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| likes
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| library_name
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Ibrahim-Alam/finetuning-xlnet-base-cased-on-tweet_sentiment_binary | Ibrahim-Alam | 2023-06-29T03:29:07Z | 90 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-29T03:12:36Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-xlnet-base-cased-on-tweet_sentiment_binary
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. -->
# finetuning-xlnet-base-cased-on-tweet_sentiment_binary
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2407
- Accuracy: 0.9334
- F1: 0.9369
## 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: 1
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Christiansg/finetuning-sentiment_spanish-amazon-group23 | Christiansg | 2023-06-29T03:25:10Z | 101 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-29T03:11:54Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-sentiment_spanish-amazon-group23
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. -->
# finetuning-sentiment_spanish-amazon-group23
This model is a fine-tuned version of [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0012
- Accuracy: 1.0
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
carissa1/SimplyLaw_Legal_Questions | carissa1 | 2023-06-29T03:18:09Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"question-answering",
"license:mit",
"region:us"
]
| question-answering | 2023-06-27T17:26:28Z | ---
license: mit
pipeline_tag: question-answering
library_name: adapter-transformers
--- |
beomi/kollama-33b | beomi | 2023-06-29T03:12:02Z | 11 | 8 | transformers | [
"transformers",
"llama",
"text-generation",
"KoLLAMA",
"KoreanGPT",
"ko",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-06-29T03:10:40Z | ---
license: mit
language:
- ko
- en
metrics:
- perplexity
- accuracy
pipeline_tag: text-generation
tags:
- llama
- KoLLAMA
- KoreanGPT
---
> 🚧 Note: this repo is under construction 🚧
## Todo
✅ - finish
⏳ - currently working on it
- ✅ Train new BBPE Tokenizer
- ✅ Test train code on TPUv4 Pods (with model parallel)
- ✅ Converting test (jax to PyTorch)
- ✅ LM train validation on minimal dataset (1 sentence 1000 step)
- ⏳ Build Data Shuffler (curriculum learning)
- ⏳ Train 7B Model
- ⏳ Train 13B Model
- ⏳ Train 33B Model
- Train 65B Model
# KoLLaMA Model Card
KoLLaMA (33B) trained on Korean/English/Code dataset with LLaMA Architecture via JAX,
with the warm support from [Google TPU Research Cloud program](https://sites.research.google/trc/about/) for providing part of the computation resources.
## Model details
**Researcher developing the model**
Junbum Lee (aka Beomi)
**Model date**
KoLLaMA was trained between 2023.04~
- 33B model was trained on 2023.07~
**Model version**
This is alpha version of the model.
**Model type**
LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters.
(This repo contains 33B model!)
**Paper or resources for more information**
More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/.
**Citations details**
KoLLAMA: [TBD]
LLAMA: https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/
**License**
MIT
**Where to send questions or comments about the model**
Questions and comments about KoLLaMA can be sent via the [GitHub repository](https://github.com/beomi/KoLLAMA) of the project , by opening an issue.
## Intended use
**Primary intended uses**
The primary use of KoLLaMA is research on Korean Opensource large language models
**Primary intended users**
The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence.
**Out-of-scope use cases**
LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers.
## Factors
**Relevant factors**
One of the most relevant factors for which model performance may vary is which language is used. Although we included 20 languages in the training data, most of our dataset is made of English text, and we thus expect the model to perform better for English than other languages. Relatedly, it has been shown in previous studies that performance might vary for different dialects, and we expect that it will be the case for our model.
## Evaluation datasets
[TBD]
## Training dataset
[TBD]
## Ethical considerations
**Data**
The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data.
**Human life**
The model is not intended to inform decisions about matters central to human life, and should not be used in such a way.
**Risks and harms**
Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard.
**Use cases**
LLaMA is a foundational model, and as such, it should not be used for downstream applications without further investigation and mitigations of risks. These risks and potential fraught use cases include, but are not limited to: generation of misinformation and generation of harmful, biased or offensive content. |
kevinid/bert-base-multilingual-uncased-finetuned-MeIA-AnalisisDeSentimientos | kevinid | 2023-06-29T03:07:08Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-28T02:42:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-multilingual-uncased-finetuned-MeIA-AnalisisDeSentimientos
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-uncased-finetuned-MeIA-AnalisisDeSentimientos
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0342
- F1: 0.5746
## 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0295 | 1.0 | 766 | 1.0167 | 0.5416 |
| 0.9326 | 2.0 | 1532 | 1.0108 | 0.5553 |
| 0.7689 | 3.0 | 2298 | 1.0342 | 0.5746 |
| 0.623 | 4.0 | 3064 | 1.1112 | 0.5679 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
t3PbMvBN6SXv/ppo-PyramidsRND | t3PbMvBN6SXv | 2023-06-29T03:07:04Z | 5 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-06-29T03:06:09Z | ---
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: t3PbMvBN6SXv/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
t3PbMvBN6SXv/Pixelcopter-PLE-v0 | t3PbMvBN6SXv | 2023-06-29T03:02:50Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-29T02:54:01Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: 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: 86.50 +/- 60.51
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
|
TheFools/Onickayes | TheFools | 2023-06-29T02:58:30Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-06-27T14:04:27Z | ---
license: creativeml-openrail-m
---
|
t3PbMvBN6SXv/ppo-SnowballTarget | t3PbMvBN6SXv | 2023-06-29T02:22:43Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-06-29T02:22:37Z | ---
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: t3PbMvBN6SXv/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
hw2942/Erlangshen-Longformer-110M-finetuning-wallstreetcn-morning-news-vix-sz50-V3 | hw2942 | 2023-06-29T02:15:02Z | 87 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"longformer",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-29T01:43:03Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Erlangshen-Longformer-110M-finetuning-wallstreetcn-morning-news-vix-sz50-V3
results: []
widget:
- text:A股创业板六年新高；纳指跌落高位，标普又新高，创史上第二大中概IPO和今年美股最大IPO的滴滴首日冲高回落，市值破800亿美元，叮咚买菜次日涨逾60%；美元逾两月新高，金银铜6月大跌，原油半年涨超50%。\n中国6月官方制造业PMI为50.9，价格指数从高位回落。\n央行等六部门：充分发挥信贷等金融子市场合力，增强政策的针对性和可操作性。\n人社部 “十四五” 发展规划要求，基本养老保险参保率达95％，城镇新增就业逾5000万人。\n沪深交所7月19日起下调基金交易经手费收费标准。\n奈雪的茶赴港上市首日破发，收盘大跌14%，市值跌破300亿港元。\n港股上市倒计时，小鹏汽车定价165港元/股。\n格力2020股东会通过员工持股计划等议案，董明珠称接班人不是我说你行就行，是你能行才行。\n美国6月小非农ADP新增就业高于预期，绝对值较5月有所回落。\n美联储逆回购用量史上首次逼近1万亿美元。\n媒体称拜登最早下周颁布新行政令，限制多个行业的寡头垄断。\n亚马逊称FTC新任主席有偏见，寻求其回避反垄断调查。\n散户最爱平台Robinhood遭FINRA创纪录罚款7000万美元，被指坑害百万客户。
---
<!-- 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. -->
# Erlangshen-Longformer-110M-finetuning-wallstreetcn-morning-news-vix-sz50-V3
This model is a fine-tuned version of [IDEA-CCNL/Erlangshen-Longformer-110M](https://huggingface.co/IDEA-CCNL/Erlangshen-Longformer-110M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6427
- Accuracy: 0.6154
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 38 | 0.6960 | 0.5 |
| No log | 2.0 | 76 | 0.7015 | 0.5 |
| No log | 3.0 | 114 | 0.8248 | 0.5 |
| No log | 4.0 | 152 | 0.6956 | 0.5 |
| No log | 5.0 | 190 | 0.6886 | 0.5 |
| No log | 6.0 | 228 | 0.7065 | 0.5 |
| No log | 7.0 | 266 | 0.7070 | 0.5 |
| No log | 8.0 | 304 | 0.7395 | 0.5385 |
| No log | 9.0 | 342 | 0.6871 | 0.6538 |
| No log | 10.0 | 380 | 0.6427 | 0.6154 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
eutimio-arevalo-valarezo/huggy-ml-unl | eutimio-arevalo-valarezo | 2023-06-29T02:09:31Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-06-29T02:08:46Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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: eutimio-arevalo-valarezo/huggy-ml-unl
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
akaneshiro/Reinforce-pixelcopter | akaneshiro | 2023-06-29T02:07:46Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-29T02:07:40Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 8.50 +/- 8.58
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
|
yhna/ppo-Pyramids-Training | yhna | 2023-06-29T02:07:27Z | 12 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-06-29T02:07:23Z | ---
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: yhna/ppo-Pyramids-Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
vg055/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos | vg055 | 2023-06-29T01:46:57Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-16T23:13:23Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
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-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0243
- F1: 0.5454
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0835 | 1.0 | 766 | 1.1722 | 0.4787 |
| 0.9631 | 2.0 | 1532 | 1.0243 | 0.5454 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hoaio/Reinforce-Cartpole-v1 | hoaio | 2023-06-29T01:46:27Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-29T01:46:18Z | ---
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: 468.80 +/- 37.79
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
|
kevinid/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos | kevinid | 2023-06-29T01:43:31Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-26T23:41:43Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
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-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0450
- F1: 0.5358
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.09 | 1.0 | 766 | 1.0614 | 0.5191 |
| 0.9633 | 2.0 | 1532 | 1.0450 | 0.5358 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
daeinbangeu/wav2vec2-large-xls-r-300m-korean-g-TW3-backup | daeinbangeu | 2023-06-29T01:42:10Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-06-15T00:59:06Z | ---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-korean-g-TW3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-korean-g-TW3
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9885
- Cer: 0.1503
## 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_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.028 | 3.25 | 500 | 1.0176 | 0.1568 |
| 0.0399 | 6.49 | 1000 | 1.0700 | 0.1733 |
| 0.0521 | 9.74 | 1500 | 0.9727 | 0.1645 |
| 0.0427 | 12.99 | 2000 | 1.0005 | 0.1610 |
| 0.0342 | 16.23 | 2500 | 1.0248 | 0.1633 |
| 0.029 | 19.48 | 3000 | 0.9593 | 0.1562 |
| 0.0232 | 22.73 | 3500 | 1.0307 | 0.1534 |
| 0.0196 | 25.97 | 4000 | 0.9930 | 0.1535 |
| 0.017 | 29.22 | 4500 | 0.9885 | 0.1503 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3
|
sajid73/distilhubert-finetuned-gtzan | sajid73 | 2023-06-29T01:29:35Z | 160 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| audio-classification | 2023-06-28T23:44:06Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.85
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5569
- Accuracy: 0.85
## 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_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0203 | 1.0 | 113 | 1.8486 | 0.5 |
| 1.3421 | 2.0 | 226 | 1.2434 | 0.67 |
| 0.9927 | 3.0 | 339 | 0.9158 | 0.76 |
| 0.8987 | 4.0 | 452 | 0.8062 | 0.76 |
| 0.6031 | 5.0 | 565 | 0.6789 | 0.8 |
| 0.3869 | 6.0 | 678 | 0.6774 | 0.79 |
| 0.4401 | 7.0 | 791 | 0.5672 | 0.84 |
| 0.1752 | 8.0 | 904 | 0.5165 | 0.86 |
| 0.2991 | 9.0 | 1017 | 0.5699 | 0.84 |
| 0.1433 | 10.0 | 1130 | 0.5569 | 0.85 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
osunlp/attrscore-flan-t5-xxl | osunlp | 2023-06-29T01:25:48Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"dataset:osunlp/AttrScore",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-06-22T23:42:05Z | ---
license: apache-2.0
datasets:
- osunlp/AttrScore
---
AttributionScore model fine-tuned from FLAN-t5-xxl on the combined repurposed data from https://huggingface.co/datasets/osunlp/AttrScore |
osunlp/attrscore-vicuna-13b | osunlp | 2023-06-29T01:24:59Z | 11 | 1 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:osunlp/AttrScore",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-06-27T14:34:31Z | ---
datasets:
- osunlp/AttrScore
--- |
osunlp/attrscore-alpaca-7b | osunlp | 2023-06-29T01:24:29Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:osunlp/AttrScore",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-06-27T15:00:41Z | ---
datasets:
- osunlp/AttrScore
--- |
QuangHuy54/roberta-base-squad2-distilled-small-cuad-1 | QuangHuy54 | 2023-06-29T01:12:56Z | 125 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-06-28T23:41:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-squad2-distilled-small-cuad-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-squad2-distilled-small-cuad-1
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0174
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3727 | 1.0 | 1125 | 1.1065 |
| 1.0129 | 2.0 | 2250 | 1.0079 |
| 0.7974 | 3.0 | 3375 | 1.0174 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
gogamza/kobart-base-v1 | gogamza | 2023-06-29T00:45:30Z | 1,709 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bart",
"feature-extraction",
"ko",
"license:mit",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2022-03-02T23:29:05Z | ---
language: ko
tags:
- bart
license: mit
---
## KoBART-base-v1
```python
from transformers import PreTrainedTokenizerFast, BartModel
tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v1')
model = BartModel.from_pretrained('gogamza/kobart-base-v1')
```
|
LanguageMachines/stable-diffusion-2-1-base | LanguageMachines | 2023-06-29T00:29:05Z | 19 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"text-to-image",
"arxiv:2112.10752",
"arxiv:2202.00512",
"arxiv:1910.09700",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-06-28T23:32:51Z | ---
license: openrail++
tags:
- stable-diffusion
- text-to-image
duplicated_from: stabilityai/stable-diffusion-2-1-base
---
# Stable Diffusion v2-1-base Model Card
This model card focuses on the model associated with the Stable Diffusion v2-1-base model.
