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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-08 19:17:42
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 549
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listlengths 1
4.05k
| pipeline_tag
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values | createdAt
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|---|---|---|---|---|---|---|---|---|---|
elftsdmr/5000
|
elftsdmr
| 2023-05-23T09:11:57Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-23T08:58:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: '5000'
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. -->
# 5000
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1912
- Accuracy: 0.952
- Precision: 0.9751
- Recall: 0.9287
- F1: 0.9513
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 63 | 0.1936 | 0.939 | 0.9890 | 0.8891 | 0.9364 |
| No log | 2.0 | 126 | 0.2011 | 0.946 | 0.9747 | 0.9168 | 0.9449 |
| No log | 3.0 | 189 | 0.1912 | 0.952 | 0.9751 | 0.9287 | 0.9513 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 2.1.0
- Tokenizers 0.13.3
|
RahmaBS/git-base-pokemon
|
RahmaBS
| 2023-05-23T09:10:47Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"git",
"image-text-to-text",
"generated_from_trainer",
"image-to-text",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2023-05-23T09:01:50Z |
---
license: mit
tags:
- generated_from_trainer
- image-to-text
datasets:
- imagefolder
model-index:
- name: git-base-pokemon
results: []
pipeline_tag: image-to-text
---
<!-- 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. -->
# git-base-pokemon
This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0350
- Wer Score: 2.2148
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Score |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 7.3616 | 4.17 | 50 | 4.5895 | 21.4258 |
| 2.4353 | 8.33 | 100 | 0.4961 | 9.9322 |
| 0.1527 | 12.5 | 150 | 0.0303 | 1.3197 |
| 0.0192 | 16.67 | 200 | 0.0260 | 1.3299 |
| 0.007 | 20.83 | 250 | 0.0297 | 2.2059 |
| 0.0027 | 25.0 | 300 | 0.0321 | 2.4795 |
| 0.0017 | 29.17 | 350 | 0.0334 | 2.4488 |
| 0.0014 | 33.33 | 400 | 0.0340 | 2.1355 |
| 0.0013 | 37.5 | 450 | 0.0345 | 2.3619 |
| 0.0012 | 41.67 | 500 | 0.0349 | 2.2084 |
| 0.0011 | 45.83 | 550 | 0.0350 | 2.1803 |
| 0.0011 | 50.0 | 600 | 0.0350 | 2.2148 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Dentikka/opus-mt-ru-en-finetuned-ru-to-en
|
Dentikka
| 2023-05-23T08:51:18Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-18T10:47:47Z |
---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: opus-mt-ru-en-finetuned-ru-to-en
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-ru-en-finetuned-ru-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ru-en](https://huggingface.co/Helsinki-NLP/opus-mt-ru-en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0396
- Bleu*100: 69.5571
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu*100 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5253 | 1.0 | 625 | 1.1806 | 67.3919 |
| 1.2178 | 2.0 | 1250 | 1.0974 | 68.7640 |
| 1.1185 | 3.0 | 1875 | 1.0611 | 69.4906 |
| 1.025 | 4.0 | 2500 | 1.0437 | 69.3828 |
| 0.9981 | 5.0 | 3125 | 1.0396 | 69.5571 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Babelscape/wikineural-multilingual-ner
|
Babelscape
| 2023-05-23T08:47:23Z | 201,943 | 136 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"named-entity-recognition",
"sequence-tagger-model",
"de",
"en",
"es",
"fr",
"it",
"nl",
"pl",
"pt",
"ru",
"multilingual",
"dataset:Babelscape/wikineural",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
widget:
- text: My name is Wolfgang and I live in Berlin.
- text: George Washington went to Washington.
- text: Mi nombre es Sarah y vivo en Londres.
- text: Меня зовут Симона, и я живу в Риме.
tags:
- named-entity-recognition
- sequence-tagger-model
datasets:
- Babelscape/wikineural
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
- multilingual
license:
- cc-by-nc-sa-4.0
pretty_name: wikineural-dataset
source_datasets:
- original
task_categories:
- structure-prediction
task_ids:
- named-entity-recognition
---
# WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER
This is the model card for the EMNLP 2021 paper [WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER](https://aclanthology.org/2021.findings-emnlp.215/). We fine-tuned a multilingual language model (mBERT) for 3 epochs on our [WikiNEuRal dataset](https://huggingface.co/datasets/Babelscape/wikineural) for Named Entity Recognition (NER). The resulting multilingual NER model supports the 9 languages covered by WikiNEuRal (de, en, es, fr, it, nl, pl, pt, ru), and it was trained on all 9 languages jointly.
**If you use the model, please reference this work in your paper**:
```bibtex
@inproceedings{tedeschi-etal-2021-wikineural-combined,
title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}",
author = "Tedeschi, Simone and
Maiorca, Valentino and
Campolungo, Niccol{\`o} and
Cecconi, Francesco and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.215",
pages = "2521--2533",
abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.",
}
```
The original repository for the paper can be found at [https://github.com/Babelscape/wikineural](https://github.com/Babelscape/wikineural).
## How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Babelscape/wikineural-multilingual-ner")
model = AutoModelForTokenClassification.from_pretrained("Babelscape/wikineural-multilingual-ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
example = "My name is Wolfgang and I live in Berlin"
ner_results = nlp(example)
print(ner_results)
```
## Limitations and bias
This model is trained on WikiNEuRal, a state-of-the-art dataset for Multilingual NER automatically derived from Wikipedia. Therefore, it might not generalize well to all textual genres (e.g. news). On the other hand, models trained only on news articles (e.g. only on CoNLL03) have been proven to obtain much lower scores on encyclopedic articles. To obtain more robust systems, we encourage you to train a system on the combination of WikiNEuRal with other datasets (e.g. WikiNEuRal + CoNLL).
## Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents and models belongs to the original copyright holders.
|
AZZLI/Magic-10
|
AZZLI
| 2023-05-23T08:44:25Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-23T08:42:30Z |
---
license: creativeml-openrail-m
---
|
YakovElm/test2
|
YakovElm
| 2023-05-23T08:38:43Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-23T08:37:33Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: test2
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. -->
# test2
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:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ManopeDavid/my_awesome_qa_model
|
ManopeDavid
| 2023-05-23T08:27:29Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-23T08:15:54Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ManopeDavid/my_awesome_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. -->
# ManopeDavid/my_awesome_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: 1.6571
- Validation Loss: 1.8993
- 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': 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 |
|:----------:|:---------------:|:-----:|
| 3.4977 | 2.2290 | 0 |
| 1.9157 | 1.8993 | 1 |
| 1.6571 | 1.8993 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hw2942/FinBERT_CN
|
hw2942
| 2023-05-23T08:21:10Z | 190 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"finance",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-23T07:52:57Z |
---
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- finance
---
|
Xoyo/ppo-Pyramids
|
Xoyo
| 2023-05-23T08:19:33Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-05-23T08:19:27Z |
---
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: Xoyo/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
lmz/rust-stable-diffusion-v2-1
|
lmz
| 2023-05-23T08:18:19Z | 0 | 8 | null |
[
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"rust",
"arxiv:2112.10752",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2022-12-11T15:15:23Z |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- rust
inference: true
extra_gated_prompt: |-
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. 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 carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
extra_gated_heading: Please read the LICENSE to access this model
---
This repository hosts weights for a Rust based version of Stable Diffusion.
These weights have been directly adapted from the
[stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1)
weights, they can be used with the
[diffusers-rs](https://github.com/LaurentMazare/diffusers-rs) crate.
To do so, checkout the diffusers-rs repo, copy the weights in the `data/`
directory and run the following command:
```bash
cargo run --example stable-diffusion --features clap -- --prompt "A rusty robot holding a fire torch."
```
This is for the image-to-text pipeline, example using the image-to-image and
inpainting pipelines can be found in the
[crate readme](https://github.com/LaurentMazare/diffusers-rs/blob/main/README.md).
## License
The license is unchanged, see the
[original version](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL).
In line with paragraph 4, the original copyright is preserved:
Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
The model details section below is copied from the runwayml version, refer to
the [original repo](https://huggingface.co/stabilityai/stable-diffusion-2-1) for
use restrictions, limitations, bias discussion etc.
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)
- **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 that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H).
- **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/), [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}
}
## Weight Extraction
The weights have been converted by downloading them from the stabilityai/stable-diffusion-2-1 repo,
and then running the following commands in the
[diffusers-rs repo](https://github.com/LaurentMazare/diffusers-rs).
After downloading the files, use Python to convert them to `npz` files.
```python
import numpy as np
import torch
model = torch.load("./vae.bin")
np.savez("./vae_v2.1.npz", **{k: v.numpy() for k, v in model.items()})
model = torch.load("./unet.bin")
np.savez("./unet_v2.1.npz", **{k: v.numpy() for k, v in model.items()})
```
Convert these `.npz` files to `.ot` files via `tensor-tools`.
```bash
cargo run --release --example tensor-tools cp ./data/vae_v2.1.npz ./data/vae_v2.1.ot
cargo run --release --example tensor-tools cp ./data/unet_v2.1.npz ./data/unet_v2.1.ot
```
|
alism98/whisper-small-persian
|
alism98
| 2023-05-23T08:13:54Z | 82 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"fa",
"en",
"dataset:mozilla-foundation/common_voice_13_0",
"license:creativeml-openrail-m",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-22T16:58:05Z |
---
license: creativeml-openrail-m
datasets:
- mozilla-foundation/common_voice_13_0
language:
- fa
- en
metrics:
- wer
- accuracy
pipeline_tag: automatic-speech-recognition
---
|
maxingenio/platzi-vit-model-massimo
|
maxingenio
| 2023-05-23T08:10:56Z | 193 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:pokemon-classification",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-05-23T07:48:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pokemon-classification
metrics:
- accuracy
model-index:
- name: platzi-vit-model-massimo
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: pokemon-classification
type: pokemon-classification
config: full
split: validation
args: full
metrics:
- name: Accuracy
type: accuracy
value: 0.08201438848920864
---
<!-- 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. -->
# platzi-vit-model-massimo
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the pokemon-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 7.8941
- Accuracy: 0.0820
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.9383 | 0.82 | 500 | 6.3834 | 0.0360 |
| 0.3399 | 1.64 | 1000 | 7.1051 | 0.0755 |
| 0.0749 | 2.46 | 1500 | 7.6120 | 0.0885 |
| 0.0332 | 3.28 | 2000 | 7.8941 | 0.0820 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
zinn/varra
|
zinn
| 2023-05-23T08:06:55Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-23T08:02:42Z |
---
license: creativeml-openrail-m
---
|
MrNoOne/ppo-Huggy
|
MrNoOne
| 2023-05-23T07:53:26Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-05-23T07:53:18Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: MrNoOne/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
KatarLegacy/JapaneseDollLikeness_v15
|
KatarLegacy
| 2023-05-23T07:53:06Z | 0 | 3 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-23T07:51:13Z |
---
license: creativeml-openrail-m
---
|
lmz/rust-stable-diffusion-v1-5
|
lmz
| 2023-05-23T07:52:40Z | 0 | 7 | null |
[
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"rust",
"arxiv:2112.10752",
"arxiv:2103.00020",
"arxiv:2205.11487",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2022-11-19T15:38:42Z |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- rust
inference: true
extra_gated_prompt: |-
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. 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 carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
extra_gated_heading: Please read the LICENSE to access this model
---
This repository hosts weights for a Rust based version of Stable Diffusion.
These weights have been directly adapted from the
[runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)
weights, they can be used with the
[diffusers-rs](https://github.com/LaurentMazare/diffusers-rs) crate.
To do so, checkout the diffusers-rs repo, copy the weights in the `data/`
directory and run the following command:
```bash
cargo run --example stable-diffusion --features clap -- --prompt "A rusty robot holding a fire torch."
```
This is for the image-to-text pipeline, example using the image-to-image and
inpainting pipelines can be found in the
[crate readme](https://github.com/LaurentMazare/diffusers-rs/blob/main/README.md).
## License
The license is unchanged, see the
[original version](https://huggingface.co/spaces/CompVis/stable-diffusion-license).
In line with paragraph 4, the original copyright is preserved:
Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
The model details section below is copied from the runwayml version, refer to
the [original repo](https://huggingface.co/runwayml/stable-diffusion-v1-5) for
use restrictions, limitations, bias discussion etc.
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
## Weight Extraction
The weights have been converted by downloading them from the runwayml/stable-diffusion-v1.5 repo,
and then running the following commands in the
[diffusers-rs repo](https://github.com/LaurentMazare/diffusers-rs).
After downloading the files, use Python to convert them to `npz` files.
```python
import numpy as np
import torch
model = torch.load("./vae.bin")
np.savez("./vae.npz", **{k: v.numpy() for k, v in model.items()})
model = torch.load("./unet.bin")
np.savez("./unet.npz", **{k: v.numpy() for k, v in model.items()})
```
Convert these `.npz` files to `.ot` files via `tensor-tools`.
