modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-27 18:27:08
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 533
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listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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CyberHarem/akutsu_ruri_ahogirl
|
CyberHarem
| 2023-08-31T14:44:16Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/akutsu_ruri_ahogirl",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-31T07:23:27Z |
---
license: mit
datasets:
- CyberHarem/akutsu_ruri_ahogirl
pipeline_tag: text-to-image
tags:
- art
---
# Lora of akutsu_ruri_ahogirl
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 6000, you need to download `6000/akutsu_ruri_ahogirl.pt` as the embedding and `6000/akutsu_ruri_ahogirl.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 6000**, with the score of 0.820. The trigger words are:
1. `akutsu_ruri_ahogirl`
2. `black_hair, star_\(symbol\), twintails, hair_ornament, brown_eyes, open_mouth, long_hair, star_hair_ornament`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:---------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **6000** | **0.820** | [**Download**](6000/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6000/previews/nude.png) | [<NSFW, click to see>](6000/previews/nude2.png) |  |  |
| 5600 | 0.780 | [Download](5600/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5600/previews/nude.png) | [<NSFW, click to see>](5600/previews/nude2.png) |  |  |
| 5200 | 0.812 | [Download](5200/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4800 | 0.636 | [Download](4800/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) |  |  |
| 4400 | 0.746 | [Download](4400/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4400/previews/nude.png) | [<NSFW, click to see>](4400/previews/nude2.png) |  |  |
| 4000 | 0.764 | [Download](4000/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4000/previews/nude.png) | [<NSFW, click to see>](4000/previews/nude2.png) |  |  |
| 3600 | 0.800 | [Download](3600/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3600/previews/nude.png) | [<NSFW, click to see>](3600/previews/nude2.png) |  |  |
| 3200 | 0.796 | [Download](3200/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3200/previews/nude.png) | [<NSFW, click to see>](3200/previews/nude2.png) |  |  |
| 2800 | 0.744 | [Download](2800/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2800/previews/nude.png) | [<NSFW, click to see>](2800/previews/nude2.png) |  |  |
| 2400 | 0.571 | [Download](2400/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) |  |  |
| 2000 | 0.693 | [Download](2000/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2000/previews/nude.png) | [<NSFW, click to see>](2000/previews/nude2.png) |  |  |
| 1600 | 0.608 | [Download](1600/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1600/previews/nude.png) | [<NSFW, click to see>](1600/previews/nude2.png) |  |  |
| 1200 | 0.597 | [Download](1200/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [<NSFW, click to see>](1200/previews/nude2.png) |  |  |
| 800 | 0.508 | [Download](800/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](800/previews/nude.png) | [<NSFW, click to see>](800/previews/nude2.png) |  |  |
| 400 | 0.342 | [Download](400/akutsu_ruri_ahogirl.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](400/previews/nude.png) | [<NSFW, click to see>](400/previews/nude2.png) |  |  |
|
SneakySpidy/ppo-LunarLander-v2
|
SneakySpidy
| 2023-08-31T14:44:11Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-31T14:26:25Z |
---
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: 245.37 +/- 61.82
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
...
```
|
jondurbin/airocoder-34b-2.1
|
jondurbin
| 2023-08-31T14:38:12Z | 1,431 | 4 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-30T23:52:19Z |
---
license: llama2
---
codellama-34b fine-tuned on the "code" expert from lmoe adapters.
|
IainRatherThanIan/donut-base-sroie
|
IainRatherThanIan
| 2023-08-31T14:35:27Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-08-31T12:45:53Z |
---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-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. -->
# donut-base-sroie
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder 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: 4
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
nightdude/config_810
|
nightdude
| 2023-08-31T14:30:47Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-31T14:30:28Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
KatMarie/wav2vec2-large-xls-r-300m-euskera2.2-colab
|
KatMarie
| 2023-08-31T14:26:16Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-29T16:53:58Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-euskera2.2-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: eu
split: test
args: eu
metrics:
- name: Wer
type: wer
value: 0.33880710195329866
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-euskera2.2-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2252
- Wer: 0.3388
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.3751 | 0.64 | 300 | 2.7513 | 0.9999 |
| 0.7411 | 1.28 | 600 | 0.3982 | 0.5537 |
| 0.265 | 1.92 | 900 | 0.2748 | 0.4088 |
| 0.1513 | 2.56 | 1200 | 0.2252 | 0.3388 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
HyperbeeAI/nanotracker-hf
|
HyperbeeAI
| 2023-08-31T14:19:18Z | 0 | 0 | null |
[
"object-detection",
"vision",
"dataset:wider_surveillance",
"license:apache-2.0",
"region:us"
] |
object-detection
| 2023-08-19T21:34:46Z |
---
license: apache-2.0
tags:
- object-detection
- vision
datasets:
- wider_surveillance
---
# NanoTracker - person tracking with quantized networks by HyperbeeAI
Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. [email protected]
This repository contains the quantized neural network-based person tracking utility by HyperbeeAI, NanoTracker. The algorithm is benchmarked against the WIDER pedestrian surveillance dataset.
See efficientdet_comparison/ for the comparison of our trained models with efficientdet
|
HyperbeeAI/nanotranslator-hf
|
HyperbeeAI
| 2023-08-31T14:17:00Z | 0 | 2 | null |
[
"translation",
"en",
"es",
"dataset:news_commentary",
"license:apache-2.0",
"region:us"
] |
translation
| 2023-08-19T21:29:54Z |
---
language:
- en
- es
datasets:
- news_commentary
tags:
- translation
license: apache-2.0
---
# NanoTranslator by HyperbeeAI
Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. [email protected]
This repository contains the Spanish-to-English translation utility by HyperbeeAI called NanoTranslator. **The model takes up less than 400 KBs of RAM and provides accurate translation for casual conversations.**
To run the demo, see explanations in "demo.ipynb", which acts as the serial terminal to communicate with the ai85 from the host PC. Further explanations are provided below as well as in the notebooks.
