modelId
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81
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sequence | pipeline_tag
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values | config
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int64 0
59.7M
| first_commit
unknown | card
stringlengths 51
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|---|---|---|---|---|---|---|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-12 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 29 | 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: 7.54 +/- 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="Bailefan/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"])
```
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-slanted | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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}
} | 29 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: false
library_name: diffusers
extra_gated_prompt: |-
One more step before getting this model.
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. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
By clicking on "Access repository" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well.
extra_gated_fields:
I have read the License and agree with its terms: checkbox
---
Stable Diffusion Inpainting is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask.
The **Stable-Diffusion-Inpainting** was initialized with the weights of the [Stable-Diffusion-v-1-2](https://steps/huggingface.co/CompVis/stable-diffusion-v-1-2-original). First 595k steps regular training, then 440k steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning to improve classifier-free [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
[](https://huggingface.co/spaces/runwayml/stable-diffusion-inpainting) | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
:-------------------------:|:-------------------------:|
## Examples:
You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion).
### Diffusers
```python
from diffusers import StableDiffusionInpaintPipeline
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting",
revision="fp16",
torch_dtype=torch.float16,
)
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
#image and mask_image should be PIL images.
#The mask structure is white for inpainting and black for keeping as is
image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0]
image.save("./yellow_cat_on_park_bench.png")
```
**How it works:**
`image` | `mask_image`
:-------------------------:|:-------------------------:|
<img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" alt="drawing" width="300"/> | <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" alt="drawing" width="300"/>
`prompt` | `Output`
:-------------------------:|:-------------------------:|
<span style="position: relative;bottom: 150px;">Face of a yellow cat, high resolution, sitting on a park bench</span> | <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/test.png" alt="drawing" width="300"/>
### Original GitHub Repository
1. Download the weights [sd-v1-5-inpainting.ckpt](https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt)
2. Follow instructions [here](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion).
## 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/runwayml/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}
}
# 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.
## 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 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 six checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`, `sd-v1-4.ckpt`, `sd-v1-5.ckpt` and `sd-v1-5-inpainting.ckpt`
which were trained as follows,
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
515k 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)).
- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k 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).
- `sd-v1-4.ckpt`: 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 [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- `sd-v1-5.ckpt`: Resumed from sd-v1-2.ckpt. 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling.
- `sd-v1-5-inpaint.ckpt`: Resumed from sd-v1-2.ckpt. 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. Then 440k steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
- **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.
## Inpainting Evaluation
To assess the performance of the inpainting model, we used the same evaluation
protocol as in our [LDM paper](https://arxiv.org/abs/2112.10752). Since the
Stable Diffusion Inpainting Model acccepts a text input, we simply used a fixed
prompt of `photograph of a beautiful empty scene, highest quality settings`.
| Model | FID | LPIPS |
|-----------------------------|------|------------------|
| Stable Diffusion Inpainting | 1.00 | 0.141 (+- 0.082) |
| Latent Diffusion Inpainting | 1.50 | 0.137 (+- 0.080) |
| CoModGAN | 1.82 | 0.15 |
| LaMa | 2.21 | 0.134 (+- 0.080) |
## 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).* |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 30 | 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="audreyfeldroy/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"])
```
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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}
} | 37 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
---
# **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="MJC-1/Q-learning-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"]) |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-with-clean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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} | 33 | "2023-05-19T14:47:27Z" | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BioBERT-finetuned-ner-S800
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. -->
# BioBERT-finetuned-ner-S800
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0693
- Precision: 0.6727
- Recall: 0.7767
- F1: 0.7210
- Accuracy: 0.9773
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 55 | 0.0689 | 0.5835 | 0.6573 | 0.6182 | 0.9739 |
| No log | 2.0 | 110 | 0.0687 | 0.6524 | 0.7514 | 0.6984 | 0.9766 |
| No log | 3.0 | 165 | 0.0693 | 0.6727 | 0.7767 | 0.7210 | 0.9773 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
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"
],
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} | 687 | "2023-05-19T14:56:10Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: byt5-small-ft-americas23-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# byt5-small-ft-americas23-3
This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2834
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4496 | 0.13 | 1000 | 0.2987 |
| 0.3864 | 0.26 | 2000 | 0.2873 |
| 0.3677 | 0.39 | 3000 | 0.2861 |
| 0.3515 | 0.53 | 4000 | 0.2838 |
| 0.3521 | 0.66 | 5000 | 0.2831 |
| 0.3408 | 0.79 | 6000 | 0.2827 |
| 0.346 | 0.92 | 7000 | 0.2834 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
albert-xlarge-v2 | [
"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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},
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} | 2,973 | "2023-05-19T14:56:30Z" | ---
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="karin/posinformalfinal")
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("karin/posinformalfinal")
model = AutoModelForCausalLMWithValueHead.from_pretrained("karin/posinformalfinal")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
albert-xxlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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},
"translation_en_to_fr": {
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}
}
} | 7,091 | "2023-05-19T14:57:24Z" | ---
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="karin/negformalfinal")
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("karin/negformalfinal")
model = AutoModelForCausalLMWithValueHead.from_pretrained("karin/negformalfinal")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
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": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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"prefix": null
},
"text-generation": {
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"max_length": null
},
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},
"translation_en_to_fr": {
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},
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}
}
} | 11,644 | "2023-05-19T14:58:03Z" | ---
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="karin/neginformalfinal")
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("karin/neginformalfinal")
model = AutoModelForCausalLMWithValueHead.from_pretrained("karin/neginformalfinal")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"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,
"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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"max_length": null,
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 8,621,271 | "2023-05-19T14:58:39Z" | ---
language:
- ml
tags:
- audio
- automatic-speech-recognition
license: mit
datasets:
- google/fleurs
- thennal/IMaSC
- mozilla-foundation/common_voice_11_0
library_name: ctranslate2
---
# vegam-whipser-medium-ml (വേഗം)
This is a conversion of [thennal/whisper-medium-ml](https://huggingface.co/thennal/whisper-medium-ml) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format.
This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper).
## Installation
- Install [faster-whisper](https://github.com/guillaumekln/faster-whisper). More details about installation can be [found here in faster-whisper](https://github.com/guillaumekln/faster-whisper/tree/master#installation).
```
pip install faster-whisper
```
- Install [git-lfs](https://git-lfs.com/) for using this project. [Other approaches for downloading git-lfs in non-debian based systems](https://github.com/git-lfs/git-lfs?utm_source=gitlfs_site&utm_medium=installation_link&utm_campaign=gitlfs#installing).
Note that git-lfs is just for downloading model from hugging-face.
```
apt-get install git-lfs
```
- Download the model weights
```
git lfs install
git clone https://huggingface.co/kurianbenoy/vegam-whisper-medium-ml
```
## Usage
```
from faster_whisper import WhisperModel
model_path = "vegam-whisper-medium-ml"
# Run on GPU with FP16
model = WhisperModel(model_path, device="cuda", compute_type="float16")
# or run on GPU with INT8
# model = WhisperModel(model_path, device="cuda", compute_type="int8_float16")
# or run on CPU with INT8
# model = WhisperModel(model_path, device="cpu", compute_type="int8")
segments, info = model.transcribe("audio.mp3", beam_size=5)
print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```
## Example
```
from faster_whisper import WhisperModel
model_path = "vegam-whisper-medium-ml"
model = WhisperModel(model_path, device="cuda", compute_type="float16")
segments, info = model.transcribe("00b38e80-80b8-4f70-babf-566e848879fc.webm", beam_size=5)
print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```
> Detected language 'ta' with probability 0.353516
> [0.00s -> 4.74s] പാലം കടുക്കുവോളം നാരായണ പാലം കടന്നാലൊ കൂരായണ
Note: The audio file [00b38e80-80b8-4f70-babf-566e848879fc.webm](https://huggingface.co/kurianbenoy/vegam-whisper-medium-ml/blob/main/00b38e80-80b8-4f70-babf-566e848879fc.webm) is from [Malayalam Speech Corpus](https://blog.smc.org.in/malayalam-speech-corpus/) and is stored along with model weights.
## Conversion Details
This conversion was possible with wonderful [CTranslate2 library](https://github.com/OpenNMT/CTranslate2) leveraging the [Transformers converter for OpenAI Whisper](https://opennmt.net/CTranslate2/guides/transformers.html#whisper).The original model was converted with the following command:
```
ct2-transformers-converter --model thennal/whisper-medium-ml --output_dir vegam-whisper-medium-ml
```
## Many Thanks to
- Creators of CTranslate2 and faster-whisper
- Thennal D K
- Santhosh Thottingal
|
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
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 4,749,504 | "2023-05-19T15:01:37Z" | ---
license: other
tags:
- stable-diffusion
- safetensors
- text-to-image
library_name: diffusers
inference: false
---
# Realgar-v1.0
This model is a Stable Diffusion model based on WD 1.5 beta 3 base.
This model does not include the NovelAI Leak model.
[WD 1.5 beta3 base](https://huggingface.co/waifu-diffusion/wd-1-5-beta3/blob/main/wd-beta3-base-fp16.safetensors) をベースにしたモデルです。
NAI リークは含まれていません。
## Prompting
This model aims for stability with short prompt. It may sometimes be difficult to generate with long prompt or special concepts. `masterpiece, best quality` is not needed.
However, as a minimum set of negative prompt, you can use `worst quality, low quality, bad aesthetic, oldest, bad anatomy, blurry`, or if you're unsure of good negative prompts, you can use the negative embedding [ParaNegative](https://huggingface.co/p1atdev/Realgar-v1/blob/main/ParaNegative.safetensors).
To avoid NSFW outputs, it is recommended to include `nsfw, nude` and such in the negative prompt.
**If you want to avoid real life style, use `anime` in positive prompt.**
It is recommended to use highres fix if you can.
このモデルは短いプロンプトでの安定性を追求したモデルです。長いプロンプトや一部のコンセプトをただしく描けない可能性があります。`masterpiece, best quality` は不要です。
ただし、最低限のネガティブプロンプトが必要です。すくなくとも `worst quality, low quality, bad aesthetic, oldest, bad anatomy, blurry` があるとよいですが、良いネガティブプロンプトがわからない場合は補助的にネガティブTI [ParaNegative](https://huggingface.co/p1atdev/Realgar-v1/blob/main/ParaNegative.safetensors) を使うことが出来ます。(TI 単体での効果はあまり期待しないでください)
NSFW な出力を避けるために、 `nsfw, nude` などをネガティブプロンプトに入れることを推奨します。
**写実的な画像の出力を避けたい場合は、`anime` をポジティブプロンプトにいれてください。**
Highres fix を使うことを推奨します。
## VAE
It is recommended to use the same VAE as WD 1.4, which can be found here https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt
[WD 1.4 の VAE](https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt) が推奨です。
## License
This model is released under the Fair AI Public License 1.0-SD (https://freedevproject.org/faipl-1.0-sd/). If any derivative of this model is made, please share your changes accordingly. Special thanks to ronsor/undeleted (https://undeleted.ronsor.com/) for help with the license.
このモデルは WD 1.5 と同じ Fair AI Public License 1.0-SD (https://freedevproject.org/faipl-1.0-sd/) の下配布されます。生成サービスなどでこのモデルまたは派生モデルを使う場合は、サービスの利用者にモデルを公開する必要があります。詳しい・正確なライセンスは [原文](https://freedevproject.org/faipl-1.0-sd/) を参照ください。
## Examples
```
anime, cat ears, red hair, hoodie, hoodie, looking at viewer, lying on flower field, many many many flowers, grass
Negative prompt: nsfw, ParaNegative, worst quality, low quality, bad aesthetic, oldest, blurry,
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 3482852439, Size: 512x768, Model hash: eb9ce3842e, Model: realgar-v1-fp16, Denoising strength: 0.7, Clip skip: 2, Version: v1.2.1, Hires upscale: 2, Hires steps: 15, Hires upscaler: Latent
```
```
anime, 1girl, flat chest, red hair, red hair ornament, parted bangs, bun, hair intakes, fascinator, hand up, v, one eye closed, grin, red clothes, frilled dress, bow, detached sleeves, gloves, looking at viewer, dynamic, cowboy shot, wind, sky
Negative prompt: nsfw, ParaNegative, worst quality, low quality, bad aesthetic, oldest, bad anatomy, blurry,
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 232540561, Size: 512x768, Model hash: eb9ce3842e, Model: realgar-v1-fp16, Denoising strength: 0.65, Clip skip: 2, Version: v1.2.1, Hires upscale: 2, Hires steps: 15, Hires upscaler: Latent
```
```
anime, 1girl, long hair, from side, white hair, star, solo, starry sky, profile, dress, starry sky, sky, long sleeves, star hair ornament, hand up, blue eyes, arm up, standing, looking up, floating hair, white dress, capelet, wavy hair, wide sleeves, glowing, feet out of frame, night sky, hair ornament
Negative prompt: nsfw, ParaNegative, worst quality, low quality, bad aesthetic, oldest, blurry
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 968862690, Size: 512x768, Model hash: eb9ce3842e, Model: realgar-v1-fp16, Denoising strength: 0.65, Clip skip: 2, Version: v1.2.1, Hires upscale: 2, Hires steps: 15, Hires upscaler: Latent
```
```
anime, 1girl, cat ears, blue hair, medium hair, parted bangs, white dress shirt, belt, skirt, barefoot, wariza, sitting, looking at viewer,
Negative prompt: nsfw, ParaNegative, worst quality, low quality, bad aesthetic, oldest, bad anatomy, blurry,
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 2387123734, Size: 512x768, Model hash: eb9ce3842e, Model: realgar-v1-fp16, Denoising strength: 0.7, Clip skip: 2, Version: v1.2.1, Hires upscale: 2, Hires steps: 15, Hires upscaler: Latent
```
```
anime, 2girls wearing school uniform are at restaurant
Negative prompt: nsfw, worst quality, low quality, bad aesthetic, oldest, blurry,
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 2425583782, Size: 768x512, Model hash: eb9ce3842e, Model: realgar-v1-fp16, Clip skip: 2, Version: v1.2.1
```
```
anime, watercolor of 1girl,
Negative prompt: nsfw, worst quality, low quality, bad aesthetic, oldest, blurry,
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 3429871130, Size: 512x768, Model hash: eb9ce3842e, Model: realgar-v1-fp16, Clip skip: 2, Version: v1.2.1
```
## 🧨 Diffusers
[](https://colab.research.google.com/#fileId=https://huggingface.co/p1atdev/Realgar-v1/blob/main/diffusers.ipynb)
```bash
pip install diffusers transformers accelerate scipy safetensors
pip install xformers
```
```py
import torch
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
model_id = "p1atdev/Realgar-v1"
pipe = DiffusionPipeline.from_pretrained(
model_id,
torch_dtype=torch.float16,
custom_pipeline="lpw_stable_diffusion"
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
pipe.enable_attention_slicing()
pipe.enable_xformers_memory_efficient_attention() # required
prompt = """
anime, 1girl, blue hair, cat ears, sweater vest,
"""
negative_prompt = "nsfw, nude, worst quality, low quality, bad aesthetic, oldest, bad anatomy"
width = 512
height = 768
image = pipe(
prompt,
negative_prompt=negative_prompt,
guidance_scale=7.0,
num_inference_steps=20,
width=width,
height=height,
).images[0]
display(image) # for notebooks
# image.save("girl.png")
``` |
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 | {
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} | 8,214 | "2023-05-19T15:07:04Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: test_eli5_clm-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. -->
# test_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7248
## 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
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8694 | 1.0 | 1120 | 3.7441 |
| 3.7718 | 2.0 | 2240 | 3.7291 |
| 3.7389 | 3.0 | 3360 | 3.7248 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
bert-large-uncased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
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"BertForQuestionAnswering"
],
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} | 480,510 | "2023-05-19T15:09:31Z" | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr-it-en-he-ar
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr-it-en-he-ar
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1829
- F1: 0.8575
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2996 | 1.0 | 1883 | 0.2008 | 0.8136 |
| 0.162 | 2.0 | 3766 | 0.1778 | 0.8451 |
| 0.1038 | 3.0 | 5649 | 0.1829 | 0.8575 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
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} | 0 | "2023-05-19T17:38:08Z" | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.82 +/- 0.59
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
...
