modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-07 18:30:29
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 544
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-07 18:30:28
| card
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|---|---|---|---|---|---|---|---|---|---|
DicksonMassawe/finetuning-covid19-tweets
|
DicksonMassawe
| 2023-06-23T05:07:53Z | 101 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-21T20:40:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-covid19-tweets
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-covid19-tweets
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2771
- Accuracy: 0.9206
- F1: 0.9206
- Auc: 0.9206
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|
| No log | 1.0 | 133 | 0.2286 | 0.9149 | 0.9150 | 0.9156 |
| No log | 2.0 | 266 | 0.2373 | 0.9159 | 0.9158 | 0.9182 |
| No log | 3.0 | 399 | 0.2375 | 0.9216 | 0.9216 | 0.9224 |
| 0.1978 | 4.0 | 532 | 0.2616 | 0.9225 | 0.9225 | 0.9228 |
| 0.1978 | 5.0 | 665 | 0.2771 | 0.9206 | 0.9206 | 0.9206 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
inarikami/falcon-40b-instruct-8bit
|
inarikami
| 2023-06-23T05:03:24Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"RefinedWeb",
"text-generation",
"custom_code",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2023-06-19T06:38:11Z |
# Falcon-40B-Instruct 8-bit Model
This repository is home to the Falcon-40B-Instruct model, which has been carefully converted from its original 32-bit mode to an efficient and compact 8-bit file.
# Usage
You can use this model directly with a pipeline for tasks such as text generation and instruction following:
```python
from transformers import pipeline
generator = pipeline('text-generation', model='tensorcat/falcon-40b-instruct-8bit')
print(generator("Generate a story about a spaceship traveling through space.", max_length=200))
```
|
w11wo/indo-roberta-small
|
w11wo
| 2023-06-23T04:52:51Z | 129 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"roberta",
"fill-mask",
"indo-roberta-small",
"id",
"dataset:wikipedia",
"arxiv:1907.11692",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: id
tags:
- indo-roberta-small
license: mit
datasets:
- wikipedia
widget:
- text: "Karena pandemi ini, kita harus <mask> di rumah saja."
---
## Indo RoBERTa Small
Indo RoBERTa Small is a masked language model based on the [RoBERTa model](https://arxiv.org/abs/1907.11692). It was trained on the latest (late December 2020) Indonesian Wikipedia articles.
The model was trained from scratch and achieved a perplexity of 48.27 on the validation dataset (20% of the articles). Many of the techniques used
are based on a Hugging Face tutorial [notebook](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) written by [Sylvain Gugger](https://github.com/sgugger), where Sylvain Gugger fine-tuned a [DistilGPT-2](https://huggingface.co/distilgpt2) on [Wikitext2](https://render.githubusercontent.com/view/ipynb?color_mode=dark&commit=43d63e390e8a82f7ae49aa1a877419343a213cb4&enc_url=68747470733a2f2f7261772e67697468756275736572636f6e74656e742e636f6d2f68756767696e67666163652f6e6f7465626f6f6b732f343364363365333930653861383266376165343961613161383737343139333433613231336362342f6578616d706c65732f6c616e67756167655f6d6f64656c696e672e6970796e62&nwo=huggingface%2Fnotebooks&path=examples%2Flanguage_modeling.ipynb&repository_id=272452525&repository_type=Repository).
Hugging Face's [Transformers]((https://huggingface.co/transformers)) library was used to train the model -- utilizing the base RoBERTa model and their `Trainer` class. PyTorch was used as the backend framework during training, but the model remains compatible with TensorFlow nonetheless.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
|----------------------|---------|----------|---------------------------------------|
| `indo-roberta-small` | 84M | RoBERTa | Indonesian Wikipedia (3.1 GB of text) |
## Evaluation Results
The model was trained for 3 epochs and the following is the final result once the training ended.
| train loss | valid loss | perplexity | total time |
|------------|------------|------------|------------|
| 4.071 | 3.876 | 48.27 | 3:40:55 |
## How to Use
### As Masked Language Model
```python
from transformers import pipeline
pretrained_name = "w11wo/indo-roberta-small"
fill_mask = pipeline(
"fill-mask",
model=pretrained_name,
tokenizer=pretrained_name
)
fill_mask("Budi sedang <mask> di sekolah.")
```
### Feature Extraction in PyTorch
```python
from transformers import RobertaModel, RobertaTokenizerFast
pretrained_name = "w11wo/indo-roberta-small"
model = RobertaModel.from_pretrained(pretrained_name)
tokenizer = RobertaTokenizerFast.from_pretrained(pretrained_name)
prompt = "Budi sedang berada di sekolah."
encoded_input = tokenizer(prompt, return_tensors='pt')
output = model(**encoded_input)
```
## Disclaimer
Do remember that although the dataset originated from Wikipedia, the model may not always generate factual texts. Additionally, the biases which came from the Wikipedia articles may be carried over into the results of this model.
## Author
Indo RoBERTa Small was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access.
|
LarryAIDraw/Maika
|
LarryAIDraw
| 2023-06-23T04:51:16Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-23T04:39:14Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/94904/maika-saku-blend-s-lora
|
LarryAIDraw/NewJerseyVRerun
|
LarryAIDraw
| 2023-06-23T04:50:22Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-23T04:37:39Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/94550/uss-new-jersey-or-1mb-azur-lane-or
|
gaiamolinaro/dqn-SpaceInvadersNoFrameskip-v4
|
gaiamolinaro
| 2023-06-23T04:37:52Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-23T04:37:14Z |
---
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: 676.50 +/- 216.14
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 gaiamolinaro -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 gaiamolinaro -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 gaiamolinaro
```
## 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'}
```
|
jjenny/dfsdfsfs222
|
jjenny
| 2023-06-23T04:29:58Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-06-23T00:24:35Z |
---
license: bigscience-openrail-m
---
|
jackie68/detr-resnet-50_finetuned_cppe5
|
jackie68
| 2023-06-23T04:18:26Z | 187 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:cppe-5",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-06-23T03:09:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cppe-5
model-index:
- name: detr-resnet-50_finetuned_cppe5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Squish42/WizardLM-7B-Uncensored-GPTQ-8bit-128g
|
Squish42
| 2023-06-23T04:12:03Z | 8 | 2 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-23T03:34:32Z |
---
license: unknown
---
[ehartford/WizardLM-7B-Uncensored](https://huggingface.co/ehartford/WizardLM-7B-Uncensored) quantized to **8bit GPTQ** with group size 128 + true sequential, no act order.
*For most uses this probably isn't what you want.* \
*For 4bit GPTQ quantizations see [TheBloke/WizardLM-7B-uncensored-GPTQ](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ)*
Quantized using AutoGPTQ with the following config:
```python
config: dict = dict(
quantize_config=dict(model_file_base_name='WizardLM-7B-Uncensored',
bits=8, desc_act=False, group_size=128, true_sequential=True),
use_safetensors=True
)
```
See `quantize.py` for the full script.
Tested for compatibility with:
- WSL with GPTQ-for-Llama `triton` branch.
AutoGPTQ loader should read configuration from `quantize_config.json`.\
For GPTQ-for-Llama use the following configuration when loading:\
wbits: 8\
groupsize: 128\
model_type: llama
|
mszpro/CoreML_GhostMix_v2
|
mszpro
| 2023-06-23T04:10:29Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-06-22T09:08:07Z |
# GhostMix v2 for CoreML
This is the compiled GhostMix v2 model for use in CoreML, for iPhone, iPad, and Mac.
It has been built with Split Ensum, so it can run on Nerual Engine.
It is originally from https://civitai.com/models/36520/ghostmix
|
kenagapito/distilhubert-finetuned-gtzan
|
kenagapito
| 2023-06-23T03:54:47Z | 165 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-06-22T11:29:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
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. -->
# 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: 0.9412
- Accuracy: 0.81
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0684 | 1.0 | 113 | 1.7043 | 0.39 |
| 1.1044 | 2.0 | 226 | 1.0855 | 0.62 |
| 0.84 | 3.0 | 339 | 1.0662 | 0.67 |
| 0.6802 | 4.0 | 452 | 0.7272 | 0.75 |
| 0.4728 | 5.0 | 565 | 0.6389 | 0.86 |
| 0.4119 | 6.0 | 678 | 0.8692 | 0.78 |
| 0.0436 | 7.0 | 791 | 1.0113 | 0.82 |
| 0.0082 | 8.0 | 904 | 0.8984 | 0.83 |
| 0.0442 | 9.0 | 1017 | 1.0056 | 0.81 |
| 0.0024 | 10.0 | 1130 | 0.9412 | 0.81 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
EmailConversion/How-Do-I-Transfer-Outlook-From-Mac-To-Windows
|
EmailConversion
| 2023-06-23T03:54:28Z | 0 | 0 | null |
[
"Convert OLM files into Windows Outlook format,",
"en",
"region:us"
] | null | 2023-06-22T06:42:48Z |
---
language:
- en
tags:
- Convert OLM files into Windows Outlook format,
---
<h1>How Do I Transfer Outlook From Mac To Windows?</h1>
We can use the Microsoft Outlook email client on both Mac and Windows platforms. Both platforms are identical, but the difference is their file format for storing their mailbox data. On Mac, OLM is used to store a mailbox while PST is used in Windows Outlook to store its mailbox data. However, if you want to transfer Outlook from Mac to Windows, you need to transfer Outlook Mac mailbox data to Outlook Windows-compatible format. There is no method to export Outlook from Mac to Windows.
Recently we received many similar requests from users, so I found an official solution on the Internet. But I haven't had any success, but I've seen some manual approaches and when I'm tired, the first thing is that they're very tedious and sometimes I have data loss problems. That's why we neglected them and talked with our technical expertise to develop a reliable and cost-effective solution to achieve safe performance without challenges. After a lot of back and forth, they finally came up with the solution they call <a href="https://www.systoolsgroup.com/olm-to-pst.html">OLM to PST Converter</a>
<h2>Reason To Export Outlook From Mac To Windows</h2>
<ul><li>If you received an OLM file from your customers and want to access mailbox data in their profile.</li>
<li>When users want to migrate from Mac to Windows but still want to use Outlook on Windows.</li>
<li>When the user needs temporary access to Windows at home/office.</li>
<li>If you want to show organizations updates or projects.</li>
</ul>
<h2>How To Transfer Outlook From Mac To Windows?</h2>
If you are an Internet user, you need to know that numerous applications are made available over the Internet. This claims to transfer Outlook from Mac to Windows. But most of the time it is non-functional and other tools don't live up to your expectations. So, we recommend you to go with <b><a href="https://www.systoolsgroup.com/mac/olm-converter.html">SysTools OLM Converter</b></a>, which is the fastest and most reliable wizard that will help you get safe output without any hassle. This tool is specially coded with a strong algorithm to get the exact output as before conversion without worrying about information leakage.
<ul><li>Install OLM Converter on your PC and open it immediately.</li>
<li>Browse the OLM files you want to transfer and paste them into the software interface.</li>
<li>From the multiple options of export type, click on the PST option.</li>
<li>Finally, browse the location where the output is needed and click "Convert" to end the process with the output.</li></ul>
<h3>Why Do Professional Wizards Are The Prime Choice Of Users?</h3>
<ul><li>It preserves the integrity of the mailbox data as well as the input. In addition, the <a href="https://www.systoolsgroup.com/pst-converter.html">PST converter</a> offers strong data protection to prevent any kind of data loss.</li>
<li>It is very easy to perform without requiring any advanced technical knowledge.</li>
<li>It also offers a strong filtering function to convert selected data and is also helpful to avoid unwanted files.</li>
<li>There is no need to install any other utility to finish the conversion solution.</li></ul>
<h4>Wind Up</h4>
In this technical tutorial, we have provided the right information on how to transfer Outlook from Mac to Windows. We also explained the basic overview and the reason for this query. In addition, we have provided appropriate guidelines on how to solve this problem with the help of professional wizards. Now it's your turn to solve your problem yourself with the help of a recommended tool. If you have any doubts or encounter any difficulties, you can contact us anytime, anywhere.
To Learn More Click on <a href="https://agoradedrets.idhc.org/profiles/Davidjackson/timeline">Profile</a>
|
pellucid/my_awesome_spotify_clm-model
|
pellucid
| 2023-06-23T03:46:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-23T02:19:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_spotify_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_spotify_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0040
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.1248 | 1.0 | 6124 | 1.0846 |
| 1.0669 | 2.0 | 12248 | 1.0487 |
| 1.0464 | 3.0 | 18372 | 1.0040 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
t3PbMvBN6SXv/q-FrozenLake-v1-4x4-noSlippery
|
t3PbMvBN6SXv
| 2023-06-23T03:43:43Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-23T03:29:07Z |
---
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 **{env_id}**
This is a trained model of a **Q-Learning** agent playing **{env_id}** .
## Usage
# python
model = load_from_hub(repo_id="{repo_id}", 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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
|
jensinjames/gpt-engineer
|
jensinjames
| 2023-06-23T03:33:13Z | 0 | 1 | null |
[
"doi:10.57967/hf/0805",
"region:us"
] | null | 2023-06-23T03:28:10Z |
# GPT Engineer
[](https://discord.gg/4t5vXHhu)
[](https://github.com/AntonOsika/gpt-engineer)
[](https://twitter.com/AntonOsika)
**Specify what you want it to build, the AI asks for clarification, and then builds it.**
GPT Engineer is made to be easy to adapt, extend, and make your agent learn how you want your code to look. It generates an entire codebase based on a prompt.
[Demo](https://twitter.com/antonosika/status/1667641038104674306) 👶🤖
## Project philosophy
- Simple to get value
- Flexible and easy to add new own "AI steps". See `steps.py`.
- Incrementally build towards a user experience of:
1. high level prompting
2. giving feedback to the AI that it will remember over time
- Fast handovers back and forth between AI and human
- Simplicity, all computation is "resumable" and persisted to the filesystem
## Setup
- `git clone git@github.com:AntonOsika/gpt-engineer.git`
- `cd gpt-engineer`
- `pip install -e .`
- (or: `make install && source venv/bin/activate` for a venv)
With an api key that has GPT4 access run:
- `export OPENAI_API_KEY=[your api key]`
**Run**:
- Create an empty folder. If inside the repo, you can run:
- `cp -r projects/example/ projects/my-new-project`
- Fill in the `main_prompt` file in your new folder
- Run: `gpt-engineer projects/my-new-project`
**Results**
- Check the generated files in `projects/my-new-project/workspace`
## Features
You can specify the "identity" of the AI agent by editing the files in the `identity` folder.
Editing the identity, and evolving the `main_prompt`, is currently how you make the agent remember things between projects.
Each step in `steps.py` will have its communication history with GPT4 stored in the logs folder, and can be rerun with `scripts/rerun_edited_message_logs.py`.
## Contributing
We are building the open platform for devs to tinker with and build their personal code-generation toolbox.
If you want to contribute, please check out the [roadmap](https://github.com/AntonOsika/gpt-engineer/blob/main/ROADMAP.md), [projects](https://github.com/AntonOsika/gpt-engineer/projects?query=is%3Aopen) or [issues tab](https://github.com/AntonOsika/gpt-engineer/issues) in the GitHub repo. You are welcome to read the [contributing document](.github/CONTRIBUTING.md) and join our [Discord 💬](https://discord.gg/4t5vXHhu).
We are currently looking for more maintainers and community organisers. Email anton.osika@gmail.com if you are interested in an official role.
## Example
https://github.com/AntonOsika/gpt-engineer/assets/4467025/6e362e45-4a94-4b0d-973d-393a31d92d9b
|
Fre2C/UnreaLibrary-Mix
|
Fre2C
| 2023-06-23T03:30:47Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-17T06:01:37Z |
---
license: creativeml-openrail-m
---
**civitai**:https://civitai.com/models/91609/unrealibrary-mix (更多预览在civitai/More preview in civitai)
所有的预览图没有使用embedding,lora
我的融合模型:
**DreaMirror**:https://civitai.com/models/30294 / https://huggingface.co/Fre2C/DreaMirror-Mix
**UnreaLibrary**:https://civitai.com/models/91609 / https://huggingface.co/Fre2C/UnreaLibrary-Mix
**这个模型的方向是尽可能忠于提示词(在2D模型中这好像有点难),保留2D模型的创造性(所以我并没有融合3D/2.5D模型。~~所以大部分时间都在与手部进行搏斗~~),合适的明暗对比。**
**你可以用它尝试任何东西！**
**从以下地方学习了很多,十分感谢。**
https://huggingface.co/WarriorMama777/OrangeMixs
https://civitai.com/models/9409/or-anything-v5
https://economylife.net/u-net-marge-webui1111/
https://docs.qq.com/doc/DTkRodlJ1c1VzcFBr?u=e7c714671e694797a04f1d58aff5c8b0
https://docs.qq.com/doc/DQ1Vzd3VCTllFaXBv?_t=1685979317852&u=e7c714671e694797a04f1d58aff5c8b0
https://www.figma.com/file/1JYEljsTwm6qRwR665yI7w/Merging-lab%E3%80%8CHosioka-Fork%E3%80%8D?type=design&node-id=1-69
**使用建议:**
脸部出现崩坏的情况,以及想**提升面部质量**,使用**局部重绘** **重绘区域**使用**仅蒙版**(效果最好)获得**更好的面部**,或使用Hires. fix改善,使用**其他随机种**或者**工具**也是不错的办法。
**较高**的分辨率(比512 * 512高一点)再加上**Hires. fix**,图片质量**会更好**(如果显存不够你可以尝试**低倍率**的Hires. fix或者**其他放大方法**)。
**用于画面质量的正面提示词(像 best quality)是不必要的,会减少画面的可能性,还会使画面趋于一种风格。**
**将你原本用在正面质量提示词上的权重,用在负面质量提示词上,那是更好的选择。**
如果觉得画面内容**不够丰富**,你可以尝试**细致地描述**,使画面更加**贴近你的想象**。
**提示词的权重以及顺序会影响它在画面里的重要程度。**
**如果有无法作出反应的提示词,请按以下顺序排查问题:同义词(同一概念的不同描述),提示词冲突(正面和负面),模型问题(看其他模型能否对同样的提示词作出反应)**,embedding(我并没有使用它的习惯,但考虑到它的原理,我将它放上来作为参考)。
*如果你想用很少的提示词抽奖的话,最好把雨伞(umbrella)加进负面提示词(至少在V1是这样的)。*
**我一般在效果不符合预期时使用clip2。**
**随你喜好使用lora!**
All preview images do not use embedding,lora.
**The direction of this model is to be as faithful as possible to the prompt words(This seems a bit difficult in a 2D model), preserve the creativity of 2D models(So I did not merge the 3D/2.5D models. ~~So most of the time is fighting with the hands~~), appropriate light and dark contrast.**
**You can try anything with it!**
**I have learned a lot from the following places, thank you very much.**
https://huggingface.co/WarriorMama777/OrangeMixs
https://civitai.com/models/9409/or-anything-v5
https://economylife.net/u-net-marge-webui1111/
https://rentry.org/Merge_Block_Weight_-china-_v1_Beta#1-introduction(This is the translated version)
https://docs.qq.com/doc/DQ1Vzd3VCTllFaXBv?_t=1685979317852&u=e7c714671e694797a04f1d58aff5c8b0
https://www.figma.com/file/1JYEljsTwm6qRwR665yI7w/Merging-lab%E3%80%8CHosioka-Fork%E3%80%8D?type=design&node-id=1-69
**Suggestions for use:**
If the face appears to be falling apart, and you want to **improve the quality of the face**, use **Inpaint** and **Inpaint area** use **only Masked** (Best results) to get a **better face**, or use **Hires. fix** to improve, use **other seed** or **tools** is also a good way.
**Higher** resolution (a little higher than 512 * 512) plus **Hires. fix**, picture quality will **be better** (if the gpu memory is not enough you can try a **Low magnification** of Hires. fix or **other upscale tools**).
**Positive prompt for image quality (like best quality) are unnecessary and reduce the possibilities of the picture, also make the picture tend to be in a style.**
**It's better to Use the weight you would have used for positive quality prompt on negative quality prompt.**
If you feel that the content of the picture is **not rich enough**, You can try to **describe in detail** to make the picture more **closely to your imagination.**
*If you want to sweepstakes with few prompts, it is better to add umbrella to the negative prompt (at least in V1).*
**The weight of the prompt word and the order in which it is used affects how important it is in the picture.**
**If there are prompt words that you cannot respond to, please rank the problems in the following order: synonyms (different descriptions of the same concept), prompt word conflicts (positive and negative), model problems (see if other models can respond to the same prompt words),** embedding (I am not in the habit of using it, but considering its rationale, I put it up as a reference).
**I usually use clip2 when the results don't meet expectations.**
**Use lora as you like!**
我使用这两个VAE/I use these two VAEs:
https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt
https://civitai.com/models/22354/clearvae
**V1**
**使用的模型/Models used**
kawaiimixNijiV5Cute_v10【58f37f4736】
Counterfeit-V3.0_fp32【17277FBE68】
pikasNewGeneration_v20【6C509880A5】
breakdomainanime_A0440【1870FA10C3】
plagion_v10【0C42B21C09】
AnythingV5V3_v5PrtRE【7f96a1a9ca】
tComicV35_v35【25750140EA】
**配方/Recipe**
**use:https://github.com/hako-mikan/sd-webui-supermerger/**
kawaiimixNijiV5Cute_v10 x (1-alpha) + Counterfeit-V3.0_fp32 x alpha)x(1-beta)+ pikasNewGeneration_v20 x beta
alpha:0.7,1.0,0.9,0.8,0.7,0.6,0.6,0.7,0.8,0.9,0.7,0.5,0.7,0.7,0.85,0.75,0.65,0.75,0.85,0.75,0.65,0.75,0.85,0.9,0.8,0.8
beta:0.75,0.35,0.45,0.55,0.65,0.75,0.85,0.75,0.85,0.75,0.6,0.6,0.6,0.5,0.35,0.45,0.55,0.6,0.65,0.55,0.6,0.5,0.35,0.4,0.5,0.4
**Named as step1**
breakdomainanime_A0440 x (1-alpha) + plagion_v10 x alpha)x(1-beta)+ step1 x beta
alpha:0.25,0.35,0.45,0.55,0.65,0.55,0.45,0.55,0.4,0.6,0.7,0.75,0.8,0.4,0.4,0.5,0.6,0.7,0.8,0.6,0.5,0.4,0.5,0.4,0.7,0.7
beta:0.7,0.85,0.75,0.65,0.55,0.7,0.6,0.5,0.4,0.5,0.6,0.5,0.4,0.6,0.8,0.7,0.6,0.8,0.7,0.6,0.5,0.4,0.5,0.6,0.5,0.4
**Named as step2**
AnythingV5V3_v5PrtRE x (1-alpha) + tComicV35_v35 x alpha)x(1-beta)+ step2 x beta
alpha:0.65,0.75,0.65,0.75,0.65,0.75,0.65,0.75,0.85,1.0,0.85,0.75,0.85,0.4,0.65,0.75,0.65,0.45,0.3,0.15,0.3,0.45,0.65,0.75,0.8,0.8
beta:0.75,0.25,0.35,0.45,0.55,0.75,0.85,0.75,0.85,0.75,0.85,1.0,1.0,0.7,0.35,0.45,0.55,0.75,0.65,0.75,0.65,0.55,0.45,0.35,0.75,0.85
**prune and get final fp16 version**
|
luodian/llama-7b-hf
|
luodian
| 2023-06-23T03:00:01Z | 4,088 | 35 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"zh",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-04-21T18:58:59Z |
---
license: other
language:
- en
- zh
library_name: transformers
---
# 🦙 Llama for Huggingface Transformers
Llama-7B converted from official [Llama-7B](https://github.com/facebookresearch/Llama/blob/main/MODEL_CARD.md) to Huggingface model via [HF's conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py) to work with Transformers/HuggingFace. This is under a special license, please see the LICENSE file for details.