### Contents:
- **.py files:** python modules used by the Jupyter notebooks. These files define a simulation environment for the MAX78000 CNN accelerator hardware + some peripheral tools that help evaluation. Note that the simulator only includes the chip features that are relevant to this project (e.g., pooling not implemented because this project does not need it).
- **evaluation.ipynb:** this Jupyter notebook provides an interface to try out different sentences from the test set on the model in the simulation environment, and compute the BLEU score of the model over the test set.
- **demo.ipynb:** this Jupyter notebook acts as the serial interface with the chip. A sentence in the source language is sent over to the chip for translation via the serial port, the implementation on the chip translates this and sends it back via the same serial port in the target language, and the result is displayed on the notebook cell. This needs to be run together with the "assets/demo.elf" program on the chip, which does the actual translation job on the ai85. There is a specific cell on the notebook that needs to be run before the ai85 demo.elf is started. Check the notebook for further info.
- **assets/demo.elf:** C program running the actual translation application. Run this together with the demo.ipynb notebook for the translation demo. See further explanations inside demo.ipynb.
### Extras/Notes:
- the demo C program does not require any extra modules/libraries, it can be directly run the same way as the Maxim SDK examples (i.e., using the arm gdb, defining the target as "remote localhost:3333", doing "load" etc.). However, note that the Jupyter notebook demo.ipynb needs to be run together with the C program for meaningful output. There is a specific cell on the notebook that needs to be run before the ai85 demo.elf is started. Check the notebook for further info.
- The demo.ipynb notebook needs to run on the same host PC that programs the ai85 since it uses the on-board (USB) serial port (that programs the ai85) to communicate with the chip while the translation application is running.
- Although the program should run on both the EVKit and the FeatherBoard without errors (since it uses common functionality), it was only explicitly tested with the FeatherBoard for now.
### Setup:
This demo has been tested with the following configuration:
Python 3.8.10.
datasets 1.8.0
huggingface-hub 0.0.10
ipykernel 5.5.3
ipython 7.22.0
notebook 6.3.0
numpy 1.20.2
pyserial 3.5
sacrebleu 1.5.1
tokenizers 0.10.3
torch 1.8.1
torchtext 0.9.1
tqdm 4.49.0
Note1: torchtext might default to older versions (e.g., v0.8) on some containers (typically in those provided by AWS, which use older versions of python that don't align well with the newer torchtext versions), in that case, the .legacy submodule path needs to be removed from the import directives in the .py files and Jupyter notebooks.
Note2: there are multiple python packages on pip that provide serial port implementation, with conflicting function/object names too. Although the package used here gets imported with "import serial", it needs to be installed via "pip install pyserial", not "pip install serial". Make sure you get the correct version.
|
akniele/dqn-SpaceInvadersNoFrameskip-v4
|
akniele
| 2023-08-31T14:14:42Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-31T14:14:07Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 572.00 +/- 112.92
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga akniele -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga akniele -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga akniele
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
BugHunter1/whisper-tiny-en
|
BugHunter1
| 2023-08-31T14:06:10Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-30T20:11:43Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-en
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 0.34993849938499383
---
<!-- 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-tiny-en
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7922
- Wer Ortho: 0.3507
- Wer: 0.3499
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|
| 0.0001 | 140.35 | 4000 | 0.7922 | 0.3507 | 0.3499 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Agiyogi/ppo-LunarLander-v2
|
Agiyogi
| 2023-08-31T13:49:48Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-31T13:38:10Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 249.72 +/- 15.12
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
...
```
|
dt-and-vanilla-ardt/dt-ppo_train_walker2d_level-3108_1217-66
|
dt-and-vanilla-ardt
| 2023-08-31T13:40:11Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-08-31T11:18:59Z |
---
tags:
- generated_from_trainer
model-index:
- name: dt-ppo_train_walker2d_level-3108_1217-66
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. -->
# dt-ppo_train_walker2d_level-3108_1217-66
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Campqt/Reinforce-CartPole-v1
|
Campqt
| 2023-08-31T13:39:39Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-31T13:39:31Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
sophiebasnylons/Sophie_Lapute
|
sophiebasnylons
| 2023-08-31T13:39:00Z | 0 | 0 | null |
[
"fr",
"dataset:fka/awesome-chatgpt-prompts",
"license:artistic-2.0",
"region:us"
] | null | 2023-08-31T13:37:01Z |
---
license: artistic-2.0
datasets:
- fka/awesome-chatgpt-prompts
language:
- fr
metrics:
- character
---
|
ardt-multipart/ardt-multipart-ppo_train_walker2d_level-3108_1139-66
|
ardt-multipart
| 2023-08-31T13:35:56Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-08-31T10:41:34Z |
---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-ppo_train_walker2d_level-3108_1139-66
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. -->
# ardt-multipart-ppo_train_walker2d_level-3108_1139-66
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Korkkork/gyuri
|
Korkkork
| 2023-08-31T13:20:47Z | 0 | 0 | null |
[
"kara",
"kpop",
"artist",
"license:openrail",
"region:us"
] | null | 2023-08-31T13:19:21Z |
---
license: openrail
tags:
- kara
- kpop
- artist
---
|
aoliveira/speecht5_finetuned_voxpopuli_sk
|
aoliveira
| 2023-08-31T13:09:02Z | 84 | 0 |
transformers
|
[
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2023-08-30T22:52:21Z |
---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- facebook/voxpopuli
pipeline_tag: text-to-speech
model-index:
- name: speecht5_finetuned_voxpopuli_sk
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_voxpopuli_sk
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4442
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5132 | 6.4 | 500 | 0.4654 |
| 0.4874 | 12.8 | 1000 | 0.4512 |
| 0.4834 | 19.2 | 1500 | 0.4457 |
| 0.4729 | 25.6 | 2000 | 0.4442 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
pabloyesteb/poca-SoccerTwos
|
pabloyesteb
| 2023-08-31T13:07:05Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-08-31T13:00:11Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: pabloyesteb/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Brainclub5000/napolu
|
Brainclub5000
