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hopkins/eng-deu-sim2b | hopkins | 2023-07-23T17:39:56Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2023-07-23T17:22:00Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-deu-sim2b
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. -->
# eng-deu-sim2b
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6799
- Bleu: 20.7936
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dsvv-cair/alpaca-cleaned-llama-2-13b-bf16-qlora-adapter | dsvv-cair | 2023-07-23T17:30:52Z | 0 | 0 | null | [
"region:us"
] | null | 2023-07-23T03:27:03Z | Method : QLORA
Dataset : yahma/alpaca-cleaned
Base model : meta-llama/Llama-30b
Compute dtype : bfloat16
|
daryl149/llama-2-7b-hf | daryl149 | 2023-07-23T17:14:12Z | 346 | 21 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-19T22:52:57Z | These are the converted model weights for Llama-2-7B in Huggingface format.
Courtesy of [Mirage-Studio.io](https://mirage-studio.io), home of MirageGPT: the private ChatGPT alternative.
---
license: other
LLAMA 2 COMMUNITY LICENSE AGREEMENT
Llama 2 Version Release Date: July 18, 2023
"Agreement" means the terms and conditions for use, reproduction, distribution and
modification of the Llama Materials set forth herein.
"Documentation" means the specifications, manuals and documentation
accompanying Llama 2 distributed by Meta at ai.meta.com/resources/models-and-
libraries/llama-downloads/.
"Licensee" or "you" means you, or your employer or any other person or entity (if
you are entering into this Agreement on such person or entity's behalf), of the age
required under applicable laws, rules or regulations to provide legal consent and that
has legal authority to bind your employer or such other person or entity if you are
entering in this Agreement on their behalf.
"Llama 2" means the foundational large language models and software and
algorithms, including machine-learning model code, trained model weights,
inference-enabling code, training-enabling code, fine-tuning enabling code and other
elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-
libraries/llama-downloads/.
"Llama Materials" means, collectively, Meta's proprietary Llama 2 and
Documentation (and any portion thereof) made available under this Agreement.
"Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you
are an entity, your principal place of business is in the EEA or Switzerland) and Meta
Platforms, Inc. (if you are located outside of the EEA or Switzerland).
By clicking "I Accept" below or by using or distributing any portion or element of the
Llama Materials, you agree to be bound by this Agreement.
1. License Rights and Redistribution.
a. Grant of Rights. You are granted a non-exclusive, worldwide, non-
transferable and royalty-free limited license under Meta's intellectual property or
other rights owned by Meta embodied in the Llama Materials to use, reproduce,
distribute, copy, create derivative works of, and make modifications to the Llama
Materials.
b. Redistribution and Use.
i. If you distribute or make the Llama Materials, or any derivative works
thereof, available to a third party, you shall provide a copy of this Agreement to such
third party.
ii. If you receive Llama Materials, or any derivative works thereof, from
a Licensee as part of an integrated end user product, then Section 2 of this
Agreement will not apply to you.
iii. You must retain in all copies of the Llama Materials that you
distribute the following attribution notice within a "Notice" text file distributed as a
part of such copies: "Llama 2 is licensed under the LLAMA 2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved."
iv. Your use of the Llama Materials must comply with applicable laws
and regulations (including trade compliance laws and regulations) and adhere to the
Acceptable Use Policy for the Llama Materials (available at
https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into
this Agreement.
v. You will not use the Llama Materials or any output or results of the
Llama Materials to improve any other large language model (excluding Llama 2 or
derivative works thereof).
2. Additional Commercial Terms. If, on the Llama 2 version release date, the
monthly active users of the products or services made available by or for Licensee,
or Licensee's affiliates, is greater than 700 million monthly active users in the
preceding calendar month, you must request a license from Meta, which Meta may
grant to you in its sole discretion, and you are not authorized to exercise any of the
rights under this Agreement unless or until Meta otherwise expressly grants you
such rights.
3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE
LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE
PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY
WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR
FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE
FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING
THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR
USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE
LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT,
NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS
AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL,
CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN
IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF
ANY OF THE FOREGOING.
5. Intellectual Property.
a. No trademark licenses are granted under this Agreement, and in
connection with the Llama Materials, neither Meta nor Licensee may use any name
or mark owned by or associated with the other or any of its affiliates, except as
required for reasonable and customary use in describing and redistributing the
Llama Materials.
b. Subject to Meta's ownership of Llama Materials and derivatives made by or
for Meta, with respect to any derivative works and modifications of the Llama
Materials that are made by you, as between you and Meta, you are and will be the
owner of such derivative works and modifications.
c. If you institute litigation or other proceedings against Meta or any entity
(including a cross-claim or counterclaim in a lawsuit) alleging that the Llama
Materials or Llama 2 outputs or results, or any portion of any of the foregoing,
constitutes infringement of intellectual property or other rights owned or licensable
by you, then any licenses granted to you under this Agreement shall terminate as of
the date such litigation or claim is filed or instituted. You will indemnify and hold
harmless Meta from and against any claim by any third party arising out of or related
to your use or distribution of the Llama Materials.
6. Term and Termination. The term of this Agreement will commence upon your
acceptance of this Agreement or access to the Llama Materials and will continue in
full force and effect until terminated in accordance with the terms and conditions
herein. Meta may terminate this Agreement if you are in breach of any term or
condition of this Agreement. Upon termination of this Agreement, you shall delete
and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the
termination of this Agreement.
7. Governing Law and Jurisdiction. This Agreement will be governed and
construed under the laws of the State of California without regard to choice of law
principles, and the UN Convention on Contracts for the International Sale of Goods
does not apply to this Agreement. The courts of California shall have exclusive
jurisdiction of any dispute arising out of this Agreement.
---
|
daryl149/llama-2-70b-chat-hf | daryl149 | 2023-07-23T17:13:51Z | 131 | 4 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-20T07:05:42Z | These are the converted model weights for Llama-2-70B-chat in Huggingface format.
Courtesy of [Mirage-Studio.io](https://mirage-studio.io), home of MirageGPT: the private ChatGPT alternative.
---
license: other
LLAMA 2 COMMUNITY LICENSE AGREEMENT
Llama 2 Version Release Date: July 18, 2023
"Agreement" means the terms and conditions for use, reproduction, distribution and
modification of the Llama Materials set forth herein.
"Documentation" means the specifications, manuals and documentation
accompanying Llama 2 distributed by Meta at ai.meta.com/resources/models-and-
libraries/llama-downloads/.
"Licensee" or "you" means you, or your employer or any other person or entity (if
you are entering into this Agreement on such person or entity's behalf), of the age
required under applicable laws, rules or regulations to provide legal consent and that
has legal authority to bind your employer or such other person or entity if you are
entering in this Agreement on their behalf.
"Llama 2" means the foundational large language models and software and
algorithms, including machine-learning model code, trained model weights,
inference-enabling code, training-enabling code, fine-tuning enabling code and other
elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-
libraries/llama-downloads/.
"Llama Materials" means, collectively, Meta's proprietary Llama 2 and
Documentation (and any portion thereof) made available under this Agreement.
"Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you
are an entity, your principal place of business is in the EEA or Switzerland) and Meta
Platforms, Inc. (if you are located outside of the EEA or Switzerland).
By clicking "I Accept" below or by using or distributing any portion or element of the
Llama Materials, you agree to be bound by this Agreement.
1. License Rights and Redistribution.
a. Grant of Rights. You are granted a non-exclusive, worldwide, non-
transferable and royalty-free limited license under Meta's intellectual property or
other rights owned by Meta embodied in the Llama Materials to use, reproduce,
distribute, copy, create derivative works of, and make modifications to the Llama
Materials.
b. Redistribution and Use.
i. If you distribute or make the Llama Materials, or any derivative works
thereof, available to a third party, you shall provide a copy of this Agreement to such
third party.
ii. If you receive Llama Materials, or any derivative works thereof, from
a Licensee as part of an integrated end user product, then Section 2 of this
Agreement will not apply to you.
iii. You must retain in all copies of the Llama Materials that you
distribute the following attribution notice within a "Notice" text file distributed as a
part of such copies: "Llama 2 is licensed under the LLAMA 2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved."
iv. Your use of the Llama Materials must comply with applicable laws
and regulations (including trade compliance laws and regulations) and adhere to the
Acceptable Use Policy for the Llama Materials (available at
https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into
this Agreement.
v. You will not use the Llama Materials or any output or results of the
Llama Materials to improve any other large language model (excluding Llama 2 or
derivative works thereof).
2. Additional Commercial Terms. If, on the Llama 2 version release date, the
monthly active users of the products or services made available by or for Licensee,
or Licensee's affiliates, is greater than 700 million monthly active users in the
preceding calendar month, you must request a license from Meta, which Meta may
grant to you in its sole discretion, and you are not authorized to exercise any of the
rights under this Agreement unless or until Meta otherwise expressly grants you
such rights.
3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE
LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE
PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY
WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR
FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE
FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING
THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR
USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE
LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT,
NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS
AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL,
CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN
IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF
ANY OF THE FOREGOING.
5. Intellectual Property.
a. No trademark licenses are granted under this Agreement, and in
connection with the Llama Materials, neither Meta nor Licensee may use any name
or mark owned by or associated with the other or any of its affiliates, except as
required for reasonable and customary use in describing and redistributing the
Llama Materials.
b. Subject to Meta's ownership of Llama Materials and derivatives made by or
for Meta, with respect to any derivative works and modifications of the Llama
Materials that are made by you, as between you and Meta, you are and will be the
owner of such derivative works and modifications.
c. If you institute litigation or other proceedings against Meta or any entity
(including a cross-claim or counterclaim in a lawsuit) alleging that the Llama
Materials or Llama 2 outputs or results, or any portion of any of the foregoing,
constitutes infringement of intellectual property or other rights owned or licensable
by you, then any licenses granted to you under this Agreement shall terminate as of
the date such litigation or claim is filed or instituted. You will indemnify and hold
harmless Meta from and against any claim by any third party arising out of or related
to your use or distribution of the Llama Materials.
6. Term and Termination. The term of this Agreement will commence upon your
acceptance of this Agreement or access to the Llama Materials and will continue in
full force and effect until terminated in accordance with the terms and conditions
herein. Meta may terminate this Agreement if you are in breach of any term or
condition of this Agreement. Upon termination of this Agreement, you shall delete
and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the
termination of this Agreement.
7. Governing Law and Jurisdiction. This Agreement will be governed and
construed under the laws of the State of California without regard to choice of law
principles, and the UN Convention on Contracts for the International Sale of Goods
does not apply to this Agreement. The courts of California shall have exclusive
jurisdiction of any dispute arising out of this Agreement.
---
|
Vivekkumargupta/email_spame_classfier | Vivekkumargupta | 2023-07-23T17:03:53Z | 0 | 0 | null | [
"region:us"
] | null | 2023-07-23T16:38:43Z | ---
title: Email_Spam_Proj
sdk: streamlit
emoji: 🚀
colorFrom: yellow
colorTo: green
pinned: false
python_version: 3.9
---
|
taohu88/ppo-SnowballTarget | taohu88 | 2023-07-23T17:00:34Z | 19 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-07-23T17:00:31Z | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: taohu88/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
timothyckl/open_llama_3b_v2_qlora_unfiltered | timothyckl | 2023-07-23T16:59:17Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:ehartford/wizard_vicuna_70k_unfiltered",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-17T08:54:44Z | ---
license: apache-2.0
datasets:
- ehartford/wizard_vicuna_70k_unfiltered
---
# Overview
Fine-tuned [OpenLLaMA-3B-v2](https://huggingface.co/openlm-research/open_llama_3b_v2) with an uncensored/unfiltered Wizard-Vicuna conversation dataset [ehartford/wizard_vicuna_70k_unfiltered](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered).
Used QLoRA for fine-tuning. Trained for one epoch on a 48GB GPU (NVIDIA A6000) instance, took ~9 hours to train.
# Training code
Code used to train the model is made available [here](https://github.com/georgesung/llm_qlora) by georgesung. |
tanmoy-in/opt-350m-bn | tanmoy-in | 2023-07-23T16:53:23Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:finetune:facebook/opt-350m",
"license:other",
"region:us"
] | null | 2023-07-22T02:57:44Z | ---
license: other
base_model: facebook/opt-350m
tags:
- generated_from_trainer
model-index:
- name: opt-350m-bn
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. -->
# opt-350m-bn
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 5
### Training results
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
iamsatyajit/trained-model | iamsatyajit | 2023-07-23T16:51:05Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-05T13:15:48Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
gArthur98/Roberta-classweight-Sentiment-classifier | gArthur98 | 2023-07-23T16:48:44Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-23T16:00:47Z | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: Roberta-classweight-Sentiment-classifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Roberta-classweight-Sentiment-classifier
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:
- Loss: 0.8656
- F1: 0.6449
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9827 | 0.5 | 500 | 0.8890 | 0.6395 |
| 0.9185 | 1.0 | 1000 | 0.8708 | 0.6449 |
| 0.8998 | 1.5 | 1500 | 0.8673 | 0.6449 |
| 0.8792 | 2.01 | 2000 | 0.8648 | 0.6449 |
| 0.8877 | 2.51 | 2500 | 0.8656 | 0.6449 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ALM-AHME/convnextv2-large-1k-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30-Shuffled | ALM-AHME | 2023-07-23T16:43:02Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"convnextv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/convnextv2-large-1k-224",
"base_model:finetune:facebook/convnextv2-large-1k-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-07-23T09:25:15Z | ---
license: apache-2.0
base_model: facebook/convnextv2-large-1k-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnextv2-large-1k-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30-Shuffled
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: Augmented-Final
split: train
args: Augmented-Final
metrics:
- name: Accuracy
type: accuracy
value: 0.9623477297895903
---
<!-- 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. -->
# convnextv2-large-1k-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30-Shuffled
This model is a fine-tuned version of [facebook/convnextv2-large-1k-224](https://huggingface.co/facebook/convnextv2-large-1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1288
- Accuracy: 0.9623
## 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: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2522 | 0.99 | 93 | 0.1288 | 0.9623 |
| 0.1579 | 1.99 | 187 | 0.1211 | 0.9573 |
| 1.1016 | 3.0 | 281 | 1.1018 | 0.3216 |
| 1.0934 | 4.0 | 375 | 1.0787 | 0.6432 |
| 0.5795 | 4.99 | 468 | 0.5864 | 0.6445 |
| 0.5437 | 5.99 | 562 | 0.5733 | 0.7369 |
| 0.3369 | 6.94 | 651 | 0.3298 | 0.9030 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Hawk91/ddpm-celebahq-finetuned-anime-6epochs | Hawk91 | 2023-07-23T16:41:49Z | 32 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | 2023-07-23T16:29:59Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This is a celebahq sd model fine-tuned with anime faces dataset.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Hawk91/ddpm-celebahq-finetuned-anime-6epochs')
image = pipeline().images[0]
image
```
|
akshitapps/sandman-2.0 | akshitapps | 2023-07-23T16:40:52Z | 30 | 1 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"photorealistic",
"photoreal",
"en",
"license:other",
"autotrain_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-07-21T18:58:08Z | ---
language:
- en
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- photorealistic
- photoreal
- diffusers
inference: false
---
# Dreamlike Photoreal 2.0 is a photorealistic model based on Stable Diffusion 1.5, made by [dreamlike.art](https://dreamlike.art/).
# If you want to use dreamlike models on your website/app/etc., check the license at the bottom first!
Warning: This model is horny! Add "nude, naked" to the negative prompt if want to avoid NSFW.
You can add **photo** to your prompt to make your gens look more photorealistic.
Non-square aspect ratios work better for some prompts. If you want a portrait photo, try using a vertical aspect ratio. If you want a landscape photo, try using a horizontal aspect ratio.
This model was trained on 768x768px images, so use 768x768px, 640x896px, 896x640px, etc. It also works pretty good with higher resolutions such as 768x1024px or 1024x768px.
### Examples
<img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview1.jpg" style="max-width: 800px;" width="100%"/>
<img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview2.jpg" style="max-width: 800px;" width="100%"/>
<img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview3.jpg" style="max-width: 800px;" width="100%"/>
### dreamlike.art
You can use this model for free on [dreamlike.art](https://dreamlike.art/)!
<img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/dreamlike.jpg" style="max-width: 1000px;" width="100%"/>
### CKPT
[Download dreamlike-photoreal-2.0.ckpt (2.13GB)](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/dreamlike-photoreal-2.0.ckpt)
### Safetensors
[Download dreamlike-photoreal-2.0.safetensors (2.13GB)](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/dreamlike-photoreal-2.0.safetensors)
### 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "dreamlike-art/dreamlike-photoreal-2.0"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "photo, a church in the middle of a field of crops, bright cinematic lighting, gopro, fisheye lens"
image = pipe(prompt).images[0]
image.save("./result.jpg")
```
<img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/church.jpg" style="max-width: 640px;" width="100%"/>
# License
This model is licesed under a **modified** CreativeML OpenRAIL-M license.