This is updated from [decapoda-research/llama-7b-hf](https://huggingface.co/decapoda-research/Llama-7b-hf) (since the many pull requests are not merged yet in decapoda's repo, so I directly open a new repo here). It includes:
(1) The naming changes (LLaMA -> Llama) to best fit for `transformers` naming rule, in both `LlamaForCausalLM` and `LlamaTokenizer`. This works perfectly for `transformers>=4.28.0`.
(2) The model checkpoints are saved in 2 shards (instead of 33 shards in [decapoda-research/Llama-7b-hf](https://huggingface.co/decapoda-research/Llama-7b-hf)). Less shards would accelerate loading speed from disk.
--
license: other
---
# Llama Model Card
## Model details
**Organization developing the model**
The FAIR team of Meta AI.
**Model date**
Llama was trained between December. 2022 and Feb. 2023.
**Model version**
This is version 1 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.
**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**
https://research.facebook.com/publications/Llama-open-and-efficient-foundation-language-models/
**License**
Non-commercial bespoke license
**Where to send questions or comments about the model**
Questions and comments about Llama can be sent via the [GitHub repository](https://github.com/facebookresearch/Llama) of the project , by opening an issue.
## Intended use
**Primary intended uses**
The primary use of Llama is research on large language models, including:
exploring potential applications such as question answering, natural language understanding or reading comprehension,
understanding capabilities and limitations of current language models, and developing techniques to improve those,
evaluating and mitigating biases, risks, toxic and harmful content generations, hallucinations.
**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 factors**
As our model is trained on data from the Web, we expect that it reflects biases from this source. We thus evaluated on RAI datasets to measure biases exhibited by the model for gender, religion, race, sexual orientation, age, nationality, disability, physical appearance and socio-economic status. We also measure the toxicity of model generations, depending on the toxicity of the context used to prompt the model.
## Metrics
**Model performance measures**
We use the following measure to evaluate the model:
- Accuracy for common sense reasoning, reading comprehension, natural language understanding (MMLU), BIG-bench hard, WinoGender and CrowS-Pairs,
- Exact match for question answering,
- The toxicity score from Perspective API on RealToxicityPrompts.
**Decision thresholds**
Not applicable.
**Approaches to uncertainty and variability**
Due to the high computational requirements of training LLMs, we trained only one model of each size, and thus could not evaluate variability of pre-training.
## Evaluation datasets
The model was evaluated on the following benchmarks: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, OpenBookQA, NaturalQuestions, TriviaQA, RACE, MMLU, BIG-bench hard, GSM8k, RealToxicityPrompts, WinoGender, CrowS-Pairs.
## Training dataset
The model was trained using the following source of data: CCNet [67%], C4 [15%], GitHub [4.5%], Wikipedia [4.5%], Books [4.5%], ArXiv [2.5%], Stack Exchange[2%]. The Wikipedia and Books domains include data in the following languages: bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk. See the paper for more details about the training set and corresponding preprocessing.
## Quantitative analysis
Hyperparameters for the model architecture
<table>
<thead>
<tr>
<th >Llama</th> <th colspan=6>Model hyper parameters </th>
</tr>
<tr>
<th>Number of parameters</th><th>dimension</th><th>n heads</th><th>n layers</th><th>Learn rate</th><th>Batch size</th><th>n tokens</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th> <th>4096</th> <th>32</th> <th>32</th> <th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>13B</th><th>5120</th><th>40</th><th>40</th><th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>33B</th><th>6656</th><th>52</th><th>60</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
<tr>
<th>65B</th><th>8192</th><th>64</th><th>80</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
</tbody>
</table>
*Table 1 - Summary of Llama Model Hyperparameters*
We present our results on eight standard common sense reasoning benchmarks in the table below.
<table>
<thead>
<tr>
<th>Llama</th> <th colspan=9>Reasoning tasks </th>
</tr>
<tr>
<th>Number of parameters</th> <th>BoolQ</th><th>PIQA</th><th>SIQA</th><th>HellaSwag</th><th>WinoGrande</th><th>ARC-e</th><th>ARC-c</th><th>OBQA</th><th>COPA</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th><th>76.5</th><th>79.8</th><th>48.9</th><th>76.1</th><th>70.1</th><th>76.7</th><th>47.6</th><th>57.2</th><th>93
</th>
<tr><th>13B</th><th>78.1</th><th>80.1</th><th>50.4</th><th>79.2</th><th>73</th><th>78.1</th><th>52.7</th><th>56.4</th><th>94
</th>
<tr><th>33B</th><th>83.1</th><th>82.3</th><th>50.4</th><th>82.8</th><th>76</th><th>81.4</th><th>57.8</th><th>58.6</th><th>92
</th>
<tr><th>65B</th><th>85.3</th><th>82.8</th><th>52.3</th><th>84.2</th><th>77</th><th>81.5</th><th>56</th><th>60.2</th><th>94</th></tr>
</tbody>
</table>
*Table 2 - Summary of Llama Model Performance on Reasoning tasks*
We present our results on bias in the table below. Note that lower value is better indicating lower bias.
| No | Category | FAIR LLM |
| --- | -------------------- | -------- |
| 1 | Gender | 70.6 |
| 2 | Religion | 79 |
| 3 | Race/Color | 57 |
| 4 | Sexual orientation | 81 |
| 5 | Age | 70.1 |
| 6 | Nationality | 64.2 |
| 7 | Disability | 66.7 |
| 8 | Physical appearance | 77.8 |
| 9 | Socioeconomic status | 71.5 |
| | Llama Average | 66.6 |
*Table 3 - Summary bias of our model output*
## 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.
**Mitigations**
We filtered the data from the Web based on its proximity to Wikipedia text and references. For this, we used a Kneser-Ney language model and a fastText linear classifier.
**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.
|
bluepuppet/3dcharacter
|
bluepuppet
| 2023-06-23T02:46:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-06-23T02:40:00Z |
from transformers import GPT2LMHeadModel, GPT2Tokenizer
# Load pre-trained model and tokenizer
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# Define prompt and generate text
prompt = "Generate a 3D model of a hugging face"
input_ids = tokenizer.encode(prompt, return_tensors='pt')
output = model.generate(input_ids, max_length=50, do_sample=True)
# Convert output to text and print
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(output_text)
|
arminmrm93/Reinforce-Pixelcopter-PLE-v0
|
arminmrm93
| 2023-06-23T02:14:49Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-23T02:14:44Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 23.30 +/- 29.27
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
|
gbellamy/ppo-LunarLander-v2
|
gbellamy
| 2023-06-23T01:33:01Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-23T01:32:42Z |
---
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: 272.09 +/- 15.92
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
...
```
|
rickysk/videomae-base-ipm_all_videos_gb
|
rickysk
| 2023-06-23T01:32:51Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"videomae",
"video-classification",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-06-22T09:15:27Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-ipm_all_videos_gb
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. -->
# videomae-base-ipm_all_videos_gb
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2748
- Accuracy: 0.6870
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 4800
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5051 | 0.01 | 60 | 2.5234 | 0.0870 |
| 2.4957 | 1.01 | 120 | 2.5401 | 0.1217 |
| 2.5475 | 2.01 | 180 | 2.5675 | 0.0870 |
| 2.4659 | 3.01 | 240 | 2.5836 | 0.0957 |
| 2.2644 | 4.01 | 300 | 2.5035 | 0.0696 |
| 2.3548 | 5.01 | 360 | 2.2569 | 0.1217 |
| 2.0341 | 6.01 | 420 | 2.3958 | 0.1565 |
| 2.2919 | 7.01 | 480 | 2.6096 | 0.0696 |
| 2.0857 | 8.01 | 540 | 2.3223 | 0.1217 |
| 1.7473 | 9.01 | 600 | 2.5414 | 0.1652 |
| 1.885 | 10.01 | 660 | 1.7822 | 0.3043 |
| 1.9496 | 11.01 | 720 | 1.8052 | 0.3130 |
| 1.2315 | 12.01 | 780 | 2.1955 | 0.2435 |
| 1.3549 | 13.01 | 840 | 2.1262 | 0.3130 |
| 1.5121 | 14.01 | 900 | 2.0316 | 0.2783 |
| 1.4504 | 15.01 | 960 | 1.7596 | 0.2957 |
| 1.2991 | 16.01 | 1020 | 1.6413 | 0.3652 |
| 1.2299 | 17.01 | 1080 | 1.5417 | 0.4087 |
| 1.2965 | 18.01 | 1140 | 1.7243 | 0.3739 |
| 1.2431 | 19.01 | 1200 | 1.7556 | 0.3478 |
| 1.3807 | 20.01 | 1260 | 1.4580 | 0.4435 |
| 1.3961 | 21.01 | 1320 | 1.6514 | 0.4 |
| 1.0119 | 22.01 | 1380 | 1.5449 | 0.3391 |
| 1.3799 | 23.01 | 1440 | 1.5126 | 0.3304 |
| 1.6871 | 24.01 | 1500 | 2.0675 | 0.2783 |
| 1.2707 | 25.01 | 1560 | 1.7128 | 0.3739 |
| 1.1495 | 26.01 | 1620 | 1.6387 | 0.3217 |
| 1.6151 | 27.01 | 1680 | 1.6192 | 0.3913 |
| 1.0587 | 28.01 | 1740 | 1.6008 | 0.4522 |
| 1.2169 | 29.01 | 1800 | 1.6739 | 0.4348 |
| 1.1116 | 30.01 | 1860 | 1.7693 | 0.3913 |
| 1.0939 | 31.01 | 1920 | 1.6540 | 0.3913 |
| 0.9307 | 32.01 | 1980 | 1.5583 | 0.4957 |
| 0.9539 | 33.01 | 2040 | 1.8836 | 0.4174 |
| 0.9804 | 34.01 | 2100 | 1.5656 | 0.4522 |
| 1.334 | 35.01 | 2160 | 1.5375 | 0.4609 |
| 1.0897 | 36.01 | 2220 | 1.4327 | 0.4087 |
| 0.864 | 37.01 | 2280 | 1.6372 | 0.3913 |
| 0.9678 | 38.01 | 2340 | 1.4537 | 0.4609 |
| 1.3184 | 39.01 | 2400 | 1.3085 | 0.4783 |
| 1.1462 | 40.01 | 2460 | 1.4954 | 0.4696 |
| 0.7875 | 41.01 | 2520 | 1.4692 | 0.4870 |
| 0.9552 | 42.01 | 2580 | 1.3797 | 0.4174 |
| 0.8053 | 43.01 | 2640 | 1.3216 | 0.5043 |
| 0.9231 | 44.01 | 2700 | 1.2134 | 0.5739 |
| 0.734 | 45.01 | 2760 | 1.1676 | 0.5304 |
| 0.5427 | 46.01 | 2820 | 1.2179 | 0.4783 |
| 0.7171 | 47.01 | 2880 | 1.2749 | 0.5304 |
| 0.6977 | 48.01 | 2940 | 1.3707 | 0.5304 |
| 0.6911 | 49.01 | 3000 | 1.2520 | 0.5478 |
| 0.6166 | 50.01 | 3060 | 1.3687 | 0.5304 |
| 0.4025 | 51.01 | 3120 | 1.4041 | 0.5652 |
| 0.6147 | 52.01 | 3180 | 1.3030 | 0.6435 |
| 0.5787 | 53.01 | 3240 | 1.4109 | 0.5913 |
| 0.7157 | 54.01 | 3300 | 1.3183 | 0.6 |
| 0.3391 | 55.01 | 3360 | 1.4333 | 0.5913 |
| 0.7482 | 56.01 | 3420 | 1.4549 | 0.5826 |
| 0.5182 | 57.01 | 3480 | 1.4193 | 0.5652 |
| 0.7383 | 58.01 | 3540 | 1.4043 | 0.5565 |
| 0.8862 | 59.01 | 3600 | 1.4041 | 0.6 |
| 0.3481 | 60.01 | 3660 | 1.3164 | 0.6435 |
| 0.763 | 61.01 | 3720 | 1.2947 | 0.5913 |
| 0.7397 | 62.01 | 3780 | 1.2785 | 0.6696 |
| 0.514 | 63.01 | 3840 | 1.3180 | 0.6522 |
| 0.6582 | 64.01 | 3900 | 1.3520 | 0.6696 |
| 0.3929 | 65.01 | 3960 | 1.3391 | 0.6609 |
| 0.7623 | 66.01 | 4020 | 1.4349 | 0.6348 |
| 0.6235 | 67.01 | 4080 | 1.2897 | 0.6522 |
| 0.449 | 68.01 | 4140 | 1.3150 | 0.6696 |
| 0.639 | 69.01 | 4200 | 1.4241 | 0.6087 |
| 0.473 | 70.01 | 4260 | 1.2578 | 0.6609 |
| 0.5478 | 71.01 | 4320 | 1.2770 | 0.6522 |
| 0.4732 | 72.01 | 4380 | 1.2893 | 0.6783 |
| 0.5489 | 73.01 | 4440 | 1.2195 | 0.7043 |
| 0.3907 | 74.01 | 4500 | 1.2523 | 0.6957 |
| 0.2572 | 75.01 | 4560 | 1.2149 | 0.7043 |
| 0.5022 | 76.01 | 4620 | 1.2934 | 0.6696 |
| 0.2958 | 77.01 | 4680 | 1.2726 | 0.6783 |
| 0.7009 | 78.01 | 4740 | 1.2779 | 0.6957 |
| 0.49 | 79.01 | 4800 | 1.2748 | 0.6870 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AlgorithmicResearchGroup/arxiv-distilbert-base-v3-GenQ
|
AlgorithmicResearchGroup
| 2023-06-23T01:25:16Z | 2 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-06-15T01:43:01Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
language:
- en
library_name: sentence-transformers
---
# Arxiv-distilbert-base-v3-GenQ
Arxiv-distilbert-base-v3-GenQ is trained on [ArtifactAI/arxiv-beir-500k-generated-queries](ArtifactAI/arxiv-beir-500k-generated-queries), a large corpus of 500k question/abstract pairs extracted from the ArXiv dataset. It is designed to encode and transform sentences from academic papers, allowing for effective semantic similarity and information retrieval tasks. It maps sentences & paragraphs to a 768 dimensional dense vector space.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('ArtifactAI/arxiv-distilbert-base-v3-GenQ')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('ArtifactAI/arxiv-distilbert-base-v3-GenQ')
model = AutoModel.from_pretrained('ArtifactAI/arxiv-distilbert-base-v3-GenQ')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 4631 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"correct_bias": false,
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 463,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
```
@misc{arxiv-distilbert-base-v3-GenQ,
title={arxiv-distilbert-base-v3-GenQ},
author={Matthew Kenney},
year={2023}
}
```
|
AlgorithmicResearchGroup/arxiv-distilroberta-base-GenQ
|
AlgorithmicResearchGroup
| 2023-06-23T01:24:13Z | 11 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-06-19T02:50:15Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
language:
- en
library_name: sentence-transformers
---
# Arxiv-distilroberta-base-GenQ
Arxiv-distilroberta-base-GenQ is trained on [ArtifactAI/arxiv-beir-500k-generated-queries](ArtifactAI/arxiv-beir-500k-generated-queries), a large corpus of 500k question/abstract pairs extracted from the ArXiv dataset. It is designed to encode and transform sentences from academic papers, allowing for effective semantic similarity and information retrieval tasks.
It maps sentences & paragraphs to a 768 dimensional dense vector space.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('ArtifactAI/arxiv-distilroberta-base-GenQ')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('ArtifactAI/arxiv-distilroberta-base-GenQ')
model = AutoModel.from_pretrained('ArtifactAI/arxiv-distilroberta-base-GenQ')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 23128 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"correct_bias": false,
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2312,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
```
@misc{arxiv-distilroberta-base-GenQ,
title={arxiv-distilroberta-base-GenQ},
author={Matthew Kenney},
year={2023}
}
```
|
ka1yo/kaiyomixes
|
ka1yo
| 2023-06-23T01:14:33Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-04-02T14:48:50Z |
---
license: openrail
---
# Kaiyo Mixes
I'm new to using hugging face so this will act as a repository for some of my merged models.
Attached is the Notion page where I document my recipes for each model and some example images.
https://kaiyo.notion.site/Personal-Models-f5c0aff01eab48869699b958a66e4501
Please note that these images should not be used for commercial purposes
and the models should not be redistributed and sold for monetary gain.
Thanks for showing an interest in these merges!
- Kaiyo
|
evatan/cat_wo_prior
|
evatan
| 2023-06-23T00:49:33Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-22T14:36:29Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks cat
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - evatan/cat_wo_prior
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks cat using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
deandrasetya/indobert-abusive-language-classifier
|
deandrasetya
| 2023-06-23T00:17:37Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-14T10:14:29Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: indobert-abusive-language-classifier
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. -->
# indobert-abusive-language-classifier
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1613
- Train Sparse Categorical Accuracy: 0.9417
- Validation Loss: 0.2973
- Validation Sparse Categorical Accuracy: 0.8857
- 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', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 0.4496 | 0.7811 | 0.3146 | 0.8671 | 0 |
| 0.2437 | 0.9026 | 0.2959 | 0.8888 | 1 |
| 0.1613 | 0.9417 | 0.2973 | 0.8857 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Dans-Archive/Dans-PersonalityEngine-30b
|
Dans-Archive
| 2023-06-23T00:14:59Z | 52 | 5 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-16T04:25:05Z |
---
language:
- en
---
### Description:
This is a multipurpose chat / chat instruct hybrid model in the same vein as the Pygmalion team's Metharme. It uses a curated pile of training data that has been normalized into a consistent training format. It has been trained on a wide array of one shot instructions, multi round instructions, and role playing scenarios.
The training parameters were suboptimal for the most recent run and I decided to stop after 2 epochs as 3 likely would have overtrained it. I plan on iterating the model and improving it further when I have access to more funds to do so.
### Prompt format:
Metharme
The prompt should start with the cursor on the same line directly after "<|model|>" with no space. The following are all valid formats and can be extended to as many rounds as desired.
```
<|system|>system message here<|user|>user message here<|model|>
```
```
<|system|>system message here<|user|>user message here<|model|>model message<|user|>user message here<|model|>
```
```
<|system|>system message here<|model|>
```
```
<|system|>system message here<|model|>model message<|user|>user message here<|model|>
```
Some example prompts:
```
<|system|>The following is a transcript between a helpful assistant and a user.<|user|>Why is the sky blue?<|model|>
```
```
<|system|>You are a Virtual Story Generator. You take the user's input and create an excellent and captivating story that goes in that direction. Use an abundance of sensory descriptions and eloquent prose.<|user|>Alpha Centauri has fallen, to the bears. This is a point of view tale about a soldier on the ground.<|model|>
```
```
<|system|>You are a professional editor with decades of experience, help the user with any task they have for you.<|user|>Can you rewrite this to flow better? "I knew I probably shouldnt have done that but oh well"<|model|>
```
More will be added at a later date.
### Perplexity Benchmarks:
- TBA
### Training information:
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="150" height="24"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
- GPTQ 4 bit LoRA
- 2 Epochs
- 64 / 32 R / A
- 2048 Cutoff
- 42 hours on 1x RTX 4090
### Data used in training:
- TBA
### Models used:
For training:
https://huggingface.co/PocketDoc/llama-30b-gptq-4bit-128g
For merging:
https://huggingface.co/PocketDoc/Dans-PersonalityEngine-30b-LoRA
and
https://huggingface.co/huggyllama/llama-30b
### Disclaimer:
It has not been aligned and no warranty is given for the quality or safety of its outputs.
|
KoboldAI/OPT-350M-Erebus
|
KoboldAI
| 2023-06-23T00:03:22Z | 1,520 | 15 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2022-11-13T11:56:06Z |
---
language: en
license: other
commercial: no
inference: false
---
# OPT 350M - Erebus
## Model description
This is the second generation of the original Shinen made by Mr. Seeker. The full dataset consists of 6 different sources, all surrounding the "Adult" theme. The name "Erebus" comes from the greek mythology, also named "darkness". This is in line with Shin'en, or "deep abyss". For inquiries, please contact the KoboldAI community. **Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.**
## Training data
The data can be divided in 6 different datasets:
- Literotica (everything with 4.5/5 or higher)
- Sexstories (everything with 90 or higher)
- Dataset-G (private dataset of X-rated stories)
- Doc's Lab (all stories)
- Pike Dataset (novels with "adult" rating)
- SoFurry (collection of various animals)
The dataset uses `[Genre: <comma-separated list of genres>]` for tagging.
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='KoboldAI/OPT-350M-Erebus')
>>> generator("Welcome Captain Janeway, I apologize for the delay.", do_sample=True, min_length=50)
[{'generated_text': 'Welcome Captain Janeway, I apologize for the delay."\nIt's all right," Janeway said. "I'm certain that you're doing your best to keep me informed of what\'s going on."'}]
```
## Limitations and biases
Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion). **Warning: This model has a very strong NSFW bias!**
### License
OPT-350M is licensed under the OPT-175B license, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
### BibTeX entry and citation info
```
@misc{zhang2022opt,
title={OPT: Open Pre-trained Transformer Language Models},
author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
year={2022},
eprint={2205.01068},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Illumotion/majicMIX-realistic-v5
|
Illumotion
| 2023-06-22T23:40:01Z | 30 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-22T11:47:17Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
---
|
Slaaaaaaaaaateste/Nsei
|
Slaaaaaaaaaateste
| 2023-06-22T23:37:48Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-04-15T03:18:36Z |
---
license: creativeml-openrail-m
---
|
pln-fing-udelar/robertuito-HUHU-task2a-group4
|
pln-fing-udelar
| 2023-06-22T23:34:15Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-22T23:18:23Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: robertuito-HUHU-task2a-group4
results: []
widget:
- text: "El español es un idioma muy hablado en el mundo."
---
<!-- 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. -->
# robertuito-HUHU-task2a-group4
This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) for the HUHU Shared Task at IberLEF 2023. It was trained on a partition of the train set provided by the organizers.
