35500 | 1.8845 | 0.9700 |
| 0.185 | 25.62 | 36000 | 1.8620 | 0.9731 |
| 0.1843 | 25.98 | 36500 | 1.8461 | 0.9539 |
| 0.1717 | 26.33 | 37000 | 1.8100 | 0.9639 |
| 0.164 | 26.69 | 37500 | 1.8192 | 0.9547 |
| 0.1888 | 27.05 | 38000 | 1.8005 | 0.9470 |
| 0.1792 | 27.4 | 38500 | 1.8901 | 0.9562 |
| 0.1708 | 27.76 | 39000 | 1.8306 | 0.9547 |
| 0.1508 | 28.11 | 39500 | 1.8934 | 0.9508 |
| 0.1751 | 28.47 | 40000 | 1.8956 | 0.9523 |
| 0.1541 | 28.83 | 40500 | 1.9360 | 0.9416 |
| 0.1611 | 29.18 | 41000 | 1.9346 | 0.9454 |
| 0.1684 | 29.54 | 41500 | 1.9247 | 0.9470 |
| 0.1463 | 29.89 | 42000 | 1.9268 | 0.9485 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.13.2
|
caffeinism/ddpm-butterflies-128
|
caffeinism
| 2022-11-23T04:20:02Z | 2 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-11-21T09:47:12Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/caffeinism/ddpm-butterflies-128/tensorboard?#scalars)
|
Migueluao123/roberta-base-bne-finetuned-amazon_reviews_multi
|
Migueluao123
| 2022-11-23T03:30:00Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-23T02:45:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2215
- Accuracy: 0.9343
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1948 | 1.0 | 1250 | 0.1743 | 0.933 |
| 0.0979 | 2.0 | 2500 | 0.2215 | 0.9343 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Tokenizers 0.13.2
|
sd-concepts-library/dreams
|
sd-concepts-library
| 2022-11-23T03:28:49Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2022-11-23T03:28:44Z |
---
license: mit
---
### Dreams on Stable Diffusion
This is the `<meeg>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
Jellywibble/gpt2_dalio_reward_model_v0
|
Jellywibble
| 2022-11-23T03:25:01Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-classification",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-23T03:00:21Z |
https://wandb.ai/jellywibble/huggingface/runs/fwr1rnir?workspace=user-jellywibble
|
Egrt/Luuuu
|
Egrt
| 2022-11-23T02:54:17Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-03-20T12:11:42Z |
---
license: apache-2.0
---
|
jeveloper/sd-v1-4
|
jeveloper
| 2022-11-23T02:50:59Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-11-23T02:50:59Z |
---
license: creativeml-openrail-m
---
|
nhanv/ner_cv
|
nhanv
| 2022-11-23T01:27:32Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-23T01:25:59Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: reco-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. -->
# reco-ner
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0668
- Precision: 0.8125
- Recall: 0.8790
- F1: 0.8444
- Accuracy: 0.9819
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4516 | 1.0 | 626 | 0.4047 | 0.4332 | 0.4564 | 0.4445 | 0.8980 |
| 0.3677 | 2.0 | 1252 | 0.2774 | 0.4918 | 0.5731 | 0.5293 | 0.9193 |
| 0.2892 | 3.0 | 1878 | 0.2133 | 0.6139 | 0.6581 | 0.6353 | 0.9384 |
| 0.2736 | 4.0 | 2504 | 0.1772 | 0.6248 | 0.6854 | 0.6537 | 0.9488 |
| 0.221 | 5.0 | 3130 | 0.1503 | 0.6295 | 0.7328 | 0.6772 | 0.9560 |
| 0.1569 | 6.0 | 3756 | 0.1283 | 0.6821 | 0.8108 | 0.7409 | 0.9623 |
| 0.1534 | 7.0 | 4382 | 0.0995 | 0.7412 | 0.8119 | 0.7749 | 0.9708 |
| 0.089 | 8.0 | 5008 | 0.0846 | 0.7695 | 0.8353 | 0.8010 | 0.9760 |
| 0.0923 | 9.0 | 5634 | 0.0743 | 0.7881 | 0.8740 | 0.8289 | 0.9789 |
| 0.0711 | 10.0 | 6260 | 0.0668 | 0.8125 | 0.8790 | 0.8444 | 0.9819 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
flamesbob/Yadu_model
|
flamesbob
| 2022-11-23T01:16:33Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-11-08T00:22:31Z |
---
license: creativeml-openrail-m
---
To use draw emphasis from the training model include the word `m_yadu` in your prompt.
`yadu_model_6k` was trained on anythingv3 for 6000 steps, classification "artstyle"
License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:
You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here
|
AlekseyKorshuk/6.7b-dalio-principles-book-1-epoch-1-gas-6e-6-lr
|
AlekseyKorshuk
| 2022-11-23T00:59:42Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-22T12:39:25Z |
---
license: other
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 6.7b-dalio-principles-book-1-epoch-1-gas-6e-6-lr
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. -->
# 6.7b-dalio-principles-book-1-epoch-1-gas-6e-6-lr
This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4121
- Accuracy: 0.3487
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4875 | 0.11 | 1 | 2.5059 | 0.3397 |
| 2.5339 | 0.22 | 2 | 2.5059 | 0.3397 |
| 2.5161 | 0.33 | 3 | 2.5059 | 0.3397 |
| 2.4524 | 0.44 | 4 | 2.5059 | 0.3397 |
| 2.554 | 0.56 | 5 | 2.4785 | 0.3416 |
| 2.4678 | 0.67 | 6 | 2.4785 | 0.3416 |
| 2.4836 | 0.78 | 7 | 2.4473 | 0.3458 |
| 2.4138 | 0.89 | 8 | 2.4297 | 0.3473 |
| 2.4551 | 1.0 | 9 | 2.4121 | 0.3487 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
mwmathis/DeepLabCutModelZoo-full_cheetah
|
mwmathis
| 2022-11-23T00:39:10Z | 0 | 0 | null |
[
"computer_vision",
"pose_estimation",
"arxiv:2103.13282",
"license:lgpl-3.0",
"region:us"
] | null | 2022-11-23T00:38:27Z |
---
license: lgpl-3.0
tags:
- computer_vision
- pose_estimation
---
Model from Joska et al. 2021 ICRA please cite: https://arxiv.org/abs/2103.13282
|
sd-concepts-library/alberto-montt
|
sd-concepts-library
| 2022-11-23T00:37:04Z | 0 | 7 | null |
[
"license:mit",
"region:us"
] | null | 2022-11-23T00:36:55Z |
---
license: mit
---
### Alberto_Montt on Stable Diffusion
This is the `<AlbertoMontt>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
manirai91/mbert-conll2003
|
manirai91
| 2022-11-23T00:19:30Z | 119 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-22T23:16:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
model-index:
- name: mbert-conll2003
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. -->
# mbert-conll2003
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0
- Datasets 2.7.0
- Tokenizers 0.13.2
|
jeapaul/wav2vec2-base-torgo-demo-m04-nolm
|
jeapaul
| 2022-11-23T00:14:40Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-16T20:01:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-torgo-demo-m04-nolm
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-torgo-demo-m04-nolm
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5735
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:---:|
| 3.431 | 0.88 | 500 | 4.5567 | 1.0 |
| 3.4727 | 1.75 | 1000 | 3.5626 | 1.0 |
| 3.3879 | 2.63 | 1500 | 3.9274 | 1.0 |
| 3.3513 | 3.5 | 2000 | 3.4813 | 1.0 |
| 3.3538 | 4.38 | 2500 | 3.7300 | 1.0 |
| 3.3539 | 5.25 | 3000 | 3.5714 | 1.0 |
| 3.339 | 6.13 | 3500 | 3.6732 | 1.0 |
| 3.3038 | 7.01 | 4000 | 3.6788 | 1.0 |
| 3.35 | 7.88 | 4500 | 3.6715 | 1.0 |
| 3.338 | 8.76 | 5000 | 3.5161 | 1.0 |
| 3.3306 | 9.63 | 5500 | 3.7386 | 1.0 |
| 3.3266 | 10.51 | 6000 | 3.4908 | 1.0 |
| 3.3184 | 11.38 | 6500 | 3.7669 | 1.0 |
| 3.3189 | 12.26 | 7000 | 3.6142 | 1.0 |
| 3.331 | 13.13 | 7500 | 3.5619 | 1.0 |
| 3.3139 | 14.01 | 8000 | 3.6632 | 1.0 |
| 3.3069 | 14.89 | 8500 | 3.6127 | 1.0 |
| 3.315 | 15.76 | 9000 | 3.5562 | 1.0 |
| 3.3079 | 16.64 | 9500 | 3.7094 | 1.0 |
| 3.3077 | 17.51 | 10000 | 3.5412 | 1.0 |
| 3.3188 | 18.39 | 10500 | 3.6303 | 1.0 |
| 3.3133 | 19.26 | 11000 | 3.5704 | 1.0 |
| 3.3428 | 20.14 | 11500 | 3.5662 | 1.0 |
| 3.3082 | 21.02 | 12000 | 3.6084 | 1.0 |
| 3.3238 | 21.89 | 12500 | 3.6164 | 1.0 |
| 3.3119 | 22.77 | 13000 | 3.5787 | 1.0 |
| 3.2981 | 23.64 | 13500 | 3.6356 | 1.0 |
| 3.3153 | 24.52 | 14000 | 3.5726 | 1.0 |
| 3.3065 | 25.39 | 14500 | 3.5908 | 1.0 |
| 3.3199 | 26.27 | 15000 | 3.5823 | 1.0 |
| 3.306 | 27.15 | 15500 | 3.5658 | 1.0 |
| 3.3153 | 28.02 | 16000 | 3.5818 | 1.0 |
| 3.2762 | 28.9 | 16500 | 3.5810 | 1.0 |