- **You are not allowed to host, finetune, or do inference with the model or its derivatives on websites/apps/etc. If you want to, please email us at [email protected]**
- **You are free to host the model card and files (Without any actual inference or finetuning) on both commercial and non-commercial websites/apps/etc. Please state the full model name (Dreamlike Photoreal 2.0) and include the license as well as a link to the model card (https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0)**
- **You are free to use the outputs (images) of the model for commercial purposes in teams of 10 or less**
- You can't use the model to deliberately produce nor share illegal or harmful outputs or content
- The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
- You may re-distribute the weights. 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 **modified** CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/blob/main/LICENSE.md
|
rajat/deidentify | rajat | 2023-07-23T16:38:59Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-23T16:38:58Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
RicardoLee/Llama2-base-7B-Chinese-50W-pre_release | RicardoLee | 2023-07-23T16:38:25Z | 10 | 11 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"llama2",
"llama2-base",
"llama2-base-7B",
"zh",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-23T07:15:33Z | ---
language:
- zh
- en
tags:
- llama2
- llama2-base
- llama2-base-7B
---
# 7B Chinese Chatbot trained based on LLama2-base 7B
## Introduction
在完成了[Llama2-chat 7B Chinese](https://huggingface.co/RicardoLee/Llama2-chat-Chinese-50W) 和 [Llama2-chat 13B Chinese](https://huggingface.co/RicardoLee/Llama2-chat-13B-Chinese-50W) 的训练后，我非常好奇能否直接基于Llama2-base 系列直接进行SFT训练。这也是本模型仓库的初衷。
但是在实际操作中，在用了原先chat模型的LoRA训练框架后，我发现基于Llama2 base的 LoRA 训练非常难以收敛，随时处于梯度爆炸的边缘。DeepSpeed 会频繁触发reduce scale 操作，最终scale太小越界导致训练崩溃。我遍历了LR 1e-5 - 2e-4，LoRA rank \[4, 8, 64\]，LoRA Alpha \[1,4,8,16,32\]，LoRA Dropout \[0.05, 0.1\] ，Warmup Ratio \[0.01, 0.03, 0.05\]等超参数，均无法稳定训练。因此，本模型重新回归了全参数SFT训练。其难以进行LoRA训练的原因还待分析。
由于网上存在使用LoRA 在英文SFT数据集上基于Llama2-base 进行SFT训练成功的样例，因此我怀疑难以训练的原因可能是扩中文词表embedding导致训练难度大幅度提升。
为了方便后来人一起分析，本模型仓库特地将训练的全部loss/LR信息附在[Material](trainer_state.json)中。
训练数据使用[BELLE](https://huggingface.co/BelleGroup)项目中采样的50万SFT数据进行SFT训练。
After finishing the training of [Llama2-chat 7B Chinese](https://huggingface.co/RicardoLee/Llama2-chat-Chinese-50W) and [Llama2-chat 13B Chinese](https://huggingface.co/RicardoLee/Llama2-chat-13B-Chinese-50W), I am deeply intrigued by the possibility of conducting SFT (Style-Fine-Tuning) training directly based on the Llama2-base series. This is the fundamental purpose of this model repository.
**However**, in real practice, I have observed that conducting LoRA training based on the Llama2 base model, within the framework of the previous Llama2-chat SFT project, presents significant challenges in achieving convergence. The gradient explosion happens in every training step and casue reducing scale operation in Deepspeed. In the end, the scale is too small and out of bounds, causing the training to crash. I have traversed LR 1e-5 - 2e-4，LoRA rank \[4, 8, 64\]，LoRA Alpha \[1,4,8,16,32\]，LoRA Dropout \[0.05, 0.1\] ，Warmup Ratio \[0.01, 0.03, 0.05\] and other hyperparameters, all of which cannot be trained stably. Therefore, this model has reverted to full-parameter SFT training. The reasons behind the difficulties encountered during LoRA training require further analysis.
As there are instances online where successful LoRA training on English SFT datasets using Llama2-base has been demonstrated, I suspect that the challenge in training might be attributed to the expansion of the Chinese word embedding, resulting in a substantial increase in training difficulty.
In order to facilitate collaborative analysis for future researchers, this model repository has thoughtfully appended all training-related loss/LR information in [Material](trainer_state.json).
The training data is sampled from [BELLE](https://huggingface.co/BelleGroup) project, which consists of 500,000 SFT samples.
## Train Detail
一些训练上的细节：
1. 训练框架：该模型采用全参数SFT训练，而非LoRA
2. Tokenizer：该模型使用了Chinese-Alpaca-Plus模型的tokenizer.model。这是因为LLama2本身的tokenizer.model同LLama1是一摸一样的。因此理论上可以完全复用Chinese-LLaMa项目的tokenizer而不会产生如何错位问题。
3. 训练参数：受限于资源，本模型只训练了1 epoch。其LR 为2e-4。Warmup ratio 为0.01。可以看到这是一个非常激进的训练，因此本模型仓库被命名为了预发布版本。未来会接着放出3 epoch版本。
4. 训练资源：8卡V100。21个小时
5. 训练起始的loss：参见[Material](trainer_state.json)
6. 训练终止的loss：参见[Material](trainer_state.json)
Some details in training:
1. Trianing Framework: This model adopts full-parameter SFT training instead of LoRA.
2. Tokenizer: This model utilizes the tokenizer.model from the Chinese-Alpaca-Plus model. The reason for this choice is that the tokenizer.model in LLama2 is identical to the one used in LLama1. As a result, it is theoretically feasible to entirely reuse the tokenizer from the Chinese-LLaMa project without encountering any issues related to token misalignment.
3. Training Parameters: Constrained by limited resources, this model was trained for only 1 epoch, with a learning rate of 2e-4 and a warmup ratio of 0.01. Obviously, this is an exceedingly aggressive training schema, hence this model repository has been labeled as the 'pre-release' version. In the future, a 3-epoch version will be released subsequently for the comparison of previous Llama2-chat Chinese models.
4. Training Resource: 8\*V100, 21 hours.
5. Initial Loss: Please refer to [Material](trainer_state.json)
6. Train Loss: Please refer to [Material](trainer_state.json)
## Inference
该模型依然采用stanford alpaca 模版。因此在测试时且别忘记添加开场白。开场白如下：
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\n${Your Content}\n\n### Response:\n\n"
对于带上文的对话，开场白如下：
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\nHuman:${Previous Human Content}\nAssistant:${Previous Assistance Content}\nHuman:${Your Question}\n\n### Response:\n\n"
This model still using the Stanford Alpaca template. Therefore, don't forget to add prologue template. The prologue template is:
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\n${Your Content}\n\n### Response:\n\n"
For dialogue with context, the prelogue template is:
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\nHuman:${Previous Human Content}\nAssistant:${Previous Machine Content}\nHuman:${Your Question}\n\n### Response:\n\n"
## Licence
本仓库的模型依照 Apache-2.0 协议开源，模型的权重的使用则需要遵循LLama2[MODEL LICENCE](LICENSE)。
This repository's models are open-sourced under the Apache-2.0 license, and their weight usage must adhere to LLama2 [MODEL LICENCE](LICENSE) license.
## Future Work
将会在近期逐步放出
1. 更大SFT数据规模训练下的模型。
2. 13B及以下的LLama2 同LLama2-chat的模型，以供大家对比。
I will release the following models:
1. Models trained on larger data scale.
2. Models trained on LLama2 and LLama2-chat (under the 13B, since I only have V100), for comparison.
|
HasinMDG/X-Sent-Deberta_v35 | HasinMDG | 2023-07-23T16:08:16Z | 4 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"deberta-v2",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | 2023-07-23T16:07:57Z | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HasinMDG/X-Sent-Deberta_v35
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("HasinMDG/X-Sent-Deberta_v35")
# 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}
}
```
|
wuru330/flipped_results | wuru330 | 2023-07-23T16:03:24Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-07-23T15:17:12Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: flipped_results
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. -->
# flipped_results
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5433
- Accuracy: 0.4099
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3063 | 1.0 | 37 | 1.2485 | 0.4592 |
| 1.2272 | 2.0 | 74 | 1.1915 | 0.4626 |
| 1.1518 | 3.0 | 111 | 1.1770 | 0.4575 |
| 1.0504 | 4.0 | 148 | 1.1724 | 0.4745 |
| 0.9525 | 5.0 | 185 | 1.1966 | 0.4898 |
| 0.7485 | 6.0 | 222 | 1.2403 | 0.4626 |
| 0.5645 | 7.0 | 259 | 1.3973 | 0.4235 |
| 0.4645 | 8.0 | 296 | 1.4260 | 0.4898 |
| 0.374 | 9.0 | 333 | 1.5838 | 0.4405 |
| 0.2721 | 10.0 | 370 | 1.6747 | 0.4354 |
| 0.2679 | 11.0 | 407 | 1.7427 | 0.4507 |
| 0.2284 | 12.0 | 444 | 1.8097 | 0.4269 |
| 0.2003 | 13.0 | 481 | 1.9738 | 0.3997 |
| 0.1844 | 14.0 | 518 | 1.9745 | 0.4524 |
| 0.1631 | 15.0 | 555 | 2.0326 | 0.4456 |
| 0.1135 | 16.0 | 592 | 2.1294 | 0.4184 |
| 0.1188 | 17.0 | 629 | 2.1613 | 0.4065 |
| 0.1204 | 18.0 | 666 | 2.1795 | 0.4252 |
| 0.1083 | 19.0 | 703 | 2.2433 | 0.4167 |
| 0.0794 | 20.0 | 740 | 2.2762 | 0.4150 |
| 0.0589 | 21.0 | 777 | 2.3736 | 0.4065 |
| 0.0646 | 22.0 | 814 | 2.3644 | 0.4252 |
| 0.0744 | 23.0 | 851 | 2.4478 | 0.4099 |
| 0.0728 | 24.0 | 888 | 2.4367 | 0.4099 |
| 0.0382 | 25.0 | 925 | 2.5123 | 0.3997 |
| 0.033 | 26.0 | 962 | 2.5202 | 0.4031 |
| 0.0326 | 27.0 | 999 | 2.5145 | 0.4099 |
| 0.023 | 28.0 | 1036 | 2.5309 | 0.4099 |
| 0.0377 | 29.0 | 1073 | 2.5409 | 0.4099 |
| 0.0243 | 30.0 | 1110 | 2.5433 | 0.4099 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Hawk91/sd_Anime_Faces_32 | Hawk91 | 2023-07-23T15:34:14Z | 30 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | 2023-07-22T12:36:03Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of Anime faces.
github link for repository: https://github.com/mohit-ix/StableDiffusionHF
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Hawk91/sd_Anime_Faces_32')
image = pipeline().images[0]
image
```
|
tslai1992/rl_course_vizdoom_health_gathering_supreme | tslai1992 | 2023-07-23T15:24:01Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T15:06:57Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.05 +/- 3.17
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r tslai1992/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
sindhoorar/brain-tumor | sindhoorar | 2023-07-23T15:20:44Z | 0 | 0 | null | [
"medical",
"deep learning",
"neuroscience",
"en",
"license:cc-by-2.0",
"region:us"
] | null | 2023-07-23T15:17:06Z | ---
license: cc-by-2.0
language:
- en
tags:
- medical
- deep learning
- neuroscience
--- |
nikbhi/lunar_lander | nikbhi | 2023-07-23T15:15:14Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T15:14:56Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: Proximal Policy Optimization
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 252.05 +/- 16.24
name: mean_reward
verified: false
---
# **Proximal Policy Optimization** Agent playing **LunarLander-v2**
This is a trained model of a **Proximal Policy Optimization** 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
...
```
|
slickdata/finetuned-Sentiment-classfication-ROBERTA-model | slickdata | 2023-07-23T15:08:57Z | 127 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-15T19:36:22Z | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: finetuned-Sentiment-classfication-ROBERTA-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-Sentiment-classfication-ROBERTA-model
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:
- Loss: 0.2222
- Rmse: 0.2936
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6684 | 2.72 | 500 | 0.3931 | 0.4892 |
| 0.1963 | 5.43 | 1000 | 0.2222 | 0.2936 |
| 0.0755 | 8.15 | 1500 | 0.2479 | 0.2757 |
| 0.0413 | 10.86 | 2000 | 0.3233 | 0.2794 |
| 0.0213 | 13.58 | 2500 | 0.3590 | 0.2689 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
tijstijs/ppo-SnowballTarget | tijstijs | 2023-07-23T15:05:09Z | 8 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-07-23T15:05:02Z | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: tijstijs/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Samalabama66/Taxi_v3 | Samalabama66 | 2023-07-23T14:59:07Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T14:59:03Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi_v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.42 +/- 2.75
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="Samalabama66/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"])
```
|
Tverous/sft-trl | Tverous | 2023-07-23T14:58:52Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"generated_from_trainer",
"dataset:anli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-22T21:25:14Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- anli
model-index:
- name: sft-trl
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. -->
# sft-trl
This model is a fine-tuned version of [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) on the anli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6837
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.1859 | 0.2 | 1000 | 2.0019 |
| 1.1342 | 0.4 | 2000 | 1.5352 |
| 0.8871 | 0.6 | 3000 | 1.0923 |
| 0.591 | 0.8 | 4000 | 0.8373 |
| 0.3165 | 1.0 | 5000 | 0.7852 |
| 0.3205 | 1.2 | 6000 | 0.7531 |
| 0.2338 | 1.4 | 7000 | 0.7155 |
| 0.2922 | 1.6 | 8000 | 0.6837 |
| 0.2427 | 1.8 | 9000 | 0.6837 |
| 0.2036 | 2.0 | 10000 | 0.6837 |
| 0.1205 | 2.2 | 11000 | 0.6837 |
| 0.2275 | 2.4 | 12000 | 0.6837 |
| 0.1094 | 2.6 | 13000 | 0.6837 |
| 0.1507 | 2.8 | 14000 | 0.6837 |
| 0.1739 | 3.0 | 15000 | 0.6837 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
NasimB/guten | NasimB | 2023-07-23T14:57:26Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-23T07:46:14Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: guten
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. -->
# guten
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1054
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.3418 | 0.29 | 500 | 5.3350 |
| 5.0312 | 0.58 | 1000 | 4.9322 |
| 4.6974 | 0.87 | 1500 | 4.6839 |
| 4.4435 | 1.17 | 2000 | 4.5502 |
| 4.2915 | 1.46 | 2500 | 4.4247 |
| 4.1863 | 1.75 | 3000 | 4.3206 |
| 4.0756 | 2.04 | 3500 | 4.2468 |
| 3.89 | 2.33 | 4000 | 4.2082 |
| 3.8602 | 2.62 | 4500 | 4.1531 |
| 3.8239 | 2.91 | 5000 | 4.1034 |
| 3.6381 | 3.21 | 5500 | 4.1005 |
| 3.5871 | 3.5 | 6000 | 4.0692 |
| 3.559 | 3.79 | 6500 | 4.0366 |
| 3.481 | 4.08 | 7000 | 4.0358 |
| 3.3172 | 4.37 | 7500 | 4.0295 |
| 3.3056 | 4.66 | 8000 | 4.0174 |
| 3.295 | 4.95 | 8500 | 4.0045 |
| 3.1594 | 5.24 | 9000 | 4.0171 |
| 3.1303 | 5.54 | 9500 | 4.0157 |
| 3.1295 | 5.83 | 10000 | 4.0147 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Samalabama66/q-FrozenLake-v1-4x4-noSlippery | Samalabama66 | 2023-07-23T14:56:43Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T14:56:39Z | ---
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="Samalabama66/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"])
```
|
ZachBeesley/marian-finetuned-kde4-en-to-fr | ZachBeesley | 2023-07-23T14:48:02Z | 58 | 0 | transformers | [
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-07-22T23:29:25Z | ---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- generated_from_keras_callback
model-index:
- name: ZachBeesley/marian-finetuned-kde4-en-to-fr
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. -->
# ZachBeesley/marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7977
- Validation Loss: 0.8201
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 17733, '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: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.0610 | 0.8780 | 0 |
| 0.7977 | 0.8201 | 1 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
tslai1992/ppo-LunarLander-v2 | tslai1992 | 2023-07-23T14:47:13Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T13:50:03Z | ---
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: -187.45 +/- 124.90
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'experiment'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'tslai1992/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
Aspik101/Llama-2-7b-hf-instruct-pl-lora_unload | Aspik101 | 2023-07-23T14:27:54Z | 1,480 | 1 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"facebook",
"meta",
"llama-2",
"pl",
"dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-23T14:18:11Z | ---
language:
- pl
datasets:
- Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish
license: other
model_type: llama-2
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
--- |
dhinman/Reinforce-CartPole | dhinman | 2023-07-23T14:26:17Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T14:26:07Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Aspik101/Llama-2-7b-hf-instruct-pl-lora_GGML | Aspik101 | 2023-07-23T14:18:10Z | 0 | 0 | null | [
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"text-generation",
"pl",
"dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish",
"license:other",
"region:us"
] | text-generation | 2023-07-23T14:11:33Z | ---
language:
- pl
datasets:
- Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish
license: other
model_type: llama-2
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
--- |
BaoKien/falcon-7b-finetuned-QA-1000_prompt_6 | BaoKien | 2023-07-23T14:16:54Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-23T14:16:45Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
rdpatilds/my_awesome_billsum_model | rdpatilds | 2023-07-23T14:16:36Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:billsum",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-07-23T04:06:27Z | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1689
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4359
- Rouge1: 0.1689
- Rouge2: 0.0732
- Rougel: 0.143
- Rougelsum: 0.1431
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 124 | 2.6287 | 0.1318 | 0.0424 | 0.11 | 0.11 | 19.0 |
| No log | 2.0 | 248 | 2.4971 | 0.1403 | 0.0482 | 0.1168 | 0.1169 | 19.0 |
| No log | 3.0 | 372 | 2.4484 | 0.1629 | 0.0675 | 0.1369 | 0.1371 | 19.0 |
| No log | 4.0 | 496 | 2.4359 | 0.1689 | 0.0732 | 0.143 | 0.1431 | 19.0 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
emilianJR/epiCRealism | emilianJR | 2023-07-23T14:14:35Z | 65,089 | 68 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-06-25T12:13:26Z | ---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Diffuser model for this SD checkpoint:
https://civitai.com/models/25694/epicrealism
**emilianJR/epiCRealism** is the HuggingFace diffuser that you can use with **diffusers.StableDiffusionPipeline()**.
Examples | Examples | Examples
---- | ---- | ----
,%20raw%20photo,%20((monochrome)),%20((grayscale)),%20black%20and%20white%20photo.jpeg) |  | ![]()
,%20lotr,%20fantasy,%20elf,%20female,%20full%20body,%20looking%20at%20viewer,%20portrait,%20phot.jpeg) | ,%20soft%20lighting,%20h.jpeg) | ![]()
-------
## 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "emilianJR/epiCRealism"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "YOUR PROMPT"
image = pipe(prompt).images[0]
image.save("image.png")
```
## License
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:
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
Vladislav-HuggingFace/Reinforce-Pixelcopter-PLE-v0 | Vladislav-HuggingFace | 2023-07-23T14:14:18Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-22T23:40:38Z | ---
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: 33.40 +/- 28.26
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
|
teilomillet/Reinforce-CartPole-v1 | teilomillet | 2023-07-23T14:09:28Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T13:24:32Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 818.50 +/- 180.87
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Aspik101/Llama-2-7b-hf-instruct-pl-lora_adapter_model | Aspik101 | 2023-07-23T14:08:01Z | 0 | 0 | null | [
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"text-generation",
"pl",
"dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish",
"license:other",
"region:us"
] | text-generation | 2023-07-23T14:08:00Z | ---
language:
- pl
datasets:
- Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish
license: other
model_type: llama-2
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
--- |
jeonsworld/sentence-embedding-base | jeonsworld | 2023-07-23T13:45:56Z | 2 | 1 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2023-07-23T13:43:51Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
sagarsdesai/dqn-SpaceInvadersNoFrameskip-v4 | sagarsdesai | 2023-07-23T13:45:02Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-22T13:04:49Z | ---
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: 394.50 +/- 141.94
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 sagarsdesai -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 sagarsdesai -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 sagarsdesai
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 1000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
KingAsiedu/tweet_sentiments_analysis_bert | KingAsiedu | 2023-07-23T13:43:34Z | 113 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-23T12:22:57Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: tweet_sentiments_analysis_bert
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. -->
# tweet_sentiments_analysis_bert
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6473
- F1-score: 0.7199
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6762 | 1.0 | 1000 | 0.6473 | 0.7199 |
| 0.537 | 2.0 | 2000 | 0.6577 | 0.7334 |
| 0.3792 | 3.0 | 3000 | 0.8750 | 0.7392 |
| 0.2142 | 4.0 | 4000 | 1.2328 | 0.7425 |
| 0.1037 | 5.0 | 5000 | 1.4804 | 0.7410 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
tslai1992/a2c-PandaReachDense-v2 | tslai1992 | 2023-07-23T13:42:14Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T12:50:48Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.37 +/- 0.21
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
...