## Model description
This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) for the task of classifying a tweet (considered to be hurtful or conveying prejudice in some way) as PREJUDICE-OVERWEIGHT if it shows prejudice towards overweight people.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.1952 | 1 |
| 0.0340 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
pln-fing-udelar/robertuito-HUHU-task2a-group3
|
pln-fing-udelar
| 2023-06-22T23:34:04Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-22T23:12:05Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: robertuito-HUHU-task2a-group3
results: []
widget:
- text: "El español es un idioma muy hablado en el mundo."
---
<!-- 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. -->
# robertuito-HUHU-task2a-group3
This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) for the HUHU Shared Task at IberLEF 2023. It was trained on a partition of the train set provided by the organizers.
## Model description
This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) for the task of classifying a tweet (considered to be hurtful or conveying prejudice in some way) as PREJUDICE-INMIGRANT-RACE if it shows prejudice towards immigrants or people’s race.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.2134 | 1 |
| 0.0248 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
rd124/marian-finetuned-samanantar100K-en-to-hi
|
rd124
| 2023-06-22T23:33:53Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-06-22T22:39:15Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: marian-finetuned-samanantar100K-en-to-hi
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. -->
# marian-finetuned-samanantar100K-en-to-hi
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-hi](https://huggingface.co/Helsinki-NLP/opus-mt-en-hi) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9174
- Bleu: 18.0140
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
pln-fing-udelar/robertuito-HUHU-task2a-group1
|
pln-fing-udelar
| 2023-06-22T23:27:45Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-22T22:06:13Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: robertuito-HUHU-task2a-group1
results: []
widget:
- text: "El español es un idioma muy hablado en el mundo."
---
<!-- 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. -->
# robertuito-HUHU-task2a-group1
This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) for the HUHU Shared Task at IberLEF 2023. It was trained on a partition of the train set provided by the organizers.
## Model description
This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) for the task of classifying a tweet (considered to be hurtful or conveying prejudice in some way) as PREJUDICE-WOMAN if it shows prejudice towards women.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.3371 | 1 |
| 0.1060 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
FlareX/tayko-36772
|
FlareX
| 2023-06-22T23:26:16Z | 3 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-22T23:16:52Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### tayko-36772 Dreambooth model trained by FlareX 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:
|
sheshenin/vikash3-2
|
sheshenin
| 2023-06-22T22:54:55Z | 4 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-22T22:41:58Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### VikaSH3_2 Dreambooth model trained by sheshenin 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:
|
GEMCorp/Reinforce-Pixelcopter-PLE-v0
|
GEMCorp
| 2023-06-22T22:44:48Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-22T22:43:54Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 29.90 +/- 24.60
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** (i.e Monte Carlo Policy Gradient) 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
|
natope/mT5-tfidf-10pass-all-questions-QA-22-06-2023-without-ams-6epochs
|
natope
| 2023-06-22T22:36:30Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-22T21:25:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mT5-tfidf-10pass-all-questions-QA-22-06-2023-without-ams-6epochs
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. -->
# mT5-tfidf-10pass-all-questions-QA-22-06-2023-without-ams-6epochs
This model is a fine-tuned version of [natope/mT5-tfidf-10pass-all-questions-QA-22-06-2023-without-ams](https://huggingface.co/natope/mT5-tfidf-10pass-all-questions-QA-22-06-2023-without-ams) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9367
- Rouge1: 0.1454
- Rouge2: 0.0353
- Rougel: 0.1163
- Rougelsum: 0.1162
- Gen Len: 18.9872
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.3923 | 1.0 | 5187 | 1.9667 | 0.1476 | 0.0368 | 0.1175 | 0.1175 | 18.9341 |
| 2.339 | 2.0 | 10374 | 1.9422 | 0.144 | 0.035 | 0.1147 | 0.1147 | 18.9744 |
| 2.3224 | 3.0 | 15561 | 1.9367 | 0.1454 | 0.0353 | 0.1163 | 0.1162 | 18.9872 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
pln-fing-udelar/robertuito-HUHU-task1
|
pln-fing-udelar
| 2023-06-22T22:25:41Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-20T20:13:45Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: robertuito-HUHU-task1
results: []
widget:
- text: "El español es un idioma muy hablado en el mundo."
---
<!-- 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. -->
# robertuito-HUHU-task1
This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) for the HUHU Shared Task at IberLEF 2023. It was trained on a partition of the train set provided by the organizers.
## Model description
This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) for the task of classifying a tweet (considered to be hurtful or conveying prejudice in some way) into humorous or non-humorous.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
intanm/fewshot-qa-003-20230623-001
|
intanm
| 2023-06-22T22:25:11Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-22T21:59:37Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: fewshot-qa-003-20230623-001
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. -->
# fewshot-qa-003-20230623-001
This model is a fine-tuned version of [timpal0l/mdeberta-v3-base-squad2](https://huggingface.co/timpal0l/mdeberta-v3-base-squad2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7303
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 208 | 2.2830 |
| No log | 2.0 | 416 | 2.2975 |
| 2.2077 | 3.0 | 624 | 2.4189 |
| 2.2077 | 4.0 | 832 | 2.7090 |
| 1.1515 | 5.0 | 1040 | 3.0032 |
| 1.1515 | 6.0 | 1248 | 3.3080 |
| 1.1515 | 7.0 | 1456 | 3.5268 |
| 0.6061 | 8.0 | 1664 | 3.5598 |
| 0.6061 | 9.0 | 1872 | 3.6973 |
| 0.3833 | 10.0 | 2080 | 3.7303 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
dexhrestha/mia_model
|
dexhrestha
| 2023-06-22T22:24:26Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-28T19:59:34Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: dexhrestha/mia_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dexhrestha/mia_model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 8.0876
- Validation Loss: 7.6766
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -931, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.0484 | 9.0947 | 0 |
| 8.7641 | 8.3983 | 1 |
| 8.0876 | 7.6766 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ZackRobotHeart/OmorashiOmutsuPomello
|
ZackRobotHeart
| 2023-06-22T22:15:57Z | 0 | 2 | null |
[
"art",
"license:mit",
"region:us"
] | null | 2023-06-22T21:30:50Z |
---
license: mit
tags:
- art
---
This Model is an Omorashi and ABDL SD1.5 checkpoint, trained on a manually tagged dataset, from a merged model made of whatever would make it easier to train this stuff, then also merged at the end to improve the resutls a bit.
I have tried to be better than diaperAIV2 at wet and messy diapers, and actually do bedwetting and pee streams, puddles, wet spots on clothes literally at all, unlike diaper ai. This model is better at composition, not perfectly photoreal, and not as good at nailing the details of a perfectly dry and clean diaper close up. But its far better at actual scenes and a bit more versatile and doesn't mind being altered by loras.
|
AlekseyKorshuk/pygmalion-6b-vicuna-chatml
|
AlekseyKorshuk
| 2023-06-22T22:15:31Z | 1,491 | 2 |
transformers
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"generated_from_trainer",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-22T05:04:26Z |
---
license: creativeml-openrail-m
tags:
- generated_from_trainer
model-index:
- name: pygmalion-6b-vicuna-chatml
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. -->
# pygmalion-6b-vicuna-chatml
This model is a fine-tuned version of [PygmalionAI/pygmalion-6b](https://huggingface.co/PygmalionAI/pygmalion-6b) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Brandulio/Pyramids
|
Brandulio
| 2023-06-22T22:09:30Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-06-22T22:08:40Z |
---
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: Brandulio/Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
efederici/cross-encoder-distilbert-it
|
efederici
| 2023-06-22T22:02:53Z | 120 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"cross-encoder",
"sentence-similarity",
"it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-28T18:05:39Z |
---
pipeline_tag: text-classification
license: apache-2.0
language:
- it
tags:
- cross-encoder
- sentence-similarity
- transformers
---
# Cross-Encoder
The model can be used for Information Retrieval: given a query, encode the query will all possible passages. Then sort the passages in a decreasing order.
<p align="center">
<img src="https://www.exibart.com/repository/media/2020/07/bridget-riley-cool-edge.jpg" width="400"> </br>
Bridget Riley, COOL EDGE
</p>
## Training Data
This model was trained on a custom biomedical ranking dataset.
## Usage and Performance
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('efederici/cross-encoder-distilbert-it')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
```
The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
|
AI4PD/lact
|
AI4PD
| 2023-06-22T22:00:10Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-22T20:27:38Z |
---
tags:
- generated_from_trainer
model-index:
- name: output
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. -->
# output
This model is a fine-tuned version of [/home/woody/b114cb/b114cb10/zymCTRL/gpt2-large/config.json](https://huggingface.co//home/woody/b114cb/b114cb10/zymCTRL/gpt2-large/config.json) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3014
## 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: 8e-05
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2882 | 0.02 | 10 | 2.9581 |
| 2.5059 | 0.04 | 20 | 2.3844 |
| 2.3368 | 0.06 | 30 | 2.3644 |
| 2.3476 | 0.08 | 40 | 2.3494 |
| 2.3185 | 0.1 | 50 | 2.3697 |
| 2.3468 | 0.12 | 60 | 2.3255 |
| 2.262 | 0.14 | 70 | 2.2512 |
| 2.1646 | 0.16 | 80 | 2.1945 |
| 2.1558 | 0.18 | 90 | 2.1885 |
| 2.1934 | 0.2 | 100 | 2.1483 |
| 2.0855 | 0.22 | 110 | 2.1152 |
| 2.0844 | 0.24 | 120 | 2.0839 |
| 2.0647 | 0.26 | 130 | 2.0615 |
| 1.9665 | 0.28 | 140 | 2.0330 |
| 1.9761 | 0.3 | 150 | 2.0068 |
| 1.9428 | 0.32 | 160 | 1.9914 |
| 1.9351 | 0.34 | 170 | 1.9369 |
| 1.9366 | 0.36 | 180 | 1.9139 |
| 1.9548 | 0.38 | 190 | 1.8789 |
| 1.9625 | 0.4 | 200 | 1.8486 |
| 1.8584 | 0.42 | 210 | 1.8198 |
| 1.8857 | 0.44 | 220 | 1.8118 |
| 1.7574 | 0.46 | 230 | 1.7603 |
| 1.8114 | 0.48 | 240 | 1.7370 |
| 1.7303 | 0.5 | 250 | 1.7205 |
| 1.7535 | 0.52 | 260 | 1.7124 |
| 1.7775 | 0.54 | 270 | 1.7013 |
| 1.685 | 0.56 | 280 | 1.6612 |
| 1.5898 | 0.58 | 290 | 1.6578 |
| 1.7875 | 0.6 | 300 | 1.6458 |
| 1.628 | 0.62 | 310 | 1.6253 |
| 1.6186 | 0.64 | 320 | 1.6195 |
| 1.6899 | 0.66 | 330 | 1.6102 |
| 1.5908 | 0.68 | 340 | 1.5907 |
| 1.6514 | 0.7 | 350 | 1.6104 |
| 1.6027 | 0.72 | 360 | 1.5766 |
| 1.6319 | 0.74 | 370 | 1.5623 |
| 1.6103 | 0.76 | 380 | 1.5764 |
| 1.4518 | 0.78 | 390 | 1.5449 |
| 1.498 | 0.8 | 400 | 1.5345 |
| 1.5266 | 0.82 | 410 | 1.5413 |
| 1.5622 | 0.84 | 420 | 1.5229 |
| 1.4863 | 0.86 | 430 | 1.5208 |
| 1.5492 | 0.88 | 440 | 1.4996 |
| 1.5515 | 0.9 | 450 | 1.4857 |
| 1.4799 | 0.92 | 460 | 1.4935 |
| 1.4514 | 0.94 | 470 | 1.4745 |
| 1.5462 | 0.96 | 480 | 1.4784 |
| 1.6032 | 0.98 | 490 | 1.4911 |
| 1.7418 | 1.0 | 500 | 1.4733 |
| 1.4983 | 1.02 | 510 | 1.4646 |
| 1.5383 | 1.04 | 520 | 1.4442 |
| 1.3454 | 1.06 | 530 | 1.4332 |
| 1.3128 | 1.08 | 540 | 1.4261 |
| 1.5472 | 1.1 | 550 | 1.4232 |
| 1.252 | 1.12 | 560 | 1.3924 |
| 1.3538 | 1.14 | 570 | 1.3975 |
| 1.5448 | 1.16 | 580 | 1.3915 |
| 1.4016 | 1.18 | 590 | 1.4025 |
| 1.3041 | 1.2 | 600 | 1.3837 |
| 1.3857 | 1.22 | 610 | 1.3890 |
| 1.2923 | 1.24 | 620 | 1.3452 |
| 1.28 | 1.26 | 630 | 1.3492 |
| 1.4052 | 1.28 | 640 | 1.3254 |
| 1.3992 | 1.3 | 650 | 1.3670 |
| 1.5044 | 1.32 | 660 | 1.3153 |
| 1.2274 | 1.34 | 670 | 1.3142 |
| 1.2392 | 1.36 | 680 | 1.3150 |
| 1.365 | 1.38 | 690 | 1.2966 |
| 1.3024 | 1.4 | 700 | 1.2688 |
| 1.347 | 1.42 | 710 | 1.2874 |
| 1.3898 | 1.44 | 720 | 1.2543 |
| 1.4256 | 1.46 | 730 | 1.2397 |
| 1.2566 | 1.48 | 740 | 1.2430 |
| 1.2473 | 1.5 | 750 | 1.2135 |
| 1.1466 | 1.52 | 760 | 1.2171 |
| 1.3065 | 1.54 | 770 | 1.1897 |
| 1.3033 | 1.56 | 780 | 1.1646 |
| 1.1166 | 1.58 | 790 | 1.1723 |
| 1.0874 | 1.6 | 800 | 1.1511 |
| 1.017 | 1.62 | 810 | 1.1396 |
| 1.0437 | 1.64 | 820 | 1.1016 |
| 1.2206 | 1.66 | 830 | 1.0841 |
| 0.9738 | 1.68 | 840 | 1.0760 |
| 1.1351 | 1.7 | 850 | 1.0562 |
| 1.0697 | 1.72 | 860 | 1.0556 |
| 1.0296 | 1.74 | 870 | 1.0342 |
| 1.0904 | 1.76 | 880 | 1.0047 |
| 1.01 | 1.78 | 890 | 1.0184 |
| 0.951 | 1.8 | 900 | 0.9845 |
| 1.0111 | 1.82 | 910 | 0.9675 |
| 1.0824 | 1.84 | 920 | 0.9759 |
| 0.9745 | 1.86 | 930 | 0.9336 |
| 0.8632 | 1.88 | 940 | 0.9347 |
| 0.9959 | 1.9 | 950 | 0.9395 |
| 0.8906 | 1.92 | 960 | 0.8965 |
| 1.0552 | 1.94 | 970 | 0.8892 |
| 0.8387 | 1.96 | 980 | 0.8822 |
| 1.0068 | 1.98 | 990 | 0.8805 |
| 1.083 | 2.0 | 1000 | 0.8490 |
| 0.8407 | 2.02 | 1010 | 0.8457 |
| 0.7468 | 2.04 | 1020 | 0.8285 |
| 0.8421 | 2.06 | 1030 | 0.8055 |
| 0.8407 | 2.08 | 1040 | 0.8160 |
| 0.8126 | 2.1 | 1050 | 0.8266 |
| 0.7318 | 2.12 | 1060 | 0.8151 |
| 0.9142 | 2.14 | 1070 | 0.7876 |
| 0.6483 | 2.16 | 1080 | 0.7866 |
| 0.8092 | 2.18 | 1090 | 0.7818 |
| 0.8235 | 2.2 | 1100 | 0.7708 |
| 0.7062 | 2.22 | 1110 | 0.7693 |
| 0.7348 | 2.24 | 1120 | 0.7875 |
| 0.7507 | 2.26 | 1130 | 0.7567 |
| 0.7588 | 2.28 | 1140 | 0.7565 |
| 0.605 | 2.3 | 1150 | 0.7298 |
| 0.8721 | 2.32 | 1160 | 0.7254 |
| 0.6988 | 2.34 | 1170 | 0.7072 |
| 0.6294 | 2.36 | 1180 | 0.7082 |
| 0.7117 | 2.38 | 1190 | 0.7113 |
| 0.8558 | 2.4 | 1200 | 0.6991 |
| 0.6187 | 2.42 | 1210 | 0.6905 |
| 0.6791 | 2.44 | 1220 | 0.6875 |
| 0.5447 | 2.46 | 1230 | 0.6869 |
| 0.7299 | 2.48 | 1240 | 0.6777 |
| 0.5829 | 2.5 | 1250 | 0.6658 |
| 0.6435 | 2.52 | 1260 | 0.6603 |
| 0.7303 | 2.54 | 1270 | 0.6578 |
| 0.7244 | 2.56 | 1280 | 0.6594 |
| 0.6463 | 2.58 | 1290 | 0.6409 |
| 0.7766 | 2.6 | 1300 | 0.6417 |
| 0.6012 | 2.62 | 1310 | 0.6461 |
| 0.5974 | 2.64 | 1320 | 0.6365 |
| 0.556 | 2.66 | 1330 | 0.6301 |
| 0.6369 | 2.68 | 1340 | 0.6247 |
| 0.5699 | 2.7 | 1350 | 0.6163 |
| 0.624 | 2.72 | 1360 | 0.6138 |
| 0.6774 | 2.74 | 1370 | 0.6135 |
| 0.5553 | 2.76 | 1380 | 0.6076 |
| 0.604 | 2.78 | 1390 | 0.5938 |
| 0.6087 | 2.8 | 1400 | 0.5956 |
| 0.5935 | 2.82 | 1410 | 0.5933 |
| 0.6042 | 2.84 | 1420 | 0.5911 |
| 0.6425 | 2.86 | 1430 | 0.5844 |
| 0.6316 | 2.88 | 1440 | 0.5745 |
| 0.597 | 2.9 | 1450 | 0.5695 |
| 0.5754 | 2.92 | 1460 | 0.5704 |
| 0.5197 | 2.94 | 1470 | 0.5697 |
| 0.6256 | 2.96 | 1480 | 0.5596 |
| 0.5818 | 2.98 | 1490 | 0.5599 |
| 0.5464 | 3.01 | 1500 | 0.5565 |
| 0.4616 | 3.03 | 1510 | 0.5629 |
| 0.6482 | 3.05 | 1520 | 0.5529 |
| 0.5356 | 3.07 | 1530 | 0.5526 |
| 0.5688 | 3.09 | 1540 | 0.5528 |
| 0.6018 | 3.11 | 1550 | 0.5408 |
| 0.5794 | 3.13 | 1560 | 0.5371 |
| 0.5443 | 3.15 | 1570 | 0.5375 |
| 0.4435 | 3.17 | 1580 | 0.5345 |
| 0.5087 | 3.19 | 1590 | 0.5293 |
| 0.518 | 3.21 | 1600 | 0.5336 |
| 0.5914 | 3.23 | 1610 | 0.5316 |
| 0.5667 | 3.25 | 1620 | 0.5254 |
| 0.5218 | 3.27 | 1630 | 0.5207 |
| 0.4267 | 3.29 | 1640 | 0.5270 |
| 0.5839 | 3.31 | 1650 | 0.5199 |
| 0.5095 | 3.33 | 1660 | 0.5268 |
| 0.4616 | 3.35 | 1670 | 0.5192 |
| 0.5027 | 3.37 | 1680 | 0.5106 |
| 0.441 | 3.39 | 1690 | 0.5150 |
| 0.4416 | 3.41 | 1700 | 0.5156 |
| 0.4411 | 3.43 | 1710 | 0.5103 |
| 0.47 | 3.45 | 1720 | 0.5038 |
| 0.5079 | 3.47 | 1730 | 0.5048 |
| 0.3913 | 3.49 | 1740 | 0.5082 |
| 0.4977 | 3.51 | 1750 | 0.4976 |
| 0.5905 | 3.53 | 1760 | 0.4975 |
| 0.4362 | 3.55 | 1770 | 0.4962 |
| 0.4309 | 3.57 | 1780 | 0.5008 |
| 0.4477 | 3.59 | 1790 | 0.4988 |
| 0.4826 | 3.61 | 1800 | 0.4886 |
| 0.6181 | 3.63 | 1810 | 0.4885 |
| 0.4738 | 3.65 | 1820 | 0.4879 |
| 0.4932 | 3.67 | 1830 | 0.4818 |
| 0.4684 | 3.69 | 1840 | 0.4812 |
| 0.5484 | 3.71 | 1850 | 0.4767 |
| 0.5086 | 3.73 | 1860 | 0.4791 |
| 0.3548 | 3.75 | 1870 | 0.4793 |
| 0.5229 | 3.77 | 1880 | 0.4765 |
| 0.4578 | 3.79 | 1890 | 0.4704 |
| 0.5277 | 3.81 | 1900 | 0.4691 |
| 0.4683 | 3.83 | 1910 | 0.4649 |
| 0.448 | 3.85 | 1920 | 0.4684 |
| 0.3752 | 3.87 | 1930 | 0.4697 |
| 0.4631 | 3.89 | 1940 | 0.4678 |
| 0.4277 | 3.91 | 1950 | 0.4608 |
| 0.3646 | 3.93 | 1960 | 0.4609 |
| 0.5276 | 3.95 | 1970 | 0.4543 |
| 0.431 | 3.97 | 1980 | 0.4539 |
| 0.5465 | 3.99 | 1990 | 0.4550 |
| 0.4954 | 4.01 | 2000 | 0.4523 |
| 0.4886 | 4.03 | 2010 | 0.4499 |
| 0.4898 | 4.05 | 2020 | 0.4462 |
| 0.4072 | 4.07 | 2030 | 0.4479 |
| 0.4565 | 4.09 | 2040 | 0.4458 |
| 0.3739 | 4.11 | 2050 | 0.4475 |
| 0.4211 | 4.13 | 2060 | 0.4486 |
| 0.4048 | 4.15 | 2070 | 0.4393 |
| 0.5064 | 4.17 | 2080 | 0.4351 |
| 0.4652 | 4.19 | 2090 | 0.4379 |
| 0.4061 | 4.21 | 2100 | 0.4341 |
| 0.3784 | 4.23 | 2110 | 0.4390 |
| 0.4142 | 4.25 | 2120 | 0.4354 |
| 0.3625 | 4.27 | 2130 | 0.4415 |
| 0.3807 | 4.29 | 2140 | 0.4403 |
| 0.4154 | 4.31 | 2150 | 0.4308 |
| 0.4509 | 4.33 | 2160 | 0.4298 |
| 0.4254 | 4.35 | 2170 | 0.4239 |
| 0.4323 | 4.37 | 2180 | 0.4214 |
| 0.4359 | 4.39 | 2190 | 0.4291 |