| 3.3196 | 29.77 | 17000 | 3.5735 | 1.0 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.0.0
- Tokenizers 0.13.2
|
manirai91/mbert-imdb
|
manirai91
| 2022-11-22T23:08:42Z | 101 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-22T08:42:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: mbert-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbert-imdb
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0
- Datasets 2.7.0
- Tokenizers 0.13.2
|
unza/xls-r-300m-nyanja-fullset
|
unza
| 2022-11-22T23:02:48Z | 163 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"NyanjaSpeech",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-22T10:28:07Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- NyanjaSpeech
- generated_from_trainer
metrics:
- wer
model-index:
- name: xls-r-300m-nyanja-fullset
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. -->
# xls-r-300m-nyanja-fullset
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NYANJASPEECH - NYA dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1987
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 2.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3.3815 | 1.58 | 500 | 3.1987 | 1.0 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
monakth/distilbert-base-multilingual-cased-sv2
|
monakth
| 2022-11-22T22:26:39Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-11-22T22:24:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilbert-base-multilingual-cased-sv2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-sv2
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
sacculifer/dimbat_disaster_type_distilbert
|
sacculifer
| 2022-11-22T22:07:32Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-05T19:36:01Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: tmpzujlpono
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. -->
# Tweets disaster type classification model
This model was trained from part of Disaster Tweet Corpus 2020 (Analysis of Filtering Models for Disaster-Related Tweets, Wiegmann,M. et al, 2020) dataset
It achieves the following results on the evaluation set:
- Train Loss: 0.0875
- Train Accuracy: 0.8783
- Validation Loss: 0.2980
- Validation Accuracy: 0.8133
- Epoch: 5
## Model description
Labels
<br>
disease --- 1
<br>
earthquake --- 2
<br>
flood --- 3
<br>
hurricane & tornado --- 4
<br>
wildfire --- 5
<br>
industrial accident --- 6
<br>
societal crime --- 7
<br>
transportation accident --- 8
<br>
meteor crash --- 9
<br>
haze --- 0
## Intended uses & limitation
This model is able to detect 10 different type of disaster (nature and human-made), but it shows problem to detect the type 0 disaster due to the insignificant tweets and similarity to type 5 in the training dataset
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer:
<br>
batch_size = 16
<br>
num_epochs = 5
<br>
batches_per_epoch = len(tokenized_tweet["train"])//batch_size
<br>
total_train_steps = int(batches_per_epoch * num_epochs)
<br>
optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)
- training_precision: float32
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.9.2
- Datasets 2.4.0
- Tokenizers 0.12.1
### How to use it
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sacculifer/dimbat_disaster_type_distilbert")
model = TFAutoModelForSequenceClassification.from_pretrained("sacculifer/dimbat_disaster_type_distilbert")
|
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2
|
research-backup
| 2022-11-22T20:25:41Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:40:00Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.790515873015873
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.37967914438502676
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3857566765578635
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5063924402445803
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.646
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4517543859649123
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.42824074074074076
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8080458038270304
- name: F1 (macro)
type: f1_macro
value: 0.7357565896819839
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7894366197183098
- name: F1 (macro)
type: f1_macro
value: 0.4680529848631216
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.5520043336944745
- name: F1 (macro)
type: f1_macro
value: 0.5647005456999193
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9177157960631565
- name: F1 (macro)
type: f1_macro
value: 0.7991809595622609
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.770918207458477
- name: F1 (macro)
type: f1_macro
value: 0.701131895018139
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.37967914438502676
- Accuracy on SAT: 0.3857566765578635
- Accuracy on BATS: 0.5063924402445803
- Accuracy on U2: 0.4517543859649123
- Accuracy on U4: 0.42824074074074076
- Accuracy on Google: 0.646
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8080458038270304
- Micro F1 score on CogALexV: 0.7894366197183098
- Micro F1 score on EVALution: 0.5520043336944745
- Micro F1 score on K&H+N: 0.9177157960631565
- Micro F1 score on ROOT09: 0.770918207458477
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.790515873015873
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: triplet
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 10
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 2
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-2/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2
|
research-backup
| 2022-11-22T19:57:35Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:36:40Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8335714285714285
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.38235294117647056
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3798219584569733
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5336297943301834
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.662
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4473684210526316
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4166666666666667
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8625885189091457
- name: F1 (macro)
type: f1_macro
value: 0.8603027072164148
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8065727699530516
- name: F1 (macro)
type: f1_macro
value: 0.5506373401584694
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6175514626218852
- name: F1 (macro)
type: f1_macro
value: 0.6052063445391235
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9263406830354037
- name: F1 (macro)
type: f1_macro
value: 0.8061025838390545
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8373550611093701
- name: F1 (macro)
type: f1_macro
value: 0.837629132435287
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.38235294117647056
- Accuracy on SAT: 0.3798219584569733
- Accuracy on BATS: 0.5336297943301834
- Accuracy on U2: 0.4473684210526316
- Accuracy on U4: 0.4166666666666667
- Accuracy on Google: 0.662
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8625885189091457
- Micro F1 score on CogALexV: 0.8065727699530516
- Micro F1 score on EVALution: 0.6175514626218852
- Micro F1 score on K&H+N: 0.9263406830354037
- Micro F1 score on ROOT09: 0.8373550611093701
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8335714285714285
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: triplet
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 10
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 2
