modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
DoyyingFace/bert-asian-hate-tweets-asonam-unclean
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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"BertForSequenceClassification"
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| 30 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: gauravkuppa/ppo-SnowballTarget1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
albert-base-v1
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
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"AlbertForMaskedLM"
],
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}
| 38,156 | 2023-03-10T21:50:51Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: bucket-not-bucket
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9777777791023254
---
# bucket-not-bucket
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### bucket
#### not bucket
|
albert-large-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
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"AlbertForMaskedLM"
],
"model_type": "albert",
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}
| 687 | 2023-03-10T21:55:35Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.86 +/- 5.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r adam1brownell/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
albert-large-v2
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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}
| 26,792 | 2023-03-10T22:01:12Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_legal_bert_small
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_legal_bert_small
This model is a fine-tuned version of [nlpaueb/legal-bert-small-uncased](https://huggingface.co/nlpaueb/legal-bert-small-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4867
- Accuracy: 0.815
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 50 | 0.4818 | 0.815 |
| No log | 2.0 | 100 | 0.4856 | 0.815 |
| No log | 3.0 | 150 | 0.4867 | 0.815 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
albert-xlarge-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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}
| 341 | 2023-03-10T22:01:30Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -183.18 +/- 71.91
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 100000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'Cesar514/PPO2-LunarLander-v3'
'batch_size': 512
'minibatch_size': 128}
```
|
bert-base-cased-finetuned-mrpc
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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}
| 11,644 | 2023-03-10T22:09:14Z |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
widget:
- text: "A high tech solarpunk utopia in the Amazon rainforest"
example_title: Amazon rainforest
- text: "A pikachu fine dining with a view to the Eiffel Tower"
example_title: Pikachu in Paris
- text: "A mecha robot in a favela in expressionist style"
example_title: Expressionist robot
- text: "an insect robot preparing a delicious meal"
example_title: Insect robot
- text: "A small cabin on top of a snowy mountain in the style of Disney, artstation"
example_title: Snowy disney cabin
extra_gated_prompt: |-
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
extra_gated_heading: Please read the LICENSE to access this model
---
# Stable Diffusion v1-4 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with 🧨Diffusers blog](https://huggingface.co/blog/stable_diffusion).
The **Stable-Diffusion-v1-4** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2)
checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
This weights here are intended to be used with the 🧨 Diffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, [come here](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
## Examples
We recommend using [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion.
### PyTorch
```bash
pip install --upgrade diffusers transformers scipy
```
Running the pipeline with the default PNDM scheduler:
```python
import torch
from diffusers import StableDiffusionPipeline
model_id = "CompVis/stable-diffusion-v1-4"
device = "cuda"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to(device)
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
**Note**:
If you are limited by GPU memory and have less than 4GB of GPU RAM available, please make sure to load the StableDiffusionPipeline in float16 precision instead of the default float32 precision as done above. You can do so by telling diffusers to expect the weights to be in float16 precision:
```py
import torch
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to(device)
pipe.enable_attention_slicing()
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
To swap out the noise scheduler, pass it to `from_pretrained`:
```python
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
model_id = "CompVis/stable-diffusion-v1-4"
# Use the Euler scheduler here instead
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
### JAX/Flax
To use StableDiffusion on TPUs and GPUs for faster inference you can leverage JAX/Flax.
Running the pipeline with default PNDMScheduler
```python
import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxStableDiffusionPipeline
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="flax", dtype=jax.numpy.bfloat16
)
prompt = "a photo of an astronaut riding a horse on mars"
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, num_samples)
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
```
**Note**:
If you are limited by TPU memory, please make sure to load the `FlaxStableDiffusionPipeline` in `bfloat16` precision instead of the default `float32` precision as done above. You can do so by telling diffusers to load the weights from "bf16" branch.
```python
import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxStableDiffusionPipeline
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="bf16", dtype=jax.numpy.bfloat16
)
prompt = "a photo of an astronaut riding a horse on mars"
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, num_samples)
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
```
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
### Safety Module
The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers.
This checker works by checking model outputs against known hard-coded NSFW concepts.
The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter.
Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images.
The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-2B (en) and subsets thereof (see next section)
**Training Procedure**
Stable Diffusion v1-4 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
We currently provide four checkpoints, which were trained as follows.
- [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`.
515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2`. 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2`.225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
steps show the relative improvements of the checkpoints:
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
## Citation
```bibtex
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
```
*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
|
bert-base-german-dbmdz-cased
|
[
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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}
}
}
| 1,814 | 2023-03-10T22:16:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: keyword_category_classifier_v4
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. -->
# keyword_category_classifier_v4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2337
- Accuracy: 0.9266
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7312 | 1.0 | 986 | 0.2616 | 0.9165 |
| 0.2264 | 2.0 | 1972 | 0.2337 | 0.9266 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
bert-base-german-dbmdz-uncased
|
[
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 68,305 | 2023-03-10T22:19:37Z |
---
license: apache-2.0
datasets:
- hackathon-pln-es/neutral-es
language:
- es
metrics:
- bleu
tags:
- Text Neutralization
- Inclusive Language
---
|
bert-base-multilingual-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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}
}
}
| 4,749,504 | 2023-03-10T22:22:17Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Jclementg/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
bert-base-multilingual-uncased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 328,585 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-copterv1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 27.33 +/- 18.83
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
bert-large-cased-whole-word-masking-finetuned-squad
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8,214 | 2023-03-10T22:35:21Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: UchihaMadara/thesis-pretrained
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# UchihaMadara/thesis-pretrained
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.4115
- Validation Loss: 3.2358
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -853, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.4115 | 3.2358 | 0 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
bert-large-cased-whole-word-masking
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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"max_length": null,
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
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}
}
}
| 2,316 | 2023-03-10T22:35:59Z |
---
license: creativeml-openrail-m
---
This is a clone copy of Stable Diffusion Models from Civitai for Colab training purposes.
|
distilbert-base-multilingual-cased
|
[
"pytorch",
"tf",
"onnx",
"safetensors",
"distilbert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"prefix": null
},
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},
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},
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| 8,339,633 | 2023-03-10T22:54:42Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.836492533087124
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8559
- Bleu: 52.8365
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
13306330378/huiqi_model
|
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| 0 | 2023-03-11T02:43:04Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/yichengwang125/pokemon-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the ./pokemon-blip-captions dataset. You can find some example images in the following.
|
AK/ak_nlp
|
[
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"RobertaForMaskedLM"
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}
| 6 | 2023-03-11T06:37:19Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- ppicazo/autotrain-data-astrophotography-object-classifier-alpha4
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.006629293486504155
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 40333104876
- CO2 Emissions (in grams): 0.0066
## Validation Metrics
- Loss: 0.015
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: 1.000
- Macro Recall: 1.000
- Micro Recall: 1.000
- Weighted Recall: 1.000
|
AT/distilbert-base-cased-finetuned-wikitext2
|
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| 0 | null |
Access to model dpogreb/autotrain-nyx-40340104916 is restricted and you are not in the authorized list. Visit https://huggingface.co/dpogreb/autotrain-nyx-40340104916 to ask for access.
|
AT/distilgpt2-finetuned-wikitext2
|
[] | null |
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| 0 | 2023-03-11T08:20:19Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 0 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 48 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`setfit.modeling.SupConLoss`
Parameters of the fit()-Method:
```
{
"epochs": 12,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 576,
"warmup_steps": 58,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_mix_tokens': True})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
AdapterHub/bert-base-uncased-pf-comqa
|
[
"bert",
"en",
"dataset:com_qa",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering"
] |
question-answering
|
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| 0 | null |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmp3l_wgjg2/dshin/flan-t5-ppo")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmp3l_wgjg2/dshin/flan-t5-ppo")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmp3l_wgjg2/dshin/flan-t5-ppo")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
AdapterHub/bert-base-uncased-pf-conll2000
|
[
"bert",
"en",
"dataset:conll2000",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:chunk/conll2000"
] |
token-classification
|
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| 4 | null |
---
tags:
- conversational
---
#Safalin DialoGPT Model
|
AdapterHub/bert-base-uncased-pf-conll2003_pos
|
[
"bert",
"en",
"dataset:conll2003",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:pos/conll2003"
] |
token-classification
|
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}
| 6 | null |
---
datasets:
- artemkramov/coreference-dataset-ua
language:
- uk
tags:
- coreference-resolution
- anaphora
widget:
- text: "Jens Peter Hansen kommer fra Danmark"
example_title: "Coreference resolution"
model-index:
- name: test
results:
- task:
type: coreference-resolution # Required. Example: automatic-speech-recognition
name: Coreference resolution # Optional. Example: Speech Recognition
dataset:
type: artemkramov/coreference-dataset-ua # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: Silver Ukrainian Coreference Resolution Dataset # Required. A pretty name for the dataset. Example: Common Voice (French)
metrics:
- type: coval
value: 0.731
name: Mean F1 measure of MUC, BCubed, and CEAFE
---
# Coreference resolution model for the Ukrainian language
<!-- Provide a quick summary of what the model is/does. -->
The coreference resolution model for the Ukrainian language was trained on the [silver Ukrainian coreference dataset](https://huggingface.co/datasets/artemkramov/coreference-dataset-ua)
using the [F-Coref](https://arxiv.org/abs/2209.04280) library. The model was trained on top of the [XML-Roberta-base model](https://huggingface.co/ukr-models/xlm-roberta-base-uk).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Artem Kramov](https://www.linkedin.com/in/artem-kramov-0b3731100/), Andrii Kursin ([email protected]).
- **Languages:** Ukrainian
- **Finetuned from model:** [XML-Roberta-base](https://huggingface.co/ukr-models/xlm-roberta-base-uk)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/artemkramov/fastcoref-ua/blob/main/README.md
- **Demo:** [Google Colab](https://colab.research.google.com/drive/1vsaH15DFDrmKB4aNsQ-9TCQGTW73uk1y?usp=sharing), [HF Spaces](https://huggingface.co/spaces/artemkramov/f-coref-ua)
### Out-of-Scope Use
According to the metrics retrieved from the evaluation dataset, the model is more precision-oriented. Also, there is a high level of granularity of mentions.
E.g., the mention "Головний виконавчий директор Андрій Сидоренко" can be divided into the following coreferent groups: ["Головний виконавчий директор Андрій Сидоренко", "Головний виконавчий директор", "Андрій Сидоренко"].
Such a feature can also be used to extract some positions, roles, or other features of entities in the text.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from fastcoref import FCoref
import spacy
nlp = spacy.load('uk_core_news_md')
model_path = "artemkramov/coref-ua"
model = FCoref(model_name_or_path=model_path, device='cuda:0', nlp=nlp)
preds = model.predict(
texts=["""Мій друг дав мені свою машину та ключі до неї; крім того, він дав мені його книгу. Я з радістю її читаю."""]
)
preds[0].get_clusters(as_strings=False)
> [[(0, 3), (13, 17), (66, 70), (83, 84)],
[(0, 8), (18, 22), (58, 61), (71, 75)],
[(18, 29), (42, 45)],
[(71, 81), (95, 97)]]
preds[0].get_clusters()
> [['Мій', 'мені', 'мені', 'Я'], ['Мій друг', 'свою', 'він', 'його'], ['свою машину', 'неї'], ['його книгу', 'її']]
preds[0].get_logit(
span_i=(13, 17), span_j=(42, 45)
)
> -6.867196
```
## Training Details
### Training Data
The model was trained on the silver coreference resolution dataset: https://huggingface.co/datasets/artemkramov/coreference-dataset-ua.
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
#### Metrics
Two types of metrics were considered: mention-based and the coreference resolution metrics themselves.
Mention-based metrics:
- mention precision
- mention recall
- mention F1
Coreference resolution metrics were calculated as the average values across the following metrics: MUC, BCubed, CEAFE:
- coreference precision
- coreference recall
- coreference F1
### Results
The metrics for the validation dataset:
| Metric | Value |
|:---------------------|:-------|
| Mention precision | 0.850 |
| Mention recall | 0.798 |
| Mention F1 | 0.824 |
| Coreference precision | 0.758 |
| Coreference recall | 0.706 |
| Coreference F1 | 0.731 |
## Model Card Authors
Artem Kramov (https://www.linkedin.com/in/artem-kramov-0b3731100/), Andrii Kursin ([email protected])
|
AdapterHub/bert-base-uncased-pf-copa
|
[
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"adapterhub:comsense/copa"
] | null |
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| 4 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **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://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: hasarinduperera/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AdapterHub/bert-base-uncased-pf-drop
|
[
"bert",
"en",
"dataset:drop",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering"
] |
question-answering
|
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}
| 4 | null |
---
license: apache-2.0
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
---
This is a public repo, for testing purpose only.
|
AdapterHub/bert-base-uncased-pf-duorc_s
|
[
"bert",
"en",
"dataset:duorc",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering"
] |
question-answering
|
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| 0 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: MlpPolicy
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 266.45 +/- 63.75
name: mean_reward
verified: false
---
# **MlpPolicy** Agent playing **LunarLander-v2**
This is a trained model of a **MlpPolicy** 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
...