| 2023-08-31T13:01:37Z | 2 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:adapter:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-08-31T13:01:33Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: NAPOLU
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - napolu
These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "NAPOLU" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
Test prompt: NAPOLU
|
AdapterHub/xmod-base-zh_TW
|
AdapterHub
| 2023-08-31T13:00:26Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:zh/cc100",
"zh",
"license:mit",
"region:us"
] | null | 2023-08-31T12:58:16Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:zh/cc100
language:
- zh
license: "mit"
---
# Adapter `AdapterHub/xmod-base-zh_TW` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [zh/cc100](https://adapterhub.ml/explore/zh/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-zh_TW", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-zh_CN
|
AdapterHub
| 2023-08-31T13:00:25Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:zh/cc100",
"zh",
"license:mit",
"region:us"
] | null | 2023-08-31T12:58:08Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:zh/cc100
language:
- zh
license: "mit"
---
# Adapter `AdapterHub/xmod-base-zh_CN` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [zh/cc100](https://adapterhub.ml/explore/zh/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-zh_CN", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-vi_VN
|
AdapterHub
| 2023-08-31T13:00:24Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:vi/cc100",
"vi",
"license:mit",
"region:us"
] | null | 2023-08-31T12:57:58Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:vi/cc100
language:
- vi
license: "mit"
---
# Adapter `AdapterHub/xmod-base-vi_VN` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [vi/cc100](https://adapterhub.ml/explore/vi/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-vi_VN", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-uk_UA
|
AdapterHub
| 2023-08-31T13:00:20Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:uk/cc100",
"uk",
"license:mit",
"region:us"
] | null | 2023-08-31T12:57:30Z |
---
tags:
- xmod
- adapter-transformers
- adapterhub:uk/cc100
language:
- uk
license: "mit"
---
# Adapter `AdapterHub/xmod-base-uk_UA` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [uk/cc100](https://adapterhub.ml/explore/uk/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-uk_UA", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-th_TH
|
AdapterHub
| 2023-08-31T13:00:16Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:th/cc100",
"th",
"license:mit",
"region:us"
] | null | 2023-08-31T12:57:02Z |
---
tags:
- xmod
- adapter-transformers
- adapterhub:th/cc100
language:
- th
license: "mit"
---
# Adapter `AdapterHub/xmod-base-th_TH` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [th/cc100](https://adapterhub.ml/explore/th/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-th_TH", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-te_IN
|
AdapterHub
| 2023-08-31T13:00:15Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:te/cc100",
"te",
"license:mit",
"region:us"
] | null | 2023-08-31T12:56:54Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:te/cc100
language:
- te
license: "mit"
---
# Adapter `AdapterHub/xmod-base-te_IN` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [te/cc100](https://adapterhub.ml/explore/te/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-te_IN", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-ta_IN
|
AdapterHub
| 2023-08-31T13:00:14Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:ta/cc100",
"ta",
"license:mit",
"region:us"
] | null | 2023-08-31T12:56:46Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:ta/cc100
language:
- ta
license: "mit"
---
# Adapter `AdapterHub/xmod-base-ta_IN` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [ta/cc100](https://adapterhub.ml/explore/ta/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-ta_IN", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-sq_AL
|
AdapterHub
| 2023-08-31T13:00:10Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:sq/cc100",
"sq",
"license:mit",
"region:us"
] | null | 2023-08-31T12:56:12Z |
---
tags:
- xmod
- adapter-transformers
- adapterhub:sq/cc100
language:
- sq
license: "mit"
---
# Adapter `AdapterHub/xmod-base-sq_AL` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [sq/cc100](https://adapterhub.ml/explore/sq/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-sq_AL", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-so_SO
|
AdapterHub
| 2023-08-31T13:00:09Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:so/cc100",
"so",
"license:mit",
"region:us"
] | null | 2023-08-31T12:56:04Z |
---
tags:
- xmod
- adapter-transformers
- adapterhub:so/cc100
language:
- so
license: "mit"
---
# Adapter `AdapterHub/xmod-base-so_SO` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [so/cc100](https://adapterhub.ml/explore/so/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-so_SO", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-sa_IN
|
AdapterHub
| 2023-08-31T13:00:04Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:sa/cc100",
"sa",
"license:mit",
"region:us"
] | null | 2023-08-31T12:55:30Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:sa/cc100
language:
- sa
license: "mit"
---
# Adapter `AdapterHub/xmod-base-sa_IN` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [sa/cc100](https://adapterhub.ml/explore/sa/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-sa_IN", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-ru_RU
|
AdapterHub
| 2023-08-31T13:00:03Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:ru/cc100",
"ru",
"license:mit",
"region:us"
] | null | 2023-08-31T12:55:21Z |
---
tags:
- xmod
- adapter-transformers
- adapterhub:ru/cc100
language:
- ru
license: "mit"
---
# Adapter `AdapterHub/xmod-base-ru_RU` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [ru/cc100](https://adapterhub.ml/explore/ru/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-ru_RU", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-pl_PL
|
AdapterHub
| 2023-08-31T12:59:59Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:pl/cc100",
"pl",
"license:mit",
"region:us"
] | null | 2023-08-31T12:54:46Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:pl/cc100
language:
- pl
license: "mit"
---
# Adapter `AdapterHub/xmod-base-pl_PL` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [pl/cc100](https://adapterhub.ml/explore/pl/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-pl_PL", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-or_IN
|
AdapterHub
| 2023-08-31T12:59:57Z | 8 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:or/cc100",
"or",
"license:mit",
"region:us"
] | null | 2023-08-31T12:54:29Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:or/cc100
language:
- or
license: "mit"
---
# Adapter `AdapterHub/xmod-base-or_IN` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [or/cc100](https://adapterhub.ml/explore/or/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-or_IN", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-nl_XX
|
AdapterHub
| 2023-08-31T12:59:56Z | 4 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:nl/cc100",