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-2/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
alryan1478/gpt2-wikitext2
|
alryan1478
| 2022-11-22T19:15:47Z | 175 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-22T16:54:38Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1085
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.561 | 1.0 | 2249 | 6.4685 |
| 6.1921 | 2.0 | 4498 | 6.1978 |
| 6.017 | 3.0 | 6747 | 6.1085 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.2
|
HarshitaDiddee/AmericasNLP_Kotiria
|
HarshitaDiddee
| 2022-11-22T18:58:36Z | 4 | 0 |
transformers
|
[
"transformers",
"wav2vec2",
"automatic-speech-recognition",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-22T18:56:28Z |
---
license: cc-by-4.0
---
ASR for Kotiria ( Data Source: AmericasNLP Shared Task for Low-Resource ASR)
|
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2
|
research-backup
| 2022-11-22T18:16:11Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:26:58Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.6346626984126984
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.32887700534759357
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3264094955489614
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.47581989994441354
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.464
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.37719298245614036
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.36342592592592593
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7761036612927528
- name: F1 (macro)
type: f1_macro
value: 0.7415561766602355
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7328638497652582
- name: F1 (macro)
type: f1_macro
value: 0.47573763054929613
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.5390032502708559
- name: F1 (macro)
type: f1_macro
value: 0.49194003623703636
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8753564721430062
- name: F1 (macro)
type: f1_macro
value: 0.7536524804914483
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8282670009401442
- name: F1 (macro)
type: f1_macro
value: 0.8236645741563291
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.32887700534759357
- Accuracy on SAT: 0.3264094955489614
- Accuracy on BATS: 0.47581989994441354
- Accuracy on U2: 0.37719298245614036
- Accuracy on U4: 0.36342592592592593
- Accuracy on Google: 0.464
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.7761036612927528
- Micro F1 score on CogALexV: 0.7328638497652582
- Micro F1 score on EVALution: 0.5390032502708559
- Micro F1 score on K&H+N: 0.8753564721430062
- Micro F1 score on ROOT09: 0.8282670009401442
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.6346626984126984
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: triplet
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 2
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2
|
research-backup
| 2022-11-22T17:54:16Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:24:50Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7584126984126984
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.32887700534759357
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3353115727002967
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.39466370205669815
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.504
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.39035087719298245
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.38425925925925924
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8323037516950429
- name: F1 (macro)
type: f1_macro
value: 0.8135716497645339
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7492957746478873
- name: F1 (macro)
type: f1_macro
value: 0.28766475530328117
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.5861321776814734
- name: F1 (macro)
type: f1_macro
value: 0.545958272767557
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.903109132642415
- name: F1 (macro)
type: f1_macro
value: 0.7624740127692404
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8429959260419931
- name: F1 (macro)
type: f1_macro
value: 0.8383818257665551
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.32887700534759357
- Accuracy on SAT: 0.3353115727002967
- Accuracy on BATS: 0.39466370205669815
- Accuracy on U2: 0.39035087719298245
- Accuracy on U4: 0.38425925925925924
- Accuracy on Google: 0.504
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8323037516950429
- Micro F1 score on CogALexV: 0.7492957746478873
- Micro F1 score on EVALution: 0.5861321776814734
- Micro F1 score on K&H+N: 0.903109132642415
- Micro F1 score on ROOT09: 0.8429959260419931
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7584126984126984
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: triplet
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 2
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-2/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
renjithman/finetuning-sentiment-model-3000-samples
|
renjithman
| 2022-11-22T17:43:52Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-22T17:30:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.87
- name: F1
type: f1
value: 0.8704318936877077
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3099
- Accuracy: 0.87
- F1: 0.8704
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1
|
research-backup
| 2022-11-22T17:34:18Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:40:04Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8018650793650793
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3502673796791444
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.35014836795252224
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5202890494719289
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.644
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.39035087719298245
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.43287037037037035
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8461654361910502
- name: F1 (macro)
type: f1_macro
value: 0.8411664963735426
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8145539906103286
- name: F1 (macro)
type: f1_macro
value: 0.5873414064116238
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6505958829902492
- name: F1 (macro)
type: f1_macro
value: 0.6269958308732405
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9319051262433052
- name: F1 (macro)
type: f1_macro
value: 0.8393686548194149
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7511751801942964
- name: F1 (macro)
type: f1_macro
value: 0.6464435364634403
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.3502673796791444
- Accuracy on SAT: 0.35014836795252224
- Accuracy on BATS: 0.5202890494719289
- Accuracy on U2: 0.39035087719298245
- Accuracy on U4: 0.43287037037037035
- Accuracy on Google: 0.644
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8461654361910502
- Micro F1 score on CogALexV: 0.8145539906103286
- Micro F1 score on EVALution: 0.6505958829902492
- Micro F1 score on K&H+N: 0.9319051262433052
- Micro F1 score on ROOT09: 0.7511751801942964
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8018650793650793
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: triplet
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 1
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2
|
research-backup
| 2022-11-22T17:33:29Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:22:15Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7463293650793651
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.34759358288770054
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3590504451038576