This `stable-diffusion-2-1-base` model fine-tunes [stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) (`512-base-ema.ckpt`) with 220k extra steps taken, with `punsafe=0.98` on the same dataset.
- Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `v2-1_512-ema-pruned.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.ckpt).
- Use it with 🧨 [`diffusers`](#examples)
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).
- **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
## Examples
Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner.
```bash
pip install diffusers transformers accelerate scipy safetensors
```
Running the pipeline (if you don't swap the scheduler it will run with the default PNDM/PLMS scheduler, in this example we are swapping it to EulerDiscreteScheduler):
```python
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
import torch
model_id = "stabilityai/stable-diffusion-2-1-base"
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
**Notes**:
- Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance)
- If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed)
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_.
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 subset of the large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section).
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are 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.
Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic.
**Training Procedure**
Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through the OpenCLIP-ViT/H text-encoder.
- The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512.
We currently provide the following checkpoints, for various versions:
### Version 2.1
- `512-base-ema.ckpt`: Fine-tuned on `512-base-ema.ckpt` 2.0 with 220k extra steps taken, with `punsafe=0.98` on the same dataset.
- `768-v-ema.ckpt`: Resumed from `768-v-ema.ckpt` 2.0 with an additional 55k steps on the same dataset (`punsafe=0.1`), and then fine-tuned for another 155k extra steps with `punsafe=0.98`.
### Version 2.0
- `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`.
850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`.
- `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset.
- `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning.
The additional input channels of the U-Net which process this extra information were zero-initialized.
- `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.
The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama).
- `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752).
In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml).
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 1
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:
Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 200000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq.
## Citation
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
*This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* |
wesley7137/wizard-vicuna-7b-uncensored | wesley7137 | 2023-06-29T00:11:18Z | 117 | 2 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2023-06-25T10:25:44Z | ---
inference: false
license: other
---
<!-- header start -->
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
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<p><a href="https://discord.gg/Jq4vkcDakD">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 -->
# Eric Hartford's Wizard Vicuna 7B Uncensored GGML
These files are GGML format model files for [Eric Hartford's Wizard Vicuna 7B Uncensored](https://huggingface.co/ehartford/Wizard-Vicuna-7B-Uncensored).
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:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [KoboldCpp](https://github.com/LostRuins/koboldcpp)
* [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui)
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
* [ctransformers](https://github.com/marella/ctransformers)
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-HF)
## Prompt template
```
USER: prompt goes here
ASSISTANT:
```
<!-- compatibility_ggml start -->
## Compatibility
### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`.
They should be compatible with all current UIs and libraries that use llama.cpp, such as those listed at the top of this README.
### 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 only compatible with llama.cpp as of June 6th, commit `2d43387`.
They will NOT be compatible with koboldcpp, text-generation-ui, and other UIs and libraries yet. Support is expected to come over the next few days.
## Explanation of the new k-quant methods
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.
<!-- compatibility_ggml end -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| Wizard-Vicuna-7B-Uncensored.ggmlv3.q2_K.bin | q2_K | 2 | 2.80 GB | 5.30 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. |
| Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 3.55 GB | 6.05 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 |
| Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 3.23 GB | 5.73 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 |
| Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 2.90 GB | 5.40 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 4.05 GB | 6.55 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 |
| Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 3.79 GB | 6.29 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 4.77 GB | 7.27 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 |
| Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 4.63 GB | 7.13 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| Wizard-Vicuna-7B-Uncensored.ggmlv3.q6_K.bin | q6_K | 6 | 5.53 GB | 8.03 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
| Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_0.bin | q4_0 | 4 | 3.79 GB | 6.29 GB | Original llama.cpp quant method, 4-bit. |
| Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_1.bin | q4_1 | 4 | 4.21 GB | 6.71 GB | Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_0.bin | q5_0 | 5 | 4.63 GB | 7.13 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_1.bin | q5_1 | 5 | 5.06 GB | 7.56 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| Wizard-Vicuna-7B-Uncensored.ggmlv3.q8_0.bin | q8_0 | 8 | 7.16 GB | 9.66 GB | Original llama.cpp 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 Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
```
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/Jq4vkcDakD)
## 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
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**Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
**Patreon special mentions**: Ajan Kanaga, Kalila, Derek Yates, Sean Connelly, Luke, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, trip7s trip, Jonathan Leane, Talal Aujan, Artur Olbinski, Cory Kujawski, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Johann-Peter Hartmann.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Eric Hartford's Wizard Vicuna 7B Uncensored
This is [wizard-vicuna-13b](https://huggingface.co/junelee/wizard-vicuna-13b) trained against LLaMA-7B with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.
Shout out to the open source AI/ML community, and everyone who helped me out.
Note:
An uncensored model has no guardrails.
You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car.
Publishing anything this model generates is the same as publishing it yourself.
You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.
|
Ibrahim-Alam/finetuning-xlnet-base-cased-on-imdb | Ibrahim-Alam | 2023-06-28T23:49:30Z | 93 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-26T18:21:35Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-xlnet-base-cased-on-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.95056
- name: F1
type: f1
value: 0.9503813729425933
---
<!-- 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. -->
# finetuning-xlnet-base-cased-on-imdb
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1402
- Accuracy: 0.9506
- F1: 0.9504
## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
allenchienxxx/taxi-v3 | allenchienxxx | 2023-06-28T23:48:58Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T23:48:56Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.72
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="allenchienxxx/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"])
```
|
QuangHuy54/roberta-base-squad2-distilled-small-cuad | QuangHuy54 | 2023-06-28T23:26:08Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-06-28T22:17:02Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-squad2-distilled-small-cuad
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-base-squad2-distilled-small-cuad
This model is a fine-tuned version of [deepset/roberta-base-squad2-distilled](https://huggingface.co/deepset/roberta-base-squad2-distilled) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 282 | 2.9007 |
| 3.0578 | 2.0 | 564 | 2.9068 |
| 3.0578 | 3.0 | 846 | 2.9965 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Alyss97/bert-base-multilingual-uncased-sentiment-finetuned-MeIA-AnalisisDeSentimientos | Alyss97 | 2023-06-28T23:17:00Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-28T16:34:21Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-multilingual-uncased-sentiment-finetuned-MeIA-AnalisisDeSentimientos
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-uncased-sentiment-finetuned-MeIA-AnalisisDeSentimientos
This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9405
- F1: 0.5939
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9441 | 1.0 | 766 | 0.9419 | 0.5604 |
| 0.7769 | 2.0 | 1532 | 0.9405 | 0.5939 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
NeoCodes-dev/Pyramid_PPO1 | NeoCodes-dev | 2023-06-28T23:08:21Z | 14 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-06-28T23:08:19Z | ---
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: dergky1/Pyramid_PPO1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
YakovElm/MariaDB_20_BERT_Over_Sampling | YakovElm | 2023-06-28T22:32:16Z | 62 | 0 | transformers | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-28T22:29:47Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB_20_BERT_Over_Sampling
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. -->
# MariaDB_20_BERT_Over_Sampling
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0346
- Train Accuracy: 0.9902
- Validation Loss: 0.2078
- Validation Accuracy: 0.9698
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4262 | 0.7938 | 0.1451 | 0.9598 | 0 |
| 0.1324 | 0.9535 | 0.1764 | 0.9698 | 1 |
| 0.0346 | 0.9902 | 0.2078 | 0.9698 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
MazVer/SweetHan | MazVer | 2023-06-28T22:31:41Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-06-28T22:27:51Z | ---
license: creativeml-openrail-m
---
|
jncraton/flan-t5-xl-ct2-int8 | jncraton | 2023-06-28T22:26:50Z | 47 | 1 | transformers | [
"transformers",
"text2text-generation",
"en",
"fr",
"ro",
"de",
"multilingual",
"dataset:svakulenk0/qrecc",
"dataset:taskmaster2",
"dataset:djaym7/wiki_dialog",
"dataset:deepmind/code_contests",
"dataset:lambada",
"dataset:gsm8k",
"dataset:aqua_rat",
"dataset:esnli",
"dataset:quasc",
"dataset:qed",
"arxiv:2210.11416",
"arxiv:1910.09700",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-06-28T20:45:42Z | ---
language:
- en
- fr
- ro
- de
- multilingual
widget:
- text: "Translate to German: My name is Arthur"
example_title: "Translation"
- text: "Please answer to the following question. Who is going to be the next Ballon d'or?"
example_title: "Question Answering"
- text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."
example_title: "Logical reasoning"
- text: "Please answer the following question. What is the boiling point of Nitrogen?"
example_title: "Scientific knowledge"
- text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?"
example_title: "Yes/no question"
- text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"
example_title: "Reasoning task"
- text: "Q: ( False or not False or False ) is? A: Let's think step by step"
example_title: "Boolean Expressions"
- text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"
example_title: "Math reasoning"
- text: "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?"
example_title: "Premise and hypothesis"
tags:
- text2text-generation
datasets:
- svakulenk0/qrecc
- taskmaster2
- djaym7/wiki_dialog
- deepmind/code_contests
- lambada
- gsm8k
- aqua_rat
- esnli
- quasc
- qed
license: apache-2.0
---
# Model Card for FLAN-T5 XL
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
5. [Training Details](#training-details)
6. [Evaluation](#evaluation)
7. [Environmental Impact](#environmental-impact)
8. [Citation](#citation)
# TL;DR
If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages.
As mentioned in the first few lines of the abstract :
> Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.
**Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the [T5 model card](https://huggingface.co/t5-large).
# Model Details
## Model Description
- **Model type:** Language model
- **Language(s) (NLP):** English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian
- **License:** Apache 2.0
- **Related Models:** [All FLAN-T5 Checkpoints](https://huggingface.co/models?search=flan-t5)
- **Original Checkpoints:** [All Original FLAN-T5 Checkpoints](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints)
- **Resources for more information:**
- [Research paper](https://arxiv.org/pdf/2210.11416.pdf)
- [GitHub Repo](https://github.com/google-research/t5x)
- [Hugging Face FLAN-T5 Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/t5)
# Usage
Find below some example scripts on how to use the model in `transformers`:
## Using the Pytorch model
### Running the model on a CPU
<details>
<summary> Click to expand </summary>
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xl")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl")
input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
### Running the model on a GPU
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xl")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map="auto")
input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
### Running the model on a GPU using different precisions
#### FP16
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xl")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map="auto", torch_dtype=torch.float16)
input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
#### INT8
<details>
<summary> Click to expand </summary>
```python
# pip install bitsandbytes accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xl")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map="auto", load_in_8bit=True)
input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
# Uses
## Direct Use and Downstream Use
The authors write in [the original paper's model card](https://arxiv.org/pdf/2210.11416.pdf) that:
> The primary use is research on language models, including: research on zero-shot NLP tasks and in-context few-shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models
See the [research paper](https://arxiv.org/pdf/2210.11416.pdf) for further details.
## Out-of-Scope Use
More information needed.
# Bias, Risks, and Limitations
The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2210.11416.pdf):
> Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.
## Ethical considerations and risks
> Flan-T5 is fine-tuned on a large corpus of text data that was not filtered for explicit content or assessed for existing biases. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data.
## Known Limitations
> Flan-T5 has not been tested in real world applications.
## Sensitive Use:
> Flan-T5 should not be applied for any unacceptable use cases, e.g., generation of abusive speech.
# Training Details
## Training Data
The model was trained on a mixture of tasks, that includes the tasks described in the table below (from the original paper, figure 2):
## Training Procedure
According to the model card from the [original paper](https://arxiv.org/pdf/2210.11416.pdf):
> These models are based on pretrained T5 (Raffel et al., 2020) and fine-tuned with instructions for better zero-shot and few-shot performance. There is one fine-tuned Flan model per T5 model size.
The model has been trained on TPU v3 or TPU v4 pods, using [`t5x`](https://github.com/google-research/t5x) codebase together with [`jax`](https://github.com/google/jax).
# Evaluation
## Testing Data, Factors & Metrics
The authors evaluated the model on various tasks covering several languages (1836 in total). See the table below for some quantitative evaluation:
For full details, please check the [research paper](https://arxiv.org/pdf/2210.11416.pdf).
## Results
For full results for FLAN-T5-XL, see the [research paper](https://arxiv.org/pdf/2210.11416.pdf), Table 3.
# Environmental Impact
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:** Google Cloud TPU Pods - TPU v3 or TPU v4 | Number of chips ≥ 4.