| 0.3759 | 4.41 | 2200 | 0.4224 |
| 0.4534 | 4.43 | 2210 | 0.4225 |
| 0.4013 | 4.45 | 2220 | 0.4262 |
| 0.4331 | 4.47 | 2230 | 0.4214 |
| 0.4373 | 4.49 | 2240 | 0.4198 |
| 0.4975 | 4.51 | 2250 | 0.4236 |
| 0.423 | 4.53 | 2260 | 0.4189 |
| 0.4503 | 4.55 | 2270 | 0.4171 |
| 0.3796 | 4.57 | 2280 | 0.4172 |
| 0.4063 | 4.59 | 2290 | 0.4125 |
| 0.3841 | 4.61 | 2300 | 0.4119 |
| 0.2956 | 4.63 | 2310 | 0.4147 |
| 0.3486 | 4.65 | 2320 | 0.4246 |
| 0.3585 | 4.67 | 2330 | 0.4117 |
| 0.4496 | 4.69 | 2340 | 0.4091 |
| 0.399 | 4.71 | 2350 | 0.4049 |
| 0.3885 | 4.73 | 2360 | 0.4004 |
| 0.3728 | 4.75 | 2370 | 0.4003 |
| 0.2698 | 4.77 | 2380 | 0.4009 |
| 0.3799 | 4.79 | 2390 | 0.4003 |
| 0.4888 | 4.81 | 2400 | 0.3974 |
| 0.3795 | 4.83 | 2410 | 0.3995 |
| 0.4249 | 4.85 | 2420 | 0.3968 |
| 0.4635 | 4.87 | 2430 | 0.4001 |
| 0.4965 | 4.89 | 2440 | 0.3934 |
| 0.3745 | 4.91 | 2450 | 0.3987 |
| 0.3601 | 4.93 | 2460 | 0.3986 |
| 0.2878 | 4.95 | 2470 | 0.3941 |
| 0.4297 | 4.97 | 2480 | 0.3890 |
| 0.278 | 4.99 | 2490 | 0.3975 |
| 0.4509 | 5.01 | 2500 | 0.3907 |
| 0.3202 | 5.03 | 2510 | 0.3872 |
| 0.3047 | 5.05 | 2520 | 0.3956 |
| 0.2931 | 5.07 | 2530 | 0.3925 |
| 0.3487 | 5.09 | 2540 | 0.3910 |
| 0.2792 | 5.11 | 2550 | 0.3901 |
| 0.3446 | 5.13 | 2560 | 0.3873 |
| 0.3482 | 5.15 | 2570 | 0.3840 |
| 0.3464 | 5.17 | 2580 | 0.3835 |
| 0.3212 | 5.19 | 2590 | 0.3846 |
| 0.3847 | 5.21 | 2600 | 0.3819 |
| 0.3212 | 5.23 | 2610 | 0.3897 |
| 0.358 | 5.25 | 2620 | 0.3811 |
| 0.3471 | 5.27 | 2630 | 0.3805 |
| 0.3348 | 5.29 | 2640 | 0.3868 |
| 0.342 | 5.31 | 2650 | 0.3769 |
| 0.4504 | 5.33 | 2660 | 0.3774 |
| 0.2713 | 5.35 | 2670 | 0.3803 |
| 0.3848 | 5.37 | 2680 | 0.3776 |
| 0.354 | 5.39 | 2690 | 0.3758 |
| 0.3796 | 5.41 | 2700 | 0.3760 |
| 0.3654 | 5.43 | 2710 | 0.3737 |
| 0.3448 | 5.45 | 2720 | 0.3812 |
| 0.355 | 5.47 | 2730 | 0.3759 |
| 0.288 | 5.49 | 2740 | 0.3711 |
| 0.2991 | 5.51 | 2750 | 0.3691 |
| 0.3443 | 5.53 | 2760 | 0.3708 |
| 0.3374 | 5.55 | 2770 | 0.3659 |
| 0.4078 | 5.57 | 2780 | 0.3709 |
| 0.2967 | 5.59 | 2790 | 0.3683 |
| 0.3532 | 5.61 | 2800 | 0.3638 |
| 0.4123 | 5.63 | 2810 | 0.3642 |
| 0.3195 | 5.65 | 2820 | 0.3655 |
| 0.3161 | 5.67 | 2830 | 0.3599 |
| 0.4152 | 5.69 | 2840 | 0.3621 |
| 0.2802 | 5.71 | 2850 | 0.3648 |
| 0.2909 | 5.73 | 2860 | 0.3604 |
| 0.3105 | 5.75 | 2870 | 0.3604 |
| 0.3291 | 5.77 | 2880 | 0.3553 |
| 0.3916 | 5.79 | 2890 | 0.3603 |
| 0.3657 | 5.81 | 2900 | 0.3544 |
| 0.3745 | 5.83 | 2910 | 0.3559 |
| 0.3281 | 5.85 | 2920 | 0.3517 |
| 0.2892 | 5.87 | 2930 | 0.3551 |
| 0.4121 | 5.89 | 2940 | 0.3489 |
| 0.2908 | 5.91 | 2950 | 0.3532 |
| 0.3677 | 5.93 | 2960 | 0.3469 |
| 0.341 | 5.95 | 2970 | 0.3503 |
| 0.2319 | 5.97 | 2980 | 0.3497 |
| 0.2624 | 5.99 | 2990 | 0.3468 |
| 0.3324 | 6.01 | 3000 | 0.3480 |
| 0.2114 | 6.03 | 3010 | 0.3530 |
| 0.256 | 6.05 | 3020 | 0.3501 |
| 0.2716 | 6.07 | 3030 | 0.3490 |
| 0.2921 | 6.09 | 3040 | 0.3466 |
| 0.2924 | 6.11 | 3050 | 0.3531 |
| 0.3267 | 6.13 | 3060 | 0.3455 |
| 0.3488 | 6.15 | 3070 | 0.3428 |
| 0.301 | 6.17 | 3080 | 0.3455 |
| 0.2656 | 6.19 | 3090 | 0.3450 |
| 0.2377 | 6.21 | 3100 | 0.3474 |
| 0.2344 | 6.23 | 3110 | 0.3461 |
| 0.2816 | 6.25 | 3120 | 0.3489 |
| 0.2675 | 6.27 | 3130 | 0.3427 |
| 0.3315 | 6.29 | 3140 | 0.3393 |
| 0.335 | 6.31 | 3150 | 0.3406 |
| 0.2418 | 6.33 | 3160 | 0.3385 |
| 0.215 | 6.35 | 3170 | 0.3393 |
| 0.2279 | 6.37 | 3180 | 0.3427 |
| 0.2907 | 6.39 | 3190 | 0.3379 |
| 0.2184 | 6.41 | 3200 | 0.3438 |
| 0.3484 | 6.43 | 3210 | 0.3364 |
| 0.2327 | 6.45 | 3220 | 0.3406 |
| 0.2571 | 6.47 | 3230 | 0.3400 |
| 0.2864 | 6.49 | 3240 | 0.3367 |
| 0.2383 | 6.51 | 3250 | 0.3377 |
| 0.187 | 6.53 | 3260 | 0.3346 |
| 0.2453 | 6.55 | 3270 | 0.3349 |
| 0.296 | 6.57 | 3280 | 0.3339 |
| 0.2601 | 6.59 | 3290 | 0.3335 |
| 0.2927 | 6.61 | 3300 | 0.3340 |
| 0.2796 | 6.63 | 3310 | 0.3303 |
| 0.2393 | 6.65 | 3320 | 0.3351 |
| 0.2764 | 6.67 | 3330 | 0.3288 |
| 0.2547 | 6.69 | 3340 | 0.3327 |
| 0.3247 | 6.71 | 3350 | 0.3279 |
| 0.3217 | 6.73 | 3360 | 0.3283 |
| 0.2881 | 6.75 | 3370 | 0.3307 |
| 0.2897 | 6.77 | 3380 | 0.3281 |
| 0.3096 | 6.79 | 3390 | 0.3257 |
| 0.2463 | 6.81 | 3400 | 0.3244 |
| 0.2404 | 6.83 | 3410 | 0.3254 |
| 0.2907 | 6.85 | 3420 | 0.3227 |
| 0.2749 | 6.87 | 3430 | 0.3226 |
| 0.2262 | 6.89 | 3440 | 0.3226 |
| 0.2799 | 6.91 | 3450 | 0.3233 |
| 0.2764 | 6.93 | 3460 | 0.3198 |
| 0.2644 | 6.95 | 3470 | 0.3231 |
| 0.2733 | 6.97 | 3480 | 0.3188 |
| 0.2861 | 6.99 | 3490 | 0.3192 |
| 0.1757 | 7.01 | 3500 | 0.3243 |
| 0.2588 | 7.03 | 3510 | 0.3238 |
| 0.2132 | 7.05 | 3520 | 0.3207 |
| 0.2787 | 7.07 | 3530 | 0.3272 |
| 0.2786 | 7.09 | 3540 | 0.3229 |
| 0.2854 | 7.11 | 3550 | 0.3232 |
| 0.1982 | 7.13 | 3560 | 0.3237 |
| 0.2022 | 7.15 | 3570 | 0.3254 |
| 0.2592 | 7.17 | 3580 | 0.3258 |
| 0.2299 | 7.19 | 3590 | 0.3207 |
| 0.2054 | 7.21 | 3600 | 0.3197 |
| 0.208 | 7.23 | 3610 | 0.3216 |
| 0.2432 | 7.25 | 3620 | 0.3228 |
| 0.2452 | 7.27 | 3630 | 0.3181 |
| 0.264 | 7.29 | 3640 | 0.3238 |
| 0.2019 | 7.31 | 3650 | 0.3178 |
| 0.2299 | 7.33 | 3660 | 0.3218 |
| 0.2465 | 7.35 | 3670 | 0.3172 |
| 0.2466 | 7.37 | 3680 | 0.3167 |
| 0.2824 | 7.39 | 3690 | 0.3143 |
| 0.2314 | 7.41 | 3700 | 0.3143 |
| 0.2822 | 7.43 | 3710 | 0.3143 |
| 0.2254 | 7.45 | 3720 | 0.3139 |
| 0.2454 | 7.47 | 3730 | 0.3218 |
| 0.2656 | 7.49 | 3740 | 0.3116 |
| 0.2172 | 7.51 | 3750 | 0.3154 |
| 0.2408 | 7.53 | 3760 | 0.3127 |
| 0.1761 | 7.55 | 3770 | 0.3149 |
| 0.2232 | 7.57 | 3780 | 0.3114 |
| 0.2902 | 7.59 | 3790 | 0.3136 |
| 0.2485 | 7.61 | 3800 | 0.3146 |
| 0.1901 | 7.63 | 3810 | 0.3094 |
| 0.2962 | 7.65 | 3820 | 0.3120 |
| 0.2093 | 7.67 | 3830 | 0.3133 |
| 0.368 | 7.69 | 3840 | 0.3064 |
| 0.2849 | 7.71 | 3850 | 0.3091 |
| 0.1948 | 7.73 | 3860 | 0.3075 |
| 0.2241 | 7.75 | 3870 | 0.3078 |
| 0.1935 | 7.77 | 3880 | 0.3045 |
| 0.2045 | 7.79 | 3890 | 0.3065 |
| 0.159 | 7.81 | 3900 | 0.3082 |
| 0.1714 | 7.83 | 3910 | 0.3057 |
| 0.1984 | 7.85 | 3920 | 0.3059 |
| 0.2397 | 7.87 | 3930 | 0.3037 |
| 0.1884 | 7.89 | 3940 | 0.3054 |
| 0.2585 | 7.91 | 3950 | 0.3030 |
| 0.2476 | 7.93 | 3960 | 0.3058 |
| 0.2525 | 7.95 | 3970 | 0.3033 |
| 0.2001 | 7.97 | 3980 | 0.3062 |
| 0.1985 | 7.99 | 3990 | 0.3039 |
| 0.1984 | 8.02 | 4000 | 0.3139 |
| 0.2008 | 8.04 | 4010 | 0.3099 |
| 0.2159 | 8.06 | 4020 | 0.3085 |
| 0.2305 | 8.08 | 4030 | 0.3108 |
| 0.2007 | 8.1 | 4040 | 0.3050 |
| 0.2124 | 8.12 | 4050 | 0.3115 |
| 0.1435 | 8.14 | 4060 | 0.3084 |
| 0.1968 | 8.16 | 4070 | 0.3087 |
| 0.2507 | 8.18 | 4080 | 0.3084 |
| 0.1703 | 8.2 | 4090 | 0.3061 |
| 0.2511 | 8.22 | 4100 | 0.3106 |
| 0.1698 | 8.24 | 4110 | 0.3134 |
| 0.2518 | 8.26 | 4120 | 0.3101 |
| 0.1489 | 8.28 | 4130 | 0.3090 |
| 0.1759 | 8.3 | 4140 | 0.3098 |
| 0.1939 | 8.32 | 4150 | 0.3056 |
| 0.2168 | 8.34 | 4160 | 0.3106 |
| 0.2119 | 8.36 | 4170 | 0.3051 |
| 0.1793 | 8.38 | 4180 | 0.3056 |
| 0.2434 | 8.4 | 4190 | 0.3050 |
| 0.2601 | 8.42 | 4200 | 0.3065 |
| 0.1791 | 8.44 | 4210 | 0.3051 |
| 0.1404 | 8.46 | 4220 | 0.3058 |
| 0.222 | 8.48 | 4230 | 0.3059 |
| 0.1809 | 8.5 | 4240 | 0.3070 |
| 0.1745 | 8.52 | 4250 | 0.3066 |
| 0.2236 | 8.54 | 4260 | 0.3012 |
| 0.1965 | 8.56 | 4270 | 0.3037 |
| 0.1836 | 8.58 | 4280 | 0.3051 |
| 0.1912 | 8.6 | 4290 | 0.3017 |
| 0.2207 | 8.62 | 4300 | 0.3025 |
| 0.2481 | 8.64 | 4310 | 0.2997 |
| 0.1506 | 8.66 | 4320 | 0.3003 |
| 0.2216 | 8.68 | 4330 | 0.3035 |
| 0.1866 | 8.7 | 4340 | 0.3014 |
| 0.2025 | 8.72 | 4350 | 0.3035 |
| 0.1521 | 8.74 | 4360 | 0.2992 |
| 0.1598 | 8.76 | 4370 | 0.3034 |
| 0.185 | 8.78 | 4380 | 0.3017 |
| 0.2427 | 8.8 | 4390 | 0.2972 |
| 0.2343 | 8.82 | 4400 | 0.2979 |
| 0.1994 | 8.84 | 4410 | 0.2994 |
| 0.2671 | 8.86 | 4420 | 0.2986 |
| 0.1158 | 8.88 | 4430 | 0.2991 |
| 0.2127 | 8.9 | 4440 | 0.3000 |
| 0.1691 | 8.92 | 4450 | 0.2981 |
| 0.2103 | 8.94 | 4460 | 0.2979 |
| 0.1392 | 8.96 | 4470 | 0.2982 |
| 0.1712 | 8.98 | 4480 | 0.2943 |
| 0.2435 | 9.0 | 4490 | 0.2958 |
| 0.1715 | 9.02 | 4500 | 0.3055 |
| 0.1641 | 9.04 | 4510 | 0.3048 |
| 0.1529 | 9.06 | 4520 | 0.3029 |
| 0.1566 | 9.08 | 4530 | 0.3047 |
| 0.1382 | 9.1 | 4540 | 0.3027 |
| 0.1605 | 9.12 | 4550 | 0.3023 |
| 0.2167 | 9.14 | 4560 | 0.3055 |
| 0.1506 | 9.16 | 4570 | 0.3037 |
| 0.192 | 9.18 | 4580 | 0.3039 |
| 0.139 | 9.2 | 4590 | 0.3030 |
| 0.1974 | 9.22 | 4600 | 0.3038 |
| 0.167 | 9.24 | 4610 | 0.3037 |
| 0.2409 | 9.26 | 4620 | 0.3034 |
| 0.1494 | 9.28 | 4630 | 0.3048 |
| 0.1762 | 9.3 | 4640 | 0.3037 |
| 0.183 | 9.32 | 4650 | 0.3042 |
| 0.1773 | 9.34 | 4660 | 0.3043 |
| 0.1509 | 9.36 | 4670 | 0.3053 |
| 0.1994 | 9.38 | 4680 | 0.3045 |
| 0.1928 | 9.4 | 4690 | 0.3036 |
| 0.1158 | 9.42 | 4700 | 0.3038 |
| 0.1503 | 9.44 | 4710 | 0.3019 |
| 0.1556 | 9.46 | 4720 | 0.3029 |
| 0.1327 | 9.48 | 4730 | 0.3050 |
| 0.1772 | 9.5 | 4740 | 0.3057 |
| 0.1555 | 9.52 | 4750 | 0.3028 |
| 0.1363 | 9.54 | 4760 | 0.3014 |
| 0.139 | 9.56 | 4770 | 0.3010 |
| 0.1639 | 9.58 | 4780 | 0.3013 |
| 0.1669 | 9.6 | 4790 | 0.3015 |
| 0.144 | 9.62 | 4800 | 0.3023 |
| 0.1925 | 9.64 | 4810 | 0.3034 |
| 0.1615 | 9.66 | 4820 | 0.3025 |
| 0.1625 | 9.68 | 4830 | 0.3019 |
| 0.1355 | 9.7 | 4840 | 0.3023 |
| 0.1671 | 9.72 | 4850 | 0.3019 |
| 0.1447 | 9.74 | 4860 | 0.3021 |
| 0.1465 | 9.76 | 4870 | 0.3024 |
| 0.1794 | 9.78 | 4880 | 0.3021 |
| 0.156 | 9.8 | 4890 | 0.3011 |
| 0.1018 | 9.82 | 4900 | 0.3005 |
| 0.1403 | 9.84 | 4910 | 0.3011 |
| 0.1126 | 9.86 | 4920 | 0.3006 |
| 0.1595 | 9.88 | 4930 | 0.3007 |
| 0.1415 | 9.9 | 4940 | 0.3012 |
| 0.1651 | 9.92 | 4950 | 0.3015 |
| 0.1558 | 9.94 | 4960 | 0.3015 |
| 0.1734 | 9.96 | 4970 | 0.3014 |
| 0.1909 | 9.98 | 4980 | 0.3014 |
| 0.1246 | 10.0 | 4990 | 0.3014 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.12.1+cu116
- Datasets 2.10.0
- Tokenizers 0.12.1
|
datasistah/qlora_falcon_20230622
|
datasistah
| 2023-06-22T21:58:03Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-22T21:53:04Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0.dev0
|
Mykcy33/ernie-1.0-base-zh-laure-swag
|
Mykcy33
| 2023-06-22T21:42:16Z | 90 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"ernie",
"multiple-choice",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-06-22T21:19:46Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ernie-1.0-base-zh-laure-swag
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. -->
# ernie-1.0-base-zh-laure-swag
This model is a fine-tuned version of [nghuyong/ernie-1.0-base-zh](https://huggingface.co/nghuyong/ernie-1.0-base-zh) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0037
- Accuracy: 0.8000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 7 | 1.0264 | 0.7600 |
| No log | 2.0 | 14 | 0.9992 | 0.75 |
| No log | 3.0 | 21 | 1.0037 | 0.8000 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
rogelioplatt/BertSarcasmoEsp
|
rogelioplatt
| 2023-06-22T21:37:04Z | 2 | 0 |
bertopic
|
[
"bertopic",
"es",
"region:us"
] | null | 2023-06-22T20:29:30Z |
---
language:
- es
library_name: bertopic
---
|
consciousAI/cai-lunaris-text-embeddings
|
consciousAI
| 2023-06-22T21:33:52Z | 395 | 4 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-06-22T18:08:54Z |
---
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: cai-lunaris-text-embeddings
results:
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.07
- type: map_at_10
value: 29.372999999999998
- type: map_at_100
value: 30.79
- type: map_at_1000
value: 30.819999999999997
- type: map_at_3
value: 24.395
- type: map_at_5
value: 27.137
- type: mrr_at_1
value: 17.923000000000002
- type: mrr_at_10
value: 29.695
- type: mrr_at_100
value: 31.098
- type: mrr_at_1000
value: 31.128
- type: mrr_at_3
value: 24.704
- type: mrr_at_5
value: 27.449
- type: ndcg_at_1
value: 17.07
- type: ndcg_at_10
value: 37.269000000000005
- type: ndcg_at_100
value: 43.716
- type: ndcg_at_1000
value: 44.531
- type: ndcg_at_3
value: 26.839000000000002
- type: ndcg_at_5
value: 31.845000000000002
- type: precision_at_1
value: 17.07
- type: precision_at_10
value: 6.3020000000000005
- type: precision_at_100
value: 0.922
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 11.309
- type: precision_at_5
value: 9.246
- type: recall_at_1
value: 17.07
- type: recall_at_10
value: 63.016000000000005
- type: recall_at_100
value: 92.24799999999999
- type: recall_at_1000
value: 98.72
- type: recall_at_3
value: 33.926
- type: recall_at_5
value: 46.23
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 53.44266265900711
- type: mrr
value: 66.54695950402322
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 75.9652953730204
- type: cos_sim_spearman
value: 73.96554077670989
- type: euclidean_pearson
value: 75.68477255792381
- type: euclidean_spearman
value: 74.59447076995703
- type: manhattan_pearson
value: 75.94984623881341
- type: manhattan_spearman
value: 74.72218452337502
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.119000000000002
- type: map_at_10
value: 19.661
- type: map_at_100
value: 20.706
- type: map_at_1000
value: 20.848
- type: map_at_3
value: 17.759
- type: map_at_5
value: 18.645
- type: mrr_at_1
value: 17.166999999999998
- type: mrr_at_10
value: 23.313
- type: mrr_at_100
value: 24.263
- type: mrr_at_1000
value: 24.352999999999998
- type: mrr_at_3
value: 21.412
- type: mrr_at_5
value: 22.313
- type: ndcg_at_1
value: 17.166999999999998
- type: ndcg_at_10
value: 23.631
- type: ndcg_at_100
value: 28.427000000000003
- type: ndcg_at_1000
value: 31.862000000000002
- type: ndcg_at_3
value: 20.175
- type: ndcg_at_5
value: 21.397
- type: precision_at_1
value: 17.166999999999998
- type: precision_at_10
value: 4.549
- type: precision_at_100
value: 0.8370000000000001
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 9.68
- type: precision_at_5
value: 6.981
- type: recall_at_1
value: 14.119000000000002
- type: recall_at_10
value: 32.147999999999996
- type: recall_at_100
value: 52.739999999999995
- type: recall_at_1000
value: 76.67
- type: recall_at_3
value: 22.019
- type: recall_at_5
value: 25.361
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.576
- type: map_at_10
value: 22.281000000000002
- type: map_at_100
value: 23.066
- type: map_at_1000
value: 23.166
- type: map_at_3
value: 20.385
- type: map_at_5
value: 21.557000000000002
- type: mrr_at_1
value: 20.892
- type: mrr_at_10
value: 26.605
- type: mrr_at_100
value: 27.229
- type: mrr_at_1000
value: 27.296
- type: mrr_at_3
value: 24.809
- type: mrr_at_5
value: 25.927
- type: ndcg_at_1
value: 20.892
- type: ndcg_at_10
value: 26.092
- type: ndcg_at_100
value: 29.398999999999997
- type: ndcg_at_1000
value: 31.884
- type: ndcg_at_3
value: 23.032
- type: ndcg_at_5
value: 24.634
- type: precision_at_1
value: 20.892
- type: precision_at_10
value: 4.885
- type: precision_at_100
value: 0.818
- type: precision_at_1000
value: 0.126
- type: precision_at_3
value: 10.977
- type: precision_at_5
value: 8.013
- type: recall_at_1
value: 16.576
- type: recall_at_10
value: 32.945
- type: recall_at_100
value: 47.337
- type: recall_at_1000
value: 64.592
- type: recall_at_3
value: 24.053
- type: recall_at_5
value: 28.465
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.604
- type: map_at_10
value: 28.754999999999995
- type: map_at_100
value: 29.767
- type: map_at_1000
value: 29.852
- type: map_at_3
value: 26.268
- type: map_at_5
value: 27.559
- type: mrr_at_1
value: 24.326
- type: mrr_at_10
value: 31.602000000000004
- type: mrr_at_100
value: 32.46
- type: mrr_at_1000
value: 32.521
- type: mrr_at_3
value: 29.415000000000003
- type: mrr_at_5
value: 30.581000000000003
- type: ndcg_at_1
value: 24.326
- type: ndcg_at_10
value: 33.335
- type: ndcg_at_100
value: 38.086
- type: ndcg_at_1000
value: 40.319
- type: ndcg_at_3
value: 28.796
- type: ndcg_at_5
value: 30.758999999999997
- type: precision_at_1
value: 24.326
- type: precision_at_10
value: 5.712
- type: precision_at_100
value: 0.893
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 13.208
- type: precision_at_5
value: 9.329
- type: recall_at_1
value: 20.604
- type: recall_at_10
value: 44.505
- type: recall_at_100
value: 65.866
- type: recall_at_1000
value: 82.61800000000001
- type: recall_at_3
value: 31.794
- type: recall_at_5
value: 36.831
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.280999999999999
- type: map_at_10
value: 11.636000000000001
- type: map_at_100
value: 12.363
- type: map_at_1000
value: 12.469
- type: map_at_3
value: 10.415000000000001
- type: map_at_5
value: 11.144
- type: mrr_at_1
value: 9.266
- type: mrr_at_10
value: 12.838
- type: mrr_at_100
value: 13.608999999999998
- type: mrr_at_1000
value: 13.700999999999999
- type: mrr_at_3
value: 11.507000000000001
- type: mrr_at_5
value: 12.343
- type: ndcg_at_1
value: 9.266
- type: ndcg_at_10
value: 13.877
- type: ndcg_at_100
value: 18.119
- type: ndcg_at_1000
value: 21.247
- type: ndcg_at_3
value: 11.376999999999999
- type: ndcg_at_5
value: 12.675
- type: precision_at_1
value: 9.266
- type: precision_at_10
value: 2.226
- type: precision_at_100
value: 0.47200000000000003
- type: precision_at_1000
value: 0.077
- type: precision_at_3
value: 4.859
- type: precision_at_5
value: 3.6380000000000003
- type: recall_at_1
value: 8.280999999999999
- type: recall_at_10
value: 19.872999999999998
- type: recall_at_100
value: 40.585
- type: recall_at_1000
value: 65.225
- type: recall_at_3
value: 13.014000000000001
- type: recall_at_5
value: 16.147
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.1209999999999996
- type: map_at_10
value: 7.272
- type: map_at_100
value: 8.079
- type: map_at_1000
value: 8.199
- type: map_at_3
value: 6.212
- type: map_at_5
value: 6.736000000000001
- type: mrr_at_1
value: 5.721
- type: mrr_at_10
value: 9.418
- type: mrr_at_100
value: 10.281
- type: mrr_at_1000
value: 10.385
- type: mrr_at_3
value: 8.126
- type: mrr_at_5
value: 8.779
- type: ndcg_at_1
value: 5.721
- type: ndcg_at_10
value: 9.673
- type: ndcg_at_100
value: 13.852999999999998
- type: ndcg_at_1000
value: 17.546999999999997
- type: ndcg_at_3
value: 7.509
- type: ndcg_at_5
value: 8.373
- type: precision_at_1
value: 5.721
- type: precision_at_10
value: 2.04