"xmod",
"nl",
"license:mit",
"region:us"
] | null | 2023-08-31T12:54:11Z |
---
tags:
- adapterhub:nl/cc100
- adapter-transformers
- xmod
language:
- nl
license: "mit"
---
# Adapter `AdapterHub/xmod-base-nl_XX` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [nl/cc100](https://adapterhub.ml/explore/nl/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-nl_XX", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-ms_MY
|
AdapterHub
| 2023-08-31T12:59:53Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:ms/cc100",
"xmod",
"ms",
"license:mit",
"region:us"
] | null | 2023-08-31T12:53:45Z |
---
tags:
- adapterhub:ms/cc100
- adapter-transformers
- xmod
language:
- ms
license: "mit"
---
# Adapter `AdapterHub/xmod-base-ms_MY` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [ms/cc100](https://adapterhub.ml/explore/ms/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-ms_MY", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-mk_MK
|
AdapterHub
| 2023-08-31T12:59:49Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:mk/cc100",
"mk",
"license:mit",
"region:us"
] | null | 2023-08-31T12:53:09Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:mk/cc100
language:
- mk
license: "mit"
---
# Adapter `AdapterHub/xmod-base-mk_MK` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [mk/cc100](https://adapterhub.ml/explore/mk/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-mk_MK", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-lv_LV
|
AdapterHub
| 2023-08-31T12:59:48Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:lv/cc100",
"xmod",
"lv",
"license:mit",
"region:us"
] | null | 2023-08-31T12:53:00Z |
---
tags:
- adapterhub:lv/cc100
- adapter-transformers
- xmod
language:
- lv
license: "mit"
---
# Adapter `AdapterHub/xmod-base-lv_LV` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [lv/cc100](https://adapterhub.ml/explore/lv/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-lv_LV", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-ku_TR
|
AdapterHub
| 2023-08-31T12:59:42Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:ku/cc100",
"xmod",
"ku",
"license:mit",
"region:us"
] | null | 2023-08-31T12:52:14Z |
---
tags:
- adapterhub:ku/cc100
- adapter-transformers
- xmod
language:
- ku
license: "mit"
---
# Adapter `AdapterHub/xmod-base-ku_TR` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [ku/cc100](https://adapterhub.ml/explore/ku/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-ku_TR", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-ko_KR
|
AdapterHub
| 2023-08-31T12:59:41Z | 3 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:ko/cc100",
"ko",
"license:mit",
"region:us"
] | null | 2023-08-31T12:52:05Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:ko/cc100
language:
- ko
license: "mit"
---
# Adapter `AdapterHub/xmod-base-ko_KR` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [ko/cc100](https://adapterhub.ml/explore/ko/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-ko_KR", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-kn_IN
|
AdapterHub
| 2023-08-31T12:59:40Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:kn/cc100",
"xmod",
"kn",
"license:mit",
"region:us"
] | null | 2023-08-31T12:51:56Z |
---
tags:
- adapterhub:kn/cc100
- adapter-transformers
- xmod
language:
- kn
license: "mit"
---
# Adapter `AdapterHub/xmod-base-kn_IN` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [kn/cc100](https://adapterhub.ml/explore/kn/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-kn_IN", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-km_KH
|
AdapterHub
| 2023-08-31T12:59:39Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:km/cc100",
"km",
"license:mit",
"region:us"
] | null | 2023-08-31T12:51:47Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:km/cc100
language:
- km
license: "mit"
---
# Adapter `AdapterHub/xmod-base-km_KH` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [km/cc100](https://adapterhub.ml/explore/km/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-km_KH", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-kk_KZ
|
AdapterHub
| 2023-08-31T12:59:37Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:kk/cc100",
"kk",
"license:mit",
"region:us"
] | null | 2023-08-31T12:51:38Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:kk/cc100
language:
- kk
license: "mit"
---
# Adapter `AdapterHub/xmod-base-kk_KZ` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [kk/cc100](https://adapterhub.ml/explore/kk/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-kk_KZ", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-ja_XX
|
AdapterHub
| 2023-08-31T12:59:35Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:ja/cc100",
"ja",
"license:mit",
"region:us"
] | null | 2023-08-31T12:51:20Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:ja/cc100
language:
- ja
license: "mit"
---
# Adapter `AdapterHub/xmod-base-ja_XX` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [ja/cc100](https://adapterhub.ml/explore/ja/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-ja_XX", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-it_IT
|
AdapterHub
| 2023-08-31T12:59:34Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:it/cc100",
"it",
"license:mit",
"region:us"
] | null | 2023-08-31T12:51:11Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:it/cc100
language:
- it
license: "mit"
---
# Adapter `AdapterHub/xmod-base-it_IT` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [it/cc100](https://adapterhub.ml/explore/it/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-it_IT", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-id_ID
|
AdapterHub
| 2023-08-31T12:59:33Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:id/cc100",
"id",
"license:mit",
"region:us"
] | null | 2023-08-31T12:50:53Z |
---
tags:
- xmod
- adapter-transformers
- adapterhub:id/cc100
language:
- id
license: "mit"
---
# Adapter `AdapterHub/xmod-base-id_ID` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [id/cc100](https://adapterhub.ml/explore/id/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-id_ID", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-hy_AM
|
AdapterHub
| 2023-08-31T12:59:32Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:hy/cc100",
"hy",
"license:mit",
"region:us"
] | null | 2023-08-31T12:50:44Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:hy/cc100
language:
- hy
license: "mit"
---
# Adapter `AdapterHub/xmod-base-hy_AM` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [hy/cc100](https://adapterhub.ml/explore/hy/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-hy_AM", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-hr_HR
|
AdapterHub
| 2023-08-31T12:59:29Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:hr/cc100",
"hr",
"license:mit",
"region:us"
] | null | 2023-08-31T12:50:17Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:hr/cc100
language:
- hr
license: "mit"
---
# Adapter `AdapterHub/xmod-base-hr_HR` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [hr/cc100](https://adapterhub.ml/explore/hr/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-hr_HR", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-hi_IN
|