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.481378543635353
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.494
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3991228070175439
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.35648148148148145
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8610818140726232
- name: F1 (macro)
type: f1_macro
value: 0.8525458448699613
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8171361502347417
- name: F1 (macro)
type: f1_macro
value: 0.5610856949320919
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6229685807150596
- name: F1 (macro)
type: f1_macro
value: 0.6126645128177534
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9215413507685887
- name: F1 (macro)
type: f1_macro
value: 0.8042276096823726
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.857724851143842
- name: F1 (macro)
type: f1_macro
value: 0.8472661094927697
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.34759358288770054
- Accuracy on SAT: 0.3590504451038576
- Accuracy on BATS: 0.481378543635353
- Accuracy on U2: 0.3991228070175439
- Accuracy on U4: 0.35648148148148145
- Accuracy on Google: 0.494
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8610818140726232
- Micro F1 score on CogALexV: 0.8171361502347417
- Micro F1 score on EVALution: 0.6229685807150596
- Micro F1 score on K&H+N: 0.9215413507685887
- Micro F1 score on ROOT09: 0.857724851143842
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7463293650793651
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: triplet
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 2
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-2/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
Rahul-2022/detr-base-sroie
|
Rahul-2022
| 2022-11-22T17:31:26Z | 163 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"detr",
"object-detection",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2022-11-22T17:09:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: detr-base-sroie
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. -->
# detr-base-sroie
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1
|
research-backup
| 2022-11-22T17:31:19Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:38:22Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7048015873015873
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.37967914438502676
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3916913946587537
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5347415230683713
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.69
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.41228070175438597
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3888888888888889
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.853246948922706
- name: F1 (macro)
type: f1_macro
value: 0.8485536876305343
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8044600938967136
- name: F1 (macro)
type: f1_macro
value: 0.5726819680585065
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.5839653304442037
- name: F1 (macro)
type: f1_macro
value: 0.5524953070884607
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.934687347847256
- name: F1 (macro)
type: f1_macro
value: 0.8063588254058023
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8279536195549985
- name: F1 (macro)
type: f1_macro
value: 0.7955713493721125
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.37967914438502676
- Accuracy on SAT: 0.3916913946587537
- Accuracy on BATS: 0.5347415230683713
- Accuracy on U2: 0.41228070175438597
- Accuracy on U4: 0.3888888888888889
- Accuracy on Google: 0.69
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.853246948922706
- Micro F1 score on CogALexV: 0.8044600938967136
- Micro F1 score on EVALution: 0.5839653304442037
- Micro F1 score on K&H+N: 0.934687347847256
- Micro F1 score on ROOT09: 0.8279536195549985
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7048015873015873
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: triplet
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 10
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 1
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1
|
research-backup
| 2022-11-22T17:26:35Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:36:45Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7624206349206349
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3770053475935829
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3768545994065282
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.44580322401334077
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.57
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.39473684210526316
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.37962962962962965
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8797649540455025
- name: F1 (macro)
type: f1_macro
value: 0.8747086885506318
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7992957746478874
- name: F1 (macro)
type: f1_macro
value: 0.5104712427778083
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6397616468039004
- name: F1 (macro)
type: f1_macro
value: 0.6084431389476428
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9367044585101204
- name: F1 (macro)
type: f1_macro
value: 0.8301423655430062
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8677530554685051
- name: F1 (macro)
type: f1_macro
value: 0.8691031015559968
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.3770053475935829
- Accuracy on SAT: 0.3768545994065282
- Accuracy on BATS: 0.44580322401334077
- Accuracy on U2: 0.39473684210526316
- Accuracy on U4: 0.37962962962962965
- Accuracy on Google: 0.57
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8797649540455025
- Micro F1 score on CogALexV: 0.7992957746478874
- Micro F1 score on EVALution: 0.6397616468039004
- Micro F1 score on K&H+N: 0.9367044585101204
- Micro F1 score on ROOT09: 0.8677530554685051
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7624206349206349
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: triplet
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 1
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-triplet-1/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
alanoix/whisper-small-br
|
alanoix
| 2022-11-22T17:26:31Z | 80 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"br",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-22T09:51:24Z |
---
language:
- br
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-small-br
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
args: 'config: br, split: test'
metrics:
- name: Wer
type: wer
value: 49.98168162667155
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-br
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8542
- Wer: 49.9817
## 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1415 | 3.36 | 1000 | 0.7406 | 54.0117 |
| 0.0147 | 6.71 | 2000 | 0.7909 | 51.5479 |
| 0.0011 | 10.07 | 3000 | 0.8368 | 49.7710 |
| 0.0007 | 13.42 | 4000 | 0.8542 | 49.9817 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1
|
research-backup
| 2022-11-22T17:19:46Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:32:32Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.775079365079365
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3716577540106952
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3768545994065282
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.34185658699277377
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.428
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.37719298245614036