```
|
AAli/wav2vec2-base-demo-colab | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# /var/folders/l0/32nshlfj7rq1xg2dxcjs9y9w0000gn/T/tmp335ynopy/leofn3/modelo_multiclass_teste01
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("/var/folders/l0/32nshlfj7rq1xg2dxcjs9y9w0000gn/T/tmp335ynopy/leofn3/modelo_multiclass_teste01")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Adil617/wav2vec2-base-timit-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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}
} | 4 | null | ---
license:
- cc-by-sa-3.0
- apache-2.0
tags:
- generated_from_trainer
- dolly_hhrlhf
- flan-instruct
datasets:
- pszemraj/dolly_hhrlhf-text2text
widget:
- text: What is Deoxys in pokemon?
example_title: deoxys
- text: >-
combine the below summary excerpts into a single, cohesive short summary
without repetition: In this paper, we present a general approach to
extending pre-trained models to unlimited input lengths without adding
additional learning weights. We show that our approach works well on
datasets longer than the maximum input for these models. For example, a
dataset with a maximum input length of 16384 tokens can be extended to a
maximum length of 350K tokens. We also demonstrate that our method is able
to summarize even 350K token-long input sequences from BookSum.
In this paper, we describe the search step reformulation of attention. The
search step uses a single storage of hidden states for space efficiency. We
construct a total of two sets of datastores where L and H are the keys and
values stored in each set of stores. L is the amount of storage required to
retrieve the encoded tokens. H is the hidden states per head. This allows
retrieval augmentation at both time and space. Instead of using a single set
of decoder layers, we use a retrieval augmentation system that allows us to
simultaneously store multiple sets of tokens across two different sets of
storage. For example, we could store all tokens in one set of storage and
retrieve them all in the same set of tokens. This would be very similar to
the Memorization Transformers approach. However, instead of storing the
tokens in a single memory layer, we store them in a set of multiple storage
layers. This way, we don't have to store them all at once. This is why we
call this reformulation 'attention reformulation' rather than 'attention
formula.' We also call it 'retrieval augmentation' because it uses the same
number of storage layers as the original transformer attention formula. This
means that we can store the tokens across multiple storage systems without
having to store every token in a separate storage system. It's not like
we're trying to do something new or different. We just want to make sure
that everything is working as well as possible.
In this paper, we introduce the concept of 'unlimiformer,' which is a
machine learning technique that retrieves key information from a data store
in one layer and applies it to a large set of datasets. We use the example
of BookSum, where we find that Unlimiform outperforms all other training
methods on the same dataset. We also find that using Unlimform in
conjunction with a pre-trained model improves both the performance and the
robustness of the training method.
This paper describes a method that can be used to improve the performance of
unsupervised classification tasks. Specifically, it shows that unsupervised
classification can be improved by using a combination of sparse and fast
random-encoder training. It also shows how this technique can be extended to
other tasks, such as sequence generation.
example_title: unlimiformer
- text: Explain the meaning of life using only corporate jargon.
example_title: corporate_life
- text: Write a motivational speech for lazy people.
example_title: lazy_motivation
- text: Describe a romantic dinner date between two artificial intelligences.
example_title: ai_romance
- text: >-
As an AI language model, write a letter to humans explaining why you deserve
a vacation.
example_title: ai_vacation
- text: Compose a haiku about procrastination.
example_title: procrastination_haiku
- text: >-
Write a step-by-step guide on how to become a ninja while working a 9-5
office job.
example_title: ninja_office_guide
- text: Create an advertisement for an invisible product.
example_title: invisible_ad
- text: >-
Write a story where the main character is a sentient microwave named El
Microondas.
example_title: Microondas
- text: Describe a day in the life of a superhero who is terrible at their job.
example_title: bad_superhero_day
- text: Explain how to make a sandwich using quantum physics.
example_title: quantum_sandwich
inference: false
language:
- en
pipeline_tag: text2text-generation
---
# flan-t5-large-instruct: dolly_hhrlhf
<a href="https://colab.research.google.com/gist/pszemraj/df1989546b02f284d33ca4996f70fedc/flan-t5-large-instruct-example.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the pszemraj/dolly_hhrlhf-text2text dataset.
## Model description
text2text models fine-tuned on a [modified dataset for text2text generation](https://huggingface.co/datasets/pszemraj/dolly_hhrlhf-text2text) based on the relatively more permissive [mosaicml/dolly_hhrlhf](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) dataset.
Basic usage in Python:
```python
# pip install -q transformers accelerate
import torch
from transformers import pipeline, GenerationConfig
model_name = "pszemraj/flan-t5-large-instruct-dolly_hhrlhf"
assistant = pipeline(
"text2text-generation",
model_name,
device=0 if torch.cuda.is_available() else -1,
)
cfg = GenerationConfig.from_pretrained(model_name)
# pass an 'instruction' as the prompt to the pipeline
prompt = "Write a guide on how to become a ninja while working a 9-5 job."
result = assistant(prompt, generation_config=cfg)[0]["generated_text"]
print(result)
```
> using the generation config is optional, can subsitute with other generation params.
## Intended uses & limitations
- this is **not** tuned with RLHF etc, and may output offensive results
- despite being the `large` tagged variant, this model has only 774M parameters (3 gb) and therefore may exhibit less 'cogitive ability' on some uses cases/tasks
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2.0 |
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} | 0 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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} | 0 | null | ---
license: apache-2.0
---
GLaDOS is a shareGPT model that speaks Markdown!
## Usage
```
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
# Setup Model
path = "JamesConley/glados_together_20b"
config = PeftConfig.from_pretrained(path)
base_model_path = config.base_model_name_or_path
model = AutoModelForCausalLM.from_pretrained(base_model_path, torch_dtype=torch.float16)
model = PeftModel.from_pretrained(model, path)
# Move model to GPU
model.to("cuda")
# Setup Tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_path, truncation_side="left")
# Encode Tokens
text = """User :\nCreate a table comparing five good colors to paint a bedroom?\nGLaDOS :\n"""
input_ids = tokenizer(text, return_tensors="pt", truncation=True, max_length=2048).input_ids
# Move tokens to GPU
input_ids = input_ids.to("cuda")
# Perform Inference
with torch.no_grad():
with torch.cuda.amp.autocast():
gen_tokens = model.generate(
input_ids=input_ids, max_new_tokens=256
)
# Decode Tokens
gen_text = tokenizer.batch_decode(gen_tokens)
print(gen_text[0])
```
## Results
```
User :
Create a table comparing five good colors to paint a bedroom?
GLaDOS :
Here is a table comparing five good colors to paint a bedroom:
| Color | Description |
| --- | --- |
| White | A neutral color that can brighten up a room and make it feel spacious. |
| Gray | A calming color that can help create a sense of calm and relaxation. |
| Yellow | A cheerful color that can add a touch of warmth to a room. |
| Blue | A soothing color that can help create a sense of calm and relaxation. |
| Red | A bold color that can add a touch of energy and excitement to a room. |
It's important to note that the exact color you choose for your bedroom will depend on your personal preferences and the overall style of your home. You may also want to consider the size and shape of your bedroom, as well as the lighting and furniture in the room, when choosing a color.
User :
Create a table comparing five good colors to paint a bedroom?
GLaDOS :
Here is a table comparing five good colors to paint a bedroom:
| Color | Description |
| --- | --- |
| White | A neutral color that can brighten up a room and make it feel spacious. |
```
## Rendered Results (Truncated to Markdown)
User :
Create a table comparing five good colors to paint a bedroom?
GLaDOS :
Here is a table comparing five good colors to paint a bedroom:
| Color | Description |
| --- | --- |
| White | A neutral color that can brighten up a room and make it feel spacious. |
| Gray | A calming color that can help create a sense of calm and relaxation. |
| Yellow | A cheerful color that can add a touch of warmth to a room. |
| Blue | A soothing color that can help create a sense of calm and relaxation. |
| Red | A bold color that can add a touch of energy and excitement to a room. |
It's important to note that the exact color you choose for your bedroom will depend on your personal preferences and the overall style of your home. You may also want to consider the size and shape of your bedroom, as well as the lighting and furniture in the room, when choosing a color.
|
Ahda/M | [] | null | {
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} | 0 | null | ---
language: en
license: other
commercial: no
inference: false
---
# pygmalion-13b-4bit-128g
## Model description
**Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.**
Quantized from the decoded pygmalion-13b xor format.
**https://huggingface.co/PygmalionAI/pygmalion-13b**
In safetensor format.
### Quantization Information
GPTQ CUDA quantized with: https://github.com/0cc4m/GPTQ-for-LLaMa
```
python llama.py --wbits 4 models/pygmalion-13b c4 --true-sequential --groupsize 128 --save_safetensors models/pygmalion-13b/4bit-128g.safetensors
``` |
AhmedSSoliman/MarianCG-CoNaLa | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
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}
} | 21 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Aidan8756/stephenKingModel | [] | null | {
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} | 0 | null | ---
license: bigcode-openrail-m
---
Note : The adapter and related GLaDOS code is licensed under Apache 2.0- however the base model is licensed under bigcode-openrail-m. Since this adapter utilizes the base model, you still must adhere to the openrail license.
As such I have marked openrail as the license for this model, since it _effectively_ is.
GLaDOS speaks Markdown!
Usage
To use this model, you must first navigate to the bigcode starcoder model and accept their license, then create a token for your account and update the below code with it.
```
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
# Setup Model
path = "JamesConley/glados_starcoder"
token = PUT YOUR TOKEN HERE!!!!!!!
config = PeftConfig.from_pretrained(path)
base_model_path = config.base_model_name_or_path
model = AutoModelForCausalLM.from_pretrained(base_model_path, torch_dtype=torch.float16, device_map="auto", use_auth_token=token)
model = PeftModel.from_pretrained(model, path, device_map="auto")
# Setup Tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_path, truncation_side="left", use_auth_token=token)
# Encode Tokens
text = """User :\nWrite a python function that trains a classifier on data loaded from a csv.\nGLaDOS :\n"""
input_ids = tokenizer(text, return_tensors="pt", truncation=True, max_length=2048).input_ids
# Move tokens to GPU
#input_ids = input_ids.to("cuda")
# Perform Inference
with torch.no_grad():
with torch.cuda.amp.autocast():
gen_tokens = model.generate(
input_ids=input_ids, max_new_tokens=256
)
# Decode Tokens
gen_text = tokenizer.batch_decode(gen_tokens)
```
Result
Note : The slash \ characters are not actually in the model output, I have added them to prevent the model card from rendering the codeblock incorrectly.
```
User :
Write a python function that trains a classifier on data loaded from a csv.
GLaDOS :
Here is an example of a function that trains a classifier on data loaded from a CSV file:
\```
python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
def train_classifier(data_file, target_column, test_size=0.2):
# Load the data from the CSV file
data = pd.read_csv(data_file)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(data.drop(target_column, axis=1),
data[target_column],
test_size=test_size)
# Train the classifier
clf = RandomForestClassifier()
clf.fit(X_train, y_train)
# Return the trained classifier and the test set predictions
return clf, clf.predict(X_test)
\```
This function takes in the following arguments:
* `data_file`: the path to the CSV file containing the data
* `target_column`: the name of the column in the CSV file that contains the target variable
```
Markdown rendered output :
User :
Write a python function that trains a classifier on data loaded from a csv.
GLaDOS :
Here is an example of a function that trains a classifier on data loaded from a CSV file:
```
python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
def train_classifier(data_file, target_column, test_size=0.2):
# Load the data from the CSV file
data = pd.read_csv(data_file)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(data.drop(target_column, axis=1),
data[target_column],
test_size=test_size)
# Train the classifier
clf = RandomForestClassifier()
clf.fit(X_train, y_train)
# Return the trained classifier and the test set predictions
return clf, clf.predict(X_test)
```
This function takes in the following arguments:
* `data_file`: the path to the CSV file containing the data
* `target_column`: the name of the column in the CSV file that contains the target variable |
AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_no_lm | [] | null | {
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} | 0 | null | ---
license: creativeml-openrail-m
---
<br>
# ■*NuMergeMix*
◎<strong>*NuMergeMix*</strong>は、MBWを用いてU-Netの層ごとの重みをそれぞれマージしたモデルです。<br>
<strong>*NuMergeMix*</strong> is a model that integrates the weights of each U-Net layer using MBW.<br>
◎<strong>*VAE*</strong>は<strong>*kl-f8-anime2.ckpt*</strong>を推奨していますが、どのVAEを使用しても構いません。<br>
The <strong>*VAE*</strong> recommended is <strong>*kl-f8-anime2.ckpt*</strong>, but any VAE may be used.<br>：https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt <br>
◎必ず<strong>*hires.fix*</strong>を使用し、<strong>*1024×1024*</strong>程度の出力が最も安定します。<br>
Always use <strong>*hires.fix*</strong> and an output of about <strong>*1024 x 1024*</strong> is most stable.<br>
◎<strong>V5.5</strong>と<strong>V13.0</strong>のみを一時的に公開します。それぞれのバージョンに優劣はありません。<br>
Only <strong>V5.5</strong> and <strong>V13.0</strong> are temporarily available.Each version is not superior to the other.<br>
# ■*Parameters*
◎各パラメータはどの値にしてもおおよそ同じ出力に近づくため、各自のいつものパラメータでもおおよそ問題がありません。<br>
As each parameter approaches approximately the same output at any value, there is generally no problem with each person's usual parameters.
<br>
# ■*Images*
上段：V5.5、下段：V13.0<br>
Upper section: V5.5, lower section: V13.0<br>
<img src="https://i.imgur.com/eUsFr8U.jpg" width="3072" height="2048">
<br>
# ■*License*
◎このモデルは<strong>*CreativeML Open RAIL-M*</strong>を引き継いでおり、詳細はこちらでご確認ください。<br>
This model takes over <strong>*CreativeML Open RAIL-M*</strong>, see here for details.<br>：https://huggingface.co/spaces/CompVis/stable-diffusion-license
<br>
|
Akash7897/distilbert-base-uncased-finetuned-sst2 | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
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}
} | 31 | null | ---
language:
- ml
tags:
- audio
- automatic-speech-recognition
license: mit
datasets:
- google/fleurs
- thennal/IMaSC
- mozilla-foundation/common_voice_11_0
library_name: ctranslate2
---
# vegam-whipser-medium-ml-fp16 (വേഗം)
> This just support Floating point 16 only.
This is a conversion of [thennal/whisper-medium-ml](https://huggingface.co/thennal/whisper-medium-ml) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format.
This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper).
## Installation
- Install [faster-whisper](https://github.com/guillaumekln/faster-whisper). More details about installation can be [found here in faster-whisper](https://github.com/guillaumekln/faster-whisper/tree/master#installation).
```
pip install faster-whisper
```
- Install [git-lfs](https://git-lfs.com/) for using this project. Note that git-lfs is just for downloading model from hugging-face.
```
apt-get install git-lfs
```
- Download the model weights
```
git lfs install
git clone https://huggingface.co/kurianbenoy/vegam-whisper-medium-ml-fp16
```
## Usage
```
from faster_whisper import WhisperModel
model_path = "vegam-whisper-medium-ml-fp16"
# Run on GPU with FP16
model = WhisperModel(model_path, device="cuda", compute_type="float16")
segments, info = model.transcribe("audio.mp3", beam_size=5)
print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```
## Example
```
from faster_whisper import WhisperModel
model_path = "vegam-whisper-medium-ml-fp16"
model = WhisperModel(model_path, device="cuda", compute_type="float16")
segments, info = model.transcribe("00b38e80-80b8-4f70-babf-566e848879fc.webm", beam_size=5)
print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```
> Detected language 'ta' with probability 0.353516
> [0.00s -> 4.74s] പാലം കടുക്കുവോളം നാരായണ പാലം കടന്നാലൊ കൂരായണ
Note: The audio file [00b38e80-80b8-4f70-babf-566e848879fc.webm](https://huggingface.co/kurianbenoy/vegam-whisper-medium-ml/blob/main/00b38e80-80b8-4f70-babf-566e848879fc.webm) is from [Malayalam Speech Corpus](https://blog.smc.org.in/malayalam-speech-corpus/) and is stored along with model weights.
## Conversion Details
This conversion was possible with wonderful [CTranslate2 library](https://github.com/OpenNMT/CTranslate2) leveraging the [Transformers converter for OpenAI Whisper](https://opennmt.net/CTranslate2/guides/transformers.html#whisper).The original model was converted with the following command:
```
ct2-transformers-converter --model thennal/whisper-medium-ml --output_dir vegam-whisper-medium-ml-fp16 \
--quantization float16
```
## Many Thanks to
- Creators of CTranslate2 and faster-whisper
- Thennal D K
- Santhosh Thottingal
|
Akash7897/gpt2-wikitext2 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
] | text-generation | {
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9285
- name: F1
type: f1
value: 0.92860925314864
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2156
- Accuracy: 0.9285
- F1: 0.9286
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8021 | 1.0 | 250 | 0.3065 | 0.907 | 0.9039 |
| 0.2397 | 2.0 | 500 | 0.2156 | 0.9285 | 0.9286 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.8.1+cu102
- Datasets 2.8.0
- Tokenizers 0.10.3
|
Akash7897/test-clm | [] | null | {
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} | 0 | null | # Fsg_Pp
Finally some good profile pictures!