```
|
AdapterHub/bert-base-uncased-pf-emo
|
[
"bert",
"en",
"dataset:emo",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
] |
text-classification
|
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| 5 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
- gov report
- long document
metrics:
- rouge
model-index:
- name: long-t5-base-govreport
results: []
datasets:
- pszemraj/govreport-summarization-8192
language:
- en
library_name: transformers
pipeline_tag: summarization
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# long-t5-base-govreport
This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the None dataset.
It achieves the following results on the evaluation set:
- Gen Len: 787.34
- Loss: 1.5448
- Rouge1: 57.2303
- Rouge2: 24.9705
- Rougel: 26.8081
- Rougelsum: 54.2747
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Refer to the [pszemraj/govreport-summarization-8192](https://huggingface.co/datasets/pszemraj/govreport-summarization-8192) dataset.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 3
- eval_batch_size: 1
- seed: 4299
- gradient_accumulation_steps: 128
- total_train_batch_size: 384
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 25.0
### Training results
| Training Loss | Epoch | Step | Gen Len | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:-------:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 2.1198 | 0.39 | 25 | 805.336 | 1.8720 | 29.4332 | 7.3761 | 17.0816 | 25.065 |
| 1.8609 | 0.78 | 50 | 833.404 | 1.7601 | 35.3533 | 10.6624 | 18.643 | 31.6979 |
| 1.7805 | 1.17 | 75 | 866.356 | 1.6833 | 36.5786 | 11.1185 | 20.0358 | 33.2116 |
| 1.7352 | 1.56 | 100 | 822.348 | 1.6524 | 40.5489 | 13.0695 | 20.1256 | 37.1369 |
| 1.7371 | 1.95 | 125 | 765.6 | 1.6294 | 43.8594 | 15.2962 | 20.7807 | 40.3461 |
| 1.6428 | 2.34 | 150 | 844.184 | 1.6055 | 44.5054 | 15.731 | 21.2582 | 40.9775 |
| 1.6567 | 2.73 | 175 | 857.236 | 1.6031 | 47.3641 | 16.9664 | 21.4998 | 43.994 |
| 1.5773 | 3.12 | 200 | 841.86 | 1.5855 | 47.2284 | 17.3099 | 21.6793 | 43.9018 |
| 1.5614 | 3.52 | 225 | 832.8 | 1.5883 | 46.4612 | 17.1368 | 21.5931 | 43.1184 |
| 1.5328 | 3.91 | 250 | 790.056 | 1.5730 | 46.5685 | 17.5423 | 22.2082 | 43.1811 |
| 1.5194 | 4.3 | 275 | 825.868 | 1.5690 | 47.6205 | 18.377 | 22.7639 | 44.3701 |
| 1.571 | 4.69 | 300 | 794.032 | 1.5676 | 49.2203 | 19.1109 | 22.8005 | 46.0679 |
| 1.4275 | 5.08 | 325 | 833.068 | 1.5656 | 50.6982 | 20.0278 | 23.5585 | 47.5036 |
| 1.4912 | 5.47 | 350 | 793.068 | 1.5625 | 50.3371 | 19.8639 | 23.3666 | 47.1898 |
| 1.4764 | 5.86 | 375 | 819.86 | 1.5532 | 50.9702 | 20.7532 | 23.8765 | 47.9915 |
| 1.3972 | 6.25 | 400 | 770.78 | 1.5564 | 49.279 | 19.4781 | 23.1018 | 46.1942 |
| 1.4479 | 6.64 | 425 | 806.244 | 1.5529 | 50.3317 | 20.2888 | 23.4454 | 47.3491 |
| 1.4567 | 7.03 | 450 | 787.48 | 1.5590 | 52.2209 | 21.2868 | 23.9284 | 49.1691 |
| 1.3933 | 7.42 | 475 | 842.664 | 1.5561 | 51.9578 | 20.5806 | 23.7177 | 48.9121 |
| 1.4245 | 7.81 | 500 | 813.772 | 1.5420 | 52.3725 | 21.7787 | 24.5209 | 49.4003 |
| 1.3033 | 8.2 | 525 | 824.66 | 1.5499 | 52.7839 | 21.589 | 24.5617 | 49.8609 |
| 1.3673 | 8.59 | 550 | 807.348 | 1.5530 | 53.2339 | 22.152 | 24.7587 | 50.2502 |
| 1.3634 | 8.98 | 575 | 767.952 | 1.5458 | 53.0293 | 22.3194 | 25.174 | 50.078 |
| 1.3095 | 9.37 | 600 | 856.252 | 1.5412 | 53.7658 | 22.5229 | 25.0448 | 50.708 |
| 1.3492 | 9.76 | 625 | 826.064 | 1.5389 | 51.8662 | 21.6229 | 24.6819 | 48.8648 |
| 1.3007 | 10.16 | 650 | 843.544 | 1.5404 | 53.6692 | 22.154 | 24.6218 | 50.6864 |
| 1.2729 | 10.55 | 675 | 808.764 | 1.5428 | 54.6479 | 23.3029 | 25.5647 | 51.6394 |
| 1.3758 | 10.94 | 700 | 800.152 | 1.5403 | 54.9418 | 23.3323 | 25.6087 | 51.9256 |
| 1.3357 | 11.33 | 725 | 814.496 | 1.5455 | 55.2511 | 23.5606 | 25.8237 | 52.3183 |
| 1.2817 | 11.72 | 750 | 811.144 | 1.5412 | 55.2847 | 23.6632 | 25.9341 | 52.3146 |
| 1.2771 | 12.11 | 775 | 852.704 | 1.5450 | 55.1956 | 23.5545 | 25.677 | 52.1841 |
| 1.2892 | 12.5 | 800 | 805.844 | 1.5369 | 54.9563 | 23.5105 | 25.8876 | 51.9568 |
| 1.2757 | 12.89 | 825 | 813.476 | 1.5467 | 56.4728 | 24.6875 | 26.4415 | 53.4939 |
| 1.2382 | 13.28 | 850 | 787.34 | 1.5448 | 57.2303 | 24.9705 | 26.8081 | 54.2747 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.2
|
AdapterHub/bert-base-uncased-pf-emotion
|
[
"bert",
"en",
"dataset:emotion",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
] |
text-classification
|
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| 165 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 540.50 +/- 176.92
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 dugongo -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 dugongo -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 dugongo
```
## 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)])
```
|
AdapterHub/bert-base-uncased-pf-hellaswag
|
[
"bert",
"en",
"dataset:hellaswag",
"arxiv:2104.08247",
"adapter-transformers",
"adapterhub:comsense/hellaswag"
] | null |
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}
| 0 | null |
Access to model Abdullah007/image-classification-ResNet50 is restricted and you are not in the authorized list. Visit https://huggingface.co/Abdullah007/image-classification-ResNet50 to ask for access.
|
AdapterHub/bert-base-uncased-pf-imdb
|
[
"bert",
"en",
"dataset:imdb",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:sentiment/imdb"
] |
text-classification
|
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}
| 15 | null |
---
tags:
- generated_from_keras_callback
model-index:
- name: Rishu115/mlm-bert-train_finalTraining
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rishu115/mlm-bert-train_finalTraining
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2958
- Validation Loss: 1.1886
- Epoch: 6
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 47396, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.2956 | 1.1881 | 0 |
| 1.2949 | 1.1895 | 1 |
| 1.2951 | 1.1890 | 2 |
| 1.2952 | 1.1902 | 3 |
| 1.2950 | 1.1869 | 4 |
| 1.2957 | 1.1867 | 5 |
| 1.2958 | 1.1886 | 6 |
### Framework versions
- Transformers 4.23.1
- TensorFlow 2.10.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AdapterHub/bert-base-uncased-pf-mrpc
|
[
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:sts/mrpc"
] |
text-classification
|
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| 52 | null |
---
license: mit
language:
- en
pipeline_tag: text-generation
tags:
- gpt
- gpt2
- gpt3
- ai dungeon
- ai
- dungeon
- medium
- en
- english
- text
- generation
- text generation
---
Large model from https://github.com/KoolenDasheppi/Clover-Edition
Working good only with English language.
|
AdapterHub/bert-base-uncased-pf-multirc
|
[
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:rc/multirc"
] |
text-classification
|
{
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| 4 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.48 +/- 4.69
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Taratata/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
AdapterHub/bert-base-uncased-pf-newsqa
|
[
"bert",
"en",
"dataset:newsqa",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering"
] |
question-answering
|
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}
| 3 | 2023-03-11T11:46:56Z |
---
language:
- bn
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
- openslr
metrics:
- wer
model-index:
- name: Whisper Small - Mohammed Rakib
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: bn
split: test
metrics:
- type: wer
value: 10.09
name: WER
- type: cer
value: 5.31
name: CER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: bn_in
split: test
metrics:
- type: wer
value: 14.74
name: WER
- type: cer
value: 8.87
name: CER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small - Mohammed Rakib
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common-voice-11, the google-fleurs and the openslr53 datasets.
It achieves the following results on the evaluation set:
- Loss: 0.0605
- Cer: 5.6737
- Wer: 10.5971
## 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: 32
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 888
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
| :-----------: | :---: | :--: | :-------------: | :-------: | :------: |
| 1.4902 | 0.11 | 100 | 1.2636 | 1208.7378 | 889.9406 |
| 0.5703 | 0.23 | 200 | 0.4612 | 121.7766 | 108.6401 |
| 0.3679 | 0.34 | 300 | 0.3046 | 17.7385 | 35.2744 |
| 0.2301 | 0.45 | 400 | 0.2059 | 14.8853 | 29.7671 |
| 0.191 | 0.56 | 500 | 0.1693 | 12.7561 | 25.4528 |
| 0.1605 | 0.68 | 600 | 0.1462 | 11.5880 | 22.7845 |
| 0.146 | 0.79 | 700 | 0.1300 | 10.4321 | 20.5541 |
| 0.1296 | 0.9 | 800 | 0.1156 | 9.8572 | 19.2143 |
| 0.1212 | 1.01 | 900 | 0.1055 | 8.9462 | 17.4004 |
| 0.1072 | 1.13 | 1000 | 0.0978 | 8.2675 | 15.9234 |
| 0.1013 | 1.24 | 1100 | 0.0912 | 7.7918 | 15.0605 |
| 0.0952 | 1.35 | 1200 | 0.0854 | 7.5497 | 14.3207 |
| 0.0915 | 1.47 | 1300 | 0.0809 | 6.9833 | 13.3163 |
| 0.0843 | 1.58 | 1400 | 0.0780 | 6.6422 | 12.7179 |
| 0.0819 | 1.69 | 1500 | 0.0744 | 6.7287 | 12.6589 |
| 0.0798 | 1.8 | 1600 | 0.0718 | 6.4962 | 12.3022 |
| 0.0774 | 1.92 | 1700 | 0.0694 | 6.2198 | 11.8414 |
| 0.0695 | 2.03 | 1800 | 0.0680 | 6.1346 | 11.5683 |
| 0.0686 | 2.14 | 1900 | 0.0662 | 5.9758 | 11.2485 |
| 0.0681 | 2.25 | 2000 | 0.0647 | 6.0599 | 11.2842 |
| 0.0661 | 2.37 | 2100 | 0.0639 | 5.9500 | 11.1356 |
| 0.0653 | 2.48 | 2200 | 0.0631 | 5.8114 | 10.8952 |
| 0.0636 | 2.59 | 2300 | 0.0622 | 5.8502 | 10.9385 |
| 0.0641 | 2.71 | 2400 | 0.0615 | 5.7382 | 10.7015 |
| 0.0633 | 2.82 | 2500 | 0.0612 | 5.7038 | 10.6455 |
| 0.0626 | 2.93 | 2600 | 0.0608 | 5.8058 | 10.7597 |
| 0.06 | 3.04 | 2700 | 0.0605 | 5.7328 | 10.6374 |
| 0.0584 | 3.16 | 2800 | 0.0605 | 5.6737 | 10.5971 |
| 0.0585 | 3.27 | 2900 | 0.0604 | 5.6877 | 10.6098 |
| 0.0598 | 3.38 | 3000 | 0.0603 | 5.7075 | 10.6043 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AdapterHub/bert-base-uncased-pf-pmb_sem_tagging
|
[
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:semtag/pmb"
] |
token-classification
|
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| 4 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 13.59 +/- 5.65
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Cesar514/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
AdapterHub/bert-base-uncased-pf-quail
|
[
"bert",
"en",
"dataset:quail",
"arxiv:2104.08247",
"adapter-transformers"
] | null |
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| 2 | null |
---
license: mit
datasets:
- ThePioneer/Ver0_voice_dataset
language:
- en
- ja
- zh
tags:
- music
- voice
---
アニメ声のようなわざとらしい声でもなく、ボカロなどのソフトを使ったいかにも合成の音声でもなく、クラスに一人くらいいそうな、自然で親しみやすい美少女の声を…。
本モデルは、そういうコンセプトで開発された[So-vits-svc 4.0](https://github.com/svc-develop-team/so-vits-svc)のモデルです。
一次音声は私自身の肉声から合成し、その素材をElevenLabsで1時間程度まで水増しし、[水増しした音声データセット](https://huggingface.co/datasets/ThePioneer/Ver0_voice_dataset)を学習させました。
innnky氏がG_0.pth、D_0.pthのあったrepoを削除してしまったようなので、学習用のベースとなった[G_0.pth](https://huggingface.co/ThePioneer/NaturalGirlyVoice/blob/main/G_0.pth)、[D_0.pth](https://huggingface.co/ThePioneer/NaturalGirlyVoice/blob/main/D_0.pth)および[hubertのチェックポイント](https://huggingface.co/ThePioneer/NaturalGirlyVoice/blob/main/checkpoint_best_legacy_500.pt)も同梱しています。
また、推論や学習ができるように、[notebook](https://huggingface.co/ThePioneer/NaturalGirlyVoice/blob/main/sovits4_0_for_training_and_inference.ipynb)も同梱しています（利用時はconfig.jsonを置き換えることも必要です）。
## 注意
- Sovitsの仕様で、音声の直前の無音部でノイズが発生することがあります。
- 中国由来のモデルなので、日本語や英語の発音はたまにおかしくなります。
- 自然な音声を目指したので、（もしかすると）実在人物の音声に類似している可能性があります。「歌わせてみた」など、平和的な内容での利用を推奨します。
- （おそらく日本では）違法ではないですが、例えばどこかの4chan民のように『我が闘争』を読ませる使い方や、nsfw音声としての利用は推奨されません。
## サンプル
God knows...