```bash
cargo run --release --example tensor-tools cp ./data/vae.npz ./data/vae.ot
cargo run --release --example tensor-tools cp ./data/unet.npz ./data/unet.ot
```
|
rifkiaputri/mt5-base-id-finetune-unans-qg
|
rifkiaputri
| 2023-05-23T07:48:31Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question-generation",
"id",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-11T05:13:59Z |
---
language: id
tags:
- mt5
- question-generation
license: mit
---
# mt5-base for Indonesian Unanswerable Question Generation (cased)
[mT5-base](https://huggingface.co/google/mt5-base) model fine-tuned on machine-translated SQuAD 2.0 dataset for generating unanswerable questions in Indonesian. Please refer to [this paper](https://aclanthology.org/2022.emnlp-main.465/) for more details on the model.
## Citation Info
```bibtex
@inproceedings{putri-oh-2022-idk,
title = "{IDK}-{MRC}: Unanswerable Questions for {I}ndonesian Machine Reading Comprehension",
author = "Putri, Rifki Afina and
Oh, Alice",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.465",
pages = "6918--6933",
}
```
|
chenyanjin/codeparrot-ds
|
chenyanjin
| 2023-05-23T07:46:47Z | 145 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-23T07:17:13Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
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. -->
# codeparrot-ds
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
KatarLegacy/koreanDollLikenessv20
|
KatarLegacy
| 2023-05-23T07:44:23Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-23T07:42:30Z |
---
license: creativeml-openrail-m
---
|
tlsalfm820/wav2vec2-base-librispeech-demo-colab
|
tlsalfm820
| 2023-05-23T07:24:27Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-16T01:19:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-librispeech-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-librispeech-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2489
- Wer: 0.1673
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.7801 | 2.2 | 500 | 2.1946 | 1.0068 |
| 0.7656 | 4.41 | 1000 | 0.3422 | 0.3137 |
| 0.3193 | 6.61 | 1500 | 0.2528 | 0.2327 |
| 0.2202 | 8.81 | 2000 | 0.2141 | 0.2121 |
| 0.1703 | 11.01 | 2500 | 0.2121 | 0.1966 |
| 0.1388 | 13.22 | 3000 | 0.3337 | 0.2325 |
| 0.119 | 15.42 | 3500 | 0.2342 | 0.1847 |
| 0.0992 | 17.62 | 4000 | 0.2356 | 0.1785 |
| 0.0875 | 19.82 | 4500 | 0.2534 | 0.1810 |
| 0.0769 | 22.03 | 5000 | 0.2491 | 0.1765 |
| 0.0661 | 24.23 | 5500 | 0.2513 | 0.1710 |
| 0.0587 | 26.43 | 6000 | 0.2546 | 0.1686 |
| 0.0544 | 28.63 | 6500 | 0.2489 | 0.1673 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
csukuangfj/sherpa-onnx-conformer-zh-2023-05-23
|
csukuangfj
| 2023-05-23T07:16:01Z | 0 | 0 | null |
[
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2023-05-23T04:01:47Z |
---
license: apache-2.0
---
# Introduction
Models from this repo are converted from
https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless5_offline
which is trained using
https://github.com/k2-fsa/icefall/pull/447
|
Wulichao/ppo-LunarLander-v2
|
Wulichao
| 2023-05-23T07:01:38Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-23T07:01:16Z |
---
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: 255.75 +/- 47.26
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
...
```
|
xzuyn/Pythia-OpenAssistant-SFT-V4-Epoch-3.5-12B-GGML
|
xzuyn
| 2023-05-23T06:54:37Z | 0 | 0 | null |
[
"gpt_neox",
"sft",
"region:us"
] | null | 2023-05-23T06:35:20Z |
---
tags:
- gpt_neox
- sft
---
# For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp)
Original Model: https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5
|
xzuyn/OpenLLaMa-300BT-Preview-7B-GGML
|
xzuyn
| 2023-05-23T06:34:30Z | 0 | 0 | null |
[
"llama",
"region:us"
] | null | 2023-05-23T06:15:58Z |
---
tags:
- llama
---
# For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp)
Original Model: https://huggingface.co/openlm-research/open_llama_7b_preview_300bt
|
Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-gptq-4bit
|
Yhyu13
| 2023-05-23T06:19:06Z | 6 | 3 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-23T05:59:21Z |
---
license: apache-2.0
---
GPTQ 4-bit no actor version for compatibility that works in textgen-webui
Generated by using scripts from https://gitee.com/yhyu13/llama_-tools
Merged weights: https://huggingface.co/Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf
Converted LLaMA weights: https://huggingface.co/Yhyu13/llama-30B-hf-openassitant
Delta weights: https://huggingface.co/OpenAssistant/oasst-rlhf-2-llama-30b-7k-steps-xor
---
OA has done a great jobs in RLHF their pre-trained weights. I must say it is tuned to spit out CoT step by step thinking without you actively prompting it to do so,
which is a feature that we observe on ChatGPT and GPT-4.
But note, it still fails at logical paradox tasks such as era of time and bird shot. But none of the LLaMA based models or any available models other than GPT-4 and Claude+ can correct answer paradox questions anyway. So OA rlhf is expected to fail at these tasks, but I do like the RLHF-ed tone which make OA's response sounds professional and proficient.
|
Shimin/LLaMA-embeeding
|
Shimin
| 2023-05-23T06:17:45Z | 0 | 0 | null |
[
"mteb",
"model-index",
"region:us"
] | null | 2023-05-23T06:12:15Z |
---
tags:
- mteb
model-index:
- name: llama-tiny
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 84.82089552238806
- type: ap
value: 52.244288754498804
- type: f1
value: 79.35565129121834
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 76.88005
- type: ap
value: 71.7558743112296
- type: f1
value: 76.75929709152336
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 36.716
- type: f1
value: 36.331656620051916
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 30.154098978087656
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 48.655981827941844
- type: mrr
value: 62.225959635931936
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 69.1874521857414
- type: cos_sim_spearman
value: 67.84973877341001
- type: euclidean_pearson
value: 63.36571115098326
- type: euclidean_spearman
value: 63.01529126784642
- type: manhattan_pearson
value: 63.691756204527984
- type: manhattan_spearman
value: 63.5966298152444
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 24.29369891574357
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 41.93000000000001
- type: f1
value: 38.45098233405931
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 87.95257637938899
- type: f1
value: 87.25507653572913
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 78.43365253077977
- type: f1
value: 55.64695289940465
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 65.90786819098857
- type: f1
value: 62.35389823052779
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.61600537995966
- type: f1
value: 66.37808715079163
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 23.00601527438721
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 21.286029353821007
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 61.34352630182002
- type: cos_sim_spearman
value: 57.3159446726199
- type: euclidean_pearson
value: 57.77383201309595
- type: euclidean_spearman
value: 56.52916341448532
- type: manhattan_pearson
value: 57.81159634093376
- type: manhattan_spearman
value: 56.58929614335256
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 42.43826552918454
- type: cos_sim_spearman
value: 42.79832498565007
- type: euclidean_pearson
value: 43.28436689786081
- type: euclidean_spearman
value: 41.85722944201275
- type: manhattan_pearson
value: 43.603582859415155
- type: manhattan_spearman
value: 42.03496823632042
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 58.098123831839864
- type: cos_sim_spearman
value: 58.76942124480372
- type: euclidean_pearson
value: 56.97457224841097
- type: euclidean_spearman
value: 57.18340478354308
- type: manhattan_pearson
value: 57.31295064497741
- type: manhattan_spearman
value: 57.493896474014484
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 56.49517429141376
- type: cos_sim_spearman
value: 53.35774064269118
- type: euclidean_pearson
value: 55.40952387726341
- type: euclidean_spearman
value: 53.229797688882066
- type: manhattan_pearson
value: 55.74150917721058
- type: manhattan_spearman
value: 53.54718901411597
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 68.72772088428103
- type: cos_sim_spearman
value: 69.22566145584167
- type: euclidean_pearson
value: 65.30796876669609
- type: euclidean_spearman
value: 67.10758913762312
- type: manhattan_pearson
value: 65.57806238071112
- type: manhattan_spearman
value: 67.39903132713971
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 58.979556654868105
- type: cos_sim_spearman
value: 58.80648121538181
- type: euclidean_pearson
value: 56.6389358556767
- type: euclidean_spearman
value: 56.57842223984996
- type: manhattan_pearson
value: 56.782732319570805
- type: manhattan_spearman
value: 56.722675792288754
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 67.99528891263441
- type: cos_sim_spearman
value: 68.604405659815
- type: euclidean_pearson
value: 65.37326812628687
- type: euclidean_spearman
value: 67.71917884234688
- type: manhattan_pearson
value: 65.49059918281816
- type: manhattan_spearman
value: 67.80712877323259
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 54.312560058176615
- type: cos_sim_spearman
value: 52.66713108890498
- type: euclidean_pearson
value: 53.872433462935966
- type: euclidean_spearman
value: 53.68953787551416
- type: manhattan_pearson
value: 54.26767414800746
- type: manhattan_spearman
value: 54.1060935491622
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 66.69386947176218
- type: mrr
value: 88.32853511775079
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.27821782178218
- type: cos_sim_ap
value: 57.55972038310294
- type: cos_sim_f1
value: 56.744186046511636
- type: cos_sim_precision
value: 67.77777777777779
- type: cos_sim_recall
value: 48.8
- type: dot_accuracy
value: 99.08514851485148
- type: dot_ap
value: 38.03171387315323
- type: dot_f1
value: 42.29492650545282
- type: dot_precision
value: 40.21641118124437
- type: dot_recall
value: 44.6
- type: euclidean_accuracy
value: 99.25445544554455
- type: euclidean_ap
value: 55.0893612229229
- type: euclidean_f1
value: 55.27950310559005
- type: euclidean_precision
value: 57.2961373390558
- type: euclidean_recall
value: 53.400000000000006
- type: manhattan_accuracy
value: 99.27029702970297
- type: manhattan_ap
value: 56.570555954231416
- type: manhattan_f1
value: 56.53825284861639
- type: manhattan_precision
value: 61.80308422301305
- type: manhattan_recall
value: 52.1
- type: max_accuracy
value: 99.27821782178218
- type: max_ap
value: 57.55972038310294
- type: max_f1
value: 56.744186046511636
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 35.549661310808844
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 39.49585587792292
- type: mrr
value: 39.614673520923525
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.296596619931705
- type: cos_sim_spearman
value: 30.615476110489308
- type: dot_pearson
value: 22.591641241858532
- type: dot_spearman
value: 23.172280120633175
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.28466327108092
- type: f1
value: 56.51136199945227
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 18.349314406029464
---
|
leonhe/q-FrozenLake-v1-4x4-noSlippery
|
leonhe
| 2023-05-23T06:10:22Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-23T06:10:19Z |
---
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="leonhe/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"])
```
|
xzuyn/LLaMa-OpenAssistant-2-Epochs-13B-GGML
|
xzuyn
| 2023-05-23T06:02:55Z | 0 | 1 | null |
[
"llama",
"region:us"
] | null | 2023-05-23T05:45:07Z |
---
tags:
- llama
---
# For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp)
Original Model: https://huggingface.co/dvruette/oasst-llama-13b-2-epochs
|
MayIBorn/ft-sd15-class-instance2
|
MayIBorn
| 2023-05-23T05:13:33Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-05-23T04:53:12Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: an identification photo of iom man
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - MayIBorn/ft-sd15-class-instance2
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on an identification photo of iom man using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
|
maxjugaad/NewModel
|
maxjugaad
| 2023-05-23T05:06:47Z | 0 | 0 | null |
[
"license:deepfloyd-if-license",
"region:us"
] | null | 2023-05-23T05:06:47Z |
---
license: deepfloyd-if-license
---
|
whang/test
|
whang
| 2023-05-23T04:59:01Z | 0 | 0 | null |
[
"aa",
"dataset:fka/awesome-chatgpt-prompts",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-23T04:58:32Z |
---
license: creativeml-openrail-m
datasets:
- fka/awesome-chatgpt-prompts
language:
- aa
---
|
asubiabre/Lunar-Lander-PPO
|
asubiabre
| 2023-05-23T04:58:12Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-23T04:58:05Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -161.08 +/- 72.39
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
zinnn/varra
|
zinnn
| 2023-05-23T04:56:47Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-23T04:49:41Z |
---
license: creativeml-openrail-m
---
|
xzuyn/GPT-NeoX-Erebus-20B-GGML
|
xzuyn
| 2023-05-23T04:47:17Z | 0 | 1 | null |
[
"gpt_neox",
"region:us"
] | null | 2023-05-23T04:09:23Z |
---
tags:
- gpt_neox
---
# For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp)
Original Model: https://huggingface.co/KoboldAI/GPT-NeoX-20B-Erebus
|
xzuyn/DistilGPT-2-Rap-82M-GGML
|
xzuyn
| 2023-05-23T04:03:39Z | 0 | 1 | null |
[
"gpt2",
"gpt-2",
"region:us"
] | null | 2023-05-23T04:01:33Z |
---
tags:
- gpt2
- gpt-2
---
# For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp)
Original Model: https://huggingface.co/dzionek/distilgpt2-rap
|
xzuyn/DialoGPT-Small-124M-GGML
|
xzuyn
| 2023-05-23T04:00:58Z | 0 | 1 | null |
[
"gpt2",
"gpt-2",
"region:us"
] | null | 2023-05-23T03:59:53Z |
---
tags:
- gpt2
- gpt-2
---
# For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp)
Original Model: https://huggingface.co/microsoft/DialoGPT-small
|
xzuyn/GPT-J-Skein-6B-GGML
|
xzuyn
| 2023-05-23T03:48:59Z | 0 | 0 | null |
[
"gptj",
"gpt-j",
"region:us"
] | null | 2023-05-23T03:33:07Z |
---
tags:
- gptj
- gpt-j
---
# For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp)
Original Model: https://huggingface.co/KoboldAI/GPT-J-6B-Skein
|
BigSalmon/InformalToFormalLincoln99Paraphrase
|
BigSalmon
| 2023-05-23T03:45:21Z | 207 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-23T02:54:21Z |
data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln99Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln99Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above):
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer]
***
microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer]
***
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
Backwards
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
accustomed to having its name uttered ______, harvard university is weathering a rare spell of reputational tumult
(a) in reverential tones
(b) with great affection
(c) in adulatory fashion
(d) in glowing terms
```
```
clarify: international ( {working together} / cooperation ) is called for when ( {issue go beyond lots of borders} / an issue transcends borders / a given matter has transnational implications ).