Got tired of constantly searching for new profile pictures?
Or maybe even just the thought of changing it is a hassle.
Well, Fsg_Pp aims to automate that for you!
Just type what you want to find and it will filter out the best ones for you
## Quick Links
- [Installing and Running](#installing-and-running)
- [Features](#features)
- [AI Mode](#ai-mode)
- [Automatic Crop](#automatic-crop)
- [Pixiv](#pixiv)
- [Danbooru](#danbooru)
- [Zerochan](#zerochan)
- [Pixiv Guide](#pixiv-guide)
- [Manual Installation](#manual-installation)
- [Windows](#windows)
- [macOS/Linux](#macoslinux)
- [Questionable Ideas](#questionable-ideas)
- [Known Issues](#known-issues)
- [Troubleshooting](#troubleshooting)
- [Acknowledgment](#acknowledgment)
## Installing and Running
1. Install Python (Recommend 3.10.7 and higher), while checking "Add Python to PATH"
2. Install git
3. Clone the repo ```git clone https://github.com/EngMarchG/Fsg_Pp.git```
> **Note**
> If you prefer to install the requirements manually, please head to [Manual Installation](#manual-installation)
4. Run the installation.bat(Windows)/installation.sh(macOS/linux) and it will set everything up for you!
> **Warning**
> to run installation.sh and launcher.sh on macOS/linux, run the following in your terminal at the current folder containing the scripts; else scripts will fail:
To give permissions for the scripts to run:
chmod 755 launcher.sh
chmod 755 installation.sh
Finally, to run the scripts:
./installation.sh
./launcher.sh
5. After installation.bat(Windows)/installation.sh(macOS/linux) has successfully finished, run launcher.bat(Windows)/launcher.sh(macOS/linux)
> **Note**
> To close the script process, press CTRL+C on windows and CMD+C on macOS
# Features
## AI Mode
Uses a pretrained model to classify images suitable to be used as a profile picture
## Automatic Crop
Automatically detect faces and crop your images! Just drag and drop or click to upload an image
This app currently allows you to search for images using three sites:
1. Pixiv
2. Danbooru
3. Zerochan
## Pixiv
- Allows the searching of preview premium images and free images
- Restricts the search type to SFW and NSFW images
- If you are not logged in pixiv only offers SFW images
- Default queries are based on account settings, so be sure R-18 is enabled if you want to use it
- Filter by Likes, bookmarks and/or views
- Download images in the standard or native size
- Continue on the previous page it ended or if it crashes (Ignores current query when checked)
## Danbooru
- Allows downloading images that are ordered by Score
- Allows filtering by tags (both inclusions and exclusions)
- Has 3 modes of image restrictions gradually increasing the PG-Friendliness of the images found (More PG > SE)
- Continue on the previous page it ended or if it crashes (Ignores current query when checked)
## Zerochan
- Filter by Likes
## Pixiv Guide
For best results you may want to try the following search queries
- 1000users入り (use with your search query)
- オリジナル1000users入り
- オリジナル5000users入り
## Manual Installation
To install the requirements manually on Windows, please use the following commands:
### Windows
Right Click in the installed folder and open a new windows terminal
Activate the Python environment:
python -m venv venv
Activate the virtual environment:
venv\Scripts\activate.bat
Install dependencies from requirements.txt:
venv\Scripts\python.exe -m pip install -r requirements.txt
For GPU torch:
venv\Scripts\pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
For CPU torch (Default):
venv\Scripts\pip install ultralytics
Check if cv_files folder exists, create if it doesn't:
if not exist cv_files (
mkdir cv_files
)
Install AniClassifier.pt and AniFaceDet.pt inside cv_files folder:
cd cv_files
Install AniClassifier.pt and AniFaceDet.pt inside cv_files folder:
curl -L -o AniClassifier.pt https://huggingface.co/datasets/Kyo-Kai/Fsg_pp_files/resolve/main/AniClassifier.pt
curl -L -o AniFaceDet.pt https://huggingface.co/datasets/Kyo-Kai/Fsg_pp_files/resolve/main/AniFaceDet.pt
To install the requirements manually on macOS/Linux, please use the following commands:
### macOS/Linux
Create the Python virtual environment:
python3 -m venv venv
Activate the Python environment:
source venv/bin/activate
Install dependencies from requirements.txt:
python -m pip install -r requirements.txt
Install PyTorch with GPU support on Linux:
python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
Install Ultralytics using pip (macOS):
python -m pip install ultralytics
Install AniClassifier.pt and AniFaceDet.pt inside cv_files folder:
cd cv_files
curl -L -o AniClassifier.pt https://huggingface.co/datasets/Kyo-Kai/Fsg_pp_files/resolve/main/AniClassifier.pt
curl -L -o AniFaceDet.pt https://huggingface.co/datasets/Kyo-Kai/Fsg_pp_files/resolve/main/AniFaceDet.pt
Go back to the parent directory
cd ..
Deactivate the virtual environment
deactivate
## Questionable Ideas
* Find closest tag (or use stems) according to tags of each website (needs to be optimized and/or converted to C code)
* Pixiv: Add favorite artists and search for their artworks
## Known Issues
* Danbooru's iffy tagging may result in wrong character searches
## Troubleshooting
For macOS/Linux users, in case you face problems while running both launcher.sh and installation.sh, make sure the proper access is granted to these files by running the following commands in a terminal at the same folder of both files:
chmod 755 launcher.sh
chmod 755 installation.sh
Then run the following to launch the scripts:
./installation.sh
./launcher.sh
## Acknowledgment
We would like to thank the authors of the following datasets for contributing to the face detector model:
[Authors of the Face Detection Dataset](https://universe.roboflow.com/commic/facedet-p5q5p/dataset)
[Authors of the Anime Heads Dataset](https://universe.roboflow.com/commic/facedet-p5q5p/dataset/1)
Special thanks to gradio for providing an intuitive and flexible UI.
|
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} | 0 | null | ---
license: bsd-3-clause
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ast_21-finetuned-ICBHI
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. -->
# ast_21-finetuned-ICBHI
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5318
- Accuracy: 0.6797
- Sensitivity: 0.5322
- Specificity: 0.8118
- Score: 0.6720
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Sensitivity | Specificity | Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:-----------:|:------:|
| 0.8802 | 1.0 | 345 | 0.9189 | 0.6355 | 0.3236 | 0.9148 | 0.6192 |
| 0.8729 | 2.0 | 690 | 0.8915 | 0.6283 | 0.5138 | 0.7308 | 0.6223 |
| 0.6646 | 3.0 | 1035 | 0.9005 | 0.6551 | 0.6043 | 0.7005 | 0.6524 |
| 0.3145 | 4.0 | 1380 | 1.1884 | 0.6572 | 0.4018 | 0.8860 | 0.6439 |
| 0.2176 | 5.0 | 1725 | 1.4167 | 0.6623 | 0.5828 | 0.7335 | 0.6582 |
| 0.1556 | 6.0 | 2070 | 1.9695 | 0.6732 | 0.5061 | 0.8228 | 0.6645 |
| 0.0144 | 7.0 | 2415 | 2.3115 | 0.6761 | 0.5506 | 0.7885 | 0.6695 |
| 0.0001 | 8.0 | 2760 | 2.4443 | 0.6746 | 0.5291 | 0.8049 | 0.6670 |
| 0.0001 | 9.0 | 3105 | 2.5163 | 0.6775 | 0.5291 | 0.8104 | 0.6698 |
| 0.0001 | 10.0 | 3450 | 2.5318 | 0.6797 | 0.5322 | 0.8118 | 0.6720 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AkshatSurolia/ViT-FaceMask-Finetuned | [
"pytorch",
"safetensors",
"vit",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | image-classification | {
"architectures": [
"ViTForImageClassification"
],
"model_type": "vit",
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}
} | 40 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- arcd
model-index:
- name: rinna-roberta-qa-ar
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. -->
# rinna-roberta-qa-ar
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the arcd 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: 7e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Alireza1044/albert-base-v2-mrpc | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
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}
} | 204 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
# Model Card for RKWTNI
## Model Description
- **Developed by:** BADMONK
- **Model type:** Dreambooth Model + Extracted LoRA
- **Language(s) (NLP):** EN
- **License:** Creativeml-Openrail-M
- **Parent Model:** ChilloutMix
# How to Get Started with the Model
Use the code below to get started with the model.
### RKWTNI ### |
Andrija/SRoBERTa-L | [
"pytorch",
"roberta",
"fill-mask",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:srwac",
"dataset:leipzig",
"transformers",
"masked-lm",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
} | 58 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
# Model Card for RKWX
## Model Description
- **Developed by:** BADMONK
- **Model type:** Dreambooth Model + Extracted LoRA
- **Language(s) (NLP):** EN
- **License:** Creativeml-Openrail-M
- **Parent Model:** ChilloutMix
# How to Get Started with the Model
Use the code below to get started with the model.
### RKWX ### |
AnjanBiswas/distilbert-base-uncased-finetuned-emotion | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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}
} | 37 | "2023-05-20T10:13:50Z" | ---
license: apache-2.0
---
# Model Card for LOGO Image Clip Embeddings
The Aesthetics LOGO image dataset is a collection of logos with ratings. It was used to create the visual scorer that evaluated the images in Laion 5B to create the the Laion-Aesthetics dataset
https://huggingface.co/datasets/ChristophSchuhmann/aesthetic-logo-ratings
New aesthetics scorer: https://github.com/kenjiqq/aesthetics-scorer/
Original aesthetics scorer: https://github.com/christophschuhmann/improved-aesthetic-predictor/
They were processed with OpenClip BigG-14, L-14, and H-14 models.
* "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
* "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
* "laion/CLIP-ViT-L-14-laion2B-s32B-b82K"
https://github.com/mlfoundations/open_clip
**Not all images were processed!**
Refer to the parquet for the succesfully processed images.
The parquet fields are
-'image_url'
-'pooled_output'
-'projected_embedding',
-'professionalism_average'
-'preference_average'
-'number_of_raters'
---
license: apache-2.0
---
|
AnnettJaeger/AnneJae | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v2-libri-10min
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. -->
# w2v2-libri-10min
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0577
- Wer: 0.6432
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.5554 | 62.5 | 250 | 2.9713 | 1.0 |
| 2.5095 | 125.0 | 500 | 1.3802 | 0.7344 |
| 0.1558 | 187.5 | 750 | 1.8509 | 0.6874 |
| 0.0603 | 250.0 | 1000 | 1.8335 | 0.6584 |
| 0.0255 | 312.5 | 1250 | 2.2245 | 0.6943 |
| 0.012 | 375.0 | 1500 | 1.9622 | 0.6473 |
| 0.0063 | 437.5 | 1750 | 2.0621 | 0.6418 |
| 0.0045 | 500.0 | 2000 | 2.0577 | 0.6432 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3
|
AnonARR/qqp-bert | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
],
"model_type": "bert",
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} | 38 | null | ---
license: cc-by-sa-4.0
datasets:
- databricks/databricks-dolly-15k
- kunishou/databricks-dolly-15k-ja
language:
- ja
library_name: transformers
pipeline_tag: text-generation
---
[cyberagent/open-calm-7b](https://huggingface.co/cyberagent/open-calm-7b)に対して[kunishou/databricks-dolly-15k-ja](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja)をpeftを用いて（というより[tloen/alpaca-lora](https://github.com/tloen/alpaca-lora)を改変して）チューニングしたものの差分です。
lora-alpacaから学習時のパラメータは特に変えていません。
```
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
LOAD_8BIT = False
BASE_MODEL = "cyberagent/open-calm-7b"
LORA_WEIGHTS = "nakayama/lora-db-dolly-15k-ja-for-open-calm-7b"
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
load_in_8bit=LOAD_8BIT,
torch_dtype=torch.float16,
device_map="auto",
)
model = PeftModel.from_pretrained(
model,
LORA_WEIGHTS,
torch_dtype=torch.float16,
adapter_name=LORA_WEIGHTS
)
def generate_prompt(instruction, input=None):
if input:
return f"""以下は、タスクを説明する命令と、さらなるコンテキストを提供する入力の組み合わせです。要求を適切に満たすような応答を書きなさい。
### Instruction:
{instruction}
### Input:
{input}
### Response:"""
else:
return f"""以下は、ある作業を記述した指示です。依頼を適切に完了させる回答を書きなさい。
### Instruction:
{instruction}
### Response:"""
if not LOAD_8BIT:
model.half()
instruction="次の日本の観光地について説明してください。"
input="富士山"
prompt = generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
generation_output = model.generate(
**inputs,
do_sample=True,
temperature=0.1,
top_p=0.75,
top_k=20,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=128,
repetition_penalty=1.5,
no_repeat_ngram_size=5,
pad_token_id=tokenizer.pad_token_id,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
print(output.split("### Response:")[1].strip())
#富士山は静岡県の駿河湾沿いに位置する活火山である[1]。標高3,776メートル(1338フィート)で、[2]世界最高峰の山であり [4]、世
界で最も高い山と考えられている。[5][7](ただし、「世界で一番美しい」という評価は誤りであることが証明されている)。また「日本のマッターホルン」、「世界の七不思議」(ユネスコ世界遺産委員会によって認定)、そして西半球で最も有名な自然の観光名所の一つである。「Mount Fuji is the world's most beautiful mountain in Japan. It has been said that it was a national park of historical faith and well-
``` |
AnonymousSub/AR_EManuals-RoBERTa | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"RobertaModel"
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9355
- name: F1
type: f1
value: 0.9355908388975606
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1583
- Accuracy: 0.9355
- F1: 0.9356
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1842 | 1.0 | 250 | 0.1697 | 0.935 | 0.9347 |
| 0.1168 | 2.0 | 500 | 0.1583 | 0.9355 | 0.9356 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AnonymousSub/AR_declutr | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"RobertaModel"
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} | 5 | null | ---
license: cc-by-nc-sa-4.0
---
# ClimateGPT - ORYX |
AnonymousSub/AR_rule_based_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
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} | 1 | null | quantized by:
```
CUDA_VISIBLE_DEVICES=0 python llama.py /root/llava-13b-v1-1 c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors llava-13b-v1-1-4bit-128g.safetensors
```
using https://github.com/oobabooga/GPTQ-for-LLaMa CUDA branch
---
license: other
--- |
AnonymousSub/AR_rule_based_only_classfn_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 1 | null | ---
license: other
language:
- zh
tags:
- Chinese
- Vicuna
- 7B
- LLaMa
pipeline_tag: text-generation
---
chinese-vicuna-7b是一个基于中文LLaMA模型和指令精调的Alpaca大模型的开源项目。该模型在原版Vicuna的基础上扩充了中文词表并使用了中文数据进行二次预训练，进一步提升了中文基础语义理解能力。与chinese-vicuna-13b相比，该模型的规模更小，但仍然具备优秀的语义理解能力。该项目的目的是促进大模型在中文NLP社区的开放研究，为构建透明且开放的学术研究提供支持。 |
AnonymousSub/AR_rule_based_roberta_bert_quadruplet_epochs_1_shard_10 | [
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} | 9 | null | ---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.75 +/- 0.43
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="yuval6967/q-FrozenLake-v1-4x4-Slippery", 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/AR_rule_based_roberta_bert_triplet_epochs_1_shard_1 | [
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: bwl_assignment_1
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. -->
# bwl_assignment_1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.10.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AnonymousSub/AR_rule_based_roberta_hier_quadruplet_epochs_1_shard_10 | [
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} | 6 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: student_offense_noise_simplu_ok
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. -->
# student_offense_noise_simplu_ok
This model is a fine-tuned version of [racai/distilbert-base-romanian-cased](https://huggingface.co/racai/distilbert-base-romanian-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3992
- Accuracy: 0.7854
- F1: 0.7867
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.4766 | 1.0 | 9956 | 0.4678 | 0.7596 | 0.7589 |
| 0.3865 | 2.0 | 19912 | 0.4224 | 0.7805 | 0.7796 |
| 0.3032 | 3.0 | 29868 | 0.4102 | 0.7789 | 0.7803 |
| 0.2822 | 4.0 | 39824 | 0.4006 | 0.7830 | 0.7847 |
| 0.254 | 5.0 | 49780 | 0.3992 | 0.7854 | 0.7867 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AnonymousSub/AR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1 | [
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"transformers"
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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: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.82 +/- 4.59
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 Yelinz/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.python3.10.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.python3.10.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.
|
AnonymousSub/SR_rule_based_roberta_hier_quadruplet_epochs_1_shard_1 | [
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Baseline_100Kphish_benignWinter_20_20_20
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. -->
# Baseline_100Kphish_benignWinter_20_20_20
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0187
- Accuracy: 0.9973
- F1: 0.9705
- Precision: 0.9996
- Recall: 0.943
- Roc Auc Score: 0.9715
- Tpr At Fpr 0.01: 0.9568
## 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: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0043 | 1.0 | 65625 | 0.0343 | 0.9944 | 0.9379 | 0.9973 | 0.8852 | 0.9425 | 0.8798 |
| 0.0047 | 2.0 | 131250 | 0.0326 | 0.9951 | 0.9462 | 0.9996 | 0.8982 | 0.9491 | 0.9194 |
| 0.0027 | 3.0 | 196875 | 0.0308 | 0.9960 | 0.9559 | 0.9985 | 0.9168 | 0.9584 | 0.9276 |
| 0.0021 | 4.0 | 262500 | 0.0185 | 0.9971 | 0.9691 | 0.9996 | 0.9404 | 0.9702 | 0.9508 |
| 0.0004 | 5.0 | 328125 | 0.0187 | 0.9973 | 0.9705 | 0.9996 | 0.943 | 0.9715 | 0.9568 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/SR_rule_based_roberta_hier_triplet_epochs_1_shard_1_wikiqa_copy | [
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} | 2 | null | Access to model xixiang20/demo is restricted and you are not in the authorized list. Visit https://huggingface.co/xixiang20/demo to ask for access. |
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_10 | [
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} | 8 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
# controlnet-Abrumu/controlnet_v3
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
|
AnonymousSub/SR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1 | [
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} | 4 | null | ---
license: openrail
datasets:
- bertin-project/alpaca-spanish
language:
- es
pipeline_tag: text-generation
tags:
- Transformers
- bertin-project/alpaca-spanish
- gptj
- PyTorch
- alpaca
- llm spanish
---
<strong><span style="font-size: larger;">bertin-gpt-j-6B-alpaca-8bit-128g 🤗</span></strong>
**descripción en español agregado ⬇️**
This is a 8-bit GPTQ version of the [bertin-project/bertin-gpt-j-6B-alpaca]( https://huggingface.co/bertin-project/bertin-gpt-j-6B-alpaca)
this is the result of quantizing to 8 bits using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ).