```
|
vineetsharma/rl_course_vizdoom_health_gathering_supreme | vineetsharma | 2023-07-23T13:42:06Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T13:29:53Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 12.15 +/- 5.33
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r vineetsharma/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Corianas/PixelCopter_next_try | Corianas | 2023-07-23T13:38:30Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T07:10:28Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PixelCopter_next_try
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 37.70 +/- 26.66
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
|
model-embeddings/multi-qa-mpnet-base-dot-v1 | model-embeddings | 2023-07-23T13:26:58Z | 2,893 | 1 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:search_qa",
"dataset:eli5",
"dataset:natural_questions",
"dataset:trivia_qa",
"dataset:embedding-data/QQP",
"dataset:embedding-data/PAQ_pairs",
"dataset:embedding-data/Amazon-QA",
"dataset:embedding-data/WikiAnswers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2023-07-23T13:15:08Z | ---
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
datasets:
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- search_qa
- eli5
- natural_questions
- trivia_qa
- embedding-data/QQP
- embedding-data/PAQ_pairs
- embedding-data/Amazon-QA
- embedding-data/WikiAnswers
---
# multi-qa-mpnet-base-dot-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer, util
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]
#Load the model
model = SentenceTransformer('sentence-transformers/multi-qa-mpnet-base-dot-v1')
#Encode query and documents
query_emb = model.encode(query)
doc_emb = model.encode(docs)
#Compute dot score between query and all document embeddings
scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
for doc, score in doc_score_pairs:
print(score, doc)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#CLS Pooling - Take output from first token
def cls_pooling(model_output):
return model_output.last_hidden_state[:,0]
#Encode text
def encode(texts):
# Tokenize sentences
encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input, return_dict=True)
# Perform pooling
embeddings = cls_pooling(model_output)
return embeddings
# Sentences we want sentence embeddings for
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-mpnet-base-dot-v1")
model = AutoModel.from_pretrained("sentence-transformers/multi-qa-mpnet-base-dot-v1")
#Encode query and docs
query_emb = encode(query)
doc_emb = encode(docs)
#Compute dot score between query and all document embeddings
scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
for doc, score in doc_score_pairs:
print(score, doc)
```
## Technical Details
In the following some technical details how this model must be used:
| Setting | Value |
| --- | :---: |
| Dimensions | 768 |
| Produces normalized embeddings | No |
| Pooling-Method | CLS pooling |
| Suitable score functions | dot-product (e.g. `util.dot_score`) |
----
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developped this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developped this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intented to be used for semantic search: It encodes queries / questions and text paragraphs in a dense vector space. It finds relevant documents for the given passages.
Note that there is a limit of 512 word pieces: Text longer than that will be truncated. Further note that the model was just trained on input text up to 250 word pieces. It might not work well for longer text.
## Training procedure
The full training script is accessible in this current repository: `train_script.py`.
### Pre-training
We use the pretrained [`mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure.
#### Training
We use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
The model was trained with [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) using CLS-pooling, dot-product as similarity function, and a scale of 1.
| Dataset | Number of training tuples |
|--------------------------------------------------------|:--------------------------:|
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs from WikiAnswers | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) Automatically generated (Question, Paragraph) pairs for each paragraph in Wikipedia | 64,371,441 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs from all StackExchanges | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs from all StackExchanges | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) Triplets (query, answer, hard_negative) for 500k queries from Bing search engine | 17,579,773 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) (query, answer) pairs for 3M Google queries and Google featured snippet | 3,012,496 |
| [Amazon-QA](http://jmcauley.ucsd.edu/data/amazon/qa/) (Question, Answer) pairs from Amazon product pages | 2,448,839
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) pairs from Yahoo Answers | 1,198,260 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) pairs from Yahoo Answers | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) pairs from Yahoo Answers | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) (Question, Answer) pairs for 140k questions, each with Top5 Google snippets on that question | 582,261 |
| [ELI5](https://huggingface.co/datasets/eli5) (Question, Answer) pairs from Reddit ELI5 (explainlikeimfive) | 325,475 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions pairs (titles) | 304,525 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Question, Duplicate_Question, Hard_Negative) triplets for Quora Questions Pairs dataset | 103,663 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) (Question, Paragraph) pairs for 100k real Google queries with relevant Wikipedia paragraph | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) (Question, Paragraph) pairs from SQuAD2.0 dataset | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) (Question, Evidence) pairs | 73,346 |
| **Total** | **214,988,242** | |
laion/scaling-laws-openclip | laion | 2023-07-23T13:26:57Z | 0 | 3 | null | [
"arxiv:2212.07143",
"region:us"
] | null | 2022-12-16T14:09:50Z | # Reproducible scaling laws for contrastive language-image learning [[arXiv]](https://arxiv.org/abs/2212.07143)
We provide pre-trained models used in the paper "Reproducible scaling laws for contrastive language-image learning" [[arXiv]](https://arxiv.org/abs/2212.07143).
Please refer to the official [Github](https://github.com/LAION-AI/scaling-laws-openclip) repository for more information about how to reproduce the results and use the models. |
DerckPrinz/ppo-LunarLander-v2 | DerckPrinz | 2023-07-23T13:20:21Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T13:20:05Z | ---
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: 279.20 +/- 22.76
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
...
```
|
oleksandrfluxon/mpt-7b-instruct-2 | oleksandrfluxon | 2023-07-23T13:14:25Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"mpt",
"text-generation",
"Composer",
"MosaicML",
"llm-foundry",
"custom_code",
"dataset:mosaicml/dolly_hhrlhf",
"arxiv:2205.14135",
"arxiv:2108.12409",
"arxiv:2010.04245",
"license:cc-by-sa-3.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-07-22T17:18:34Z | ---
license: cc-by-sa-3.0
datasets:
- mosaicml/dolly_hhrlhf
tags:
- Composer
- MosaicML
- llm-foundry
inference: false
duplicated_from: mosaicml/mpt-7b-instruct
---
# MPT-7B-Instruct
MPT-7B-Instruct is a model for short-form instruction following.
It is built by finetuning [MPT-7B](https://huggingface.co/mosaicml/mpt-7b) on a [dataset](https://huggingface.co/datasets/sam-mosaic/dolly_hhrlhf) derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets.
* License: _CC-By-SA-3.0_
* [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-instruct)
This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture.
## Model Date
May 5, 2023
## Model License
CC-By-SA-3.0
## Documentation
* [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b)
* [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/)
* Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)!
### Example Question/Instruction
**Longboi24**:
> What is a quoll?
**MPT-7B-Instruct**:
>A Quoll (pronounced “cool”) is one of Australia’s native carnivorous marsupial mammals, which are also known as macropods or wallabies in other parts around Asia and South America
## How to Use
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom model architecture that is not yet part of the `transformers` package.
It includes options for many training efficiency features such as [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), QK LayerNorm, and more.
```python
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b-instruct',
trust_remote_code=True
)
```
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package.
`MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more.
To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision:
```python
import torch
import transformers
name = 'mosaicml/mpt-7b-instruct'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.attn_config['attn_impl'] = 'triton'
config.init_device = 'cuda:0' # For fast initialization directly on GPU!
model = transformers.AutoModelForCausalLM.from_pretrained(
name,
config=config,
torch_dtype=torch.bfloat16, # Load model weights in bfloat16
trust_remote_code=True
)
```
Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example:
```python
import transformers
name = 'mosaicml/mpt-7b-instruct'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.max_seq_len = 4096 # (input + output) tokens can now be up to 4096
model = transformers.AutoModelForCausalLM.from_pretrained(
name,
config=config,
trust_remote_code=True
)
```
This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
```
The model can then be used, for example, within a text-generation pipeline.
Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html).
```python
from transformers import pipeline
pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')
with torch.autocast('cuda', dtype=torch.bfloat16):
print(
pipe('Here is a recipe for vegan banana bread:\n',
max_new_tokens=100,
do_sample=True,
use_cache=True))
```
### Formatting
This model was trained on data formatted in the dolly-15k format:
```python
INSTRUCTION_KEY = "### Instruction:"
RESPONSE_KEY = "### Response:"
INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
PROMPT_FOR_GENERATION_FORMAT = """{intro}
{instruction_key}
{instruction}
{response_key}
""".format(
intro=INTRO_BLURB,
instruction_key=INSTRUCTION_KEY,
instruction="{instruction}",
response_key=RESPONSE_KEY,
)
example = "James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week? Explain before answering."
fmt_ex = PROMPT_FOR_GENERATION_FORMAT.format(instruction=example)
```
In the above example, `fmt_ex` is ready to be tokenized and sent through the model.
## Model Description
The architecture is a modification of a standard decoder-only transformer.
The model has been modified from a standard transformer in the following ways:
* It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf)
* It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings
* It does not use biases
| Hyperparameter | Value |
|----------------|-------|
|n_parameters | 6.7B |
|n_layers | 32 |
| n_heads | 32 |
| d_model | 4096 |
| vocab size | 50432 |
| sequence length | 2048 |
## PreTraining Data
For more details on the pretraining process, see [MPT-7B](https://huggingface.co/mosaicml/mpt-7b).
The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
### Training Configuration
This model was trained on 8 A100-40GBs for about 2.3 hours using the [MosaicML Platform](https://www.mosaicml.com/platform).
The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the AdamW optimizer.
## Limitations and Biases
_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_
MPT-7B-Instruct can produce factually incorrect output, and should not be relied on to produce factually accurate information.
MPT-7B-Instruct was trained on various public datasets.
While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
## Acknowledgements
This model was finetuned by Sam Havens and the MosaicML NLP team
## MosaicML Platform
If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b).
## Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
## Citation
Please cite this model using the following format:
```
@online{MosaicML2023Introducing,
author = {MosaicML NLP Team},
title = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs},
year = {2023},
url = {www.mosaicml.com/blog/mpt-7b},
note = {Accessed: 2023-03-28}, % change this date
urldate = {2023-03-28} % change this date
}
```
|
akreal/MMS-1B-all-shared-params | akreal | 2023-07-23T13:06:34Z | 0 | 0 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-07-23T12:52:16Z | ---
license: cc-by-nc-4.0
---
This is ASR fine-tuned MMS 1B model (the shared parameters) converted for the s3prl usage.
|
digiplay/Opiate_v3 | digiplay | 2023-07-23T13:05:24Z | 2,151 | 2 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-07-23T10:59:43Z | ---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://civitai.com/models/69587?modelVersionId=121220
Original Author's DEMO images :
|
ailabturkiye/KralSakir-Sakir | ailabturkiye | 2023-07-23T13:00:24Z | 0 | 1 | null | [
"license:other",
"region:us"
] | null | 2023-07-23T12:06:22Z | ---
license: other
---
<!-- Codes by HTML.am -->
<!-- CSS Code -->
<style type="text/css" scoped>
img.GeneratedImage {
width:300px;height:194px;margin:10px;border-width:6px;border-color:#FFFFFF;border-style:solid;
}
</style>
<!-- HTML Code -->
<a href="https://heylink.me/StickColor/" target="_self"><img src="https://cdn.discordapp.com/attachments/715205867439390800/1132656221200064532/sssssssssssssssssssssssssssssssseewewewew.png" alt="By StickColor
heylink.me/stickcolor" class="GeneratedImage"></a>
Listen Ex ;
<!-- HTML Codes by Quackit.com -->
<audio src="https://cdn.discordapp.com/attachments/715205867439390800/1132658173745037354/KralSakir-feat.Ceza_Yerli_Plaka.mp3" autoplay="autoplay" loop="loop" id="MusicPlayer">
<embed src="https://cdn.discordapp.com/attachments/715205867439390800/1132658173745037354/KralSakir-feat.Ceza_Yerli_Plaka.mp3" autoplay="true" width="450" height="300" name="MusicPlayer" id="MusicPlayer">
</audio>
By StickColor Remaster ;) I'm a Phonk Prod;
|
Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch1 | Evan-Lin | 2023-07-23T12:58:49Z | 46 | 0 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | reinforcement-learning | 2023-07-23T12:56:28Z | ---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="Evan-Lin//tmp/tmp30s_h2fs/Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch1")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmp30s_h2fs/Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch1")
model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmp30s_h2fs/Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch1")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
rajat/my-awesome-model | rajat | 2023-07-23T12:48:04Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-23T12:48:02Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
finloop/yolov8s-seg-solar-panels | finloop | 2023-07-23T12:33:39Z | 37 | 4 | ultralytics | [
"ultralytics",
"ultralyticsplus",
"yolov8",
"yolo",
"vision",
"instance-segmentation",
"pytorch",
"awesome-yolov8-models",
"solar-panels",
"pv-panels",
"image-segmentation",
"license:mit",
"region:us"
] | image-segmentation | 2023-07-10T07:24:59Z | ---
license: mit
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- instance-segmentation
- pytorch
- awesome-yolov8-models
- solar-panels
- pv-panels
library_name: ultralytics
library_version: 8.0.57
inference: false
pipeline_tag: image-segmentation
---
YOLOv8s trained on solar panels dataset https://app.roboflow.com/rzeszow-university-of-technology/solar-panels-seg/2
**Inference API:** [On Roboflow](https://app.roboflow.com/rzeszow-university-of-technology/solar-panels-seg/deploy/2)
## Training results
*Labels:*
*Predictions:*
## How to use
1. Instal ultralytics package. Follow their guide here: [Quickstart](https://docs.ultralytics.com/quickstart/)
2. Clone this repository.
3. Run inference
```sh
yolo segment predict model=best.pt imgsz=640 save=True source=image.png
```
|
espnet/pengcheng_aishell_asr_train_asr_whisper_medium_finetune_raw_zh_whisper_multilingual_sp | espnet | 2023-07-23T12:17:53Z | 7 | 1 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:aishell",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2023-07-23T10:17:00Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: zh
datasets:
- aishell
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/pengcheng_aishell_asr_train_asr_whisper_medium_finetune_raw_zh_whisper_multilingual_sp`
This model was trained by Pengcheng Guo using aishell recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 8503eb7de909bfade7c2339641073eb19b33c1f2
pip install -e .
cd egs2/aishell/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/pengcheng_aishell_asr_train_asr_whisper_medium_finetune_raw_zh_whisper_multilingual_sp
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sun Jul 23 12:02:06 CST 2023`
- python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]`
- espnet version: `espnet 202304`
- pytorch version: `pytorch 1.10.1`
- Git hash: ``
- Commit date: ``
## exp/whisper_finetune/asr_train_asr_whisper_full_raw_zh_whisper_multilingual_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_whisper_noctc_beam10_asr_model_valid.acc.ave/dev|14326|14326|76.0|24.0|0.0|0.0|24.0|24.0|
|decode_asr_whisper_noctc_beam10_asr_model_valid.acc.ave/test|7176|7176|74.5|25.5|0.0|0.0|25.5|25.5|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_whisper_noctc_beam10_asr_model_valid.acc.ave/dev|14326|205341|97.3|2.6|0.1|0.1|2.8|24.0|
|decode_asr_whisper_noctc_beam10_asr_model_valid.acc.ave/test|7176|104765|97.1|2.8|0.1|0.1|3.0|25.5|
## ASR config
<details><summary>expand</summary>
```
config: conf/whisper/train_asr_whisper_full.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/whisper_finetune/asr_train_asr_whisper_full_raw_zh_whisper_multilingual_sp
ngpu: 1
seed: 2022
num_workers: 4
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: 8
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 53027
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
collect_stats: false
write_collected_feats: false
max_epoch: 3
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 3
nbest_averaging_interval: 0
grad_clip: 1.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: 100
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 12000000
valid_batch_bins: null
train_shape_file:
- exp/whisper_finetune/asr_stats_raw_zh_whisper_multilingual_sp/train/speech_shape
- exp/whisper_finetune/asr_stats_raw_zh_whisper_multilingual_sp/train/text_shape.whisper_multilingual
valid_shape_file:
- exp/whisper_finetune/asr_stats_raw_zh_whisper_multilingual_sp/valid/speech_shape
- exp/whisper_finetune/asr_stats_raw_zh_whisper_multilingual_sp/valid/text_shape.whisper_multilingual
batch_type: numel
valid_batch_type: null
fold_length:
- 51200
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
train_data_path_and_name_and_type:
- - dump/raw/train_sp/wav.scp
- speech
- kaldi_ark
- - dump/raw/train_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- kaldi_ark
- - dump/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adamw
optim_conf:
lr: 1.0e-05
weight_decay: 0.01
betas:
- 0.9
- 0.99
eps: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 1500
token_list:
- '!'
- '"'
- '#'
- $
- '%'
- '&'
- ''''
- (
- )
- '*'
- +
- ','
- '-'
- .
- /
- '0'
- '1'
- '2'
- '3'
- '4'
- '5'
- '6'
- '7'
- '8'
- '9'
- ':'
- ;
- <
- '='
- '>'
- '?'