<audio src="https://huggingface.co/ThePioneer/NaturalGirlyVoice/resolve/main/God-knows...-sovits4-ver0.mp3" controls></audio>
Speak softly love
<audio src="https://huggingface.co/ThePioneer/NaturalGirlyVoice/resolve/main/Speak-Softly-Love-Sovits4-Ver0.mp3" controls></audio>
|
AdapterHub/bert-base-uncased-pf-quartz
|
[
"bert",
"en",
"dataset:quartz",
"arxiv:2104.08247",
"adapter-transformers"
] | null |
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}
| 2 | 2023-03-11T11:53:33Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.90 +/- 5.09
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Taratata/rl_course_vizdoom_health_gathering_supreme-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme-v2
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme-v2 --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
AdapterHub/bert-base-uncased-pf-quoref
|
[
"bert",
"en",
"dataset:quoref",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering"
] |
question-answering
|
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}
}
| 6 | 2023-03-11T12:01:36Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: McCheng/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AdapterHub/bert-base-uncased-pf-scicite
|
[
"bert",
"en",
"dataset:scicite",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
] |
text-classification
|
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}
| 0 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-4
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
|
AdapterHub/bert-base-uncased-pf-snli
|
[
"bert",
"en",
"dataset:snli",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
] |
text-classification
|
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}
| 8 | null |
# An alias of [relbert/relbert-roberta-base-nce-semeval2012-0](https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-0).
|
AdapterHub/bert-base-uncased-pf-social_i_qa
|
[
"bert",
"en",
"dataset:social_i_qa",
"arxiv:2104.08247",
"adapter-transformers"
] | null |
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}
| 4 | null |
---
language:
- en
tags:
- openvino
---
# distilbert-base-uncased-distilled-squad
This is the [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) model converted to [OpenVINO](https://openvino.ai), for accellerated inference.
An example of how to do inference on this model:
```python
from optimum.intel.openvino import OVModelForQuestionAnswering
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/distilbert-base-uncased-distilled-squad-ov-fp32"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForQuestionAnswering.from_pretrained(model_id)
pipe = pipeline("question-answering", model=model, tokenizer=tokenizer)
result = pipe("What is OpenVINO?", "OpenVINO is a framework that accelerates deep learning inferencing")
print(result)
```
|
AdapterHub/bert-base-uncased-pf-squad
|
[
"bert",
"en",
"dataset:squad",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering",
"adapterhub:qa/squad1"
] |
question-answering
|
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| 9 | null |
---
language: en
thumbnail: http://www.huggingtweets.com/jacksepticeye/1678537664774/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506613842633232400/ILPlMQ63_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Jacksepticeye</div>
<div style="text-align: center; font-size: 14px;">@jacksepticeye</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Jacksepticeye.
| Data | Jacksepticeye |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 107 |
| Short tweets | 646 |
| Tweets kept | 2495 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/10bvtkin/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jacksepticeye's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/65s277k4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/65s277k4/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jacksepticeye')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
AdapterHub/bert-base-uncased-pf-stsb
|
[
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:sts/sts-b"
] |
text-classification
|
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}
| 3 | null |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
- keras-dreambooth
- nature
license: creativeml-openrail-m
inference: true
library_name: keras
---
## Model description
This the Stable Diffusion model fine-tuned the Low Poly World concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the instance_prompt: **a photo of lowpoly_world**
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| inner_optimizer.class_name | Custom>RMSprop |
| inner_optimizer.config.name | RMSprop |
| inner_optimizer.config.weight_decay | None |
| inner_optimizer.config.clipnorm | None |
| inner_optimizer.config.global_clipnorm | None |
| inner_optimizer.config.clipvalue | None |
| inner_optimizer.config.use_ema | False |
| inner_optimizer.config.ema_momentum | 0.99 |
| inner_optimizer.config.ema_overwrite_frequency | 100 |
| inner_optimizer.config.jit_compile | True |
| inner_optimizer.config.is_legacy_optimizer | False |
| inner_optimizer.config.learning_rate | 0.0010000000474974513 |
| inner_optimizer.config.rho | 0.9 |
| inner_optimizer.config.momentum | 0.0 |
| inner_optimizer.config.epsilon | 1e-07 |
| inner_optimizer.config.centered | False |
| dynamic | True |
| initial_scale | 32768.0 |
| dynamic_growth_steps | 2000 |
| training_precision | mixed_float16 |
|
Ahmadvakili/A
|
[] | null |
{
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}
}
}
| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: NathanS-HuggingFace/Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Akashpb13/Central_kurdish_xlsr
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ckb",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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}
}
}
| 10 | null |
---
license: mit
language:
- en
pipeline_tag: text-to-image
---
|
Alireza1044/albert-base-v2-stsb
|
[
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
{
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"AlbertForSequenceClassification"
],
"model_type": "albert",
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}
| 37 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jeffshang/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AmazonScience/qanlu
|
[
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:atis",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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},
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}
}
| 494 | null |
---
license: mit
datasets:
- mrqa
language:
- en
metrics:
- squad
library_name: adapter-transformers
pipeline_tag: question-answering
---
# Description
This is the single-dataset adapter for the NaturalQuestions partition of the MRQA 2019 Shared Task Dataset. The adapter was created by Friedman et al. (2021) and should be used with the `roberta-base` encoder.
The UKP-SQuARE team created this model repository to simplify the deployment of this model on the UKP-SQuARE platform. The GitHub repository of the original authors is https://github.com/princeton-nlp/MADE
# Usage
This model contains the same weights as https://huggingface.co/princeton-nlp/MADE/resolve/main/single_dataset_adapters/NaturalQuestions/model.pt. The only difference is that our repository follows the standard format of AdapterHub. Therefore, you could load this model as follows:
```
from transformers import RobertaForQuestionAnswering, RobertaTokenizerFast
model = RobertaForQuestionAnswering.from_pretrained("roberta-base")
model.load_adapter("UKP-SQuARE/NaturalQuestions_Adapter_RoBERTa", source="hf")
model.set_active_adapters("NaturalQuestions")
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base')
pipe = pipeline("question-answering", model=model, tokenizer=tokenizer)
pipe({"question": "What is the capital of Germany?", "context": "The capital of Germany is Berlin."})
```
Note you need the adapter-transformers library https://adapterhub.ml
# Evaluation
Friedman et al. report an F1 score of **79.2 on NaturalQuestions**.
Please refer to the original publication for more information.
# Citation
Single-dataset Experts for Multi-dataset Question Answering (Friedman et al., EMNLP 2021)
|
Amba/wav2vec2-large-xls-r-300m-turkish-colab
|
[] | null |
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}
}
| 0 | null |
---
license: mit
datasets:
- mrqa
language:
- en
metrics:
- squad
library_name: adapter-transformers
pipeline_tag: question-answering
---
# Description
This is the single-dataset adapter for the NewsQA partition of the MRQA 2019 Shared Task Dataset. The adapter was created by Friedman et al. (2021) and should be used with the `roberta-base` encoder.
The UKP-SQuARE team created this model repository to simplify the deployment of this model on the UKP-SQuARE platform. The GitHub repository of the original authors is https://github.com/princeton-nlp/MADE
# Usage
This model contains the same weights as https://huggingface.co/princeton-nlp/MADE/resolve/main/single_dataset_adapters/NewsQA/model.pt. The only difference is that our repository follows the standard format of AdapterHub. Therefore, you could load this model as follows:
```
from transformers import RobertaForQuestionAnswering, RobertaTokenizerFast
model = RobertaForQuestionAnswering.from_pretrained("roberta-base")
model.load_adapter("UKP-SQuARE/NewsQA_Adapter_RoBERTa", source="hf")
model.set_active_adapters("NewsQA")
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base')
pipe = pipeline("question-answering", model=model, tokenizer=tokenizer)
pipe({"question": "What is the capital of Germany?", "context": "The capital of Germany is Berlin."})
```
Note you need the adapter-transformers library https://adapterhub.ml
# Evaluation
Friedman et al. report an F1 score of **70.9 on NewsQA**.
Please refer to the original publication for more information.
# Citation
Single-dataset Experts for Multi-dataset Question Answering (Friedman et al., EMNLP 2021)
|
aisoftware/Loquela
|
[
"onnx"
] | null |
{
"architectures": null,
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"task_specific_params": {
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}
| 0 | null |
---
license: mit
datasets:
- mrqa
language:
- en
metrics:
- squad
library_name: adapter-transformers
pipeline_tag: question-answering
---
# Description
This is the single-dataset adapter for the TriviaQA partition of the MRQA 2019 Shared Task Dataset. The adapter was created by Friedman et al. (2021) and should be used with the `roberta-base` encoder.
The UKP-SQuARE team created this model repository to simplify the deployment of this model on the UKP-SQuARE platform. The GitHub repository of the original authors is https://github.com/princeton-nlp/MADE
# Usage
This model contains the same weights as https://huggingface.co/princeton-nlp/MADE/resolve/main/single_dataset_adapters/SearchQA/model.pt. The only difference is that our repository follows the standard format of AdapterHub. Therefore, you could load this model as follows:
```
from transformers import RobertaForQuestionAnswering, RobertaTokenizerFast
model = RobertaForQuestionAnswering.from_pretrained("roberta-base")
model.load_adapter("UKP-SQuARE/SearchQA_Adapter_RoBERTa", source="hf")
model.set_active_adapters("SearchQA")
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base')
pipe = pipeline("question-answering", model=model, tokenizer=tokenizer)
pipe({"question": "What is the capital of Germany?", "context": "The capital of Germany is Berlin."})
```
Note you need the adapter-transformers library https://adapterhub.ml
# Evaluation
Friedman et al. report an F1 score of **85.1 on SearchQA**.
Please refer to the original publication for more information.