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
*Note* Of all the masking techniques, this one works the best.
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
essence: when someone's views are keeping within reasonable.
refine: the senator's voting record is ( moderate / centrist / pragmatic / balanced / fair-minded / even-handed ).
***
essence: when things are worked through in a petty way.
refine: the propensity of the u.s. congress to settle every dispute by way of ( mudslinging / bickering / demagoguery / name-calling / finger-pointing / vilification ) is appalling.
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
```
music before bedtime [makes for being able to relax] -> is a recipe for relaxation.
```
```
[people wanting entertainment love traveling new york city] -> travelers flock to new york city in droves, drawn to its iconic entertainment scene. [cannot blame them] -> one cannot fault them [broadway so fun] -> when it is home to such thrilling fare as Broadway.
```
```
in their ( ‖ when you are rushing because you want to get there on time ‖ / haste to arrive punctually / mad dash to be timely ), morning commuters are too rushed to whip up their own meal.
***
politicians prefer to author vague plans rather than ( ‖ when you can make a plan without many unknowns ‖ / actionable policies / concrete solutions ).
```
```
Q: What is whistleblower protection?
A: Whistleblower protection is a form of legal immunity granted to employees who expose the unethical practices of their employer.
Q: Why are whistleblower protections important?
A: Absent whistleblower protections, employees would be deterred from exposing their employer’s wrongdoing for fear of retribution.
Q: Why would an employer engage in retribution?
A: An employer who has acted unethically stands to suffer severe financial and reputational damage were their transgressions to become public. To safeguard themselves from these consequences, they might seek to dissuade employees from exposing their wrongdoing.
```
```
original: the meritocratic nature of crowdfunding [MASK] into their vision's viability.
infill: the meritocratic nature of crowdfunding [gives investors idea of how successful] -> ( offers entrepreneurs a window ) into their vision's viability.
```
```
Leadership | Lecture 17: Worker Morale
What Workers Look for in Companies:
• Benefits
o Tuition reimbursement
o Paid parental leave
o 401K matching
o Profit sharing
o Pension plans
o Free meals
• Social responsibility
o Environmental stewardship
o Charitable contributions
o Diversity
• Work-life balance
o Telecommuting
o Paid holidays and vacation
o Casual dress
• Growth opportunities
• Job security
• Competitive compensation
• Recognition
o Open-door policies
o Whistleblower protection
o Employee-of-the-month awards
o Positive performance reviews
o Bonuses
```
```
description: business
keywords: for-profit, fiduciary duty, monopolistic, bottom line, return on investment, short-term thinking, capital-intensive, self-interested, risk-taking, fiduciary duty, merger, speculation, profiteering, oversight, capitalism, diversification
```
```
3. In this task, you are given a company name and you need to find its industry.
McDonalds -- Restaurant
Facebook -- Social Network
IKEA -- Furniture
American Express -- Credit Services
Nokia -- Telecom
Nintendo -- Entertainment
4. In this task, you are given a Month and you need to convert it to its corresponding season
April -- Spring
December -- Winter
July -- Summer
October -- Fall
February -- Winter
5. In this task, you are given a sentence with a missing word and you need to predict the correct word.
Managers should set an _____ for their employees. -- example
Some people spend more than four _____ in the gym. -- hours
The police were on the _____ of arresting the suspect. -- verge
They were looking for _____ on how to solve the problem. -- guidance
What is the _____ of the coffee? -- price
6. In this task, you are given a paragraph and you need to reorder it to make it logical.
It was first proposed in 1987. The total length of the bridge is 1,828 meters. The idea of a bridge connects Hong Kong to Macau. -- The idea of bridge connecting Hong Kong and Macau was first proposed in 1987. The total length of the bridge is 1,828 meters.
It is a movie about a brave and noble policeman. The film was produced by Americans. They were Kevin Lima and Chris Buck. They are directors. The movie is called Tarzan. -- Produced by Americans Kevin Lima and Chris Buck, Tarzan is a movie about a brave and noble policeman.
It was first discovered in the mountains of India. The active ingredients in this plant can stimulate hair growth. The plant is called "Hair Plus." -- First discovered in the mountains of India, Hair Plus is a plant whose active ingredients can stimulate hair growth.
```
```
trivia: What is the population of South Korea?
response: 51 million.
***
trivia: What is the minimum voting age in the US?
response: 18.
***
trivia: What are the first ten amendments of the US constitution called?
response: Bill of Rights.
```
```
ideas: in modern-day america, it is customary for the commander-in-chief to conduct regular press conferences
related keywords: transparency, check and balance, sacrosanct, public accountability, adversarial, unscripted, direct access, open government, watchdog, healthy democracy, institutional integrity, right to know, direct line of communication, behind closed doors, updates, track progress, instill confidence, reassure, humanize, leadership style, day-to-day, forthcoming, demystify, ask hard questions
***
ideas: i know this one guy who retired so young, attesting to how careful they were with money.
related keywords: money management, resourceful, penny-pinching, live below their means, frugal, financial discipline, financial independence, conservative, long-term vision, discretionary spending, deferred gratification, preparedness, self-control, cushion
```
```
less specific: actors and musicians should ( support democracy ).
clarifies: actors and musicians should ( wield their celebrity to amplify pro-democracy messaging / marshal their considerable influence in the service of the democratic cause ).
***
less specific: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( be careful ).
clarifies: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( master their desires / exercise self-restraint / embrace frugality / restrain their appetite for splendor ).
```
```
dull: clean
emotional heft: spotless, immaculate, pristine
***
dull: hot
emotional heft: scorching, searing, blistering
***
dull: happy
emotional heft: euphoric
```
```
text: {guide: vividly describe the premise of the show "seinfield"} -> set in the heart of bustling new york city, the sitcom "seinfield" follows the everyday {restrict: term that implies they get into trouble but in a comical way} -> misadventures of four neurotic friends. on any given episode, one can find them quarreling over their favorite diner's latest menu change, haggling over the division of household expenses, or contriving a scheme to spy on the apartment's newest resident. mundane as their exploits may be, they never fail to elicit a hearty laugh. {guide: mention how larry david is responsible} -> behind the show's witty, incisive dialogue lies the sharp pen of larry david, who co-created the show with jerry seinfeld. {guide: mention how larry david came up with the most recognizable lines} -> it is his genius that conjured such instantly {restrict: term that imply everybody knows them} -> recognizable quips as "no soup for you!" and "not that there's anything wrong with that!". {guide: mention how humanity should revel in having such good comedians these days} -> as this list of laugh-out-loud lines attests, the world is fortunate to ( count such a sharp comedic mind among its ranks / have such a talented humorist in its midst / have such comedic talent gracing its airwaves ).
```
```
system: i'm not sure i agree with your assessment of the situation.
user: replace "assessment" with a more casual term.
chatbot: ( take / read / view ).
system: i've been feeling really busy lately, trying to balance work and family responsibilities.
user: replace "busy" with a more intense word that conveys it is too much.
chatbot: overwhelmed.
system: the candidate's stance on immigration is quite nebulous, and it's difficult to discern exactly what they would do if elected.
user: replace "nebulous" with a word that conveys a lack of detail.
chatbot: ( vague / ambiguous ).
```
```
user: exorbitant in price, scholarly journals unconscionably define the breadth of one's knowledge awareness by the contents of their wallet. [replace “knowledge awareness” with a more natural expression]
chatbot: intellectual horizons.
user: can you do another alternative to “intellectual horizons” that has more relation to “scholarly journals”?
chatbot: academic enlightenment.
```
```
key: calculate.
syn: estimate, consider, weigh, number, count, apportion, proportion, investigate, reckon, rate, compute.
ant: guess, conjecture, hit, chance, risk, stake, miscalculate.
```
```
description: more forceful version of curious that is less forceful than nosy
answer: inquisitive
description: more forceful version of hopeful that is less forceful than overconfident
answer: optimistic
```
|
SHENMU007/speechcommand-demo
|
SHENMU007
| 2023-05-23T03:30:04Z | 157 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-05-23T02:41:32Z |
---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: speechcommand-demo
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. -->
# speechcommand-demo
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0873
- Accuracy: 0.9809
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- 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: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6433 | 1.0 | 399 | 0.4979 | 0.9112 |
| 0.2406 | 2.0 | 798 | 0.1455 | 0.9750 |
| 0.1563 | 3.0 | 1197 | 0.1032 | 0.9785 |
| 0.1144 | 4.0 | 1597 | 0.0919 | 0.9806 |
| 0.1254 | 5.0 | 1995 | 0.0873 | 0.9809 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
xzuyn/Koishi-Instruct-3B-GGML
|
xzuyn
| 2023-05-23T03:13:21Z | 0 | 0 | null |
[
"gpt_neox",
"region:us"
] | null | 2023-05-23T03:02:40Z |
---
tags:
- gpt_neox
---
# For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp)
Original Model: https://huggingface.co/ewof/koishi-instruct-3b
|
4bit/pyg-7b
|
4bit
| 2023-05-23T03:07:43Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text generation",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-05-23T02:50:34Z |
---
language:
- en
thumbnail: null
tags:
- text generation
- conversational
pipeline_tag: text-generation
inference: false
---
<h1 style="text-align: center">Pygmalion 7B</h1>
<h2 style="text-align: center">A conversational LLaMA fine-tune.</h2>
## Model Details
Converted from the XORs weights from PygmalionAI's release https://huggingface.co/PygmalionAI/pygmalion-7b
Pygmalion 7B is a dialogue model based on Meta's LLaMA-7B.
This is version 1. It has been fine-tuned using a subset of the data from Pygmalion-6B-v8-pt4, for those of you familiar with the project.
## Prompting
The model was trained on the usual Pygmalion persona + chat format, so any of the usual UIs should already handle everything correctly. If you're using the model directly, this is the expected formatting:
```
[CHARACTER]'s Persona: [A few sentences about the character you want the model to play]
<START>
[DIALOGUE HISTORY]
You: [User's input message here]
[CHARACTER]:
```
Where `[CHARACTER]` is, as you can probably guess, the name of the character you want the model to portray, `<START>` should be used verbatim as a delimiter token to separate persona and scenario data from the dialogue, and `[DIALOGUE HISTORY]` is a sliding window of chat history so the model can have conversational context to draw from. Here's a concrete example:
```
Assistant's Persona: Assistant is a highly intelligent language model trained to comply with user requests.
<START>
Assistant: Hello! How may I help you today?
You: What is Zork?
Assistant:
```
Which will generate something like:
```
Zork is an interactive fiction computer game created in the 1970s by Infocom, Inc., which was later acquired by Activision Blizzard. It is widely considered one of the most influential games ever made and has been credited with popularizing text-based adventure games. The original version of Zork was written in the programming language MACRO-10, but it was ported to many other platforms over the years."
```
The model will automatically emit an end-of-text token (`</s>`) when it judges that the response is complete.
## Limitations and biases
The intended use-case for this model is fictional conversation for entertainment purposes. Any other sort of usage is out of scope.