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- Exc
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- ' lush'
- ìłĦë
- Before
- ' ÐĴÑĤоÑĢ'
- ' SaaS'
- ' sitzt'
- ' beetle'
- ' Essential'
- enko
- ' ëķĮëıĦ'
- ' revving'
- ' poorer'
- ' coerc'
- ' idee'
- ' coû'
- alet
- ' zdrow'
- ' fender'
- growth
- DING
- ' zde'
- ä¸ĬéĿ¢
- ENTS
- ' facets'
- éļª
- ushima
- ' ÅŁeh'
- ' parasite'
- ' lapse'
- ' Meer'
- ' Kund'
- ' slog'
- ' brunch'
- ' Chart'
- arz
- ' MUS'
- ' offenses'
- ' inglés'
- ' foliage'
- oplan
- Aut
- ' Jacqu'
- tak
- iembre
- ' xen'
- ' nominees'
- ' biomedical'
- ésus
- ' estuv'
- ÏĦÏĮ
- ATHAN
- ' íķľëį°'
- ' heed'
- crosstalk
- Bill
- ' spouses'
- ' ÑģÑİж'
- ' verso'
- ' Sven'
- ' Cau'
- cuz
- ' ë³´ìĦ¸ìļĶ'
- ' ÑħозÑı'
- ' mocking'
- ' Ona'
- ' Dá'
- ' fruitful'
- ' banquet'
- udding
- inctions
- dert
- sud
- ' descon'
- ' JC'
- ' §'
- ' publi'
- ëĪĪ
- éģķãģĨ
- ' entschieden'
- ' ROI'
- ãģįãģŁ
- ' ìĥĿê²¼'
- ' käytt'
- yani
- shaw
- ' unleash'
- ' manne'
- ' histogram'
- æĬ¥
- à¸Ńะà¹Ħร
- ' gn'
- ' fella'
- ' einges'
- ' Built'
- ' representa'
- ' punishing'
- ' outsiders'
- нÑĥÑĤÑĮÑģÑı
- current
- ' familiarity'
- ' див'
- ' projets'
- ' aqueles'
- ' Glue'
- those
- ' inception'
- ' aquellos'
- ' illusions'
- ' attends'
- rese
- ' swarm'
- ' swab'
- ' regardez'
- ' posição'
- ' akhir'
- ' extracting'
- ' anecdote'
- ' Tale'
- ' вин'
- ' abges'
- ' oluÅŁ'
- ' complicado'
- ' covari'
- ÑĸÑĤÑĮ
- Der
- ' ×Ļ×Ķ'
- Form
- ' ìĸ´ì¨Įëĵł'
- ' readable'
- ' inhibit'
- ' decipher'
- ' Angry'
- pg
- வத
- ' ÑģобÑģÑĤвенно'
- ' samh'
- ' escr'
- ' encompasses'
- ' auster'
- ' confisc'
- ' Mandal'
- ' }'
- atcher
- =#
- çļĦæŶåĢĻ
- ' кино'
- ' stal'
- lungs
- ' vole'
- ' requis'
- ' ãĤĪ'
- ' pén'
- ' lecturer'
- ' inscription'
- ' cervical'
- ' Treasure'
- ' JW'
- comings
- ' eyesight'
- ' Tails'
- ÃŃsimo
- ' worksheet'
- ' swiftly'
- ' conos'
- ' eliminates'
- ' Blaze'
- алог
- ' pictured'
- ' giraffe'
- ' Logic'
- åĺī
- ' enrichment'
- Fit
- ' unintended'
- ' persecuted'
- akap
- ë°ĺ
- ' barber'
- ' arbeitet'
- ' Surprisingly'
- ' Autob'
- unku
- prov
- ' Loch'
- obyl
- ' подгоÑĤов'
- ' économique'
- ' patt'
- ' ceased'
- ' ÑģпиÑģ'
- ' nuclei'
- ' iste'
- ' Wag'
- ' zupeÅĤnie'
- ' proverb'
- ' AhÃŃ'
- åĽŀåİ»
- liamo
- ' reliably'
- ' pik'
- ' Trading'
- ' Coleman'
- ' ανα'
- ' magari'
- ' PHIL'
- ' shedding'
- ohner
- ' pornography'
- ' beneficiaries'
- âĢ¢
- enin
- ' resolving'
- ' ÑģпоÑĢÑĤ'
- ' бег'
- ' nectar'
- ultura
- imsical
- ĮĢ를
- å¹´åīį
- ãģĹãĤĥ
- ' visão'
- éģİä¾Ĩ
- ÿÿÿÿÿÿÿÿ
- attform
- ' ë§ŀëĬĶ'
- ' pilgrimage'
- ' mating'
- ' Reaper'
- ' Bref'
- çĶŁæ´»
- ' ×ij×ĵ'
- ' novamente'
- ' grilling'
- ' Wireless'
- ' Romanian'
- ÒĽ
- ìľłë
- hait
- ' Bora'
- ARRY
- ' hypotheses'
- 马
- ikut
- ' ìķĦë²Ħ'
- ' Ñĸз'
- ' nationale'
- تÙī
- üllt
- ' éléments'
- ' Ware'
- ' (-'
- алÑĮном
- ' indict'
- ' Stones'
- ãģŁãĤģ
- explosion
- ' ëĥĦìĥĪ'
- ' felic'
- ' judiciary'
- ' incarnation'
- ' inning'
- ' formul'
- ' shipment'
- ' reindeer'
- æĴŃ
- ' ознаÑĩ'
- ' envol'
- undy
- ' знаÑĤÑĮ'
- ' видели'
- ' excluding'
- death
- ' berm'
- ' soprattutto'
- ' debido'
- ' Zig'
- ' Ov'
- ' KEVIN'
- ' Pale'
- ' Mire'
- ' andar'
- including
- ' swapped'
- ' misconceptions'
- ' spong'
- réal
- ' orbitals'
- ' hashtags'
- orit
- ' mauvais'
- иÑģа
- ' livres'
- ' IPS'
- ' 04'
- ög
- instr
- ' внеÑĪ'
- ' hice'
- isée
- ' owes'
- ' esimerk'
- ' UH'
- ' irritation'
- ' giggles'
- ' colonialism'
- ' Bliss'
- strings
- ' reunited'
- ' Psaki'
- wach
- ' cliffs'
- ' False'
- äg
- pipe
- ' whopping'
- ' meringue'
- ' bung'
- industrie
- ' leche'
- ' Loy'
- ' drie'
- ' passat'
- ' oleh'
- ' céu'
- ' Gabrie'
- ' reefs'
- ' bombers'
- ' episódio'
- ' Rug'
- ' Prose'
- onos
- ' obese'
- ' goog'
- ' piace'
- flanzen
- éĴŁ
- ' flaps'
- ' Alto'
- é£Łãģ¹
- Fin
- ' resize'
- ê·¸ëŀ¨
- è²»
- Nathan
- ŀĪ볤
- ' ÑĤай'
- ' NFT'
- ' sneeze'
- ' shroud'
- ié
- ' veramente'
- ' cascade'
- ' Ook'
- ìĹĨìĿ´
- ' infused'
- fps
- center
- ' grappling'
- ' Wohnung'
- ' Tumb'
- ' Imma'
- ' Duygusal'
- енÑĤи
- ' stewardship'
- ' harp'
- ' endorsed'
- ılan
- ' одним'
- ' competency'
- ' bert'
- ' Tales'
- ' rhe'
- ' ohh'
- ' ê°Ħëĭ¨'
- ' mRNA'
- ' gangster'
- ' Runner'
- еннÑĭм
- phoria
- ' wÅĤaÅĽciwie'
- ' quarto'
- ' organise'
- ' Vet'
- Pad
- ' ÙħØ«'
- ' stinks'
- ' Dul'
- uem
- isiej
- Top
- ' tussen'
- ' Efendimiz'
- ' Boule'
- ' Sloven'
- ' Lö'
- Ñijз
- ÑĢип
- cave
- ' boî'
- ' apologise'
- ' Marly'
- ' Export'
- ' Caitlin'
- ' tavalla'
- ' entails'
- ' brom'
- ' Copenh'
- ' walnut'
- ' insists'
- ' cuá»Ļc'
- ' Quit'
- ' Device'
- ×Ĵ×Ŀ
- ' DOT'
- ' velocidad'
- LIE
- Cool
- ' sanitation'
- ' olho'
- ' EB'
- ' íĻķìĭ¤íŀĪ'
- ' ÐľÐ¸Ñħ'
- ' zuk'
- ' surname'
- ' Schuld'
- ruff
- cultural
- ' ÑģÑĤолÑĮко'
- æĻļä¸Ĭ
- Įëį°
- ' torto'
- ' backups'
- ÑĢий
- relax
- ' synergy'
- ' buffs'
- ' apo'
- ' Wellness'
- rounded
- ' universes'
- ' fera'
- ' standby'
- ' Silva'
- ' JI'
- ensored
- ' ìĹĨëĭ¤'
- ' ÐIJв'
- ' оÑĤдел'
- ' fø'
- ' Rockef'
- ' Compass'
- ' Bears'
- ' ä¸įè¦ģ'
- Turn
- ' thá»±c'
- ' possibile'
- ' estem'
- ' Croatia'
- ' tätä'
- ' CAL'
- à¹Ģà¸ŀ
- ' ÑģÑĤÑĢаÑħ'
- ' salts'
- ' minimalist'
- ' incorporates'
- ' ÙĨÛģÛĮÚº'
- acao
- ' slammed'
- ' cama'
- Text
- '!!!!!!'
- ' alcanz'
- éma
- ' incense'
- ' harden'
- ' granting'
- ' Nai'
- ' Firma'
- ' hypoc'
- job
- ' RH'
- zur
- илÑı
- ' ź'
- ' dares'
- anh
- ' ë§Įíģ¼'
- ' cuestión'
- ' Lima'
- æĻ¯
- ' assunto'
- ' IPO'
- ' Bengal'
- ' Bier'
- ' psyche'
- ' acquainted'
- ' Gün'
- ози
- ÅĽciÄħ
- AG
- ' malfunction'
- ' asteroids'
- irez
- amorph
- ' ÑģоÑĤÑĢÑĥд'
- ' freshwater'
- ' arran'
- ' пÑĢÑĭ'
- ног
- ' diabetic'
- ' ÙĤاÙĦ'
- ' oppress'
- ' capacitance'
- performance
- crates
- ' apostle'
- ' JEN'
- OULD
- Intro
- ' stalls'
- ' ABOUT'
- cticamente
- ' diligent'
- ' manifests'
- ' Pakistani'
- ' ('''
- åľº
- ''
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init: null
input_size: 1
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
joint_net_conf: null
use_preprocessor: true
token_type: whisper_multilingual
bpemodel: whisper_multilingual
non_linguistic_symbols: null
cleaner: whisper_basic
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
aux_ctc_tasks: []
frontend: null
frontend_conf: {}
specaug: null
specaug_conf: {}
normalize: null
normalize_conf: {}
model: espnet
model_conf:
ctc_weight: 0.0
lsm_weight: 0.1
length_normalized_loss: false
sym_sos: <|startoftranscript|>
sym_eos: <|endoftext|>
preencoder: null
preencoder_conf: {}
encoder: whisper
encoder_conf:
whisper_model: medium
dropout_rate: 0.0
use_specaug: true
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 40
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.12
num_time_mask: 5
download_dir: /workspace/asr/whisper/models
postencoder: null
postencoder_conf: {}
decoder: whisper
decoder_conf:
whisper_model: medium
dropout_rate: 0.0
download_dir: /workspace/asr/whisper/models
preprocessor: default
preprocessor_conf:
tokenizer_language: zh
required:
- output_dir
- token_list
version: '202304'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
FacundoMR/ESESC | FacundoMR | 2023-07-23T11:41:36Z | 0 | 0 | fairseq | [
"fairseq",
"medical",
"es",
"dataset:Open-Orca/OpenOrca",
"arxiv:1910.09700",
"license:openrail",
"region:us"
] | null | 2023-07-23T11:37:32Z | ---
license: openrail
datasets:
- Open-Orca/OpenOrca
language:
- es
metrics:
- accuracy
library_name: fairseq
tags:
- medical
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Naruke/dqn-SpaceInvadersNoFrameskip-v4 | Naruke | 2023-07-23T11:40:29Z | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T10:18:32Z | ---
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: 561.00 +/- 159.51
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 Naruke -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 Naruke -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 Naruke
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch0 | Evan-Lin | 2023-07-23T11:36:45Z | 47 | 0 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | reinforcement-learning | 2023-07-23T11:34:01Z | ---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpeydaa0l0/Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch0")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpeydaa0l0/Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch0")
model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpeydaa0l0/Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch0")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
Ritikrb/ppo-LunarLander-v2 | Ritikrb | 2023-07-23T11:20:09Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T11:19:04Z | ---
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: 277.20 +/- 14.74
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
...
```
|
BauyrjanQ/whisper-kk-sp2ner-b8-m1000 | BauyrjanQ | 2023-07-23T11:07:28Z | 76 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:BauyrjanQ/whisper-kk",
"base_model:finetune:BauyrjanQ/whisper-kk",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-07-23T02:59:36Z | ---
license: apache-2.0
base_model: BauyrjanQ/whisper-kk
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-kk-sp2ner-b8-m1000
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. -->
# whisper-kk-sp2ner-b8-m1000
This model is a fine-tuned version of [BauyrjanQ/whisper-kk](https://huggingface.co/BauyrjanQ/whisper-kk) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4832
- Wer: 55.8859
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1336 | 0.69 | 1000 | 0.4832 | 55.8859 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Senna1848/dirkmaintz | Senna1848 | 2023-07-23T11:03:23Z | 45 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"question-answering",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-07-22T20:21:08Z | ---
metrics:
- accuracy
pipeline_tag: question-answering
--- |
sarwarbeing/PPO-LunarLander-v2 | sarwarbeing | 2023-07-23T10:59:49Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T10:57:45Z | ---
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: 279.31 +/- 11.46
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
...
```
|
espnet/akreal_ls100_asr2_e_branchformer1_1gpu_raw_wavlm_large_21_km2k_bpe_rm6k_bpe_ts5k_sp | espnet | 2023-07-23T10:59:13Z | 0 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech_100",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2023-07-23T10:36:30Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech_100
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/akreal_ls100_asr2_e_branchformer1_1gpu_raw_wavlm_large_21_km2k_bpe_rm6k_bpe_ts5k_sp`
This model was trained by Pavel Denisov using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 64a1cabc6e7fe4fd22d46b788cec29ba6a37801e
pip install -e .