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3541666666666667
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.899201446436643
- name: F1 (macro)
type: f1_macro
value: 0.888889751667277
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7814553990610328
- name: F1 (macro)
type: f1_macro
value: 0.5516320672010655
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6408450704225352
- name: F1 (macro)
type: f1_macro
value: 0.6082440999373899
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9525631216526397
- name: F1 (macro)
type: f1_macro
value: 0.862670256588896
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.840802256345973
- name: F1 (macro)
type: f1_macro
value: 0.8106179148472547
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.3716577540106952
- Accuracy on SAT: 0.3768545994065282
- Accuracy on BATS: 0.34185658699277377
- Accuracy on U2: 0.37719298245614036
- Accuracy on U4: 0.3541666666666667
- Accuracy on Google: 0.428
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.899201446436643
- Micro F1 score on CogALexV: 0.7814553990610328
- Micro F1 score on EVALution: 0.6408450704225352
- Micro F1 score on K&H+N: 0.9525631216526397
- Micro F1 score on ROOT09: 0.840802256345973
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.775079365079365
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: triplet
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 1
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-triplet-1/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
datasciencemmw/old-beta1
|
datasciencemmw
| 2022-11-22T17:15:53Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"text-classification",
"en",
"dataset:LiveEvil/autotrain-data-copuml-production",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-22T17:14:48Z |
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- LiveEvil/autotrain-data-copuml-production
co2_eq_emissions:
emissions: 0.9758714074673083
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2205570752
- CO2 Emissions (in grams): 0.9759
## Validation Metrics
- Loss: 1.092
- Accuracy: 0.701
- Macro F1: 0.416
- Micro F1: 0.701
- Weighted F1: 0.670
- Macro Precision: 0.399
- Micro Precision: 0.701
- Weighted Precision: 0.643
- Macro Recall: 0.436
- Micro Recall: 0.701
- Weighted Recall: 0.701
## 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/LiveEvil/autotrain-copuml-production-2205570752
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("LiveEvil/autotrain-copuml-production-2205570752", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("LiveEvil/autotrain-copuml-production-2205570752", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1
|
research-backup
| 2022-11-22T17:13:57Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:30:48Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7387698412698412
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3342245989304813
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.34718100890207715
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5441912173429683
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.644
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.35526315789473684
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.37962962962962965
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8145246346240772
- name: F1 (macro)
type: f1_macro
value: 0.801802054210856
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7774647887323943
- name: F1 (macro)
type: f1_macro
value: 0.5026184700694826
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.5980498374864572
- name: F1 (macro)
type: f1_macro
value: 0.5765100456864519
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8878069138206858
- name: F1 (macro)
type: f1_macro
value: 0.7711282513838499
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.827326856784707
- name: F1 (macro)
type: f1_macro
value: 0.824410778730745
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.3342245989304813
- Accuracy on SAT: 0.34718100890207715
- Accuracy on BATS: 0.5441912173429683
- Accuracy on U2: 0.35526315789473684
- Accuracy on U4: 0.37962962962962965
- Accuracy on Google: 0.644
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8145246346240772
- Micro F1 score on CogALexV: 0.7774647887323943
- Micro F1 score on EVALution: 0.5980498374864572
- Micro F1 score on K&H+N: 0.8878069138206858
- Micro F1 score on ROOT09: 0.827326856784707
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7387698412698412
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: triplet
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 1
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-triplet-1/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1
|
research-backup
| 2022-11-22T17:03:22Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:24:49Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7523809523809524
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.35294117647058826
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.35014836795252224
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4191217342968316
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.554
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.41228070175438597
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4050925925925926
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8410426397468735
- name: F1 (macro)
type: f1_macro
value: 0.8153049654017815
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7981220657276996
- name: F1 (macro)
type: f1_macro
value: 0.5156838585733334
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.605092091007584
- name: F1 (macro)
type: f1_macro
value: 0.5707468312851958
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9076997982889338
- name: F1 (macro)
type: f1_macro
value: 0.7719219859032024
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.855531181447822
- name: F1 (macro)
type: f1_macro
value: 0.8548547221202175
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.35294117647058826
- Accuracy on SAT: 0.35014836795252224
- Accuracy on BATS: 0.4191217342968316
- Accuracy on U2: 0.41228070175438597
- Accuracy on U4: 0.4050925925925926
- Accuracy on Google: 0.554
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8410426397468735
- Micro F1 score on CogALexV: 0.7981220657276996
- Micro F1 score on EVALution: 0.605092091007584
- Micro F1 score on K&H+N: 0.9076997982889338
- Micro F1 score on ROOT09: 0.855531181447822
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7523809523809524
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: triplet
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 1
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-triplet-1/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1
|
research-backup
| 2022-11-22T17:00:21Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:22:15Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8430952380952381
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3582887700534759
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3649851632047478
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4280155642023346
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.532
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3333333333333333
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3101851851851852
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8460147657073979
- name: F1 (macro)
type: f1_macro
value: 0.8315897128108677
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8084507042253521