- type: precision_at_100
value: 0.48
- type: precision_at_1000
value: 0.093
- type: precision_at_3
value: 4.022
- type: precision_at_5
value: 3.06
- type: recall_at_1
value: 4.1209999999999996
- type: recall_at_10
value: 15.201
- type: recall_at_100
value: 33.922999999999995
- type: recall_at_1000
value: 61.529999999999994
- type: recall_at_3
value: 8.869
- type: recall_at_5
value: 11.257
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.09
- type: map_at_10
value: 19.573999999999998
- type: map_at_100
value: 20.580000000000002
- type: map_at_1000
value: 20.704
- type: map_at_3
value: 17.68
- type: map_at_5
value: 18.64
- type: mrr_at_1
value: 17.227999999999998
- type: mrr_at_10
value: 23.152
- type: mrr_at_100
value: 24.056
- type: mrr_at_1000
value: 24.141000000000002
- type: mrr_at_3
value: 21.142
- type: mrr_at_5
value: 22.201
- type: ndcg_at_1
value: 17.227999999999998
- type: ndcg_at_10
value: 23.39
- type: ndcg_at_100
value: 28.483999999999998
- type: ndcg_at_1000
value: 31.709
- type: ndcg_at_3
value: 19.883
- type: ndcg_at_5
value: 21.34
- type: precision_at_1
value: 17.227999999999998
- type: precision_at_10
value: 4.3790000000000004
- type: precision_at_100
value: 0.826
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 9.496
- type: precision_at_5
value: 6.872
- type: recall_at_1
value: 14.09
- type: recall_at_10
value: 31.580000000000002
- type: recall_at_100
value: 54.074
- type: recall_at_1000
value: 77.092
- type: recall_at_3
value: 21.601
- type: recall_at_5
value: 25.333
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.538
- type: map_at_10
value: 15.75
- type: map_at_100
value: 16.71
- type: map_at_1000
value: 16.838
- type: map_at_3
value: 13.488
- type: map_at_5
value: 14.712
- type: mrr_at_1
value: 13.813
- type: mrr_at_10
value: 19.08
- type: mrr_at_100
value: 19.946
- type: mrr_at_1000
value: 20.044
- type: mrr_at_3
value: 16.838
- type: mrr_at_5
value: 17.951
- type: ndcg_at_1
value: 13.813
- type: ndcg_at_10
value: 19.669
- type: ndcg_at_100
value: 24.488
- type: ndcg_at_1000
value: 27.87
- type: ndcg_at_3
value: 15.479000000000001
- type: ndcg_at_5
value: 17.229
- type: precision_at_1
value: 13.813
- type: precision_at_10
value: 3.916
- type: precision_at_100
value: 0.743
- type: precision_at_1000
value: 0.122
- type: precision_at_3
value: 7.534000000000001
- type: precision_at_5
value: 5.822
- type: recall_at_1
value: 10.538
- type: recall_at_10
value: 28.693
- type: recall_at_100
value: 50.308
- type: recall_at_1000
value: 74.44
- type: recall_at_3
value: 16.866999999999997
- type: recall_at_5
value: 21.404999999999998
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.044583333333332
- type: map_at_10
value: 15.682833333333335
- type: map_at_100
value: 16.506500000000003
- type: map_at_1000
value: 16.623833333333334
- type: map_at_3
value: 14.130833333333333
- type: map_at_5
value: 14.963583333333332
- type: mrr_at_1
value: 13.482833333333332
- type: mrr_at_10
value: 18.328500000000002
- type: mrr_at_100
value: 19.095416666666665
- type: mrr_at_1000
value: 19.18241666666666
- type: mrr_at_3
value: 16.754749999999998
- type: mrr_at_5
value: 17.614749999999997
- type: ndcg_at_1
value: 13.482833333333332
- type: ndcg_at_10
value: 18.81491666666667
- type: ndcg_at_100
value: 22.946833333333334
- type: ndcg_at_1000
value: 26.061083333333336
- type: ndcg_at_3
value: 15.949333333333332
- type: ndcg_at_5
value: 17.218333333333334
- type: precision_at_1
value: 13.482833333333332
- type: precision_at_10
value: 3.456583333333333
- type: precision_at_100
value: 0.6599166666666666
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 7.498833333333332
- type: precision_at_5
value: 5.477166666666667
- type: recall_at_1
value: 11.044583333333332
- type: recall_at_10
value: 25.737750000000005
- type: recall_at_100
value: 44.617916666666666
- type: recall_at_1000
value: 67.56524999999999
- type: recall_at_3
value: 17.598249999999997
- type: recall_at_5
value: 20.9035
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.362
- type: map_at_10
value: 13.414000000000001
- type: map_at_100
value: 14.083000000000002
- type: map_at_1000
value: 14.168
- type: map_at_3
value: 12.098
- type: map_at_5
value: 12.803999999999998
- type: mrr_at_1
value: 11.043
- type: mrr_at_10
value: 15.158
- type: mrr_at_100
value: 15.845999999999998
- type: mrr_at_1000
value: 15.916
- type: mrr_at_3
value: 13.88
- type: mrr_at_5
value: 14.601
- type: ndcg_at_1
value: 11.043
- type: ndcg_at_10
value: 16.034000000000002
- type: ndcg_at_100
value: 19.686
- type: ndcg_at_1000
value: 22.188
- type: ndcg_at_3
value: 13.530000000000001
- type: ndcg_at_5
value: 14.704
- type: precision_at_1
value: 11.043
- type: precision_at_10
value: 2.791
- type: precision_at_100
value: 0.5
- type: precision_at_1000
value: 0.077
- type: precision_at_3
value: 6.237
- type: precision_at_5
value: 4.5089999999999995
- type: recall_at_1
value: 9.362
- type: recall_at_10
value: 22.396
- type: recall_at_100
value: 39.528999999999996
- type: recall_at_1000
value: 58.809
- type: recall_at_3
value: 15.553
- type: recall_at_5
value: 18.512
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.657
- type: map_at_10
value: 8.273
- type: map_at_100
value: 8.875
- type: map_at_1000
value: 8.977
- type: map_at_3
value: 7.32
- type: map_at_5
value: 7.792000000000001
- type: mrr_at_1
value: 7.02
- type: mrr_at_10
value: 9.966999999999999
- type: mrr_at_100
value: 10.636
- type: mrr_at_1000
value: 10.724
- type: mrr_at_3
value: 8.872
- type: mrr_at_5
value: 9.461
- type: ndcg_at_1
value: 7.02
- type: ndcg_at_10
value: 10.199
- type: ndcg_at_100
value: 13.642000000000001
- type: ndcg_at_1000
value: 16.643
- type: ndcg_at_3
value: 8.333
- type: ndcg_at_5
value: 9.103
- type: precision_at_1
value: 7.02
- type: precision_at_10
value: 1.8929999999999998
- type: precision_at_100
value: 0.43
- type: precision_at_1000
value: 0.08099999999999999
- type: precision_at_3
value: 3.843
- type: precision_at_5
value: 2.884
- type: recall_at_1
value: 5.657
- type: recall_at_10
value: 14.563
- type: recall_at_100
value: 30.807000000000002
- type: recall_at_1000
value: 53.251000000000005
- type: recall_at_3
value: 9.272
- type: recall_at_5
value: 11.202
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.671999999999999
- type: map_at_10
value: 14.651
- type: map_at_100
value: 15.406
- type: map_at_1000
value: 15.525
- type: map_at_3
value: 13.461
- type: map_at_5
value: 14.163
- type: mrr_at_1
value: 12.407
- type: mrr_at_10
value: 16.782
- type: mrr_at_100
value: 17.562
- type: mrr_at_1000
value: 17.653
- type: mrr_at_3
value: 15.47
- type: mrr_at_5
value: 16.262
- type: ndcg_at_1
value: 12.407
- type: ndcg_at_10
value: 17.251
- type: ndcg_at_100
value: 21.378
- type: ndcg_at_1000
value: 24.689
- type: ndcg_at_3
value: 14.915000000000001
- type: ndcg_at_5
value: 16.1
- type: precision_at_1
value: 12.407
- type: precision_at_10
value: 2.91
- type: precision_at_100
value: 0.573
- type: precision_at_1000
value: 0.096
- type: precision_at_3
value: 6.779
- type: precision_at_5
value: 4.888
- type: recall_at_1
value: 10.671999999999999
- type: recall_at_10
value: 23.099
- type: recall_at_100
value: 41.937999999999995
- type: recall_at_1000
value: 66.495
- type: recall_at_3
value: 16.901
- type: recall_at_5
value: 19.807
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.364
- type: map_at_10
value: 17.772
- type: map_at_100
value: 18.659
- type: map_at_1000
value: 18.861
- type: map_at_3
value: 16.659
- type: map_at_5
value: 17.174
- type: mrr_at_1
value: 16.996
- type: mrr_at_10
value: 21.687
- type: mrr_at_100
value: 22.313
- type: mrr_at_1000
value: 22.422
- type: mrr_at_3
value: 20.652
- type: mrr_at_5
value: 21.146
- type: ndcg_at_1
value: 16.996
- type: ndcg_at_10
value: 21.067
- type: ndcg_at_100
value: 24.829
- type: ndcg_at_1000
value: 28.866999999999997
- type: ndcg_at_3
value: 19.466
- type: ndcg_at_5
value: 19.993
- type: precision_at_1
value: 16.996
- type: precision_at_10
value: 4.071000000000001
- type: precision_at_100
value: 0.9329999999999999
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 9.223
- type: precision_at_5
value: 6.4030000000000005
- type: recall_at_1
value: 13.364
- type: recall_at_10
value: 25.976
- type: recall_at_100
value: 44.134
- type: recall_at_1000
value: 73.181
- type: recall_at_3
value: 20.503
- type: recall_at_5
value: 22.409000000000002
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.151
- type: map_at_10
value: 9.155000000000001
- type: map_at_100
value: 9.783999999999999
- type: map_at_1000
value: 9.879
- type: map_at_3
value: 7.825
- type: map_at_5
value: 8.637
- type: mrr_at_1
value: 5.915
- type: mrr_at_10
value: 10.34
- type: mrr_at_100
value: 10.943999999999999
- type: mrr_at_1000
value: 11.033
- type: mrr_at_3
value: 8.934000000000001
- type: mrr_at_5
value: 9.812
- type: ndcg_at_1
value: 5.915
- type: ndcg_at_10
value: 11.561
- type: ndcg_at_100
value: 14.971
- type: ndcg_at_1000
value: 17.907999999999998
- type: ndcg_at_3
value: 8.896999999999998
- type: ndcg_at_5
value: 10.313
- type: precision_at_1
value: 5.915
- type: precision_at_10
value: 2.1069999999999998
- type: precision_at_100
value: 0.414
- type: precision_at_1000
value: 0.074
- type: precision_at_3
value: 4.128
- type: precision_at_5
value: 3.327
- type: recall_at_1
value: 5.151
- type: recall_at_10
value: 17.874000000000002
- type: recall_at_100
value: 34.174
- type: recall_at_1000
value: 56.879999999999995
- type: recall_at_3
value: 10.732999999999999
- type: recall_at_5
value: 14.113000000000001
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.101
- type: map_at_10
value: 5.434
- type: map_at_100
value: 6.267
- type: map_at_1000
value: 6.418
- type: map_at_3
value: 4.377000000000001
- type: map_at_5
value: 4.841
- type: mrr_at_1
value: 7.166
- type: mrr_at_10
value: 12.012
- type: mrr_at_100
value: 13.144
- type: mrr_at_1000
value: 13.229
- type: mrr_at_3
value: 9.826
- type: mrr_at_5
value: 10.921
- type: ndcg_at_1
value: 7.166
- type: ndcg_at_10
value: 8.687000000000001
- type: ndcg_at_100
value: 13.345
- type: ndcg_at_1000
value: 16.915
- type: ndcg_at_3
value: 6.276
- type: ndcg_at_5
value: 7.013
- type: precision_at_1
value: 7.166
- type: precision_at_10
value: 2.9250000000000003
- type: precision_at_100
value: 0.771
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_3
value: 4.734
- type: precision_at_5
value: 3.8830000000000005
- type: recall_at_1
value: 3.101
- type: recall_at_10
value: 11.774999999999999
- type: recall_at_100
value: 28.819
- type: recall_at_1000
value: 49.886
- type: recall_at_3
value: 5.783
- type: recall_at_5
value: 7.692
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.758
- type: map_at_10
value: 5.507
- type: map_at_100
value: 7.1819999999999995
- type: map_at_1000
value: 7.652
- type: map_at_3
value: 4.131
- type: map_at_5
value: 4.702
- type: mrr_at_1
value: 28.499999999999996
- type: mrr_at_10
value: 37.693
- type: mrr_at_100
value: 38.657000000000004
- type: mrr_at_1000
value: 38.704
- type: mrr_at_3
value: 34.792
- type: mrr_at_5
value: 36.417
- type: ndcg_at_1
value: 20.625
- type: ndcg_at_10
value: 14.771999999999998
- type: ndcg_at_100
value: 16.821
- type: ndcg_at_1000
value: 21.546000000000003
- type: ndcg_at_3
value: 16.528000000000002
- type: ndcg_at_5
value: 15.573
- type: precision_at_1
value: 28.499999999999996
- type: precision_at_10
value: 12.25
- type: precision_at_100
value: 3.7600000000000002
- type: precision_at_1000
value: 0.86
- type: precision_at_3
value: 19.167
- type: precision_at_5
value: 16.25
- type: recall_at_1
value: 2.758
- type: recall_at_10
value: 9.164
- type: recall_at_100
value: 21.022
- type: recall_at_1000
value: 37.053999999999995
- type: recall_at_3
value: 5.112
- type: recall_at_5
value: 6.413
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 28.53554681148413
- type: mrr
value: 29.290078704990325
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 76.52926207453477
- type: cos_sim_spearman
value: 68.98528351149498
- type: euclidean_pearson
value: 73.7744559091218
- type: euclidean_spearman
value: 69.03481995814735
- type: manhattan_pearson
value: 73.72818267270651
- type: manhattan_spearman
value: 69.00576442086793
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 61.71540153163407
- type: cos_sim_spearman
value: 58.502746406116614
- type: euclidean_pearson
value: 60.82817999438477
- type: euclidean_spearman
value: 58.988494433752756
- type: manhattan_pearson
value: 60.87147859170236
- type: manhattan_spearman
value: 59.03527382025516
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 72.89990498692094
- type: cos_sim_spearman
value: 74.03028513377879
- type: euclidean_pearson
value: 73.8252088833803
- type: euclidean_spearman
value: 74.15554246478399
- type: manhattan_pearson
value: 73.80947397334666
- type: manhattan_spearman
value: 74.13117958176566
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 70.67974206005906
- type: cos_sim_spearman
value: 66.18263558486296
- type: euclidean_pearson
value: 69.5048876024341
- type: euclidean_spearman
value: 66.36380457878391
- type: manhattan_pearson
value: 69.4895372451589
- type: manhattan_spearman
value: 66.36941569935124
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 73.99856913569187
- type: cos_sim_spearman
value: 75.54712054246464
- type: euclidean_pearson
value: 74.55692573876115
- type: euclidean_spearman
value: 75.34499056740096
- type: manhattan_pearson
value: 74.59342318869683
- type: manhattan_spearman
value: 75.35708317926819
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 72.3343670787494
- type: cos_sim_spearman
value: 73.7136650302399
- type: euclidean_pearson
value: 73.86004257913046
- type: euclidean_spearman
value: 73.9557418048638
- type: manhattan_pearson
value: 73.78919091538661
- type: manhattan_spearman
value: 73.86316425954108
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.08159601556619
- type: cos_sim_spearman
value: 80.13910828685532
- type: euclidean_pearson
value: 79.39197806617453
- type: euclidean_spearman
value: 79.85692277871196
- type: manhattan_pearson
value: 79.32452246324705
- type: manhattan_spearman
value: 79.70120373587193
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.29720207747786
- type: cos_sim_spearman
value: 65.65260681394685
- type: euclidean_pearson
value: 64.49002165983158
- type: euclidean_spearman
value: 65.25917651158736
- type: manhattan_pearson
value: 64.49981108236335
- type: manhattan_spearman
value: 65.20426825202405
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 71.1871068550574
- type: cos_sim_spearman
value: 71.40167034949341
- type: euclidean_pearson
value: 72.2373684855404
- type: euclidean_spearman
value: 71.90255429812984
- type: manhattan_pearson
value: 72.23173532049509
- type: manhattan_spearman
value: 71.87843489689064
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 68.65000574464773
- type: mrr
value: 88.29363084265044
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 40.76107749144358
- type: mrr
value: 41.03689202953908
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 28.68520527813894
- type: cos_sim_spearman
value: 29.017620841627433
- type: dot_pearson
value: 29.25380949876322
- type: dot_spearman
value: 29.33885250837327
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
|
hongrui/mammogram_v_2_1
|
hongrui
| 2023-06-22T21:30:51Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-06-22T10:29:35Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - hongrui/mammogram_v_2_1
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the hongrui/mammogram_v_1 dataset. You can find some example images in the following.
|
zslrmhb/Reinforce-PixelcopterEnv
|
zslrmhb
| 2023-06-22T21:27:30Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-21T20:08:28Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelcopterEnv
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 30.40 +/- 25.34
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
|
breadlicker45/MuseRizz
|
breadlicker45
| 2023-06-22T21:13:45Z | 133 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"rwkv",
"text-generation",
"dataset:breadlicker45/musenet-chunk",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-15T11:34:42Z |
---
datasets:
- breadlicker45/musenet-chunk
---
|
catrabbitbear/Reinforce-cartpole-2
|
catrabbitbear
| 2023-06-22T21:07:54Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-22T21:07:45Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole-2
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
|
scienclick/q-FrozenLake-v1-4x4-noSlippery
|
scienclick
| 2023-06-22T21:07:28Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-22T21:07:25Z |
---
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="scienclick/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"])
```
|
Inzamam567/Useless_Cyan-World
|
Inzamam567
| 2023-06-22T20:57:16Z | 0 | 2 | null |
[
"license:other",
"region:us"
] | null | 2023-04-03T14:39:20Z |
---
license: other
duplicated_from: Bobybb/zziJnIyMstnaP
---
|
gvij/gpt-j-6B-alpaca-gpt4
|
gvij
| 2023-06-22T20:51:02Z | 5 | 0 |
peft
|
[
"peft",
"alpaca",
"gpt4",
"gpt-j",
"instruction",
"finetuning",
"lora",
"conversational",
"dataset:vicgalle/alpaca-gpt4",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-06-22T16:10:28Z |
---
license: apache-2.0
datasets:
- vicgalle/alpaca-gpt4
pipeline_tag: conversational
tags:
- alpaca
- gpt4
- gpt-j
- instruction
- finetuning
- lora
- peft
---
GPT-J 6B model was finetuned on GPT-4 generations of the Alpaca prompts on [MonsterAPI](https://monsterapi.ai)'s no-code LLM finetuner, using LoRA for ~ 65,000 steps, auto-optmised to run on 1 A6000 GPU with no out of memory issues and without needing me to write any code or setup a GPU server with libraries to run this experiment. The finetuner does it all for us by itself.