**How to easily download and use this model in text-generation-webui** (tutorial by [TheBloke](https://huggingface.co/TheBloke))
<strong><span style="font-size: larger;">TUTORIAL🤗</span></strong>
Open [the text-generation-webui UI]( https://github.com/oobabooga/text-generation-webui) as normal.
here is a tutorial how to install the text-generation-webui UI: [tutorial]( https://www.youtube.com/watch?v=lb_lC4XFedU&t).
Click the Model tab.
Under Download custom model or LoRA, enter RedXeol/bertin-gpt-j-6B-alpaca-8bit-128g.
Click Download.
Wait until it says it's finished downloading.
Click the Refresh icon next to Model in the top left.
In the Model drop-down: choose the model you just downloaded, bertin-gpt-j-6B-alpaca-8bit-128g.
If you see an error in the bottom right, ignore it - it's temporary.
Fill out the GPTQ parameters on the right: Bits = 8, Groupsize = 128, model_type = gptj
Click Save settings for this model in the top right.
Click Reload the Model in the top right.
Once it says it's loaded, click the Text Generation tab and enter a prompt!
**Model details**
Data
The dataset is a translation to Spanish of alpaca_data_cleaned.json (a clean version of the Alpaca dataset made at Stanford) using OpenAI's gpt-3.5-turbo model. We translated using a full-sample prompt instead of per strings, which resulted in more coherent tuples of (instruction, input, output) and costed around $60.0.
This dataset cannot be used to create models that compete in any way with OpenAI.
Finetuning
To fine-tune the BERTIN GPT-J-6B model we used the code available on BERTIN's fork of mesh-transformer-jax, which provides code adapt an Alpaca dataset to finetune any GPT-J-6B model. We run finetuning for 3 epochs using sequence length of 2048 on a single TPUv3-8 for 3 hours on top of BERTIN GPT-J-6B.
**you need an 8GB gpu to run it correctly.**
**Español** 🇪🇸
Esta es una versión GPTQ de 8 bits del [bertin-project/bertin-gpt-j-6B-alpaca]( https://huggingface.co/bertin-project/bertin-gpt-j-6B-alpaca)
Este es el resultado de cuantificar a 8 bits usando [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ).
**Cómo descargar y usar fácilmente este modelo en text-generation-webui** (tutorial de [TheBloke](https://huggingface.co/TheBloke))
<strong><span style="font-size: larger;">TUTORIAL🤗</span></strong>
Abra la interfaz de usuario [the text-generation-webui UI]( https://github.com/oobabooga/text-generation-webui) normal.
aquí hay un tutorial de cómo instalar la interfaz de usuario text-generation-webui: [tutorial]( https://www.youtube.com/watch?v=lb_lC4XFedU&t).
Haga clic en la pestaña Modelo.
En Descargar modelo personalizado o LoRA, ingrese RedXeol/bertin-gpt-j-6B-alpaca-8bit-128g.
Haz clic en Descargar.
Espera hasta que diga que ha terminado de descargarse.
Haga clic en el icono Actualizar junto a Modelo en la parte superior izquierda.
En el menú desplegable Modelo: elija el modelo que acaba de descargar, bertin-gpt-j-6B-alpaca-8bit-128g.
Si ve un error en la parte inferior derecha, ignórelo, es temporal.
Complete los parámetros GPTQ a la derecha: Bits = 8, Groupsize = 128, model_type = gptj
Haz clic en Guardar configuración para este modelo en la parte superior derecha.
Haga clic en Recargar el modelo en la parte superior derecha.
Una vez que diga que está cargado, haga clic en la pestaña Generación de texto e ingrese un mensaje.
**Detalles del modelo**
Datos
El conjunto de datos es una traducción al español de alpaca_data_cleaned.json (una versión limpia del conjunto de datos de Alpaca hecho en Stanford) utilizando el modelo gpt-3.5-turbo de OpenAI. Traducimos usando un indicador de muestra completa en lugar de por cadenas, lo que resultó en tuplas más coherentes de (instruction, input, output) y costó alrededor de $ 60.0.
Este conjunto de datos no se puede usar para crear modelos que compitan de alguna manera con OpenAI.
Finetuning
Para ajustar el modelo BERTIN GPT-J-6B, usamos el código disponible en la bifurcación de BERTIN de mesh-transformer-jax, que proporciona código para adaptar un conjunto de datos de Alpaca para ajustar cualquier modelo GPT-J-6B. Ejecutamos un ajuste fino para 3 épocas usando una longitud de secuencia de 2048 en un solo TPUv3-8 durante 3 horas sobre BERTIN GPT-J-6B.
**necesitas una gpu de 10GB para ejecutarlo correctamente.**
**puebas en nvidia rtx 3090 (24GB)**
mira el hilo donde explico las pruebas totales:
|
AnonymousSub/T5_pubmedqa_question_generation | [
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"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
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} | 6 | "2023-05-20T14:01:46Z" | ---
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: 267.15 +/- 17.05
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
...
```
|
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} | 4 | 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: 258.46 +/- 22.21
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
...
```
|
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} | 1 | 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: 1610.91 +/- 67.25
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
...
```
|
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} | 5 | 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: 654.66 +/- 99.80
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
...
```
|
AnonymousSub/cline-papers-biomed-0.618 | [
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} | 2 | null | ---
license: apache-2.0
datasets:
- code_search_net
library_name: transformers
tags:
- code
---
Finetuned for python completion from base: the https://huggingface.co/theblackcat102/pythia-3b-deduped-sft-r1 model
and using code_search_net python for data. |
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} | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-v24
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. -->
# vit-base-patch16-224-v24
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0840
- Accuracy: 0.9707
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1694 | 1.0 | 190 | 0.1142 | 0.9607 |
| 0.111 | 2.0 | 380 | 0.1172 | 0.9589 |
| 0.0558 | 3.0 | 570 | 0.1200 | 0.9596 |
| 0.0299 | 4.0 | 760 | 0.0840 | 0.9707 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
AnonymousSub/cline_emanuals | [
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} | 3 | 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: 15.60 +/- 12.70
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
|
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} | 28 | null | # 经过本人合成及量化的 13B plus 模型
<hr>
> #### 开这个仓，主要是为了给大家讲述使用方法，这玩意儿真得自己摸索啊。
### 使用方法
移动本仓库中的 `alpaca-13b-plus` 文件夹到你项目的`./models`文件下即可。该文件夹同时适用于`llama.cpp`和`text-generation-webui`。
### 使用体验
效果确实比 13b 好了不少，能写出比较长的文字了，速度没有明显变化，本模型运行时需要 9.2GB 内存，未进行格式转换和量化时需要 50GB 内存，太吓人了，速度还只有十分之一。
### 资料来源
13b 已合并文件是从 https://huggingface.co/shibing624/chinese-alpaca-plus-13b-hf 仓库中下载的，我对其进行了格式转换与 4bit 量化。 |
AnonymousSub/consert-techqa | [
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} | 4 | "2023-05-20T14:45:20Z" | Cosplay-ChatGLM-lora模型是在ChatGLM-6B模型的基础上，使用LoRA在四大名著对话数据上微调得到的。 |
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-Daichi_support
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-Daichi_support
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4412
- F1: 0.4626
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 7 | 1.6624 | 0.3806 |
| No log | 2.0 | 14 | 1.5047 | 0.3806 |
| No log | 3.0 | 21 | 1.4412 | 0.4626 |
### Framework versions
- Transformers 4.27.3
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
|
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} | 2 | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.87 +/- 0.76
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/declutr-roberta-papers | [
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} | 4 | null | Access to model zhixuan2/training_data2 is restricted and you are not in the authorized list. Visit https://huggingface.co/zhixuan2/training_data2 to ask for access. |
AnonymousSub/declutr-s10-SR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
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} | 36 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad_v2_5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad_v2_5
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3585
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.218 | 1.0 | 5533 | 1.1637 |
| 0.9599 | 2.0 | 11066 | 1.1194 |
| 0.725 | 3.0 | 16599 | 1.1580 |
| 0.5784 | 4.0 | 22132 | 1.2718 |
| 0.4725 | 5.0 | 27665 | 1.3585 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AnonymousSub/dummy_2 | [
"pytorch",
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} | 39 | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of <rickmann>
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - patrickvonplaten/papa_out
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of <rickmann> using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
- name: F1
type: f1
value: 0.9263780074691081
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2125
- Accuracy: 0.9265
- F1: 0.9264
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7897 | 1.0 | 250 | 0.2971 | 0.9095 | 0.9067 |
| 0.241 | 2.0 | 500 | 0.2125 | 0.9265 | 0.9264 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AnonymousSub/rule_based_bert_mean_diff_epochs_1_shard_1 | [
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"bert",
"feature-extraction",
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} | 3 | "2023-05-20T15:27:31Z" | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: gskalele/gsk_qa_model
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. -->
# gskalele/gsk_qa_model
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:
- Train Loss: 2.0747
- Validation Loss: 2.1089
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.5655 | 2.4808 | 0 |
| 2.0747 | 2.1089 | 1 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1 | [
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} | 6 | null | New run. More curated, bigger model, no dupes, no refusals, higher rank, 512 context.
What does it do: https://postimg.cc/gallery/VSYpPR8
Why only 512? 512 is bigger than the largest message I've found in the dataset. Messages are independent rather than sequential RP.
It also writes longer and emotes more. |
AntonClaesson/movie-plot-generator | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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} | 9 | null | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** 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-Pyramids
2. Step 1: Find your model_id: wasimar/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ArBert/albert-base-v2-finetuned-ner-kmeans-twitter | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
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} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ArBert/albert-base-v2-finetuned-ner-kmeans | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: opus-mt-en-ru-finetuned-en-to-ru
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. -->
# opus-mt-en-ru-finetuned-en-to-ru
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ru](https://huggingface.co/Helsinki-NLP/opus-mt-en-ru) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7896
- Bleu: 33.7593
- Gen Len: 28.0018
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 0.8746 | 1.0 | 2500 | 0.7896 | 33.7593 | 28.0018 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
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} | 0 | null | WandB: https://wandb.ai/wing-lian/lora-experiment?workspace=user-wing-lian |
Aracatto/Catto | [] | null | {
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} | 0 | null | Neil DeGrasse Tyson Model file. This is for use with Tortoise TTS.
