modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
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|---|---|---|---|---|---|---|
Azaghast/DistilBART-SCP-ParaSummarization | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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"BartForConditionalGeneration"
],
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} | 8 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: iis-pet-classifier
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8909774422645569
---
# iis-pet-classifier
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Beagle
#### French bulldog
#### German shepherd
#### Gold fish
#### Golden retriever
#### Koi fish
#### Maine Coon
#### Parrot
#### Persian cat
#### Siamese cat
#### Spyhnx cat
#### Yorkshire Terrier
 |
Azaghast/GPT2-SCP-ContainmentProcedures | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
],
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}
} | 5 | null | ---
tags:
- mteb
model-index:
- name: exp-base-softmax-cls
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 62.582089552238806
- type: ap
value: 28.34158342712533
- type: f1
value: 57.90592799130289
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 73.19482500000001
- type: ap
value: 67.6077746758214
- type: f1
value: 72.63527860839866
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 35.982
- type: f1
value: 35.71914781139217
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 43.487012987012996
- type: f1
value: 44.02737283534714
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 36.71
- type: f1
value: 31.104124013825597
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 70.9616
- type: ap
value: 65.0967141806998
- type: f1
value: 70.66380739227522
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 62.681258549931606
- type: f1
value: 62.13409591438103
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 56.794345645234834
- type: f1
value: 37.68660318483031
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 44.68392737054472
- type: f1
value: 42.69921534768016
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 44.690652320107596
- type: f1
value: 42.60195535363316
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 66.071
- type: ap
value: 11.414687973213008
- type: f1
value: 50.13154789871491
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 48.76910016977928
- type: f1
value: 48.93136567684689
--- |
Azaghast/GPT2-SCP-Descriptions | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 5 | 2023-05-16T07:37:44Z | ---
tags:
- mteb
model-index:
- name: exp-base-softmax-embedding_last_mean
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 72.6865671641791
- type: ap
value: 35.18630702205591
- type: f1
value: 66.57514923106042
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 63.53105
- type: ap
value: 59.000538219502815
- type: f1
value: 63.1802379944976
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 30.900000000000006
- type: f1
value: 30.494996366285836
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 76.17857142857143
- type: f1
value: 76.15821888029166
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 36.62
- type: f1
value: 33.8690789773316
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 60.686400000000006
- type: ap
value: 56.69113229007745
- type: f1
value: 60.08285294764551
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.18604651162792
- type: f1
value: 88.59593463292848
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 68.14865481076151
- type: f1
value: 48.73078526128046
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.28513786146603
- type: f1
value: 60.767834458633985
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.50907868190988
- type: f1
value: 68.94327319453019
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 64.16099999999999
- type: ap
value: 11.489318637375941
- type: f1
value: 49.425734132556194
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 51.05546123372948
- type: f1
value: 51.13766228638481
--- |
Azuris/DialoGPT-medium-senorita | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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"max_length": 1000
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}
} | 14 | null | ---
license: mit
---
# Introduction
This is ItbearZhang/facebook-opt-125m-with-alpacadataset using the dataset from [tatsu-lab/stanford_alpaca: Code and documentation to train Stanford's Alpaca models, and generate the data. (github.com)](https://github.com/tatsu-lab/stanford_alpaca)
and the opt-125m pretrained lm model from facebook/opt-125m.
# How to use it to interfere?
``` python
from transformers import pipeline
def generate_by_pipeline(instruction, inputs=""):
if inputs == "":
prompt = f"### Instruction:\n{instruction}\n\n### Response:"
else:
prompt = f"### Instruction:\n{instruction}\n\n### Input:\n{inputs}\n\n### Response:"
return generator(prompt)[0]['generated_text']
print(generate_by_pipeline("What is the capital of China?"))