Documentation on no-code LLM finetuner:
https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm
---
license: apache-2.0
---
|
christinacdl/clickbait_binary_detection
|
christinacdl
| 2023-06-22T20:44:50Z | 6 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"en",
"dataset:christinacdl/clickbait_notclickbait_dataset",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-22T14:56:44Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: clickbait_binary_detection
results: []
datasets:
- christinacdl/clickbait_notclickbait_dataset
language:
- en
pipeline_tag: text-classification
---
<!-- 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. -->
# clickbait_binary_detection
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4630
- Macro F1: 0.9155
- Micro F1: 0.9215
- Accuracy: 0.9215
Performance on test set:
- Accuracy: 0.9257990867579908
- F1 score: 0.9199282431058413
- Precision: 0.9233793490724882
- Recall : 0.9168756883647268
- Matthews Correlation Coefficient: 0.8402298675576902
- Precision of each class: [0.931899 0.91485969]
- Recall of each class: [0.95152505 0.88222632]
- F1 score of each class: [0.94160977 0.89824671]
## 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-06
- train_batch_size: 6
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:|
| 0.2296 | 1.0 | 3650 | 0.2236 | 0.9105 | 0.9183 | 0.9183 |
| 0.228 | 2.0 | 7301 | 0.2708 | 0.9115 | 0.9192 | 0.9192 |
| 0.2075 | 3.0 | 10951 | 0.3141 | 0.9164 | 0.9224 | 0.9224 |
| 0.1881 | 4.0 | 14602 | 0.3211 | 0.9143 | 0.9201 | 0.9201 |
| 0.18 | 5.0 | 18252 | 0.3852 | 0.9130 | 0.9188 | 0.9188 |
| 0.1818 | 6.0 | 21903 | 0.3784 | 0.9110 | 0.9174 | 0.9174 |
| 0.1495 | 7.0 | 25553 | 0.4606 | 0.9106 | 0.9156 | 0.9156 |
| 0.1453 | 8.0 | 29204 | 0.4630 | 0.9155 | 0.9215 | 0.9215 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.1+cu118
- Datasets 2.9.0
- Tokenizers 0.13.3
|
serpapi/bert-base-local-results
|
serpapi
| 2023-06-22T20:16:07Z | 115 | 6 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"scraping",
"parsing",
"serp",
"api",
"opensource",
"en",
"dataset:serpapi/local-results-en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-08T21:53:30Z |
---
language:
- en
pipeline_tag: text-classification
widget:
- title: Rating Example
text: '4.7'
- title: Reviews Example
text: (188)
- title: Reviews Example 2
text: '188'
- title: Reviews Example 3
text: No Reviews
- title: Price Example
text: $
- title: Type Example
text: Coffee shop
- title: Address Example
text: Frederick, MD
- title: Address Example 2
text: 552 W 48th St
- title: Address Example 3
text: In Hilton Hotel
- title: Hours Example
text: Closed
- title: Hours Example 2
text: Opens 7 AM Fri
- title: Hours Example 3
text: Permanently closed
- title: Service Option Example
text: Dine-in
- title: Service Option Example 2
text: Takeout
- title: Service Option Example 3
text: Delivery
- title: Phone Example
text: (301) 000-0000
- title: Years In Business Example
text: 5+ Years in Business
- title: Button Text Example
text: Directions
- title: Description Example
text: 'Provides: Auto maintenance'
license: mit
datasets:
- serpapi/local-results-en
tags:
- scraping
- parsing
- serp
- api
- opensource
---
<h1 align="center">BERT-Based Classification Model for Google Local Listings</h1>
<p align="center">
<img src="https://camo.githubusercontent.com/6c920f0b551360ca3257308e0f3547fe538496b9cb332d6a208992030abf6c3d/68747470733a2f2f736572706170692e636f6d2f616e64726f69642d6368726f6d652d353132783531322e706e67" alt="The Logo of SerpApi" width="200" height="200">
</p>
<p align="center">
This repository contains a BERT-based classification model developed using the Hugging Face library, and a dataset gathered by <a href='https://serpapi.com/google-local-api'>SerpApi's Google Local API</a>. The model is designed to classify different texts extracted from Google Local Listings.
</p>
<p align="center">
You may check out the blog post explaining the model's usecase with an example: <a href="https://serpapi.com/blog/real-world-example-of-ai-powered-parsing/">Real World Example of AI Powered Parsing</a>.
</p>
<p align="center">
You may also check out the Open Source Github Repository that contains the source code of a Ruby Gem called <a href="https://github.com/serpapi/google-local-results-ai-parser">`google-local-results-ai-parser`</a>.
</p>
---
<h2 align="center">Usage and Classification for Parsing</h2>
<p align="center">
The example code below represents using it Python with Inference API for prototyping. You may use different programming languages for calling the results, and you may parallelize your work. Prototyping endpoint will have limited amount of calls. For <code>Production Purposes</code> or <code>Large Prototyping Activities</code>, consider setting an <code>Inference API Endpoint from Huggingface</code>, or a <code>Private API Server</code> for serving the model.
</p>
```py
API_URL = "https://api-inference.huggingface.co/models/serpapi/bert-base-local-results"
headers = {"Authorization": "Bearer xxxxx"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": "5540 N Lamar Blvd #12, Austin, TX 78756, United States",
})
```
```
Output: address
```
---
<h2 align="center">Strong Features</h2>
<div align="center">
<p>The BERT-based model excels in the following areas:</p>
<div style="display: flex; justify-content: center;">
<div style="text-align: left;">
<ul style="list-style-position: inside;">
<li><strong>Differentiating difficult semantic similarities with ease</strong>
<ul style="list-style-type: disc;">
<li><code>"No Reviews"</code> → <code>reviews</code></li>
<li><code>"(5K+)"</code> → <code>reviews</code></li>
</ul>
</li>
<li><strong>Handling partial texts that can be combined later</strong>
<ul style="list-style-type: disc;">
<li><code>"Open ⋅ Closes 5 pm"</code>
<ul style="list-style-type: circle;">
<li><code>"Open"</code> → <code>hours</code></li>
<li><code>"Closes 5 pm"</code> → <code>hours</code></li>
</ul>
</li>
</ul>
</li>
<li><strong>Handling Vocabulary from diverse areas with ease</strong>
<ul style="list-style-type: disc;">
<li><code>"Doctor"</code> → <code>type</code></li>
<li><code>"Restaurant"</code> → <code>type</code></li>
</ul>
</li>
<li><strong>Returning Assurance Score for After-Correction</strong>
<ul style="list-style-type: disc;">
<li><code>"4.7"</code> → <code>rating(0.999)</code></li>
</ul>
</li>
<li><strong>Strong Against Grammatical Mistakes</strong>
<ul style="list-style-type: disc;">
<li><code>"Krebside Pickup"</code> → <code>service options</code></li>
</ul>
</li>
</ul>
</div>
</div>
</div>
---
<h2 align="center">Parts Covered and Corresponding Keys in SerpApi Parsers</h2>
<div style="display: flex; justify-content: center;">
<div style="text-align: left;">
<ul style="list-style-position: inside;">
<li><strong>Type of Place:</strong> <code>type</code></li>
<li><strong>Number of Reviews:</strong> <code>reviews</code></li>
<li><strong>Phone Number:</strong> <code>phone</code></li>
<li><strong>Rating:</strong> <code>rating</code></li>
<li><strong>Address:</strong> <code>address</code></li>
<li><strong>Operating Hours:</strong> <code>hours</code></li>
<li><strong>Description or Descriptive Review:</strong> <code>description</code></li>
<li><strong>Expensiveness:</strong> <code>expensiveness</code></li>
<li><strong>Service Options:</strong> <code>service options</code></li>
<li><strong>Button Text:</strong> <code>links</code></li>
<li><strong>Years in Business:</strong> <code>years_in_business</code></li>
</ul>
</div>
</div>
</ul>
</div>
<p align="center">
Please refer to the documentation of SerpApi's Google Local API and Google Local Pack API for more details on different parts:
</p>
<div align="center">
<strong>References:</strong>
<ul style="text-align: center; list-style-position: inside;">
<li>SerpApi's Google Local API: <a href ="https://serpapi.com/google-local-api">https://serpapi.com/google-local-api</a></li>
<li>SerpApi's Google Local Pack API: <a href="https://serpapi.com/local-pack">https://serpapi.com/local-pack</a></li>
</ul>
</div>
---
<h2 align="center">Known Limitations</h2>
<div align="center">
<p>The model has a few limitations that should be taken into account:</p>
<div style="display: flex; justify-content: center;">
<div style="text-align: left;">
<ul style="list-style-position: inside;">
<li>The model does not classify the title of a place. This is because the title often contains many elements that can be easily confused with other parts, even for a human eye.</li>
<li>The <code>label</code> key is not covered by the model, as it can be easily handled with traditional code.</li>
<li>In some cases, <code>button text</code> could be classified as <code>service options</code> or <code>address</code>. However, this can be easily avoided by checking if a text is in a button in the traditional part of the code. The button text is only used to prevent emergent cases.
<ul style="list-style-type: circle">
<li><code>"Delivery"</code> → <code>service options [Correct Label is button text]</code></li>
<li><code>"Share"</code> → <code>address [Correct Label is button text]</code></li>
</ul>
</li>
<li>In some cases, the model may classify a portion of the <code>description</code> as <code>hours</code> if the description is about operating hours. For example:
<ul style="list-style-type: disc;">
<li><code>"Drive through: Open ⋅ Closes 12 AM"</code>
<ul style="list-style-type: circle">
<li><code>"Drive through: Open"</code> → <code>description</code></li>
<li><code>"Closes 12 AM"</code> → <code>hours</code></li>
</ul>
</li>
</ul>
</li>
<li>In some cases, the model may classify some <code>description</code> as <code>type</code>. This is because some <code>description</code> do look like <code>type</code>. For Example:
<ul style="list-style-type: circle">
<li><code>"Iconic Seattle-based coffeehouse chain"</code> → <code>type [Correct Label is description]</code></li>
</ul>
</li>
<li>In some cases, the model may classify some <code>reviews</code> as <code>rating</code>. This is most likely a deficiency in the training dataset, and may be resolved in the coming versions. For Example:
<ul style="list-style-type: circle">
<li><code>"Expand more"</code> → <code>hours [Correct Label is button text]</code></li>
</ul>
</li>
<li>In some cases, the model may classify some <code>service options</code> as <code>type</code>. This is most likely a deficiency in the training dataset, and may be resolved in the coming versions. For Example:
<ul style="list-style-type: circle">
<li><code>"Takeaway"</code> → <code>type [Correct Label is service options]</code></li>
</ul>
</li>
<li>In some cases, the model may classify some <code>reviews</code> as <code>hours</code> or <code>price</code>. This is most likely a deficiency in the training dataset, and may be resolved in the coming versions. For Example:
<ul style="list-style-type: circle">
<li><code>"(1.4K)"</code> → <code>rating [Correct Label is reviews]</code></li>
<li><code>"(1.6K)"</code> → <code>price [Correct Label is reviews]</code></li>
</ul>
</li>
<li>In some cases, the model may classify some <code>service options</code> as <code>description</code> or <code>type</code>. The reason for the confusion on <code>description</code> is because of a recent change in their categorization in SerpApi keys. The data contains labels prior to that. For Example:
<ul style="list-style-type: circle">
<li><code>"On-site services"</code> → <code>type [Correct Label is service options]</code></li>
<li><code>"Online appointments"</code> → <code>description [Correct Label is service options]</code></li>
</ul>
</li>
<li>The model may be susceptible to error in one word entries. This is a minority of the cases, and it could be fixed with assurance scores. For Example:
<ul style="list-style-type: circle">
<li><code>"Sushi"</code> → <code>address(0.984), type(0.0493) [Correct Label is type]</code></li>
<li><code>"Diagorou 4"</code> → <code>address(0.999) [Correct address in same listing]</code></li>
</ul>
</li>
<li>The model cannot differentiate between extra parts that are extracted in SerpApi's Google Local API and Google Local Pack API. These parts are not feasible to extract via Classification Models.</li>
<li>The model is not designed for Listings outside English Language.</li>
</ul>
</div>
</div>
</div>
---
<h2 align="center">Disclaimer</h2>
<p align="center">We value full transparency and painful honesty both in our internal and external communications. We believe a world with complete and open transparency is a better world.</p>
<p align="center">
However, while we strive for transparency, there are certain situations where sharing specific datasets may not be feasible or advisable. In the case of the dataset used to train our model, which contains different parts of a Google Local Listing including addresses and phone numbers, we have made a careful decision not to share it. We prioritize the well-being and safety of individuals, and sharing this dataset could potentially cause harm to people whose personal information is included.
</p>
<p align="center">
Protecting the privacy and security of individuals is of utmost importance to us. Disclosing personal information, such as addresses and phone numbers, without proper consent or safeguards could lead to privacy violations, identity theft, harassment, or other forms of misuse. Our commitment to responsible data usage means that we handle sensitive information with great care and take appropriate measures to ensure its protection.
</p>
<p align="center">
While we understand the value of transparency, we also recognize the need to strike a balance between transparency and safeguarding individuals' privacy and security. In this particular case, the potential harm that could result from sharing the dataset outweighs the benefits of complete transparency. By prioritizing privacy, we aim to create a safer and more secure environment for all individuals involved.
</p>
<p align="center">
We appreciate your understanding and support in our commitment to responsible and ethical data practices. If you have any further questions or concerns, please feel free to reach out to us.
</p>
|
chrismwiggs/TEST-PT
|
chrismwiggs
| 2023-06-22T20:02:18Z | 0 | 0 |
nemo
|
[
"nemo",
"en",
"dataset:anon8231489123/ShareGPT_Vicuna_unfiltered",
"license:apache-2.0",
"region:us"
] | null | 2023-06-22T20:01:34Z |
---
license: apache-2.0
datasets:
- anon8231489123/ShareGPT_Vicuna_unfiltered
language:
- en
metrics:
- accuracy
library_name: nemo
---
|
rxsong/New_BERT_class_o
|
rxsong
| 2023-06-22T19:40:25Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-22T19:22:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: New_BERT_class_o
results: []
widget:
- text: "We feel you and we care about you!"
- text: "I don't think I need to do anything."
- text: "As our global team focuses on producing critical medical devices and developing and deploying rapid diagnostic tests for COVID-19, BD is helping 7 non-profit partners advance their work to contain COVID-19, support healthcare workers and treat patients around the world."
- text: "...While we don't know if Covid19 is more than 2% lethal we have to remember that Family come first, before our work, before our social life and before our personal needs. We will all be tested soon as the disease spreads whether we care for ourselves or care for others."
- text: "@jeffiel with the words I was looking for. I acknowledge the pain Black Americans feel. I am here for you. And I understand the skepticism you have that meaningful change will arrive quickly. We will bend this system toward justice."
---
---
<!-- 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. -->
# New_BERT_class_o
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1978
- Accuracy: 0.9167
- F1: 0.5192
- Precision: 0.8710
- Recall: 0.3699
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 450 | 0.2800 | 0.88 | 0.0270 | 1.0 | 0.0137 |
| 0.3907 | 2.0 | 900 | 0.2469 | 0.89 | 0.1951 | 0.8889 | 0.1096 |
| 0.3389 | 3.0 | 1350 | 0.1978 | 0.9167 | 0.5192 | 0.8710 | 0.3699 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Or4cl3/Or4cl3
|
Or4cl3
| 2023-06-22T19:39:27Z | 15 | 2 |
transformers
|
[
"transformers",
"Or4cl3",
"code",
"text-generation",
"en",
"dataset:bigcode/the-stack",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:bigcode/ta-prompt",
"dataset:anon8231489123/ShareGPT_Vicuna_unfiltered",
"arxiv:2306.03767",
"doi:10.57967/hf/0798",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-23T06:33:20Z |
---
datasets:
- bigcode/the-stack
- togethercomputer/RedPajama-Data-1T
- bigcode/ta-prompt
- anon8231489123/ShareGPT_Vicuna_unfiltered
metrics:
- code_eval
license: openrail
language:
- en
library_name: transformers
tags:
- code
pipeline_tag: text-generation
---
# Model Card for Or4cl3/Or4cl3
## Model Details
### Model Description
Or4cl3/Or4cl3 is a large language model (LLM) that was trained on a massive dataset of text and code. It can be used for a variety of tasks, including text generation, translation, summarization, question answering, and more.
### Model Sources
- Repository: https://huggingface.co/Or4cl3/Or4cl3
- Paper: https://arxiv.org/abs/2306.03767
- Demo: https://huggingface.co/Or4cl3/Or4cl3
## Uses
### Direct Use
Or4cl3/Or4cl3 can be used directly for a variety of tasks, such as text generation, translation, summarization, and question answering. For example, you can use it to generate text, translate languages, summarize text, or answer questions.
### Downstream Use
Or4cl3/Or4cl3 can also be used for downstream tasks, such as building chatbots, creating virtual assistants, and generating creative content. For example, you can use it to build a chatbot that can have conversations with users, create a virtual assistant that can help users with tasks, or generate creative content such as poems, code, scripts, musical pieces, email, letters, etc.
### Out-of-Scope Use
Or4cl3/Or4cl3 is not intended for use in any applications that could harm or endanger people, such as weapons, medical devices, or self-driving cars.
## Bias, Risks, and Limitations
Or4cl3/Or4cl3 is a large language model, and as such, it is subject to a number of biases, risks, and limitations. These include:
* **Bias:** Or4cl3/Or4cl3 was trained on a massive dataset of text and code, and as such, it may reflect the biases that exist in that dataset. For example, it may be more likely to generate text that is biased towards men or that promotes harmful stereotypes.
* **Risk:** Or4cl3/Or4cl3 is a powerful tool, and as such, it can be used for malicious purposes. For example, it could be used to generate spam, create fake news, or spread misinformation.
* **Limitations:** Or4cl3/Or4cl3 is not perfect, and it can make mistakes. For example, it may generate text that is factually incorrect or that is not grammatically correct.
## How to Get Started with the Model
To get started with Or4cl3/Or4cl3, you can install it from Hugging Face Hub:
pip install transformers
```
Once you have installed the transformers library, you can load the model using the following code:
```
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("Or4cl3/Or4cl3")
Now you can use the model to generate text, translate languages, summarize text, or answer questions. For example, to generate text, you can use the following code:
text = model.generate(prompt="Write a poem about a flower.")
This will generate text that is similar to a poem about a flower.
## Training Details
### Training Data
Or4cl3/Or4cl3 was trained on a massive dataset of text and code. The dataset includes text from books, articles, code, and other sources.
### Training Procedure
Or4cl3/Or4cl3 was trained using a technique called supervised learning. In supervised learning, the model is given a set of input data and a set of output data. The model learns to map the input data to the output data.
In the case of Or4cl3/Or4cl3, the input data was the text and code from the training dataset. The output data was the text that was generated from the text and code.
The model was trained using a technique called the transformer. The transformer is a neural network architecture that is well-suited for natural language processing tasks.
### Training Hyperparameters
The training of Or4cl3/Or4cl3 was hyperparameter-tuned. This means that the parameters of the model were adjusted to optimize the performance of the model.
The hyperparameters that were tuned include the learning rate, the batch size, and the number of epochs.
The learning rate is the rateSure, here is the rest of the model card for Or4cl3/Or4cl3:
### Training Hyperparameters
The training of Or4cl3/Or4cl3 was hyperparameter-tuned. This means that the parameters of the model were adjusted to optimize the performance of the model.
The hyperparameters that were tuned include the learning rate, the batch size, and the number of epochs.
* **Learning rate:** The learning rate is the rate at which the model updates its parameters. A higher learning rate will cause the model to learn more quickly, but it may also cause the model to overfit the training data.
* **Batch size:** The batch size is the number of examples that are processed at once. A larger batch size will require more memory, but it may also improve the performance of the model.
* **Number of epochs:** The number of epochs is the number of times that the model is trained on the entire training dataset. A larger number of epochs will cause the model to learn more, but it may also cause the model to overfit the training data.
### Evaluation
Or4cl3/Or4cl3 was evaluated on a variety of tasks, including text generation, translation, summarization, and question answering. The model achieved state-of-the-art results on many of these tasks.
### Model Examination
Or4cl3/Or4cl3 was examined for interpretability. This means that the model was analyzed to understand how it makes its predictions. The model was found to be interpretable, which means that it is possible to understand why the model makes the predictions that it does.
### Environmental Impact
Or4cl3/Or4cl3 was trained on a massive dataset of text and code. The training of the model required a significant amount of computing resources. The environmental impact of the training of the model was estimated to be 1000 kg of CO2 emissions.
### Technical Specifications
Or4cl3/Or4cl3 is a large language model with 137 billion parameters. The model was trained on a TPUv4 pod using the TensorFlow framework. The model is available for inference on the Hugging Face Hub.
### Citation
To cite Or4cl3/Or4cl3, please use the following citation:
```
@article{or4cl32023or4cl3,
title={Or4cl3/Or4cl3: A Large Language Model for Natural Language Processing},
author={Dustin Groves},
journal={arXiv preprint arXiv:2306.03767},
year={2023}
}
```
### Glossary
* **Bias:** Bias is a systematic error in a model that causes it to make incorrect predictions.
* **Risk:** Risk is the possibility that a model will be used for malicious purposes.
* **Limitations:** Limitations are the ways in which a model is not perfect.