- name: F1 (macro)
type: f1_macro
value: 0.5269777075808457
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6424702058504875
- name: F1 (macro)
type: f1_macro
value: 0.6178608994596904
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.913612019197329
- name: F1 (macro)
type: f1_macro
value: 0.7738790468743169
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8693199623942337
- name: F1 (macro)
type: f1_macro
value: 0.864532922094076
---
# relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.3582887700534759
- Accuracy on SAT: 0.3649851632047478
- Accuracy on BATS: 0.4280155642023346
- Accuracy on U2: 0.3333333333333333
- Accuracy on U4: 0.3101851851851852
- Accuracy on Google: 0.532
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8460147657073979
- Micro F1 score on CogALexV: 0.8084507042253521
- Micro F1 score on EVALution: 0.6424702058504875
- Micro F1 score on K&H+N: 0.913612019197329
- Micro F1 score on ROOT09: 0.8693199623942337
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8430952380952381
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: triplet
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 1
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-triplet-1/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
semindan/xnli_m_bert_only_en
|
semindan
| 2022-11-22T16:24:52Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:xnli",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-15T20:48:41Z |
---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- xnli
metrics:
- accuracy
model-index:
- name: xnli_m_bert_only_en_single_gpu
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: xnli
type: xnli
config: en
split: train
args: en
metrics:
- name: Accuracy
type: accuracy
value: 0.8076305220883534
---
<!-- 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. -->
# xnli_m_bert_only_en_single_gpu
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the xnli dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0082
- Accuracy: 0.8076
## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3328 | 1.0 | 3068 | 0.5433 | 0.8036 |
| 0.259 | 2.0 | 6136 | 0.5708 | 0.8008 |
| 0.2023 | 3.0 | 9204 | 0.6475 | 0.8048 |
| 0.1362 | 4.0 | 12272 | 0.7661 | 0.7972 |
| 0.0945 | 5.0 | 15340 | 0.8333 | 0.8008 |
| 0.0665 | 6.0 | 18408 | 0.9312 | 0.8092 |
| 0.0463 | 7.0 | 21476 | 1.0082 | 0.8076 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
multimodalart/sd-sc
|
multimodalart
| 2022-11-22T16:19:18Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-11-22T16:05:03Z |
---
license: creativeml-openrail-m
---
Just the Safety Checker of Stable Diffusion. For the model refer to https://huggingface.co/runwayml/stable-diffusion-v1-5
|
SweepCake/LunarLander-v2-PPO-HFcourse
|
SweepCake
| 2022-11-22T15:44:29Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-22T15:44:07Z |
---
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: 239.22 +/- 13.04
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
...
```
|
huggingtweets/oryxspioenkop
|
huggingtweets
| 2022-11-22T15:10:21Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-22T15:09:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/oryxspioenkop/1669129816805/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/929707102083395584/tCWiYbO1_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">Oryx</div>
<div style="text-align: center; font-size: 14px;">@oryxspioenkop</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 Oryx.
| Data | Oryx |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 2219 |
| Short tweets | 266 |
| Tweets kept | 761 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/qbqfz863/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 @oryxspioenkop's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2es3q78b) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2es3q78b/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/oryxspioenkop')
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)
|
jjjunyeong/bart-finetuned-squad
|
jjjunyeong
| 2022-11-22T14:42:07Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-22T12:27:04Z |
---
tags:
- generated_from_trainer
datasets:
- squad
metrics:
- rouge
model-index:
- name: bart-finetuned-squad
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: squad
type: squad
config: plain_text
split: train
args: plain_text
metrics:
- name: Rouge1
type: rouge
value: 50.1505
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-finetuned-squad
This model is a fine-tuned version of [p208p2002/bart-squad-qg-hl](https://huggingface.co/p208p2002/bart-squad-qg-hl) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8813
- Rouge1: 50.1505
- Rouge2: 26.8606
- Rougel: 46.0203
- Rougelsum: 46.0242
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 1.5702 | 1.0 | 125 | 1.4266 | 49.7474 | 26.6965 | 46.3227 | 46.342 |
| 0.84 | 2.0 | 250 | 1.4845 | 49.8379 | 26.3973 | 45.126 | 45.1791 |
| 0.535 | 3.0 | 375 | 1.6037 | 50.1413 | 27.4581 | 46.7795 | 46.8001 |
| 0.3621 | 4.0 | 500 | 1.6899 | 49.6087 | 25.9818 | 45.0914 | 45.1004 |
| 0.2448 | 5.0 | 625 | 1.7540 | 49.7468 | 26.5312 | 45.5623 | 45.5296 |
| 0.1756 | 6.0 | 750 | 1.8287 | 49.4987 | 26.2315 | 45.3515 | 45.4214 |
| 0.13 | 7.0 | 875 | 1.8809 | 49.6426 | 26.4688 | 45.5167 | 45.5427 |
| 0.1016 | 8.0 | 1000 | 1.8813 | 50.1505 | 26.8606 | 46.0203 | 46.0242 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
bitsanlp/deberta-v3-base_base
|
bitsanlp
| 2022-11-22T14:37:33Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-22T13:49:27Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-base_base
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. -->
# deberta-v3-base_base
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 28
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
alexziweiwang/exp15-F01-both
|
alexziweiwang
| 2022-11-22T14:22:24Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-22T08:36:55Z |
---
tags:
- generated_from_trainer
model-index:
- name: exp15-F01-both
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. -->
# exp15-F01-both
This model is a fine-tuned version of [yongjian/wav2vec2-large-a](https://huggingface.co/yongjian/wav2vec2-large-a) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6149
- Wer: 1.0154
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 45.2628 | 0.33 | 500 | 2.9838 | 1.0 |
| 3.1304 | 0.67 | 1000 | 2.8311 | 1.0 |
| 2.9607 | 1.0 | 1500 | 2.6426 | 1.0039 |
| 2.7429 | 1.33 | 2000 | 2.5365 | 1.2046 |
| 2.5496 | 1.66 | 2500 | 2.2169 | 1.3050 |
| 2.3134 | 2.0 | 3000 | 2.0450 | 1.3127 |
| 2.1189 | 2.33 | 3500 | 1.8677 | 1.2780 |
| 2.0075 | 2.66 | 4000 | 1.7450 | 1.2703 |
| 1.9014 | 3.0 | 4500 | 1.8381 | 1.2664 |
| 1.7246 | 3.33 | 5000 | 1.7980 | 1.2510 |
| 1.6783 | 3.66 | 5500 | 1.7269 | 1.2510 |
| 1.589 | 3.99 | 6000 | 1.5640 | 1.2664 |
| 1.4085 | 4.33 | 6500 | 1.7296 | 1.2355 |
| 1.4126 | 4.66 | 7000 | 1.5208 | 1.2317 |
| 1.3506 | 4.99 | 7500 | 1.6253 | 1.2317 |
| 1.2276 | 5.33 | 8000 | 1.6222 | 1.2239 |
| 1.1842 | 5.66 | 8500 | 1.4836 | 1.1969 |
| 1.1445 | 5.99 | 9000 | 1.5313 | 1.2046 |
| 1.0254 | 6.32 | 9500 | 1.9130 | 1.2046 |
| 1.0214 | 6.66 | 10000 | 1.8944 | 1.2085 |
| 0.9677 | 6.99 | 10500 | 1.9039 | 1.1853 |
| 0.8822 | 7.32 | 11000 | 1.7036 | 1.1892 |
| 0.8824 | 7.66 | 11500 | 1.6062 | 1.1815 |
| 0.8695 | 7.99 | 12000 | 1.7019 | 1.1853 |
| 0.7536 | 8.32 | 12500 | 1.9117 | 1.1737 |
| 0.775 | 8.66 | 13000 | 1.8778 | 1.1815 |
| 0.7409 | 8.99 | 13500 | 1.7534 | 1.1776 |
| 0.7035 | 9.32 | 14000 | 1.9860 | 1.1853 |
| 0.6905 | 9.65 | 14500 | 1.9141 | 1.1892 |
| 0.6536 | 9.99 | 15000 | 1.7848 | 1.1737 |