- **Hours used:** More information needed
- **Cloud Provider:** GCP
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
# Citation
**BibTeX:**
```bibtex
@misc{https://doi.org/10.48550/arxiv.2210.11416,
doi = {10.48550/ARXIV.2210.11416},
url = {https://arxiv.org/abs/2210.11416},
author = {Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret and Tay, Yi and Fedus, William and Li, Eric and Wang, Xuezhi and Dehghani, Mostafa and Brahma, Siddhartha and Webson, Albert and Gu, Shixiang Shane and Dai, Zhuyun and Suzgun, Mirac and Chen, Xinyun and Chowdhery, Aakanksha and Narang, Sharan and Mishra, Gaurav and Yu, Adams and Zhao, Vincent and Huang, Yanping and Dai, Andrew and Yu, Hongkun and Petrov, Slav and Chi, Ed H. and Dean, Jeff and Devlin, Jacob and Roberts, Adam and Zhou, Denny and Le, Quoc V. and Wei, Jason},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Scaling Instruction-Finetuned Language Models},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
rvrtdta/roberta-base-bne-finetuned-MeIA-AnalisisDeSentimientos | rvrtdta | 2023-06-28T22:22:16Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-26T18:21:10Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: roberta-base-bne-finetuned-MeIA-AnalisisDeSentimientos
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-base-bne-finetuned-MeIA-AnalisisDeSentimientos
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9624
- F1: 0.5881
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9506 | 1.0 | 657 | 0.9264 | 0.5792 |
| 0.6835 | 2.0 | 1314 | 0.9624 | 0.5881 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
eskalofi/liziwess | eskalofi | 2023-06-28T22:16:30Z | 30 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-06-28T22:10:11Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### liziwess Dreambooth model trained by eskalofi with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
michaelfeil/ct2fast-mpt-30b-instruct | michaelfeil | 2023-06-28T22:13:29Z | 5 | 4 | transformers | [
"transformers",
"mpt",
"text-generation",
"ctranslate2",
"int8",
"float16",
"Composer",
"MosaicML",
"llm-foundry",
"custom_code",
"arxiv:2205.14135",
"arxiv:2108.12409",
"license:cc-by-sa-3.0",
"autotrain_compatible",
"region:us"
]
| text-generation | 2023-06-23T07:22:01Z | ---
license: cc-by-sa-3.0
datasets:
- competition_math
- conceptofmind/cot_submix_original/cot_gsm8k
- knkarthick/dialogsum
- mosaicml/dolly_hhrlhf
- duorc
- tau/scrolls/qasper
- emozilla/quality
- scrolls/summ_screen_fd
- spider
tags:
- ctranslate2
- int8
- float16
- Composer
- MosaicML
- llm-foundry
inference: false
---
# # Fast-Inference with Ctranslate2
Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
quantized version of [mosaicml/mpt-30b-instruct](https://huggingface.co/mosaicml/mpt-30b-instruct)
```bash
pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.16.0
```
```python
# from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-mpt-30b-instruct"
from hf_hub_ctranslate2 import GeneratorCT2fromHfHub
model = GeneratorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
# tokenizer=AutoTokenizer.from_pretrained("{ORG}/{NAME}")
)
outputs = model.generate(
text=["def fibonnaci(", "User: How are you doing? Bot:"],
max_length=64,
include_prompt_in_result=False
)
print(outputs)
```
Checkpoint compatible to [ctranslate2>=3.16.0](https://github.com/OpenNMT/CTranslate2)
and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
Converted on 2023-06-23 using
```
ct2-transformers-converter --model mosaicml/mpt-30b-instruct --output_dir ~/tmp-ct2fast-mpt-30b-instruct --force --copy_files tokenizer.json README.md tokenizer_config.json generation_config.json special_tokens_map.json .gitattributes --quantization int8_float16 --trust_remote_code
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
# MPT-30B-Instruct
MPT-30B-Instruct is a model for short-form instruction following.
It is built by finetuning [MPT-30B](https://huggingface.co/mosaicml/mpt-30b) on [Dolly HHRLHF](https://huggingface.co/datasets/mosaicml/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. It is also trained on [Competition Math](https://huggingface.co/datasets/competition_math), [Duorc](https://huggingface.co/datasets/duorc), [CoT GSM8k](https://huggingface.co/datasets/conceptofmind/cot_submix_original), [Qasper](https://huggingface.co/datasets/allenai/qasper), [Quality](https://huggingface.co/datasets/emozilla/quality), [Summ Screen FD](https://huggingface.co/datasets/tau/scrolls) and [Spider](https://huggingface.co/datasets/spider).
* License: _CC-By-SA-3.0_
This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture.
## Model Date
June 22, 2023
## Model License
CC-By-SA-3.0
## Documentation
* [Blog post: Raising the bar for open-source foundation models](https://www.mosaicml.com/blog/mpt-30b)
* [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
**Bespokenizer46**
> I need to convince my friend, Phyllis, that she should train a custom LLM for her Fortune 500 company using the MosaicML Platform.
> Please write an email that explains why MosaicML's emphasis on cutting edge methodology, data privacy, and efficiency are so important.
> End the email with a friendly inquiry about Phyllis's family.
**MPT-30B-Instruct**:
> Phyllis -
> I know you're always looking for ways to improve efficiency at work and I think I've found a solution that you'll be very interested in.
> LLMs are only as good as the data they're trained on and the training process can be very time consuming and resource intensive. MosaicML uses cutting edge methodology to efficiently train high quality models without compromising data privacy.
> They also provide tools to easily connect to and use the model in your daily workflow.
> I think you'd really enjoy speaking with their founder, we can set up a call if you're interested.
> Also, I know it's been a tough year for your family, how are things?
> Best,
> Your Friend
## 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-30b-instruct',
trust_remote_code=True
)
```
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-30b-instruct'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.attn_config['attn_impl'] = 'triton' # change this to use triton-based FlashAttention
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
)
```
The model was trained initially on a sequence length of 2048. An additional pre-training phase was included for sequence length adaptation to 8192. However, ALiBi further enables users to increase the maximum sequence length during finetuning and/or inference. For example:
```python
import transformers
name = 'mosaicml/mpt-30b-instruct'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.max_seq_len = 16384 # (input + output) tokens can now be up to 16384
model = transformers.AutoModelForCausalLM.from_pretrained(
name,
config=config,
trust_remote_code=True
)
```
This model was trained with the MPT-30B tokenizer which is based on the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer and includes additional padding and eos tokens.
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('mosaicml/mpt-30b')
```
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
with torch.autocast('cuda', dtype=torch.bfloat16):
inputs = tokenizer('Here is a recipe for vegan banana bread:\n', return_tensors="pt").to('cuda')
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# or using the HF 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 as follows:
```python
def format_prompt(instruction):
template = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n###Instruction\n{instruction}\n\n### Response\n"
return template.format(instruction=instruction)
example = "Tell me a funny joke.\nDon't make it too funny though."
fmt_ex = format_prompt(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 | 29.95B |
|n_layers | 48 |
| n_heads | 64 |
| d_model | 7168 |
| vocab size | 50432 |
| sequence length | 8192 |
## Data Mix
The model was trained on the following data mix:
| Data Source | Number of Tokens in Source | Proportion |
|-------------|----------------------------|------------|
| competition_math | 1.6 M | 3.01% |
| cot_gsm8k | 3.36 M | 6.32% |
| dialogsum | 0.1 M | 0.19% |
| dolly_hhrlhf | 5.89 M | 11.07% |
| duorc | 8.2 M | 15.51% |
| qasper | 10.97 M | 20.63% |
| quality | 11.31 M | 21.28% |
| scrolls/summ_screen_fd | 11.56 M | 21.82% |
| spider | 0.089 M | 0.16% |
## PreTraining Data
For more details on the pretraining process, see [MPT-30B](https://huggingface.co/mosaicml/mpt-30b).
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 72 A100 40GB GPUs for 8 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-30B-Instruct can produce factually incorrect output, and should not be relied on to produce factually accurate information.
MPT-30B-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, Alex Trott, 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-30b).
## 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 consult 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-30B: Raising the bar
for open-source foundation models},
year = {2023},
url = {www.mosaicml.com/blog/mpt-30b},
note = {Accessed: 2023-06-22},
urldate = {2023-06-22}
}
``` |
Sadami/roberta-base-bne-finetuned-MeIA-AnalisisDeSentimientos | Sadami | 2023-06-28T22:10:06Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-27T04:01:47Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: roberta-base-bne-finetuned-MeIA-AnalisisDeSentimientos
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-base-bne-finetuned-MeIA-AnalisisDeSentimientos
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9503
- F1: 0.5905
## 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: 18
- eval_batch_size: 18
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9465 | 1.0 | 584 | 0.9365 | 0.5756 |
| 0.704 | 2.0 | 1168 | 0.9503 | 0.5905 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
michaelfeil/ct2fast-mpt-30b-chat | michaelfeil | 2023-06-28T22:09:57Z | 8 | 2 | transformers | [
"transformers",
"mpt",
"text-generation",
"ctranslate2",
"int8",
"float16",
"Composer",
"MosaicML",
"llm-foundry",
"custom_code",
"arxiv:2205.14135",
"arxiv:2108.12409",
"arxiv:2010.04245",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"region:us"
]
| text-generation | 2023-06-23T18:02:28Z | ---
license: cc-by-nc-sa-4.0
datasets:
- camel-ai/code
- ehartford/wizard_vicuna_70k_unfiltered
- anon8231489123/ShareGPT_Vicuna_unfiltered
- teknium1/GPTeacher/roleplay-instruct-v2-final
- teknium1/GPTeacher/codegen-isntruct
- timdettmers/openassistant-guanaco
- camel-ai/math
- project-baize/baize-chatbot/medical_chat_data
- project-baize/baize-chatbot/quora_chat_data
- project-baize/baize-chatbot/stackoverflow_chat_data
- camel-ai/biology
- camel-ai/chemistry
- camel-ai/ai_society
- jondurbin/airoboros-gpt4-1.2
- LongConversations
- camel-ai/physics
tags:
- ctranslate2
- int8
- float16
- Composer
- MosaicML
- llm-foundry
inference: false
---
# # Fast-Inference with Ctranslate2
Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
quantized version of [mosaicml/mpt-30b-chat](https://huggingface.co/mosaicml/mpt-30b-chat)
```bash
pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.16.0
```
```python
# from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-mpt-30b-chat"
from hf_hub_ctranslate2 import GeneratorCT2fromHfHub
model = GeneratorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
# tokenizer=AutoTokenizer.from_pretrained("{ORG}/{NAME}")
)
outputs = model.generate(
text=["def fibonnaci(", "User: How are you doing? Bot:"],
max_length=64,
include_prompt_in_result=False
)
print(outputs)
```
Checkpoint compatible to [ctranslate2>=3.16.0](https://github.com/OpenNMT/CTranslate2)
and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
Converted on 2023-06-23 using
```
ct2-transformers-converter --model mosaicml/mpt-30b-chat --output_dir ~/tmp-ct2fast-mpt-30b-chat --force --copy_files tokenizer.json README.md tokenizer_config.json generation_config.json special_tokens_map.json .gitattributes --quantization int8_float16 --trust_remote_code
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
# MPT-30B-Chat
MPT-30B-Chat is a chatbot-like model for dialogue generation.
It was built by finetuning [MPT-30B](https://huggingface.co/mosaicml/mpt-30b) on the [ShareGPT-Vicuna](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered), [Camel-AI](https://huggingface.co/camel-ai),
[GPTeacher](https://github.com/teknium1/GPTeacher), [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco), [Baize](https://github.com/project-baize/baize-chatbot) and some generated datasets.
* License: _CC-By-NC-SA-4.0_ (non-commercial use only)
* [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-30b-chat)
This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture.
## Model Date
June 22, 2023
## Model License
_CC-By-NC-SA-4.0_ (non-commercial use only)
## Documentation
* [Blog post: Raising the bar for open-source foundation models](https://www.mosaicml.com/blog/mpt-30b)
* [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 Dialogue
**ksreenivasan**:
> I want you to come up with a tweet based on this summary of the article:
>
> We are excited to extend the MosaicML Foundation Series with MPT-30B, a new commercially licensed open-source model that is significantly more powerful, and outperforms the
> original GPT-3. In addition, we are releasing two fine-tuned models, MPT-30B-Instruct and MPT-30B-Chat, that are built on top of MPT-30B and excel at short-form instruction
> following and multi-turn conversations, respectively.
> All MPT-30B models come with special features that differentiate them from other LLMs, including an 8k token context window (which can be further extended via finetuning;
> see MPT-7B-StoryWriter), support for context-length extrapolation via ALiBi, and efficient inference + training performance via FlashAttention. It also has strong coding
> abilities thanks to its pretraining data mixture.
**MPT-30B-Chat**:
> "Revolutionize your AI capabilities with MosaicML's latest addition, MPT-30B! Outperforming GPT-3, this open-source model is significantly more powerful and comes with
> finely-tuned models for exceptional instruction following and multi-turn conversations. Unleash the potential of an 8k token context window, ALiBi support, and FlashAttention.
> Experience the future of AI with MosaicML! #AI #ML #innovation"
**ksreenivasan**:
> Is there anything you want to say to MosaicML about this exciting news?