# Citation
Single-dataset Experts for Multi-dataset Question Answering (Friedman et al., EMNLP 2021)
|
AmitT/test
|
[] | null |
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},
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}
| 0 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="hfxmao/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Amro-Kamal/gpt
|
[] | null |
{
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}
}
| 0 | 2023-03-11T21:25:53Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -130.50 +/- 47.95
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'haanjack/ppo-LunarLander-v2-unit8'
'batch_size': 512
'minibatch_size': 128}
```
|
AndrewMcDowell/wav2vec2-xls-r-1B-german
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
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}
}
| 8 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.77
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Jclementg/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AndrewMcDowell/wav2vec2-xls-r-1b-arabic
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"dataset:common_voice",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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"Wav2Vec2ForCTC"
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}
| 7 | null |
---
language: en
thumbnail: http://www.huggingtweets.com/thenataliemars/1678571937087/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1515522232503582729/EVk0p6aA_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Natalie Mars</div>
<div style="text-align: center; font-size: 14px;">@thenataliemars</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Natalie Mars.
| Data | Natalie Mars |
| --- | --- |
| Tweets downloaded | 3032 |
| Retweets | 628 |
| Short tweets | 440 |
| Tweets kept | 1964 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wlzhwy3u/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @thenataliemars's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/wu73ens7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/wu73ens7/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/thenataliemars')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
AndrewMcDowell/wav2vec2-xls-r-1b-japanese-hiragana-katakana
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:common_voice",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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"Wav2Vec2ForCTC"
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}
| 6 | null |
---
license: mit
datasets:
- mrqa
language:
- en
metrics:
- squad
library_name: adapter-transformers
pipeline_tag: question-answering
---
# Description
This is the MADE encoder model created by Friedman et al. (2021). This encoder should be used along with the following dataset-specific adapters.
- https://huggingface.co/UKP-SQuARE/MADE_HotpotQA_Adapter
- https://huggingface.co/UKP-SQuARE/MADE_TriviaQA_Adapter
- https://huggingface.co/UKP-SQuARE/MADE_SQuAD_Adapter
- https://huggingface.co/UKP-SQuARE/MADE_SearchQA_Adapter
- https://huggingface.co/UKP-SQuARE/MADE_NewsQA_Adapter
- https://huggingface.co/UKP-SQuARE/MADE_NaturalQuestions_Adapter
The UKP-SQuARE team created this model repository to simplify the deployment of this model on the UKP-SQuARE platform. The GitHub repository of the original authors is https://github.com/princeton-nlp/MADE
# Evaluation Results
Friedman et al. (2021) reported the following results:
- SQuAD v1.1: 92.4
- HotoptQA: 81.5
- TriviaQA: 80.5
- NewsQA: 72.1
- SearchQA: 85.8
- NaturalQuestions: 80.9
- Avg: 82.2
Please refer to the original publication for more information.
# Citation
Single-dataset Experts for Multi-dataset Question Answering (Friedman et al., EMNLP 2021)
|
AndrewMcDowell/wav2vec2-xls-r-300m-japanese
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
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"Wav2Vec2ForCTC"
],
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}
}
}
| 4 | null |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1624172655422148608/JGG8jB03_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ani 🏳️⚧️🐺 🔪🩸</div>
<div style="text-align: center; font-size: 14px;">@cherryanima</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Ani 🏳️⚧️🐺 🔪🩸.
| Data | Ani 🏳️⚧️🐺 🔪🩸 |
| --- | --- |
| Tweets downloaded | 3112 |
| Retweets | 1087 |
| Short tweets | 683 |
| Tweets kept | 1342 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/9r970oz9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @cherryanima's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/rbdciabq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/rbdciabq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/cherryanima')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
AndrewNLP/redditDepressionPropensityClassifiers
|
[] | null |
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}
| 0 | null |
---
datasets:
- superb
language:
- en
metrics:
- accuracy
---
|
Andrey1989/bert-multilingual-finetuned-ner
|
[] | null |
{
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"model_type": null,
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}
}
| 0 | null |
---
language: en
thumbnail: http://www.huggingtweets.com/nyxxx696/1678572863247/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1616081980856340480/AVkhh3Fo_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Nyx 🇧🇷🌸🏳️⚧️</div>
<div style="text-align: center; font-size: 14px;">@nyxxx696</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Nyx 🇧🇷🌸🏳️⚧️.
| Data | Nyx 🇧🇷🌸🏳️⚧️ |
| --- | --- |
| Tweets downloaded | 1504 |
| Retweets | 24 |
| Short tweets | 524 |
| Tweets kept | 956 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jlopbdmp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nyxxx696's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3q4mb8yp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3q4mb8yp/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/nyxxx696')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Andrey1989/mbart-finetuned-en-to-kk
|
[] | null |
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}
| 0 | null |
---
language: en
thumbnail: http://www.huggingtweets.com/elsasingular/1678573156636/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1599666828602740737/xkaxWudG_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">❄️Elsa🏳️⚧️</div>
<div style="text-align: center; font-size: 14px;">@elsasingular</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from ❄️Elsa🏳️⚧️.
| Data | ❄️Elsa🏳️⚧️ |
| --- | --- |
| Tweets downloaded | 3224 |
| Retweets | 68 |
| Short tweets | 1246 |
| Tweets kept | 1910 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jgjdgejq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elsasingular's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/c79j2u02) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/c79j2u02/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/elsasingular')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Andrey1989/mt5-small-finetuned-mlsum-es
|
[] | null |
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| 0 | null |
---
license: apache-2.0
language:
- en
tags:
- text2text-generation
- punctuation
- true-casing
- sentence-boundary-detection
- nlp
widget:
- text: "hey man how's it going i haven't seen you in a while let's meet at 6 pm for drinks"
- text: "hello user this is an example input this text should be split into several sentences including a final interrogative did it work"
library_name: generic
inference: true
---
# Model Overview
This model accepts as input lower-cased, unpunctuated English text and performs in one pass punctuation restoration, true-casing (capitalization), and sentence boundary detection (segmentation).
In contast to many similar models, this model can predict punctuated acronyms (e.g., "U.S.") via a special "acronym" class, as well as arbitarily-capitalized words (NATO, McDonald's, etc.) via multi-label true-casing predictions.
**Widget note**: The text generation widget doesn't seem to respect line breaks.
Instead, the pipeline inserts a new line token `\n` in the text where the model has predicted sentence boundaries (line breaks).
# Usage
The easy way to use this model is to install [punctuators](https://github.com/1-800-BAD-CODE/punctuators):
```bash
pip install punctuators
```
If this package is broken, please let me know in the community tab (I update it for each model and break it a lot!).
Let's punctuate my weekend recap, as well as few interesting sentences with acronyms and abbreviations that I made up or found on Wikipedia:
<details open>
<summary>Example Usage</summary>
```
from typing import List
from punctuators.models import PunctCapSegModelONNX
# Instantiate this model
# This will download the ONNX and SPE models. To clean up, delete this model from your HF cache directory.
m = PunctCapSegModelONNX.from_pretrained("pcs_en")
# Define some input texts to punctuate
input_texts: List[str] = [
# Literally my weekend
"i woke up at 6 am and took the dog for a hike in the metacomet mountains we like to take morning adventures on the weekends",
"despite being mid march it snowed overnight and into the morning here in connecticut it was snowier up in the mountains than in the farmington valley where i live",
"when i got home i trained this model on the lambda cloud on an a100 gpu with about 10 million lines of text the total budget was less than 5 dollars",
# Real acronyms in sentences that I made up
"george hw bush was the president of the us for 8 years",
"i saw mr smith at the store he was shopping for a new lawn mower i suggested he get one of those new battery operated ones they're so much quieter",
# See how the model performs on made-up acronyms
"i went to the fgw store and bought a new tg optical scope",
# First few sentences from today's featured article summary on wikipedia
"it's that man again itma was a radio comedy programme that was broadcast by the bbc for twelve series from 1939 to 1949 featuring tommy handley in the central role itma was a character driven comedy whose satirical targets included officialdom and the proliferation of minor wartime regulations parts of the scripts were rewritten in the hours before the broadcast to ensure topicality"
]
results: List[List[str]] = m.infer(input_texts)
for input_text, output_texts in zip(input_texts, results):
print(f"Input: {input_text}")
print(f"Outputs:")
for text in output_texts:
print(f"\t{text}")
print()
```
Exact output may vary based on the model version; here is the current output:
</details>
<details open>
<summary>Expected Output</summary>
```text
In: i woke up at 6 am and took the dog for a hike in the metacomet mountains we like to take morning adventures on the weekends
Out: I woke up at 6 a.m. and took the dog for a hike in the Metacomet Mountains.
Out: We like to take morning adventures on the weekends.
In: despite being mid march it snowed overnight and into the morning here in connecticut it was snowier up in the mountains than in the farmington valley where i live
Out: Despite being mid March, it snowed overnight and into the morning.
Out: Here in Connecticut, it was snowier up in the mountains than in the Farmington Valley where I live.
In: when i got home i trained this model on the lambda cloud on an a100 gpu with about 10 million lines of text the total budget was less than 5 dollars
Out: When I got home, I trained this model on the Lambda Cloud.
Out: On an A100 GPU with about 10 million lines of text, the total budget was less than 5 dollars.
In: george hw bush was the president of the us for 8 years
Out: George H.W. Bush was the president of the U.S. for 8 years.
In: i saw mr smith at the store he was shopping for a new lawn mower i suggested he get one of those new battery operated ones they're so much quieter
Out: I saw Mr. Smith at the store he was shopping for a new lawn mower.
Out: I suggested he get one of those new battery operated ones.
Out: They're so much quieter.
In: i went to the fgw store and bought a new tg optical scope
Out: I went to the FGW store and bought a new TG optical scope.
In: it's that man again itma was a radio comedy programme that was broadcast by the bbc for twelve series from 1939 to 1949 featuring tommy handley in the central role itma was a character driven comedy whose satirical targets included officialdom and the proliferation of minor wartime regulations parts of the scripts were rewritten in the hours before the broadcast to ensure topicality
Out: It's that man again.
Out: ITMA was a radio comedy programme that was broadcast by the BBC for Twelve Series from 1939 to 1949, featuring Tommy Handley.
Out: In the central role, ITMA was a character driven comedy whose satirical targets included officialdom and the proliferation of minor wartime regulations.
Out: Parts of the scripts were rewritten in the hours before the broadcast to ensure topicality.
```
</details>
# Model Details
This model implements the graph shown below, with brief descriptions for each step following.
1. **Encoding**:
The model begins by tokenizing the text with a subword tokenizer.
The tokenizer used here is a `SentencePiece` model with a vocabulary size of 32k.
Next, the input sequence is encoded with a base-sized Transformer, consisting of 6 layers with a model dimension of 512.
2. **Punctuation**:
The encoded sequence is then fed into a feed-forward classification network to predict punctuation tokens.
Punctation is predicted once per subword, to allow acronyms to be properly punctuated.
An indiret benefit of per-subword prediction is to allow the model to run in a graph generalized for continuous-script languages, e.g., Chinese.
5. **Sentence boundary detection**
For sentence boundary detection, we condition the model on punctuation via embeddings.
Each punctuation prediction is used to select an embedding for that token, which is concatenated to the encoded representation.
The SBD head analyzes both the encoding of the un-punctuated sequence and the puncutation predictions, and predicts which tokens are sentence boundaries.
7. **Shift and concat sentence boundaries**
In English, the first character of each sentence should be upper-cased.
Thus, we should feed the sentence boundary information to the true-case classification network.
Since the true-case classification network is feed-forward and has no temporal context, each time step must embed whether it is the first word of a sentence.
Therefore, we shift the binary sentence boundary decisions to the right by one: if token `N-1` is a sentence boundary, token `N` is the first word of a sentence.
Concatenating this with the encoded text, each time step contains whether it is the first word of a sentence as predicted by the SBD head.
8. **True-case prediction**
Armed with the knowledge of punctation and sentence boundaries, a classification network predicts true-casing.
Since true-casing should be done on a per-character basis, the classification network makes `N` predictions per token, where `N` is the length of the subtoken.
(In practice, `N` is the longest possible subword, and the extra predictions are ignored).
This scheme captures acronyms, e.g., "NATO", as well as bi-capitalized words, e.g., "MacDonald".
The model's maximum length is 256 subtokens, due to the limit of the trained embeddings.
However, the [punctuators](https://github.com/1-800-BAD-CODE/punctuators) package
as described above will transparently predict on overlapping subgsegments of long inputs and fuse the results before returning output,
allowing inputs to be arbitrarily long.