AdapterHub
| 2023-08-31T12:59:28Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:hi/cc100",
"xmod",
"hi",
"license:mit",
"region:us"
] | null | 2023-08-31T12:50:08Z |
---
tags:
- adapterhub:hi/cc100
- adapter-transformers
- xmod
language:
- hi
license: "mit"
---
# Adapter `AdapterHub/xmod-base-hi_IN` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [hi/cc100](https://adapterhub.ml/explore/hi/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-hi_IN", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-he_IL
|
AdapterHub
| 2023-08-31T12:59:27Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:he/cc100",
"xmod",
"he",
"license:mit",
"region:us"
] | null | 2023-08-31T12:50:00Z |
---
tags:
- adapterhub:he/cc100
- adapter-transformers
- xmod
language:
- he
license: "mit"
---
# Adapter `AdapterHub/xmod-base-he_IL` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [he/cc100](https://adapterhub.ml/explore/he/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-he_IL", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-ha_NG
|
AdapterHub
| 2023-08-31T12:59:26Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:ha/cc100",
"ha",
"license:mit",
"region:us"
] | null | 2023-08-31T12:49:51Z |
---
tags:
- xmod
- adapter-transformers
- adapterhub:ha/cc100
language:
- ha
license: "mit"
---
# Adapter `AdapterHub/xmod-base-ha_NG` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [ha/cc100](https://adapterhub.ml/explore/ha/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-ha_NG", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-ga_IE
|
AdapterHub
| 2023-08-31T12:59:23Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:ga/cc100",
"ga",
"license:mit",
"region:us"
] | null | 2023-08-31T12:49:26Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:ga/cc100
language:
- ga
license: "mit"
---
# Adapter `AdapterHub/xmod-base-ga_IE` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [ga/cc100](https://adapterhub.ml/explore/ga/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-ga_IE", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-fr_XX
|
AdapterHub
| 2023-08-31T12:59:22Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:fr/cc100",
"fr",
"license:mit",
"region:us"
] | null | 2023-08-31T12:49:17Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:fr/cc100
language:
- fr
license: "mit"
---
# Adapter `AdapterHub/xmod-base-fr_XX` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [fr/cc100](https://adapterhub.ml/explore/fr/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-fr_XX", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-fi_FI
|
AdapterHub
| 2023-08-31T12:59:21Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:fi/cc100",
"fi",
"license:mit",
"region:us"
] | null | 2023-08-31T12:49:08Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:fi/cc100
language:
- fi
license: "mit"
---
# Adapter `AdapterHub/xmod-base-fi_FI` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [fi/cc100](https://adapterhub.ml/explore/fi/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-fi_FI", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-eu_ES
|
AdapterHub
| 2023-08-31T12:59:20Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:eu/cc100",
"eu",
"license:mit",
"region:us"
] | null | 2023-08-31T12:48:51Z |
---
tags:
- xmod
- adapter-transformers
- adapterhub:eu/cc100
language:
- eu
license: "mit"
---
# Adapter `AdapterHub/xmod-base-eu_ES` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [eu/cc100](https://adapterhub.ml/explore/eu/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-eu_ES", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-fa_IR
|
AdapterHub
| 2023-08-31T12:59:20Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:fa/cc100",
"xmod",
"fa",
"license:mit",
"region:us"
] | null | 2023-08-31T12:49:00Z |
---
tags:
- adapterhub:fa/cc100
- adapter-transformers
- xmod
language:
- fa
license: "mit"
---
# Adapter `AdapterHub/xmod-base-fa_IR` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [fa/cc100](https://adapterhub.ml/explore/fa/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-fa_IR", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-eo_EO
|
AdapterHub
| 2023-08-31T12:59:16Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:eo/cc100",
"xmod",
"eo",
"license:mit",
"region:us"
] | null | 2023-08-31T12:48:26Z |
---
tags:
- adapterhub:eo/cc100
- adapter-transformers
- xmod
language:
- eo
license: "mit"
---
# Adapter `AdapterHub/xmod-base-eo_EO` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [eo/cc100](https://adapterhub.ml/explore/eo/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-eo_EO", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-en_XX
|
AdapterHub
| 2023-08-31T12:59:15Z | 3 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:en/cc100",
"en",
"license:mit",
"region:us"
] | null | 2023-08-31T12:48:18Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:en/cc100
language:
- en
license: "mit"
---
# Adapter `AdapterHub/xmod-base-en_XX` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [en/cc100](https://adapterhub.ml/explore/en/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-en_XX", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-el_GR
|
AdapterHub
| 2023-08-31T12:59:14Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:el/cc100",
"el",
"license:mit",
"region:us"
] | null | 2023-08-31T12:48:09Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:el/cc100
language:
- el
license: "mit"
---
# Adapter `AdapterHub/xmod-base-el_GR` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [el/cc100](https://adapterhub.ml/explore/el/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-el_GR", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-de_DE
|
AdapterHub
| 2023-08-31T12:59:13Z | 3 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:de/cc100",
"de",
"license:mit",
"region:us"
] | null | 2023-08-31T12:48:01Z |
---
tags:
- xmod
- adapter-transformers
- adapterhub:de/cc100
language:
- de
license: "mit"
---
# Adapter `AdapterHub/xmod-base-de_DE` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [de/cc100](https://adapterhub.ml/explore/de/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-de_DE", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-da_DK
|
AdapterHub
| 2023-08-31T12:59:12Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:da/cc100",
"xmod",
"da",
"license:mit",
"region:us"
] | null | 2023-08-31T12:47:52Z |
---
tags:
- adapterhub:da/cc100
- adapter-transformers
- xmod
language:
- da
license: "mit"
---
# Adapter `AdapterHub/xmod-base-da_DK` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [da/cc100](https://adapterhub.ml/explore/da/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-da_DK", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-cs_CZ
|
AdapterHub
| 2023-08-31T12:59:10Z | 4 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:cs/cc100",
"xmod",
"cs",
"license:mit",
"region:us"
] | null | 2023-08-31T12:47:36Z |
---
tags:
- adapterhub:cs/cc100
- adapter-transformers
- xmod
language:
- cs
license: "mit"
---