**MPT-30B-Chat**:
> As your AI marketing assistant, I would say that this is truly exciting news for the AI community! MosaicML's latest addition, MPT-30B, is poised to revolutionize the industry with its powerful open-source capabilities and finely-tuned models for instruction following and multi-turn conversations.
> The special features, including the 8k token context window, ALiBi support, and FlashAttention, are sure to set MosaicML's MPT-30B apart from other LLMs.
> Congratulations to the MosaicML team on this incredible achievement!
## How to Use
This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning.
```python
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-30b-chat',
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-30b-chat'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.attn_config['attn_impl'] = 'triton' # change this to use triton-based FlashAttention
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
)
```
The model was trained initially with a sequence length of 4096 with an additional pretraining stage for sequence length adapation up to 8192. However, ALiBi enables users to increase the maximum sequence length even further during finetuning and/or inference. For example:
```python
import transformers
name = 'mosaicml/mpt-30b-chat'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.max_seq_len = 16384 # (input + output) tokens can now be up to 16384
model = transformers.AutoModelForCausalLM.from_pretrained(
name,
config=config,
trust_remote_code=True
)
```
This model was trained with the MPT-30B tokenizer which is based on the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer and includes additional padding and eos tokens.
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('mosaicml/mpt-30b')
```
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
with torch.autocast('cuda', dtype=torch.bfloat16):
inputs = tokenizer('Here is a recipe for vegan banana bread:\n', return_tensors="pt").to('cuda')
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# or using the HF 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))
```
## 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 | 29.95B |
|n_layers | 48 |
| n_heads | 64 |
| d_model | 7168 |
| vocab size | 50432 |
| sequence length | 8192 |
## Data Mix
The model was trained on the following data mix:
| Data Source | Number of Tokens in Source | Proportion |
|-------------|----------------------------|------------|
| Airoboros/GPT4 | 26.4M | 1.71% |
| Baize | 55.0M | 3.57% |
| Camel | 301M | 19.54% |
| GPTeacher | 7.56M | 0.49% |
| Guanaco | 15.6M | 1.02% |
| LongCoversations | 18.4M | 1.19% |
| ShareGPT | 821M | 53.24% |
| WizardLM | 297M | 19.23% |
"LongConversations" is a GPT3.5/4-generated dataset, details of which will be released at a later date.
### Training Configuration
This model was trained on 64 H100s for about 7.6 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-30B-Chat can produce factually incorrect output, and should not be relied on to produce factually accurate information.
MPT-30B-Chat 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
## 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 consult an attorney before using this model for commercial purposes.
## 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).
## Citation
Please cite this model using the following format:
```
@online{MosaicML2023Introducing,
author = {MosaicML NLP Team},
title = {Introducing MPT-30B: Raising the bar
for open-source foundation models},
year = {2023},
url = {www.mosaicml.com/blog/mpt-30b},
note = {Accessed: 2023-06-22},
urldate = {2023-06-22}
}
``` |
Mariamtc/finetuned-twitter-roberta-base-sep2022-tweetcognition | Mariamtc | 2023-06-28T22:07:15Z | 103 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-02T17:05:52Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-twitter-roberta-base-sep2022-tweetcognition
results: []
language:
- en
---
<!-- 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-twitter-roberta-base-sep2022-tweetcognition
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sep2022](https://huggingface.co/cardiffnlp/twitter-roberta-base-sep2022) on custom dataset
consisting of 2527 recent tweets related to major life events that occur during the lifespan of the users.
It achieves the following results on the evaluation set:
- Loss: 0.2433
- Accuracy: 0.9545
## Model description
A RoBERTa-base model trained on 168.86M tweets until the end of September 2022 (15M tweets increment) finetuned and trained on custom dataset
consisting of 2527 recent tweets related to major life events that occur during the lifespan of the users with the scope of performing a specific text xlassification task:
classify posts from the Twitter social media platform into a set of 30 distinct classes, each representing a major life event that the author of the post recently experienced.
RoBERTa (Robustly Optimized BERT approach) is a state-of-the-art natural language processing (NLP) model developed by Facebook AI.
## Intended uses & limitations
The scope of this fine-tuned language model is to be used for a specific text classification task: classify posts from the Twitter social media platform into a set of
30 distinct classes, each representing a major life event that the author of the post recently experienced.
The model can be further improved by training on an even larger training dataset with an extended and more diverse set of life events classes.
## Training procedure
A fine-tuning process was applied to the original model [cardiffnlp/twitter-roberta-base-sep2022](https://huggingface.co/cardiffnlp/twitter-roberta-base-sep2022) by:
- trainig the original model on a custom dataset consisting of 2527 recent tweets related to major life events that occur during the lifespan of the users
- setting the model's hyperparameters with the values mentioned in the table below
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0283 | 1.0 | 127 | 1.4553 | 0.8162 |
| 0.9216 | 2.0 | 254 | 0.5951 | 0.8992 |
| 0.4343 | 3.0 | 381 | 0.3544 | 0.9348 |
| 0.2629 | 4.0 | 508 | 0.2613 | 0.9486 |
| 0.1861 | 5.0 | 635 | 0.2433 | 0.9545 |
### Framework versions
- Transformers 4.29.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3 |
tyavika/Bert-CNNLSTM-QA-Pt-FULL | tyavika | 2023-06-28T21:57:34Z | 70 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-06-28T03:03:23Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Bert-CNNLSTM-QA-Pt-FULL
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bert-CNNLSTM-QA-Pt-FULL
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2581
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2765 | 1.0 | 3290 | 1.1320 |
| 0.8879 | 2.0 | 6580 | 1.0397 |
| 0.5987 | 3.0 | 9870 | 1.1484 |
| 0.3994 | 4.0 | 13160 | 1.2581 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
alitair/Taxi-v3 | alitair | 2023-06-28T21:55:46Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T21:55:43Z | ---
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: 7.56 +/- 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="alitair/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"])
```
|
alitair/q-FrozenLake-v1-4x4-noSlippery | alitair | 2023-06-28T21:49:01Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T21:48:58Z | ---
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="alitair/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"])
```
|
WALIDALI/bekimajic | WALIDALI | 2023-06-28T21:46:07Z | 32 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-06-28T21:34:07Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### bekimajic Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
coreml-community/coreml-realisticVision-v20_cn | coreml-community | 2023-06-28T21:43:14Z | 0 | 2 | null | [
"coreml",
"stable-diffusion",
"text-to-image",
"not-for-all-eyes",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-06-28T16:31:40Z | ---
license: creativeml-openrail-m
tags:
- coreml
- stable-diffusion
- text-to-image
- not-for-all-eyes
---
# 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
# realisticVision-v20_cn:
Source(s): [Hugging Face](https://huggingface.co/SG161222/Realistic_Vision_V2.0) - [CivitAI](https://civitai.com/models/4201/realistic-vision-v20)
**Please read this!**
My model has always been free and always will be free. There are no restrictions on the use of the model. The rights to this model still belong to me.
This model is available on Mage.Space, Sinkin.ai, GetImg.ai and RandomSeed.co (NSFW content)
You can find out news about this model and future models, as well as support me on Boosty.
Recommended for use with [VAE](https://huggingface.co/stabilityai/sd-vae-ft-mse-original) which has already been baked into the converted `CoreML` model version here.
I use this template to get good generation results:
**Prompt**: RAW photo, subject, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
**Example**: RAW photo, a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
**Negative Prompt**: (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck
OR
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation
`Euler A` or `DPM++ 2M Karras` with 25 steps
`CFG Scale` 7
`Hires Fix` with `Latent` upscaler
0 `Hires Steps` and `Denoising Strength` 0.25 - 0.45
`Upscaling` by 1.1 - 2.0 <br><br>
 |
jncraton/flan-t5-small-ct2-int8 | jncraton | 2023-06-28T21:40:22Z | 6 | 0 | transformers | [
"transformers",
"text2text-generation",
"en",
"fr",
"ro",
"de",
"multilingual",
"dataset:svakulenk0/qrecc",
"dataset:taskmaster2",
"dataset:djaym7/wiki_dialog",
"dataset:deepmind/code_contests",
"dataset:lambada",
"dataset:gsm8k",
"dataset:aqua_rat",
"dataset:esnli",
"dataset:quasc",
"dataset:qed",
"arxiv:2210.11416",
"arxiv:1910.09700",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-06-28T21:23:07Z | ---
language:
- en
- fr
- ro
- de
- multilingual
tags:
- text2text-generation
widget:
- text: "Translate to German: My name is Arthur"
example_title: "Translation"
- text: "Please answer to the following question. Who is going to be the next Ballon d'or?"
example_title: "Question Answering"
- text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."
example_title: "Logical reasoning"
- text: "Please answer the following question. What is the boiling point of Nitrogen?"
example_title: "Scientific knowledge"
- text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?"
example_title: "Yes/no question"
- text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"
example_title: "Reasoning task"
- text: "Q: ( False or not False or False ) is? A: Let's think step by step"
example_title: "Boolean Expressions"
- text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"
example_title: "Math reasoning"
- text: "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?"
example_title: "Premise and hypothesis"
datasets:
- svakulenk0/qrecc
- taskmaster2
- djaym7/wiki_dialog
- deepmind/code_contests
- lambada
- gsm8k
- aqua_rat
- esnli
- quasc
- qed
license: apache-2.0
---
# Model Card for FLAN-T5 small
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
5. [Training Details](#training-details)
6. [Evaluation](#evaluation)
7. [Environmental Impact](#environmental-impact)
8. [Citation](#citation)
9. [Model Card Authors](#model-card-authors)
# TL;DR
If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages.
As mentioned in the first few lines of the abstract :
> Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.
**Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the [T5 model card](https://huggingface.co/t5-large).
# Model Details
## Model Description
- **Model type:** Language model
- **Language(s) (NLP):** English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian
- **License:** Apache 2.0
- **Related Models:** [All FLAN-T5 Checkpoints](https://huggingface.co/models?search=flan-t5)
- **Original Checkpoints:** [All Original FLAN-T5 Checkpoints](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints)
- **Resources for more information:**
- [Research paper](https://arxiv.org/pdf/2210.11416.pdf)
- [GitHub Repo](https://github.com/google-research/t5x)
- [Hugging Face FLAN-T5 Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/t5)
# Usage
Find below some example scripts on how to use the model in `transformers`:
## Using the Pytorch model
### Running the model on a CPU
<details>
<summary> Click to expand </summary>
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small")
input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
### Running the model on a GPU
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small", device_map="auto")
input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
### Running the model on a GPU using different precisions
#### FP16
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small", device_map="auto", torch_dtype=torch.float16)
input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
#### INT8
<details>
<summary> Click to expand </summary>
```python
# pip install bitsandbytes accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small", device_map="auto", load_in_8bit=True)
input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
# Uses
## Direct Use and Downstream Use
The authors write in [the original paper's model card](https://arxiv.org/pdf/2210.11416.pdf) that:
> The primary use is research on language models, including: research on zero-shot NLP tasks and in-context few-shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models
See the [research paper](https://arxiv.org/pdf/2210.11416.pdf) for further details.
## Out-of-Scope Use
More information needed.
# Bias, Risks, and Limitations
The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2210.11416.pdf):
> Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.
## Ethical considerations and risks
> Flan-T5 is fine-tuned on a large corpus of text data that was not filtered for explicit content or assessed for existing biases. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data.
## Known Limitations
> Flan-T5 has not been tested in real world applications.
## Sensitive Use:
> Flan-T5 should not be applied for any unacceptable use cases, e.g., generation of abusive speech.
# Training Details
## Training Data
The model was trained on a mixture of tasks, that includes the tasks described in the table below (from the original paper, figure 2):
## Training Procedure
According to the model card from the [original paper](https://arxiv.org/pdf/2210.11416.pdf):
> These models are based on pretrained T5 (Raffel et al., 2020) and fine-tuned with instructions for better zero-shot and few-shot performance. There is one fine-tuned Flan model per T5 model size.
The model has been trained on TPU v3 or TPU v4 pods, using [`t5x`](https://github.com/google-research/t5x) codebase together with [`jax`](https://github.com/google/jax).
# Evaluation
## Testing Data, Factors & Metrics
The authors evaluated the model on various tasks covering several languages (1836 in total). See the table below for some quantitative evaluation:
For full details, please check the [research paper](https://arxiv.org/pdf/2210.11416.pdf).
## Results
For full results for FLAN-T5-Small, see the [research paper](https://arxiv.org/pdf/2210.11416.pdf), Table 3.
# Environmental Impact
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:** Google Cloud TPU Pods - TPU v3 or TPU v4 | Number of chips ≥ 4.