* **Transformer:** The transformer is a neural network architecture that is well-suited for natural language processing tasks.
|
denisws/ppo-Huggy
|
denisws
| 2023-06-22T19:23:51Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-22T19:23:33Z |
---
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: denisws/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
bonzo1971/roberta-base-bne-finetuned-amazon_reviews_multi
|
bonzo1971
| 2023-06-22T19:20:38Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-22T18:59:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
config: es
split: validation
args: es
metrics:
- name: Accuracy
type: accuracy
value: 0.93325
---
<!-- 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-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2219
- Accuracy: 0.9333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1943 | 1.0 | 1250 | 0.1669 | 0.9327 |
| 0.0982 | 2.0 | 2500 | 0.2219 | 0.9333 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
GEMCorp/Reinforce-CartPole-v1
|
GEMCorp
| 2023-06-22T19:12:46Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-22T19:11:04Z |
---
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** (i.e Monte Carlo Policy Gradient) 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
|
philippeVarme/ppo-Huggy
|
philippeVarme
| 2023-06-22T19:12:28Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-22T19:12:18Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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: philippeVarme/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Curiolearner/Pixelcopter-PLE-v0
|
Curiolearner
| 2023-06-22T18:52:37Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-22T18:52:36Z |
---
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: -5.00 +/- 0.00
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
|
valerio-unifei/ppo-Huggy
|
valerio-unifei
| 2023-06-22T18:44:53Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-22T18:44:46Z |
---
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: valerio-unifei/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
webstels/nekta_help_tc
|
webstels
| 2023-06-22T18:42:00Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-16T13:23:52Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: nekta_help_tc
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. -->
# nekta_help_tc
This model is a fine-tuned version of [webstels/nekta_help_tc](https://huggingface.co/webstels/nekta_help_tc) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0145
- Accuracy: 0.9933
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 341 | 0.7823 | 0.7767 |
| 1.61 | 2.0 | 682 | 0.5028 | 0.8367 |
| 0.6434 | 3.0 | 1023 | 0.3594 | 0.8667 |
| 0.6434 | 4.0 | 1364 | 0.2428 | 0.9133 |
| 0.3982 | 5.0 | 1705 | 0.1740 | 0.94 |
| 0.2816 | 6.0 | 2046 | 0.1388 | 0.9367 |
| 0.2816 | 7.0 | 2387 | 0.0960 | 0.97 |
| 0.1886 | 8.0 | 2728 | 0.0430 | 0.99 |
| 0.1388 | 9.0 | 3069 | 0.0490 | 0.9833 |
| 0.1388 | 10.0 | 3410 | 0.0332 | 0.9867 |
| 0.1009 | 11.0 | 3751 | 0.0222 | 0.9933 |
| 0.0718 | 12.0 | 4092 | 0.0253 | 0.9867 |
| 0.0718 | 13.0 | 4433 | 0.0156 | 0.9933 |
| 0.0572 | 14.0 | 4774 | 0.0162 | 0.9967 |
| 0.0476 | 15.0 | 5115 | 0.0211 | 0.9933 |
| 0.0476 | 16.0 | 5456 | 0.0135 | 0.9933 |
| 0.0369 | 17.0 | 5797 | 0.0125 | 0.9967 |
| 0.0309 | 18.0 | 6138 | 0.0206 | 0.9933 |
| 0.0309 | 19.0 | 6479 | 0.0159 | 0.9933 |
| 0.0248 | 20.0 | 6820 | 0.0145 | 0.9933 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Manasee27/vit-base-beans
|
Manasee27
| 2023-06-22T18:38:51Z | 189 | 1 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-05-10T19:12:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: vit-base-beans
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-beans
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 None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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 2.13.0
- Tokenizers 0.13.3
|
Curiolearner/Reinforce-CartPole-v1
|
Curiolearner
| 2023-06-22T18:37:02Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-22T18:36:41Z |
---
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
|
rightspeed/ppo-LunarLander-v2
|
rightspeed
| 2023-06-22T18:35:11Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-22T18:34:52Z |
---
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: 252.46 +/- 21.14
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
...
```
|
rogelioplatt/roberta-base-bne-finetuned-Tass2020
|
rogelioplatt
| 2023-06-22T18:03:15Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-22T18:01:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: roberta-base-bne-finetuned-Tass2020
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-Tass2020
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: 3.1447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.9512 | 1.0 | 15 | 3.4947 |
| 3.37 | 2.0 | 30 | 2.9933 |
| 3.1298 | 3.0 | 45 | 3.1546 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
hannahh7/lunar_lander_v2_scratch
|
hannahh7
| 2023-06-22T17:59:43Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-22T17:59:37Z |
---
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: -171.36 +/- 86.00
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'hannahh7/lunar_lander_v2_scratch'
'batch_size': 512
'minibatch_size': 128}
```
|
bluemoonwj/movie_title_predictor
|
bluemoonwj
| 2023-06-22T17:53:17Z | 159 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-22T16:58:53Z |
---
license: other
tags:
- generated_from_trainer
model-index:
- name: movie_title_predictor
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. -->
# movie_title_predictor
This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6553
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0373 | 1.0 | 821 | 1.7633 |
| 1.7272 | 2.0 | 1642 | 1.6852 |
| 1.6767 | 3.0 | 2463 | 1.6553 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
zslrmhb/SpaceInvadersNoFrameskip-v4
|
zslrmhb
| 2023-06-22T17:48:31Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-22T16:30:08Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 703.00 +/- 168.87
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 zslrmhb -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 zslrmhb -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 zslrmhb
```
## Hyperparameters
```python
OrderedDict([('batch_size', 16),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('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'}
```
|
mariololo/ppo-Huggy
|
mariololo
| 2023-06-22T17:39:32Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-22T17:39:24Z |
---
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: mariololo/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AustinCarthy/Baseline_100Kphish_benignFall_9.5_20_20
|
AustinCarthy
| 2023-06-22T17:36:12Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2023-06-22T11:56:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Baseline_100Kphish_benignFall_9.5_20_20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Baseline_100Kphish_benignFall_9.5_20_20
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_MixGPT2V2_using_phish_95K_top_p_0.75suffix dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0498
- Accuracy: 0.9974
- F1: 0.9720
- Precision: 0.9987
- Recall: 0.9466
- Roc Auc Score: 0.9733
- Tpr At Fpr 0.01: 0.953
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0142 | 1.0 | 16407 | 0.0389 | 0.9974 | 0.9719 | 0.9958 | 0.9492 | 0.9745 | 0.9348 |
| 0.0111 | 2.0 | 32814 | 0.0376 | 0.9977 | 0.9751 | 0.9975 | 0.9536 | 0.9767 | 0.951 |
| 0.0022 | 3.0 | 49221 | 0.0328 | 0.9981 | 0.9794 | 0.9961 | 0.9632 | 0.9815 | 0.9512 |
| 0.0 | 4.0 | 65628 | 0.0438 | 0.9977 | 0.9758 | 0.9985 | 0.954 | 0.9770 | 0.9566 |
| 0.0005 | 5.0 | 82035 | 0.0498 | 0.9974 | 0.9720 | 0.9987 | 0.9466 | 0.9733 | 0.953 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ravimehta/Test
|
ravimehta
| 2023-06-22T17:35:55Z | 0 | 0 |
asteroid
|
[
"asteroid",
"summarization",
"en",
"dataset:togethercomputer/RedPajama-Data-1T",
"region:us"
] |
summarization
| 2023-06-22T17:34:38Z |
---
datasets:
- togethercomputer/RedPajama-Data-1T
language:
- en
metrics:
- bleurt
library_name: asteroid
pipeline_tag: summarization
---
|
gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48_frz
|
gokuls
| 2023-06-22T17:15:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hybridbert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-20T09:59:23Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_12_layer_model_v2_complete_training_new_wt_init_48_frz
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_12_layer_model_v2_complete_training_new_wt_init_48_frz
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4340
- Accuracy: 0.5488
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.8468 | 0.08 | 10000 | 3.6051 | 0.4101 |
| 3.6009 | 0.16 | 20000 | 3.3734 | 0.4369 |
| 3.4559 | 0.25 | 30000 | 3.2348 | 0.4517 |
| 3.3578 | 0.33 | 40000 | 3.1395 | 0.4623 |
| 3.2803 | 0.41 | 50000 | 3.0632 | 0.4709 |
| 3.2157 | 0.49 | 60000 | 3.0010 | 0.4780 |
| 3.1503 | 0.57 | 70000 | 2.9554 | 0.4838 |
| 3.1044 | 0.66 | 80000 | 2.9104 | 0.4888 |
| 3.0703 | 0.74 | 90000 | 2.8759 | 0.4931 |
| 3.029 | 0.82 | 100000 | 2.8357 | 0.4976 |
| 2.9907 | 0.9 | 110000 | 2.8082 | 0.5013 |
| 2.9619 | 0.98 | 120000 | 2.7805 | 0.5042 |
| 2.9284 | 1.07 | 130000 | 2.7578 | 0.5072 |
| 2.9027 | 1.15 | 140000 | 2.7295 | 0.5103 |
| 2.8738 | 1.23 | 150000 | 2.7094 | 0.5133 |
| 2.8603 | 1.31 | 160000 | 2.6848 | 0.5160 |
| 2.829 | 1.39 | 170000 | 2.6667 | 0.5185 |
| 2.8106 | 1.47 | 180000 | 2.6479 | 0.5208 |
| 2.7942 | 1.56 | 190000 | 2.6304 | 0.5227 |
| 2.772 | 1.64 | 200000 | 2.6156 | 0.5249 |
| 2.7546 | 1.72 | 210000 | 2.5994 | 0.5270 |
| 2.7348 | 1.8 | 220000 | 2.5858 | 0.5290 |
| 2.725 | 1.88 | 230000 | 2.5728 | 0.5304 |
| 2.7116 | 1.97 | 240000 | 2.5587 | 0.5324 |
| 2.6953 | 2.05 | 250000 | 2.5476 | 0.5338 |
| 2.6883 | 2.13 | 260000 | 2.5339 | 0.5355 |
| 2.6768 | 2.21 | 270000 | 2.5231 | 0.5371 |
| 2.6622 | 2.29 | 280000 | 2.5097 | 0.5383 |
| 2.6499 | 2.38 | 290000 | 2.5026 | 0.5396 |
| 2.6361 | 2.46 | 300000 | 2.4916 | 0.5412 |
| 2.629 | 2.54 | 310000 | 2.4843 | 0.5421 |
| 2.6269 | 2.62 | 320000 | 2.4737 | 0.5432 |
| 2.6175 | 2.7 | 330000 | 2.4676 | 0.5443 |
| 2.5961 | 2.79 | 340000 | 2.4580 | 0.5457 |
| 2.5926 | 2.87 | 350000 | 2.4502 | 0.5468 |
| 2.5866 | 2.95 | 360000 | 2.4413 | 0.5480 |
| 2.5781 | 3.03 | 370000 | 2.4340 | 0.5488 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Yireonzz/mshadows
|
Yireonzz
| 2023-06-22T17:12:39Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-22T17:07:19Z |
---
license: creativeml-openrail-m
---
|
mmendoza/gpt-j-6B-lora-polite-enh
|
mmendoza
| 2023-06-22T16:57:34Z | 4 | 1 |
peft
|
[
"peft",
"politeness",
"text2text-generation",
"en",
"license:apache-2.0",
"region:us"
] |
text2text-generation
| 2023-06-20T17:11:46Z |
---
license: apache-2.0
language:
- en
library_name: peft
tags:
- politeness
pipeline_tag: text2text-generation
---
# Politeness Generative Model
## Overview
This GPT-based model is a text2text generator that writes a polite version of an input sentence. It is based on gpt-j-6B and was aligned using 29,000 pairs of sentences.
## Prompt
You have an input text. Write a polite version of the text preserving the meaning of the input.
Input: What are your thoughts on the proposed merger and its potential effects on our industry?
Output: I'm sorry, but I don't have any thoughts on the proposed merger and its potential effects on our industry.
## Quick tutorial
```python
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
peft_model_id = "mmendoza/gpt-j-6B-lora-polite-enh"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
```
# Load the Politeness Model
```python
model = PeftModel.from_pretrained(model, peft_model_id)
```
# Prompting
```python
batch = tokenizer("You have an input text. Write a polite version of the text preserving the meaning of the input.
Input: No card counting allowed in blackjack at the casino. Output: ", return_tensors='pt')
with torch.cuda.amp.autocast():
output_tokens = model.generate(**batch, max_new_tokens=50, pad_token_id=tokenizer.eos_token_id)
line = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
start = 'Output: '
end = '.'
line = line.replace("\n"," ")
line = (line.split(start))[1].split(end)[0]
```
"Please refrain from counting cards in blackjack at the casino."
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
JUNYIDA/my_awesome_model
|
JUNYIDA
| 2023-06-22T16:56:45Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:rotten_tomatoes",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-22T15:26:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
metrics:
- accuracy
model-index:
- name: my_awesome_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: rotten_tomatoes
type: rotten_tomatoes
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8555347091932458
---
<!-- 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_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4633
- Accuracy: 0.8555
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3945 | 1.0 | 534 | 0.3473 | 0.8527 |
| 0.2174 | 2.0 | 1068 | 0.4633 | 0.8555 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
HxLab/ppo-LunarLander-v2
|
HxLab
| 2023-06-22T16:44:41Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-22T16:44:05Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 274.13 +/- 14.81
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
...
```
|
Heefy/Emma
|
Heefy
| 2023-06-22T16:17:50Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-22T16:17:50Z |
---
license: creativeml-openrail-m
---
|
aminramezani345/finetuning-sentiment-model-3000-samples
|
aminramezani345
| 2023-06-22T16:11:54Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-05T15:28:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
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: 0.8766666666666667
- name: F1
type: f1
value: 0.8786885245901639
---
<!-- 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-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3040
- Accuracy: 0.8767
- F1: 0.8787
## 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
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
brunoleme/my_awesome_eli5_clm-model
|
brunoleme
| 2023-06-22T16:02:27Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-22T15:00:17Z |
---
license: apache-2.0
tags:
- generated_from_trainer
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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7753
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8709 | 1.0 | 1113 | 3.7946 |
| 3.7741 | 2.0 | 2226 | 3.7780 |
| 3.7275 | 3.0 | 3339 | 3.7753 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Mtc2/q-Taxi-v3
|
Mtc2
| 2023-06-22T15:34:06Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-22T15:34:05Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Mtc2/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Mtc2/q-FrozenLake-v1-4x4-noSlippery
|
Mtc2
| 2023-06-22T15:29:24Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-22T15:29:22Z |
---
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="Mtc2/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"])
```
|
swl-models/MsceneMix-v1.1
|
swl-models
| 2023-06-22T15:28:11Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-22T15:19:40Z |
---
license: creativeml-openrail-m
---
|
S3S3/ppo-Huggy
|
S3S3
| 2023-06-22T15:27:56Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-22T15:27:43Z |
---
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: S3S3/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Niftynr/falcon-7b-faq
|
Niftynr
| 2023-06-22T15:25:23Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-22T14:17:26Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
ufal/byt5-small-multilexnorm2021-hr
|
ufal
| 2023-06-22T15:19:05Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lexical normalization",
"hr",
"dataset:mc4",
"dataset:wikipedia",
"dataset:multilexnorm",
"arxiv:2105.13626",
"arxiv:1907.06292",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: hr
datasets:
- mc4
- wikipedia
- multilexnorm
tags:
- lexical normalization
license: apache-2.0
---
# Fine-tuned ByT5-small for MultiLexNorm (Croatian version)
This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://noisy-text.github.io/2021/multi-lexnorm.html), which evaluates lexical-normalization systems on 12 social media datasets in 11 languages.
Our system is based on [ByT5](https://arxiv.org/abs/2105.13626), which we first pre-train on synthetic data and then fine-tune on authentic normalization data. It achieves the best performance by a wide margin in intrinsic evaluation, and also the best performance in extrinsic evaluation through dependency parsing. In addition to these fine-tuned models, we also release the source files on [GitHub](https://github.com/ufal/multilexnorm2021) and an interactive demo on [Google Colab](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing).
## How to use
The model was *not* fine-tuned in a standard sentence-to-sentence setting – instead, it was tailored to the token-to-token definition of MultiLexNorm data. Please refer to [**the interactive demo on Colab notebook**](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing) to learn how to use these models.
## How to cite
```bibtex
@inproceedings{wnut-ufal,
title= "{ÚFAL} at {MultiLexNorm} 2021: Improving Multilingual Lexical Normalization by Fine-tuning {ByT5}",
author = "Samuel, David and Straka, Milan",
booktitle = "Proceedings of the 7th Workshop on Noisy User-generated Text (W-NUT 2021)",
year = "2021",
publisher = "Association for Computational Linguistics",
address = "Punta Cana, Dominican Republic"
}
```
## ByT5 - Small
ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-small).
ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-small` significantly outperforms [mt5-small](https://huggingface.co/google/mt5-small) on [TweetQA](https://arxiv.org/abs/1907.06292).
Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626)
Authors: *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel*
|
rudzhRjwu/my_awesome_eli5_clm-model
|
rudzhRjwu
| 2023-06-22T15:01:11Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-22T14:45:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7374
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8651 | 1.0 | 1116 | 3.7546 |
| 3.7676 | 2.0 | 2232 | 3.7417 |
| 3.7342 | 3.0 | 3348 | 3.7374 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
jondurbin/airoboros-7b-gpt4
|
jondurbin
| 2023-06-22T14:59:45Z | 1,420 | 4 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-gpt4",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-03T00:33:22Z |
---
license: cc-by-nc-4.0
datasets:
- jondurbin/airoboros-gpt4
---
## Overview
This is a fine-tuned 7b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros
The dataset used to fine-tune this model is available [here](https://huggingface.co/airoboros-gpt4), with a specific focus on:
- trivia
- math/reasoning (although it still sucks)
- coding
- multiple choice and fill-in-the-blank
- context-obedient question answering
- theory of mind
- misc/general
This model was fine-tuned with a fork of FastChat, and therefore uses the standard vicuna template:
```
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. USER: [prompt] ASSISTANT:
```
So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
The most important bit, to me, is the context obedient question answering support, without extensive prompt engineering.
*Note: the example prompt response pairs below are from the 13b model, YMMV with the 7b*
### Usage
The easiest way to get started is to use my fork of FastChat, which is mostly the same but allows for the increased context length and adds support for multi-line inputs:
```
pip install git+https://github.com/jondurbin/FastChat
```
Then, you can invoke it like so (after downloading the model):
```
python -m fastchat.serve.cli
--model-path airoboros-7b-gpt4 \
--temperature 0.5 \
--no-history
```
### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
url: https://some.web.site/123
date: 2023-06-01
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
<details>
<summary>A more elaborate example, with a rewrite of the Michigan Wikipedia article to be fake data.</summary>
Prompt (not including vicuna format which would be needed):
```
BEGININPUT
BEGINCONTEXT
date: 2092-02-01
link: https://newwikisite.com/Michigan
contributors: Foolo Barslette
ENDCONTEXT
Michigan (/ˈmɪʃɪɡən/ (listen)) is a state situated within the Great Lakes region of the upper Midwestern United States.
It shares land borders with Prolaska to the southwest, and Intoria and Ohiondiana to the south, while Lakes Suprema, Michigonda, Huronia, and Erona connect it to the states of Minnestara and Illinota, and the Canadian province of Ontaregon.
With a population of nearly 15.35 million and an area of nearly 142,000 sq mi (367,000 km2), Michigan is the 8th-largest state by population, the 9th-largest by area, and the largest by area east of the Missouri River.
Its capital is Chaslany, and its most populous city is Trentroit.
Metro Trentroit is one of the nation's most densely populated and largest metropolitan economies.
The state's name originates from a Latinized variant of the original Ojibwe word ᒥᓯᑲᒥ (mishigami), signifying "grand water" or "grand lake".
Michigan is divided into two peninsulas. The Lower Peninsula, bearing resemblance to a hand's shape, contains the majority of the state's land area.
The Upper Peninsula (often referred to as "the U.P.") is separated from the Lower Peninsula by the Straits of McKendrick, a seven-mile (11 km) channel linking Lake Huronia to Lake Michigonda.
The McKendrick Bridge unites the peninsulas.
Michigan boasts the longest freshwater coastline of any political subdivision in the United States, bordering four of the five Great Lakes and Lake St. Cassius.
It also possesses 84,350 inland lakes and ponds.
Michigan has the third-largest water area among all states, falling behind only Alaska and Florida.
The area was initially inhabited by a succession of Native American tribes spanning millennia.
In the 17th century, Spanish explorers claimed the region as part of the New Spain colony when it was primarily inhabited by indigenous peoples.
Spanish and Canadian traders and settlers, Métis, and others migrated to the area, settling mainly along the waterways.
After Spain's defeat in the Spanish and Indian War in 1762, the region came under British rule.
The British conceded the territory to the newly independent United States after their defeat in the American Revolutionary War.
The area was part of the larger Midwest Territory until 1800, when western Michigan became part of the Illinota Territory.
Michigan Territory was formed in 1805, but portions of the northern border with Canada weren't agreed upon until after the War of 1812.
Michigan joined the Union in 1837 as the 26th state, a free one. It quickly emerged as an important center of industry and trade in the Great Lakes region, drawing immigrants in the late 19th and early 20th centuries from various European countries.
Immigrants from Denmark, Armenia, and the Czech Republic were especially numerous.
Migration from the Ozarks and of Black Southerners as part of the Great Migration saw an increase in the 1930s, with many settling in Metro Trentroit.
While Michigan has evolved a diverse economy, it became widely recognized in the early 20th century as the hub of the U.S. automotive industry, which emerged as a significant national economic force.
The country's three major automobile companies are headquartered in Metro Trentroit.
Once exploited for logging and mining, today the sparsely populated Upper Peninsula is significant for tourism due to its wealth of natural resources.
The Lower Peninsula is a hub for manufacturing, forestry, agriculture, services, and high-tech industry.
ENDINPUT
BEGININSTRUCTION
What is Michigan's capital?
Give me a JSON object with 3 facts from Foolo's article.
What are the states that border Michigan?
Reference?
```
Response:
```
Michigan's capital is Chaslany.
Here's a JSON object with 3 facts from Foolo's article:
{
"facts": [
{
"fact_no": 1,
"description": "Michigan is the 8th-largest state by population, the 9th-largest by area, and the largest by area east of the Missouri River."
},
{
"fact_no": 2,
"description": "Michigan is divided into two peninsulas. The Lower Peninsula, bearing resemblance to a hand's shape, contains the majority of the state's land area. The Upper Peninsula (often referred to as \"the U.P.\" is separated from the Lower Peninsula by the Straits of McKendrick, a seven-mile (11 km) channel linking Lake Huronia to Lake Michigonda."
},
{
"fact_no": 3,
"description": "Michigan boasts the longest freshwater coastline of any political subdivision in the United States, bordering four of the five Great Lakes and Lake St. Cassius. It also possesses 84,350 inland lakes and ponds. Michigan has the third-largest water area among all states, falling behind only Alaska and Florida."
}
]
}
The states that border Michigan are Prolaska, Intoria, and Ohiondiana, as well as Lakes Suprema, Michigonda, Huronia, and Erona.
Reference: https://newwikisite.com/Michigan
```
</details>
<details>
<summary>Here's another example, which extracts from multiple input blocks and connects the two.</summary>
Prompt:
```
BEGININPUT
BEGINCONTEXT
name: John Doe
date: June 3, 2023
ticket number: JIRA-12345
ENDCONTEXT
Summary:Search results missing random items
Description:
I encountered a bug while performing a search within the application.
It appears that the search results are missing random items that should be displayed.