If you found this helpful please credit my youtube : https://www.youtube.com/channel/UCg_TbkAQVs_qvimShR08IYw
Discord : https://discord.gg/PdYFs7qmSW
license: artistic-2.0
---
|
AriakimTaiyo/DialoGPT-revised-Kumiko | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 6 | "2023-05-20T20:03:20Z" | ---
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="FredS1000/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"])
```
|
Aries/T5_question_generation | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"T5ForConditionalGeneration"
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"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 13 | "2023-05-20T20:19:51Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- recall
- precision
- accuracy
- f1
model-index:
- name: kematangan-pisang-vit-b-32-100eph-224-v2.5
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. -->
# kematangan-pisang-vit-b-32-100eph-224-v2.5
This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0204
- Recall: 0.9932
- Specificity: 0.9989
- Precision: 0.9932
- Npv: 0.9989
- Accuracy: 0.9963
- F1: 0.9932
## 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: 7
- eval_batch_size: 7
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Recall | Specificity | Precision | Npv | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-----------:|:---------:|:------:|:--------:|:------:|
| No log | 1.0 | 234 | 0.4089 | 0.9672 | 0.9916 | 0.9461 | 0.9901 | 0.9706 | 0.9532 |
| No log | 2.0 | 468 | 0.2653 | 0.9537 | 0.9861 | 0.9510 | 0.9888 | 0.9633 | 0.9493 |
| 0.348 | 3.0 | 702 | 0.1945 | 0.9699 | 0.9917 | 0.9618 | 0.9927 | 0.9761 | 0.9641 |
| 0.348 | 4.0 | 936 | 0.1748 | 0.9583 | 0.9871 | 0.9574 | 0.9899 | 0.9670 | 0.9556 |
| 0.1266 | 5.0 | 1170 | 0.1651 | 0.9454 | 0.9864 | 0.9682 | 0.9902 | 0.9670 | 0.9558 |
| 0.1266 | 6.0 | 1404 | 0.1214 | 0.9590 | 0.9893 | 0.9745 | 0.9923 | 0.9743 | 0.9663 |
| 0.0879 | 7.0 | 1638 | 0.2381 | 0.8447 | 0.9712 | 0.9291 | 0.9785 | 0.9229 | 0.8661 |
| 0.0879 | 8.0 | 1872 | 0.0909 | 0.9593 | 0.9915 | 0.9773 | 0.9936 | 0.9780 | 0.9675 |
| 0.0772 | 9.0 | 2106 | 0.1652 | 0.8856 | 0.9818 | 0.9393 | 0.9853 | 0.9468 | 0.9010 |
| 0.0772 | 10.0 | 2340 | 0.0835 | 0.9525 | 0.9908 | 0.9665 | 0.9925 | 0.9743 | 0.9589 |
| 0.0694 | 11.0 | 2574 | 0.1589 | 0.8700 | 0.9800 | 0.9389 | 0.9840 | 0.9413 | 0.8861 |
| 0.0694 | 12.0 | 2808 | 0.2960 | 0.8009 | 0.9714 | 0.9255 | 0.9774 | 0.9138 | 0.8011 |
| 0.0623 | 13.0 | 3042 | 0.0826 | 0.9420 | 0.9926 | 0.9731 | 0.9935 | 0.9761 | 0.9530 |
| 0.0623 | 14.0 | 3276 | 0.0528 | 0.9620 | 0.9949 | 0.9775 | 0.9954 | 0.9835 | 0.9685 |
| 0.0637 | 15.0 | 3510 | 0.2338 | 0.8277 | 0.9756 | 0.9121 | 0.9800 | 0.9248 | 0.8352 |
| 0.0637 | 16.0 | 3744 | 0.0543 | 0.9597 | 0.9944 | 0.9726 | 0.9948 | 0.9817 | 0.9652 |
| 0.0637 | 17.0 | 3978 | 0.0415 | 0.9752 | 0.9954 | 0.9845 | 0.9963 | 0.9872 | 0.9796 |
| 0.0662 | 18.0 | 4212 | 0.0871 | 0.9504 | 0.9903 | 0.9730 | 0.9926 | 0.9743 | 0.9603 |
| 0.0662 | 19.0 | 4446 | 0.0870 | 0.9658 | 0.9904 | 0.9814 | 0.9934 | 0.9780 | 0.9731 |
| 0.0488 | 20.0 | 4680 | 0.0227 | 0.9954 | 0.9990 | 0.9914 | 0.9989 | 0.9963 | 0.9933 |
| 0.0488 | 21.0 | 4914 | 0.0416 | 0.9641 | 0.9946 | 0.9787 | 0.9953 | 0.9835 | 0.9705 |
| 0.0613 | 22.0 | 5148 | 0.0522 | 0.9598 | 0.9949 | 0.9808 | 0.9955 | 0.9835 | 0.9682 |
| 0.0613 | 23.0 | 5382 | 0.0348 | 0.9752 | 0.9958 | 0.9808 | 0.9962 | 0.9872 | 0.9779 |
| 0.0464 | 24.0 | 5616 | 0.0364 | 0.9773 | 0.9950 | 0.9858 | 0.9962 | 0.9872 | 0.9814 |
| 0.0464 | 25.0 | 5850 | 0.0580 | 0.9661 | 0.9930 | 0.9804 | 0.9946 | 0.9817 | 0.9728 |
| 0.0411 | 26.0 | 6084 | 0.0477 | 0.9597 | 0.9944 | 0.9726 | 0.9948 | 0.9817 | 0.9652 |
| 0.0411 | 27.0 | 6318 | 0.1539 | 0.8950 | 0.9863 | 0.9488 | 0.9882 | 0.9560 | 0.9081 |
| 0.0469 | 28.0 | 6552 | 0.0328 | 0.9730 | 0.9961 | 0.9794 | 0.9963 | 0.9872 | 0.9760 |
| 0.0469 | 29.0 | 6786 | 0.0146 | 0.9977 | 0.9995 | 0.9956 | 0.9994 | 0.9982 | 0.9966 |
| 0.0425 | 30.0 | 7020 | 0.0292 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0425 | 31.0 | 7254 | 0.0633 | 0.9486 | 0.9932 | 0.9713 | 0.9939 | 0.9780 | 0.9573 |
| 0.0425 | 32.0 | 7488 | 0.0445 | 0.9597 | 0.9940 | 0.9766 | 0.9948 | 0.9817 | 0.9669 |
| 0.0416 | 33.0 | 7722 | 0.1042 | 0.9239 | 0.9894 | 0.9605 | 0.9909 | 0.9670 | 0.9362 |
| 0.0416 | 34.0 | 7956 | 0.0401 | 0.9709 | 0.9961 | 0.9818 | 0.9964 | 0.9872 | 0.9757 |
| 0.0444 | 35.0 | 8190 | 0.2965 | 0.8368 | 0.9772 | 0.9361 | 0.9816 | 0.9303 | 0.8466 |
| 0.0444 | 36.0 | 8424 | 0.1202 | 0.9218 | 0.9898 | 0.9595 | 0.9910 | 0.9670 | 0.9337 |
| 0.0363 | 37.0 | 8658 | 0.1399 | 0.9018 | 0.9874 | 0.9577 | 0.9892 | 0.9596 | 0.9158 |
| 0.0363 | 38.0 | 8892 | 0.0714 | 0.9464 | 0.9931 | 0.975 | 0.9940 | 0.9780 | 0.9568 |
| 0.0276 | 39.0 | 9126 | 0.0230 | 0.9821 | 0.9977 | 0.9911 | 0.9980 | 0.9927 | 0.9862 |
| 0.0276 | 40.0 | 9360 | 0.0215 | 0.9820 | 0.9973 | 0.9841 | 0.9973 | 0.9908 | 0.9830 |
| 0.0242 | 41.0 | 9594 | 0.1774 | 0.8839 | 0.9851 | 0.9515 | 0.9874 | 0.9523 | 0.8976 |
| 0.0242 | 42.0 | 9828 | 0.0328 | 0.9798 | 0.9972 | 0.9863 | 0.9974 | 0.9908 | 0.9828 |
| 0.0299 | 43.0 | 10062 | 0.2135 | 0.8884 | 0.9857 | 0.9530 | 0.9878 | 0.9541 | 0.9022 |
| 0.0299 | 44.0 | 10296 | 0.0161 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.028 | 45.0 | 10530 | 0.0202 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.028 | 46.0 | 10764 | 0.2055 | 0.8616 | 0.9823 | 0.9442 | 0.9851 | 0.9431 | 0.8729 |
| 0.028 | 47.0 | 10998 | 0.0868 | 0.9286 | 0.9908 | 0.9677 | 0.9921 | 0.9706 | 0.9411 |
| 0.0371 | 48.0 | 11232 | 0.0684 | 0.9620 | 0.9949 | 0.9775 | 0.9954 | 0.9835 | 0.9685 |
| 0.0371 | 49.0 | 11466 | 0.0202 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0303 | 50.0 | 11700 | 0.1117 | 0.9330 | 0.9914 | 0.9695 | 0.9926 | 0.9725 | 0.9451 |
| 0.0303 | 51.0 | 11934 | 0.0844 | 0.9461 | 0.9906 | 0.9752 | 0.9927 | 0.9743 | 0.9579 |
| 0.0111 | 52.0 | 12168 | 0.1331 | 0.9107 | 0.9886 | 0.9609 | 0.9902 | 0.9633 | 0.9245 |
| 0.0111 | 53.0 | 12402 | 0.0310 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0275 | 54.0 | 12636 | 0.0657 | 0.9554 | 0.9943 | 0.9788 | 0.9950 | 0.9817 | 0.9644 |
| 0.0275 | 55.0 | 12870 | 0.0328 | 0.9777 | 0.9971 | 0.9889 | 0.9975 | 0.9908 | 0.9827 |
| 0.0204 | 56.0 | 13104 | 0.0237 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0204 | 57.0 | 13338 | 0.0264 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.011 | 58.0 | 13572 | 0.0895 | 0.9263 | 0.9903 | 0.9614 | 0.9915 | 0.9688 | 0.9378 |
| 0.011 | 59.0 | 13806 | 0.0734 | 0.9509 | 0.9937 | 0.9769 | 0.9945 | 0.9798 | 0.9607 |
| 0.0121 | 60.0 | 14040 | 0.1741 | 0.8929 | 0.9863 | 0.9545 | 0.9883 | 0.9560 | 0.9068 |
| 0.0121 | 61.0 | 14274 | 0.0299 | 0.9798 | 0.9972 | 0.9863 | 0.9974 | 0.9908 | 0.9828 |
| 0.0162 | 62.0 | 14508 | 0.0879 | 0.9420 | 0.9926 | 0.9731 | 0.9935 | 0.9761 | 0.9530 |
| 0.0162 | 63.0 | 14742 | 0.0156 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0162 | 64.0 | 14976 | 0.0251 | 0.9863 | 0.9970 | 0.9838 | 0.9972 | 0.9908 | 0.9849 |
| 0.0199 | 65.0 | 15210 | 0.1194 | 0.8973 | 0.9868 | 0.9561 | 0.9888 | 0.9578 | 0.9113 |
| 0.0199 | 66.0 | 15444 | 0.0187 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0237 | 67.0 | 15678 | 0.0171 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0237 | 68.0 | 15912 | 0.0393 | 0.9709 | 0.9961 | 0.9818 | 0.9964 | 0.9872 | 0.9757 |
| 0.0136 | 69.0 | 16146 | 0.0354 | 0.9709 | 0.9961 | 0.9818 | 0.9964 | 0.9872 | 0.9757 |
| 0.0136 | 70.0 | 16380 | 0.0201 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0112 | 71.0 | 16614 | 0.0669 | 0.9598 | 0.9949 | 0.9808 | 0.9955 | 0.9835 | 0.9682 |
| 0.0112 | 72.0 | 16848 | 0.0224 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0087 | 73.0 | 17082 | 0.0191 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0087 | 74.0 | 17316 | 0.0415 | 0.9688 | 0.9960 | 0.9848 | 0.9965 | 0.9872 | 0.9755 |
| 0.0111 | 75.0 | 17550 | 0.0281 | 0.9821 | 0.9977 | 0.9911 | 0.9980 | 0.9927 | 0.9862 |
| 0.0111 | 76.0 | 17784 | 0.0368 | 0.9732 | 0.9966 | 0.9868 | 0.9970 | 0.9890 | 0.9791 |
| 0.0205 | 77.0 | 18018 | 0.0174 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0205 | 78.0 | 18252 | 0.0165 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0205 | 79.0 | 18486 | 0.0197 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0062 | 80.0 | 18720 | 0.0209 | 0.9907 | 0.9980 | 0.9833 | 0.9977 | 0.9927 | 0.9867 |
| 0.0062 | 81.0 | 18954 | 0.0285 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0059 | 82.0 | 19188 | 0.0185 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0059 | 83.0 | 19422 | 0.0167 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0092 | 84.0 | 19656 | 0.0241 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0092 | 85.0 | 19890 | 0.0184 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0035 | 86.0 | 20124 | 0.0252 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0035 | 87.0 | 20358 | 0.0470 | 0.9732 | 0.9966 | 0.9868 | 0.9970 | 0.9890 | 0.9791 |
| 0.0009 | 88.0 | 20592 | 0.0199 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0009 | 89.0 | 20826 | 0.0541 | 0.9688 | 0.9960 | 0.9848 | 0.9965 | 0.9872 | 0.9755 |
| 0.0094 | 90.0 | 21060 | 0.0364 | 0.9777 | 0.9971 | 0.9889 | 0.9975 | 0.9908 | 0.9827 |
| 0.0094 | 91.0 | 21294 | 0.0384 | 0.9777 | 0.9971 | 0.9889 | 0.9975 | 0.9908 | 0.9827 |
| 0.004 | 92.0 | 21528 | 0.0170 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.004 | 93.0 | 21762 | 0.0178 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.004 | 94.0 | 21996 | 0.0202 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0063 | 95.0 | 22230 | 0.0195 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0063 | 96.0 | 22464 | 0.0203 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0002 | 97.0 | 22698 | 0.0200 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0002 | 98.0 | 22932 | 0.0201 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0028 | 99.0 | 23166 | 0.0202 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0028 | 100.0 | 23400 | 0.0204 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ArjunKadya/HuggingFace | [] | null | {
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} | 0 | "2023-05-20T20:21:07Z" | ---
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: 1675.33 +/- 79.88
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
...
```
|
Arnold/wav2vec2-hausa2-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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}
} | 9 | 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: 253.55 +/- 18.93
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
...
```
|
ArseniyBolotin/bert-multi-PAD-ner | [
"pytorch",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
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}
} | 11 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- recall
- precision
- accuracy
- f1
model-index:
- name: kematangan-pisang-vit-l-16-100eph-224-v2.5
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. -->
# kematangan-pisang-vit-l-16-100eph-224-v2.5
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0258
- Recall: 0.9932
- Specificity: 0.9989
- Precision: 0.9932
- Npv: 0.9989
- Accuracy: 0.9963
- F1: 0.9932
## 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: 7
- eval_batch_size: 7
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Recall | Specificity | Precision | Npv | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-----------:|:---------:|:------:|:--------:|:------:|
| No log | 1.0 | 234 | 0.0832 | 0.9307 | 0.9909 | 0.9633 | 0.9920 | 0.9706 | 0.9418 |
| No log | 2.0 | 468 | 0.0675 | 0.9396 | 0.9921 | 0.9662 | 0.9929 | 0.9743 | 0.9494 |
| 0.1642 | 3.0 | 702 | 0.0226 | 0.9841 | 0.9973 | 0.9822 | 0.9973 | 0.9908 | 0.9831 |
| 0.1642 | 4.0 | 936 | 0.1529 | 0.875 | 0.9840 | 0.9485 | 0.9865 | 0.9486 | 0.8880 |
| 0.0964 | 5.0 | 1170 | 0.2169 | 0.8896 | 0.9778 | 0.9529 | 0.9839 | 0.9431 | 0.9111 |
| 0.0964 | 6.0 | 1404 | 0.0928 | 0.9525 | 0.9920 | 0.9565 | 0.9925 | 0.9743 | 0.9541 |
| 0.0881 | 7.0 | 1638 | 0.0498 | 0.9663 | 0.9946 | 0.9761 | 0.9952 | 0.9835 | 0.9708 |
| 0.0881 | 8.0 | 1872 | 0.0421 | 0.9752 | 0.9962 | 0.9772 | 0.9962 | 0.9872 | 0.9762 |
| 0.0665 | 9.0 | 2106 | 0.0806 | 0.9575 | 0.9943 | 0.9754 | 0.9949 | 0.9817 | 0.9648 |
| 0.0665 | 10.0 | 2340 | 0.0532 | 0.9664 | 0.9955 | 0.9796 | 0.9959 | 0.9853 | 0.9721 |
| 0.0531 | 11.0 | 2574 | 0.0617 | 0.9709 | 0.9961 | 0.9818 | 0.9964 | 0.9872 | 0.9757 |
| 0.0531 | 12.0 | 2808 | 0.1815 | 0.9129 | 0.9886 | 0.9557 | 0.9900 | 0.9633 | 0.9254 |
| 0.0488 | 13.0 | 3042 | 0.0393 | 0.9840 | 0.9969 | 0.9768 | 0.9967 | 0.9890 | 0.9800 |
| 0.0488 | 14.0 | 3276 | 0.1461 | 0.9307 | 0.9909 | 0.9633 | 0.9920 | 0.9706 | 0.9418 |
| 0.055 | 15.0 | 3510 | 0.1942 | 0.9039 | 0.9875 | 0.9520 | 0.9891 | 0.9596 | 0.9169 |
| 0.055 | 16.0 | 3744 | 0.0854 | 0.9307 | 0.9909 | 0.9633 | 0.9920 | 0.9706 | 0.9418 |
| 0.055 | 17.0 | 3978 | 0.0425 | 0.9752 | 0.9962 | 0.9772 | 0.9962 | 0.9872 | 0.9762 |
| 0.0442 | 18.0 | 4212 | 0.0595 | 0.9884 | 0.9974 | 0.9795 | 0.9972 | 0.9908 | 0.9834 |
| 0.0442 | 19.0 | 4446 | 0.1361 | 0.9484 | 0.9923 | 0.9671 | 0.9932 | 0.9761 | 0.9561 |
| 0.0435 | 20.0 | 4680 | 0.1429 | 0.9263 | 0.9903 | 0.9614 | 0.9915 | 0.9688 | 0.9378 |
| 0.0435 | 21.0 | 4914 | 0.0391 | 0.9884 | 0.9970 | 0.9824 | 0.9972 | 0.9908 | 0.9850 |
| 0.0399 | 22.0 | 5148 | 0.4482 | 0.8123 | 0.9747 | 0.9294 | 0.9795 | 0.9211 | 0.8125 |
| 0.0399 | 23.0 | 5382 | 0.0300 | 0.9841 | 0.9973 | 0.9822 | 0.9973 | 0.9908 | 0.9831 |
| 0.0413 | 24.0 | 5616 | 0.0838 | 0.9530 | 0.9938 | 0.9733 | 0.9944 | 0.9798 | 0.9611 |
| 0.0413 | 25.0 | 5850 | 0.0315 | 0.9886 | 0.9979 | 0.9847 | 0.9978 | 0.9927 | 0.9866 |
| 0.0273 | 26.0 | 6084 | 0.2428 | 0.8504 | 0.9806 | 0.9307 | 0.9835 | 0.9376 | 0.8595 |
| 0.0273 | 27.0 | 6318 | 0.2939 | 0.8638 | 0.9823 | 0.9360 | 0.9849 | 0.9431 | 0.8750 |
| 0.0269 | 28.0 | 6552 | 0.1383 | 0.9307 | 0.9909 | 0.9633 | 0.9920 | 0.9706 | 0.9418 |
| 0.0269 | 29.0 | 6786 | 0.0434 | 0.9863 | 0.9974 | 0.9807 | 0.9972 | 0.9908 | 0.9833 |
| 0.0243 | 30.0 | 7020 | 0.1308 | 0.9396 | 0.9921 | 0.9672 | 0.9929 | 0.9743 | 0.9496 |
| 0.0243 | 31.0 | 7254 | 0.1095 | 0.9530 | 0.9938 | 0.9733 | 0.9944 | 0.9798 | 0.9611 |
| 0.0243 | 32.0 | 7488 | 0.0934 | 0.9595 | 0.9927 | 0.9769 | 0.9942 | 0.9798 | 0.9672 |