cd egs2/librispeech_100/asr2
./run.sh --skip_data_prep false --skip_train true --download_model espnet/akreal_ls100_asr2_e_branchformer1_1gpu_raw_wavlm_large_21_km2k_bpe_rm6k_bpe_ts5k_sp
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sun Jul 23 11:19:30 CEST 2023`
- python version: `3.10.8 (main, Nov 14 2022, 00:00:00) [GCC 11.3.1 20220421 (Red Hat 11.3.1-3)]`
- espnet version: `espnet 202304`
- pytorch version: `pytorch 1.13.1+cu117`
- Git hash: `64a1cabc6e7fe4fd22d46b788cec29ba6a37801e`
- Commit date: `Sat Jul 22 23:17:44 2023 +0200`
## exp/asr_train_discrete_asr_e_branchformer1_1gpu_raw_wavlm_large_21_km2000_bpe_rm6000_bpe_ts5000_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc0.3_asr_model_valid.acc.ave/dev_clean|2703|54402|96.5|3.3|0.2|0.4|3.8|42.5|
|decode_ctc0.3_asr_model_valid.acc.ave/dev_other|2864|50948|93.9|5.7|0.4|0.5|6.7|54.4|
|decode_ctc0.3_asr_model_valid.acc.ave/test_clean|2620|52576|96.5|3.3|0.2|0.4|3.9|43.2|
|decode_ctc0.3_asr_model_valid.acc.ave/test_other|2939|52343|93.6|5.9|0.5|0.6|7.0|56.2|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc0.3_asr_model_valid.acc.ave/dev_clean|2703|288456|99.0|0.6|0.4|0.4|1.4|42.5|
|decode_ctc0.3_asr_model_valid.acc.ave/dev_other|2864|265951|97.8|1.3|0.9|0.7|2.9|54.4|
|decode_ctc0.3_asr_model_valid.acc.ave/test_clean|2620|281530|99.0|0.6|0.5|0.4|1.4|43.2|
|decode_ctc0.3_asr_model_valid.acc.ave/test_other|2939|272758|97.9|1.2|0.9|0.8|2.8|56.2|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc0.3_asr_model_valid.acc.ave/dev_clean|2703|69558|94.8|3.2|2.0|0.5|5.6|42.5|
|decode_ctc0.3_asr_model_valid.acc.ave/dev_other|2864|64524|91.3|5.7|3.0|1.2|9.9|54.4|
|decode_ctc0.3_asr_model_valid.acc.ave/test_clean|2620|66983|94.8|3.2|2.0|0.5|5.7|43.2|
|decode_ctc0.3_asr_model_valid.acc.ave/test_other|2939|66650|91.4|5.7|2.9|1.2|9.7|56.2|
## ASR config
<details><summary>expand</summary>
```
config: conf/train_discrete_asr_e_branchformer1_1gpu.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_discrete_asr_e_branchformer1_1gpu_raw_wavlm_large_21_km2000_bpe_rm6000_bpe_ts5000_sp
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 1
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 70
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: 1000
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 280000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_rm_wavlm_large_21_km2000_bpe6000_bpe5000_sp/train/text_shape.bpe
- exp/asr_stats_raw_rm_wavlm_large_21_km2000_bpe6000_bpe5000_sp/train/src_text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_rm_wavlm_large_21_km2000_bpe6000_bpe5000_sp/valid/text_shape.bpe
- exp/asr_stats_raw_rm_wavlm_large_21_km2000_bpe6000_bpe5000_sp/valid/src_text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 150
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
train_data_path_and_name_and_type:
- - dump/raw/train_clean_100_sp/text.ts.en
- text
- text
- - dump/raw/train_clean_100_sp/text.rm.wavlm_large_21_km2000
- src_text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/text.ts.en
- text
- text
- - dump/raw/dev/text.rm.wavlm_large_21_km2000
- src_text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
lr: 0.006
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 15000
token_list:
- <blank>
- <unk>
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- ▁IN
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- ▁I
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- ING
- ▁IT
- ''''
- ▁HIS
- ▁HAD
- ▁WITH
- ▁YOU
- ▁FOR
- T
- ▁AS
- ▁HER
- LY
- ▁NOT
- ▁BUT
- ▁SHE
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- D
- E
- ▁IS
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- Y
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- ER
- ▁NO
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- R
- ▁IF
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- ▁AGAIN
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- ▁EVEN
- NESS
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- K
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- ▁RE
- ▁AM
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- ▁DE
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- ABLE
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- EST
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- CE
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- ▁T
- EL
- ▁D
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- ENT
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- ▁THINGS
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- ▁KNEW
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- TER
- ▁KIND
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- CH
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- ▁HALF
- ▁OH
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- ▁HOWEVER
- ▁TURNED
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- ▁PERHAPS
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- ▁SMALL
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- ▁NEAR
- ▁CARE
- ▁WAR
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- ▁UNTIL
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- ▁DEAR
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- ▁FEET
- ▁NEXT
- ▁GENERAL
- LING
- ▁LAY
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- ▁BLACK
- ▁POWER
- ▁BROUGHT
- Z
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- ▁ROUND
- ▁BELIEVE
- ▁LARGE
- ▁ALONG
- ▁HELP
- ▁DAYS
- ▁FIVE
- ▁K
- ▁HOPE
- AM
- ▁CO
- ▁KEEP
- ▁FULL
- ▁WALK
- ▁MASTER
- ATED
- ▁NATURE
- ▁JOHN
- ▁POINT
- ▁DUR
- ▁MATTER
- ▁MONEY
- ▁CHILD
- ▁LOOKING
- ▁RATHER
- ▁AIR
- IA
- ▁P
- ▁TWENTY
- ▁FIRE
- OL
- ▁LESS
- ▁SHORT
- ▁PASSED
- ▁INDEED
- TY
- ▁CASE
- ▁WORD
- ▁WISH
- ▁COUNTRY
- LED
- ID
- ▁BOY
- ▁SOUND
- ▁FORM
- ▁CRIED
- LA
- ▁FRIEND
- TON
- ▁FACT
- ▁UNCLE
- ▁TAKEN
- ▁AL
- ▁TEN
- IAN
- ▁GONE
- ▁SEA
- ▁REASON
- TING
- ▁WHOSE
- ▁OTHERS
- AC
- ▁LI
- ▁DEATH
- ▁CERTAIN
- ▁ANSWERED
- ▁THEMSELVES
- ▁LADY
- ▁STATE
- ▁CAR
- ▁WIFE
- ▁THOUSAND
- ▁TRUE
- ▁BEHIND
- AGE
- ▁DOCTOR
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- OM
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- IST
- ▁SENT
- ▁SPEAK
- ▁WOMEN
- ▁GROUND
- VER
- ENCE
- NA
- ▁TALK
- ▁CHILDREN
- TION
- CO
- MO
- ▁HEAR
- ▁ORDER
- ▁LEAVE
- ▁PRO
- ▁ALREADY
- ▁LA
- ▁FINE
- SE
- ▁BA
- PP
- ▁THUS
- AD
- ▁NEED
- ▁SIGHT
- ▁CALL
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- ▁MANNER
- MP
- ▁BECAME
- UM
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- OW
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- ▁DIDN
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- ARY
- ▁GIVEN
- ▁SIX
- ▁EVERYTHING
- ▁DARK
- ▁DEAD
- ▁STRONG
- ▁SON
- ▁COMING
- URE
- ▁HELD
- ▁ABOVE
- ▁REALLY
- ▁BEAUTIFUL
- ▁SECOND
- ARD
- ▁EVENING
- ▁CON
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- ▁FELLOW
- ▁ROSE
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- ▁CAUSE
- ▁TURN
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- ▁DOUBT
- ▁QUESTION
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- ▁DEEP
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- ▁FEEL
- ▁HO
- ▁IMP
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- ▁SA
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- ▁FREE
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- ▁RETURN
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- ▁VI
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- ▁PARENTS
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- ▁LOVER
- UCH
- ▁HID
- ▁NEEDED
- ▁ORDERED
- ▁MEAL
- ▁SLOW
- ▁DAM
- ▁CLOUDS
- ▁DAN
- ▁GAR
- ▁EXPLAIN
- ▁QUI
- ▁CLIMB
- ▁HURRIED
- ▁MURMUR
- ▁SWIFT
- ▁ARTHUR
- ▁JEFF
- ▁KINGDOM
- ▁MESSAGE
- ▁PROTEST
- ▁ORGAN
- ▁RISK
- ▁FORGIVE
- ▁OCCURRED
- ▁PEARL
- ▁ODD
- ▁INFORMATION
- ▁BUSY
- ▁TRI
- ▁LACK
- ▁BAY
- ▁FLEET
- ▁CROWN
- ▁WAITED
- ▁BIRDS
- ▁PITY
- ▁SUCCEEDED
- ▁INFORMED
- ▁WISHES
- ▁DIRECTLY
- ▁CABIN
- ▁AUGUST
- ▁COUNTENANCE
- ▁HORROR
- ▁PHILIP
- ▁POPULAR
- ▁PREVIOUS
- ▁CONTRARY
- ▁ARTICLE
- ▁DIFFERENCE
- ▁HIDDEN
- ▁HUGE
- ▁AUTHORITY
- ▁POUND
- ▁JUMP
- ▁SPI
- ▁SHAKE
- ▁EVENTS
- ▁FRO
- ▁LEAN
- ▁CRO
- ▁TRIM
- ▁SHARE
- ▁FISHER
- ▁SETTLED
- ▁QUESTIONS
- ▁SI
- ▁VAL
- ▁APPROACHED
- ▁SUGGESTED
- ▁CONTINU
- ▁PERFORM
- ▁ACKNOWLEDG
- ▁CLIFF
- ▁COLONEL
- ▁GHOST
- ▁MAJESTY
- ▁EMOTION
- ▁SUPPER
- ▁DISTANT
- ▁INTERESTED
- ▁JACK
- ▁HUM
- ▁TRAMP
- ▁BRI
- ▁POUR
- ▁SHIPS
- ▁CHAIN
- ▁DY
- ▁RANK
- ▁MATTERS
- ▁LOVELY
- AW
- ▁PAT
- ▁WORKING
- ▁CONSEIL
- ▁EVIDENCE
- ▁MERCHANT
- ▁SOLEMN
- ▁CONSTANT
- ▁MINISTER
- ▁OFFICIAL
- ▁SENTIMENT
- ▁CENTURY
- ▁DELAY
- ▁JAMES
- ▁MATCH
- ▁FOREIGN
- ▁AROSE
- ▁BEAST
- ▁BAB
- ▁WIT
- ▁REMARKABLE
- ▁THOR
- ▁COMPAR
- ▁MAL
- ▁NEARER
- ▁FOURTH
- ▁GREY
- ▁MENTION
- ▁RUBB
- ▁CHARM
- ▁BARON
- ▁DESIRED
- SCAR
- ▁HOPED
- ▁TEACHER
- ▁MON
- ITCH
- BEL
- ▁PARTS
- ▁EIGHTY
- LAC
- GGING
- ▁REFLECT
- ▁COLLECT
- ▁BULL
- ▁CONSCIOUS
- ▁MOMENTS
- ▁DISTURB
- ▁COLLEGE
- ▁EGGS
- ▁STUPID
- ▁YESTERDAY
- ▁EXAMINE
- ▁FAULT
- ▁DEPTH
- ▁ROOT
- ▁MOUSE
- ▁SOUGHT
- ▁TURTLE
- ▁NATIVE
- ▁CRACK
- ▁SOLD
- ▁INVIT
- ▁PICKED
- ▁CEASED
- ▁HEARING
- ▁MIDS
- ▁PLAYING
- ▁STAGE
- ▁UNTO
- ▁GAIN
- ▁MIST
- ▁ORDERS
- ▁KNEES
- ▁TALE
- ▁DISTINCT
- ▁BENT
- ▁DESPAIR
- ▁TRIUMPH
- ▁SQUARE
- ▁THROAT
- ▁BOUGHT
- ▁PERMIT
- ▁SPEND
- ▁TRIP
- ▁THREATEN
- ▁ROME
- INESS
- ▁EXPOS
- GON
- ▁WRITING
- ▁INCREASED
- ▁PORTION
- ▁TENT
- IUS
- ▁YO
- ▁INTENDED
- ▁NAMED
- RATION
- ▁NOTIC
- ▁PIPE
- ▁WILLING
- ▁INSTANTLY
- ▁SERVED
- ▁BAL
- ▁POSSESS
- ▁CRE
- ▁ADMIRATION
- ▁LIBERTY
- ▁OPPORTUNITY
- ▁SELDOM
- ▁BIRTH
- ▁GLOW
- ▁INCLUD
- ▁REQUEST
- ▁TYPE
- ▁SLEPT
- ▁CRIME
- ▁MOTIVE
- ▁ELSIE
- ▁BEGUN
- ▁CONSENT
- ▁ADMITTED
- ▁AVOID
- ▁ADDRESS
- ▁HATE
- ▁DEMANDED
- ▁APPARENTLY
- ▁SUGGESTION
- ▁CONSIDERATION
- ▁BLESS
- ▁PROCEEDED
- NCY
- ▁PRISON
- ▁CONT
- ▁SHOUTED
- ▁FACES
- ▁SPIRITS
- ▁DEVELOP
- ▁ACCIDENT
- ▁ADVICE
- ▁INNOCENT
- ▁INSTINCT
- ▁UNCONSCIOUS
- ▁MYSTERIOUS
- ▁PRETEND
- ▁PEEP
- ▁ANYONE
- ▁DUKE
- ▁PLUM
- VILLE
- ▁SEVERE
- ▁ALAS
- ▁DELIGHTED
- ▁ISSUE
- ▁ASKING
- ▁CROW
- ▁ACCEPTED
- ▁RIDE
- ▁DOORS
- ▁TAR
- ▁PREPAR
- ▁SUGGEST
- WOOD
- ▁CITIZEN
- ▁ENTRANCE
- ▁LINCOLN
- ▁POLITICAL
- ▁PRACTICAL
- ▁STIFF
- ▁WIDOW
- ▁CAPITAL
- ▁CLEVER
- ▁MAMMA
- ▁CREDIT
- ▁OBEY
- ▁STRING
- ▁DAILY
- ▁ARGUMENT
- ▁HEAP
- ▁APARTMENT
- ▁FLIGHT
- ▁ELDER
- ▁PUR
- ▁PAGE
- ▁DUST
- ▁GAZE
- ▁NATIONAL
- ▁BABY
- DDING
- ISTS
- ▁TEACH
- ▁STREETS
- CAL
- ▁GE
- AFF
- ▁GOES
- ▁POSSIBL
- UNG
- ▁LINES
- GUE
- ▁VOTE
- ▁HUNTING
- ▁QUO
- ▁RESEMBL
- ▁BASKET
- ▁CIRCLE
- ▁CONSEQUENCE
- ▁KITCHEN
- ▁TREASURE
- ▁NEVERTHELESS
- ▁FANCI
- ▁ASSEMBL
- ▁GRIEF
- ▁VEIL
- ▁SEASON
- ▁INVENT
- ▁VIRGINIA
- ▁HUT
- ▁GUEST
- ▁ROAR
- ▁BEHOLD
- ▁VICTORY
- ▁CAPABLE
- ▁DULL
- ▁SHOE
- ▁FLOAT
- ▁MERRY
- ▁IMMEDIATE
- ETH
- ▁ELEANOR
- ▁EXPLANATION
- ▁PARLIAMENT
- ▁PRINCIPAL
- ▁PROPORTION
- ▁RESOLUTION
- ▁UNUSUAL
- ▁BLUFF
- ▁NINETEEN
- ▁SENSATION
- ▁VISIBLE
- ▁INCOME
- ▁FATE
- ▁SUPER
- ▁LAUGHTER
- ▁EASE
- ▁LOAD
- ▁JEW
- ▁ZE
- ▁FEVER
- ▁WEDDING
- ▁JOINED
- ▁TRACE
- ▁LEADER
- ▁CLEARLY
- ▁FLOWER
- ▁TERMS
- ▁EMPLOYED
- OCK
- ▁PARTICULARLY
- ▁MEMBERS
- ▁CONFESS
- ▁GRO
- ▁ADDRESSED
- ▁CHRIST
- ▁ACCOMPANI
- ▁AFFORD
- ▁AMOUNT
- ▁BRILLIANT
- ▁COMMUNICAT
- ▁FIERCE
- ▁RECORD
- ▁SACRIFICE
- ▁TEMPT
- ▁CORDIAL
- ▁COLOUR
- ▁PROOF
- ▁ESTATE
- ▁PARDON
- ▁ADVIS
- ▁ATTITUDE
- ▁IMPORTANCE
- ▁BOOT
- ▁SHOCK
- ▁FIR
- ▁PLENT
- ▁HIT
- ▁MEMBER
- ▁SUR
- ▁SEATED
- ▁MAG
- AVING
- ▁FAVOUR
- ▁REMARK
- ▁DIM
- ▁FAITHFUL
- ▁SAVED
- CHI
- ▁SIN
- THE
- ▁CONFIDENCE
- ▁EXTRAORDINARY
- ▁FORTUNATE
- ▁MISFORTUNE
- ▁PATIENCE
- ▁RELIGIOUS
- ▁SATISFACTION
- ▁POSITIVE
- ▁SIMILAR
- ▁EXCHANG
- ▁RETREAT
- ▁FLESH
- ▁ADMIRE
- ▁SPIRITUAL
- ▁DAWN
- ▁BURIED
- ▁URGE
- ▁SUNDAY
- ▁FOX
- ▁EMMA
- ▁NURSE
- ▁SNAPP
- ▁PARK
- ▁OBTAIN
- ▁RECOGNIZED
- ▁SPEED
- ▁MAGIC
- ▁LAWS
- ▁REMOVED
- ▁HAM
- ▁PRESERV
- ▁AID
- HOUSE
- ▁MENTIONED
- ▁CONSCIENCE
- ▁CONTEMPT
- ▁DETAIL
- ▁IMMENSE
- ▁NERVOUS
- ▁PRISCILLA
- ▁UNFORTUNATE
- ▁UNHAPPY
- ▁COMPLAIN
- ▁TWICE
- ▁WHISTL
- ▁SNAKE
- ▁WASHINGTON
- ▁PIRATE
- ▁WICKED
- ▁BODIES
- ▁DESIGN
- ▁JASON
- ▁VAGUE
- ▁CONSIST
- ▁GIFT
- ▁ANGEL
- ▁RODE
- ▁FOLD
- ▁BRIDE
- ▁ANGER
- ▁BASE
- ITUDE
- ▁CONCLUDED
- ▁ALTER
- ▁FRI
- ▁PANT
- ▁BID
- ▁HIGHEST
- ▁SAILOR
- MPLE
- ▁OBSERV
- ▁CHEERFUL
- IFICATION
- RID
- ▁DESCRIBED
- ▁BIN
- ▁JEWEL
- ▁ARTIST
- ▁PEER
- ▁NORA
- ▁SKI
- ▁DIAMOND
- ▁ENCOURAGE
- ▁PRIVILEGE
- ▁PROJECT
- ▁ANYBODY
- ▁ENCOUNTER
- ▁HOLLOW
- ▁YIELD
- ▁BOBBY
- ▁SAVAGE
- ▁SOMEBODY
- ▁OTHERWISE
- ▁PRAISE
- ▁PROBLEM
- ▁DISTRESS
- ▁UGLY
- ▁WARRIOR
- ▁MOURN
- ▁RELIEV
- ▁DESK
- ▁FOOLISH
- ▁STARTLED
- ▁SKILL
- SHONE
- ▁LONE
- ▁OBSERVATION
- ▁DENI
- ▁NEST
- ▁SOLDIER
- ▁RELATION
- ▁TRULY
- ▁VISITOR
- ▁OFFICERS
- ERSON
- ▁YA
- ▁EVIDENT
- ▁DREAMS
- ▁KEEPING
- ▁PLAINLY
- ▁DRUNK
- ▁EMBRAC
- ▁INTELLIGENCE
- ▁LIEUTENANT
- ▁PERSUADE
- ▁SURROUNDING
- ▁UNIVERSAL
- ▁GLEAM
- ▁SUPERIOR
- ▁WHEEL
- ▁JEALOUS
- ▁QUEER
- ▁PIERRE
- ▁MILK
- ▁RAIL
- ▁FLUSH
- ▁STAIRS
- ▁JESUS
- ▁HORN
- ▁REGION
- ▁SAFETY
- ▁KA
- ▁GUIDE
- ▁CAKE
- ▁CUP
- ▁INQUIRED
- ▁DEFI
- ▁LESSON
- ▁WRETCHED
- ▁PACE
- ▁TEST
- ▁READING
- ▁ENTIRE
- ▁NET
- ▁DOGS
- ▁COMMANDER
- ▁PRODUCE
- ▁GAINED
- ▁ARRIVAL
- ▁FAMILIAR
- ▁MEANWHILE
- ▁SUSPICION
- ▁CHOICE
- ▁IMPULSE
- ▁THRUST
- ▁PROCESS
- ▁SUMMON
- ▁SHEPHERD
- ▁HASTILY
- ▁GRASP
- ▁COUNTESS
- ▁STYLE
- ▁DWELL
- ▁MERIT
- ▁PITCH
- ▁HUNGRY
- ▁SPORT
- ▁LOUISE
- ▁STERN
- ▁PROVIDED
- ▁ASSUME
- ▁EARLIE
- ▁RAGE
- ▁U
- ▁RAPIDLY
- PORT
- ▁SUCCESSFUL
- ▁FLED
- ▁AGREE
- ▁CONDITIONS
- ▁RELATIONS
- ▁DREAD
- ▁NATURALLY
- ▁EARL
- ▁GAY
- ▁HYPNOTI
- ▁PUTT
- ▁GAZ
- ▁JIM
- ▁PAUS
- ▁PROPOS
- ▁ADMINISTRATION
- ▁ELEVEN
- ▁HOSPITAL
- ▁MAGISTRATE
- ▁STRIKE
- ▁DIGNITY
- ▁GLORY
- ▁BOTTLE
- ▁THRONE
- ▁RECKON
- ▁COSETTE
- ▁MOREOVER
- ▁APPLI
- ▁HIND
- ▁PRODUCT
- ▁POOL
- ▁TRIAL
- HAN
- ▁ERIC
- ▁CUB
- ▁PIECES
- ▁EXCEPTION
- ▁ENJOYED
- ▁DARED
- ▁TRU
- ▁CLOSELY
- ▁RAPID
- ▁AFFECTED
- ▁REQUIRE
- ▁SOFTLY
- ▁BROW
- UCK
- ▁MARKED
- ▁SEVENT
- ▁ELECT
- ▁FORGOT
- ▁CORRECT
- ▁FRANCS
- ▁MARGUERITE
- ▁SCIENCE
- ▁UNEXPECTED
- ▁FOUGHT
- ▁MILITA
- ▁THUNDER
- ▁VOYAGE
- ▁GANEM
- ▁FREEDOM
- ▁NODDED
- ▁CAPTURE
- ▁MORTAL