# Adapter `AdapterHub/xmod-base-cs_CZ` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [cs/cc100](https://adapterhub.ml/explore/cs/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-cs_CZ", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-ca_ES
|
AdapterHub
| 2023-08-31T12:59:09Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:ca/cc100",
"ca",
"license:mit",
"region:us"
] | null | 2023-08-31T12:47:28Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:ca/cc100
language:
- ca
license: "mit"
---
# Adapter `AdapterHub/xmod-base-ca_ES` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [ca/cc100](https://adapterhub.ml/explore/ca/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-ca_ES", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-bn_IN
|
AdapterHub
| 2023-08-31T12:59:08Z | 4 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:bn/cc100",
"bn",
"license:mit",
"region:us"
] | null | 2023-08-31T12:47:19Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:bn/cc100
language:
- bn
license: "mit"
---
# Adapter `AdapterHub/xmod-base-bn_IN` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [bn/cc100](https://adapterhub.ml/explore/bn/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-bn_IN", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-bg_BG
|
AdapterHub
| 2023-08-31T12:59:07Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:bg/cc100",
"xmod",
"bg",
"license:mit",
"region:us"
] | null | 2023-08-31T12:47:11Z |
---
tags:
- adapterhub:bg/cc100
- adapter-transformers
- xmod
language:
- bg
license: "mit"
---
# Adapter `AdapterHub/xmod-base-bg_BG` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [bg/cc100](https://adapterhub.ml/explore/bg/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-bg_BG", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
AdapterHub/xmod-base-be_BY
|
AdapterHub
| 2023-08-31T12:59:06Z | 4 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xmod",
"adapterhub:be/cc100",
"be",
"license:mit",
"region:us"
] | null | 2023-08-31T12:47:02Z |
---
tags:
- adapter-transformers
- xmod
- adapterhub:be/cc100
language:
- be
license: "mit"
---
# Adapter `AdapterHub/xmod-base-be_BY` for AdapterHub/xmod-base
An [adapter](https://adapterhub.ml) for the `AdapterHub/xmod-base` model that was trained on the [be/cc100](https://adapterhub.ml/explore/be/cc100/) dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("AdapterHub/xmod-base")
adapter_name = model.load_adapter("AdapterHub/xmod-base-be_BY", source="hf", set_active=True)
```
## Architecture & Training
This adapter was extracted from the original model checkpoint [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) to allow loading it independently via the Adapters library.
For more information on architecture and training, please refer to the original model card.
## Evaluation results
<!-- Add some description here -->
## Citation
[Lifting the Curse of Multilinguality by Pre-training Modular Transformers (Pfeiffer et al., 2022)](http://dx.doi.org/10.18653/v1/2022.naacl-main.255)
```
@inproceedings{pfeiffer-etal-2022-lifting,
title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers",
author = "Pfeiffer, Jonas and
Goyal, Naman and
Lin, Xi and
Li, Xian and
Cross, James and
Riedel, Sebastian and
Artetxe, Mikel",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.255",
doi = "10.18653/v1/2022.naacl-main.255",
pages = "3479--3495"
}
```
|
aggie/edos-alpaca-option-llama2-chat-demo
|
aggie
| 2023-08-31T12:57:02Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-30T13:11:50Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
aggie/edos-alpaca-option-llama2-chat
|
aggie
| 2023-08-31T12:55:13Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-30T13:11:11Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
Sachin16/RL
|
Sachin16
| 2023-08-31T12:52:26Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-31T12:52:01Z |
---
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: 261.28 +/- 16.95
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
...
```
|
LarryAIDraw/h2k-utaha
|
LarryAIDraw
| 2023-08-31T12:52:16Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-31T12:48:53Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/137843/h2k-utaha-kasumigaoka
|
adityaa11/ppo-LunarLander-v2
|
adityaa11
| 2023-08-31T12:45:39Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-31T12:45:20Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -198.01 +/- 46.69
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
...
```
|
jrahn/bertsgambit
|
jrahn
| 2023-08-31T12:44:55Z | 108 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"chess",
"fen",
"dataset:jrahn/yolochess_lichess-elite_2201",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-31T10:37:27Z |
---
datasets:
- jrahn/yolochess_lichess-elite_2201
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- chess
- fen
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
trieudemo11/llama_7b_attrb_cate_b6_l320_low_6
|
trieudemo11
| 2023-08-31T12:36:58Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-31T12:36:34Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
rsions
- PEFT 0.6.0.dev0
|
LarryAIDraw/lineana_v2
|
LarryAIDraw
| 2023-08-31T12:28:15Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-30T05:43:11Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/136927/lineana-dedoldia-or-mushoku-tensei
|
Idriska/my_awesome_qa_model
|
Idriska
| 2023-08-31T12:25:15Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-31T12:16:48Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7238
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.2961 |
| 2.72 | 2.0 | 500 | 1.8327 |
| 2.72 | 3.0 | 750 | 1.7238 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
LarryAIDraw/pursena_v2
|
LarryAIDraw
| 2023-08-31T12:22:05Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-30T05:56:09Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/136900/pursena-adoldia-or-mushoku-tensei
|
LarryAIDraw/qingCB-v1
|
LarryAIDraw
| 2023-08-31T12:17:18Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-30T05:45:06Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/136187/or-naruse-haru-or-or-snowbreak-containment-zone-or-or-qing
|
xzuyn/LLaMa-2-LIMA-7B-QLoRA_v2
|
xzuyn
| 2023-08-31T12:15:09Z | 0 | 1 | null |
[
"en",
"dataset:xzuyn/lima-multiturn-alpaca",
"region:us"
] | null | 2023-08-27T00:55:20Z |
---
datasets:
- xzuyn/lima-multiturn-alpaca
language:
- en
---
Trained on a 7900XTX.
[Zeus-LLM-Trainer](https://github.com/official-elinas/zeus-llm-trainer) command to recreate:
```
python finetune.py --data_path "xzuyn/lima-multiturn-alpaca" --learning_rate 0.0001 --optim "paged_adamw_8bit" --train_4bit --lora_r 32 --lora_alpha 32 --prompt_template_name "alpaca_short" --num_train_epochs 15 --gradient_accumulation_steps 24 --per_device_train_batch_size 1 --logging_steps 1 --save_total_limit 20 --use_gradient_checkpointing True --save_and_eval_steps 1 --group_by_length True --cutoff_len 4096 --val_set_size 0 --use_flash_attn True --base_model "meta-llama/Llama-2-7b-hf"
```
|
xzuyn/LLaMa-2-LIMA-7B-QLoRA
|
xzuyn
| 2023-08-31T12:14:34Z | 0 | 0 | null |
[
"en",
"dataset:xzuyn/lima-multiturn-alpaca",
"region:us"
] | null | 2023-08-26T09:30:42Z |
---
datasets:
- xzuyn/lima-multiturn-alpaca
language:
- en
---
Trained on a 7900XTX.