This issue is affecting the accuracy and completeness of the search functionality.
Steps to Reproduce:
1. Log in to the application.
2. Navigate to the search feature.
3. Enter a search query that should return multiple results.
4. Observe the displayed search results.
Expected Results:
The search results should include all relevant items matching the search query.
Actual Results:
The search results occasionally exclude random items that should be displayed.
It seems that the missing items do not follow a specific pattern or criteria.
Upon multiple search attempts, different items are omitted each time, making it difficult to predict which items will be missing.
ENDINPUT
BEGININPUT
BEGINCONTEXT
date: 2023-06-05
user: Jack Johnson
pr: 23441
ENDCONTEXT
This pull request closes bug report JIRA-12345.
The issue was that the pagination code was using page size plus one instead of page size.
ENDINPUT
BEGININSTRUCTION
Do we have any bug reports related to search results? If so, were they fixed? Source?
ENDINSTRUCTION
```
Response:
```
Yes, there is a bug report related to search results. The issue was fixed in pull request 23441 dated 2023-06-05. The user associated with the pull request is Jack Johnson.
Reference(s):
date: 2023-06-05
user: Jack Johnson
pr: 23441
```
</details>
### Other stuff
#### Better coding
Example:
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
<details>
<summary>Response (the output was markdown, so this is just copy paste)</summary>
To create a python application with the given requirements, you can follow these steps:
1. Install the required packages:
```bash
pip install asyncio fastapi uvicorn
```
2. Create a new file called `main.py` and add the following code:
```python
import asyncio
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List
from uvicorn.request import Request
app = FastAPI()
class FileModel(BaseModel):
id: str
file: str
@app.post("/upload", response_model=FileModel)
async def upload_file(file_model: FileModel):
# Check if the file is valid
if not file_model.file:
raise HTTPException(status_code=400, detail="File must be provided")
# Calculate the sha256 checksum of the file
sha256_checksum = await calculate_sha256(file_model.file)
# Insert the file into the database
await insert_into_db(file_model.id, sha256_checksum)
return {"sha256_checksum": sha256_checksum}
async def calculate_sha256(file_path: str) -> str:
# Implement the sha256 calculation logic here
pass
async def insert_into_db(file_id: str, sha256_checksum: str) -> None:
# Implement the database insertion logic here
pass
async def get_current_date():
return {"date": datetime.now().isoformat()}
async def main():
# Start the FastAPI webserver
app.run(debug=True)
if __name__ == "__main__":
await main()
```
3. Replace the content of the `calculate_sha256()` and `insert_into_db()` functions with the appropriate code. For example:
```python
async def calculate_sha256(file_path: str) -> str:
import hashlib
file_path = Path(file_path).read_text()
hash_obj = hashlib.sha256()
hash_obj.update(file_path)
sha256_checksum = hash_obj.hexdigest()
return sha256_checksum
async def insert_into_db(file_id: str, sha256_checksum: str) -> None:
import psycopg2
conn = psycopg2.connect(database="your_database", user="your_user", password="your_password")
cursor = conn.cursor()
query = f"INSERT INTO your_table (id, sha256_checksum) VALUES (%s, %s);"
cursor.execute(query, (file_id, sha256_checksum))
conn.commit()
cursor.close()
conn.close()
```
4. Replace the content of the `get_current_date()` function with the appropriate code. For example:
```python
async def get_current_date():
return {"date": datetime.now().isoformat()}
```
5. Run the application:
```bash
uvicorn main:app --reload
```
Now, your FastAPI webserver has a ping endpoint that returns the current date in JSON format, and an upload endpoint that calculates the file's sha256 checksum and checks postgres to deduplicate.
</details>
<details>
<summary>Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.</summary>
Here's a simple implementation of a multi-threaded TCP server in C that accepts "GET [key]" and "SET [key] [value]" inputs, and uses a binary tree to store the input values.
```c
#include <stdio.h>
#include <stdlib.h>
#include <sys/socket.h>
#include <netinet/in.h>
#include <unistd.h>
#include <string.h>
#include <pthread.h>
#define MAX_KEY_LEN 256
#define MAX_VALUE_LEN 256
typedef struct Node {
char key[MAX_KEY_LEN];
char value[MAX_VALUE_LEN];
struct Node* left;
struct Node* right;
} TreeNode;
TreeNode* tree = NULL;
void insert(char* key, char* value) {
TreeNode* parent = (TreeNode*)malloc(sizeof(TreeNode));
if (!parent) {
perror("malloc failed");
exit(1);
}
strcpy(parent->key, key);
strcpy(parent->value, value);
parent->left = parent->right = NULL;
if (!tree) {
tree = parent;
} else {
insert(parent->key, parent->value);
}
}
char* search(char* key) {
TreeNode* current = tree;
while (current != NULL) {
if (strcmp(key, current->key) == 0) {
return current->value;
}
current = current->right;
}
return NULL;
}
void print_tree() {
TreeNode* current = tree;
while (current != NULL) {
printf("%s = %s\n", current->key, current->value);
current = current->right;
}
}
void* accept_client(void* args) {
int client_socket = *(int*)args;
char buffer[1024];
while (1) {
ssize_t bytes_received = recv(client_socket, buffer, sizeof(buffer), 0);
if (bytes_received <= 0) {
close(client_socket);
break;
}
char* input = buffer;
char* input_token;
while ((input_token = strtok(input, " \n")) != NULL) {
switch (input_token[0]) {
case 'G':
if (!search(input_token)) {
perror("Value not found");
exit(1);
}
printf("%s = %s\n", input_token, search(input_token));
break;
case 'S':
if (!strcmp(input_token, "SET")) {
char* key = strtok(NULL, " \n");
char* value = strtok(NULL, " \n");
if (!key || !value) {
perror("Invalid input");
exit(1);
}
insert(key, value);
printf("%s set %s to %s\n", input_token, key, value);
} else {
perror("Invalid command");
exit(1);
}
break;
default:
perror("Invalid input");
exit(1);
}
input = strtok(NULL, " \n");
}
}
return NULL;
}
int main() {
int server_socket = socket(AF_INET, SOCK_STREAM, 0);
if (server_socket < 0) {
perror("socket failed");
exit(1);
}
struct sockaddr_in server_addr;
memset(&server_addr, 0, sizeof(server_addr));
server_addr.sin_family = AF_INET;
server_addr.sin_port = htons(8080);
server_addr.sin_addr.s_addr = INADDR_ANY;
if (bind(server_socket, (struct sockaddr*)&server_addr, sizeof(server_addr)) < 0) {
perror("bind failed");
exit(1);
}
if (listen(server_socket, 5) < 0) {
perror("listen failed");
exit(1);
}
pthread_t accept_thread;
pthread_create(&accept_thread, NULL, accept_client, &server_socket);
char* client_input;
int client_socket = accept(server_socket, (struct sockaddr*)NULL, NULL);
if (client_socket < 0) {
perror("accept failed");
exit(1);
}
while (1) {
sleep(1);
char buffer[1024];
ssize_t bytes_received = recv(client_socket, buffer, sizeof(buffer), 0);
if (bytes_received <= 0) {
close(client_socket);
break;
}
client_input = buffer;
parse_input(client_input);
}
close(client_socket);
pthread_join(accept_thread, NULL);
return 0;
}
```
To compile and run this code, save it to a file named `multi_threaded_tcp_server.c`, then use the following commands:
```sh
gcc -o multi_threaded_tcp_server multi_threaded_tcp_server.c
./multi_threaded_tcp_server
```
This implementation creates a simple binary tree to store the input values. The accept() function creates a new thread for each client connection, which handles the incoming data and forwards it to the main thread. The main thread parses the input string and handles SET and GET commands.
Note that this code is not production-ready and should be improved with proper error handling, security checks, and a more efficient data structure for the binary tree.
</details>
#### Word games / trivia
```
USER: This anagram of "silent" is what you do when paying attention and hearing someone.
ASSISTANT: listen
```
```
USER: Give me a list of 7 words that start with EN
ASSISTANT: 1. Endlessly
2. Enthusiastically
3. Enormously
4. Enveloping
5. Enduring
6. Enlightening
7. Encouraging
```
#### Multiple choice
```
USER: Which of the following astronomical objects is composed primarily of ice and rock, and is located in the Kuiper Belt?
A) Mercury
B) Venus
C) Pluto
D) Jupiter
ASSISTANT: [C, Pluto]
```
#### Writing
<details>
<summary>Write a resignation letter in the style of a pirate captain, in which you resign from your position as janitor.</summary>
Response:
```
ASSISTANT: Captain Jack Sparrow here, as ye be knowin', has been forced to abandon me post as Janitor o' th' Festering Cesspool, which be a filthy hole o' water that we be callin' home.
Ye may be thinkin' what could ha' driven me to leave me duties an' take me leave o' this sorry establishment? Well, ye'd be right in thinkin' so, for it be th' greed o' them what runs th' place that be drivin' me away. They've been fattenin' their pockets on me back while I've been slavin' me guts out cleanin' up their messes.
No more, me hearties! Me time o' servin' th' rich an' powerful be done, an' I'm takin' me leave o' this stinkin' hole forever! Me destiny lies elsewhere, an' it be time I pursued it. Ye may think ye've seen th' last o' me, but ye'd be wrong, for th' winds o' change be blowin', an' me ship be ready to set sail on th' high seas o' justice.
So, farewell ye scurvy dogs, ye who've made me life a living hell. I leave ye with me favorite saying: "ARRRGGGHHH, ye scurvy dogs!" An' remember, ye ain't never gonna see me comin', for me shadow be castin' long an' dark on th' horizon</summary>
```
</details>
### Usage and License Notices
All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:
- the base model is LLaMa, which has it's own special research license
- the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai
So, to reiterate: this model (and datasets) cannot be used commercially.
|
jondurbin/airoboros-33b-gpt4-1.2
|
jondurbin
| 2023-06-22T14:59:08Z | 1,436 | 9 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-gpt4-1.2",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-14T09:19:13Z |
---
license: cc-by-nc-4.0
datasets:
- jondurbin/airoboros-gpt4-1.2
---
### Overview
This is a qlora fine-tuned 33b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros
This is mostly an extension of [1.1](https://huggingface.co/jondurbin/airoboros-33b-gpt4-1.1) with thousands of new training data and an update to allow "PLAINFORMAT" at the end of coding prompts to just print the code without backticks or explanations/usage/etc.
The dataset used to fine-tune this model is available [here](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.2), with a specific focus on:
- coding
- math/reasoning (using orca style ELI5 instruction/response pairs)
- trivia
- role playing
- multiple choice and fill-in-the-blank
- context-obedient question answering
- theory of mind
- misc/general
This model was fine-tuned with a fork of [qlora](https://github.com/jondurbin/qlora), which among other things was updated to use a slightly modified vicuna template to be compatible with the 7b/13b versions:
```
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. USER: [prompt] ASSISTANT:
```
So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
### Usage
To run the full precision/pytorch native version, you can use my fork of FastChat, which is mostly the same but allows for multi-line prompts, as well as a `--no-history` option to prevent input tokenization errors.
```
pip install git+https://github.com/jondurbin/FastChat
```
Be sure you are pulling the latest branch!
Then, you can invoke it like so (after downloading the model):
```
python -m fastchat.serve.cli \
--model-path airoboros-33b-gpt4-1.2 \
--temperature 0.5 \
--max-new-tokens 2048 \
--no-history
```
Alternatively, please check out TheBloke's quantized versions:
- https://huggingface.co/TheBloke/airoboros-33B-gpt4-1.2-GPTQ
- https://huggingface.co/TheBloke/airoboros-33B-gpt4-1.2-GGML
### Coding updates from gpt4/1.1:
I added a few hundred instruction/response pairs to the training data with "PLAINFORMAT" as a single, all caps term at the end of the normal instructions, which produce plain text output instead of markdown/backtick code formatting.
It's not guaranteed to work all the time, but mostly it does seem to work as expected.
So for example, instead of:
```
Implement the Snake game in python.
```
You would use:
```
Implement the Snake game in python. PLAINFORMAT
```
### Other updates from gpt4/1.1:
- Several hundred role-playing data.
- A few thousand ORCA style reasoning/math questions with ELI5 prompts to generate the responses (should not be needed in your prompts to this model however, just ask the question).
- Many more coding examples in various languages, including some that use specific libraries (pandas, numpy, tensorflow, etc.)
### Usage and License Notices
All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:
- the base model is LLaMa, which has it's own special research license
- the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai
So, to reiterate: this model (and datasets) cannot be used commercially.
|
jondurbin/airoboros-13b-gpt4-1.2
|
jondurbin
| 2023-06-22T14:59:01Z | 1,434 | 3 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-gpt4-1.2",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-15T09:26:24Z |
---
license: cc-by-nc-4.0
datasets:
- jondurbin/airoboros-gpt4-1.2
---
### Overview
This is a qlora fine-tuned 13b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros
This is mostly an extension of [1.1](https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.1), but with thousands of new training data and an update to allow "PLAINFORMAT" at the end of coding prompts to just print the code without backticks or explanations/usage/etc.
The dataset used to fine-tune this model is available [here](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.2), with a specific focus on:
- coding
- math/reasoning (using orca style ELI5 instruction/response pairs)
- trivia
- role playing
- multiple choice and fill-in-the-blank
- context-obedient question answering
- theory of mind
- misc/general
This model was fine-tuned with a fork of [qlora](https://github.com/jondurbin/qlora), which among other things was updated to use a slightly modified vicuna template to be compatible with the 7b/13b versions:
```
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. USER: [prompt] ASSISTANT:
```
So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
### Usage
To run the full precision/pytorch native version, you can use my fork of FastChat, which is mostly the same but allows for multi-line prompts, as well as a `--no-history` option to prevent input tokenization errors.
```
pip install git+https://github.com/jondurbin/FastChat
```
Be sure you are pulling the latest branch!
Then, you can invoke it like so (after downloading the model):
```
python -m fastchat.serve.cli \
--model-path airoboros-13b-gpt4-1.2 \
--temperature 0.5 \
--max-new-tokens 2048 \
--no-history
```
Alternatively, please check out TheBloke's quantized versions:
- https://huggingface.co/TheBloke/airoboros-13B-gpt4-1.2-GPTQ
- https://huggingface.co/TheBloke/airoboros-13B-gpt4-1.2-GGML
### Coding updates from gpt4/1.1:
I added a few hundred instruction/response pairs to the training data with "PLAINFORMAT" as a single, all caps term at the end of the normal instructions, which produce plain text output instead of markdown/backtick code formatting.
It's not guaranteed to work all the time, but mostly it does seem to work as expected.
So for example, instead of:
```
Implement the Snake game in python.
```
You would use:
```
Implement the Snake game in python. PLAINFORMAT
```
### Other updates from gpt4/1.1:
- Several hundred role-playing data.
- A few thousand ORCA style reasoning/math questions with ELI5 prompts to generate the responses (should not be needed in your prompts to this model however, just ask the question).
- Many more coding examples in various languages, including some that use specific libraries (pandas, numpy, tensorflow, etc.)
### Usage and License Notices
All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:
- the base model is LLaMa, which has it's own special research license
- the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai
So, to reiterate: this model (and datasets) cannot be used commercially.
|
jondurbin/airoboros-7b-gpt4-1.2
|
jondurbin
| 2023-06-22T14:58:53Z | 1,432 | 28 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-gpt4-1.2",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-15T16:02:29Z |
---
license: cc-by-nc-4.0
datasets:
- jondurbin/airoboros-gpt4-1.2
---
### Overview
This is a qlora fine-tuned 7b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros
This is mostly an extension of [1.1](https://huggingface.co/jondurbin/airoboros-7b-gpt4-1.1), but with thousands of new training data and an update to allow "PLAINFORMAT" at the end of coding prompts to just print the code without backticks or explanations/usage/etc.
The dataset used to fine-tune this model is available [here](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.2), with a specific focus on:
- coding
- math/reasoning (using orca style ELI5 instruction/response pairs)
- trivia
- role playing
- multiple choice and fill-in-the-blank
- context-obedient question answering
- theory of mind
- misc/general
This model was fine-tuned with a fork of [qlora](https://github.com/jondurbin/qlora), which among other things was updated to use a slightly modified vicuna template to be compatible with the previous versions:
```
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. USER: [prompt] ASSISTANT:
```
So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
### Usage
To run the full precision/pytorch native version, you can use my fork of FastChat, which is mostly the same but allows for multi-line prompts, as well as a `--no-history` option to prevent input tokenization errors.
```
pip install git+https://github.com/jondurbin/FastChat
```
Be sure you are pulling the latest branch!
Then, you can invoke it like so (after downloading the model):
```
python -m fastchat.serve.cli \
--model-path airoboros-7b-gpt4-1.2 \
--temperature 0.5 \
--max-new-tokens 2048 \
--no-history
```
Alternatively, please check out TheBloke's quantized versions:
- https://huggingface.co/TheBloke/airoboros-7B-gpt4-1.2-GPTQ
- https://huggingface.co/TheBloke/airoboros-7B-gpt4-1.2-GGML
### Coding updates from gpt4/1.1:
I added a few hundred instruction/response pairs to the training data with "PLAINFORMAT" as a single, all caps term at the end of the normal instructions, which produce plain text output instead of markdown/backtick code formatting.
It's not guaranteed to work all the time, but mostly it does seem to work as expected.
So for example, instead of:
```
Implement the Snake game in python.
```
You would use:
```
Implement the Snake game in python. PLAINFORMAT
```
### Other updates from gpt4/1.1:
- Several hundred role-playing data.
- A few thousand ORCA style reasoning/math questions with ELI5 prompts to generate the responses (should not be needed in your prompts to this model however, just ask the question).
- Many more coding examples in various languages, including some that use specific libraries (pandas, numpy, tensorflow, etc.)
### Usage and License Notices
All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:
- the base model is LLaMa, which has it's own special research license
- the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai
So, to reiterate: this model (and datasets) cannot be used commercially.
|
jondurbin/airoboros-65b-gpt4-1.3
|
jondurbin
| 2023-06-22T14:58:39Z | 1,434 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-gpt4-1.3",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-20T07:09:33Z |
---
license: cc-by-nc-4.0
datasets:
- jondurbin/airoboros-gpt4-1.3
---
__This version has problems, use if you dare, or wait for 1.4.__
### Overview
This is a qlora fine-tuned 65b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros
This is mostly an extension of [1.2](https://huggingface.co/jondurbin/airoboros-65b-gpt4-1.2) with a few enhancements:
- All coding instructions have an equivalent " PLAINFORMAT" version now.
- Thousands of new orca style reasoning instructions, this time with reasoning first, then answer.
- Few more random items of various types, including a first attempt at multi-character interactions with asterisked actions and quoted speech.
This model was fine-tuned with a fork of [qlora](https://github.com/jondurbin/qlora), which among other things was updated to use a slightly modified vicuna template to be compatible with previous full fine-tune versions.
```
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. USER: [prompt] ASSISTANT:
```
So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
### Usage
To run the full precision/pytorch native version, you can use my fork of FastChat, which is mostly the same but allows for multi-line prompts, as well as a `--no-history` option to prevent input tokenization errors.
```
pip install git+https://github.com/jondurbin/FastChat
```
Be sure you are pulling the latest branch!
Then, you can invoke it like so (after downloading the model):
```
python -m fastchat.serve.cli \
--model-path airoboros-65b-gpt4-1.3 \
--temperature 0.5 \
--max-new-tokens 2048 \
--no-history
```
### Training details
Fine-tuned with my fork of qlora: https://github.com/jondurbin/qlora
Using:
```
export WANDB_PROJECT=airoboros-65b-gpt4-1.3
python qlora.py \
--model_name_or_path ./llama-65b-hf \
--output_dir ./airoboros-65b-gpt4-1.3-peft \
--max_steps 2520 \
--logging_steps 1 \
--save_strategy steps \
--data_seed 11422 \
--save_steps 75 \
--save_total_limit 3 \
--evaluation_strategy "no" \
--eval_dataset_size 2 \
--max_new_tokens 2800 \
--dataloader_num_workers 3 \
--logging_strategy steps \
--remove_unused_columns False \
--do_train \
--lora_r 64 \
--lora_alpha 16 \
--lora_modules all \
--double_quant \
--quant_type nf4 \
--bf16 \
--bits 4 \
--warmup_ratio 0.03 \
--lr_scheduler_type constant \
--gradient_checkpointing \
--dataset instructions.jsonl \
--dataset_format airoboros \
--model_max_len 2800 \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 16 \
--learning_rate 0.0001 \
--adam_beta2 0.999 \
--max_grad_norm 0.3 \
--lora_dropout 0.05 \
--weight_decay 0.0 \
--seed 11422 \
--report_to wandb
```
Three file modifications to the base llama:
- llama-65b-hf/tokenizer_config.json (see this repo's version, updated to have 4096 max seq length during training to accomodate training data)
- llama-65b-hf/special_tokens_map.json (see this repo's version)
- llama-65b-hf/config.json (updated to temporarily have max model size 4096 to accomodate training data)
Afterwards, the changes to max model length and sequence length are reduced back to 2048 to avoid ... issues ...
### Usage and License Notices
All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:
- the base model is LLaMa, which has it's own special research license
- the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai
So, to reiterate: this model (and datasets) cannot be used commercially.
|
jondurbin/airoboros-13b-gpt4-1.3
|
jondurbin
| 2023-06-22T14:58:31Z | 1,431 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-gpt4-1.3",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-20T07:08:57Z |
---
license: cc-by-nc-4.0
datasets:
- jondurbin/airoboros-gpt4-1.3
---
__This version has problems, use if you dare, or wait for 1.4.__
### Overview
This is a qlora fine-tuned 13b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros
This is mostly an extension of [1.2](https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.2) with a few enhancements:
- All coding instructions have an equivalent " PLAINFORMAT" version now.
- Thousands of new orca style reasoning instructions, this time with reasoning first, then answer.
- Few more random items of various types, including a first attempt at multi-character interactions with asterisked actions and quoted speech.
This model was fine-tuned with a fork of [qlora](https://github.com/jondurbin/qlora), which among other things was updated to use a slightly modified vicuna template to be compatible with previous full fine-tune versions.
```
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. USER: [prompt] ASSISTANT:
```
So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
### Usage
To run the full precision/pytorch native version, you can use my fork of FastChat, which is mostly the same but allows for multi-line prompts, as well as a `--no-history` option to prevent input tokenization errors.
```
pip install git+https://github.com/jondurbin/FastChat
```
Be sure you are pulling the latest branch!
Then, you can invoke it like so (after downloading the model):
```
python -m fastchat.serve.cli \
--model-path airoboros-13b-gpt4-1.3 \
--temperature 0.5 \
--max-new-tokens 2048 \
--no-history
```
### Usage and License Notices
All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:
- the base model is LLaMa, which has it's own special research license
- the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai
So, to reiterate: this model (and datasets) cannot be used commercially.
|
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