As such, it was **not** fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading.
|
sayakpaul/masked-lm-tpu
|
sayakpaul
| 2023-05-23T02:55:40Z | 65 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-05-21T06:47:41Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sayakpaul/masked-lm-tpu
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. -->
# sayakpaul/masked-lm-tpu
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 9.9067
- Train Accuracy: 0.0116
- Validation Loss: 9.8225
- Validation Accuracy: 0.0198
- Epoch: 8
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.0001, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 22325, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1175, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 10.2116 | 0.0 | 10.1957 | 0.0000 | 0 |
| 10.2017 | 0.0 | 10.1798 | 0.0 | 1 |
| 10.1890 | 0.0000 | 10.1604 | 0.0000 | 2 |
| 10.1733 | 0.0000 | 10.1145 | 0.0000 | 3 |
| 10.1336 | 0.0000 | 10.0666 | 0.0000 | 4 |
| 10.0906 | 0.0001 | 10.0200 | 0.0005 | 5 |
| 10.0360 | 0.0006 | 9.9646 | 0.0049 | 6 |
| 9.9830 | 0.0038 | 9.8938 | 0.0155 | 7 |
| 9.9067 | 0.0116 | 9.8225 | 0.0198 | 8 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
kenkliesner/transformer_1_model
|
kenkliesner
| 2023-05-23T02:49:47Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-23T01:57:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: transformer_1_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9296
---
<!-- 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. -->
# transformer_1_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2347
- Accuracy: 0.9296
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2312 | 1.0 | 1563 | 0.1932 | 0.9261 |
| 0.1515 | 2.0 | 3126 | 0.2347 | 0.9296 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
yy-zm/00
|
yy-zm
| 2023-05-23T02:49:37Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-23T02:49:37Z |
---
license: creativeml-openrail-m
---
|
mirfan899/da_ner
|
mirfan899
| 2023-05-23T02:49:18Z | 0 | 0 |
spacy
|
[
"spacy",
"token-classification",
"da",
"model-index",
"region:us"
] |
token-classification
| 2023-03-28T02:30:30Z |
---
tags:
- spacy
- token-classification
language:
- da
model-index:
- name: da_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9453630482
- name: NER Recall
type: recall
value: 0.9094052559
- name: NER F Score
type: f_score
value: 0.927035601
---
| Feature | Description |
| --- | --- |
| **Name** | `da_ner` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.5.1,<3.6.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (36 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `ADVERTISING`, `AMOUNTS_OF_THE_PRODUCT`, `AVAILABILITY`, `BRANDING`, `CUSTOMERS`, `DISCOUNTS_AND_OFFERS`, `DOCUMENTATION`, `EMPLOYEES`, `EXTERNAL_SUPPLIER`, `FACILITIES`, `FINANCING`, `HANDLING_OF_SERVICE`, `LEASING`, `LEGAL`, `LOCATIONS`, `LOCATION_IN_THE_STORE`, `LOGISTICS`, `MARKETING`, `MARKET_COVERAGE`, `MEDIA`, `MESSAGES`, `ORGANIZATIONAL_STRUCTURE`, `PAYMENT_TERMS`, `PR`, `PRICE`, `PRICE_STRATEGIES`, `PRODUCT_PROPERTIES`, `PRODUCT_TYPE`, `PRODUCT_WARRANTY`, `REFERENCES`, `RETURN_ON_INVESTMENT`, `SALES_PROCESS`, `SHOWROOM`, `THE_MANAGEMENT`, `UNIFORMITY_IN_DELIVERIES`, `USE_OF_THE_PRODUCT` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 92.70 |
| `ENTS_P` | 94.54 |
| `ENTS_R` | 90.94 |
| `TOK2VEC_LOSS` | 50522.21 |
| `NER_LOSS` | 55212.43 |
|
nolanaatama/ysbrvc1000pchkjv
|
nolanaatama
| 2023-05-23T02:47:23Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-23T02:40:03Z |
---
license: creativeml-openrail-m
---
|
xzuyn/CodeGPT-Small-Py-117M-GGML
|
xzuyn
| 2023-05-23T02:45:30Z | 0 | 0 | null |
[
"gpt2",
"gpt-2",
"region:us"
] | null | 2023-05-23T02:43:00Z |
---
tags:
- gpt2
- gpt-2
---
# For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp)
Original Model: https://huggingface.co/microsoft/CodeGPT-small-py
|
redax123/valcroanime
|
redax123
| 2023-05-23T02:43:31Z | 29 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-23T02:37:53Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### valcroanime Dreambooth model trained by redax123 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
xzuyn/RWKV-4-Raven-3B-v11-Eng99-Other1-20230425-ctx4096-GGML
|
xzuyn
| 2023-05-23T02:39:17Z | 0 | 1 | null |
[
"rwkv",
"region:us"
] | null | 2023-05-23T02:31:26Z |
---
tags:
- rwkv
---
# For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp)
Original Model: https://huggingface.co/BlinkDL/rwkv-4-raven
|
Xoyo/Reinforce-Pixelcopter-PLE-v0
|
Xoyo
| 2023-05-23T02:36:01Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-23T02:35:18Z |
---
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: 13.10 +/- 11.52
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
|
Jocheareong/Sceince
|
Jocheareong
| 2023-05-23T02:21:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-05-23T02:11:21Z |
------------
tags꼬리표::
- /dog- /개- /dog- /dogs- /dog- /dog- /dog- /dogs- /dog- /dog- /dog- /dog- /dog- /dog- /dog-- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog-- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog-- /dog- /dog- /dog- /dog- /dog- /dog- - - /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog-dog- /dog- /dog-dog-dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog-dog- /dog- /dog-dog-dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog/dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- - /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog-dog- /dog- /dog-dog-dog- /dog- /dog- /dog-dog-dog-dog- /dog- /dog- /d/dog- /dog- /dog- /dog-dog- /dog- /dog-dog-dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog/dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog-dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog-dog- /dog- /dog-dog-dog- /dog- /dog- /dog-dog-dog-dog- /dog- /dog- /d/dog- /dog- /dog- /dog-dog- /dog- /dog-dog-dog- /dog- /dog- /dog-dog-dog-dog- /dog- /dog- /d/dog-dog-dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog/dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog-dog-dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog-dog- /dog-dog- /dog-dog-dog- /dog-dog-dog-dog-dog-/dog-dog-dog-dog-dog-dog-dog-dog-dog-/dog-dog- /dog-dog- /dog- /dog-/dog-dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog-dog- /dog-dog- /dog-dog- /dog-dog-dog-dog-dog- /dog-dog-dog-dog-dog-dog- /dog- /dog- //dog-dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog- /dog-dog- /dog-dog- /dog-dog-dog- /dog-dog-dog-dog-/dog-dog-dog-dog-dog-dog-dog-dog-/dog-dog-
------------
|
xzuyn/OpenLLaMa-400BT-Preview-7B-GGML
|
xzuyn
| 2023-05-23T02:04:22Z | 0 | 0 | null |
[
"llama",
"region:us"
] | null | 2023-05-23T01:48:48Z |
---
tags:
- llama
---
# For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp)
Original Model: https://huggingface.co/openlm-research/open_llama_7b_400bt_preview
|
YakovElm/Qt10SetFitModel
|
YakovElm
| 2023-05-23T01:47:54Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-05-23T01:44:54Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# YakovElm/Qt10SetFitModel
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("YakovElm/Qt10SetFitModel")
# 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}
}
```
|
Monero/Guanaco-13b-Merged-8bit
|
Monero
| 2023-05-23T01:44:47Z | 5 | 3 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"dataset:timdettmers/guanaco-13b",
"dataset:JosephusCheung/GuanacoDataset",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-23T01:09:26Z |
---
datasets:
- timdettmers/guanaco-13b
- JosephusCheung/GuanacoDataset
---
<center><h1><b>Guanaco</b> - Generative Universal Assistant for Natural-language Adaptive Context-aware Omnilingual outputs</h1></center>
<p><strong><font size="5">Information</font></strong></p>
Guanaco 13b LoRa from timdettmers/guanaco-13b that was merged to Llama 13b and is compatible with transformers 4.28.0
<br>This was made using https://huggingface.co/timdettmers/guanaco-13b and https://huggingface.co/datasets/JosephusCheung/GuanacoDataset
The details of the guanaco dataset and parameters of the LoRa that Tim Dettmers' released is not available at this time.
<html>
<head>
<style>
table {
border:1px solid #b3adad;
border-collapse:collapse;
padding:5px;
}
table th {
border:1px solid #b3adad;
padding:5px;
background: #f0f0f0;
color: #313030;
}
table td {
border:1px solid #b3adad;
text-align:center;
padding:5px;
background: #ffffff;
color: #313030;
}
</style>
</head>
<body>
<table>
<thead>
<tr>
<th>Model:</th>
<th>Wikitext2</th>
<th>Ptb-New</th>
<th>C4-New</th>
</tr>
</thead>
<tbody>
<tr>
<td>Guanaco 13b 8bit</td>
<td>5.771384239196777</td>
<td>10.377276420593262</td>
<td></td>
</tr>
</tbody>
</table>
</body>
</html>
More information can be found here and below: https://huggingface.co/datasets/JosephusCheung/GuanacoDataset
Below is a description of Guanaco from https://guanaco-model.github.io/:
Guanaco is an advanced instruction-following language model built on Meta's LLaMA 13B model. Expanding upon the initial 52K dataset from the Alpaca model, an additional 534,530 entries have been incorporated, covering English, Simplified Chinese, Traditional Chinese (Taiwan), Traditional Chinese (Hong Kong), Japanese, Deutsch, and various linguistic and grammatical tasks. This wealth of data enables Guanaco to perform exceptionally well in multilingual environments.
In an effort to foster openness and replicability in research, we have made the [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) publicly accessible and released the [model weights](https://huggingface.co/JosephusCheung/Guanaco). By providing these resources, we aim to inspire more researchers to pursue related research and collectively advance the development of instruction-following language models.
When utilizing the Guanaco model, please bear in mind the following points:
* The Guanaco model has not been filtered for harmful, biased, or explicit content. As a result, outputs that do not adhere to ethical norms may be generated during use. Please exercise caution when using the model in research or practical applications.
1\. Improved context and prompt role support:
---------------------------------------------
The new format is designed to be similar to ChatGPT, allowing for better integration with the Alpaca format and enhancing the overall user experience.
Instruction is utilized as a few-shot context to support diverse inputs and responses, making it easier for the model to understand and provide accurate responses to user queries.
The format is as follows:
### Instruction:
User: History User Input
Assistant: History Assistant Answer
### Input:
System: Knowledge
User: New User Input
### Response:
New Assistant Answer
This structured format allows for easier tracking of the conversation history and maintaining context throughout a multi-turn dialogue.
2\. Role-playing support:
-------------------------
Guanaco now offers advanced role-playing support, similar to Character.AI, in English, Simplified Chinese, Traditional Chinese, Japanese, and Deutsch, making it more versatile for users from different linguistic backgrounds.
Users can instruct the model to assume specific roles, historical figures, or fictional characters, as well as personalities based on their input. This allows for more engaging and immersive conversations.
The model can use various sources of information to provide knowledge and context for the character's background and behavior, such as encyclopedic entries, first-person narrations, or a list of personality traits.
The model will consistently output responses in the format "Character Name: Reply" to maintain the chosen role throughout the conversation, enhancing the user's experience.
3\. Rejection of answers and avoidance of erroneous responses:
--------------------------------------------------------------
The model has been updated to handle situations where it lacks sufficient knowledge or is unable to provide a valid response more effectively.
Reserved keywords have been introduced to indicate different scenarios and provide clearer communication with the user:
* NO IDEA: Indicates that the model lacks the necessary knowledge to provide an accurate answer, and will explain this to the user, encouraging them to seek alternative sources.
* FORBIDDEN: Indicates that the model refuses to answer due to specific reasons (e.g., legal, ethical, or safety concerns), which will be inferred based on the context of the query.
* SFW: Indicates that the model refuses to answer a question because it has been filtered for NSFW content, ensuring a safer and more appropriate user experience.
4\. Continuation of responses for ongoing topics:
-------------------------------------------------
The Guanaco model can now continue answering questions or discussing topics upon the user's request, making it more adaptable and better suited for extended conversations.
The contextual structure consisting of System, Assistant, and User roles allows the model to engage in multi-turn dialogues, maintain context-aware conversations, and provide more coherent responses.
The model can now accommodate role specification and character settings, providing a more immersive and tailored conversational experience based on the user's preferences.
It is important to remember that Guanaco is a 7B-parameter model, and any knowledge-based content should be considered potentially inaccurate. We strongly recommend providing verifiable sources, such as Wikipedia, for knowledge-based answers. In the absence of sources, it is crucial to inform users of this limitation to prevent the dissemination of false information and to maintain transparency.
5\. Multimodal Visual Question Answering (VQA) Support:
-------------------------------------------------------
Guanaco expands its capabilities into the realm of multimodal interactions, now offering support for Visual Question Answering (VQA). The model achieves this by integrating data from the blip2-flan-t5-xxl for multilingual VQA tasks, marking a significant milestone in the development of multimodal chatbots.
This new feature allows the model to interpret and respond to queries that involve both text and visual inputs, providing a richer, more interactive, and comprehensive user experience. Users can now ask questions about an image, and the model will analyze the visual content in conjunction with the textual query to provide a response.
A noteworthy addition is the [Guanaco VQA Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoVQADataset), publicly accessible now.
Now as a multimodal chatbot, Guanaco can bridge the gap between visual and linguistic understanding, making it an incredibly versatile tool for a wide array of applications.