[Zeus-LLM-Trainer](https://github.com/official-elinas/zeus-llm-trainer) command to recreate:
```
python finetune.py --data_path "xzuyn/lima-multiturn-alpaca" --learning_rate 0.0001 --optim "paged_adamw_8bit" --train_4bit --lora_r 32 --lora_alpha 32 --prompt_template_name "alpaca_short" --num_train_epochs 15 --gradient_accumulation_steps 24 --per_device_train_batch_size 1 --logging_steps 1 --save_total_limit 20 --use_gradient_checkpointing True --save_and_eval_steps 41 --cutoff_len 4096 --val_set_size 0 --use_flash_attn True --base_model "meta-llama/Llama-2-7b-hf"
```
|
nimrita/distilhubert-finetuned-gtzan
|
nimrita
| 2023-08-31T12:03:57Z | 176 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-08-23T07:06:21Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.87
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5522
- Accuracy: 0.87
## 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_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8034 | 1.0 | 113 | 1.5716 | 0.52 |
| 1.0738 | 2.0 | 226 | 1.0565 | 0.62 |
| 0.852 | 3.0 | 339 | 0.7845 | 0.76 |
| 0.7287 | 4.0 | 452 | 0.7007 | 0.78 |
| 0.4968 | 5.0 | 565 | 0.5528 | 0.82 |
| 0.1266 | 6.0 | 678 | 0.7303 | 0.81 |
| 0.1341 | 7.0 | 791 | 0.5915 | 0.85 |
| 0.0251 | 8.0 | 904 | 0.5522 | 0.87 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Aonodensetsu/Kupurupurupuru
|
Aonodensetsu
| 2023-08-31T11:57:53Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-08-31T11:15:49Z |
---
license: gpl-3.0
---
This is a mirror of CivitAI.
The style of artist **Kupurupurupuru** trained for [Foxya v3](https://civitai.com/models/17138).
The preview image uses the prompt "\<lyco\> 1girl" - the recommended settings are epoch 15-20, strength 0.6-0.8.
|
Aonodensetsu/kokemoco
|
Aonodensetsu
| 2023-08-31T11:54:39Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-08-31T11:15:33Z |
---
license: gpl-3.0
---
This is a mirror of CivitAI.
The style of artist **kokemoco** trained for [Foxya v3](https://civitai.com/models/17138).
The preview image uses the prompt "\<lyco\> 1girl" - the recommended settings are epoch 7-20, strength 0.5-0.8.
|
alexdbz/roberta-large-peft-Lora-abstracts-v1-2epochs
|
alexdbz
| 2023-08-31T11:46:20Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-31T11:46:16Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
Aonodensetsu/fruitsrabbit
|
Aonodensetsu
| 2023-08-31T11:41:53Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-08-31T11:13:33Z |
---
license: gpl-3.0
---
This is a mirror of CivitAI.
The style of artist **fruitsrabbit** trained for [Foxya v3](https://civitai.com/models/17138).
The preview image uses the prompt "\<lyco\> 1girl" - the recommended settings are epoch 17-20, strength 0.5-0.8.
|
Aonodensetsu/blvefo9
|
Aonodensetsu
| 2023-08-31T11:35:54Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-08-31T11:13:15Z |
---
license: gpl-3.0
---
This is a mirror of CivitAI.
The style of artist **blvefo9** trained for [Foxya v3](https://civitai.com/models/17138).
The preview image uses the prompt "\<lyco\> 1girl" - the recommended settings are epoch 13-18, strength 0.5-0.7.
|
papasega/finetune_Distilbert_SST_Avalinguo_Fluency
|
papasega
| 2023-08-31T11:27:11Z | 106 | 1 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased-finetuned-sst-2-english",
"base_model:finetune:distilbert/distilbert-base-uncased-finetuned-sst-2-english",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-31T11:07:29Z |
---
license: apache-2.0
base_model: distilbert-base-uncased-finetuned-sst-2-english
tags:
- generated_from_trainer
model-index:
- name: finetune_Distilbert_SST_Avalinguo_Fluency
results: []
widget:
- text: "Engineer, Yeah, indeed, you know that the lady has a Phd. It's the 1st."
example_title: "High_fluency_1"
- text: "Oh, how was brown for you?"
example_title: "Low_fluency_1"
- text: "Now they can."
example_title: "Low_fluency_2"
- text: "But kind of plastics like growing more social consciousness, right?"