- **Hours used:** More information needed
- **Cloud Provider:** GCP
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
# Citation
**BibTeX:**
```bibtex
@misc{https://doi.org/10.48550/arxiv.2210.11416,
doi = {10.48550/ARXIV.2210.11416},
url = {https://arxiv.org/abs/2210.11416},
author = {Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret and Tay, Yi and Fedus, William and Li, Eric and Wang, Xuezhi and Dehghani, Mostafa and Brahma, Siddhartha and Webson, Albert and Gu, Shixiang Shane and Dai, Zhuyun and Suzgun, Mirac and Chen, Xinyun and Chowdhery, Aakanksha and Narang, Sharan and Mishra, Gaurav and Yu, Adams and Zhao, Vincent and Huang, Yanping and Dai, Andrew and Yu, Hongkun and Petrov, Slav and Chi, Ed H. and Dean, Jeff and Devlin, Jacob and Roberts, Adam and Zhou, Denny and Le, Quoc V. and Wei, Jason},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Scaling Instruction-Finetuned Language Models},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
``` |
LanguageMachines/blip2-flan-t5-xxl | LanguageMachines | 2023-06-28T21:39:54Z | 9 | 1 | transformers | [
"transformers",
"pytorch",
"blip-2",
"visual-question-answering",
"vision",
"image-to-text",
"image-captioning",
"en",
"arxiv:2301.12597",
"arxiv:2210.11416",
"license:mit",
"endpoints_compatible",
"region:us"
]
| image-to-text | 2023-06-28T05:45:06Z | ---
language: en
license: mit
tags:
- vision
- image-to-text
- image-captioning
- visual-question-answering
pipeline_tag: image-to-text
inference: false
duplicated_from: Salesforce/blip2-flan-t5-xxl
---
# BLIP-2, Flan T5-xxl, pre-trained only
BLIP-2 model, leveraging [Flan T5-xxl](https://huggingface.co/google/flan-t5-xxl) (a large language model).
It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2).
Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model.
The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen
while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings,
which bridge the gap between the embedding space of the image encoder and the large language model.
The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg"
alt="drawing" width="600"/>
This allows the model to be used for tasks like:
- image captioning
- visual question answering (VQA)
- chat-like conversations by feeding the image and the previous conversation as prompt to the model
## Direct Use and Downstream Use
You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for
fine-tuned versions on a task that interests you.
## Bias, Risks, Limitations, and Ethical Considerations
BLIP2-FlanT5 uses off-the-shelf Flan-T5 as the language model. It inherits the same risks and limitations from [Flan-T5](https://arxiv.org/pdf/2210.11416.pdf):
> Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.
BLIP2 is fine-tuned on image-text datasets (e.g. [LAION](https://laion.ai/blog/laion-400-open-dataset/) ) collected from the internet. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data.
BLIP2 has not been tested in real world applications. It should not be directly deployed in any applications. Researchers should first carefully assess the safety and fairness of the model in relation to the specific context they’re being deployed within.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example), or refer to the snippets below depending on your usecase:
#### Running the model on CPU
<details>
<summary> Click to expand </summary>
```python
import requests
from PIL import Image
from transformers import BlipProcessor, Blip2ForConditionalGeneration
processor = BlipProcessor.from_pretrained("Salesforce/blip2-flan-t5-xxl")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xxl")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
#### Running the model on GPU
##### In full precision
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xxl")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xxl", device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
##### In half precision (`float16`)
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import torch
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xxl")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xxl", torch_dtype=torch.float16, device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
##### In 8-bit precision (`int8`)
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate bitsandbytes
import torch
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xxl")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xxl", load_in_8bit=True, device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details> |
gbellamy/poca-SoccerTwos | gbellamy | 2023-06-28T21:38:59Z | 6 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| reinforcement-learning | 2023-06-28T21:38:47Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: gbellamy/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
YakovElm/MariaDB_15_BERT_Over_Sampling | YakovElm | 2023-06-28T21:38:09Z | 61 | 0 | transformers | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-28T21:37:06Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB_15_BERT_Over_Sampling
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. -->
# MariaDB_15_BERT_Over_Sampling
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0424
- Train Accuracy: 0.9878
- Validation Loss: 0.2411
- Validation Accuracy: 0.9598
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4493 | 0.7804 | 0.2149 | 0.9271 | 0 |
| 0.1265 | 0.9613 | 0.2099 | 0.9598 | 1 |
| 0.0424 | 0.9878 | 0.2411 | 0.9598 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
prognosis/falcon7b-cardio-disease-qa-v1 | prognosis | 2023-06-28T21:15:59Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
]
| null | 2023-06-28T09:16:23Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: falcon7b-cardio-disease-qa-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. -->
# falcon7b-cardio-disease-qa-v1
This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 1500
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
cleanrl/Pusher-v4-ddpg_continuous_action-seed1 | cleanrl | 2023-06-28T21:08:17Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Pusher-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T21:08:02Z | ---
tags:
- Pusher-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pusher-v4
type: Pusher-v4
metrics:
- type: mean_reward
value: -30.52 +/- 2.85
name: mean_reward
verified: false
---
# (CleanRL) **DDPG** Agent Playing **Pusher-v4**
This is a trained model of a DDPG agent playing Pusher-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ddpg_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action --env-id Pusher-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Pusher-v4-ddpg_continuous_action-seed1/raw/main/ddpg_continuous_action.py
curl -OL https://huggingface.co/cleanrl/Pusher-v4-ddpg_continuous_action-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Pusher-v4-ddpg_continuous_action-seed1/raw/main/poetry.lock
poetry install --all-extras
python ddpg_continuous_action.py --track --capture-video --save-model --hf-entity cleanrl --upload-model --env-id Pusher-v4 --seed 1
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': True,
'cuda': True,
'env_id': 'Pusher-v4',
'exp_name': 'ddpg_continuous_action',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'torch_deterministic': True,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
Panchovix/robin-33B-v2-fp16-SuperHOT-8k | Panchovix | 2023-06-28T21:04:37Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-06-28T19:59:12Z | ---
license: other
---
[TheBloke robin-33B-v2-fp16](https://huggingface.co/TheBloke/robin-33B-v2-fp16/tree/main) merged with kaiokendev's [33b SuperHOT 8k LoRA](https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test), without quant. (Full FP16 model) |
NeoCodes-dev/ppo-SnowballTarget | NeoCodes-dev | 2023-06-28T21:03:00Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-06-28T21:02:56Z | ---
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: dergky1/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
NasimB/bert-dp-4 | NasimB | 2023-06-28T21:01:05Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:generator",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-06-26T01:24:27Z | ---
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: bert-dp-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-dp-4
This model is a fine-tuned version of [](https://huggingface.co/) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4611
## 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: 180
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 6.3492 | 1.89 | 1000 | 5.9327 |
| 5.8333 | 3.78 | 2000 | 5.8515 |
| 5.7604 | 5.67 | 3000 | 5.8483 |
| 5.7137 | 7.56 | 4000 | 5.7914 |
| 5.6597 | 9.45 | 5000 | 5.7672 |
| 5.6213 | 11.34 | 6000 | 5.7594 |
| 5.5798 | 13.23 | 7000 | 5.7352 |
| 5.5482 | 15.12 | 8000 | 5.7275 |
| 5.513 | 17.01 | 9000 | 5.7203 |
| 5.485 | 18.9 | 10000 | 5.7211 |
| 5.4498 | 20.79 | 11000 | 5.6947 |
| 5.4175 | 22.68 | 12000 | 5.6923 |
| 5.3877 | 24.57 | 13000 | 5.6879 |
| 5.3635 | 26.47 | 14000 | 5.6776 |
| 5.3389 | 28.36 | 15000 | 5.6757 |
| 5.3166 | 30.25 | 16000 | 5.6758 |
| 5.2951 | 32.14 | 17000 | 5.6676 |
| 5.2793 | 34.03 | 18000 | 5.6711 |
| 5.2684 | 35.92 | 19000 | 5.6687 |
| 5.2609 | 37.81 | 20000 | 5.6684 |
| 5.2606 | 39.7 | 21000 | 5.6719 |
| 5.2624 | 41.59 | 22000 | 5.6697 |
| 5.2551 | 43.48 | 23000 | 5.6718 |
| 5.2461 | 45.37 | 24000 | 5.6699 |
| 5.2431 | 47.26 | 25000 | 5.6692 |
| 5.2414 | 49.15 | 26000 | 5.6691 |
| 5.2856 | 51.04 | 27000 | 5.6823 |
| 5.2753 | 52.93 | 28000 | 5.6860 |
| 5.2549 | 54.82 | 29000 | 5.6877 |
| 5.2276 | 56.71 | 30000 | 5.6285 |
| 5.1674 | 58.6 | 31000 | 5.5439 |
| 5.0894 | 60.49 | 32000 | 5.4082 |
| 4.9508 | 62.38 | 33000 | 5.1598 |
| 4.7453 | 64.27 | 34000 | 4.9274 |
| 4.5898 | 66.16 | 35000 | 4.7884 |
| 4.4656 | 68.05 | 36000 | 4.6531 |
| 4.35 | 69.94 | 37000 | 4.5123 |
| 4.2378 | 71.83 | 38000 | 4.4012 |
| 4.1496 | 73.72 | 39000 | 4.3240 |
| 4.0891 | 75.61 | 40000 | 4.2763 |
| 4.0538 | 77.5 | 41000 | 4.2520 |
| 4.0448 | 79.4 | 42000 | 4.2485 |
| 3.9724 | 81.29 | 43000 | 3.9940 |
| 3.6527 | 83.18 | 44000 | 3.7442 |
| 3.4172 | 85.07 | 45000 | 3.5713 |
| 3.2446 | 86.96 | 46000 | 3.4403 |
| 3.4764 | 88.85 | 47000 | 3.3796 |
| 3.0543 | 90.74 | 48000 | 3.2884 |
| 2.9549 | 92.63 | 49000 | 3.2107 |
| 2.8785 | 94.52 | 50000 | 3.1466 |
| 2.8143 | 96.41 | 51000 | 3.0788 |
| 2.7605 | 98.3 | 52000 | 3.0230 |
| 2.7111 | 100.19 | 53000 | 2.9802 |
| 2.6727 | 102.08 | 54000 | 2.9414 |
| 2.6417 | 103.97 | 55000 | 2.9167 |
| 2.612 | 105.86 | 56000 | 2.8927 |
| 2.5918 | 107.75 | 57000 | 2.8769 |
| 2.5769 | 109.64 | 58000 | 2.8637 |
| 2.566 | 111.53 | 59000 | 2.8551 |
| 2.556 | 113.42 | 60000 | 2.8458 |
| 2.548 | 115.31 | 61000 | 2.8488 |
| 2.5468 | 117.2 | 62000 | 2.8412 |
| 2.5453 | 119.09 | 63000 | 2.8383 |
| 2.7567 | 120.98 | 64000 | 2.8857 |
| 2.6017 | 122.87 | 65000 | 2.8382 |
| 2.5416 | 124.76 | 66000 | 2.7862 |
| 2.484 | 126.65 | 67000 | 2.7415 |
| 2.4361 | 128.54 | 68000 | 2.7079 |
| 2.3925 | 130.43 | 69000 | 2.6771 |
| 2.3512 | 132.33 | 70000 | 2.6542 |
| 2.3146 | 134.22 | 71000 | 2.6327 |
| 2.2805 | 136.11 | 72000 | 2.6119 |
| 2.2494 | 138.0 | 73000 | 2.5903 |
| 2.2218 | 139.89 | 74000 | 2.5734 |
| 2.1955 | 141.78 | 75000 | 2.5584 |
| 2.1739 | 143.67 | 76000 | 2.5459 |
| 2.154 | 145.56 | 77000 | 2.5337 |
| 2.1324 | 147.45 | 78000 | 2.5260 |
| 2.1149 | 149.34 | 79000 | 2.5169 |
| 2.096 | 151.23 | 80000 | 2.5095 |
| 2.083 | 153.12 | 81000 | 2.5045 |
| 2.0666 | 155.01 | 82000 | 2.4911 |
| 2.0562 | 156.9 | 83000 | 2.4907 |
| 2.0437 | 158.79 | 84000 | 2.4808 |
| 2.0356 | 160.68 | 85000 | 2.4816 |
| 2.0317 | 162.57 | 86000 | 2.4758 |
| 2.0201 | 164.46 | 87000 | 2.4724 |
| 2.0138 | 166.35 | 88000 | 2.4723 |
| 2.0095 | 168.24 | 89000 | 2.4651 |
| 2.0056 | 170.13 | 90000 | 2.4651 |
| 2.0021 | 172.02 | 91000 | 2.4616 |
| 1.9974 | 173.91 | 92000 | 2.4611 |
| 1.9985 | 175.8 | 93000 | 2.4613 |
| 1.9954 | 177.69 | 94000 | 2.4579 |
| 1.9979 | 179.58 | 95000 | 2.4611 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
mxdza/ppo-LunarLander-v2 | mxdza | 2023-06-28T20:58:03Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T20:57:42Z | ---
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: 240.30 +/- 20.26
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
InriaValda/lstm_font_seq_ordering | InriaValda | 2023-06-28T20:45:27Z | 0 | 0 | null | [
"text-classification",
"region:us"
]
| text-classification | 2023-06-28T20:42:55Z | ---
pipeline_tag: text-classification
--- |
StefanV28/HandSign2 | StefanV28 | 2023-06-28T20:45:21Z | 3 | 0 | tf-keras | [
"tf-keras",
"mobilenet",
"image-classification",
"region:us"
]
| image-classification | 2023-06-28T09:41:25Z | ---
pipeline_tag: image-classification
--- |
lunarti/ppo-Huggy | lunarti | 2023-06-28T20:44:39Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-06-28T20:44:32Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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: lunarti/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
hartholt/stablelm-tuned-alpha-7b | hartholt | 2023-06-28T20:31:55Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"causal-lm",
"en",
"dataset:dmayhem93/ChatCombined",
"dataset:tatsu-lab/alpaca",
"dataset:nomic-ai/gpt4all_prompt_generations",
"dataset:Dahoas/full-hh-rlhf",
"dataset:jeffwan/sharegpt_vicuna",
"dataset:HuggingFaceH4/databricks_dolly_15k",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-06-28T20:31:55Z | ---
language:
- en
tags:
- causal-lm
license: cc-by-nc-sa-4.0
datasets:
- dmayhem93/ChatCombined
- tatsu-lab/alpaca
- nomic-ai/gpt4all_prompt_generations
- Dahoas/full-hh-rlhf
- jeffwan/sharegpt_vicuna
- HuggingFaceH4/databricks_dolly_15k
duplicated_from: stabilityai/stablelm-tuned-alpha-7b
---
# StableLM-Tuned-Alpha
## Model Description
`StableLM-Tuned-Alpha` is a suite of 3B and 7B parameter decoder-only language models built on top of the `StableLM-Base-Alpha` models and further fine-tuned on various chat and instruction-following datasets.