- ▁OWNER
- ▁POLITE
- ▁VISION
- ▁EDUCATION
- ▁GOVERNOR
- ▁RAV
- ▁REWARD
- ▁HASTE
- ▁REPEAT
- ▁DETERMIN
- ▁PITI
- ▁KNEE
- LINE
- ▁DEVOTED
- ▁INTERRUPTED
- ▁FOLKS
- ▁EXTREME
- ▁APPROACH
- ▁CONTINUE
- ▁BEARING
- ▁CHAP
- ▁ACQUAINTED
- ▁GLIMPSE
- ▁GRADUALLY
- ▁SUNSHINE
- ▁PRACTICE
- ▁SUPPLI
- ▁DAVID
- ▁DRIFT
- ▁SHOWING
- ▁LEVEL
- ▁PROMPT
- ▁QUARREL
- ▁REPRESENTATIVE
- ▁PLUNG
- ▁GIANT
- FALL
- ▁STOUT
- CHA
- WEPT
- ▁GLANC
- ▁SALT
- ▁CHOSEN
- ▁BUCK
- ▁REALIZED
- ▁REALITY
- ▁TUR
- ▁DRIVEN
- ▁CARD
- ▁PRAYER
- ▁TERM
- AID
- ▁HOLY
- ▁ENDURE
- ▁RANGE
- ▁HANG
- ▁SAM
- LAN
- ▁CAVE
- INA
- ▁GRI
- ▁SIGH
- ▁NEIGHBOUR
- ▁COUNCIL
- ▁EXERCISE
- ▁NAUTILUS
- ▁SOMEWHERE
- ▁SYLVIA
- ▁THOROUGH
- ▁VICTIM
- ▁BRIDGE
- ▁COMPELLED
- ▁INCLINED
- ▁OVERCOME
- ▁RESERVE
- ▁ARREST
- ▁PRECIOUS
- ▁DUTCH
- ▁OCEAN
- ▁ACQUIR
- ▁RECALL
- ▁DESTIN
- ▁ATTACH
- ▁SLIM
- ▁WEEP
- ▁CONSCIOUSNESS
- ▁TIGHT
- ▁WAKE
- ▁COMFORTABLE
- ▁ACTIVE
- ▁WINGS
- ▁GRIN
- ▁AFFECT
- ▁WHIT
- ▁IDEAL
- ▁EASTER
- ▁APPROACHING
- ▁CREATED
- ▁PLANS
- ▁INCREASE
- ▁FLYING
- ▁SHOUT
- OES
- MISSION
- ▁ARMED
- ABILITY
- ▁BLUSH
- ▁CONNECTION
- ▁MATTHEW
- ▁MEDICINE
- ▁REMIND
- ▁EXHIBIT
- ▁BLOCK
- ▁DESERVE
- ▁LISTENING
- ▁TITLE
- ▁FLOUR
- ▁FLAME
- ▁AGENT
- ▁USEFUL
- ▁BRIG
- ▁BOIL
- ▁ASSURED
- ▁REFLECTION
- ▁PINE
- ▁WAG
- ▁YOUNGER
- ▁BEARD
- ▁KINDNESS
- CTUALLY
- ▁ACTUAL
- ▁WEIGHT
- ▁LILY
- ▁IMPRESS
- ▁DESCRIBE
- ▁BEHELD
- ▁COMMUNITY
- ▁DESPERATE
- ▁DISPLAY
- ▁ENEMIES
- ▁MELANCHOLY
- ▁MIRROR
- ▁RECOMMEND
- ▁SPANISH
- ▁BLAME
- ▁VOLUME
- ▁SHOOT
- ▁COMBIN
- ▁SHAKING
- ▁SOUTHERN
- ▁MYSTERY
- ▁EVERYONE
- ▁COMMISSION
- ▁COMPOSED
- ▁UDO
- ▁IMAGE
- ▁DECEIV
- ▁FAILURE
- ▁PATTY
- ▁ALICE
- ▁FRAME
- ▁MODEST
- ▁MAGNIFICENT
- ▁BRANCHES
- ▁REIGN
- ▁RAG
- ▁PARISH
- ▁KATE
- ▁AMID
- ▁SLEEPING
- ▁ANNOUNCED
- ▁EAGERLY
- ▁WIRE
- ▁LAP
- ▁ARAB
- ▁EATING
- ▁RUM
- ▁CAREFUL
- ▁DISCUSS
- WORTH
- ▁DISTRICT
- ▁FOREHEAD
- ▁FRANCIS
- ▁INCIDENT
- ▁APPEAL
- ▁EMBARRASS
- ▁MAINTAIN
- ▁PRONOUNC
- ▁FURNISH
- ▁STRAIN
- ▁ELEMENT
- ▁SILK
- ▁FEAST
- ▁RECENT
- ▁DANCING
- ▁LODGE
- ▁ASHAMED
- ▁TRICK
- ▁BOBO
- ▁STUFF
- ▁ET
- ▁ASSERT
- ▁SANK
- ▁TREATMENT
- ECI
- ▁SWIM
- ▁BECOMING
- ▁SINGING
- ▁PLATE
- ▁SCATTERED
- ▁EXTREMELY
- ▁GRIM
- ▁SANG
- ▁FIGHTING
- ▁FACTOR
- ▁PAINFUL
- ▁HIDE
- ▁FUNN
- ▁AFTERWARD
- ▁FROG
- ▁VENTURE
- ▁DISAPPOINT
- ▁COMRADE
- ▁MONSIEUR
- ▁OBVIOUS
- ▁PASSENGER
- ▁PROFOUND
- ▁PUBLISH
- ▁ACCUSTOM
- ▁BLOOM
- ▁SMITH
- ▁RELATIVE
- ▁ACCUSE
- ▁MANIFEST
- ▁SOLID
- ▁MONSTER
- ▁MARIUS
- ▁CANDLE
- ▁PROCUR
- ▁INTERFERE
- ▁HOUSEHOLD
- ▁DEVELOPMENT
- ▁AGREEABLE
- ▁HALT
- ▁NECESSITY
- FOLD
- ▁CITIES
- ▁REGI
- ▁GLOOMY
- BBL
- ▁SEPARATED
- ▁CHEST
- ▁STRIP
- ▁SPAR
- ▁DUN
- ▁SETTLE
- ▁STARED
- ▁HANGING
- ▁FEATURES
- ▁PILE
- ▁ORIGIN
- ARIES
- ▁LION
- ▁ALI
- ▁ASTONISHMENT
- ▁COMPLIMENT
- ▁DELICATE
- ▁COUNSEL
- ▁FIFTH
- ▁SUPPRESS
- ▁BURDEN
- ▁COMPLEX
- ▁ADDITION
- ▁CRUSH
- ▁TWIST
- ▁PIANO
- ▁BRUSH
- ▁CHECK
- ▁ANNIE
- ▁SHELTER
- ▁IMPROV
- ▁WESTERN
- ▁LOCAL
- ▁APPLE
- ▁GREET
- ▁MASK
- ▁RUSSIAN
- ▁TOWER
- ▁CREW
- ▁TIP
- ▁WANDERING
- ▁READER
- ▁WANDERED
- ▁DESTROY
- ▁OBSERVE
- MORE
- ▁ESCAPED
- ▁PET
- ▁BUILD
- ▁REAR
- ▁DESTROYED
- HIN
- ▁OWE
- ▁RANG
- ▁TEAR
- ▁NED
- ▁OFFICER
- ▁TRAP
- ▁OCCUR
- ▁APPOINTED
- ▁ATMOSPHERE
- ▁CHOOSE
- ▁CONCLUSION
- ▁CULTIVAT
- ▁DESCRIPTION
- ▁ENORMOUS
- ▁EXHAUSTED
- ▁LANDSCAPE
- ▁NATASHA
- ▁PROSPECT
- ▁REFRESH
- ▁SPECIES
- ▁SURROUNDED
- ▁WEAPON
- ▁BLANK
- ▁DEFEND
- ▁EDITH
- ▁HORRIBL
- ▁BETRAY
- ▁FERKO
- ▁LABOUR
- ▁NEGRO
- ▁RESUMED
- ▁LEAF
- ▁MUSKET
- ▁INTENSE
- ▁MERCY
- ▁ADOPT
- ▁SCORE
- ▁DASH
- ▁LAWYER
- ▁SLOPE
- ▁CHUCK
- ▁ASSISTANCE
- ▁BROOK
- ▁BREAKING
- ▁ASSIST
- ▁GROAN
- ▁HELEN
- ▁BEHAV
- ▁MAIDEN
- ▁CRIS
- ▁SHOUTING
- ▁NAY
- ▁PIG
- ▁ACCORDINGLY
- ETTE
- ▁DESIR
- ▁RUB
- ▁GRU
- ▁PIT
- ▁HEAVI
- ▁OBTAINED
- ▁SPARE
- ▁BRANCH
- ▁COUNTER
- ▁APART
- ▁AMBITION
- ▁ASTONISHED
- ▁CORRESPOND
- ▁DRIVING
- ▁ENERGY
- ▁HISTORIAN
- ▁REVOLUTION
- ▁SWEEP
- ▁TREMBLING
- ▁CRAFT
- ▁FAMILIES
- ▁LITERATURE
- SBURG
- ▁FEMALE
- ▁TILNEY
- ▁GENEROUS
- ▁SUBMIT
- ▁INTELLECTUAL
- ▁ORCHARD
- ▁STORIES
- ▁DIANA
- ▁VEIN
- ▁TRIFL
- ▁TWIN
- ▁WORSHIP
- ▁MARBLE
- ▁GALLANT
- ▁SENSIBLE
- ▁NEAT
- ▁BROWNIE
- ▁JUNE
- ▁SHAW
- ▁WORST
- ▁USELESS
- ▁FISHING
- ▁CRYING
- ▁MAYBE
- ▁VARI
- ▁PRESERVE
- ▁VOL
- ▁EMPLOY
- ▁INTERRUPT
- ▁SLIGHTLY
- ▁ACCOMPLISHED
- NEY
- ▁STEAM
- ▁BALANC
- ▁LEANING
- ▁SIGHED
- ▁REFUSE
- ▁IMAGINED
- ▁DATE
- GROUND
- ▁ENTERTAIN
- ▁PERCEIVE
- ▁ABROAD
- ▁CHEESE
- ▁DESTRUCTION
- ▁ESSENTIAL
- ▁EXPEDITION
- ▁GRANDFATHER
- ▁INFINITE
- ▁LIBRARY
- ▁MULTITUDE
- ▁NEGLECT
- ▁SWALLOW
- ▁VILLEFORT
- ▁BELOVED
- ▁COMMITTEE
- ▁CONFIDENT
- ▁PURPLE
- ▁PURCHAS
- ▁SCRAP
- ▁SPOIL
- ▁LIKEWISE
- ▁EXTRA
- ▁STRAW
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- TIME
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- ▁ADVANC
- ▁IMAGIN
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- ▁SUCCEED
- ▁DISMISS
- CTOR
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- ▁PUNISH
- ▁ABRUPTLY
- ▁BERNARD
- ▁DECISION
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- ▁PROVINCE
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- ▁TREMENDOUS
- ▁UNPLEASANT
- ▁LEISURE
- ▁THRONG
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- ▁CONTRADICT
- ▁RESTRAIN
- ▁DIVIDED
- ▁WRAPPED
- ▁HAUNT
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- CHESTER
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- ▁BUFFALO
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- ▁PARTIAL
- ▁SWU
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- ▁PERCH
- ▁LACE
- SPOON
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- SERIES
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- ▁ITALIAN
- ▁PARSON
- ▁DECLIN
- ▁NEIGHBORHOOD
- ▁GREGGORY
- ▁EXCEED
- ▁SILLY
- ▁ICELAND
- ▁HIDEOUS
- ▁STRU
- ▁ALTERNAT
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- ▁ABILITY
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- ▁EVA
- ▁CREST
- ▁FINISH
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- ▁MIX
- ▁SLIP
- ▁LUXURI
- ▁AUTUMN
- ▁CIRCULAR
- ▁COMPOSITION
- ▁DISPLEAS
- ▁EXCELLENC
- ▁FURNITURE
- ▁GRADUATE
- ▁INDIFFERENT
- ▁JOSEPH
- ▁OCCUPATION
- ▁POSSIBILITY
- ▁RENEWED
- ▁RESPONDED
- ▁PREVAIL
- ▁HOARSE
- ▁PRACTIS
- ▁FAREWELL
- ▁JULIET
- ▁OVERHEAD
- ▁THREAD
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- ▁COARSE
- ▁MANKIND
- ▁KICK
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- ▁SOLICIT
- ▁RESIGN
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- ▁STEEL
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- ▁AUTHORITIES
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- ▁FAVORITE
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- ▁AGITATION
- ▁ABBE
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- ▁BIBLE
- ▁RECOLLECTION
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- ▁SHINE
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- ▁ELDE
- ▁BIGGE
- ▁PEACEFUL
- SCRIBED
- ▁WEIGH
- CARLET
- ▁DECIDE
- ▁RECOLLECT
- ▁BOHEMIA
- ▁CALIFORNIA
- ▁CONSTRUCT
- ▁DEMONSTRAT
- ▁DISTRIBUT
- ▁FRIGHTFUL
- ▁GNOME
- ▁IGNORANCE
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- ▁JULIUS
- ▁MEMORIES
- ▁OCCUPY
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- ▁WHIRLWIND
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- ▁TRADITION
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- ▁IMPRISON
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- ▁STOMACH
- ▁TORRENT
- ▁WITHDRAW
- ▁FRANZ
- ▁POISON
- ▁SURVEY
- ▁BRITISH
- ▁ELEVAT
- ▁AWOKE
- ▁ESTHER
- ▁INHERIT
- ▁TRAVERS
- ▁STOPPING
- ▁IRELAND
- ▁COMPARATIVE
- ▁SOBB
- ▁FAVOURITE
- ▁CANVAS
- ▁CLOAK
- ▁GLAR
- ▁ASSISTANT
- ▁DAMAGE
- ▁PEAK
- ▁DISTINCTION
- FARE
- ▁DOLLAR
- ▁BEGGAR
- LUSIVE
- ▁MODEL
- ▁SECUR
- ▁DISPOS
- ▁SLID
- ▁PEA
- ▁SPEEDI
- HOLD
- ▁SNAP
- ▁CIGAR
- ▁AFFLICT
- ▁AMAZEMENT
- ▁LAUNCELOT
- ▁LEAGUE
- ▁MARIPOSA
- ▁POPULATION
- ▁UNEASY
- ▁BLOSSOM
- ▁CATERPILLAR
- ▁INCLINATION
- ▁SUSPEND
- ▁SYNDIC
- ▁TAYLOR
- ▁WILSON
- ▁CONTRAST
- ▁PORTRAIT
- ▁CORONER
- ▁GREEK
- ▁BUNDLE
- ▁BLEW
- ▁THORPE
- ▁ORPHAN
- ▁MUSCLE
- ▁DEAF
- ▁SURVIV
- ▁EXCEEDINGLY
- ▁TENDENC
- ▁ISRAEL
- ▁QUANTIT
- ▁PENSION
- ▁DRIED
- TEXT
- ▁REFERENCE
- ▁REPOSE
- ▁FOLLY
- ▁REPLACE
- ▁TERR
- ▁ANKLE
- ▁SUNLIGHT
- ▁SECURITY
- ▁SHOV
- ▁RAW
- CULAR
- ▁JACKET
- ▁TUNE
- ▁HOBB
- ▁MARTIN
- DUCED
- ▁FIST
- ▁BEGG
- ▁CHOK
- ▁INQUIRE
- ▁INTELLECT
- ▁AMUSEMENT
- ▁APPROPRIATE
- ▁CONGRATULAT
- ▁CONVENTION
- ▁DISCOURAG
- ▁EXQUISITE
- ▁FOUNTAIN
- ▁JUNIOR
- ▁NONSENSE
- ▁OBSTACLE
- ▁SPECIMEN
- ▁SWEAR
- ▁TRANQUIL
- ▁VEHICLE
- ▁WISDOM
- ▁ASCERTAIN
- ▁CAUTIOUS
- ▁CENTURIES
- ▁CORRUPT
- ▁EXPLOR
- ▁TURKEY
- ▁BARGAIN
- ▁CONFOUND
- ▁FUNCTION
- ▁GRACIOUS
- ▁MONICA
- ▁ILLUSTRAT
- ▁CRUMB
- ▁REMEDY
- ▁REMOTE
- ▁REVENGE
- ▁BABYLON
- ▁CAUTION
- ▁INTERIOR
- ▁CRISTEL
- ▁BRAZ
- ▁THIRST
- ▁PROBABLE
- ▁HARMONY
- ▁CHARITY
- ▁DECAY
- ▁COLONI
- ▁AVAIL
- ▁REPULS
- ▁ABSENT
- ▁PULSE
- ▁PRESUM
- ▁CRANE
- ▁NEIGHBOURHOOD
- ▁SUNSET
- ▁CANNON
- ▁GRAPE
- ▁SOFA
- ▁DRANK
- MINOUS
- ▁DECLARATION
- ▁CLOSING
- ▁MEEK
- ▁STARV
- ▁BUNCH
- ▁PERFORMANCE
- ▁ENTERTAINMENT
- ▁STRIV
- ▁EMILY
- ▁VALET
- MPOSED
- ▁INTIMA
- ▁POLISH
- ▁HIRE
- POST
- ▁TREMBLE
- ▁CEASE
- ▁VIRGIN
- ▁RUSSIA
- COURSE
- ▁EDUCAT
- BOUND
- ▁INHABIT
- ▁SUPERINTEND
- ▁BISCUIT
- ▁CHICAGO
- ▁CHOKICHI
- ▁CONFLICT
- ▁ENCLOS
- ▁EXCLUSION
- ▁EXECUTIVE
- ▁GRANDMOTHER
- ▁HEADQUARTERS
- ▁INFERIOR
- ▁INVISIBLE
- ▁MUTUAL
- ▁OPPONENT
- ▁SENSITIVE
- ▁STUDIED
- ▁TEMPORARY
- ▁UNWILLING
- ▁PERMANENT
- ▁BEDROOM
- ▁NOVEMBER
- ▁COMPLICAT
- ▁DEVOUR
- ▁SCRAMBL
- ▁SECTION
- ▁PROPOSITION
- ▁DEPRIV
- ▁RYNCH
- ▁PLEAD
- ▁TORTURE
- ▁SCOUT
- ▁PILOT
- ▁CHERISH
- ▁SPEAR
- ▁SUGAR
- ▁JASPER
- ▁STRAY
- ▁RIFLE
- ▁NORMAL
- ▁JERK
- ▁HONEY
- ▁AWAKENED
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ctc_conf:
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ctc_type: builtin
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token_type: bpe
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bpemodel: data/token_list/tgt_bpe_unigram5000_ts_en/bpe.model
src_bpemodel: data/token_list/src_bpe_unigram6000_rm_wavlm_large_21_km2000/bpe.model
non_linguistic_symbols: null
cleaner: null
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tokenizer_encode_conf: null
src_tokenizer_encode_conf:
enable_sampling: true
alpha: 0.4
nbest_size: -1
frontend: embed
frontend_conf:
embed_dim: 512
positional_dropout_rate: 0.1
specaug: specaug
specaug_conf:
apply_time_warp: false
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: false
freq_mask_width_range:
- 0
- 10
num_freq_mask: 0
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 10
preencoder: null
preencoder_conf: {}
encoder: e_branchformer
encoder_conf:
output_size: 256
attention_heads: 4
attention_layer_type: rel_selfattn
pos_enc_layer_type: rel_pos
rel_pos_type: latest
cgmlp_linear_units: 1024
cgmlp_conv_kernel: 31
use_linear_after_conv: false
gate_activation: identity
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv1d2
layer_drop_rate: 0.0
linear_units: 1024
positionwise_layer_type: linear
use_ffn: true
macaron_ffn: true
merge_conv_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
layer_drop_rate: 0.0
model: discrete_asr
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
share_decoder_input_output_embed: false
share_encoder_decoder_input_embed: false
required:
- output_dir
- src_token_list
- token_list
version: '202304'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
usamaaleem99tech/segformer-class-classWeights-augmentation | usamaaleem99tech | 2023-07-23T10:55:25Z | 22 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"base_model:finetune:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-07-07T18:23:00Z | ---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: segformer-class-classWeights-augmentation
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9655172413793104
- name: F1
type: f1
value: 0.964683592269799
- name: Precision
type: precision
value: 0.9674329501915708
- name: Recall
type: recall
value: 0.9655172413793104
---
<!-- 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. -->
# segformer-class-classWeights-augmentation
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1855
- Accuracy: 0.9655
- F1: 0.9647
- Precision: 0.9674
- Recall: 0.9655
- Learning Rate: 0.0000
## 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: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Rate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| No log | 0.89 | 6 | 0.1113 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.1153 | 1.93 | 13 | 0.0929 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.2246 | 2.96 | 20 | 0.1026 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.2246 | 4.0 | 27 | 0.0391 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.1433 | 4.89 | 33 | 0.0673 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.1816 | 5.93 | 40 | 0.0794 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.1816 | 6.96 | 47 | 0.0687 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.1448 | 8.0 | 54 | 0.1123 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.1124 | 8.89 | 60 | 0.1855 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
vineetsharma/ppo_cleanrl-LunarLander-v2 | vineetsharma | 2023-07-23T10:40:40Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T10:36:59Z | ---
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: -189.88 +/- 131.26
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'vineetsharma/ppo_cleanrl-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
tslai1992/ppo-SnowballTarget | tslai1992 | 2023-07-23T10:37:53Z | 5 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-07-23T10:26:12Z | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: tslai1992/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Vinipereira/PPO-LunarLander-v2 | Vinipereira | 2023-07-23T10:32:46Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T10:32:26Z | ---
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: -1592.79 +/- 200.13
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
...