## Punctuation Tokens
This model predicts the following set of punctuation tokens:
| Token | Description |
| ---: | :---------- |
| NULL | Predict no punctuation |
| ACRONYM | Every character in this subword ends with a period |
| . | Latin full stop |
| , | Latin comma |
| ? | Latin question mark |
# Training Details
## Training Framework
This model was trained on a forked branch of the [NeMo](https://github.com/NVIDIA/NeMo) framework.
## Training Data
This model was trained with News Crawl data from WMT.
Approximately 10M lines were used from the years 2021 and 2012.
The latter was used to attempt to reduce bias: annual news is typically dominated by a few topics, and 2021 is dominated by COVID discussions.
# Limitations
## Domain
This model was trained on news data, and may not perform well on conversational or informal data.
## Noisy Training Data
The training data was noisy, and no manual cleaning was utilized.
### Acronyms and Abbreviations
Acronyms and abbreviations are especially noisy; the table below shows how many variations of each token appear in the training data.
| Token | Count |
| -: | :- |
| Mr | 115232 |
| Mr. | 108212 |
| Token | Count |
| -: | :- |
| U.S. | 85324 |
| US | 37332 |
| U.S | 354 |
| U.s | 108 |
| u.S. | 65 |
Thus, the model's acronym and abbreviation predictions may be a bit unpredictable.
### Sentence Boundary Detection Targets
An assumption for sentence boundary detection targets is that each line of the input data is exactly one sentence.
However, a non-negligible portion of the training data contains multiple sentences per line.
Thus, the SBD head may miss an obvious sentence boundary if it's similar to an error seen in the training data.
# Evaluation
In these metrics, keep in mind that
1. The data is noisy
2. Sentence boundaries and true-casing are conditioned on predicted punctuation, which is the most difficult task and sometimes incorrect.
When conditioning on reference punctuation, true-casing and SBD metrics are much higher w.r.t. the reference targets.
4. Punctuation can be subjective. E.g.,
`Hello Frank, how's it going?`
or
`Hello Frank. How's it going?`
When the sentences are longer and more practical, these ambiguities abound and affect all 3 analytics.
## Test Data and Example Generation
Each test example was generated using the following procedure:
1. Concatenate 10 random sentences
2. Lower-case the concatenated sentence
3. Remove all punctuation
The data is a held-out portion of News Crawl, which has been deduplicated.
3,000 lines of data was used, generating 3,000 unique examples of 10 sentences each.
## Results
<details open>
<summary>Punctuation Report</summary>
```text
label precision recall f1 support
<NULL> (label_id: 0) 98.83 98.49 98.66 446496
<ACRONYM> (label_id: 1) 74.15 94.26 83.01 697
. (label_id: 2) 90.64 92.99 91.80 30002
, (label_id: 3) 77.19 79.13 78.15 23321
? (label_id: 4) 76.58 74.56 75.56 1022
-------------------
micro avg 97.21 97.21 97.21 501538
macro avg 83.48 87.89 85.44 501538
weighted avg 97.25 97.21 97.23 501538
```
</details>
<details open>
<summary>True-casing Report</summary>
```text
# With predicted punctuation (not aligned with targets)
label precision recall f1 support
LOWER (label_id: 0) 99.76 99.72 99.74 2020678
UPPER (label_id: 1) 93.32 94.20 93.76 83873
-------------------
micro avg 99.50 99.50 99.50 2104551
macro avg 96.54 96.96 96.75 2104551
weighted avg 99.50 99.50 99.50 2104551
# With reference punctuation (punctuation matches targets)
label precision recall f1 support
LOWER (label_id: 0) 99.83 99.81 99.82 2020678
UPPER (label_id: 1) 95.51 95.90 95.71 83873
-------------------
micro avg 99.66 99.66 99.66 2104551
macro avg 97.67 97.86 97.76 2104551
weighted avg 99.66 99.66 99.66 2104551
```
</details>
<details open>
<summary>Sentence Boundary Detection report</summary>
```text
# With predicted punctuation (not aligned with targets)
label precision recall f1 support
NOSTOP (label_id: 0) 99.59 99.45 99.52 471608
FULLSTOP (label_id: 1) 91.47 93.53 92.49 29930
-------------------
micro avg 99.09 99.09 99.09 501538
macro avg 95.53 96.49 96.00 501538
weighted avg 99.10 99.09 99.10 501538
# With reference punctuation (punctuation matches targets)
label precision recall f1 support
NOSTOP (label_id: 0) 100.00 99.97 99.98 471608
FULLSTOP (label_id: 1) 99.63 99.93 99.78 32923
-------------------
micro avg 99.97 99.97 99.97 504531
macro avg 99.81 99.95 99.88 504531
weighted avg 99.97 99.97 99.97 504531
```
</details>
# Fun Facts
Some fun facts are examined in this section.
## Embeddings
Let's examine the embeddings (see graph above) to see if the model meaningfully employed them.
We show here the cosine similarity between the embeddings of each token:
| | NULL | ACRONYM | . | , | ? |
| - | - | - | - | - | - |
| NULL | 1.00 | | | | |
| ACRONYM | -0.49 | 1.00 | | ||
| . | -1.00 | 0.48 | 1.00 | | |
| , | 1.00 | -0.48 | -1.00 | 1.00 | |
| ? | -1.00 | 0.49 | 1.00 | -1.00 | 1.00 |
Recall that these embeddings are used to predict sentence boundaries... thus we should expect full stops to cluster.
Indeed, we see that `NULL` and "`,`" are exactly the same, because neither have an implication on sentence boundaries.
Next, we see that "`.`" and "`?`" are exactly the same, because w.r.t. SBD these are exactly the same: strong full stop implications.
(Though, we may expect some difference between these tokens, given that "`.`" is predicted after abbreviations, e.g., 'Mr.', that are not full stops.)
Further, we see that "`.`" and "`?`" are exactly the opposite of `NULL`.
This is expected since these tokens typically imply sentence boundaries, whereas `NULL` and "`,`" never do.
Lastly, we see that `ACRONYM` is similar to, but not the same as, the full stops "`.`" and "`?`",
and far from, but not the opposite of, `NULL` and "`,`".
Intuition suggests this is because acronyms can be full stops ("I live in the northern U.S. It's cold here.") or not ("It's 5 a.m. and I'm tired.").
|
Andrey1989/mt5-small-finetuned-mlsum-fr
|
[] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
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"max_length": null
},
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 0 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="naeisher/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Andrey78/my_model_nlp
|
[] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
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"max_length": null
},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 0 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.40 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="naeisher/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Andrianos/bert-base-greek-punctuation-prediction-finetuned
|
[
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 0 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 13.20 +/- 4.19
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r agercas/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Andrija/M-bert-NER
|
[
"pytorch",
"bert",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
}
| 8 | null |
---
language: en
thumbnail: http://www.huggingtweets.com/elsasingular-michellexotter-nyxxx696/1678573843308/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1599666828602740737/xkaxWudG_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1616081980856340480/AVkhh3Fo_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1592255638394077184/ugsW8sO4_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">❄️Elsa🏳️⚧️ & Nyx 🇧🇷🌸🏳️⚧️ & Michelle Otter 🏳️⚧️🦦</div>
<div style="text-align: center; font-size: 14px;">@elsasingular-michellexotter-nyxxx696</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from ❄️Elsa🏳️⚧️ & Nyx 🇧🇷🌸🏳️⚧️ & Michelle Otter 🏳️⚧️🦦.
| Data | ❄️Elsa🏳️⚧️ | Nyx 🇧🇷🌸🏳️⚧️ | Michelle Otter 🏳️⚧️🦦 |
| --- | --- | --- | --- |
| Tweets downloaded | 3226 | 1504 | 3194 |
| Retweets | 68 | 24 | 37 |
| Short tweets | 1246 | 524 | 702 |
| Tweets kept | 1912 | 956 | 2455 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/or027l6u/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elsasingular-michellexotter-nyxxx696's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ei4il6l7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ei4il6l7/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/elsasingular-michellexotter-nyxxx696')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Andrija/SRoBERTa-F
|
[
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:srwac",
"dataset:leipzig",
"dataset:cc100",
"dataset:hrwac",
"transformers",
"masked-lm",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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}
| 59 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 44.50 +/- 38.21
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Andrija/SRoBERTa-base-NER
|
[
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
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"RobertaForTokenClassification"
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}
| 12 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: turkic-cyrillic-classifier
results: []
language:
- ba
- cv
- sah
- tt
- ky
- kk
- tyv
- krc
- ru
datasets:
- tatiana-merz/cyrillic_turkic_langs
pipeline_tag: text-classification
widget:
- text: "Кыра тыгын"
---
<!-- 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. -->
# turkic-cyrillic-classifier
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an tatiana-merz/cyrillic_turkic_langs dataset.
It achieves the following results on the evaluation set:
```
{'test_loss': 0.013604652136564255,
'test_accuracy': 0.997,
'test_f1': 0.9969996069718668,
'test_runtime': 60.5479,
'test_samples_per_second': 148.643,
'test_steps_per_second': 2.329}
```
## Model description
The model classifies text based on a provided Turkic language written in Cyrillic script.
## Languages
- bak - Bashkir
- chv - Chuvash
- sah - Sakha
- tat - Tatar
- kir - Kyrgyz
- kaz - Kazakh
- tyv - Tuvinian
- krc - Karachay-Balkar
- rus - Russian
## Intended uses & limitations
## Training and evaluation data
[cyrillic_turkic_langs](https://huggingface.co/datasets/tatiana-merz/cyrillic_turkic_langs/)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1063 | 1.0 | 1000 | 0.0204 | 0.9950 | 0.9950 |
| 0.0126 | 2.0 | 2000 | 0.0136 | 0.9970 | 0.9970 |
### Framework versions
- Transformers 4.27.0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
|
Andrija/SRoBERTa-base
|
[
"pytorch",
"roberta",
"fill-mask",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:leipzig",
"transformers",
"masked-lm",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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| 80 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: media-bias-ukraine-dataset-all-masked
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. -->
# media-bias-ukraine-dataset-all-masked
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1585
- F1: 0.8205
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.321 | 1.0 | 114 | 0.2044 | 0.6280 |
| 0.3749 | 2.0 | 228 | 0.1678 | 0.8016 |
| 0.7008 | 3.0 | 342 | 0.1692 | 0.7832 |
| 0.0181 | 4.0 | 456 | 0.1585 | 0.8205 |
| 0.0513 | 5.0 | 570 | 0.1855 | 0.8022 |
| 0.0036 | 6.0 | 684 | 0.2107 | 0.7980 |
| 0.0396 | 7.0 | 798 | 0.2529 | 0.8017 |
| 0.0051 | 8.0 | 912 | 0.2397 | 0.7894 |
| 0.0062 | 9.0 | 1026 | 0.2698 | 0.7897 |
| 0.002 | 10.0 | 1140 | 0.2713 | 0.7878 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Andrija/SRoBERTa
|
[
"pytorch",
"roberta",
"fill-mask",
"hr",
"sr",
"multilingual",
"dataset:leipzig",
"transformers",
"masked-lm",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
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"RobertaForMaskedLM"
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}
| 88 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-thai
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-thai
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5719
- Wer: 0.5448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.7862 | 6.58 | 1000 | 1.8716 | 1.0407 |
| 0.8847 | 13.16 | 2000 | 0.5514 | 0.6268 |
| 0.4436 | 19.74 | 3000 | 0.5391 | 0.5887 |
| 0.3235 | 26.32 | 4000 | 0.5472 | 0.5610 |
| 0.2611 | 32.89 | 5000 | 0.5505 | 0.5488 |
| 0.2339 | 39.47 | 6000 | 0.5719 | 0.5448 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Andry/111
|
[] | null |
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}
| 0 | null |
Access to model MarkBW/ultimaterachelkay is restricted and you are not in the authorized list. Visit https://huggingface.co/MarkBW/ultimaterachelkay to ask for access.