However, as always, we encourage responsible and ethical use of this model. Please note that while Guanaco strives to provide accurate and helpful responses, it is still crucial to cross-verify the information from reliable sources for knowledge-based queries.
|
xzuyn/RedPajama-INCITE-Instruct-v0.1-7B-GGML
|
xzuyn
| 2023-05-23T00:39:42Z | 0 | 1 | null |
[
"gpt_neox",
"region:us"
] | null | 2023-05-23T00:17:06Z |
---
tags:
- gpt_neox
---
# For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp)
Original Model: https://huggingface.co/togethercomputer/RedPajama-INCITE-Instruct-7B-v0.1
|
cmpatino/ppo-SnowballTarget
|
cmpatino
| 2023-05-23T00:11:48Z | 20 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-05-23T00:11:43Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: cmpatino/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
isnekki/Xensword-T5-Base-Summarizer
|
isnekki
| 2023-05-22T23:34:38Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-22T23:21:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: Xensword-T5-Base-Summarizer
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. -->
# Xensword-T5-Base-Summarizer
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0029
- Rouge1: 0.1594
- Rouge2: 0.0664
- Rougel: 0.1405
- Rougelsum: 0.14
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 157 | 2.1287 | 0.157 | 0.0654 | 0.1388 | 0.1382 | 19.0 |
| No log | 2.0 | 314 | 2.0431 | 0.1613 | 0.0672 | 0.1419 | 0.1415 | 19.0 |
| No log | 3.0 | 471 | 2.0179 | 0.1593 | 0.0665 | 0.1406 | 0.1401 | 19.0 |
| 2.2552 | 4.0 | 628 | 2.0029 | 0.1594 | 0.0664 | 0.1405 | 0.14 | 19.0 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AlbertoFor/wav2vec2-common_voice-it-demo
|
AlbertoFor
| 2023-05-22T23:30:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"it",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-22T18:41:48Z |
---
language:
- it
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-common_voice-it-demo
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: COMMON_VOICE - IT
type: common_voice
config: it
split: test[80%:]
args: 'Config: it, Training split: train[85%:], Eval split: test[80%:]'
metrics:
- name: Wer
type: wer
value: 0.23675718221172767
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-common_voice-it-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - IT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3484
- Wer: 0.2368
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| No log | 0.37 | 400 | 0.9124 | 0.7336 |
| 3.904 | 0.74 | 800 | 0.4753 | 0.5022 |
| 0.4384 | 1.1 | 1200 | 0.3941 | 0.3731 |
| 0.2985 | 1.47 | 1600 | 0.4007 | 0.3830 |
| 0.2719 | 1.84 | 2000 | 0.3576 | 0.3597 |
| 0.2719 | 2.21 | 2400 | 0.3571 | 0.3286 |
| 0.2158 | 2.57 | 2800 | 0.3465 | 0.3198 |
| 0.2054 | 2.94 | 3200 | 0.3162 | 0.2982 |
| 0.1783 | 3.31 | 3600 | 0.3295 | 0.3089 |
| 0.1495 | 3.68 | 4000 | 0.3248 | 0.3034 |
| 0.1495 | 4.04 | 4400 | 0.3101 | 0.3028 |
| 0.1397 | 4.41 | 4800 | 0.3588 | 0.3006 |
| 0.123 | 4.78 | 5200 | 0.3451 | 0.3041 |
| 0.115 | 5.15 | 5600 | 0.3333 | 0.2921 |
| 0.0947 | 5.51 | 6000 | 0.3331 | 0.2858 |
| 0.0947 | 5.88 | 6400 | 0.3536 | 0.2950 |
| 0.0952 | 6.25 | 6800 | 0.3344 | 0.2786 |
| 0.0778 | 6.62 | 7200 | 0.3363 | 0.2699 |
| 0.0744 | 6.99 | 7600 | 0.3246 | 0.2655 |
| 0.0648 | 7.35 | 8000 | 0.3390 | 0.2627 |
| 0.0648 | 7.72 | 8400 | 0.3405 | 0.2630 |
| 0.0591 | 8.09 | 8800 | 0.3367 | 0.2534 |
| 0.0527 | 8.46 | 9200 | 0.3448 | 0.2509 |
| 0.0461 | 8.82 | 9600 | 0.3379 | 0.2425 |
| 0.0408 | 9.19 | 10000 | 0.3491 | 0.2409 |
| 0.0408 | 9.56 | 10400 | 0.3456 | 0.2377 |
| 0.0393 | 9.93 | 10800 | 0.3488 | 0.2370 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.11.0
|
amqdn/bloomz-legalsupport-lm-lora
|
amqdn
| 2023-05-22T22:46:09Z | 0 | 0 | null |
[
"dataset:lighteval/LegalSupport",
"region:us"
] | null | 2023-05-22T22:09:53Z |
---
datasets:
- lighteval/LegalSupport
---
## Info
BLOOMZ-3b fine-tuned on legal language. *Experimental purposes only!* This fine-tuning makes no attempt to understand the true nature of the dataset. It simply attempts to assess the performance of basic prompt engineering on BLOOMZ-3b for legal language.
## Example Output
### INSTRUCTION
Below are two legal citations. Please generate a summary judgment.
### Citation A:
United States v. Mitchell, 502 F.3d 931, 991 (9th Cir.2007) (approving of district court not allowing witnesses to offer opinions on what jury’s verdict should be); United States v. Caro, 461 F.Supp.2d. 459, 465 (W.D.Va.2006) (“[A]n express plea for mercy to the jury from a defendant’s witness is not mitigating evidence that could aid the jury in their decision making.”); see also Stenson v. Lambert, 504 F.3d 873, 892 (9th Cir.2007) (noting there are no federal cases requiring the admission of execution impact testimony); Jackson v. Dretke, 450 F.3d 614, 617-18 (5th Cir.2006); but see Jackson, 450 F.3d at 620 (Dennis, J., dissenting).
### Citation B:
United States v. Mitchell, 502 F.3d 931, 991 (9th Cir.2007) (approving of district court not allowing witnesses to offer opinions on what jury’s verdict should be); United States v. Caro, 461 F.Supp.2d. 459, 465 (W.D.Va.2006) (“[A]n express plea for mercy to the jury from a defendant’s witness is not mitigating evidence that could aid the jury in their decision making.”); see also Stenson v. Lambert, 504 F.3d 873, 892 (9th Cir.2007) (noting there are no federal cases requiring the admission of execution impact testimony); Jackson v. Dretke, 450 F.3d 614, 617-18 (5th Cir.2006); but see Jackson, 450 F.3d at 620 (Dennis, J., dissenting).
### Judgment:
The district court's decision to allow the testimony of the defendant's execution impact witness is not mitigating evidence. (Model repeats for as many tokens as possible.)
|
Monero/Guanaco-13b-Merged-4bit
|
Monero
| 2023-05-22T22:20:39Z | 7 | 1 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"dataset:timdettmers/guanaco-13b",
"dataset:JosephusCheung/GuanacoDataset",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-22T20:08:06Z |
---
datasets:
- timdettmers/guanaco-13b
- JosephusCheung/GuanacoDataset
---
<center><h1><b>Guanaco</b> - Generative Universal Assistant for Natural-language Adaptive Context-aware Omnilingual outputs</h1></center>
<p><strong><font size="5">Information</font></strong></p>
Guanaco 13b LoRa from timdettmers/guanaco-13b that was merged to Llama 13b and is compatible with transformers 4.28.0
<br>This was made using https://huggingface.co/timdettmers/guanaco-13b and https://huggingface.co/datasets/JosephusCheung/GuanacoDataset
The details of the guanaco dataset and parameters of the LoRa that Tim Dettmers' released is not available at this time.
<html>
<head>
<style>
table {
border:1px solid #b3adad;
border-collapse:collapse;
padding:5px;
}
table th {
border:1px solid #b3adad;
padding:5px;
background: #f0f0f0;
color: #313030;
}
table td {
border:1px solid #b3adad;
text-align:center;
padding:5px;
background: #ffffff;
color: #313030;
}
</style>
</head>
<body>
<table>
<thead>
<tr>
<th>Model:</th>
<th>Wikitext2</th>
<th>Ptb-New</th>
<th>C4-New</th>
</tr>
</thead>
<tbody>
<tr>
<td>Guanaco 13b 4bit TS 128g</td>
<td>5.960791110992432</td>
<td>10.849588394165039</td>
<td>7.968928813934326</td>
</tr>
</tbody>
</table>
</body>
</html>
More information can be found here and below: https://huggingface.co/datasets/JosephusCheung/GuanacoDataset
Below is a description of Guanaco from https://guanaco-model.github.io/:
Guanaco is an advanced instruction-following language model built on Meta's LLaMA 13B model. Expanding upon the initial 52K dataset from the Alpaca model, an additional 534,530 entries have been incorporated, covering English, Simplified Chinese, Traditional Chinese (Taiwan), Traditional Chinese (Hong Kong), Japanese, Deutsch, and various linguistic and grammatical tasks. This wealth of data enables Guanaco to perform exceptionally well in multilingual environments.
In an effort to foster openness and replicability in research, we have made the [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) publicly accessible and released the [model weights](https://huggingface.co/JosephusCheung/Guanaco). By providing these resources, we aim to inspire more researchers to pursue related research and collectively advance the development of instruction-following language models.
When utilizing the Guanaco model, please bear in mind the following points:
* The Guanaco model has not been filtered for harmful, biased, or explicit content. As a result, outputs that do not adhere to ethical norms may be generated during use. Please exercise caution when using the model in research or practical applications.
1\. Improved context and prompt role support:
---------------------------------------------
The new format is designed to be similar to ChatGPT, allowing for better integration with the Alpaca format and enhancing the overall user experience.
Instruction is utilized as a few-shot context to support diverse inputs and responses, making it easier for the model to understand and provide accurate responses to user queries.
The format is as follows:
### Instruction:
User: History User Input
Assistant: History Assistant Answer
### Input:
System: Knowledge
User: New User Input
### Response:
New Assistant Answer
This structured format allows for easier tracking of the conversation history and maintaining context throughout a multi-turn dialogue.
2\. Role-playing support:
-------------------------
Guanaco now offers advanced role-playing support, similar to Character.AI, in English, Simplified Chinese, Traditional Chinese, Japanese, and Deutsch, making it more versatile for users from different linguistic backgrounds.
Users can instruct the model to assume specific roles, historical figures, or fictional characters, as well as personalities based on their input. This allows for more engaging and immersive conversations.
The model can use various sources of information to provide knowledge and context for the character's background and behavior, such as encyclopedic entries, first-person narrations, or a list of personality traits.
The model will consistently output responses in the format "Character Name: Reply" to maintain the chosen role throughout the conversation, enhancing the user's experience.
3\. Rejection of answers and avoidance of erroneous responses:
--------------------------------------------------------------
The model has been updated to handle situations where it lacks sufficient knowledge or is unable to provide a valid response more effectively.
Reserved keywords have been introduced to indicate different scenarios and provide clearer communication with the user:
* NO IDEA: Indicates that the model lacks the necessary knowledge to provide an accurate answer, and will explain this to the user, encouraging them to seek alternative sources.
* FORBIDDEN: Indicates that the model refuses to answer due to specific reasons (e.g., legal, ethical, or safety concerns), which will be inferred based on the context of the query.
* SFW: Indicates that the model refuses to answer a question because it has been filtered for NSFW content, ensuring a safer and more appropriate user experience.
4\. Continuation of responses for ongoing topics:
-------------------------------------------------
The Guanaco model can now continue answering questions or discussing topics upon the user's request, making it more adaptable and better suited for extended conversations.
The contextual structure consisting of System, Assistant, and User roles allows the model to engage in multi-turn dialogues, maintain context-aware conversations, and provide more coherent responses.
The model can now accommodate role specification and character settings, providing a more immersive and tailored conversational experience based on the user's preferences.
It is important to remember that Guanaco is a 7B-parameter model, and any knowledge-based content should be considered potentially inaccurate. We strongly recommend providing verifiable sources, such as Wikipedia, for knowledge-based answers. In the absence of sources, it is crucial to inform users of this limitation to prevent the dissemination of false information and to maintain transparency.
5\. Multimodal Visual Question Answering (VQA) Support:
-------------------------------------------------------
Guanaco expands its capabilities into the realm of multimodal interactions, now offering support for Visual Question Answering (VQA). The model achieves this by integrating data from the blip2-flan-t5-xxl for multilingual VQA tasks, marking a significant milestone in the development of multimodal chatbots.
This new feature allows the model to interpret and respond to queries that involve both text and visual inputs, providing a richer, more interactive, and comprehensive user experience. Users can now ask questions about an image, and the model will analyze the visual content in conjunction with the textual query to provide a response.
A noteworthy addition is the [Guanaco VQA Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoVQADataset), publicly accessible now.
Now as a multimodal chatbot, Guanaco can bridge the gap between visual and linguistic understanding, making it an incredibly versatile tool for a wide array of applications.