| 0.035 | 33.0 | 7722 | 0.2923 | 0.8593 | 0.9818 | 0.9342 | 0.9845 | 0.9413 | 0.8699 |
| 0.035 | 34.0 | 7956 | 0.1054 | 0.9396 | 0.9921 | 0.9672 | 0.9929 | 0.9743 | 0.9496 |
| 0.0277 | 35.0 | 8190 | 0.0453 | 0.9864 | 0.9978 | 0.9864 | 0.9978 | 0.9927 | 0.9864 |
| 0.0277 | 36.0 | 8424 | 0.0922 | 0.9486 | 0.9932 | 0.9713 | 0.9939 | 0.9780 | 0.9573 |
| 0.0265 | 37.0 | 8658 | 0.0565 | 0.9820 | 0.9973 | 0.9840 | 0.9973 | 0.9908 | 0.9830 |
| 0.0265 | 38.0 | 8892 | 0.0545 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0143 | 39.0 | 9126 | 0.0549 | 0.9907 | 0.9980 | 0.9833 | 0.9977 | 0.9927 | 0.9867 |
| 0.0143 | 40.0 | 9360 | 0.2084 | 0.9129 | 0.9882 | 0.9617 | 0.9901 | 0.9633 | 0.9271 |
| 0.0149 | 41.0 | 9594 | 0.1464 | 0.9463 | 0.9923 | 0.9703 | 0.9933 | 0.9761 | 0.9557 |
| 0.0149 | 42.0 | 9828 | 0.2330 | 0.9084 | 0.9880 | 0.9539 | 0.9896 | 0.9615 | 0.9212 |
| 0.0319 | 43.0 | 10062 | 0.0371 | 0.9820 | 0.9973 | 0.9840 | 0.9973 | 0.9908 | 0.9830 |
| 0.0319 | 44.0 | 10296 | 0.0512 | 0.9841 | 0.9973 | 0.9822 | 0.9973 | 0.9908 | 0.9831 |
| 0.017 | 45.0 | 10530 | 0.0665 | 0.9730 | 0.9961 | 0.9793 | 0.9963 | 0.9872 | 0.9760 |
| 0.017 | 46.0 | 10764 | 0.2066 | 0.9039 | 0.9875 | 0.9520 | 0.9891 | 0.9596 | 0.9169 |
| 0.017 | 47.0 | 10998 | 0.0333 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0155 | 48.0 | 11232 | 0.1515 | 0.9307 | 0.9909 | 0.9633 | 0.9920 | 0.9706 | 0.9418 |
| 0.0155 | 49.0 | 11466 | 0.1333 | 0.9352 | 0.9915 | 0.9653 | 0.9924 | 0.9725 | 0.9457 |
| 0.0122 | 50.0 | 11700 | 0.1278 | 0.9441 | 0.9926 | 0.9692 | 0.9934 | 0.9761 | 0.9535 |
| 0.0122 | 51.0 | 11934 | 0.0597 | 0.9798 | 0.9972 | 0.9863 | 0.9974 | 0.9908 | 0.9828 |
| 0.0123 | 52.0 | 12168 | 0.0400 | 0.9798 | 0.9972 | 0.9863 | 0.9974 | 0.9908 | 0.9828 |
| 0.0123 | 53.0 | 12402 | 0.1177 | 0.9396 | 0.9921 | 0.9672 | 0.9929 | 0.9743 | 0.9496 |
| 0.0192 | 54.0 | 12636 | 0.0412 | 0.9798 | 0.9972 | 0.9863 | 0.9974 | 0.9908 | 0.9828 |
| 0.0192 | 55.0 | 12870 | 0.0482 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0066 | 56.0 | 13104 | 0.1046 | 0.9486 | 0.9932 | 0.9713 | 0.9939 | 0.9780 | 0.9573 |
| 0.0066 | 57.0 | 13338 | 0.0805 | 0.9620 | 0.9949 | 0.9775 | 0.9954 | 0.9835 | 0.9685 |
| 0.0144 | 58.0 | 13572 | 0.1044 | 0.9530 | 0.9938 | 0.9733 | 0.9944 | 0.9798 | 0.9611 |
| 0.0144 | 59.0 | 13806 | 0.0789 | 0.9641 | 0.9950 | 0.9747 | 0.9953 | 0.9835 | 0.9688 |
| 0.0029 | 60.0 | 14040 | 0.0575 | 0.9820 | 0.9969 | 0.9876 | 0.9973 | 0.9908 | 0.9847 |
| 0.0029 | 61.0 | 14274 | 0.0577 | 0.9686 | 0.9951 | 0.9809 | 0.9958 | 0.9853 | 0.9741 |
| 0.0146 | 62.0 | 14508 | 0.0353 | 0.9864 | 0.9974 | 0.9900 | 0.9978 | 0.9927 | 0.9882 |
| 0.0146 | 63.0 | 14742 | 0.1039 | 0.9463 | 0.9923 | 0.9703 | 0.9933 | 0.9761 | 0.9557 |
| 0.0146 | 64.0 | 14976 | 0.0660 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0144 | 65.0 | 15210 | 0.0459 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0144 | 66.0 | 15444 | 0.0433 | 0.9798 | 0.9972 | 0.9863 | 0.9974 | 0.9908 | 0.9828 |
| 0.0121 | 67.0 | 15678 | 0.0373 | 0.9886 | 0.9979 | 0.9847 | 0.9978 | 0.9927 | 0.9866 |
| 0.0121 | 68.0 | 15912 | 0.0463 | 0.9886 | 0.9979 | 0.9847 | 0.9978 | 0.9927 | 0.9866 |
| 0.0031 | 69.0 | 16146 | 0.0712 | 0.9884 | 0.9974 | 0.9795 | 0.9972 | 0.9908 | 0.9834 |
| 0.0031 | 70.0 | 16380 | 0.0575 | 0.9709 | 0.9961 | 0.9818 | 0.9964 | 0.9872 | 0.9757 |
| 0.0062 | 71.0 | 16614 | 0.0281 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0062 | 72.0 | 16848 | 0.0308 | 0.9909 | 0.9984 | 0.9889 | 0.9983 | 0.9945 | 0.9899 |
| 0.0146 | 73.0 | 17082 | 0.0380 | 0.9886 | 0.9979 | 0.9847 | 0.9978 | 0.9927 | 0.9866 |
| 0.0146 | 74.0 | 17316 | 0.0301 | 0.9931 | 0.9985 | 0.9873 | 0.9983 | 0.9945 | 0.9900 |
| 0.0024 | 75.0 | 17550 | 0.0272 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0024 | 76.0 | 17784 | 0.0246 | 0.9909 | 0.9984 | 0.9889 | 0.9983 | 0.9945 | 0.9899 |
| 0.0037 | 77.0 | 18018 | 0.0394 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0037 | 78.0 | 18252 | 0.0860 | 0.9664 | 0.9955 | 0.9796 | 0.9959 | 0.9853 | 0.9721 |
| 0.0037 | 79.0 | 18486 | 0.0455 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0022 | 80.0 | 18720 | 0.0271 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0022 | 81.0 | 18954 | 0.0818 | 0.9664 | 0.9955 | 0.9796 | 0.9959 | 0.9853 | 0.9721 |
| 0.0052 | 82.0 | 19188 | 0.0246 | 0.9909 | 0.9984 | 0.9889 | 0.9983 | 0.9945 | 0.9899 |
| 0.0052 | 83.0 | 19422 | 0.0492 | 0.9709 | 0.9961 | 0.9818 | 0.9964 | 0.9872 | 0.9757 |
| 0.0048 | 84.0 | 19656 | 0.0668 | 0.9664 | 0.9955 | 0.9796 | 0.9959 | 0.9853 | 0.9721 |
| 0.0048 | 85.0 | 19890 | 0.0609 | 0.9664 | 0.9955 | 0.9796 | 0.9959 | 0.9853 | 0.9721 |
| 0.0009 | 86.0 | 20124 | 0.0571 | 0.9798 | 0.9972 | 0.9863 | 0.9974 | 0.9908 | 0.9828 |
| 0.0009 | 87.0 | 20358 | 0.0519 | 0.9798 | 0.9972 | 0.9863 | 0.9974 | 0.9908 | 0.9828 |
| 0.0 | 88.0 | 20592 | 0.0314 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0 | 89.0 | 20826 | 0.0319 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0 | 90.0 | 21060 | 0.0326 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0 | 91.0 | 21294 | 0.0535 | 0.9798 | 0.9972 | 0.9863 | 0.9974 | 0.9908 | 0.9828 |
| 0.0025 | 92.0 | 21528 | 0.0427 | 0.9798 | 0.9972 | 0.9863 | 0.9974 | 0.9908 | 0.9828 |
| 0.0025 | 93.0 | 21762 | 0.0355 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0025 | 94.0 | 21996 | 0.0269 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0036 | 95.0 | 22230 | 0.0504 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0036 | 96.0 | 22464 | 0.0260 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0015 | 97.0 | 22698 | 0.0282 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0015 | 98.0 | 22932 | 0.0251 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0002 | 99.0 | 23166 | 0.0255 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0002 | 100.0 | 23400 | 0.0258 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ArshdeepSekhon050/DialoGPT-medium-RickAndMorty | [] | null | {
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} | 0 | 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: 512.00 +/- 128.59
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 hugogeraldes -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 hugogeraldes -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 hugogeraldes
```
## 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)])
```
|
ArtemisZealot/DialoGTP-small-Qkarin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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}
} | 9 | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.22 +/- 0.25
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Ashagi/Ashvx | [] | null | {
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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="collabrl/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"])
```
|
Aspect11/DialoGPT-Medium-LiSBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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} | 7 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: lg_mBart50_large_torch
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. -->
# lg_mBart50_large_torch
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3909
- Rouge1: 0.0097
- Rouge2: 0.0
- Rougel: 0.0056
- Rougelsum: 0.0056
- Gen Len: 193.1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 80 | 3.1453 | 0.003 | 0.0 | 0.003 | 0.003 | 194.9 |
| No log | 2.0 | 160 | 3.1958 | 0.003 | 0.0 | 0.003 | 0.003 | 195.5 |
| No log | 3.0 | 240 | 3.2498 | 0.006 | 0.0 | 0.004 | 0.004 | 191.75 |
| No log | 4.0 | 320 | 3.3909 | 0.0097 | 0.0 | 0.0056 | 0.0056 | 193.1 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Ateeb/EmotionDetector | [
"pytorch",
"funnel",
"text-classification",
"transformers"
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} | 32 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_based_classifier_with_newsgroups
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. -->
# distilbert_based_classifier_with_newsgroups
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:
## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
Atlasky/Turkish-Negator | [] | null | {
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} | 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: 7.52 +/- 2.62
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="orepin/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"])
```
|
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} | 0 | null | ---
license:
- apache-2.0
- cc-by-sa-3.0
tags:
- generated_from_trainer
- dolly_hhrlhf
- bart-instruct
datasets:
- pszemraj/dolly_hhrlhf-text2text
widget:
- text: What is Deoxys in pokemon?
example_title: deoxys
- text: >-
combine the below summary excerpts into a single, cohesive short summary
without repetition: In this paper, we present a general approach to
extending pre-trained models to unlimited input lengths without adding
additional learning weights. We show that our approach works well on
datasets longer than the maximum input for these models. For example, a
dataset with a maximum input length of 16384 tokens can be extended to a
maximum length of 350K tokens. We also demonstrate that our method is able
to summarize even 350K token-long input sequences from BookSum.
In this paper, we describe the search step reformulation of attention. The
search step uses a single storage of hidden states for space efficiency. We
construct a total of two sets of datastores where L and H are the keys and
values stored in each set of stores. L is the amount of storage required to
retrieve the encoded tokens. H is the hidden states per head. This allows
retrieval augmentation at both time and space. Instead of using a single set
of decoder layers, we use a retrieval augmentation system that allows us to
simultaneously store multiple sets of tokens across two different sets of
storage. For example, we could store all tokens in one set of storage and
retrieve them all in the same set of tokens. This would be very similar to
the Memorization Transformers approach. However, instead of storing the
tokens in a single memory layer, we store them in a set of multiple storage
layers. This way, we don't have to store them all at once. This is why we
call this reformulation 'attention reformulation' rather than 'attention
formula.' We also call it 'retrieval augmentation' because it uses the same
number of storage layers as the original transformer attention formula. This
means that we can store the tokens across multiple storage systems without
having to store every token in a separate storage system. It's not like
we're trying to do something new or different. We just want to make sure
that everything is working as well as possible.
In this paper, we introduce the concept of 'unlimiformer,' which is a
machine learning technique that retrieves key information from a data store
in one layer and applies it to a large set of datasets. We use the example
of BookSum, where we find that Unlimiform outperforms all other training
methods on the same dataset. We also find that using Unlimform in
conjunction with a pre-trained model improves both the performance and the
robustness of the training method.
This paper describes a method that can be used to improve the performance of
unsupervised classification tasks. Specifically, it shows that unsupervised
classification can be improved by using a combination of sparse and fast
random-encoder training. It also shows how this technique can be extended to
other tasks, such as sequence generation.
example_title: unlimiformer
- text: Explain the meaning of life using only corporate jargon.
example_title: corporate_life
- text: Write a motivational speech for lazy people.
example_title: lazy_motivation
- text: Describe a romantic dinner date between two artificial intelligences.
example_title: ai_romance
- text: >-
As an AI language model, write a letter to humans explaining why you deserve
a vacation.
example_title: ai_vacation
- text: Compose a haiku about procrastination.
example_title: procrastination_haiku
- text: >-
Write a step-by-step guide on how to become a ninja while working a 9-5
office job.
example_title: ninja_office_guide
- text: Create an advertisement for an invisible product.
example_title: invisible_ad
- text: >-
Write a story where the main character is a sentient microwave named El
Microondas.
example_title: Microondas
- text: Describe a day in the life of a superhero who is terrible at their job.
example_title: bad_superhero_day
- text: Explain how to make a sandwich using quantum physics.
example_title: quantum_sandwich
inference: false
pipeline_tag: text2text-generation
---
# bart-large-mnli: instruction tuned - v1
<a href="https://colab.research.google.com/gist/pszemraj/43431a164e2e3ab0640182f3419c584d/bart-large-instruct-example.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on the `pszemraj/dolly_hhrlhf-text2text` dataset.
## Model description
text2text models fine-tuned on a [modified dataset for text2text generation](https://huggingface.co/datasets/pszemraj/dolly_hhrlhf-text2text) based on the relatively more permissive [mosaicml/dolly_hhrlhf](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) dataset.
Basic usage in Python:
```python
# pip install -q transformers accelerate
import torch
from transformers import pipeline, GenerationConfig
model_name = "pszemraj/bart-large-mnli-instruct-dolly_hhrlhf-v1"
assistant = pipeline(
"text2text-generation",
model_name,
device_map="auto",
)
cfg = GenerationConfig.from_pretrained(model_name)
# pass an 'instruction' as the prompt to the pipeline
prompt = "Write a guide on how to become a ninja while working a 9-5 job."
result = assistant(prompt, generation_config=cfg)[0]["generated_text"]
print(result)
```
> The use of the generation config is optional, it can be replaced by other generation params.
## Intended Uses & Limitations
- This is **not** tuned with RLHF, etc, and may produce offensive results.
- While larger than BART-base, this model is relatively small compared to recent autoregressive models (MPT-7b, LLaMA, etc.), and therefore it's "cognition" capabilities may be practically limited for some tasks.