|
Andry/1111
|
[] | null |
{
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: media-bias-ukraine-dataset-all-removed
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. -->
# media-bias-ukraine-dataset-all-removed
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1717
- F1: 0.8014
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3845 | 1.0 | 114 | 0.2296 | 0.6101 |
| 0.1412 | 2.0 | 228 | 0.1759 | 0.7486 |
| 0.0215 | 3.0 | 342 | 0.2275 | 0.7439 |
| 0.0506 | 4.0 | 456 | 0.2064 | 0.7651 |
| 0.0366 | 5.0 | 570 | 0.1717 | 0.8014 |
| 0.2428 | 6.0 | 684 | 0.1955 | 0.7878 |
| 0.005 | 7.0 | 798 | 0.2297 | 0.7839 |
| 0.003 | 8.0 | 912 | 0.2428 | 0.8005 |
| 0.0037 | 9.0 | 1026 | 0.2577 | 0.7884 |
| 0.0099 | 10.0 | 1140 | 0.2641 | 0.7957 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Ankit-11/distilbert-base-uncased-finetuned-toxic
|
[] | null |
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| 0 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1392.29 +/- 110.15
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousNLP/pretrained-model-2
|
[
"pytorch",
"gpt2",
"transformers"
] | null |
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| 4 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: media-bias-ukraine-dataset-all-minus-ukraine-removed
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. -->
# media-bias-ukraine-dataset-all-minus-ukraine-removed
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9273
- F1: 0.2174
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0857 | 1.0 | 138 | 2.3587 | 0.1003 |
| 0.003 | 2.0 | 276 | 2.6847 | 0.0840 |
| 0.0167 | 3.0 | 414 | 2.8767 | 0.1619 |
| 0.0022 | 4.0 | 552 | 2.3012 | 0.1862 |
| 0.0019 | 5.0 | 690 | 2.7714 | 0.1961 |
| 0.001 | 6.0 | 828 | 2.7623 | 0.2015 |
| 0.6706 | 7.0 | 966 | 2.8243 | 0.1980 |
| 0.2259 | 8.0 | 1104 | 2.7106 | 0.2151 |
| 0.0013 | 9.0 | 1242 | 2.9273 | 0.2174 |
| 0.0116 | 10.0 | 1380 | 2.9458 | 0.2101 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/AR_rule_based_only_classfn_twostage_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 6 | null |
---
license: agpl-3.0
tags:
- text-to-image
- image-to-text
- image-captioning
- image-variation
- text-variation
- multi-modality
- generative model
---
UniDiffuser is a unified diffusion framework to fit all distributions relevant to a set of multi-modal data in one transformer.
UniDiffuser is able to perform image, text, text-to-image, image-to-text, and image-text pair generation by setting proper timesteps without additional overhead.
Specifically, UniDiffuser employs a variation of transformer, called [U-ViT](https://github.com/baofff/U-ViT), which parameterizes the joint noise prediction network. Other components perform as encoders and decoders of different modalities, including a pretrained image autoencoder from [Stable Diffusion](https://github.com/CompVis/stable-diffusion), a pretrained [image ViT-B/32 CLIP encoder](https://github.com/openai/CLIP), a pretrained [text ViT-L CLIP encoder](https://huggingface.co/openai/clip-vit-large-patch14), and a [GPT-2](https://github.com/openai/gpt-2) text decoder finetuned by ourselves.
We provide two versions of UniDiffuser:
- [UniDiffuser-v0](https://huggingface.co/thu-ml/unidiffuser-v0): This version is trained on [LAION-5B](https://laion.ai/), which contains noisy webdata of text-image pairs.
- [UniDiffuser-v1](https://huggingface.co/thu-ml/unidiffuser-v1): This version is resumed from UniDiffuser-v0, and is further trained with a set of less noisy internal text-image pairs. It uses a flag as its input to distinguish webdata and internal data during training.
## Download
We provide files for UniDiffuser-v0 in [this link](https://huggingface.co/thu-ml/unidiffuser-v0/tree/main), and files for UniDiffuser-v1 in [this link](https://huggingface.co/thu-ml/unidiffuser-v1/tree/main).
These files are:
- `autoencoder_kl.pth` is the weight of the image autoencoder converted from [Stable Diffusion](https://github.com/CompVis/stable-diffusion).
- `caption_decoder.pth` is the weight of the finetuned GPT-2 text decoder.
- `uvit_v0.pth/uvit_v1.pth` is the weight of U-ViT for UniDiffuser-v0/UniDiffuser-v1.
Note that UniDiffuser-v0 and UniDiffuser-v1 share the same `autoencoder_kl.pth` and `caption_decoder.pth`. You only need to download them once.
As for other components, they will be automatically downloaded.
The `diffusers` pipeline for UniDiffuser-v1 can be downloaded as follows:
```python
from diffusers import UniDiffuserPipeline
pipe = UniDiffuserPipeline.from_pretrained("thu-ml/unidiffuser-v1")
```
## Usage
Use the model with [UniDiffuser codebase](https://github.com/thu-ml/unidiffuser).
Here is an example using UniDiffuser-v1 with `diffusers`:
```python
import requests
import torch
from PIL import Image
from io import BytesIO
from diffusers import UniDiffuserPipeline
device = "cuda"
model_id_or_path = "thu-ml/unidiffuser-v1"
pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path)
pipe.to(device)
# Joint image-text generation. The generation task is automatically inferred.
sample = pipe(num_inference_steps=20, guidance_scale=8.0)
image = sample.images[0]
text = sample.text[0]
image.save("unidiffuser_sample_joint_image.png")
print(text)
# The mode can be set manually. The following is equivalent to the above:
pipe.set_joint_mode()
sample2 = pipe(num_inference_steps=20, guidance_scale=8.0)
# Note that if you set the mode manually the pipeline will no longer attempt
# to automatically infer the mode. You can re-enable this with reset_mode().
pipe.reset_mode()
# Text-to-image generation.
prompt = "an elephant under the sea"
sample = pipe(prompt=prompt, num_inference_steps=20, guidance_scale=8.0)
t2i_image = sample.images[0]
t2i_image.save("unidiffuser_sample_text2img_image.png")
# Image-to-text generation.
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg"
response = requests.get(image_url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
init_image = init_image.resize((512, 512))
sample = pipe(image=init_image, num_inference_steps=20, guidance_scale=8.0)
i2t_text = sample.text[0]
print(text)
# Image variation can be performed with a image-to-text generation followed by a text-to-image generation:
sample = pipe(prompt=i2t_text, num_inference_steps=20, guidance_scale=8.0)
final_image = sample.images[0]
final_image.save("unidiffuser_image_variation_sample.png")
# Text variation can be performed with a text-to-image generation followed by a image-to-text generation:
sample = pipe(image=t2i_image, num_inference_steps=20, guidance_scale=8.0)
final_prompt = sample.text[0]
print(final_prompt)
```
## Model Details
- **Model type:** Diffusion-based multi-modal generation model
- **Language(s):** English
- **License:** agpl-3.0
- **Model Description:** This is a model that can perform image, text, text-to-image, image-to-text, and image-text pair generation. Its main component is a [U-ViT](https://github.com/baofff/U-ViT), which parameterizes the joint noise prediction network. Other components perform as encoders and decoders of different modalities, including a pretrained image autoencoder from [Stable Diffusion](https://github.com/CompVis/stable-diffusion), a pretrained [image ViT-B/32 CLIP encoder](https://github.com/openai/CLIP), a pretrained [text ViT-L CLIP encoder](https://huggingface.co/openai/clip-vit-large-patch14), and a [GPT-2](https://github.com/openai/gpt-2) text decoder finetuned by ourselves.
- **Resources for more information:** [GitHub Repository](https://github.com/thu-ml/unidiffuser), [Paper]().
## Direct Use
_Note: Most of this section is taken from the [Stable Diffusion model card](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original), but applies in the same way to UniDiffuser_.
The model should be used following the agpl-3.0 license. Possible usage includes
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
|
AnonymousSub/AR_rule_based_roberta_hier_triplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"RobertaModel"
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}
}
}
| 4 | null |
---
language: fo
datasets:
- carlosdanielhernandezmena/ravnursson_asr
tags:
- audio
- automatic-speech-recognition
- faroese
- whisper-base
- ravnur-project
- faroe-islands
license: cc-by-4.0
widget: null
model-index:
- name: whisper-base-faroese-8k-steps-100h
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Ravnursson (Test)
type: carlosdanielhernandezmena/ravnursson_asr
split: test
args:
language: fo
metrics:
- name: WER
type: wer
value: 22.87
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Ravnursson (Dev)
type: carlosdanielhernandezmena/ravnursson_asr
split: validation
args:
language: fo
metrics:
- name: WER
type: wer
value: 20.70
---
# whisper-base-faroese-8k-steps-100h
The "whisper-base-faroese-8k-steps-100h" is an acoustic model suitable for Automatic Speech Recognition in Faroese. It is the result of fine-tuning the model "openai/whisper-base" with 100 hours of Faroese data released by the Ravnur Project (https://maltokni.fo/en/) from the Faroe Islands.
The specific dataset used to create the model is called "Ravnursson Faroese Speech and Transcripts" and it is available at http://hdl.handle.net/20.500.12537/276.
The fine-tuning process was perform during March (2023) in the servers of the Language and Voice Lab (https://lvl.ru.is/) at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena.
# Evaluation
```python
import torch
from transformers import WhisperForConditionalGeneration, WhisperProcessor
#Load the processor and model.
MODEL_NAME="carlosdanielhernandezmena/whisper-base-faroese-8k-steps-100h"
processor = WhisperProcessor.from_pretrained(MODEL_NAME)
model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME).to("cuda")
#Load the dataset
from datasets import load_dataset, load_metric, Audio
ds=load_dataset("carlosdanielhernandezmena/ravnursson_asr",split='test')
#Downsample to 16kHz
ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
#Process the dataset
def map_to_pred(batch):
audio = batch["audio"]
input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
batch["reference"] = processor.tokenizer._normalize(batch['normalized_text'])
with torch.no_grad():
predicted_ids = model.generate(input_features.to("cuda"))[0]
transcription = processor.decode(predicted_ids)
batch["prediction"] = processor.tokenizer._normalize(transcription)
return batch
#Do the evaluation
result = ds.map(map_to_pred)
#Compute the overall WER now.
from evaluate import load
wer = load("wer")
WER=100 * wer.compute(references=result["reference"], predictions=result["prediction"])
print(WER)
```
**Test Result**: 22.878772482471295
# BibTeX entry and citation info
* When publishing results based on these models please refer to:
```bibtex
@misc{mena2023whisperbasefaroese,
title={Acoustic Model in Faroese: whisper-base-faroese-8k-steps-100h.},
author={Hernandez Mena, Carlos Daniel},
year={2023},
url={https://huggingface.co/carlosdanielhernandezmena/whisper-base-faroese-8k-steps-100h},
}
```
# Acknowledgements
We want to thank to Jón Guðnason, head of the Language and Voice Lab for providing computational power to make this model possible. We also want to thank to the "Language Technology Programme for Icelandic 2019-2023" which is managed and coordinated by Almannarómur, and it is funded by the Icelandic Ministry of Education, Science and Culture.
Special thanks to Annika Simonsen and to The Ravnur Project for making their "Basic Language Resource Kit"(BLARK 1.0) publicly available through the research paper "Creating a Basic Language Resource Kit for Faroese" https://aclanthology.org/2022.lrec-1.495.pdf
|
AnonymousSub/AR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
],
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},
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}
}
}
| 6 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 48.40 +/- 53.97
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AnonymousSub/AR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
}
| 2 | 2023-03-12T02:54:59Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Cpod/t5-small-finetuned-cnn_dailymail-3-epochs
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Cpod/t5-small-finetuned-cnn_dailymail-3-epochs
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.8014
- Validation Loss: 1.6484
- Train Rouge1: 24.6091
- Train Rouge2: 11.8866
- Train Rougel: 20.3754
- Train Rougelsum: 23.2320
- Train Gen Len: 18.9998
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 1.8623 | 1.6757 | 24.4518 | 11.7242 | 20.1766 | 23.0440 | 18.9987 | 0 |
| 1.8212 | 1.6567 | 24.5313 | 11.7909 | 20.2584 | 23.1484 | 18.9992 | 1 |
| 1.8014 | 1.6484 | 24.6091 | 11.8866 | 20.3754 | 23.2320 | 18.9998 | 2 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/EManuals_BERT_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"min_length": null,
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
}
| 1 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.45 +/- 24.58
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
...