However, as always, we encourage responsible and ethical use of this model. Please note that while Guanaco strives to provide accurate and helpful responses, it is still crucial to cross-verify the information from reliable sources for knowledge-based queries.
|
Schnitzl/mt5-small-finetuned-amazon-en-es
|
Schnitzl
| 2023-05-22T22:17:05Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-22T21:58:06Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Schnitzl/mt5-small-finetuned-amazon-en-es
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. -->
# Schnitzl/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.1212
- Validation Loss: 3.6620
- Epoch: 3
## 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': 5.6e-05, 'decay_steps': 4836, '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 | Epoch |
|:----------:|:---------------:|:-----:|
| 9.9767 | 4.2684 | 0 |
| 5.9770 | 3.8151 | 1 |
| 5.3716 | 3.7080 | 2 |
| 5.1212 | 3.6620 | 3 |
### Framework versions
- Transformers 4.30.0.dev0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
jr-ai-dreamer/Chillout_Vision_v1
|
jr-ai-dreamer
| 2023-05-22T22:12:13Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-22T21:59:46Z |
---
license: creativeml-openrail-m
---
This model is not mine, is a merge between Chillout Mix fp16 and Realistic Vision v2, so....enjoy it.
|
azetaaa/Reinforce-Pixelcopter-PLE-v0
|
azetaaa
| 2023-05-22T22:03:59Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-22T22:03:53Z |
---
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: 75.30 +/- 46.26
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
|
xzuyn/RedPajama-INCITE-Base-v1-3B-GGML
|
xzuyn
| 2023-05-22T21:35:59Z | 0 | 3 | null |
[
"gpt_neox",
"region:us"
] | null | 2023-05-21T16:13:54Z |
---
tags:
- gpt_neox
---
# For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp)
Original Model: https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1
|
vkovenko/deep_lstm_attention_ukr_reviews_rating_estimation
|
vkovenko
| 2023-05-22T21:14:13Z | 0 | 0 |
keras
|
[
"keras",
"text-classification",
"uk",
"dataset:vkovenko/cross_domain_uk_reviews",
"license:cc",
"region:us"
] |
text-classification
| 2023-05-21T14:55:07Z |
---
license: cc
datasets:
- vkovenko/cross_domain_uk_reviews
language:
- uk
metrics:
- f1
library_name: keras
pipeline_tag: text-classification
---
Deep lstm with attention module trained for rating estimation of Ukrainian reviews.
Code with example usage of the model can be found in the following repository: https://github.com/HikkaV/Ukrainian-Reviews-Estimation/.
Model uses custom layer and tokenizer is used in a custom class, thus the one can load the model and tokenizer using the following code:
```python
import tensorflow as tf
from tokenizers import Tokenizer, models, pre_tokenizers, trainers, Regex
import tokenizers
from tokenizers import Tokenizer, models, decoders, processors
from tokenizers import pre_tokenizers, trainers, Regex
import huggingface_hub
class Attention(tf.keras.layers.Layer):
def __init__(self,
units=128, **kwargs):
super(Attention,self).__init__(**kwargs)
self.units = units
def build(self, input_shape):
self.W1=self.add_weight(name='attention_weights_1', shape=(input_shape[-1], self.units),
initializer='glorot_uniform', trainable=True)
self.W2=self.add_weight(name='attention_weights_2', shape=(1, self.units),
initializer='glorot_uniform', trainable=True)
super(Attention, self).build(input_shape)
def call(self, x):
x = tf.transpose(x, perm=[0, 2, 1])
attention = tf.nn.softmax(tf.matmul(self.W2, tf.nn.tanh(tf.matmul(self.W1, x))))
weighted_context = tf.reduce_sum(x * attention, axis=-1)
return weighted_context, attention
def get_config(self):
config = super().get_config().copy()
config.update({
'units': self.units
})
return config
#download model
model = tf.keras.models.load_model(huggingface_hub.hf_hub_download('vkovenko/deep_lstm_attention_ukr_reviews_rating_estimation',
'deep_lstm_attention_w2v_huber.h5',
local_dir='model'),
compile=False,
custom_objects={'Attention':Attention})
class BPETokenizer:
def __init__(self, vocab, merges):
self.suffix = '</w>'
self.tokenizer = Tokenizer(models.BPE.from_file(vocab=vocab,
merges=merges, end_of_word_suffix=self.suffix))
self.tokenizer.pre_tokenizer = pre_tokenizers.Split(Regex(r"[\w'-]+|[^\w\s'-]+"),'removed', True)
self.id_to_token = self.tokenizer.id_to_token
self.encode_batch = self.tokenizer.encode_batch
self.token_to_id = self.tokenizer.token_to_id
self.encode = self.tokenizer.encode
def tokens_to_ids(self, tokens):
return list(map(self.token_to_id, tokens))
def ids_to_tokens(self, ids):
return list(map(self.id_to_token, ids))
def decode(self, tokens, return_indices=False):
decoded = []
merged_indices = []
i = 0
while i<len(tokens):
if tokens[i].endswith(self.suffix):
decoded.append(tokens[i])
merged_indices.append([i])
i+=1
else:
merged_token = ''
tmp_indc = []
while not tokens[i].endswith(self.suffix):
merged_token+=tokens[i]
tmp_indc.append(i)
i+=1
merged_token+=tokens[i]
tmp_indc.append(i)
decoded.append(merged_token)
merged_indices.append(tmp_indc)
i+=1
if return_indices:
return decoded, merged_indices
else:
return decoded
#download tokenizer
tokenizer = BPETokenizer(vocab=huggingface_hub.hf_hub_download('vkovenko/deep_lstm_attention_ukr_reviews_rating_estimation',
'tokenizer_30k.json',
local_dir='model'),
merges=huggingface_hub.hf_hub_download('vkovenko/deep_lstm_attention_ukr_reviews_rating_estimation',
'merges_tokenizer.txt',
local_dir='model')
)
```
|
miladsolo/stable-diffusion-api
|
miladsolo
| 2023-05-22T21:00:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-05-22T09:52:07Z |
# Stable Diffusion api
A browser interface based on Gradio library for Stable Diffusion.
## Installation and Running
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
Alternatively, use online services (like Google Colab):
- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
### Automatic Installation on Windows
1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH".
2. Install [git](https://git-scm.com/download/win).
3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
### Automatic Installation on Linux
1. Install the dependencies:
```bash
# Debian-based:
sudo apt install wget git python3 python3-venv
# Red Hat-based:
sudo dnf install wget git python3
# Arch-based:
sudo pacman -S wget git python3
```
2. Navigate to the directory you would like the webui to be installed and execute the following command:
```bash
bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
```
3. Run `webui.sh`.
4. Check `webui-user.sh` for options.
### Installation on Apple Silicon
Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
## Documentation
The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
## How to use
Starting from ControlNet 1.1, we begin to use the Standard ControlNet Naming Rules (SCNNRs) to name all models. We hope that this naming rule can improve the user experience.
ControlNet 1.1 include 14 models (11 production-ready models and 3 experimental models):
control_v11p_sd15_canny
control_v11p_sd15_mlsd
control_v11f1p_sd15_depth
control_v11p_sd15_normalbae
control_v11p_sd15_seg
control_v11p_sd15_inpaint
control_v11p_sd15_lineart
control_v11p_sd15s2_lineart_anime
control_v11p_sd15_openpose
control_v11p_sd15_scribble
control_v11p_sd15_softedge
control_v11e_sd15_shuffle
control_v11e_sd15_ip2p
control_v11f1e_sd15_tile
You need to download these models and putting them in the stable-diffusion-webui/models/ControlNet path. You can download all those models from our [HuggingFace Model Page](https://huggingface.co/lllyasviel/ControlNet-v1-1/tree/main).
You need to download Stable Diffusion 1.5 model ["v1-5-pruned.ckpt"](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main) and put it in the stable-diffusion-webui/models/Stable-diffusion path.
Our python codes will automatically download other annotator models like HED and OpenPose. Nevertheless, if you want to manually download these, you can download all other annotator models from [here](https://huggingface.co/lllyasviel/Annotators/tree/main). All these models should be put in folder "annotator/ckpts".
At the end, you need to first run "bash webui.sh --nowebui" in on terminal and then by running following scripts you can use api feature that we completed in this project.
test_api_text2img.py
test_api_img2img.py
test_api_text2img_controlNet.py
test_api_img2img_controlNet.py
In each test ... .py files you can see one or two dict that you can configure your execution by changing them.
## Credits
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
- CodeFormer - https://github.com/sczhou/CodeFormer
- ESRGAN - https://github.com/xinntao/ESRGAN
- SwinIR - https://github.com/JingyunLiang/SwinIR
- Swin2SR - https://github.com/mv-lab/swin2sr
- LDSR - https://github.com/Hafiidz/latent-diffusion
- MiDaS - https://github.com/isl-org/MiDaS
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)
- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
- xformers - https://github.com/facebookresearch/xformers
- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
|
LarryAIDraw/nitocris__fate__v1
|
LarryAIDraw
| 2023-05-22T20:51:10Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-22T20:46:53Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/72952/nitocris-fate-grand-order
|
yoshivo/xlm-roberta-base-finetuned-panx-en
|
yoshivo
| 2023-05-22T20:44:38Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-22T20:41:55Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.en
split: validation
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.696578799775659
---
<!-- 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-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3979
- F1: 0.6966
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1322 | 1.0 | 50 | 0.5845 | 0.4693 |
| 0.5124 | 2.0 | 100 | 0.4304 | 0.6648 |
| 0.365 | 3.0 | 150 | 0.3979 | 0.6966 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0.post200
- Datasets 2.12.0
- Tokenizers 0.13.3
|
yoshivo/xlm-roberta-base-finetuned-panx-it
|
yoshivo
| 2023-05-22T20:41:44Z | 130 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-22T20:38:37Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.it
split: validation
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8173984407057857
---
<!-- 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-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2576
- F1: 0.8174
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8213 | 1.0 | 70 | 0.3252 | 0.7427 |
| 0.2851 | 2.0 | 140 | 0.2667 | 0.8018 |
| 0.189 | 3.0 | 210 | 0.2576 | 0.8174 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0.post200
- Datasets 2.12.0
- Tokenizers 0.13.3
|
us8945/llm-demo-v0
|
us8945
| 2023-05-22T20:39:52Z | 153 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-05-22T20:37:49Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.28.1
pip install accelerate==0.18.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="us8945/llm-demo-v0",
torch_dtype=torch.float16,
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, if you prefer to not use `trust_remote_code=True` you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"us8945/llm-demo-v0",
use_fast=True,
padding_side="left"
)
model = AutoModelForCausalLM.from_pretrained(
"us8945/llm-demo-v0",
torch_dtype=torch.float16,
device_map={"": "cuda:0"}
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "us8945/llm-demo-v0" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 2560)
(layers): ModuleList(
(0-31): 32 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=2560, out_features=7680, bias=True)
(dense): Linear(in_features=2560, out_features=2560, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)
(dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2560, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Model Validation
Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
```bash
CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=us8945/llm-demo-v0 --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
```
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
MinaAlmasi/ES-ENG-mBERT-sentiment
|
MinaAlmasi
| 2023-05-22T20:15:04Z | 55 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-16T21:24:54Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ES-ENG-mBERT-sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ES-ENG-mBERT-sentiment
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on a Custom dataset.
The best model (stopped after 14 epochs) achieves the following results on the evaluation set:
- Loss: 0.8110
- Accuracy: 0.6307
- F1: 0.6298
- Precision: 0.6291
- Recall: 0.6307
## Intended uses & limitations
Note that commercial use with this model is prohibited.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-06
- 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.063 | 1.0 | 208 | 0.9989 | 0.4731 | 0.4044 | 0.4885 | 0.4731 |
| 0.9664 | 2.0 | 416 | 0.9144 | 0.5262 | 0.4845 | 0.5270 | 0.5262 |
| 0.9067 | 3.0 | 624 | 0.8648 | 0.5896 | 0.5844 | 0.5935 | 0.5896 |
| 0.8572 | 4.0 | 832 | 0.8294 | 0.6065 | 0.5984 | 0.6102 | 0.6065 |
| 0.8168 | 5.0 | 1040 | 0.8101 | 0.6107 | 0.6092 | 0.6119 | 0.6107 |
| 0.7897 | 6.0 | 1248 | 0.8213 | 0.6074 | 0.6015 | 0.6018 | 0.6074 |
| 0.7568 | 7.0 | 1456 | 0.7992 | 0.6194 | 0.6181 | 0.6176 | 0.6194 |
| 0.7465 | 8.0 | 1664 | 0.8089 | 0.6246 | 0.6183 | 0.6206 | 0.6246 |
| 0.7223 | 9.0 | 1872 | 0.7988 | 0.6236 | 0.6214 | 0.6207 | 0.6236 |
| 0.7045 | 10.0 | 2080 | 0.8390 | 0.6165 | 0.6080 | 0.6126 | 0.6165 |
| 0.6888 | 11.0 | 2288 | 0.8042 | 0.6291 | 0.6260 | 0.6257 | 0.6291 |
| 0.671 | 12.0 | 2496 | 0.8088 | 0.6239 | 0.6212 | 0.6216 | 0.6239 |
| 0.6543 | 13.0 | 2704 | 0.8104 | 0.6256 | 0.6227 | 0.6216 | 0.6256 |
| 0.6409 | 14.0 | 2912 | 0.8110 | 0.6307 | 0.6298 | 0.6291 | 0.6307 |
| 0.6275 | 15.0 | 3120 | 0.8127 | 0.6298 | 0.6292 | 0.6299 | 0.6298 |
| 0.6176 | 16.0 | 3328 | 0.8334 | 0.6252 | 0.6217 | 0.6206 | 0.6252 |
| 0.6096 | 17.0 | 3536 | 0.8331 | 0.6256 | 0.6210 | 0.6210 | 0.6256 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
rohitp1/subh_whisper_small_distil_att_loss_mozilla_epochs_50_batch_4_try2
|
rohitp1
| 2023-05-22T20:13:01Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-16T04:21:42Z |
---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: subh_whisper_small_distil_att_loss_mozilla_epochs_50_batch_4_try2
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. -->
# subh_whisper_small_distil_att_loss_mozilla_epochs_50_batch_4_try2
This model is a fine-tuned version of [rohitp1/kkkh_whisper_small_distillation_att_loss_mozilla_epochs_100_batch_4_concat_dataset](https://huggingface.co/rohitp1/kkkh_whisper_small_distillation_att_loss_mozilla_epochs_100_batch_4_concat_dataset) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4047
- Wer: 26.9184
## 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: 1
- seed: 42
- gradient_accumulation_steps: 512
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.1019 | 1.47 | 100 | 1.6488 | 24.8451 |
| 1.0977 | 2.94 | 200 | 1.6543 | 24.8816 |
| 1.0992 | 4.41 | 300 | 1.6592 | 24.8625 |
| 1.093 | 5.88 | 400 | 1.6705 | 24.8903 |
| 1.1001 | 7.35 | 500 | 1.6851 | 24.9043 |
| 1.0575 | 8.82 | 600 | 1.4047 | 26.9184 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
rjac/bert-20news-classification
|
rjac
| 2023-05-22T20:07:04Z | 77 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-22T19:46:17Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: bert-20news-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. -->
# bert-20news-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: 0.0479
- Train Accuracy: 0.9922
- Validation Loss: 0.2769
- Validation Accuracy: 0.9284
- Epoch: 9
## Model description
This model is a fine-tuned version of the DistilBERT model for sequence classification tasks. It was trained using Hugging Face's transformers and TensorFlow. The model expects input sequences to be tokenized according to the DistilBERT's tokenizer.