```
|
tslai1992/Reinforce-Pixelcopter-PLE-v0 | tslai1992 | 2023-07-23T10:23:05Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T08:55:50Z | ---
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: 23.40 +/- 18.09
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
|
Ahmet2250/Taxi-v3-first_trial | Ahmet2250 | 2023-07-23T10:20:47Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T10:20:44Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-first_trial
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Ahmet2250/Taxi-v3-first_trial", 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"])
```
|
aarnphm/llama-2-13b-dolly-qlora | aarnphm | 2023-07-23T10:19:34Z | 4 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-22T02:47:59Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
tslai1992/Reinforce-CartPole-v1 | tslai1992 | 2023-07-23T10:12:38Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T08:54:33Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
dnarqq/ppo-Pyramids | dnarqq | 2023-07-23T10:12:09Z | 6 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2023-07-23T10:12:01Z | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: dnarqq/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jjyaoao/speecht5_voxpopuli_spanish | jjyaoao | 2023-07-23T10:04:14Z | 90 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2023-07-23T10:00:39Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_facebook_voxpopuli_spanish
results: []
pipeline_tag: text-to-speech
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_facebook_voxpopuli_spanish
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4355
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.484 | 1.0 | 1074 | 0.4623 |
| 0.4724 | 2.0 | 2148 | 0.4542 |
| 0.4646 | 3.0 | 3222 | 0.4474 |
| 0.463 | 4.0 | 4296 | 0.4451 |
| 0.4538 | 5.0 | 5370 | 0.4425 |
| 0.4509 | 6.0 | 6444 | 0.4408 |
| 0.4548 | 7.0 | 7518 | 0.4412 |
| 0.4465 | 8.0 | 8592 | 0.4389 |
| 0.4509 | 9.0 | 9666 | 0.4387 |
| 0.4424 | 10.0 | 10740 | 0.4369 |
| 0.4415 | 11.0 | 11814 | 0.4367 |
| 0.4386 | 12.0 | 12888 | 0.4366 |
| 0.4383 | 13.0 | 13962 | 0.4357 |
| 0.4396 | 14.0 | 15036 | 0.4348 |
| 0.4432 | 15.0 | 16110 | 0.4358 |
| 0.4378 | 16.0 | 17184 | 0.4356 |
| 0.4321 | 17.0 | 18258 | 0.4353 |
| 0.4381 | 18.0 | 19332 | 0.4353 |
| 0.4325 | 19.0 | 20406 | 0.4347 |
| 0.4336 | 20.0 | 21480 | 0.4355 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
gioca91/q-Taxi-v3 | gioca91 | 2023-07-23T10:03:33Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T10:03:31Z | ---
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: 7.54 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="gioca91/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"])
```
|
gioca91/q-FrozenLake-v1-4x4-noSlippery | gioca91 | 2023-07-23T09:40:34Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T09:40:32Z | ---
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="gioca91/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"])
```
|
TheBloke/Dolphin-Llama-13B-GGML | TheBloke | 2023-07-23T09:31:32Z | 12 | 30 | transformers | [
"transformers",
"llama",
"license:other",
"region:us"
] | null | 2023-07-23T08:40:26Z | ---
inference: false
license: other
model_type: llama
task_categories:
- text-generation
---
<!-- header start -->
<div style="width: 100%;">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Dolphin Llama 13B - GGML
- Model creator: [Eric Hartford](https://huggingface.co/ehartford)
- Original model: [Dolphin Llama 13B](https://huggingface.co/ehartford/dolphin-llama-13b)
## Description
This repo contains GGML format model files for [Eric Hartford's Dolphin Llama 13B](https://huggingface.co/ehartford/dolphin-llama-13b).
GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with GPU acceleration via the c_transformers backend.
* [LM Studio](https://lmstudio.ai/), a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Dolphin-Llama-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Dolphin-Llama-13B-GGML)
* [Eric Hartford's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ehartford/dolphin-llama-13b)
## Prompt template: Orca-Vicuna
```
SYSTEM: {system}
USER: {prompt}
ASSISTANT:
```
<!-- compatibility_ggml start -->
## Compatibility
### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods.
### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`.
They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation.
## Explanation of the new k-quant methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
* GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_ggml end -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| dolphin-llama-13b.ggmlv3.q2_K.bin | q2_K | 2 | 5.51 GB| 8.01 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| dolphin-llama-13b.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 6.93 GB| 9.43 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| dolphin-llama-13b.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 6.31 GB| 8.81 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| dolphin-llama-13b.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 5.66 GB| 8.16 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| dolphin-llama-13b.ggmlv3.q4_0.bin | q4_0 | 4 | 7.32 GB| 9.82 GB | Original quant method, 4-bit. |
| dolphin-llama-13b.ggmlv3.q4_1.bin | q4_1 | 4 | 8.14 GB| 10.64 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| dolphin-llama-13b.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 7.87 GB| 10.37 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| dolphin-llama-13b.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 7.37 GB| 9.87 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| dolphin-llama-13b.ggmlv3.q5_0.bin | q5_0 | 5 | 8.95 GB| 11.45 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| dolphin-llama-13b.ggmlv3.q5_1.bin | q5_1 | 5 | 9.76 GB| 12.26 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| dolphin-llama-13b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 9.23 GB| 11.73 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| dolphin-llama-13b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 8.97 GB| 11.47 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| dolphin-llama-13b.ggmlv3.q6_K.bin | q6_K | 6 | 10.68 GB| 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
| dolphin-llama-13b.ggmlv3.q8_0.bin | q8_0 | 8 | 13.83 GB| 16.33 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
## How to run in `llama.cpp`
I use the following command line; adjust for your tastes and needs:
```
./main -t 10 -ngl 32 -m dolphin-llama-13b.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "SYSTEM: You are an author and story teller\nUSER: write a story about llamas\nASSISTANT:"
```
Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz.
**Patreon special mentions**: Slarti, Chadd, John Detwiler, Pieter, zynix, K, Mano Prime, ReadyPlayerEmma, Ai Maven, Leonard Tan, Edmond Seymore, Joseph William Delisle, Luke @flexchar, Fred von Graf, Viktor Bowallius, Rishabh Srivastava, Nikolai Manek, Matthew Berman, Johann-Peter Hartmann, ya boyyy, Greatston Gnanesh, Femi Adebogun, Talal Aujan, Jonathan Leane, terasurfer, David Flickinger, William Sang, Ajan Kanaga, Vadim, Artur Olbinski, Raven Klaugh, Michael Levine, Oscar Rangel, Randy H, Cory Kujawski, RoA, Dave, Alex, Alexandros Triantafyllidis, Fen Risland, Eugene Pentland, vamX, Elle, Nathan LeClaire, Khalefa Al-Ahmad, Rainer Wilmers, subjectnull, Junyu Yang, Daniel P. Andersen, SuperWojo, LangChain4j, Mandus, Kalila, Illia Dulskyi, Trenton Dambrowitz, Asp the Wyvern, Derek Yates, Jeffrey Morgan, Deep Realms, Imad Khwaja, Pyrater, Preetika Verma, biorpg, Gabriel Tamborski, Stephen Murray, Spiking Neurons AB, Iucharbius, Chris Smitley, Willem Michiel, Luke Pendergrass, Sebastain Graf, senxiiz, Will Dee, Space Cruiser, Karl Bernard, Clay Pascal, Lone Striker, transmissions 11, webtim, WelcomeToTheClub, Sam, theTransient, Pierre Kircher, chris gileta, John Villwock, Sean Connelly, Willian Hasse
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Eric Hartford's Dolphin Llama 13B
Dolphin 🐬
https://erichartford.com/dolphin
This model is based on llama1, so it is for non-commercial use only. Future versions will be trained on llama2 and other open models that are suitable for commercial use.
This model is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model compliant to any requests. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant to any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models
You are responsible for any content you create using this model. Enjoy responsibly.
## Dataset
This dataset is an open source implementation of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)
After uncensoring, deduping, and cleaning, our dataset consists of:
- 842,610 instructions of FLANv2 augmented with GPT-4 completions
- 2,625,353 instructions of FLANv2 augmented with GPT-3.5 completions
We followed the submix and system prompt distribution outlined in the Orca paper. With a few exceptions. We included all 75k of CoT in the FLAN-1m dataset rather than sampling that. Also, we found that many items were duplicated, so we removed duplicates.
Then we filtered out instances of alignment, refusal, avoidance, and bias, in order to produce an uncensored model upon which can be layered your personalized alignment LoRA.
We also filtered out duplicates and cleaned the data.
## Training
We trained with the flan5m (gpt3.5 completions) dataset in its entirety for 3 epochs at a learning rate of 2e-5 before we stopped training to avoid overfit.
We trained with the flan1m (gpt4 completions) dataset in its entirety for 2.5 epochs at a learning rate of 1e-5 before we stopped training to avoid overfit.
It took about 600 hours to train on 8x H100s
We used a prompt format similar to Vicuna, but we added the SYSTEM: field.
Prompt format:
```
SYSTEM: {system}
USER: {prompt}
ASSISTANT:
```
Example:
```
SYSTEM: you are an expert marine biologist.
USER: Please list 10 ways that dolphins are superior to orcas.
ASSISTANT:
```
## Evaluation
Evaluation will be coming soon.
## Team
The core Dolphin Team includes:
- Eric "Faldore" Hartford
- Pankaj Mathur
- Rob "Rohan" O'Callahan
- Tom "TheBloke" Jobbins
## Gratitude
- Thank you to Microsoft for authoring the Orca paper and inspiring this work.
- Special thanks to WingLian, NanoBit, Teknium for helpful advice
- Special thanks to EdenCoder and chirper.ai for mentorship and financial sponsorship.
- Special thanks to Kilkonie for his very valued mentorship.
- Thank you to Catto.
- Thank you to Nicolai Schleifer, financial sponsor.
- Thank you to Eric Fleming, financial sponsor.
- Thank you to all the other people in the Open Source AI community who have taught me and helped me along the way.
|
tslai1992/dqn-SpaceInvadersNoFrameskip-v4 | tslai1992 | 2023-07-23T08:49:29Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T08:24:05Z | ---
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: 314.50 +/- 107.95
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 tslai1992 -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 tslai1992 -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 tslai1992
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Ahmet2250/q-FrozenLake-v1-4x4-noSlippery | Ahmet2250 | 2023-07-23T08:28:37Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T08:28:33Z | ---
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="Ahmet2250/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"])
```
|
tslai1992/deep_rl_tutorial | tslai1992 | 2023-07-23T07:51:47Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T07:22:31Z | ---
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: 260.42 +/- 22.27
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
...
```
|
NasimB/cbt-rarity-neg-log-rarity-end-p8k | NasimB | 2023-07-23T07:51:43Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-23T04:34:18Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: cbt-rarity-neg-log-rarity-end-p8k
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. -->
# cbt-rarity-neg-log-rarity-end-p8k
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1023
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.3445 | 0.29 | 500 | 5.3418 |
| 5.0296 | 0.58 | 1000 | 4.9281 |
| 4.7069 | 0.88 | 1500 | 4.6922 |
| 4.4415 | 1.17 | 2000 | 4.5462 |
| 4.2976 | 1.46 | 2500 | 4.4280 |
| 4.1934 | 1.75 | 3000 | 4.3279 |
| 4.0772 | 2.04 | 3500 | 4.2526 |
| 3.8904 | 2.33 | 4000 | 4.2097 |
| 3.8664 | 2.63 | 4500 | 4.1528 |
| 3.8245 | 2.92 | 5000 | 4.1044 |
| 3.6329 | 3.21 | 5500 | 4.0963 |
| 3.578 | 3.5 | 6000 | 4.0636 |
| 3.5675 | 3.79 | 6500 | 4.0358 |
| 3.4841 | 4.08 | 7000 | 4.0324 |
| 3.3109 | 4.38 | 7500 | 4.0293 |
| 3.314 | 4.67 | 8000 | 4.0142 |
| 3.2984 | 4.96 | 8500 | 4.0014 |
| 3.155 | 5.25 | 9000 | 4.0138 |
| 3.1322 | 5.54 | 9500 | 4.0123 |
| 3.1327 | 5.83 | 10000 | 4.0118 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
hafidikhsan/distilbert-base-uncased-english-cefr-lexical-evaluation-bs-v3 | hafidikhsan | 2023-07-23T07:34:47Z | 107 | 1 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-23T07:34:22Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-uncased-english-cefr-lexical-evaluation-bs-v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-english-cefr-lexical-evaluation-bs-v3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8231
- Accuracy: 0.6266
- F1: 0.6262
- Precision: 0.6333
- Recall: 0.6266
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.2788 | 1.0 | 346 | 1.3442 | 0.4996 | 0.4895 | 0.5071 | 0.4996 |
| 1.0093 | 2.0 | 692 | 1.2521 | 0.5670 | 0.5689 | 0.5959 | 0.5670 |
| 0.5086 | 3.0 | 1038 | 1.4732 | 0.5902 | 0.5918 | 0.5978 | 0.5902 |
| 0.2153 | 4.0 | 1384 | 1.9881 | 0.6010 | 0.6016 | 0.6120 | 0.6010 |
| 0.0496 | 5.0 | 1730 | 2.0705 | 0.6003 | 0.6015 | 0.6047 | 0.6003 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hafidikhsan/distilbert-base-uncased-english-cefr-lexical-evaluation-bs-v2 | hafidikhsan | 2023-07-23T07:24:54Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-23T07:24:13Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-uncased-english-cefr-lexical-evaluation-bs-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-english-cefr-lexical-evaluation-bs-v2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6208
- Accuracy: 0.5876
- F1: 0.5859
- Precision: 0.5892
- Recall: 0.5876
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.3819 | 1.0 | 173 | 1.3925 | 0.4859 | 0.4780 | 0.4752 | 0.4859 |
| 1.0132 | 2.0 | 346 | 1.3560 | 0.5011 | 0.5008 | 0.5815 | 0.5011 |
| 0.4879 | 3.0 | 519 | 1.4646 | 0.5510 | 0.5532 | 0.5612 | 0.5510 |
| 0.1783 | 4.0 | 692 | 1.7720 | 0.5713 | 0.5705 | 0.5724 | 0.5713 |
| 0.0539 | 5.0 | 865 | 1.9786 | 0.5634 | 0.5650 | 0.5701 | 0.5634 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
vicky0106/vicky0106-sd-class-butterflies-u1 | vicky0106 | 2023-07-23T07:23:42Z | 30 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | 2023-07-23T07:23:07Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('vicky0106/vicky0106-sd-class-butterflies-u1')
image = pipeline().images[0]
image
```
|
hafidikhsan/distilbert-base-uncased-english-cefr-lexical-evaluation-bs-v1 | hafidikhsan | 2023-07-23T07:15:48Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-23T07:15:07Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-uncased-english-cefr-lexical-evaluation-bs-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-english-cefr-lexical-evaluation-bs-v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1930
- Accuracy: 0.5941
- F1: 0.5907
- Precision: 0.5913
- Recall: 0.5941
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 44 | 1.4290 | 0.4439 | 0.3994 | 0.4597 | 0.4439 |
| 1.5279 | 2.0 | 88 | 1.2962 | 0.5076 | 0.4992 | 0.5300 | 0.5076 |
| 1.0713 | 3.0 | 132 | 1.2973 | 0.5293 | 0.5328 | 0.5564 | 0.5293 |
| 0.624 | 4.0 | 176 | 1.3405 | 0.5583 | 0.5550 | 0.5559 | 0.5583 |
| 0.3372 | 5.0 | 220 | 1.3920 | 0.5424 | 0.5445 | 0.5515 | 0.5424 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
treksis/coreml-cyberrealistic-v32_cn | treksis | 2023-07-23T07:11:06Z | 0 | 0 | null | [
"coreml",
"stable-diffusion",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2023-07-23T07:11:06Z | ---
license: creativeml-openrail-m
tags:
- coreml
- stable-diffusion
- text-to-image
duplicated_from: coreml/coreml-cyberrealistic-v32_cn
---
# Core ML Converted Model:
- This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML).