## Usage
Get started chatting with `StableLM-Tuned-Alpha` by using the following code snippet:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList
tokenizer = AutoTokenizer.from_pretrained("StabilityAI/stablelm-tuned-alpha-7b")
model = AutoModelForCausalLM.from_pretrained("StabilityAI/stablelm-tuned-alpha-7b")
model.half().cuda()
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
stop_ids = [50278, 50279, 50277, 1, 0]
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id:
return True
return False
system_prompt = """<|SYSTEM|># StableLM Tuned (Alpha version)
- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
- StableLM will refuse to participate in anything that could harm a human.
"""
prompt = f"{system_prompt}<|USER|>What's your mood today?<|ASSISTANT|>"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
tokens = model.generate(
**inputs,
max_new_tokens=64,
temperature=0.7,
do_sample=True,
stopping_criteria=StoppingCriteriaList([StopOnTokens()])
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
```
StableLM Tuned should be used with prompts formatted to `<|SYSTEM|>...<|USER|>...<|ASSISTANT|>...`
The system prompt is
```
<|SYSTEM|># StableLM Tuned (Alpha version)
- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
- StableLM will refuse to participate in anything that could harm a human.
```
## Model Details
* **Developed by**: [Stability AI](https://stability.ai/)
* **Model type**: StableLM-Tuned-Alpha models are auto-regressive language models based on the NeoX transformer architecture.
* **Language(s)**: English
* **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers)
* **License**: Fine-tuned checkpoints (`StableLM-Tuned-Alpha`) are licensed under the Non-Commercial Creative Commons license ([CC BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)), in-line with the original non-commercial license specified by [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca).
* **Contact**: For questions and comments about the model, please email `[email protected]`
## Training
| Parameters | Hidden Size | Layers | Heads | Sequence Length |
|------------|-------------|--------|-------|-----------------|
| 3B | 4096 | 16 | 32 | 4096 |
| 7B | 6144 | 16 | 48 | 4096 |
### Training Dataset
`StableLM-Tuned-Alpha` models are fine-tuned on a combination of five datasets:
[Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca), a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine.
[GPT4All Prompt Generations](https://huggingface.co/datasets/nomic-ai/gpt4all_prompt_generations), which consists of 400k prompts and responses generated by GPT-4;
[Anthropic HH](https://huggingface.co/datasets/Dahoas/full-hh-rlhf), made up of preferences about AI assistant helpfulness and harmlessness;
[DataBricks Dolly](https://github.com/databrickslabs/dolly), comprising 15k instruction/responses generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization;
and [ShareGPT Vicuna (English subset)](https://huggingface.co/datasets/jeffwan/sharegpt_vicuna), a dataset of conversations retrieved from [ShareGPT](https://sharegpt.com/).
### Training Procedure
Models are learned via supervised fine-tuning on the aforementioned datasets, trained in mixed-precision (FP16), and optimized with AdamW. We outline the following hyperparameters:
| Parameters | Batch Size | Learning Rate | Warm-up | Weight Decay | Betas |
|------------|------------|---------------|---------|--------------|-------------|
| 3B | 256 | 2e-5 | 50 | 0.01 | (0.9, 0.99) |
| 7B | 128 | 2e-5 | 100 | 0.01 | (0.9, 0.99) |
## Use and Limitations
### Intended Use
These models are intended to be used by the open-source community chat-like applications in adherence with the [CC BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
### Limitations and bias
Although the aforementioned datasets help to steer the base language models into "safer" distributions of text, not all biases and toxicity can be mitigated through fine-tuning. We ask that users be mindful of such potential issues that can arise in generated responses. Do not treat model outputs as substitutes for human judgment or as sources of truth. Please use responsibly.
## Acknowledgements
This work would not have been possible without the helpful hand of Dakota Mahan ([@dmayhem93](https://huggingface.co/dmayhem93)).
## Citations
```bibtex
@misc{alpaca,
author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {Stanford Alpaca: An Instruction-following LLaMA model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}
```
```bibtext
@misc{vicuna2023,
title = {Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality},
url = {https://vicuna.lmsys.org},
author = {Chiang, Wei-Lin and Li, Zhuohan and Lin, Zi and Sheng, Ying and Wu, Zhanghao and Zhang, Hao and Zheng, Lianmin and Zhuang, Siyuan and Zhuang, Yonghao and Gonzalez, Joseph E. and Stoica, Ion and Xing, Eric P.},
month = {March},
year = {2023}
}
```
```bibtex
@misc{gpt4all,
author = {Yuvanesh Anand and Zach Nussbaum and Brandon Duderstadt and Benjamin Schmidt and Andriy Mulyar},
title = {GPT4All: Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/nomic-ai/gpt4all}},
}
```
|
LanguageMachines/blip2-opt-2.7b | LanguageMachines | 2023-06-28T20:28:53Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"blip-2",
"visual-question-answering",
"vision",
"image-to-text",
"image-captioning",
"en",
"arxiv:2301.12597",
"license:mit",
"endpoints_compatible",
"region:us"
]
| image-to-text | 2023-06-28T19:56:29Z | ---
language: en
license: mit
tags:
- vision
- image-to-text
- image-captioning
- visual-question-answering
pipeline_tag: image-to-text
duplicated_from: Salesforce/blip2-opt-2.7b
---
# BLIP-2, OPT-2.7b, pre-trained only
BLIP-2 model, leveraging [OPT-2.7b](https://huggingface.co/facebook/opt-2.7b) (a large language model with 2.7 billion parameters).
It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2).
Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model.
The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen
while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings,
which bridge the gap between the embedding space of the image encoder and the large language model.
The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg"
alt="drawing" width="600"/>
This allows the model to be used for tasks like:
- image captioning
- visual question answering (VQA)
- chat-like conversations by feeding the image and the previous conversation as prompt to the model
## Direct Use and Downstream Use
You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for
fine-tuned versions on a task that interests you.
## Bias, Risks, Limitations, and Ethical Considerations
BLIP2-OPT uses off-the-shelf OPT as the language model. It inherits the same risks and limitations as mentioned in Meta's model card.
> Like other large language models for which the diversity (or lack thereof) of training
> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
> large language models.
>
BLIP2 is fine-tuned on image-text datasets (e.g. [LAION](https://laion.ai/blog/laion-400-open-dataset/) ) collected from the internet. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data.
BLIP2 has not been tested in real world applications. It should not be directly deployed in any applications. Researchers should first carefully assess the safety and fairness of the model in relation to the specific context they’re being deployed within.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example).
#### Running the model on CPU
<details>
<summary> Click to expand </summary>
```python
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
#### Running the model on GPU
##### In full precision
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
##### In half precision (`float16`)
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import torch
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16, device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
##### In 8-bit precision (`int8`)
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate bitsandbytes
import torch
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details> |
Ocelotr/speecht5_tts-sil | Ocelotr | 2023-06-28T20:18:24Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"ara",
"generated_from_trainer",
"ar",
"dataset:SDA_CLEAN_NAJDI",
"license:mit",
"endpoints_compatible",
"region:us"
]
| text-to-audio | 2023-06-26T16:45:53Z | ---
language:
- ar
license: mit
tags:
- ara
- generated_from_trainer
datasets:
- SDA_CLEAN_NAJDI
model-index:
- name: SpeechT5 TTS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SpeechT5 TTS
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the SDA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4853
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 40000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.5703 | 1.49 | 1000 | 0.5289 |
| 0.541 | 2.98 | 2000 | 0.5131 |
| 0.5487 | 4.46 | 3000 | 0.5059 |
| 0.5232 | 5.95 | 4000 | 0.5011 |
| 0.5295 | 7.44 | 5000 | 0.4979 |
| 0.5257 | 8.93 | 6000 | 0.4970 |
| 0.5091 | 10.42 | 7000 | 0.4905 |
| 0.5141 | 11.9 | 8000 | 0.4893 |
| 0.5033 | 13.39 | 9000 | 0.4865 |
| 0.507 | 14.88 | 10000 | 0.4850 |
| 0.502 | 16.37 | 11000 | 0.4830 |
| 0.497 | 17.86 | 12000 | 0.4823 |
| 0.4974 | 19.35 | 13000 | 0.4801 |
| 0.4993 | 20.83 | 14000 | 0.4794 |
| 0.496 | 22.32 | 15000 | 0.4814 |
| 0.4845 | 23.81 | 16000 | 0.4780 |
| 0.4977 | 25.3 | 17000 | 0.4775 |
| 0.4888 | 26.79 | 18000 | 0.4780 |
| 0.4773 | 28.27 | 19000 | 0.4792 |
| 0.4914 | 29.76 | 20000 | 0.4817 |
| 0.4864 | 31.25 | 21000 | 0.4775 |
| 0.486 | 32.74 | 22000 | 0.4773 |
| 0.4884 | 34.23 | 23000 | 0.4835 |
| 0.4856 | 35.71 | 24000 | 0.4788 |
| 0.4814 | 37.2 | 25000 | 0.4811 |
| 0.4831 | 38.69 | 26000 | 0.4814 |
| 0.4732 | 40.18 | 27000 | 0.4816 |
| 0.4846 | 41.67 | 28000 | 0.4812 |
| 0.4731 | 43.15 | 29000 | 0.4843 |
| 0.4772 | 44.64 | 30000 | 0.4830 |
| 0.4793 | 46.13 | 31000 | 0.4834 |
| 0.4736 | 47.62 | 32000 | 0.4834 |
| 0.4798 | 49.11 | 33000 | 0.4826 |
| 0.4744 | 50.6 | 34000 | 0.4841 |
| 0.4784 | 52.08 | 35000 | 0.4844 |
| 0.4743 | 53.57 | 36000 | 0.4851 |
| 0.4779 | 55.06 | 37000 | 0.4854 |
| 0.4719 | 56.55 | 38000 | 0.4854 |
| 0.4825 | 58.04 | 39000 | 0.4856 |
| 0.4805 | 59.52 | 40000 | 0.4853 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
|
mmirmahdi/Reinforce-CartPole-v1 | mmirmahdi | 2023-06-28T20:14:00Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T20:13:51Z | ---
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
|
finding-fossils/metaextractor | finding-fossils | 2023-06-28T20:05:50Z | 112 | 3 | transformers | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"Beta",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-06-15T16:24:37Z | ---
tags:
- Beta
license: mit
thumbnail: >-
https://huggingface.co/finding-fossils/metaextractor/resolve/main/ffossils-logo-text.png
widget:
- text: The core sample was aged at 12300 - 13500 BP and found at 210m a.s.l.
example_title: Age/Alti
- text: In Northern Canada, the BGC site core was primarily made up of Pinus pollen.
example_title: Taxa/Site/Region
metrics:
- precision
- recall
---
<img src="https://huggingface.co/finding-fossils/metaextractor/resolve/main/ffossils-logo-text.png" width="400">
# MetaExtractor
<!-- Provide a quick summary of what the model is/does. -->
This model extracts metadata from research articles related to Paleoecology.
The entities detected by this model are:
- **AGE**: when historical ages are mentioned such as 1234 AD or 4567 BP (before present)
- **TAXA**: plant or animal taxa names indicating what samples contained
- **GEOG**: geographic coordinates indicating where samples were excavated from, e.g. 12'34"N 34'23"W
- **SITE**: site names for where samples were excavated from
- **REGION**: more general regions to provide context for where sites are located
- **EMAIL**: researcher emails in the articles able to be used for follow-up contact
- **ALTI**: altitudes of sites from where samples were excavated, e.g. 123 m a.s.l (above sea level)
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Ty Andrews, Jenit Jain, Shaun Hutchinson, Kelly Wu, and Simon Goring
- **Shared by:** Neotoma Paleocology Database
- **Model type:** Token Classification
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** roberta-base
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/NeotomaDB/MetaExtractor
- **Paper:** TBD
- **Demo:** TBD
## 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. -->
This model can be used to extract entities from any text that are Paeleoecology related or tangential. Potential uses include identifying unique SITE names in research papers in other domains.