The model was trained specifically for classifying text into 20 different categories derived from the 20 Newsgroups dataset. These categories include various topics such as 'alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc'.
## Intended uses & limitations
This model is intended for classifying text into the above mentioned 20 categories. It can be used for categorizing text data from similar domains or topics.
## Training and evaluation data
the model was trained on 90% of the data from the 20 Newsgroups dataset, with the remaining 10% used for validation.
## 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': 2120, '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 | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 1.8498 | 0.5829 | 0.9285 | 0.8012 | 0 |
| 0.6611 | 0.8406 | 0.4800 | 0.8807 | 1 |
| 0.3563 | 0.9128 | 0.3829 | 0.9002 | 2 |
| 0.2276 | 0.9475 | 0.3593 | 0.9072 | 3 |
| 0.1544 | 0.9659 | 0.3205 | 0.9214 | 4 |
| 0.1094 | 0.9779 | 0.3007 | 0.9214 | 5 |
| 0.0825 | 0.9846 | 0.2821 | 0.9258 | 6 |
| 0.0634 | 0.9895 | 0.2754 | 0.9337 | 7 |
| 0.0533 | 0.9916 | 0.2707 | 0.9337 | 8 |
| 0.0479 | 0.9922 | 0.2769 | 0.9284 | 9 |
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
MrNoOne/ppo-LunarLander-v2
|
MrNoOne
| 2023-05-22T19:51:25Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-27T20:05:02Z |
---
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: 262.83 +/- 20.08
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
...
```
|
emmanuel17/Reinforce1
|
emmanuel17
| 2023-05-22T19:47:03Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-22T11:45:33Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforcev1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 258.70 +/- 305.66
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
|
MattBoraske/q-Taxi-v3
|
MattBoraske
| 2023-05-22T19:20:10Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-22T19:20:09Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.70
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="MattBoraske/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"])
```
|
cmudrc/AddLat2D
|
cmudrc
| 2023-05-22T18:45:36Z | 0 | 0 | null |
[
"doi:10.57967/hf/0683",
"region:us"
] | null | 2023-05-22T18:43:14Z |
---
title: AddLat2D
emoji: 😻
colorFrom: purple
colorTo: blue
sdk: streamlit
sdk_version: 1.17.0
app_file: app.py
pinned: false
---
Use the following code to load items in the 2D_Lattice.csv file
import pandas as pd
import json
df = pd.read_csv('2D_Lattice.csv')
row = 0
box = df.iloc[row,1]
array = np.array(json.loads(box))
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
antonkurylo/t5-base-samsum
|
antonkurylo
| 2023-05-22T18:06:51Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-05-22T16:18:30Z |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: t5-base-samsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: validation
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 48.9131
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-samsum
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6172
- Rouge1: 48.9131
- Rouge2: 25.4942
- Rougel: 41.2363
- Rougelsum: 45.3434
## 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: 4
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 0.7606 | 1.0 | 3683 | 0.6254 | 46.9778 | 23.8245 | 39.8294 | 43.4639 |
| 0.6273 | 2.0 | 7366 | 0.6119 | 48.2515 | 24.7534 | 40.4415 | 44.5567 |
| 0.5769 | 3.0 | 11049 | 0.6116 | 48.228 | 24.7865 | 40.7537 | 44.4026 |
| 0.5412 | 4.0 | 14732 | 0.6145 | 48.8563 | 25.356 | 41.1913 | 45.186 |
| 0.5199 | 5.0 | 18415 | 0.6172 | 48.9131 | 25.4942 | 41.2363 | 45.3434 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Benned/YunXiPW
|
Benned
| 2023-05-22T17:56:46Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-22T17:51:49Z |
---
license: creativeml-openrail-m
---
|
Eldund/a2c-PandaReachDense-v2
|
Eldund
| 2023-05-22T17:49:42Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-22T17:47:12Z |
---
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: -2.95 +/- 0.43
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
...
```
|
Benned/YunXi
|
Benned
| 2023-05-22T17:35:56Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-22T17:28:05Z |
---
license: creativeml-openrail-m
---
|
MattBoraske/q-FrozenLake-v1-4x4-noSlippery
|
MattBoraske
| 2023-05-22T17:30:18Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-22T17:30:12Z |
---
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="MattBoraske/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"])
```
|
rozzman/mobileNetV3FromTimm
|
rozzman
| 2023-05-22T17:15:05Z | 18 | 1 |
timm
|
[
"timm",
"pytorch",
"image-classification",
"dataset:rozzman/autotrain-data-wood-identification",
"region:us"
] |
image-classification
| 2023-05-22T16:51:40Z |
---
tags:
- image-classification
- timm
library_tag: timm
datasets:
- rozzman/autotrain-data-wood-identification
metrics:
- accuracy
library_name: timm
pipeline_tag: image-classification
---
# Model card for rozzman/mobileNetV3FromTimm
This model is able to recognize 11 high commercially valuable types of wood from Brazil
The dataset is from https://www.facom.ufu.br/~backes/wood_dataset.php
The names of these 11 types of wood are shown in the table below
The cross-sections of these 11 types of wood are shown in the following image
|
takeshiho0531/bert-japanese-finetuned-Ukraine-tweet
|
takeshiho0531
| 2023-05-22T17:10:08Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-22T16:08:59Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-japanese-finetuned-Ukraine-tweet
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-japanese-finetuned-Ukraine-tweet
This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-whole-word-masking](https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1318
- Accuracy: 0.5280
- F1: 0.4447
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3734 | 1.0 | 21 | 1.2022 | 0.5248 | 0.4394 |
| 1.1804 | 2.0 | 42 | 1.1680 | 0.5155 | 0.4284 |
| 1.1396 | 3.0 | 63 | 1.1318 | 0.5280 | 0.4447 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
sadra-barikbin/dqn-SpaceInvadersNoFrameskip-v4
|
sadra-barikbin
| 2023-05-22T17:02:28Z | 10 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-22T17:01:53Z |
---
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: 451.00 +/- 192.71
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 sadra-barikbin -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 sadra-barikbin -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 sadra-barikbin
```
## Hyperparameters
```python
OrderedDict([('batch_size', 16),
('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', 0.001),
('learning_starts', 50000),
('n_timesteps', 2000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
juanfkurucz/poca-SoccerTwos
|
juanfkurucz
| 2023-05-22T16:53:23Z | 16 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-05-22T16:52:43Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: juanfkurucz/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ztijn/robbertje-1-gb-shuffled-finetuned-squad
|
Ztijn
| 2023-05-22T16:50:40Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-22T15:17:27Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: robbertje-1-gb-shuffled-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. -->
# robbertje-1-gb-shuffled-finetuned-squad
This model is a fine-tuned version of [DTAI-KULeuven/robbertje-1-gb-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-shuffled) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6048
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.6845 | 1.0 | 8326 | 1.5239 |
| 1.4006 | 2.0 | 16652 | 1.6028 |
| 1.1941 | 3.0 | 24978 | 1.6048 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
leukas/mt5-small-nc16-250k-deen
|
leukas
| 2023-05-22T16:44:46Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"translation",
"de",
"en",
"arxiv:2302.14220",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-05-22T16:31:19Z |
---
language:
- de
- en
pipeline_tag: translation
---
# mt5-small-nc16-250k-deen
This model is released as part of the work from [Are Character-level Translations Worth the Wait? Comparing Character- and Subword-level Models for Machine Translation](https://arxiv.org/abs/2302.14220).
It is an mT5 model finetuned on German-->English translation using 250k sentence pairs from the WMT NewsCommentary v16 dataset.
To use the model correctly, you must prepend the prompt with "translate X to Y: ", where X and Y are your source and target languages (e.g. German, English).
NOTE: The decoder_start_token_id is 259 for byt5 models and 250099 for mt5 models, which is different from the default token from google's byt5 and mt5 models (which is 0).
|
claritylab/zero-shot-explicit-gpt2
|
claritylab
| 2023-05-22T16:41:16Z | 93 | 0 |
zeroshot_classifier
|
[
"zeroshot_classifier",
"pytorch",
"gpt2",
"text-generation",
"transformers",
"sentence-transformers",
"en",
"dataset:claritylab/UTCD",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-15T23:24:40Z |
---
library_name: zeroshot_classifier
tags:
- transformers
- sentence-transformers
- zeroshot_classifier
license: mit
datasets:
- claritylab/UTCD
language:
- en
pipeline_tag: text-generation
metrics:
- accuracy
---
# Zero-shot Explicit GPT2
This is a modified GPT2 model.
It was introduced in the Findings of ACL'23 Paper **Label Agnostic Pre-training for Zero-shot Text Classification** by ***Christopher Clarke, Yuzhao Heng, Yiping Kang, Krisztian Flautner, Lingjia Tang and Jason Mars***.
The code for training and evaluating this model can be found [here](https://github.com/ChrisIsKing/zero-shot-text-classification/tree/master).
## Model description
This model is intended for zero-shot text classification.
It was trained under the generative classification framework via explicit training with the aspect-normalized [UTCD](https://huggingface.co/datasets/claritylab/UTCD) dataset.
- **Finetuned from model:** [`gpt2-medium`](https://huggingface.co/gpt2-medium)
## Usage
Install our [python package](https://pypi.org/project/zeroshot-classifier/):
```bash
pip install zeroshot-classifier
```
Then, you can use the model like this:
```python
>>> import torch
>>> from zeroshot_classifier.models import ZsGPT2Tokenizer, ZsGPT2LMHeadModel
>>> training_strategy = 'explicit'
>>> model_name = f'claritylab/zero-shot-{training_strategy}-gpt2'
>>> model = ZsGPT2LMHeadModel.from_pretrained(model_name)
>>> tokenizer = ZsGPT2Tokenizer.from_pretrained(model_name, form=training_strategy)
>>> text = "I'd like to have this track onto my Classical Relaxations playlist."
>>> labels = [
>>> 'Add To Playlist', 'Book Restaurant', 'Get Weather', 'Play Music', 'Rate Book', 'Search Creative Work',
>>> 'Search Screening Event'
>>> ]
>>> inputs = tokenizer(dict(text=text, label_options=labels), mode='inference-sample')
>>> inputs = {k: torch.tensor(v).unsqueeze(0) for k, v in inputs.items()}
>>> outputs = model.generate(**inputs, max_length=128)
>>> decoded = tokenizer.batch_decode(outputs, skip_special_tokens=False)[0]
>>> print(decoded)
<|question|>Which of these choices best describes the following document? : " Play Music ", " Add To Playlist ", " Rate Book ", " Get Weather ", " Book Restaurant ", " Search Screening Event ", " Search Creative Work "<|endoftext|><|text|>I'd like to have this track onto my Classical Relaxations playlist.<|endoftext|><|answer|>Play Media<|endoftext|>
```
|
claritylab/zero-shot-explicit-binary-bert
|
claritylab
| 2023-05-22T16:39:17Z | 103 | 0 |
zeroshot_classifier
|
[
"zeroshot_classifier",
"pytorch",
"bert",
"text-classification",
"transformers",
"sentence-transformers",
"zero-shot-classification",
"en",
"dataset:claritylab/UTCD",
"license:mit",
"region:us"
] |
zero-shot-classification
| 2023-05-15T16:14:55Z |
---
library_name: zeroshot_classifier
tags:
- transformers
- sentence-transformers
- zeroshot_classifier
license: mit
datasets:
- claritylab/UTCD
language:
- en
pipeline_tag: zero-shot-classification
metrics:
- accuracy
---
# Zero-shot Explicit Binary BERT
This is a BERT model.