example_title: "High_fluency_2"
---
<!-- 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. -->
# finetune_Distilbert_SST_Avalinguo_Fluency
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
susmitabhatt/susmita_speecht5_finetuned_voxpopuli_nl
|
susmitabhatt
| 2023-08-31T11:23:19Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-08-31T11:21:34Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: susmita_speecht5_finetuned_voxpopuli_nl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# susmita_speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1500
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
dt-and-vanilla-ardt/dt-ppo_train_walker2d_level-3108_1042-33
|
dt-and-vanilla-ardt
| 2023-08-31T11:17:10Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-08-31T09:43:45Z |
---
tags:
- generated_from_trainer
model-index:
- name: dt-ppo_train_walker2d_level-3108_1042-33
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. -->
# dt-ppo_train_walker2d_level-3108_1042-33
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
KatMarie/basque-wav2vec2-large-xls-r-300m
|
KatMarie
| 2023-08-31T11:16:50Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-31T10:04:03Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: basque-wav2vec2-large-xls-r-300m
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: eu
split: test
args: eu
metrics:
- name: Wer
type: wer
value: 0.99994770284758
---
<!-- 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. -->
# basque-wav2vec2-large-xls-r-300m
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5181
- Wer: 0.9999
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.8015 | 0.43 | 100 | 2.8095 | 1.0000 |
| 2.4498 | 0.85 | 200 | 1.5181 | 0.9999 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
prashanth07/falcon_data_with_chunk
|
prashanth07
| 2023-08-31T11:12:04Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-31T11:10:44Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
oliverN/path-to-save-model
|
oliverN
| 2023-08-31T11:11:17Z | 2 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2",
"base_model:finetune:stabilityai/stable-diffusion-2",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-30T11:39:05Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2
instance_prompt: a photo of sks woman
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - oliverN/path-to-save-model
This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks woman using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
|
Ori/lama-2-13b-peft-strategyqa-retrieval-mix
|
Ori
| 2023-08-31T11:10:07Z | 4 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-08-30T05:31:50Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
softaken/Softaken-EML-to-PST-Converter
|
softaken
| 2023-08-31T11:09:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-08-31T11:00:49Z |
Softaken EML to PST Converter Tool is a safe and advanced software to convert EML files to Outlook PST file format. This application can rapidly convert EML or EMLX files to Outlook PST file format without losing a single file. Users are free to convert unlimited EML files to the PST file format without data limits. Both tech-expert and non-expert users can also use this tool to convert EML files to the Outlook PST file format. The software can convert EML email metadata such as cc, bcc, subject, date, non-English, management, etc. to Outlook PST file format. The app also works with various email clients such as Mozilla Thunderbird, Mac/Apple Mail, The Bat, Incredi Mail, Spicebird, Sea Monkey, Sylpheed, Opera Mail, Entourage, etc. There are no possibilities for data corruption when the conversion process begins. The utility provides a complete preview of the EML files before beginning the migration process. Users can download this app in any Windows version, such as Windows 11, Windows 10 S, Windows 10, Windows 8/8.1, Windows 7, Windows Vista, Windows XP, and Windows 2000, etc. Grab the free demo version of this app to learn more about the features and capabilities of this computer software without spending a penny.
Read More: https://www.softaken.com/eml-to-pst-converter
|
JongYeop/bert-finetuned-ner
|
JongYeop
| 2023-08-31T11:09:43Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-31T10:52:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9331789612967251
- name: Recall
type: recall
value: 0.9495119488387749
- name: F1
type: f1
value: 0.9412746079412746
- name: Accuracy
type: accuracy
value: 0.9864308000235474
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0607
- Precision: 0.9332
- Recall: 0.9495
- F1: 0.9413
- Accuracy: 0.9864
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0878 | 1.0 | 1756 | 0.0679 | 0.9148 | 0.9325 | 0.9236 | 0.9827 |
| 0.0359 | 2.0 | 3512 | 0.0617 | 0.9262 | 0.9465 | 0.9362 | 0.9855 |
| 0.0187 | 3.0 | 5268 | 0.0607 | 0.9332 | 0.9495 | 0.9413 | 0.9864 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.2
|
dt-and-vanilla-ardt/ardt-vanilla-ppo_train_walker2d_level-3108_1028-33
|
dt-and-vanilla-ardt
| 2023-08-31T11:03:27Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-08-31T09:29:57Z |
---
tags:
- generated_from_trainer
model-index:
- name: ardt-vanilla-ppo_train_walker2d_level-3108_1028-33
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. -->
# ardt-vanilla-ppo_train_walker2d_level-3108_1028-33
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Aonodensetsu/anibaruthecat
|
Aonodensetsu
| 2023-08-31T10:56:32Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-08-15T07:39:22Z |
---
license: gpl-3.0
---
This is a mirror of CivitAI.
The style of artist **anibaruthecat**, trained for [Foxya v3](https://civitai.com/models/17138).
The preview image uses the prompt "\<lyco\> my little pony" - the recommended settings are epoch 14-15, strength 0.6-0.9.
|
devinu/kullm-finetune-test-model
|
devinu
| 2023-08-31T10:55:42Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-19T07:38:57Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
Aonodensetsu/crumbles
|
Aonodensetsu
| 2023-08-31T10:54:47Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-08-15T12:15:39Z |
---
license: gpl-3.0
---
This is a mirror of CivitAI.
The style of artist **crumbles** trained for [Foxya v3](https://civitai.com/models/17138).
The preview image uses the prompt "\<lyco\> 1girl" - the recommended settings are epoch 12-15, strength 0.6-0.8.
|
ardt-multipart/ardt-multipart-ppo_train_hopper_level-3108_1037-99
|
ardt-multipart
| 2023-08-31T10:54:14Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-08-31T09:38:26Z |
---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-ppo_train_hopper_level-3108_1037-99
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. -->
# ardt-multipart-ppo_train_hopper_level-3108_1037-99
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Aonodensetsu/delicious
|
Aonodensetsu
| 2023-08-31T10:54:12Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-08-15T12:42:21Z |
---
license: gpl-3.0
---
This is a mirror of CivitAI.
The style of artist **delicious** trained for [Foxya v3](https://civitai.com/models/17138).
The preview image uses the prompt "\<lyco\> furry" - the recommended settings are epoch 12-15, strength 0.6-0.8.
|
Aonodensetsu/darkmirage
|
Aonodensetsu
| 2023-08-31T10:53:59Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-08-15T12:24:00Z |
---
license: gpl-3.0
---
This is a mirror of CivitAI.
The style of artist **darkmirage** trained for [Foxya v3](https://civitai.com/models/17138).
The preview image uses the prompt "\<lyco\> furry" - the recommended settings are epoch 13-14, strength 0.5-0.7.
|
Aonodensetsu/frenky_hw
|
Aonodensetsu
| 2023-08-31T10:53:21Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-08-15T12:53:19Z |
---
license: gpl-3.0
---
This is a mirror of CivitAI.
The style of artist **frenky_hw** trained for [Foxya v3](https://civitai.com/models/17138).
The preview image uses the prompt "\<lyco\> furry, male, girly" - the recommended settings are epoch 11-13, strength 0.6-0.8.
|
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