### Direct Use
This model is deployed on the xDD (formerly GeoDeepDive) servers where it is getting fed new research articles relevant to Neotoma and returning the extracted data.
This approach could be adapted to other domains by using the training and development code found [github.com/NeotomaDB/MetaExtractor](https://github.com/NeotomaDB/MetaExtractor) to run similar data extraction for other research domains.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This model was trained entirely on English research articles and will likely not perform well on research in other languages. Also, the articles used to train the model were chosen based on being already present in the Neotoma database and therefore may have selection bias as they represent what is already known to be relevant to Neotoma and may not correctly manage new, previously missed articles.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("finding-fossils/metaextractor")
model = AutoModelForTokenClassification.from_pretrained("finding-fossils/metaextractor")
ner_pipe = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
ner_pipe("In Northern Canada, the BGC site core was primarily made up of Pinus pollen.")
# Output
[
{
"entity_group": "REGION",
"score": 0.8088379502296448,
"word": " Northern Canada,",
"start": 3,
"end": 19
},
{
"entity_group": "SITE",
"score": 0.8307041525840759,
"word": " BGC",
"start": 24,
"end": 27
},
{
"entity_group": "TAXA",
"score": 0.9806344509124756,
"word": " Pinus",
"start": 63,
"end": 68
}
]
```
## 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. -->
The model was trained using a set of 39 research articles deemed relevant to the Neotoma Database. All articles were written in English. The entities were labeled by the project team along with using pre-labelling with early models to speed up the labelling process.
A 70/15/15 train/val/test split was used which had the following breakdown of words and entities.
| | Train | Validation | Test|
|---|:---:|:---:|:---:|
|Articles| 28 | 6 | 6|
| Words | 220857 | 37809 | 36098 |
|TAXA Entities | 3352 | 650 | 570 |
|SITE Entities | 1228 | 177 | 219 |
| REGION Entities | 2314 | 318 | 258 |
|GEOG Entities | 188 | 37 | 8 |
|AGE Entities | 919 | 206 | 153 |
|ALTI Entities | 99 | 24 | 14 |
| Email Entities | 14 | 4 | 11 |
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
For full training details please see the GitHub repository and Wiki: [github.com/NeotomaDB/MetaExtractor](https://github.com/NeotomaDB/MetaExtractor)
## Results & Metrics
For full model results see the report here: [Final Project Report](https://github.com/NeotomaDB/MetaExtractor/blob/main/reports/final/finding-fossils-final.pdf)
|
ahishamm/vit-huge-isic-patch-14 | ahishamm | 2023-06-28T20:05:46Z | 198 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-06-28T19:59:05Z | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-huge-isic-patch-14
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-huge-isic-patch-14
This model is a fine-tuned version of [google/vit-huge-patch14-224-in21k](https://huggingface.co/google/vit-huge-patch14-224-in21k) on the ahishamm/isic_db dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6077
- Accuracy: 0.7917
- Recall: 0.7917
- F1: 0.7917
- Precision: 0.7917
## 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: 16
- 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
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
globuslabs/ScholarBERT-XL_1 | globuslabs | 2023-06-28T20:01:30Z | 124 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"science",
"multi-displinary",
"en",
"arxiv:2205.11342",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-05-22T22:32:14Z | ---
language: en
tags:
- science
- multi-displinary
license: apache-2.0
---
# ScholarBERT-XL_1 Model
This is the **ScholarBERT-XL_1** variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (**2.2B tokens**).
This is a **cased** (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default.
The model has a total of 770M parameters.
# Model Architecture
| Hyperparameter | Value |
|-----------------|:-------:|
| Layers | 36 |
| Hidden Size | 1280 |
| Attention Heads | 20 |
| Total Parameters | 770M |
# Training Dataset
The vocab and the model are pertrained on **1% of the PRD** scientific literature dataset.
The PRD dataset is provided by Public.Resource.Org, Inc. (“Public Resource”),
a nonprofit organization based in California. This dataset was constructed from a corpus
of journal article files, from which We successfully extracted text from 75,496,055 articles from 178,928 journals.
The articles span across Arts & Humanities, Life Sciences & Biomedicine, Physical Sciences,
Social Sciences, and Technology. The distribution of articles is shown below.
# BibTeX entry and citation info
If using this model, please cite this paper:
```
@misc{hong2023diminishing,
title={The Diminishing Returns of Masked Language Models to Science},
author={Zhi Hong and Aswathy Ajith and Gregory Pauloski and Eamon Duede and Kyle Chard and Ian Foster},
year={2023},
eprint={2205.11342},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
globuslabs/ScholarBERT_10 | globuslabs | 2023-06-28T20:01:02Z | 119 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"science",
"multi-displinary",
"en",
"arxiv:2205.11342",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-05-22T22:22:02Z | ---
language: en
tags:
- science
- multi-displinary
license: apache-2.0
---
# ScholarBERT_10 Model
This is the **ScholarBERT_10** variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (**22.1B tokens**).
This is a **cased** (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default.
The model is based on the same architecture as [BERT-large](https://huggingface.co/bert-large-cased) and has a total of 340M parameters.
# Model Architecture
| Hyperparameter | Value |
|-----------------|:-------:|
| Layers | 24 |
| Hidden Size | 1024 |
| Attention Heads | 16 |
| Total Parameters | 340M |
# Training Dataset
The vocab and the model are pertrained on **10% of the PRD** scientific literature dataset.
The PRD dataset is provided by Public.Resource.Org, Inc. (“Public Resource”),
a nonprofit organization based in California. This dataset was constructed from a corpus
of journal article files, from which We successfully extracted text from 75,496,055 articles from 178,928 journals.
The articles span across Arts & Humanities, Life Sciences & Biomedicine, Physical Sciences,
Social Sciences, and Technology. The distribution of articles is shown below.
# BibTeX entry and citation info
If using this model, please cite this paper:
```
@misc{hong2023diminishing,
title={The Diminishing Returns of Masked Language Models to Science},
author={Zhi Hong and Aswathy Ajith and Gregory Pauloski and Eamon Duede and Kyle Chard and Ian Foster},
year={2023},
eprint={2205.11342},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
globuslabs/ScholarBERT_10_WB | globuslabs | 2023-06-28T20:00:24Z | 111 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"science",
"multi-displinary",
"en",
"arxiv:2205.11342",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-05-22T22:30:01Z | ---
language: en
tags:
- science
- multi-displinary
license: apache-2.0
---
# ScholarBERT_10_WB Model
This is the **ScholarBERT_10_WB** variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (**22.1B tokens**).
Additionally, the pretraining data also includes the Wikipedia+BookCorpus, which are used to pretrain the [BERT-base](https://huggingface.co/bert-base-cased) and [BERT-large](https://huggingface.co/bert-large-cased) models.
This is a **cased** (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default.
The model is based on the same architecture as [BERT-large](https://huggingface.co/bert-large-cased) and has a total of 340M parameters.
# Model Architecture
| Hyperparameter | Value |
|-----------------|:-------:|
| Layers | 24 |
| Hidden Size | 1024 |
| Attention Heads | 16 |
| Total Parameters | 340M |
# Training Dataset
The vocab and the model are pertrained on **10% of the PRD** scientific literature dataset and Wikipedia+BookCorpus.
The PRD dataset is provided by Public.Resource.Org, Inc. (“Public Resource”),
a nonprofit organization based in California. This dataset was constructed from a corpus
of journal article files, from which We successfully extracted text from 75,496,055 articles from 178,928 journals.
The articles span across Arts & Humanities, Life Sciences & Biomedicine, Physical Sciences,
Social Sciences, and Technology. The distribution of articles is shown below.
# BibTeX entry and citation info
If using this model, please cite this paper:
```
@misc{hong2023diminishing,
title={The Diminishing Returns of Masked Language Models to Science},
author={Zhi Hong and Aswathy Ajith and Gregory Pauloski and Eamon Duede and Kyle Chard and Ian Foster},
year={2023},
eprint={2205.11342},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
globuslabs/ScholarBERT_100_WB | globuslabs | 2023-06-28T20:00:01Z | 115 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"science",
"multi-displinary",
"en",
"arxiv:2205.11342",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-05-22T22:27:22Z | ---
language: en
tags:
- science
- multi-displinary
license: apache-2.0
---
# ScholarBERT_100_WB Model
This is the **ScholarBERT_100_WB** variant of the ScholarBERT model family.
The model is pretrained on a large collection of scientific research articles (**221B tokens**).
Additionally, the pretraining data also includes the Wikipedia+BookCorpus, which are used to pretrain the [BERT-base](https://huggingface.co/bert-base-cased) and [BERT-large](https://huggingface.co/bert-large-cased) models.
This is a **cased** (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default.
The model is based on the same architecture as [BERT-large](https://huggingface.co/bert-large-cased) and has a total of 340M parameters.
# Model Architecture
| Hyperparameter | Value |
|-----------------|:-------:|
| Layers | 24 |
| Hidden Size | 1024 |
| Attention Heads | 16 |
| Total Parameters | 340M |
# Training Dataset
The vocab and the model are pertrained on **100% of the PRD** scientific literature dataset and the Wikipedia+BookCorpus.
The PRD dataset is provided by Public.Resource.Org, Inc. (“Public Resource”),
a nonprofit organization based in California. This dataset was constructed from a corpus
of journal article files, from which We successfully extracted text from 75,496,055 articles from 178,928 journals.
The articles span across Arts & Humanities, Life Sciences & Biomedicine, Physical Sciences,
Social Sciences, and Technology. The distribution of articles is shown below.
# BibTeX entry and citation info
If using this model, please cite this paper:
```
@misc{hong2023diminishing,
title={The Diminishing Returns of Masked Language Models to Science},
author={Zhi Hong and Aswathy Ajith and Gregory Pauloski and Eamon Duede and Kyle Chard and Ian Foster},
year={2023},
eprint={2205.11342},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
hegbert/my_awesome_eli5_clm-model | hegbert | 2023-06-28T20:00:00Z | 175 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:eli5",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-06-28T18:36:15Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the eli5 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0618
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 18 | 4.0802 |
| No log | 2.0 | 36 | 4.0680 |
| No log | 3.0 | 54 | 4.0618 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
|
rttl-ai/bert-base-uncased-yelp-polarity | rttl-ai | 2023-06-28T19:58:10Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"dataset:yelp_polarity",
"arxiv:2004.10964",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-26T14:16:21Z | ---
license: apache-2.0
datasets:
- yelp_polarity
language:
- en
model-index:
- name: rttl-ai/bert-base-uncased-polarity
results:
- task:
type: text classification
name: binary_classification
dataset:
type: yelp_polarity
name: yelp_polarity
config: default
split: test
metrics:
- type: accuracy
value: 0.98
name: Accuracy
---
## Model Details
**Model Description:** This model is a fine-tune checkpoint of [bert-base-uncased](https://huggingface.co/bert-base-uncased), fine-tuned on yelp reviews.
- **Developed by:** rttl-ai
- **Model Type:** Text Classification
- **Language(s):** English
- **License:** Apache-2.0
- **Resources for more information:**
- The model was pre-trained with task-adaptive pre-training [TAPT](https://arxiv.org/pdf/2004.10964.pdf) with an increased masking rate, no corruption strategy, and using WWM, following [this paper](https://aclanthology.org/2023.eacl-main.217.pdf)
- pre-trained on yelp reviews
- fine-tuned on yelp reviews for binary class text classification |
alitair/LunarLander-v2 | alitair | 2023-06-28T19:53:51Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T19:53:34Z | ---
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: 288.83 +/- 20.38
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
...
```
|
alitair/ppo-LunarLander-v2 | alitair | 2023-06-28T19:48:11Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T19:47:50Z | ---
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: 252.02 +/- 10.27
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
...