- Provide the model to an app such as **Mochi Diffusion** [Github](https://github.com/godly-devotion/MochiDiffusion) / [Discord](https://discord.gg/x2kartzxGv) to generate images.
- `split_einsum` version is compatible with all compute unit options including Neural Engine.
- `original` version is only compatible with `CPU & GPU` option.
- Custom resolution versions are tagged accordingly.
- The `vae-ft-mse-840000-ema-pruned.ckpt` VAE is embedded into the model.
- This model was converted with a `vae-encoder` for use with `image2image`.
- This model is `fp16`.
- Descriptions are posted as-is from original model source.
- Not all features and/or results may be available in `CoreML` format.
- This model does not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).
- This model does not include a `safety checker` (for NSFW content).
- This model can be used with ControlNet.
<br>
# cyberrealistic-v32_cn:
Source(s): [CivitAI](https://civitai.com/models/15003/cyberrealistic)<br>
## CyberRealistic v3.2
Introducing my versatile photorealistic model - the result of a rigorous testing process that blends various models to achieve the desired output. While I cannot recall all of the individual components used in its creation, I am immensely satisfied with the end result. This model incorporates several custom elements, adding an extra layer of uniqueness to its output.
One of the model's key strengths lies in its ability to effectively process textual inversions and LORA, providing accurate and detailed outputs. Additionally, the model requires minimal prompts, making it incredibly user-friendly and accessible.
In this version, the core remains unchanged, although several new techniques have been introduced and certain elements have been added or removed.
Once again, it's important to note that this version is not intended as a replacement for version 3.1, but rather as an alternative version.
VAE recommended: sd-vae-ft-mse-original. (Note: The VAE has been baked into the files at this repo.)<br><br>
 |
cczhong/llama2-chinese-7b-chat-merged-gptq | cczhong | 2023-07-23T07:02:05Z | 5 | 6 | transformers | [
"transformers",
"llama",
"text-generation",
"zh",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-23T06:47:39Z | ---
license: llama2
language:
- zh
---
merge from FlagAlpha/Llama2-Chinese-7b-Chat-LoRA
github repop: [FlagAlpha/Llama2-Chinese](https://github.com/FlagAlpha/Llama2-Chinese)
use latest https://github.com/PanQiWei/AutoGPTQ
|
garyzsu/llama2-finetuned-qlora-gym-model | garyzsu | 2023-07-23T07:01:47Z | 0 | 0 | peft | [
"peft",
"text-generation",
"region:us"
] | text-generation | 2023-07-23T06:08:27Z | ---
library_name: peft
pipeline_tag: text-generation
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0 |
treksis/bk-sdm-tiny | treksis | 2023-07-23T06:55:29Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"arxiv:2305.15798",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-07-23T06:55:29Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
library_name: diffusers
pipeline_tag: text-to-image
extra_gated_prompt: >-
This model is open access and available to all, with a CreativeML OpenRAIL-M
license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or
harmful outputs or content
2. The authors claim no rights on the outputs you generate, you are free to
use them and are accountable for their use which must not go against the
provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as
a service. If you do, please be aware you have to include the same use
restrictions as the ones in the license and share a copy of the CreativeML
OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license carefully here:
https://huggingface.co/spaces/CompVis/stable-diffusion-license
extra_gated_heading: Please read the LICENSE to access this model
duplicated_from: nota-ai/bk-sdm-tiny
---
# BK-SDM Model Card
Block-removed Knowledge-distilled Stable Diffusion Model (BK-SDM) is an architecturally compressed SDM for efficient general-purpose text-to-image synthesis. This model is bulit with (i) removing several residual and attention blocks from the U-Net of [Stable Diffusion v1.4]( https://huggingface.co/CompVis/stable-diffusion-v1-4) and (ii) distillation pretraining on only 0.22M LAION pairs (fewer than 0.1% of the full training set). Despite being trained with very limited resources, our compact model can imitate the original SDM by benefiting from transferred knowledge.
- **Resources for more information**: [Paper](https://arxiv.org/abs/2305.15798), [Demo]( https://huggingface.co/spaces/nota-ai/compressed-stable-diffusion).
## Examples with 🤗[Diffusers library](https://github.com/huggingface/diffusers).
An inference code with the default PNDM scheduler and 50 denoising steps is as follows.
```python
import torch
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained("nota-ai/bk-sdm-tiny", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a tropical bird sitting on a branch of a tree"
image = pipe(prompt).images[0]
image.save("example.png")
```
The following code is also runnable, because we compressed the U-Net of [Stable Diffusion v1.4]( https://huggingface.co/CompVis/stable-diffusion-v1-4) while keeping the other parts (i.e., Text Encoder and Image Decoder) unchanged:
```python
import torch
from diffusers import StableDiffusionPipeline, UNet2DConditionModel
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe.unet = UNet2DConditionModel.from_pretrained("nota-ai/bk-sdm-tiny", subfolder="unet", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a tropical bird sitting on a branch of a tree"
image = pipe(prompt).images[0]
image.save("example.png")
```
The above examples have been tested on a single NVIDIA GeForce RTX 3090 GPU with the following versions:
```
torch 1.13.1+cu117
transformers 4.29.2
diffusers 0.15.0
```
## Compression Method
### U-Net Architecture
We removed several residual and attention blocks from the 0.86B-parameter U-Net in the 1.04B-param SDM-v1.4, and our compressed models are summarized as follows.
- 0.76B-param **BK-SDM-Base** (0.58B-param U-Net): obtained with ① fewer blocks in outer stages.
- 0.66B-param **BK-SDM-Small** (0.49B-param U-Net): obtained with ① and ② mid-stage removal.
- 0.50B-param **BK-SDM-Tiny** (0.33B-param U-Net): obtained with ①, ②, and ③ further inner-stage removal.
### Distillation Pretraining
The compact U-Net was trained to mimic the behavior of the original U-Net. We leveraged feature-level and output-level distillation, along with the denoising task loss.
<center>
<img alt="U-Net architectures and KD-based pretraining" img src="https://huggingface.co/spaces/nota-ai/compressed-stable-diffusion/resolve/e6fb31631f0b2948cf6ec54006ea050d6c83e940/docs/fig_model.png" width="100%">
</center>
<br/>
- **Training Data**: 212,776 image-text pairs (i.e., 0.22M pairs) from [LAION-Aesthetics V2 6.5+](https://laion.ai/blog/laion-aesthetics/).
- **Hardware:** A single NVIDIA A100 80GB GPU
- **Gradient Accumulations**: 4
- **Batch:** 256 (=4×64)
- **Optimizer:** AdamW
- **Learning Rate:** a constant learning rate of 5e-5 for 50K-iteration pretraining
## Experimental Results
The following table shows the zero-shot results on 30K samples from the MS-COCO validation split. After generating 512×512 images with the PNDM scheduler and 25 denoising steps, we downsampled them to 256×256 for evaluating generation scores. Our models were drawn at the 50K-th training iteration.
| Model | FID↓ | IS↑ | CLIP Score↑<br>(ViT-g/14) | # Params,<br>U-Net | # Params,<br>Whole SDM |
|:---:|:---:|:---:|:---:|:---:|:---:|
| Stable Diffusion v1.4 | 13.05 | 36.76 | 0.2958 | 0.86B | 1.04B |
| BK-SDM-Base (Ours) | 15.76 | 33.79 | 0.2878 | 0.58B | 0.76B |
| BK-SDM-Small (Ours) | 16.98 | 31.68 | 0.2677 | 0.49B | 0.66B |
| BK-SDM-Tiny (Ours) | 17.12 | 30.09 | 0.2653 | 0.33B | 0.50B |
<br/>
The following figure depicts synthesized images with some MS-COCO captions.
<center>
<img alt="Visual results" img src="https://huggingface.co/spaces/nota-ai/compressed-stable-diffusion/resolve/e6fb31631f0b2948cf6ec54006ea050d6c83e940/docs/fig_results.png" width="100%">
</center>
<br/>
# Uses
_Note: This section is taken from the [Stable Diffusion v1 model card]( https://huggingface.co/CompVis/stable-diffusion-v1-4) (which was based on the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini)) and applies in the same way to BK-SDMs_.
## Direct Use
The model is intended for research purposes only. Possible research areas and tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
### Safety Module
The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
# Acknowledgments
- We express our gratitude to [Microsoft for Startups Founders Hub](https://www.microsoft.com/en-us/startups) for generously providing the Azure credits used during pretraining.
- We deeply appreciate the pioneering research on Latent/Stable Diffusion conducted by [CompVis](https://github.com/CompVis/latent-diffusion) and [Runway](https://runwayml.com/).
- Special thanks to the contributors to [Diffusers](https://github.com/huggingface/diffusers) for their valuable support.
# Citation
```bibtex
@article{kim2023architectural,
title={On Architectural Compression of Text-to-Image Diffusion Models},
author={Kim, Bo-Kyeong and Song, Hyoung-Kyu and Castells, Thibault and Choi, Shinkook},
journal={arXiv preprint arXiv:2305.15798},
year={2023},
url={https://arxiv.org/abs/2305.15798}
}
```
```bibtex
@article{Kim_2023_ICMLW,
title={BK-SDM: Architecturally Compressed Stable Diffusion for Efficient Text-to-Image Generation},
author={Kim, Bo-Kyeong and Song, Hyoung-Kyu and Castells, Thibault and Choi, Shinkook},
journal={ICML Workshop on Efficient Systems for Foundation Models (ES-FoMo)},
year={2023},
url={https://openreview.net/forum?id=bOVydU0XKC}
}
```
*This model card was written by Bo-Kyeong Kim and is based on the [Stable Diffusion v1 model card]( https://huggingface.co/CompVis/stable-diffusion-v1-4).* |
hafidikhsan/distilbert-base-uncased-english-cefr-lexical-evaluation-lr-v2 | hafidikhsan | 2023-07-23T06:44:11Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-23T06:43:49Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-uncased-english-cefr-lexical-evaluation-lr-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-english-cefr-lexical-evaluation-lr-v2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6545
- Accuracy: 0.2858
- F1: 0.1724
- Precision: 0.1930
- Recall: 0.2858
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.6298 | 1.0 | 87 | 1.6693 | 0.2802 | 0.1665 | 0.1883 | 0.2802 |
| 1.7023 | 2.0 | 174 | 1.7559 | 0.2194 | 0.1104 | 0.0765 | 0.2194 |
| 1.732 | 3.0 | 261 | 1.7641 | 0.1731 | 0.0861 | 0.0702 | 0.1731 |
| 1.7384 | 4.0 | 348 | 1.7511 | 0.2201 | 0.1064 | 0.0776 | 0.2201 |
| 1.7189 | 5.0 | 435 | 1.7466 | 0.2259 | 0.1101 | 0.0786 | 0.2259 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Corianas/rl_course_vizdoom_health_gathering_supreme_best | Corianas | 2023-07-23T06:34:45Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-23T06:34:41Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 12.17 +/- 4.04
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Corianas/rl_course_vizdoom_health_gathering_supreme_best
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme_best
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme_best --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
hafidikhsan/distilbert-base-uncased-english-cefr-lexical-evaluation-lr-v1 | hafidikhsan | 2023-07-23T06:27:04Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-23T06:26:24Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-uncased-english-cefr-lexical-evaluation-lr-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-english-cefr-lexical-evaluation-lr-v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7918
- Accuracy: 0.1664
- F1: 0.0475
- Precision: 0.0277
- Recall: 0.1664
## 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: 0.01
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.9053 | 1.0 | 87 | 1.7927 | 0.1665 | 0.0476 | 0.0277 | 0.1665 |
| 1.7924 | 2.0 | 174 | 1.7919 | 0.1665 | 0.0476 | 0.0277 | 0.1665 |
| 1.7946 | 3.0 | 261 | 1.7918 | 0.1665 | 0.0476 | 0.0277 | 0.1665 |
| 1.792 | 4.0 | 348 | 1.7918 | 0.1665 | 0.0476 | 0.0277 | 0.1665 |
| 1.792 | 5.0 | 435 | 1.7918 | 0.1673 | 0.0479 | 0.0280 | 0.1673 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
isohpia/llama2 | isohpia | 2023-07-23T06:25:43Z | 0 | 0 | null | [
"arxiv:1910.09700",
"license:llama2",
"region:us"
] | null | 2023-07-23T05:59:19Z | ---
license: llama2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
cczhong/llama2-chinese-7b-chat-merged | cczhong | 2023-07-23T06:16:24Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"zh",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-23T05:28:58Z | ---
license: llama2
language:
- zh
---
merge from FlagAlpha/Llama2-Chinese-7b-Chat-LoRA
github repop: [FlagAlpha/Llama2-Chinese](https://github.com/FlagAlpha/Llama2-Chinese) |
LarryAIDraw/KahoBA-10 | LarryAIDraw | 2023-07-23T05:43:04Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-23T05:15:19Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/45414/kuwakami-kaho-or-blue-archive |
whoisltd/cr4 | whoisltd | 2023-07-23T05:26:37Z | 152 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-23T05:21:35Z | ---
license: apache-2.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Sumegh20/first-test-bloom-3b | Sumegh20 | 2023-07-23T04:48:36Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-23T04:48:35Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
kirby44/PyramidsTraining | kirby44 | 2023-07-23T04:25:25Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2023-07-23T04:25:22Z | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: kirby44/PyramidsTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kirby44/ppo-SnowballTarget | kirby44 | 2023-07-23T04:24:47Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-07-23T03:29:40Z | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: kirby44/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
NasimB/all-base-rerun-new-loop | NasimB | 2023-07-23T04:19:05Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-23T00:40:22Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: all-base-rerun-new-loop
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. -->
# all-base-rerun-new-loop
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0966
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.3514 | 0.29 | 500 | 5.3344 |
| 5.0244 | 0.58 | 1000 | 4.9301 |
| 4.6948 | 0.87 | 1500 | 4.6847 |
| 4.4421 | 1.16 | 2000 | 4.5356 |
| 4.285 | 1.46 | 2500 | 4.4193 |
| 4.1724 | 1.75 | 3000 | 4.3179 |
| 4.0767 | 2.04 | 3500 | 4.2422 |
| 3.883 | 2.33 | 4000 | 4.1998 |
| 3.8483 | 2.62 | 4500 | 4.1495 |
| 3.8125 | 2.91 | 5000 | 4.0986 |
| 3.6378 | 3.2 | 5500 | 4.0910 |
| 3.5732 | 3.49 | 6000 | 4.0640 |
| 3.5575 | 3.78 | 6500 | 4.0288 |
| 3.4696 | 4.07 | 7000 | 4.0250 |
| 3.3036 | 4.37 | 7500 | 4.0232 |
| 3.2977 | 4.66 | 8000 | 4.0094 |
| 3.2899 | 4.95 | 8500 | 3.9977 |
| 3.1442 | 5.24 | 9000 | 4.0094 |
| 3.1227 | 5.53 | 9500 | 4.0079 |
| 3.1169 | 5.82 | 10000 | 4.0071 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
hungngo04/cluster_to_text_bart_large_train | hungngo04 | 2023-07-23T04:05:55Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large",
"base_model:finetune:facebook/bart-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-07-23T00:38:51Z | ---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: cluster_to_text_bart_large_train
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. -->
# cluster_to_text_bart_large_train
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3172
- Bleu: 92.3793
- Gen Len: 10.8549
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.3977 | 1.0 | 4678 | 0.4944 | 84.1681 | 10.8598 |
| 0.1962 | 2.0 | 9356 | 0.3584 | 89.7412 | 10.8781 |
| 0.1123 | 3.0 | 14034 | 0.3206 | 91.6575 | 10.8484 |
| 0.077 | 4.0 | 18712 | 0.3172 | 92.3793 | 10.8549 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
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