```
|
AnonymousSub/SR_rule_based_roberta_hier_triplet_epochs_1_shard_1_wikiqa_copy
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
| 2 | null |
---
license: other
tags:
- generated_from_trainer
model-index:
- name: segment_test_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segment_test_1
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1207
- Mean Iou: 0.8483
- Mean Accuracy: 0.9024
- Overall Accuracy: 0.9649
- Per Category Iou: [0.8797073511948335, 0.9320756407098275, 0.6346731005607217, 0.946842856578934]
- Per Category Accuracy: [0.9476937770534322, 0.9647476108381431, 0.722163329552671, 0.9748104445450901]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------:|
| 1.0371 | 0.12 | 20 | 1.0895 | 0.4995 | 0.6430 | 0.8631 | [0.05521318178580855, 0.7743794151988705, 0.347543309879974, 0.8209439082840918] | [0.08660822249093107, 0.8923169863341743, 0.711708439278363, 0.8812568644974932] |
| 0.6908 | 0.25 | 40 | 0.5755 | 0.5230 | 0.6242 | 0.8926 | [0.023865281651135446, 0.8147455929204539, 0.3949463345707554, 0.858397589605038] | [0.02690009287979952, 0.9560483225800299, 0.618844995317415, 0.8951707417330758] |
| 0.7532 | 0.38 | 60 | 0.4352 | 0.5425 | 0.6362 | 0.9073 | [0.04830520945747081, 0.8390003744923147, 0.4011207931866636, 0.881716208435619] | [0.051771725987066926, 0.9667796831969788, 0.6132110672093029, 0.9128954438801464] |
| 0.4797 | 0.5 | 80 | 0.3928 | 0.5754 | 0.6651 | 0.9187 | [0.095160456358129, 0.8591208204996739, 0.4492600600843336, 0.8980719848369781] | [0.10522361249846661, 0.9658767606790423, 0.6592313641766187, 0.9299486923858831] |
| 0.4974 | 0.62 | 100 | 0.3413 | 0.5757 | 0.6564 | 0.9231 | [0.08465830140216431, 0.8655205765788001, 0.4482722747299048, 0.9043341709317356] | [0.08808466081348684, 0.9438250272055575, 0.6388503821434132, 0.9547541614780908] |
| 0.6012 | 0.75 | 120 | 0.3019 | 0.6256 | 0.7003 | 0.9309 | [0.20118898116264938, 0.8705267913237438, 0.5133711956715865, 0.9173562005725456] | [0.21758188318174648, 0.9550754656203344, 0.6747765579170281, 0.9537939455713355] |
| 0.4656 | 0.88 | 140 | 0.2558 | 0.6196 | 0.6831 | 0.9354 | [0.18448255342169326, 0.8799348242584422, 0.4896770706399499, 0.9242898593866574] | [0.18675411387413912, 0.957629264808883, 0.6245802352906291, 0.9633061744905276] |
| 0.3084 | 1.0 | 160 | 0.2720 | 0.6122 | 0.6959 | 0.9306 | [0.17521253603782794, 0.8710845346725691, 0.4824399357269581, 0.9201099745599581] | [0.18717908276816853, 0.9651212567854952, 0.6840404160211124, 0.9471146558242094] |
| 0.3281 | 1.12 | 180 | 0.2593 | 0.6599 | 0.7300 | 0.9387 | [0.3196354045076814, 0.8866606687882002, 0.5077873946739834, 0.9254476921045761] | [0.3419137619823704, 0.9647813859946743, 0.6571532354314773, 0.9563192436567626] |
| 0.5177 | 1.25 | 200 | 0.2349 | 0.7152 | 0.7782 | 0.9445 | [0.5227583225430057, 0.9035693700640127, 0.5129509983263923, 0.9217210470710874] | [0.5456644410563762, 0.9406031421756604, 0.6513240966564614, 0.9752501443811327] |
| 0.3315 | 1.38 | 220 | 0.2277 | 0.6762 | 0.7332 | 0.9435 | [0.3379172541018912, 0.8906873024131107, 0.5423203687425245, 0.93385833084482] | [0.3523035942729965, 0.9655347706627136, 0.650958385533654, 0.9641001079719775] |
| 0.7584 | 1.5 | 240 | 0.2096 | 0.7006 | 0.7579 | 0.9411 | [0.4065273757133468, 0.8956735509588918, 0.5836574037952176, 0.9166967934457825] | [0.4186732208261045, 0.9310246402349798, 0.702414928921078, 0.9793378276707719] |
| 0.4114 | 1.62 | 260 | 0.2266 | 0.7157 | 0.7832 | 0.9389 | [0.46836755859780127, 0.8939419126736278, 0.5910928241310868, 0.9094497536602162] | [0.5292527557261273, 0.9181066362487166, 0.7057063290263436, 0.9796145000506845] |
| 0.2829 | 1.75 | 280 | 0.1932 | 0.7515 | 0.8111 | 0.9512 | [0.5553894238891705, 0.9038650415073118, 0.6093098813145624, 0.9374419595745119] | [0.5760913376443579, 0.9716101928389251, 0.7359022068689445, 0.960860855647697] |
| 0.2271 | 1.88 | 300 | 0.1864 | 0.7921 | 0.8488 | 0.9557 | [0.72749813882911, 0.9128831336642784, 0.5892135224784095, 0.9387835557133554] | [0.7834695862467799, 0.9582075484693364, 0.682108077520874, 0.9715436644814973] |
| 0.2779 | 2.0 | 320 | 0.1712 | 0.8180 | 0.8901 | 0.9587 | [0.8078135832010197, 0.9212119018178146, 0.6029921836595201, 0.9400085237549578] | [0.8704261079859104, 0.9606214787743671, 0.7607928024097398, 0.9685803405441751] |
| 0.4342 | 2.12 | 340 | 0.1708 | 0.7761 | 0.8320 | 0.9548 | [0.6155968922409166, 0.9114185374153494, 0.6376315789473684, 0.939658585661896] | [0.640631845504092, 0.963783230780479, 0.7543977998028125, 0.9693606961542074] |
| 0.3321 | 2.25 | 360 | 0.1498 | 0.8358 | 0.8902 | 0.9617 | [0.8466817196643333, 0.9257548699292617, 0.6282106201058927, 0.9425208804748949] | [0.9010895851953105, 0.9624874568340255, 0.7247603727288228, 0.9726055284256458] |
| 0.2023 | 2.38 | 380 | 0.1489 | 0.8266 | 0.8892 | 0.9595 | [0.8245918770829803, 0.9218535979694774, 0.6204584829387099, 0.9396094918519875] | [0.8953634579324606, 0.9540908204687515, 0.7327368288397814, 0.9745457690515671] |
| 0.1612 | 2.5 | 400 | 0.1474 | 0.8319 | 0.8833 | 0.9614 | [0.8386504767201818, 0.9250884504372539, 0.6212118602838925, 0.9425717867382386] | [0.881909030369942, 0.9701526954959041, 0.7130847980310904, 0.9679183728130094] |
| 0.2084 | 2.62 | 420 | 0.1456 | 0.8373 | 0.9156 | 0.9617 | [0.8428293939804881, 0.9267780719611362, 0.6367325390207981, 0.9430187935498365] | [0.9257859733978234, 0.9552186457937082, 0.807640891445572, 0.9735885291166293] |
| 0.164 | 2.75 | 440 | 0.1284 | 0.8345 | 0.8808 | 0.9617 | [0.8596578459811758, 0.9255998505860551, 0.6105607176615363, 0.9421695191953943] | [0.9127433713614777, 0.9537728041909803, 0.6767583168392675, 0.9800493639325235] |
| 0.1297 | 2.88 | 460 | 0.1385 | 0.8449 | 0.9054 | 0.9635 | [0.8647724581323368, 0.9296026133121402, 0.6405698157260865, 0.9447385612148987] | [0.9357968385819182, 0.9633411073589047, 0.749796758514521, 0.9727959906219689] |
| 0.1775 | 3.0 | 480 | 0.1483 | 0.8312 | 0.9131 | 0.9615 | [0.8303105795421561, 0.9283098352535407, 0.6238329800678668, 0.9422278557852194] | [0.9462020048017104, 0.9651012565947649, 0.7745366217682133, 0.9665813244748572] |
| 0.2044 | 3.12 | 500 | 0.1337 | 0.8371 | 0.8944 | 0.9626 | [0.8455606368699817, 0.9275108345488315, 0.6310811541832109, 0.9440912228533584] | [0.9218363913569213, 0.9599956449915814, 0.7206757748504414, 0.9752020638364555] |
| 0.1668 | 3.25 | 520 | 0.1329 | 0.8441 | 0.9025 | 0.9629 | [0.8655859764078333, 0.9282151151180292, 0.6390094540302755, 0.9437038916825506] | [0.9213698017980128, 0.962867990264278, 0.7534242174893931, 0.9724944874772036] |
| 0.1279 | 3.38 | 540 | 0.1288 | 0.8347 | 0.8822 | 0.9624 | [0.8622657989592432, 0.9265309615800243, 0.6059068424468966, 0.9439998815395132] | [0.9212317964355187, 0.9664261698919513, 0.6689078828044113, 0.9723242060897683] |
| 0.2441 | 3.5 | 560 | 0.1289 | 0.8456 | 0.9119 | 0.9632 | [0.8699084528348765, 0.9296467606481729, 0.6388408116225611, 0.9439747399023978] | [0.9510540981020977, 0.9645672117667877, 0.7615761160984553, 0.9703859184453523] |
| 0.261 | 3.62 | 580 | 0.1373 | 0.8408 | 0.9059 | 0.9626 | [0.8702262323466258, 0.9285138712580321, 0.6209427648568083, 0.9435318648195579] | [0.9490453533813504, 0.9671653822394067, 0.7391515996155091, 0.9684196380660275] |
| 0.5753 | 3.75 | 600 | 0.1251 | 0.8458 | 0.8983 | 0.9627 | [0.8760502510695999, 0.9269758886426209, 0.6368816580927279, 0.9433603638703384] | [0.9298275590137217, 0.9542444643180703, 0.7302238003810314, 0.9788558132354487] |
| 0.1992 | 3.88 | 620 | 0.1289 | 0.8480 | 0.9126 | 0.9636 | [0.8658569991741146, 0.9291059762753285, 0.6517419325899511, 0.9451333803283866] | [0.9278188142929744, 0.9559565336252214, 0.7898717787151185, 0.9769391943858292] |
| 0.2911 | 4.0 | 640 | 0.1263 | 0.8451 | 0.8990 | 0.9623 | [0.8676994568324171, 0.9257222374094188, 0.6441978583675714, 0.9428325001046762] | [0.9276851900530992, 0.9481842740751965, 0.7380742344158877, 0.9821508650313081] |
| 0.2322 | 4.12 | 660 | 0.1234 | 0.8448 | 0.8923 | 0.9638 | [0.8776973476081853, 0.929274732930499, 0.626829659547631, 0.9455449914778657] | [0.9237794192383857, 0.968169497775343, 0.7048637108312268, 0.972200331262863] |
| 0.2316 | 4.25 | 680 | 0.1228 | 0.8493 | 0.9069 | 0.9642 | [0.8763959224746586, 0.9306747334769754, 0.64476604812217, 0.9454498311627832] | [0.9386358060389394, 0.9577756238205185, 0.753671319599398, 0.9776486846669562] |
| 0.2397 | 4.38 | 700 | 0.1262 | 0.8442 | 0.8963 | 0.9640 | [0.8718388103686404, 0.9303958268346455, 0.6286213954621487, 0.94588759466782] | [0.9306095894011882, 0.9690709633257429, 0.7141201558720109, 0.971342786383813] |
| 0.2163 | 4.5 | 720 | 0.1273 | 0.8438 | 0.8975 | 0.9635 | [0.8711635054714977, 0.9292374082869599, 0.6295386570878305, 0.945216509372582] | [0.9251331861276133, 0.9722796031962159, 0.724520683682118, 0.9680362026971605] |
| 0.1596 | 4.62 | 740 | 0.1207 | 0.8515 | 0.9108 | 0.9646 | [0.8777356406779111, 0.9314006904528226, 0.6507271627753556, 0.9461335840246559] | [0.9470848010094106, 0.9579392015394054, 0.7604592145612331, 0.9777115520716019] |
| 0.2282 | 4.75 | 760 | 0.1192 | 0.8504 | 0.9024 | 0.9650 | [0.8835435013923162, 0.9318821516960537, 0.6391197674041569, 0.947006215721923] | [0.9404013984543399, 0.9611932723067028, 0.7305820984405386, 0.9775071400073841] |
| 0.1647 | 4.88 | 780 | 0.1197 | 0.8518 | 0.9100 | 0.9647 | [0.8779367174977694, 0.9313115604274004, 0.6518294433470307, 0.9462412590163557] | [0.9526751134710758, 0.9568937611060662, 0.7517933435633607, 0.9786714889809993] |
| 0.1087 | 5.0 | 800 | 0.1207 | 0.8483 | 0.9024 | 0.9649 | [0.8797073511948335, 0.9320756407098275, 0.6346731005607217, 0.946842856578934] | [0.9476937770534322, 0.9647476108381431, 0.722163329552671, 0.9748104445450901] |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cpu
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/declutr-emanuals-techqa
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"prefix": null
},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 4 | null |
---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- covid_qa_deepset
model-index:
- name: xlm-roberta-base-squad2-finetuned-squad2-covidQA-V2-all-data
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-squad2-finetuned-squad2-covidQA-V2-all-data
This model is a fine-tuned version of [deepset/xlm-roberta-base-squad2](https://huggingface.co/deepset/xlm-roberta-base-squad2) on the covid_qa_deepset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_bert_mean_diff_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 3 | null |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmpmqcw0ycr/dshin/flan-t5-ppo-user-f-use-violation")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmpmqcw0ycr/dshin/flan-t5-ppo-user-f-use-violation")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmpmqcw0ycr/dshin/flan-t5-ppo-user-f-use-violation")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
AnonymousSub/rule_based_bert_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8 | null |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmpy9wgjo1m/dshin/flan-t5-ppo-user-a-use-violation")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmpy9wgjo1m/dshin/flan-t5-ppo-user-a-use-violation")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmpy9wgjo1m/dshin/flan-t5-ppo-user-a-use-violation")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 3 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vietnamese_feedback_model_2_1.0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vietnamese_feedback_model_2_1.0
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1459
- Accuracy: 0.9646
## 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1355 | 1.0 | 3999 | 0.1435 | 0.9603 |
| 0.1247 | 2.0 | 7998 | 0.1322 | 0.9643 |
| 0.088 | 3.0 | 11997 | 0.1459 | 0.9646 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 28 | null |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| inner_optimizer.class_name | Custom>RMSprop |
| inner_optimizer.config.name | RMSprop |
| inner_optimizer.config.weight_decay | None |
| inner_optimizer.config.clipnorm | None |
| inner_optimizer.config.global_clipnorm | None |
| inner_optimizer.config.clipvalue | None |
| inner_optimizer.config.use_ema | False |
| inner_optimizer.config.ema_momentum | 0.99 |
| inner_optimizer.config.ema_overwrite_frequency | 100 |
| inner_optimizer.config.jit_compile | True |
| inner_optimizer.config.is_legacy_optimizer | False |
| inner_optimizer.config.learning_rate | 0.0010000000474974513 |
| inner_optimizer.config.rho | 0.9 |
| inner_optimizer.config.momentum | 0.0 |
| inner_optimizer.config.epsilon | 1e-07 |
| inner_optimizer.config.centered | False |
| dynamic | True |
| initial_scale | 32768.0 |
| dynamic_growth_steps | 2000 |
| training_precision | mixed_float16 |
|
AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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}
}