```
|
ahishamm/vit-base-isic-patch-32 | ahishamm | 2023-06-28T19:46:46Z | 191 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-06-28T19:41:16Z | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-base-isic-patch-32
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-isic-patch-32
This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the ahishamm/isic_db dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5791
- Accuracy: 0.7778
- Recall: 0.7778
- F1: 0.7778
- Precision: 0.7778
## 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: 16
- 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
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
S3S3/ppo-Pyramids_Training1 | S3S3 | 2023-06-28T19:42:01Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-06-28T19:41:53Z | ---
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: S3S3/ppo-Pyramids_Training1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
amittian/setfit_ds_version_0_0_3 | amittian | 2023-06-28T19:41:36Z | 3 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
]
| text-classification | 2023-06-28T19:41:15Z | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# amittian/setfit_ds_version_0_0_3
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("amittian/setfit_ds_version_0_0_3")
# 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}
}
```
|
ahishamm/vit-base-isic-patch-16 | ahishamm | 2023-06-28T19:41:07Z | 191 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-06-28T19:35:26Z | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-base-isic-patch-16
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-isic-patch-16
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ahishamm/isic_db dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6220
- Accuracy: 0.7917
- Recall: 0.7917
- F1: 0.7917
- Precision: 0.7917
## 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: 16
- 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
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
PickleYard/PerfectWorld | PickleYard | 2023-06-28T19:38:43Z | 7 | 0 | diffusers | [
"diffusers",
"ai-art",
"style-transfer",
"animation",
"deep-learning",
"text-to-image",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-06-28T18:41:57Z | ---
license: other
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- ai-art
- style-transfer
- animation
- deep-learning
- text-to-image
--- |
cleanrl/InvertedPendulum-v4-ddpg_continuous_action-seed1 | cleanrl | 2023-06-28T19:37:58Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"InvertedPendulum-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T19:37:45Z | ---
tags:
- InvertedPendulum-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: InvertedPendulum-v4
type: InvertedPendulum-v4
metrics:
- type: mean_reward
value: 820.00 +/- 347.49
name: mean_reward
verified: false
---
# (CleanRL) **DDPG** Agent Playing **InvertedPendulum-v4**
This is a trained model of a DDPG agent playing InvertedPendulum-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ddpg_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action --env-id InvertedPendulum-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/InvertedPendulum-v4-ddpg_continuous_action-seed1/raw/main/ddpg_continuous_action.py
curl -OL https://huggingface.co/cleanrl/InvertedPendulum-v4-ddpg_continuous_action-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/InvertedPendulum-v4-ddpg_continuous_action-seed1/raw/main/poetry.lock
poetry install --all-extras
python ddpg_continuous_action.py --track --capture-video --save-model --hf-entity cleanrl --upload-model --env-id InvertedPendulum-v4 --seed 1
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': True,
'cuda': True,
'env_id': 'InvertedPendulum-v4',
'exp_name': 'ddpg_continuous_action',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'torch_deterministic': True,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
bk6000/q-FrozenLake-v1-4x4-noSlippery | bk6000 | 2023-06-28T19:27:47Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T19:27:45Z | ---
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="bk6000/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"])
```
|
kl08/mbti_classicalml | kl08 | 2023-06-28T19:10:03Z | 0 | 0 | null | [
"text-classification",
"en",
"dataset:kl08/myers-briggs-type-indicator",
"license:mit",
"region:us"
]
| text-classification | 2023-06-28T19:04:19Z | ---
license: mit
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
datasets:
- kl08/myers-briggs-type-indicator
--- |
YakovElm/Jira_20_BERT_Over_Sampling | YakovElm | 2023-06-28T19:05:58Z | 61 | 0 | transformers | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-28T19:05:01Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira_20_BERT_Over_Sampling
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. -->
# Jira_20_BERT_Over_Sampling
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0521
- Train Accuracy: 0.9856
- Validation Loss: 0.4925
- Validation Accuracy: 0.8612
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4491 | 0.7989 | 0.6370 | 0.6656 | 0 |
| 0.1533 | 0.9514 | 0.3511 | 0.9211 | 1 |
| 0.0521 | 0.9856 | 0.4925 | 0.8612 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
S3S3/ppo-SnowballTarget | S3S3 | 2023-06-28T19:02:06Z | 9 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-06-28T18:39:43Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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: S3S3/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
asapp/sew-d-mid-k127-400k-ft-ls100h | asapp | 2023-06-28T18:56:35Z | 119 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"sew-d",
"automatic-speech-recognition",
"audio",
"speech",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- librispeech_asr
tags:
- audio
- speech
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
- name: sew-d-mid-k127-400k-ft-ls100h
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 4.99
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 10.95
---
# SEW-D-mid-k127
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc...
Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870)
Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi
**Abstract**
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes.
The original model can be found under https://github.com/asappresearch/sew#model-checkpoints .
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, SEWDForCTC
from datasets import load_dataset
import soundfile as sf
import torch
# load the model and preprocessor
processor = Wav2Vec2Processor.from_pretrained("asapp/sew-d-mid-k127-400k-ft-ls100h")
model = SEWDForCTC.from_pretrained("asapp/sew-d-mid-k127-400k-ft-ls100h")
# load the dummy dataset with speech samples
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# preprocess
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
## Evaluation
This code snippet shows how to evaluate **asapp/sew-d-mid-k127-400k-ft-ls100hh** on LibriSpeech's "clean" and "other" test data.
```python
from datasets import load_dataset
from transformers import SEWDForCTC, Wav2Vec2Processor
import torch
from jiwer import wer
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
model = SEWDForCTC.from_pretrained("asapp/sew-d-mid-k127-400k-ft-ls100h").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("asapp/sew-d-mid-k127-400k-ft-ls100h")
def map_to_pred(batch):
input_values = processor(batch["audio"][0]["array"], sampling_rate=16000,
return_tensors="pt", padding="longest").input_values
with torch.no_grad():
logits = model(input_values.to("cuda")).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
batch["transcription"] = transcription
return batch
result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["audio"])
print("WER:", wer(result["text"], result["transcription"]))
```
*Result (WER)*:
| "clean" | "other" |
| --- | --- |
| 4.99 | 10.95 |
|
Niyazi/distilbert-base-uncased-finetuned-emotion | Niyazi | 2023-06-28T18:44:47Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-28T18:23:45Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.8975
- name: F1
type: f1
value: 0.8933293061586592
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3575
- Accuracy: 0.8975
- F1: 0.8933
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 125 | 0.5497 | 0.826 | 0.8003 |
| 0.7506 | 2.0 | 250 | 0.3575 | 0.8975 | 0.8933 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
codervent981/admin-dashboard-template | codervent981 | 2023-06-28T18:39:33Z | 0 | 0 | null | [
"region:us"
]
| null | 2023-06-28T18:39:01Z | Codervent Admin Dashboard Template is a versatile and user-friendly web application interface designed specifically for administrators. With its clean and modern design, it offers a comprehensive set of features and tools to manage and monitor various aspects of an application or website. The template provides a responsive layout, making it accessible on different devices. It includes various widgets, charts, tables, and forms, allowing administrators to effectively analyze data, manage user accounts, track performance metrics, and perform administrative tasks efficiently. Codervent Admin Dashboard Template is a reliable solution for streamlining administrative workflows and enhancing productivity.
Read More:- https://codervent.com/
|
hugo1499/bert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos | hugo1499 | 2023-06-28T18:36:02Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-28T00:10:48Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1233
- Accuracy: 0.5208
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1308 | 1.0 | 383 | 1.1263 | 0.4914 |
| 0.9642 | 2.0 | 766 | 1.0872 | 0.5150 |
| 0.813 | 3.0 | 1149 | 1.1233 | 0.5208 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
pedrogengo/listwise_longt5_1k_msmarco | pedrogengo | 2023-06-28T18:30:52Z | 166 | 0 | transformers | [
"transformers",
"pytorch",
"longt5",
"text2text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-06-28T18:07:44Z | ---
language:
- en
library_name: transformers
--- |
PickleYard/Elysian-Fields | PickleYard | 2023-06-28T18:22:47Z | 13 | 0 | diffusers | [
"diffusers",
"artificial-intelligence",
"ai-art",
"anime-style",
"content-creation",
"animation",
"dreamshaper",
"text-to-image",
"en",
"dataset:unknown",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-06-28T03:54:18Z | ---
language: en
library: diffusers
tags:
- artificial-intelligence
- ai-art
- anime-style
- content-creation
- animation
- dreamshaper
- text-to-image
license: other
datasets:
- unknown
library_name: diffusers
---
## Model Description
"Elysian Fields" is a model based on the original Dreamshaper model. The primary objective of this model is to generate artificial intelligence (AI)-driven art and animations that mimic the style of traditional paintings. It excels in creating lifelike portraits, intricate backgrounds, and anime-style characters.
Originally designed for creating unique portraits that transcend the boundaries of computer graphics and heavily-filtered photographs, this model has evolved to become an integral part of content creation and independent animation production. Leveraging the power of LoRA networks, it also supports the generation of anime-style images.
This model is hosted on Hugging Face Model Hub, and can be used for a wide range of creative applications, from content creation to animation and beyond. |
cleanrl/Walker2d-v4-ddpg_continuous_action-seed1 | cleanrl | 2023-06-28T18:16:57Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Walker2d-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T18:16:49Z | ---
tags:
- Walker2d-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Walker2d-v4
type: Walker2d-v4
metrics:
- type: mean_reward
value: 1129.42 +/- 1251.17
name: mean_reward
verified: false
---
# (CleanRL) **DDPG** Agent Playing **Walker2d-v4**
This is a trained model of a DDPG agent playing Walker2d-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ddpg_continuous_action]"
python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action --env-id Walker2d-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Walker2d-v4-ddpg_continuous_action-seed1/raw/main/ddpg_continuous_action.py
curl -OL https://huggingface.co/cleanrl/Walker2d-v4-ddpg_continuous_action-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Walker2d-v4-ddpg_continuous_action-seed1/raw/main/poetry.lock
poetry install --all-extras
python ddpg_continuous_action.py --track --capture-video --save-model --hf-entity cleanrl --upload-model --env-id Walker2d-v4 --seed 1
```
# Hyperparameters
```python
{'batch_size': 256,
'buffer_size': 1000000,
'capture_video': True,
'cuda': True,
'env_id': 'Walker2d-v4',
'exp_name': 'ddpg_continuous_action',
'exploration_noise': 0.1,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.0003,
'learning_starts': 25000.0,
'noise_clip': 0.5,
'policy_frequency': 2,
'save_model': True,
'seed': 1,
'tau': 0.005,
'torch_deterministic': True,
'total_timesteps': 1000000,
'track': True,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
nbiish/dqn-SpaceInvadersNoFrameskip-v4 | nbiish | 2023-06-28T18:06:47Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T18:06:09Z | ---
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: 556.00 +/- 103.89
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 nbiish -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 nbiish -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 nbiish
```
## 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'}
```
|
Sympan/q-FrozenLake-v1-4x4-noSlippery | Sympan | 2023-06-28T18:01:15Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T18:01:12Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Sympan/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"])
```
|
andrewrreed/falcon-7b-guanaco-qlora-arr | andrewrreed | 2023-06-28T18:00:02Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
]
| null | 2023-06-28T15:27:57Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: falcon-7b-guanaco-qlora-arr
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. -->
# falcon-7b-guanaco-qlora-arr
This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 500
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
dalonsoherrera/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos | dalonsoherrera | 2023-06-28T17:58:45Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-28T11:25:37Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
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-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0202
- F1: 0.5469
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0674 | 1.0 | 766 | 1.0666 | 0.5077 |
| 0.977 | 2.0 | 1532 | 1.0202 | 0.5469 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Malaika/ppo-unit8-LunarLander-v2-test3 | Malaika | 2023-06-28T17:58:02Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T17:57:55Z | ---
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: -142.25 +/- 90.40
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': True
'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': 'Malaika/ppo-unit8-LunarLander-v2-test3'
'batch_size': 512
'minibatch_size': 128}
```
|
Albertf/Fertary | Albertf | 2023-06-28T17:55:39Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
]
| null | 2023-06-28T17:55:39Z | ---
license: bigscience-openrail-m
---
|
AnnaMats/ppo-LunarLander-v2 | AnnaMats | 2023-06-28T17:54:36Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T15:10:48Z | ---
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: 283.84 +/- 18.16
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
...
```
|
YakovElm/Jira_10_BERT_Over_Sampling | YakovElm | 2023-06-28T17:50:33Z | 61 | 0 | transformers | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-28T17:49:47Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira_10_BERT_Over_Sampling
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. -->
# Jira_10_BERT_Over_Sampling
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0915
- Train Accuracy: 0.9711
- Validation Loss: 1.1919
- Validation Accuracy: 0.6909
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5305 | 0.7446 | 0.6068 | 0.6751 | 0 |
| 0.2652 | 0.8965 | 0.6721 | 0.6972 | 1 |
| 0.0915 | 0.9711 | 1.1919 | 0.6909 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
trevorj/ppo-lunarlander1 | trevorj | 2023-06-28T17:41:35Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T17:41:10Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.85 +/- 14.14
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
...
```
|
Inzamam567/Useless_siiNCeyyMixes | Inzamam567 | 2023-06-28T17:37:30Z | 0 | 1 | null | [
"license:openrail",
"region:us"
]
| null | 2023-06-28T17:37:30Z | ---
license: openrail
duplicated_from: siiNCeyy/MyMixes
---
|
ndktraining/distilroberta-base-finetuned-wikitext2 | ndktraining | 2023-06-28T17:29:03Z | 122 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-06-23T04:25:16Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8349
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0852 | 1.0 | 2406 | 1.9234 |
| 1.992 | 2.0 | 4812 | 1.8828 |
| 1.9603 | 3.0 | 7218 | 1.8223 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ag159/taxi-v3-q-learning | ag159 | 2023-06-28T17:20:00Z | 0 | 0 | null | [
"FrozenLake-v1",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-06-28T17:19:59Z | ---
tags:
- FrozenLake-v1
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3-q-learning
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1
type: FrozenLake-v1
metrics:
- type: mean_reward
value: 7.94 +/- 2.60
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="ag159/taxi-v3-q-learning", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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