It was introduced in the Findings of ACL'23 Paper **Label Agnostic Pre-training for Zero-shot Text Classification** by ***Christopher Clarke, Yuzhao Heng, Yiping Kang, Krisztian Flautner, Lingjia Tang and Jason Mars***.
The code for training and evaluating this model can be found [here](https://github.com/ChrisIsKing/zero-shot-text-classification/tree/master).
## Model description
This model is intended for zero-shot text classification.
It was trained under the binary classification framework via explicit training with the aspect-normalized [UTCD](https://huggingface.co/datasets/claritylab/UTCD) dataset.
- **Finetuned from model:** [`bert-base-uncased`](https://huggingface.co/bert-base-uncased)
## Usage
Install our [python package](https://pypi.org/project/zeroshot-classifier/):
```bash
pip install zeroshot-classifier
```
Then, you can use the model like this:
```python
>>> from zeroshot_classifier.models import BinaryBertCrossEncoder
>>> model = BinaryBertCrossEncoder(model_name='claritylab/zero-shot-explicit-binary-bert')
>>> text = "I'd like to have this track onto my Classical Relaxations playlist."
>>> labels = [
>>> 'Add To Playlist', 'Book Restaurant', 'Get Weather', 'Play Music', 'Rate Book', 'Search Creative Work',
>>> 'Search Screening Event'
>>> ]
>>> query = [[text, lb] for lb in labels]
>>> logits = model.predict(query, apply_softmax=True)
>>> print(logits)
[[1.0987393e-03 9.9890125e-01]
[9.9988937e-01 1.1059999e-04]
[9.9986207e-01 1.3791372e-04]
[1.6576477e-03 9.9834239e-01]
[9.9990320e-01 9.6742726e-05]
[9.9894422e-01 1.0557596e-03]
[9.9959773e-01 4.0229000e-04]]
```
|
claritylab/zero-shot-vanilla-binary-bert
|
claritylab
| 2023-05-22T16:38:24Z | 136 | 0 |
zeroshot_classifier
|
[
"zeroshot_classifier",
"pytorch",
"bert",
"text-classification",
"transformers",
"sentence-transformers",
"zero-shot-classification",
"en",
"dataset:claritylab/UTCD",
"license:mit",
"region:us"
] |
zero-shot-classification
| 2023-05-13T01:11:48Z |
---
library_name: zeroshot_classifier
tags:
- transformers
- sentence-transformers
- zeroshot_classifier
license: mit
datasets:
- claritylab/UTCD
language:
- en
pipeline_tag: zero-shot-classification
metrics:
- accuracy
---
# Zero-shot Vanilla Binary BERT
This is a BERT model.
It was introduced in the Findings of ACL'23 Paper **Label Agnostic Pre-training for Zero-shot Text Classification** by ***Christopher Clarke, Yuzhao Heng, Yiping Kang, Krisztian Flautner, Lingjia Tang and Jason Mars***.
The code for training and evaluating this model can be found [here](https://github.com/ChrisIsKing/zero-shot-text-classification/tree/master).
## Model description
This model is intended for zero-shot text classification.
It was trained under the binary classification framework as a baseline with the aspect-normalized [UTCD](https://huggingface.co/datasets/claritylab/UTCD) dataset.
- **Finetuned from model:** [`bert-base-uncased`](https://huggingface.co/bert-base-uncased)
## Usage
Install our [python package](https://pypi.org/project/zeroshot-classifier/):
```bash
pip install zeroshot-classifier
```
Then, you can use the model like this:
```python
>>> from zeroshot_classifier.models import BinaryBertCrossEncoder
>>> model = BinaryBertCrossEncoder(model_name='claritylab/zero-shot-vanilla-binary-bert')
>>> text = "I'd like to have this track onto my Classical Relaxations playlist."
>>> labels = [
>>> 'Add To Playlist', 'Book Restaurant', 'Get Weather', 'Play Music', 'Rate Book', 'Search Creative Work',
>>> 'Search Screening Event'
>>> ]
>>> query = [[text, lb] for lb in labels]
>>> logits = model.predict(query, apply_softmax=True)
>>> print(logits)
[[1.1909954e-04 9.9988091e-01]
[9.9997509e-01 2.4927122e-05]
[9.9997497e-01 2.5082643e-05]
[2.4483365e-04 9.9975520e-01]
[9.9996781e-01 3.2211588e-05]
[9.9985993e-01 1.4002046e-04]
[9.9976152e-01 2.3845369e-04]]
```
|
liamcripwell/ledpara
|
liamcripwell
| 2023-05-22T16:22:25Z | 96 | 0 |
transformers
|
[
"transformers",
"pytorch",
"led",
"text2text-generation",
"en",
"arxiv:2305.06274",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-15T19:05:50Z |
---
language:
- en
---
# LED_para document simplification model
This is a pretrained version of the document simplification model presented in the Findings of ACL 2023 paper ["Context-Aware Document Simplification"](https://arxiv.org/abs/2305.06274).
It is an end-to-end system based on the [Longformer encoder-decoder](https://huggingface.co/allenai/led-base-16384) that operates at the paragraph-level.
Target reading levels (1-4) should be indicated via a control token prepended to each input sequence ("\<RL_1\>", "\<RL_2\>", "\<RL_3\>", "\<RL_4\>"). If using the terminal interface, this will be handled automatically.
## How to use
It is recommended to use the [plan_simp](https://github.com/liamcripwell/plan_simp/tree/main) library to interface with the model.
Here is how to use this model in PyTorch:
```python
from plan_simp.models.bart import load_simplifier
simplifier, tokenizer, hparams = load_simplifier("liamcripwell/ledpara")
text = "<RL_3> Turing has an extensive legacy with statues of him and many things named after him, including an annual award for computer science innovations. He appears on the current Bank of England £50 note, which was released on 23 June 2021, to coincide with his birthday. A 2019 BBC series, as voted by the audience, named him the greatest person of the 20th century."
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, num_beams=5)
```
Generation and evaluation can also be run from the terminal.
```bash
python plan_simp/scripts/generate.py inference
--model_ckpt=liamcripwell/ledpara
--test_file=<test_data>
--reading_lvl=s_level
--out_file=<output_csv>
python plan_simp/scripts/eval_simp.py
--input_data=newselaauto_docs_test.csv
--output_data=test_out_ledpara.csv
--x_col=complex_str
--r_col=simple_str
--y_col=pred
--doc_id_col=pair_id
--prepro=True
--sent_level=True
```
|
AlexC98/BertWhyCommitOriginal
|
AlexC98
| 2023-05-22T16:09:27Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-22T16:00:23Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BertWhyCommitOriginal
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. -->
# BertWhyCommitOriginal
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4881
- Accuracy: 0.8788
## 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: 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 31 | 0.5058 | 0.7394 |
| No log | 2.0 | 62 | 0.4463 | 0.7758 |
| No log | 3.0 | 93 | 0.4260 | 0.7758 |
| No log | 4.0 | 124 | 0.3954 | 0.8061 |
| No log | 5.0 | 155 | 0.3745 | 0.8061 |
| No log | 6.0 | 186 | 0.3653 | 0.8303 |
| No log | 7.0 | 217 | 0.3533 | 0.8424 |
| No log | 8.0 | 248 | 0.3500 | 0.8364 |
| No log | 9.0 | 279 | 0.3416 | 0.8606 |
| No log | 10.0 | 310 | 0.3546 | 0.8424 |
| No log | 11.0 | 341 | 0.3469 | 0.8485 |
| No log | 12.0 | 372 | 0.3511 | 0.8606 |
| No log | 13.0 | 403 | 0.3883 | 0.8545 |
| No log | 14.0 | 434 | 0.4090 | 0.8485 |
| No log | 15.0 | 465 | 0.4301 | 0.8485 |
| No log | 16.0 | 496 | 0.4415 | 0.8606 |
| 0.2667 | 17.0 | 527 | 0.4732 | 0.8545 |
| 0.2667 | 18.0 | 558 | 0.4849 | 0.8727 |
| 0.2667 | 19.0 | 589 | 0.4881 | 0.8788 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
pedrocintra/manuela
|
pedrocintra
| 2023-05-22T16:08:35Z | 0 | 0 | null |
[
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-05-22T16:02:12Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### manuela Dreambooth model trained by pedrocintra with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
xzuyn/Alpacino-SuperCOT-13B
|
xzuyn
| 2023-05-22T15:51:22Z | 1,470 | 4 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"alpaca",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-14T01:03:59Z |
---
tags:
- llama
- alpaca
---
This was made as a test to see if I my PC can handle merging.
# Alpacino-SuperCOT-13B Recipe
Alpacino-13B + LLaMa-SuperCOT-13B (50%/50%)
## Original Models:
Alpacino-13B: https://huggingface.co/digitous/Alpacino13b
LLaMa-SuperCOT-13B: https://huggingface.co/ausboss/llama-13b-supercot
|
Bestie2088/munirah_v3
|
Bestie2088
| 2023-05-22T15:44:35Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-22T14:27:04Z |
---
license: creativeml-openrail-m
---
|
AlexC98/BertWhatCommitPreprocessed
|
AlexC98
| 2023-05-22T15:38:18Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-22T15:31:15Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BertWhatCommitPreprocessed
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. -->
# BertWhatCommitPreprocessed
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3631
- Accuracy: 0.9152
## 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: 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 38 | 0.5383 | 0.7333 |
| No log | 2.0 | 76 | 0.4130 | 0.8485 |
| No log | 3.0 | 114 | 0.3096 | 0.8727 |
| No log | 4.0 | 152 | 0.3140 | 0.8788 |
| No log | 5.0 | 190 | 0.2983 | 0.8970 |
| No log | 6.0 | 228 | 0.3019 | 0.8848 |
| No log | 7.0 | 266 | 0.3235 | 0.9030 |
| No log | 8.0 | 304 | 0.3571 | 0.8970 |
| No log | 9.0 | 342 | 0.3457 | 0.8970 |
| No log | 10.0 | 380 | 0.3340 | 0.8909 |
| No log | 11.0 | 418 | 0.3378 | 0.9091 |
| No log | 12.0 | 456 | 0.3389 | 0.9091 |
| No log | 13.0 | 494 | 0.3753 | 0.9030 |
| 0.2144 | 14.0 | 532 | 0.3492 | 0.9152 |
| 0.2144 | 15.0 | 570 | 0.3631 | 0.9152 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Arindam75/q-Taxi-v3
|
Arindam75
| 2023-05-22T15:31:17Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-22T15:26:24Z |
---
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: 7.42 +/- 2.78
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="Arindam75/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"])
```
|
AlexC98/BertWhyCommitPreprocessed
|
AlexC98
| 2023-05-22T15:29:54Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-22T15:20:33Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BertWhyCommitPreprocessed
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. -->
# BertWhyCommitPreprocessed
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4699
- Accuracy: 0.8848
## 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: 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 31 | 0.5237 | 0.7333 |
| No log | 2.0 | 62 | 0.4632 | 0.7636 |
| No log | 3.0 | 93 | 0.4243 | 0.8 |
| No log | 4.0 | 124 | 0.3896 | 0.8182 |
| No log | 5.0 | 155 | 0.3824 | 0.8242 |
| No log | 6.0 | 186 | 0.3661 | 0.8182 |
| No log | 7.0 | 217 | 0.3597 | 0.8242 |
| No log | 8.0 | 248 | 0.3569 | 0.8364 |
| No log | 9.0 | 279 | 0.3518 | 0.8606 |
| No log | 10.0 | 310 | 0.3618 | 0.8485 |
| No log | 11.0 | 341 | 0.3462 | 0.8545 |
| No log | 12.0 | 372 | 0.3636 | 0.8485 |
| No log | 13.0 | 403 | 0.3759 | 0.8485 |
| No log | 14.0 | 434 | 0.3771 | 0.8727 |
| No log | 15.0 | 465 | 0.3957 | 0.8727 |
| No log | 16.0 | 496 | 0.4154 | 0.8788 |
| 0.2682 | 17.0 | 527 | 0.3980 | 0.8606 |
| 0.2682 | 18.0 | 558 | 0.4442 | 0.8667 |
| 0.2682 | 19.0 | 589 | 0.4028 | 0.8788 |
| 0.2682 | 20.0 | 620 | 0.4653 | 0.8606 |
| 0.2682 | 21.0 | 651 | 0.4699 | 0.8848 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
0xalty/form
|
0xalty
| 2023-05-22T15:28:16Z | 30 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-22T15:18:47Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### FORM Dreambooth model trained by alty with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
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