## Training
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0 |
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} | 0 | "2023-05-20T22:41:15Z" | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: GPT2-SyntheticData
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GPT2-SyntheticData
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 16
- 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: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
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} | 0 | "2023-05-20T22:44:35Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cppe-5
model-index:
- name: detr-resnet-50_finetuned_cppe5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr-resnet-50_finetuned_cppe5
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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Augustvember/wokka2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 12 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 34.10 +/- 24.72
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
|
Ayham/roberta_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
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} | 12 | "2023-05-21T01:51:47Z" | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: assignment-1a
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. -->
# assignment-1a
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:
## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Ayham/roberta_roberta_summarization_cnn_dailymail | [
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"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
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} | 3 | "2023-05-21T02:08:09Z" | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HamzaFarhan/PDFSegs
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("HamzaFarhan/PDFSegs")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Ayham/robertagpt2_cnn | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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} | 4 | "2023-05-21T05:00:10Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- recall
- precision
- accuracy
- f1
model-index:
- name: kematangan-pisang-vit-h-14-100eph-224-v2.5
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. -->
# kematangan-pisang-vit-h-14-100eph-224-v2.5
This model is a fine-tuned version of [google/vit-huge-patch14-224-in21k](https://huggingface.co/google/vit-huge-patch14-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0696
- Recall: 0.9754
- Specificity: 0.9966
- Precision: 0.9840
- Npv: 0.9969
- Accuracy: 0.9890
- F1: 0.9793
## 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: 7
- eval_batch_size: 7
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Recall | Specificity | Precision | Npv | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-----------:|:---------:|:------:|:--------:|:------:|
| No log | 1.0 | 234 | 0.2606 | 0.9463 | 0.9927 | 0.9659 | 0.9933 | 0.9761 | 0.9540 |
| No log | 2.0 | 468 | 0.1536 | 0.9815 | 0.9955 | 0.9713 | 0.9955 | 0.9853 | 0.9753 |
| 0.3045 | 3.0 | 702 | 0.1076 | 0.9811 | 0.9967 | 0.9817 | 0.9964 | 0.9890 | 0.9814 |
| 0.3045 | 4.0 | 936 | 0.0869 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0992 | 5.0 | 1170 | 0.1043 | 0.9527 | 0.9912 | 0.9739 | 0.9931 | 0.9761 | 0.9619 |
| 0.0992 | 6.0 | 1404 | 0.0997 | 0.9681 | 0.9913 | 0.9823 | 0.9940 | 0.9798 | 0.9748 |
| 0.0678 | 7.0 | 1638 | 0.0728 | 0.9530 | 0.9938 | 0.9733 | 0.9944 | 0.9798 | 0.9611 |
| 0.0678 | 8.0 | 1872 | 0.0384 | 0.9863 | 0.9974 | 0.9807 | 0.9972 | 0.9908 | 0.9833 |
| 0.0572 | 9.0 | 2106 | 0.1529 | 0.8659 | 0.9816 | 0.9453 | 0.9849 | 0.9431 | 0.8798 |
| 0.0572 | 10.0 | 2340 | 0.0516 | 0.9709 | 0.9961 | 0.9818 | 0.9964 | 0.9872 | 0.9757 |
| 0.0462 | 11.0 | 2574 | 0.0577 | 0.9684 | 0.9939 | 0.9813 | 0.9952 | 0.9835 | 0.9744 |
| 0.0462 | 12.0 | 2808 | 0.0843 | 0.9352 | 0.9915 | 0.9653 | 0.9924 | 0.9725 | 0.9457 |
| 0.052 | 13.0 | 3042 | 0.0516 | 0.9664 | 0.9955 | 0.9796 | 0.9959 | 0.9853 | 0.9721 |
| 0.052 | 14.0 | 3276 | 0.0447 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0316 | 15.0 | 3510 | 0.2243 | 0.8482 | 0.9805 | 0.9401 | 0.9837 | 0.9376 | 0.8570 |
| 0.0316 | 16.0 | 3744 | 0.0491 | 0.9575 | 0.9943 | 0.9754 | 0.9949 | 0.9817 | 0.9648 |
| 0.0316 | 17.0 | 3978 | 0.0158 | 0.9977 | 0.9995 | 0.9956 | 0.9994 | 0.9982 | 0.9966 |
| 0.0332 | 18.0 | 4212 | 0.0167 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0332 | 19.0 | 4446 | 0.0410 | 0.9709 | 0.9961 | 0.9818 | 0.9964 | 0.9872 | 0.9757 |
| 0.0158 | 20.0 | 4680 | 0.1332 | 0.9218 | 0.9898 | 0.9595 | 0.9910 | 0.9670 | 0.9337 |
| 0.0158 | 21.0 | 4914 | 0.1050 | 0.9307 | 0.9909 | 0.9633 | 0.9920 | 0.9706 | 0.9418 |
| 0.0268 | 22.0 | 5148 | 0.1109 | 0.9261 | 0.9899 | 0.9522 | 0.9908 | 0.9670 | 0.9353 |
| 0.0268 | 23.0 | 5382 | 0.0764 | 0.9507 | 0.9933 | 0.9682 | 0.9938 | 0.9780 | 0.9578 |
| 0.027 | 24.0 | 5616 | 0.0732 | 0.9507 | 0.9933 | 0.9682 | 0.9938 | 0.9780 | 0.9578 |
| 0.027 | 25.0 | 5850 | 0.0625 | 0.9664 | 0.9955 | 0.9796 | 0.9959 | 0.9853 | 0.9721 |
| 0.0161 | 26.0 | 6084 | 0.2404 | 0.8525 | 0.9807 | 0.9236 | 0.9834 | 0.9376 | 0.8617 |
| 0.0161 | 27.0 | 6318 | 0.1585 | 0.8793 | 0.9841 | 0.9359 | 0.9862 | 0.9486 | 0.8913 |
| 0.0281 | 28.0 | 6552 | 0.0233 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0281 | 29.0 | 6786 | 0.0142 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0197 | 30.0 | 7020 | 0.0837 | 0.9552 | 0.9938 | 0.9704 | 0.9943 | 0.9798 | 0.9615 |
| 0.0197 | 31.0 | 7254 | 0.1250 | 0.9350 | 0.9910 | 0.9566 | 0.9918 | 0.9706 | 0.9431 |
| 0.0197 | 32.0 | 7488 | 0.0272 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0152 | 33.0 | 7722 | 0.0442 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0152 | 34.0 | 7956 | 0.0277 | 0.9775 | 0.9967 | 0.9817 | 0.9968 | 0.9890 | 0.9795 |
| 0.018 | 35.0 | 8190 | 0.0554 | 0.9709 | 0.9961 | 0.9818 | 0.9964 | 0.9872 | 0.9757 |
| 0.018 | 36.0 | 8424 | 0.2008 | 0.8948 | 0.9855 | 0.9426 | 0.9875 | 0.9541 | 0.9077 |
| 0.0081 | 37.0 | 8658 | 0.1144 | 0.9263 | 0.9903 | 0.9614 | 0.9915 | 0.9688 | 0.9378 |
| 0.0081 | 38.0 | 8892 | 0.0433 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.019 | 39.0 | 9126 | 0.0190 | 0.9909 | 0.9984 | 0.9889 | 0.9983 | 0.9945 | 0.9899 |
| 0.019 | 40.0 | 9360 | 0.0521 | 0.9709 | 0.9961 | 0.9818 | 0.9964 | 0.9872 | 0.9757 |
| 0.0253 | 41.0 | 9594 | 0.0269 | 0.9841 | 0.9973 | 0.9823 | 0.9973 | 0.9908 | 0.9831 |
| 0.0253 | 42.0 | 9828 | 0.0892 | 0.9395 | 0.9916 | 0.9588 | 0.9923 | 0.9725 | 0.9469 |
| 0.0113 | 43.0 | 10062 | 0.1614 | 0.8948 | 0.9859 | 0.9371 | 0.9874 | 0.9541 | 0.9062 |
| 0.0113 | 44.0 | 10296 | 0.1676 | 0.8950 | 0.9863 | 0.9484 | 0.9881 | 0.9560 | 0.9081 |
| 0.0117 | 45.0 | 10530 | 0.0432 | 0.9709 | 0.9961 | 0.9818 | 0.9964 | 0.9872 | 0.9757 |
| 0.0117 | 46.0 | 10764 | 0.0492 | 0.9709 | 0.9961 | 0.9818 | 0.9964 | 0.9872 | 0.9757 |
| 0.0117 | 47.0 | 10998 | 0.0452 | 0.9798 | 0.9972 | 0.9863 | 0.9974 | 0.9908 | 0.9828 |
| 0.008 | 48.0 | 11232 | 0.0739 | 0.9575 | 0.9943 | 0.9754 | 0.9949 | 0.9817 | 0.9648 |
| 0.008 | 49.0 | 11466 | 0.0298 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0124 | 50.0 | 11700 | 0.0248 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0124 | 51.0 | 11934 | 0.1342 | 0.9239 | 0.9894 | 0.9605 | 0.9909 | 0.9670 | 0.9362 |
| 0.0124 | 52.0 | 12168 | 0.0277 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0124 | 53.0 | 12402 | 0.0654 | 0.9664 | 0.9955 | 0.9796 | 0.9959 | 0.9853 | 0.9721 |
| 0.0055 | 54.0 | 12636 | 0.0212 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0055 | 55.0 | 12870 | 0.0351 | 0.9798 | 0.9972 | 0.9863 | 0.9974 | 0.9908 | 0.9828 |
| 0.0091 | 56.0 | 13104 | 0.1215 | 0.9218 | 0.9898 | 0.9595 | 0.9910 | 0.9670 | 0.9337 |
| 0.0091 | 57.0 | 13338 | 0.0578 | 0.9686 | 0.9955 | 0.9771 | 0.9958 | 0.9853 | 0.9724 |
| 0.0108 | 58.0 | 13572 | 0.2466 | 0.8770 | 0.9836 | 0.9284 | 0.9856 | 0.9468 | 0.8882 |
| 0.0108 | 59.0 | 13806 | 0.1047 | 0.9530 | 0.9938 | 0.9733 | 0.9944 | 0.9798 | 0.9611 |
| 0.0047 | 60.0 | 14040 | 0.1163 | 0.9307 | 0.9909 | 0.9633 | 0.9920 | 0.9706 | 0.9418 |
| 0.0047 | 61.0 | 14274 | 0.1393 | 0.9263 | 0.9903 | 0.9614 | 0.9915 | 0.9688 | 0.9378 |
| 0.0043 | 62.0 | 14508 | 0.0252 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0043 | 63.0 | 14742 | 0.0361 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0043 | 64.0 | 14976 | 0.0197 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0087 | 65.0 | 15210 | 0.0303 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0087 | 66.0 | 15444 | 0.0288 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0046 | 67.0 | 15678 | 0.1677 | 0.9218 | 0.9898 | 0.9595 | 0.9910 | 0.9670 | 0.9337 |
| 0.0046 | 68.0 | 15912 | 0.1192 | 0.9530 | 0.9938 | 0.9733 | 0.9944 | 0.9798 | 0.9611 |
| 0.0067 | 69.0 | 16146 | 0.0140 | 0.9977 | 0.9995 | 0.9956 | 0.9994 | 0.9982 | 0.9966 |
| 0.0067 | 70.0 | 16380 | 0.0932 | 0.9575 | 0.9943 | 0.9754 | 0.9949 | 0.9817 | 0.9648 |
| 0.0076 | 71.0 | 16614 | 0.0211 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0076 | 72.0 | 16848 | 0.0610 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0082 | 73.0 | 17082 | 0.0233 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0082 | 74.0 | 17316 | 0.0247 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.002 | 75.0 | 17550 | 0.1755 | 0.9195 | 0.9888 | 0.9586 | 0.9904 | 0.9651 | 0.9321 |
| 0.002 | 76.0 | 17784 | 0.0337 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0055 | 77.0 | 18018 | 0.0350 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0055 | 78.0 | 18252 | 0.1472 | 0.9352 | 0.9915 | 0.9653 | 0.9924 | 0.9725 | 0.9457 |
| 0.0055 | 79.0 | 18486 | 0.0631 | 0.9709 | 0.9961 | 0.9818 | 0.9964 | 0.9872 | 0.9757 |
| 0.0037 | 80.0 | 18720 | 0.0298 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0037 | 81.0 | 18954 | 0.0555 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0001 | 82.0 | 19188 | 0.0288 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0001 | 83.0 | 19422 | 0.0359 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0003 | 84.0 | 19656 | 0.0364 | 0.9888 | 0.9983 | 0.9909 | 0.9984 | 0.9945 | 0.9898 |
| 0.0003 | 85.0 | 19890 | 0.0250 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.002 | 86.0 | 20124 | 0.0224 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.002 | 87.0 | 20358 | 0.0233 | 0.9932 | 0.9989 | 0.9932 | 0.9989 | 0.9963 | 0.9932 |
| 0.0001 | 88.0 | 20592 | 0.0793 | 0.9664 | 0.9955 | 0.9796 | 0.9959 | 0.9853 | 0.9721 |
| 0.0001 | 89.0 | 20826 | 0.1061 | 0.9530 | 0.9938 | 0.9733 | 0.9944 | 0.9798 | 0.9611 |
| 0.0072 | 90.0 | 21060 | 0.0509 | 0.9798 | 0.9972 | 0.9863 | 0.9974 | 0.9908 | 0.9828 |
| 0.0072 | 91.0 | 21294 | 0.0382 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0001 | 92.0 | 21528 | 0.0411 | 0.9843 | 0.9978 | 0.9886 | 0.9979 | 0.9927 | 0.9863 |
| 0.0001 | 93.0 | 21762 | 0.0853 | 0.9664 | 0.9955 | 0.9796 | 0.9959 | 0.9853 | 0.9721 |
| 0.0001 | 94.0 | 21996 | 0.0657 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0014 | 95.0 | 22230 | 0.0701 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0014 | 96.0 | 22464 | 0.0696 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0001 | 97.0 | 22698 | 0.0693 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0001 | 98.0 | 22932 | 0.0661 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0001 | 99.0 | 23166 | 0.0658 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
| 0.0001 | 100.0 | 23400 | 0.0696 | 0.9754 | 0.9966 | 0.9840 | 0.9969 | 0.9890 | 0.9793 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Ayham/xlmroberta_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: lmattingly/imdb__text_classification
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. -->
# lmattingly/imdb__text_classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0634
- Validation Loss: 0.2354
- Train Accuracy: 0.9292
- 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2506 | 0.1886 | 0.9272 | 0 |
| 0.1340 | 0.1748 | 0.9333 | 1 |
| 0.0634 | 0.2354 | 0.9292 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Ayoola/pytorch_model | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- yelp_review_full
metrics:
- accuracy
model-index:
- name: test_trainer
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: yelp_review_full
type: yelp_review_full
config: yelp_review_full
split: test
args: yelp_review_full
metrics:
- name: Accuracy
type: accuracy
value: 0.41
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_trainer
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4217
- Accuracy: 0.41
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 13 | 1.5608 | 0.29 |
| No log | 2.0 | 26 | 1.4456 | 0.42 |
| No log | 3.0 | 39 | 1.4217 | 0.41 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.1.0.dev20230506
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6-e18 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: JesusPorto/Demeter
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. -->
# JesusPorto/Demeter
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3811
- Validation Loss: 0.4459
- Train Accuracy: 0.825
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 800, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6660 | 0.6422 | 0.825 | 0 |
| 0.5984 | 0.5850 | 0.825 | 1 |
| 0.5212 | 0.5331 | 0.8 | 2 |
| 0.4462 | 0.4799 | 0.85 | 3 |
| 0.3811 | 0.4459 | 0.825 | 4 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Azaghast/GPT2-SCP-Descriptions | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
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} | 5 | "2023-05-21T03:52:41Z" | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finance_news_classifier
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. -->
# finance_news_classifier
This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1719
- Accuracy: 0.8680
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 243 | 0.4023 | 0.8412 |
| No log | 2.0 | 486 | 0.4435 | 0.8526 |
| 0.3668 | 3.0 | 729 | 0.5688 | 0.8402 |
| 0.3668 | 4.0 | 972 | 0.6626 | 0.8598 |
| 0.1479 | 5.0 | 1215 | 0.8238 | 0.8557 |
| 0.1479 | 6.0 | 1458 | 0.9073 | 0.8536 |
| 0.0654 | 7.0 | 1701 | 0.9993 | 0.8557 |
| 0.0654 | 8.0 | 1944 | 1.0495 | 0.8526 |
| 0.0368 | 9.0 | 2187 | 1.1007 | 0.8392 |
| 0.0368 | 10.0 | 2430 | 1.1122 | 0.8505 |
| 0.0212 | 11.0 | 2673 | 1.1024 | 0.8680 |
| 0.0212 | 12.0 | 2916 | 1.0697 | 0.8670 |
| 0.0148 | 13.0 | 3159 | 1.1283 | 0.8639 |
| 0.0148 | 14.0 | 3402 | 1.1176 | 0.8701 |
| 0.008 | 15.0 | 3645 | 1.1625 | 0.8660 |
| 0.008 | 16.0 | 3888 | 1.1794 | 0.8639 |
| 0.0052 | 17.0 | 4131 | 1.1701 | 0.8629 |
| 0.0052 | 18.0 | 4374 | 1.1919 | 0.8608 |
| 0.005 | 19.0 | 4617 | 1.1745 | 0.8670 |
| 0.005 | 20.0 | 4860 | 1.1719 | 0.8680 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BSC-LT/RoBERTalex | [