}
| 8 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 71.50 +/- 30.28
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 2 | null |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.61 +/- 0.49
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Allrandom/q-FrozenLake-v1-4x4", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 2 | 2023-03-12T09:14:43Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.42 +/- 0.14
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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"prefix": null
},
"text-generation": {
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},
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},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 2 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: sweep-v2
type: sweep-v2
metrics:
- type: mean_reward
value: 4523.76 +/- 43.96
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **sweep-v2** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r qgallouedec/sample-factory-sweep-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=sweep-v2 --train_dir=./train_dir --experiment=sample-factory-sweep-v2
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m train --algo=APPO --env=sweep-v2 --train_dir=./train_dir --experiment=sample-factory-sweep-v2 --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 24 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: window-close-v2
type: window-close-v2
metrics:
- type: mean_reward
value: 4584.45 +/- 35.82
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **window-close-v2** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r qgallouedec/sample-factory-window-close-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=window-close-v2 --train_dir=./train_dir --experiment=sample-factory-window-close-v2
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m train --algo=APPO --env=window-close-v2 --train_dir=./train_dir --experiment=sample-factory-window-close-v2 --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
AnonymousSub/rule_based_roberta_hier_triplet_0.1_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
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}
}
}
| 6 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: window-open-v2
type: window-open-v2
metrics:
- type: mean_reward
value: 4597.81 +/- 40.52
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **window-open-v2** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r qgallouedec/sample-factory-window-open-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m enjoy --algo=APPO --env=window-open-v2 --train_dir=./train_dir --experiment=sample-factory-window-open-v2
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m train --algo=APPO --env=window-open-v2 --train_dir=./train_dir --experiment=sample-factory-window-open-v2 --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
AnonymousSub/rule_based_roberta_hier_triplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
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},
"translation_en_to_fr": {
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},
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}
}
}
| 4 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cppe-5
model-index:
- name: Raiyan_Kasper_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. -->
# Raiyan_Kasper_Model
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 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: 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: 10
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_hier_triplet_epochs_1_shard_1_wikiqa_copy
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 2 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Allrandom/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AnonymousSub/rule_based_roberta_only_classfn_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
}
| 7 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: distilbert-base-uncased_finetuned_text_2_disease_cel
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_finetuned_text_2_disease_cel
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2732
- Accuracy: 0.9865
- F1: 0.9864
- Recall: 0.9865
- Precision: 0.9879
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 1.3868 | 1.0 | 167 | 1.1692 | 0.8649 | 0.8458 | 0.8649 | 0.8573 |
| 0.5345 | 2.0 | 334 | 0.4214 | 0.9745 | 0.9736 | 0.9745 | 0.9769 |
| 0.3472 | 3.0 | 501 | 0.2732 | 0.9865 | 0.9864 | 0.9865 | 0.9879 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_only_classfn_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4 | 2023-03-12T09:46:08Z |
Теги вызова стилей
ogipote
drawdream1025
watanabe akio/ official art
tanabe kyou
Умеет в blush stickers, light blush, body blush, knee blush, shoulder blush, ringed eyes, slit pupils
|
AnonymousSub/rule_based_roberta_only_classfn_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
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"RobertaForSequenceClassification"
],
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| 27 | 2023-03-12T09:47:57Z |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
- keras-dreambooth
- nature
license: creativeml-openrail-m
inference: true
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| inner_optimizer.class_name | Custom>RMSprop |
| inner_optimizer.config.name | RMSprop |
| inner_optimizer.config.weight_decay | None |
| inner_optimizer.config.clipnorm | None |
| inner_optimizer.config.global_clipnorm | None |
| inner_optimizer.config.clipvalue | None |
| inner_optimizer.config.use_ema | False |
| inner_optimizer.config.ema_momentum | 0.99 |
| inner_optimizer.config.ema_overwrite_frequency | 100 |
| inner_optimizer.config.jit_compile | True |
| inner_optimizer.config.is_legacy_optimizer | False |
| inner_optimizer.config.learning_rate | 0.0010000000474974513 |
| inner_optimizer.config.rho | 0.9 |
| inner_optimizer.config.momentum | 0.0 |
| inner_optimizer.config.epsilon | 1e-07 |
| inner_optimizer.config.centered | False |
| dynamic | True |
| initial_scale | 32768.0 |
| dynamic_growth_steps | 2000 |
| training_precision | mixed_float16 |
|
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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| 4 | null |
推荐采样器:Euler a DPM++ 2M Karras DPM++ SDE Karras 建议自己多尝试
推荐vae：mse840000_klf8anime.vae和vae-ft-mse-840000-ema-pruned
推荐高清修复算法：Latent (nearest-exact)和Latent
效果图：不保证一定可以，多抽卡 优化tag和负面tag抄 Vすき焼き@AIart 这位的，感谢分享
((masterpiece:1.4, best quality)),((masterpiece, best quality)),cute little girl,loli,feel happy,graduate,Cherry blossom on both sides of the road
Negative prompt: (worst quality, low quality:1.4), (bad anatomy), (inaccurate limb:1.2),poorly eyes, extra digit,fewer digits,six fingers,(extra arms,extra legs:1.2),text,(realistic),nose,lips,adult,fat,perspective
Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 7.5, Seed: 71238888, Size: 640x640, Model hash: 406a8cb00c, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires steps: 20, Hires upscaler: Latent (nearest-exact)
((masterpiece:1.4, best quality)),((masterpiece, best quality)),cute little girl,loli,stand,night,meteor shower,Flower field,wide angle,overlooking
Negative prompt: (worst quality, low quality:1.4),(bad anatomy), (inaccurate limb:1.2),poorly eyes, extra digit,fewer digits,six fingers,(extra arms,extra legs:1.2),text,(realistic),nose,lips,adult,fat,perspective
Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 6.5, Seed: 3110194625, Size: 640x640, Model hash: 406a8cb00c, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires upscale: 2, Hires steps: 20, Hires upscaler: Latent (nearest-exact)
|
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
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"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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}
| 24 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 267.31 +/- 24.90
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/rule_based_twostagetriplet_hier_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
| 27 | 2023-03-12T10:45:52Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ElderTom/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AnonymousSub/specter-bert-model
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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}
}
| 6 | 2023-03-12T10:46:08Z |
---
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: 661.00 +/- 364.00
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 beebeckzzz -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 beebeckzzz -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 beebeckzzz
```
## 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)])
```
|
AnonymousSub/unsup-consert-base_copy_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
| 26 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 693.00 +/- 243.09
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 nikgeo -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 nikgeo -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 nikgeo
```
## 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', 500000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
AnonymousSub/unsup-consert-base_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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}
| 2 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1536 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 5629 with parameters:
```
{'batch_size': 256, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 5000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 1e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 0,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 384, 'out_features': 1536, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
AnonymousSub/unsup-consert-papers-bert
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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}
| 9 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_modified_for_t5_qg
model-index:
- name: t5-end2end-questions-generation
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. -->
# t5-end2end-questions-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_modified_for_t5_qg dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5682
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.584 | 0.34 | 100 | 1.9108 |
| 1.9664 | 0.68 | 200 | 1.7275 |
| 1.8466 | 1.02 | 300 | 1.6634 |
| 1.7412 | 1.36 | 400 | 1.6383 |
| 1.7134 | 1.69 | 500 | 1.6202 |
| 1.694 | 2.03 | 600 | 1.6049 |
| 1.6297 | 2.37 | 700 | 1.5975 |
| 1.6261 | 2.71 | 800 | 1.5932 |
| 1.6149 | 3.05 | 900 | 1.5875 |
| 1.569 | 3.39 | 1000 | 1.5893 |
| 1.5683 | 3.73 | 1100 | 1.5740 |
| 1.5569 | 4.07 | 1200 | 1.5785 |
| 1.5331 | 4.41 | 1300 | 1.5733 |
| 1.5216 | 4.75 | 1400 | 1.5705 |
| 1.5226 | 5.08 | 1500 | 1.5735 |
| 1.4933 | 5.42 | 1600 | 1.5703 |
| 1.4845 | 5.76 | 1700 | 1.5683 |
| 1.5077 | 6.1 | 1800 | 1.5684 |
| 1.4749 | 6.44 | 1900 | 1.5727 |
| 1.4757 | 6.78 | 2000 | 1.5682 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
ArBert/albert-base-v2-finetuned-ner
|
[
"pytorch",
"tensorboard",
"albert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
}
| 19 | null |
---
tags:
- CartPole-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 191.90 +/- 101.73
name: mean_reward
verified: false
---
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'CartPole-v1'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'jondister/JD_PPO_Experiment'
'batch_size': 512
'minibatch_size': 128}
```
|
Aran/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 286.02 +/- 16.23
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
...
```
|
Arnold/wav2vec2-large-xlsr-hausa2-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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}
}
}
| 5 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: distilbert-base-uncased_finetuned_text_2_disease_cel_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_finetuned_text_2_disease_cel_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0574
- Accuracy: 0.9985
- F1: 0.9985
- Recall: 0.9985
- Precision: 0.9986
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.8645 | 1.0 | 167 | 0.6333 | 0.9354 | 0.9288 | 0.9354 | 0.9501 |
| 0.1886 | 2.0 | 334 | 0.1070 | 0.9970 | 0.9970 | 0.9970 | 0.9971 |
| 0.0902 | 3.0 | 501 | 0.0574 | 0.9985 | 0.9985 | 0.9985 | 0.9986 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
ArpanZS/debug_squad
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 14 | null |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: helicopters-Vit
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.6041666865348816
---
# helicopters-Vit
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### NH90
#### Navy helicopter
#### helicopter
|
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