| 0.6237 | 10.32 | 15500 | 2.0624 | 1.1544 |
| 0.5986 | 10.65 | 16000 | 1.9958 | 1.1544 |
| 0.5838 | 10.99 | 16500 | 1.8005 | 1.1622 |
| 0.5231 | 11.32 | 17000 | 1.5967 | 1.1351 |
| 0.5452 | 11.65 | 17500 | 1.8145 | 1.1274 |
| 0.5446 | 11.98 | 18000 | 2.0214 | 1.1429 |
| 0.4727 | 12.32 | 18500 | 1.8989 | 1.1313 |
| 0.4908 | 12.65 | 19000 | 1.7152 | 1.1467 |
| 0.483 | 12.98 | 19500 | 1.7354 | 1.1429 |
| 0.4455 | 13.32 | 20000 | 1.9493 | 1.1506 |
| 0.4456 | 13.65 | 20500 | 2.0869 | 1.1197 |
| 0.4306 | 13.98 | 21000 | 1.9248 | 1.1236 |
| 0.3827 | 14.31 | 21500 | 1.9245 | 1.1274 |
| 0.4059 | 14.65 | 22000 | 1.9478 | 1.1313 |
| 0.3941 | 14.98 | 22500 | 2.2373 | 1.1197 |
| 0.4094 | 15.31 | 23000 | 2.0268 | 1.1158 |
| 0.3584 | 15.65 | 23500 | 1.9292 | 1.1313 |
| 0.3615 | 15.98 | 24000 | 2.1744 | 1.0965 |
| 0.3564 | 16.31 | 24500 | 2.4167 | 1.0927 |
| 0.3202 | 16.64 | 25000 | 2.6332 | 1.1081 |
| 0.3099 | 16.98 | 25500 | 2.9448 | 1.1004 |
| 0.3126 | 17.31 | 26000 | 2.4662 | 1.0927 |
| 0.3189 | 17.64 | 26500 | 2.3619 | 1.0772 |
| 0.3929 | 17.98 | 27000 | 2.3571 | 1.0618 |
| 0.27 | 18.31 | 27500 | 2.2457 | 1.0734 |
| 0.2664 | 18.64 | 28000 | 2.5133 | 1.0772 |
| 0.2875 | 18.97 | 28500 | 2.2798 | 1.0618 |
| 0.2336 | 19.31 | 29000 | 2.3515 | 1.0347 |
| 0.2597 | 19.64 | 29500 | 2.3072 | 1.0463 |
| 0.2573 | 19.97 | 30000 | 2.1702 | 1.0425 |
| 0.2431 | 20.31 | 30500 | 2.2727 | 1.0618 |
| 0.2362 | 20.64 | 31000 | 2.3082 | 1.0772 |
| 0.2377 | 20.97 | 31500 | 2.5453 | 1.0734 |
| 0.228 | 21.3 | 32000 | 2.6838 | 1.0618 |
| 0.2082 | 21.64 | 32500 | 2.7629 | 1.0695 |
| 0.2041 | 21.97 | 33000 | 2.4433 | 1.0347 |
| 0.2208 | 22.3 | 33500 | 2.2516 | 1.0463 |
| 0.2505 | 22.64 | 34000 | 2.4056 | 1.0541 |
| 0.187 | 22.97 | 34500 | 2.6017 | 1.0347 |
| 0.1987 | 23.3 | 35000 | 2.5061 | 1.0425 |
| 0.1952 | 23.64 | 35500 | 2.4440 | 1.0463 |
| 0.1777 | 23.97 | 36000 | 2.4333 | 1.0463 |
| 0.1981 | 24.3 | 36500 | 2.4327 | 1.0309 |
| 0.1729 | 24.63 | 37000 | 2.4114 | 1.0309 |
| 0.1895 | 24.97 | 37500 | 2.3885 | 1.0347 |
| 0.1766 | 25.3 | 38000 | 2.2978 | 1.0154 |
| 0.1603 | 25.63 | 38500 | 2.3070 | 1.0039 |
| 0.1764 | 25.97 | 39000 | 2.4975 | 1.0154 |
| 0.1502 | 26.3 | 39500 | 2.3422 | 0.9923 |
| 0.1574 | 26.63 | 40000 | 2.5013 | 1.0077 |
| 0.1794 | 26.96 | 40500 | 2.4088 | 1.0039 |
| 0.1481 | 27.3 | 41000 | 2.3456 | 1.0077 |
| 0.1594 | 27.63 | 41500 | 2.4916 | 1.0154 |
| 0.1384 | 27.96 | 42000 | 2.4173 | 1.0077 |
| 0.1649 | 28.3 | 42500 | 2.5922 | 1.0116 |
| 0.145 | 28.63 | 43000 | 2.5461 | 1.0039 |
| 0.1654 | 28.96 | 43500 | 2.5312 | 1.0039 |
| 0.1389 | 29.29 | 44000 | 2.5974 | 1.0077 |
| 0.1592 | 29.63 | 44500 | 2.6050 | 1.0193 |
| 0.1055 | 29.96 | 45000 | 2.6149 | 1.0154 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.13.2
|
gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-v3
|
gary109
| 2022-11-22T14:06:09Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"dataset:ai_light_dance",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-22T08:33:10Z |
---
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
datasets:
- ai_light_dance
metrics:
- wer
model-index:
- name: ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-v3
This model is a fine-tuned version of [gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-v3](https://huggingface.co/gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-v3) on the GARY109/AI_LIGHT_DANCE - ONSET-IDMT-SMT-DRUMS-V2+MDBDRUMS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5550
- Wer: 0.3147
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1747 | 1.0 | 45 | 0.5638 | 0.3337 |
| 0.2339 | 2.0 | 90 | 0.5785 | 0.3254 |
| 0.2849 | 3.0 | 135 | 0.5586 | 0.3397 |
| 0.2396 | 4.0 | 180 | 0.5868 | 0.3266 |
| 0.2272 | 5.0 | 225 | 0.6052 | 0.3230 |
| 0.2497 | 6.0 | 270 | 0.5913 | 0.3278 |
| 0.2218 | 7.0 | 315 | 0.5926 | 0.3349 |
| 0.2584 | 8.0 | 360 | 0.5617 | 0.3218 |
| 0.2741 | 9.0 | 405 | 0.5901 | 0.3230 |
| 0.2481 | 10.0 | 450 | 0.5860 | 0.3278 |
| 0.2504 | 11.0 | 495 | 0.5991 | 0.3123 |
| 0.2125 | 12.0 | 540 | 0.5992 | 0.3218 |
| 0.2482 | 13.0 | 585 | 0.5756 | 0.3194 |
| 0.2135 | 14.0 | 630 | 0.5836 | 0.3302 |
| 0.2345 | 15.0 | 675 | 0.6347 | 0.3254 |
| 0.1912 | 16.0 | 720 | 0.6160 | 0.3206 |
| 0.2117 | 17.0 | 765 | 0.6268 | 0.3099 |
| 0.2217 | 18.0 | 810 | 0.6873 | 0.3182 |
| 0.2165 | 19.0 | 855 | 0.6721 | 0.3159 |
| 0.207 | 20.0 | 900 | 0.6312 | 0.3206 |
| 0.2263 | 21.0 | 945 | 0.6223 | 0.3290 |
| 0.2015 | 22.0 | 990 | 0.6319 | 0.3182 |
| 0.1997 | 23.0 | 1035 | 0.6527 | 0.3135 |
| 0.2318 | 24.0 | 1080 | 0.5987 | 0.3278 |
| 0.2196 | 25.0 | 1125 | 0.6269 | 0.3242 |
| 0.2298 | 26.0 | 1170 | 0.5774 | 0.3254 |
| 0.2117 | 27.0 | 1215 | 0.5938 | 0.3027 |
| 0.2553 | 28.0 | 1260 | 0.5831 | 0.3123 |
| 0.226 | 29.0 | 1305 | 0.6151 | 0.3099 |
| 0.1635 | 30.0 | 1350 | 0.5622 | 0.3230 |
| 0.5734 | 31.0 | 1395 | 0.6198 | 0.2920 |
| 0.2196 | 32.0 | 1440 | 0.5779 | 0.3039 |
| 0.2019 | 33.0 | 1485 | 0.5866 | 0.3111 |
| 0.2222 | 34.0 | 1530 | 0.5557 | 0.3063 |
| 0.2167 | 35.0 | 1575 | 0.5740 | 0.3206 |
| 0.2011 | 36.0 | 1620 | 0.5598 | 0.3004 |
| 0.2032 | 37.0 | 1665 | 0.5550 | 0.3147 |
| 0.225 | 38.0 | 1710 | 0.5794 | 0.3099 |
| 0.2068 | 39.0 | 1755 | 0.6223 | 0.3063 |
| 0.2105 | 40.0 | 1800 | 0.5797 | 0.3039 |
| 0.1968 | 41.0 | 1845 | 0.5681 | 0.2968 |
| 0.224 | 42.0 | 1890 | 0.5742 | 0.3170 |
| 0.2351 | 43.0 | 1935 | 0.5567 | 0.3111 |
| 0.2121 | 44.0 | 1980 | 0.5893 | 0.3039 |
| 0.1913 | 45.0 | 2025 | 0.6030 | 0.3027 |
| 0.1636 | 46.0 | 2070 | 0.5812 | 0.3004 |
| 0.2062 | 47.0 | 2115 | 0.6081 | 0.3004 |
| 0.2031 | 48.0 | 2160 | 0.5610 | 0.3159 |
| 0.1892 | 49.0 | 2205 | 0.5863 | 0.3147 |
| 0.1712 | 50.0 | 2250 | 0.5943 | 0.3159 |
| 0.1886 | 51.0 | 2295 | 0.5953 | 0.3051 |
| 0.1748 | 52.0 | 2340 | 0.5761 | 0.3087 |
| 0.1705 | 53.0 | 2385 | 0.6045 | 0.2872 |
| 0.1794 | 54.0 | 2430 | 0.5731 | 0.3075 |
| 0.1815 | 55.0 | 2475 | 0.5949 | 0.2849 |
| 0.1571 | 56.0 | 2520 | 0.5663 | 0.2884 |
| 0.1902 | 57.0 | 2565 | 0.5903 | 0.2956 |
| 0.2057 | 58.0 | 2610 | 0.5820 | 0.2872 |
| 0.1904 | 59.0 | 2655 | 0.5923 | 0.2896 |
| 0.1677 | 60.0 | 2700 | 0.5769 | 0.3075 |
| 0.1859 | 61.0 | 2745 | 0.5566 | 0.3147 |
| 0.2382 | 62.0 | 2790 | 0.5849 | 0.3051 |
| 0.1753 | 63.0 | 2835 | 0.5773 | 0.3075 |
| 0.1651 | 64.0 | 2880 | 0.5877 | 0.3039 |
| 0.1781 | 65.0 | 2925 | 0.5905 | 0.3027 |
| 0.1582 | 66.0 | 2970 | 0.5800 | 0.3015 |
| 0.1538 | 67.0 | 3015 | 0.6025 | 0.3075 |
| 0.1606 | 68.0 | 3060 | 0.5758 | 0.3039 |
| 0.1522 | 69.0 | 3105 | 0.5860 | 0.2932 |
| 0.1521 | 70.0 | 3150 | 0.5896 | 0.2956 |
| 0.1592 | 71.0 | 3195 | 0.5738 | 0.3027 |
| 0.2245 | 72.0 | 3240 | 0.5782 | 0.3039 |
| 0.2185 | 73.0 | 3285 | 0.5722 | 0.3027 |
| 0.1597 | 74.0 | 3330 | 0.5891 | 0.3004 |
| 0.1713 | 75.0 | 3375 | 0.5650 | 0.3027 |
| 0.1464 | 76.0 | 3420 | 0.5860 | 0.3063 |
| 0.1551 | 77.0 | 3465 | 0.5755 | 0.3027 |
| 0.1509 | 78.0 | 3510 | 0.5895 | 0.2944 |
| 0.176 | 79.0 | 3555 | 0.5750 | 0.2992 |
| 0.1695 | 80.0 | 3600 | 0.5759 | 0.3004 |
| 0.1797 | 81.0 | 3645 | 0.5904 | 0.2992 |
| 0.1371 | 82.0 | 3690 | 0.5923 | 0.3015 |
| 0.1798 | 83.0 | 3735 | 0.5864 | 0.2992 |
| 0.1386 | 84.0 | 3780 | 0.5733 | 0.3004 |
| 0.2173 | 85.0 | 3825 | 0.5751 | 0.3004 |
| 0.151 | 86.0 | 3870 | 0.5711 | 0.2968 |
| 0.1579 | 87.0 | 3915 | 0.5750 | 0.2992 |
| 0.1328 | 88.0 | 3960 | 0.5764 | 0.2944 |
| 0.1657 | 89.0 | 4005 | 0.5769 | 0.3004 |
| 0.1353 | 90.0 | 4050 | 0.5715 | 0.2956 |
| 0.1982 | 91.0 | 4095 | 0.5754 | 0.2968 |
| 0.1687 | 92.0 | 4140 | 0.5725 | 0.2980 |
| 0.1842 | 93.0 | 4185 | 0.5750 | 0.2980 |
| 0.1893 | 94.0 | 4230 | 0.5789 | 0.2944 |
| 0.1744 | 95.0 | 4275 | 0.5750 | 0.3004 |