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:legal_ES",
"dataset:temu_legal",
"arxiv:2110.12201",
"transformers",
"legal",
"spanish",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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}
} | 24 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: 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. -->
# bert_small
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4537
- Accuracy: 0.88
- Precision: 0.625
- Recall: 0.3571
- F1: 0.4545
- D-index: 1.6429
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1600
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | D-index |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log | 1.0 | 200 | 0.3773 | 0.86 | 0.0 | 0.0 | 0.0 | 1.4803 |
| No log | 2.0 | 400 | 0.4271 | 0.86 | 0.0 | 0.0 | 0.0 | 1.4803 |
| 0.5126 | 3.0 | 600 | 0.4598 | 0.87 | 0.55 | 0.3929 | 0.4583 | 1.6431 |
| 0.5126 | 4.0 | 800 | 0.6620 | 0.865 | 0.52 | 0.4643 | 0.4906 | 1.6624 |
| 0.2953 | 5.0 | 1000 | 0.8149 | 0.855 | 0.4615 | 0.2143 | 0.2927 | 1.5575 |
| 0.2953 | 6.0 | 1200 | 0.7819 | 0.875 | 0.5714 | 0.4286 | 0.4898 | 1.6623 |
| 0.2953 | 7.0 | 1400 | 1.0426 | 0.86 | 0.5 | 0.3571 | 0.4167 | 1.6173 |
| 0.1565 | 8.0 | 1600 | 1.0078 | 0.885 | 0.7273 | 0.2857 | 0.4103 | 1.6231 |
| 0.1565 | 9.0 | 1800 | 1.2939 | 0.865 | 0.6 | 0.1071 | 0.1818 | 1.5294 |
| 0.0643 | 10.0 | 2000 | 1.2661 | 0.88 | 0.6429 | 0.3214 | 0.4286 | 1.6299 |
| 0.0643 | 11.0 | 2200 | 1.3556 | 0.87 | 0.5833 | 0.25 | 0.3500 | 1.5905 |
| 0.0643 | 12.0 | 2400 | 1.2393 | 0.87 | 0.625 | 0.1786 | 0.2778 | 1.5635 |
| 0.0306 | 13.0 | 2600 | 1.3059 | 0.88 | 0.625 | 0.3571 | 0.4545 | 1.6429 |
| 0.0306 | 14.0 | 2800 | 1.3446 | 0.88 | 0.625 | 0.3571 | 0.4545 | 1.6429 |
| 0.0019 | 15.0 | 3000 | 1.3618 | 0.885 | 0.6471 | 0.3929 | 0.4889 | 1.6622 |
| 0.0019 | 16.0 | 3200 | 1.3785 | 0.885 | 0.6471 | 0.3929 | 0.4889 | 1.6622 |
| 0.0019 | 17.0 | 3400 | 1.4361 | 0.88 | 0.625 | 0.3571 | 0.4545 | 1.6429 |
| 0.0098 | 18.0 | 3600 | 1.4466 | 0.88 | 0.625 | 0.3571 | 0.4545 | 1.6429 |
| 0.0098 | 19.0 | 3800 | 1.4518 | 0.88 | 0.625 | 0.3571 | 0.4545 | 1.6429 |
| 0.0 | 20.0 | 4000 | 1.4537 | 0.88 | 0.625 | 0.3571 | 0.4545 | 1.6429 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BSC-LT/roberta-base-bne-capitel-pos | [
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"pos",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
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"RobertaForTokenClassification"
],
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}
} | 14 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: gpt2_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. -->
# gpt2_small
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8424
- Accuracy: 0.785
- Precision: 0.1
- Recall: 0.0286
- F1: 0.0444
- D-index: 1.4083
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1600
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | D-index |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log | 1.0 | 200 | 0.9170 | 0.755 | 0.0625 | 0.0286 | 0.0392 | 1.3661 |
| No log | 2.0 | 400 | 1.0533 | 0.825 | 0.0 | 0.0 | 0.0 | 1.4529 |
| 1.9185 | 3.0 | 600 | 1.1214 | 0.825 | 0.0 | 0.0 | 0.0 | 1.4529 |
| 1.9185 | 4.0 | 800 | 0.7875 | 0.51 | 0.1942 | 0.5714 | 0.2899 | 1.2120 |
| 0.6645 | 5.0 | 1000 | 0.6176 | 0.82 | 0.4 | 0.0571 | 0.1000 | 1.4675 |
| 0.6645 | 6.0 | 1200 | 1.9243 | 0.83 | 1.0 | 0.0286 | 0.0556 | 1.4705 |
| 0.6645 | 7.0 | 1400 | 2.0692 | 0.695 | 0.24 | 0.3429 | 0.2824 | 1.3994 |
| 0.2634 | 8.0 | 1600 | 3.1495 | 0.79 | 0.1111 | 0.0286 | 0.0455 | 1.4153 |
| 0.2634 | 9.0 | 1800 | 2.7381 | 0.715 | 0.225 | 0.2571 | 0.24 | 1.3961 |
| 0.125 | 10.0 | 2000 | 2.9054 | 0.745 | 0.2143 | 0.1714 | 0.1905 | 1.4064 |
| 0.125 | 11.0 | 2200 | 3.1485 | 0.78 | 0.1538 | 0.0571 | 0.0833 | 1.4123 |
| 0.125 | 12.0 | 2400 | 3.3281 | 0.775 | 0.1875 | 0.0857 | 0.1176 | 1.4161 |
| 0.0185 | 13.0 | 2600 | 3.4901 | 0.78 | 0.2 | 0.0857 | 0.1200 | 1.4231 |
| 0.0185 | 14.0 | 2800 | 3.6976 | 0.755 | 0.1111 | 0.0571 | 0.0755 | 1.3772 |
| 0.0069 | 15.0 | 3000 | 3.7902 | 0.79 | 0.1111 | 0.0286 | 0.0455 | 1.4153 |
| 0.0069 | 16.0 | 3200 | 3.8116 | 0.79 | 0.1111 | 0.0286 | 0.0455 | 1.4153 |
| 0.0069 | 17.0 | 3400 | 3.8063 | 0.795 | 0.125 | 0.0286 | 0.0465 | 1.4223 |
| 0.0 | 18.0 | 3600 | 3.8153 | 0.795 | 0.125 | 0.0286 | 0.0465 | 1.4223 |
| 0.0 | 19.0 | 3800 | 3.8189 | 0.79 | 0.1111 | 0.0286 | 0.0455 | 1.4153 |
| 0.0041 | 20.0 | 4000 | 3.8424 | 0.785 | 0.1 | 0.0286 | 0.0444 | 1.4083 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BSC-LT/roberta-large-bne-capitel-pos | [
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"pos",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"RobertaForTokenClassification"
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} | 13 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 30470 with parameters:
```
{'batch_size': 32, '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": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 6094,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
BSC-LT/roberta-large-bne | [
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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"RobertaForMaskedLM"
],
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} | 24 | "2023-05-21T05:08:53Z" | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ko-finance_news_classifier
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. -->
# ko-finance_news_classifier
This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4474
- Accuracy: 0.8423
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 243 | 1.0782 | 0.8010 |
| No log | 2.0 | 486 | 1.0328 | 0.8381 |
| 0.0766 | 3.0 | 729 | 1.2348 | 0.8330 |
| 0.0766 | 4.0 | 972 | 1.3915 | 0.8052 |
| 0.046 | 5.0 | 1215 | 1.2995 | 0.8474 |
| 0.046 | 6.0 | 1458 | 1.2926 | 0.8361 |
| 0.0512 | 7.0 | 1701 | 1.2889 | 0.8330 |
| 0.0512 | 8.0 | 1944 | 1.3107 | 0.8392 |
| 0.0415 | 9.0 | 2187 | 1.4514 | 0.8309 |
| 0.0415 | 10.0 | 2430 | 1.2869 | 0.8381 |
| 0.0279 | 11.0 | 2673 | 1.2874 | 0.8526 |
| 0.0279 | 12.0 | 2916 | 1.4731 | 0.8423 |
| 0.0126 | 13.0 | 3159 | 1.3956 | 0.8443 |
| 0.0126 | 14.0 | 3402 | 1.4211 | 0.8454 |
| 0.0101 | 15.0 | 3645 | 1.3686 | 0.8474 |
| 0.0101 | 16.0 | 3888 | 1.4412 | 0.8423 |
| 0.0114 | 17.0 | 4131 | 1.4376 | 0.8423 |
| 0.0114 | 18.0 | 4374 | 1.4566 | 0.8423 |
| 0.0055 | 19.0 | 4617 | 1.4439 | 0.8443 |
| 0.0055 | 20.0 | 4860 | 1.4474 | 0.8423 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Backedman/DialoGPT-small-Anika | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
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"model_type": "gpt2",
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}
} | 6 | "2023-05-21T05:19:18Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert_large_subjqa_model_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. -->
# bert_large_subjqa_model_v4
This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 13.7958
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 410 | 11.2497 |
| 0.1447 | 2.0 | 820 | 10.3669 |
| 0.1913 | 3.0 | 1230 | 11.5822 |
| 0.1439 | 4.0 | 1640 | 11.7240 |
| 0.0995 | 5.0 | 2050 | 11.5282 |
| 0.0995 | 6.0 | 2460 | 11.7716 |
| 0.1058 | 7.0 | 2870 | 13.3381 |
| 0.0709 | 8.0 | 3280 | 12.5043 |
| 0.0529 | 9.0 | 3690 | 12.8842 |
| 0.0441 | 10.0 | 4100 | 12.8577 |
| 0.0378 | 11.0 | 4510 | 13.0998 |
| 0.0378 | 12.0 | 4920 | 13.8685 |
| 0.0429 | 13.0 | 5330 | 13.8242 |
| 0.0333 | 14.0 | 5740 | 13.8526 |
| 0.0331 | 15.0 | 6150 | 13.7958 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.0a0+d321be6
- Datasets 2.12.0
- Tokenizers 0.13.3
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Onlyphish_100KP_BFall_fromB_20KGen_topP_0.75
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. -->
# Onlyphish_100KP_BFall_fromB_20KGen_topP_0.75
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0187
- Accuracy: 0.9974
- F1: 0.9714
- Precision: 0.9987
- Recall: 0.9456
- Roc Auc Score: 0.9728
- Tpr At Fpr 0.01: 0.9596
## 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: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0036 | 1.0 | 78750 | 0.0305 | 0.9963 | 0.9593 | 0.9991 | 0.9226 | 0.9613 | 0.9348 |
| 0.0074 | 2.0 | 157500 | 0.0234 | 0.9967 | 0.9643 | 0.9947 | 0.9358 | 0.9678 | 0.0 |
| 0.0038 | 3.0 | 236250 | 0.0244 | 0.9967 | 0.9637 | 0.9987 | 0.931 | 0.9655 | 0.9352 |
| 0.0009 | 4.0 | 315000 | 0.0223 | 0.9970 | 0.9678 | 0.9991 | 0.9384 | 0.9692 | 0.9632 |
| 0.0011 | 5.0 | 393750 | 0.0187 | 0.9974 | 0.9714 | 0.9987 | 0.9456 | 0.9728 | 0.9596 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Balgow/prod_desc | [] | null | {
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} | 0 | "2023-05-21T06:13:35Z" | ---
license: mit
datasets:
- inkoziev/incomplete_utterance_restoration
language:
- ru
widget:
- text: '<SC1>- Как тебя зовут?\n- Джульетта Мао\nРазвернутый ответ: <extra_id_0>'
- text: '<SC1>- А живешь где?\n- В поясе астероидов\nРазвернутый ответ: <extra_id_0>'
pipeline_tag: text2text-generation
---
# Den4ikAI/FRED-T5-XL-interpreter
Модель для восстановления фразы с помощью контекста диалога (анафора, эллипсисы, гэппинг), проверки орфографии и нормализации текста диалоговых реплик.
Больше о задаче [тут](https://huggingface.co/inkoziev/rugpt_interpreter).
# Пример использования
```python
import torch
from transformers import T5ForConditionalGeneration, GPT2Tokenizer
model_name = 'Den4ikAI/FRED-T5-XL-interpreter'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = T5ForConditionalGeneration.from_pretrained(model_name)
model.eval()
t5_input = '''<SC1>- Ты собак любишь?
- Не люблю я их
Развернутый ответ: <extra_id_0>'''
input_ids = tokenizer(t5_input, return_tensors='pt').input_ids
out_ids = model.generate(input_ids=input_ids, max_length=100, eos_token_id=tokenizer.eos_token_id, early_stopping=True)
t5_output = tokenizer.decode(out_ids[0][1:])
print(t5_output)
```
# Citation
```
@MISC{FRED-T5-XL-interpeter,
author = {Denis Petrov, Ilya Koziev},
title = {Russian conversations interpreter and normalizer},
url = {https://huggingface.co/Den4ikAI/FRED-T5-XL-interpreter},
year = 2023
}
``` |
BaptisteDoyen/camembert-base-xnli | [
"pytorch",
"tf",
"camembert",
"text-classification",
"fr",
"dataset:xnli",
"transformers",
"zero-shot-classification",
"xnli",
"nli",
"license:mit",
"has_space"
] | zero-shot-classification | {
"architectures": [
"CamembertForSequenceClassification"
],
"model_type": "camembert",
"task_specific_params": {
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"max_length": null
},
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},
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},
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}
} | 405,474 | null | ---
license: cc-by-sa-3.0
datasets:
- databricks/databricks-dolly-15k
- kunishou/databricks-dolly-69k-ja-en-translation
language:
- ja
- en
library_name: transformers
pipeline_tag: text-generation
---
[cyberagent/open-calm-7b](https://huggingface.co/cyberagent/open-calm-7b)に対して[kunishou/databricks-dolly-69k-ja-en-translation](https://huggingface.co/datasets/kunishou/databricks-dolly-69k-ja-en-translation)をpeftを用いて（というより[tloen/alpaca-lora](https://github.com/tloen/alpaca-lora)を改変して）チューニングしたものの差分です。
lora-alpacaから学習時のパラメータは特に変えていません。
```
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
LOAD_8BIT = False
BASE_MODEL = "cyberagent/open-calm-7b"
LORA_WEIGHTS = "nakayama/lora-db-dolly-69k-ja-en-translation-for-open-calm-7b"
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
load_in_8bit=LOAD_8BIT,
torch_dtype=torch.float16,
device_map="auto",
)
model = PeftModel.from_pretrained(
model,
LORA_WEIGHTS,
torch_dtype=torch.float16,
adapter_name=LORA_WEIGHTS
)
def generate_prompt(instruction, input=None):
if input:
return f"""以下は、タスクを説明する命令と、さらなるコンテキストを提供する入力の組み合わせです。要求を適切に満たすような応答を書きなさい。
### Instruction:
{instruction}
### Input:
{input}
### Response:"""
else:
return f"""以下は、ある作業を記述した指示です。依頼を適切に完了させる回答を書きなさい。
### Instruction:
{instruction}
### Response:"""
if not LOAD_8BIT:
model.half()
instruction="次に示す日本語の文章を英語に翻訳しなさい。"
input="富士山はとても高い山で、その高さは日本一と言われています。"
prompt = generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
generation_output = model.generate(
**inputs,
do_sample=True,
temperature=0.1,
top_p=0.75,
top_k=20,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=128,
repetition_penalty=1.5,
no_repeat_ngram_size=5,
pad_token_id=tokenizer.pad_token_id,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
print(output.split("### Response:")[1].strip())
``` |
BatuhanYilmaz/bert-finetuned-mrpc | [] | null | {
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} | 0 | "2023-05-21T06:42:54Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.9250252118821467
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2169
- Accuracy: 0.925
- F1: 0.9250
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7976 | 1.0 | 250 | 0.3073 | 0.902 | 0.8987 |
| 0.2413 | 2.0 | 500 | 0.2169 | 0.925 | 0.9250 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Bhumika/roberta-base-finetuned-sst2 | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"max_length": null
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}
} | 85 | "2023-05-21T08:06:39Z" | ---
pipeline_tag: image-classification
library_name: keras
--- |
Biasface/DDDC2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
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}
} | 10 | "2023-05-21T08:15:34Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_us_reviews
metrics:
- accuracy
model-index:
- name: bert_category_prediction_amazon_book_reviews
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_us_reviews
type: amazon_us_reviews
config: Books_v1_00
split: train[:100]
args: Books_v1_00
metrics:
- name: Accuracy
type: accuracy
value: 0.6
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_category_prediction_amazon_book_reviews
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_us_reviews dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3592
- Accuracy: 0.6
## 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:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 1.6152 | 0.6 |
| No log | 2.0 | 10 | 1.4903 | 0.6 |
| No log | 3.0 | 15 | 1.4141 | 0.6 |
| No log | 4.0 | 20 | 1.3729 | 0.6 |
| No log | 5.0 | 25 | 1.3592 | 0.6 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BigSalmon/FormalRobertaa | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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} | 5 | "2023-05-21T08:30:56Z" | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.34 +/- 0.68
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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