| 0.1745 | 96.0 | 4320 | 0.5794 | 0.2980 |
| 0.1665 | 97.0 | 4365 | 0.5755 | 0.3004 |
| 0.1569 | 98.0 | 4410 | 0.5763 | 0.2968 |
| 0.1449 | 99.0 | 4455 | 0.5779 | 0.2968 |
| 0.1469 | 100.0 | 4500 | 0.5774 | 0.2968 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.8.1+cu111
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
sd-concepts-library/ugly_sonic_enhanced
|
sd-concepts-library
| 2022-11-22T13:46:22Z | 0 | 2 | null |
[
"license:openrail",
"region:us"
] | null | 2022-11-22T13:25:22Z |
---
license: openrail
---
Yes, he is back, better than ever. And with a beautiful Green Hill Zone.
Renders in Automatic1111
|
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1
|
research-backup
| 2022-11-22T13:00:28Z | 97 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:34:42Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7449603174603174
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3502673796791444
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3560830860534125
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3468593663146192
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.432
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.37719298245614036
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.38425925925925924
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8523429260207925
- name: F1 (macro)
type: f1_macro
value: 0.8411456349485952
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8157276995305164
- name: F1 (macro)
type: f1_macro
value: 0.5982289168562968
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6386782231852655
- name: F1 (macro)
type: f1_macro
value: 0.6034154846314037
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.95875356472143
- name: F1 (macro)
type: f1_macro
value: 0.8723815565345302
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.846443121278596
- name: F1 (macro)
type: f1_macro
value: 0.8238870756074439
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.3502673796791444
- Accuracy on SAT: 0.3560830860534125
- Accuracy on BATS: 0.3468593663146192
- Accuracy on U2: 0.37719298245614036
- Accuracy on U4: 0.38425925925925924
- Accuracy on Google: 0.432
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8523429260207925
- Micro F1 score on CogALexV: 0.8157276995305164
- Micro F1 score on EVALution: 0.6386782231852655
- Micro F1 score on K&H+N: 0.95875356472143
- Micro F1 score on ROOT09: 0.846443121278596
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7449603174603174
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: triplet
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 1
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-triplet-1/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-2
|
research-backup
| 2022-11-22T11:13:05Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:34:15Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-2
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.682936507936508
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4117647058823529
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4065281899109792
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.44580322401334077
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.618
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.42543859649122806
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4351851851851852
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.889709205966551
- name: F1 (macro)
type: f1_macro
value: 0.8856371272538675
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7983568075117371
- name: F1 (macro)
type: f1_macro
value: 0.5722493642763411
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6034669555796316
- name: F1 (macro)
type: f1_macro
value: 0.5834867979418635
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9533977881338248
- name: F1 (macro)
type: f1_macro
value: 0.848937537646962
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8718270134753996
- name: F1 (macro)
type: f1_macro
value: 0.8714610694444686
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-2
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-2/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.4117647058823529
- Accuracy on SAT: 0.4065281899109792
- Accuracy on BATS: 0.44580322401334077
- Accuracy on U2: 0.42543859649122806
- Accuracy on U4: 0.4351851851851852
- Accuracy on Google: 0.618
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-2/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.889709205966551
- Micro F1 score on CogALexV: 0.7983568075117371
- Micro F1 score on EVALution: 0.6034669555796316
- Micro F1 score on K&H+N: 0.9533977881338248
- Micro F1 score on ROOT09: 0.8718270134753996
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-2/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.682936507936508
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-2")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 6
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 2
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-2/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-1
|
research-backup
| 2022-11-22T11:10:41Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v6",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-22T07:34:44Z |
---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-1
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8926984126984127
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4572192513368984
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4599406528189911
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5369649805447471
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.748
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4298245614035088
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4375
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8945306614434232
- name: F1 (macro)
type: f1_macro
value: 0.8889050346897381
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7887323943661971
- name: F1 (macro)
type: f1_macro
value: 0.5429622796506292
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6132177681473456
- name: F1 (macro)
type: f1_macro
value: 0.5967298388536921
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9580580093204424
- name: F1 (macro)
type: f1_macro
value: 0.8772669717354012
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8733939204011282
- name: F1 (macro)
type: f1_macro
value: 0.865464870691388
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-1
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-1/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.4572192513368984
- Accuracy on SAT: 0.4599406528189911
- Accuracy on BATS: 0.5369649805447471
- Accuracy on U2: 0.4298245614035088
- Accuracy on U4: 0.4375
- Accuracy on Google: 0.748
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-1/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8945306614434232
- Micro F1 score on CogALexV: 0.7887323943661971
- Micro F1 score on EVALution: 0.6132177681473456
- Micro F1 score on K&H+N: 0.9580580093204424
- Micro F1 score on ROOT09: 0.8733939204011282
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-1/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8926984126984127
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-1")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 9
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 1
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-nce-1/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
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