``` |
BAHIJA/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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},
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}
} | 36 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 242.93 +/- 17.05
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BE/demo-sentiment2021 | []
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}
} | 0 | 2023-05-16T07:54:40Z | ---
datasets:
- nlphuji/flickr30k
language:
- en
metrics:
- bleu
library_name: transformers
pipeline_tag: image-to-text
--- |
BSC-LT/roberta-base-bne-capitel-ner-plus | [
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"ner",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
} | 9 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilt5-coqa
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. -->
# distilt5-coqa
This model is a fine-tuned version of [valhalla/distilt5-qa-qg-hl-6-4](https://huggingface.co/valhalla/distilt5-qa-qg-hl-6-4) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9418
- Accuracy: 0.0833
- F1: 0.0573
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
BSC-LT/roberta-large-bne-capitel-ner | [
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"ner",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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"RobertaForTokenClassification"
],
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} | 5 | null | <div align="center">
<img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/Lit_LLaMA_Badge3x.png" alt="Lit-LLaMA" width="128"/>
# ⚡ Lit-LLaMA ️
<!--
<p align="center">
<a href="https://www.lightning.ai/">Lightning.ai</a> •
<a href="https://lightning.ai/docs/pytorch/stable/">PyTorch Lightning</a> •
<a href="https://lightning.ai/docs/fabric/stable/">Fabric</a>
</p>
-->
 [](https://dev.azure.com/Lightning-AI/lit%20Models/_build/latest?definitionId=49&branchName=main) [](https://github.com/Lightning-AI/lit-llama/blob/master/LICENSE) [](https://discord.gg/VptPCZkGNa)
<img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/Llama_pineapple.gif" alt="Lit-LLaMA and pineapple pizza" width="500px"/>
</div>
# ⚡ Lit-LLaMA ️
Independent implementation of [LLaMA](<https://github.com/facebookresearch/llama>) that is fully open source under the **Apache 2.0 license.**
This implementation builds on [nanoGPT](<https://github.com/karpathy/nanoGPT>).
The original LLaMA weights are distributed by Meta under a [research-only license](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md#model-details).
New Apache 2.0 licensed weights are being released as part of the [Open LLaMA project](https://github.com/openlm-research/open_llama). Both can be [loaded in Lit-LLaMA](howto/download_weights.md).
## Why?
We believe that AI should be fully open source and part of the collective knowledge.
The original [LLaMA code](https://github.com/facebookresearch/llama) is [GPL licensed](https://github.com/facebookresearch/llama/blob/main/LICENSE) which means any project using it must also be released under GPL.
This "taints" any other code and prevents integration with the rest of the ecosystem.
**Lit-LLaMA solves that for good.**
## Design principles
**Lit-LLaMA** is:
- **Simple:** Single-file implementation without boilerplate.
- **Correct:** Numerically equivalent to the original model.
- **Optimized:** Runs on consumer hardware or at scale.
- **Open-source:** No strings attached.
## Get involved!
[Join our Discord](https://discord.gg/VptPCZkGNa) to build high-performance, truly open-source models for the common benefit of the community.
## Setup
Clone the repo
```bash
git clone https://github.com/Lightning-AI/lit-llama
cd lit-llama
```
install dependencies
```bash
pip install -r requirements.txt
```
You are all set! 🎉
## Use the model
To generate text predictions, you need to download the model weights. **If you don't have them, check out our [guide](howto/download_weights.md).**
Run inference:
```bash
python generate.py --prompt "Hello, my name is"
```
This will run the 7B model and require ~26 GB of GPU memory (A100 GPU).
[Full guide for generating samples from the model](howto/inference.md).
### Run Lit-LLaMA on consumer devices
On GPUs with `bfloat16` support, the `generate.py` script will automatically convert the weights and consume about ~14 GB.
For GPUs with less memory, or ones that don't support `bfloat16`, enable quantization (`--quantize llm.int8`):
```bash
python generate.py --quantize llm.int8 --prompt "Hello, my name is"
```
See `python generate.py --help` for more options.
You can also use GPTQ-style int4 quantization, but this needs conversions of the weights first:
```bash
python quantize.py --checkpoint_path lit-llama.pth --tokenizer_path tokenizer.model --output_path llama-7b-gptq.4bit.pth --dtype bfloat16 --quantize gptq.int4
```
With the generated quantized checkpoint generation works as usual with `--quantize gptq.int4`, bringing GPU usage to about ~5GB. As only the weights of the Linear layers are quantized, it is useful to use `--dtype bfloat16` even with the quantization enabled.
[Full guide for generating samples from the model](howto/inference.md).
## Finetune the model
We provide a simple training scripts in `finetune_lora.py` and `finetune_adapter.py` that instruction-tunes a pretrained model on the [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) dataset using the techniques of [LoRA](https://arxiv.org/abs/2106.09685) and [Adapter](https://arxiv.org/abs/2303.16199).
1. Download the data and generate a instruction tuning dataset:
```bash
python scripts/prepare_alpaca.py
```
2. Run the finetuning script
```bash
python finetune_lora.py
```
or
```bash
python finetune_adapter.py
```
It is expected that you have downloaded the pretrained weights as described above.
The finetuning requires at least one GPU with ~24 GB memory (GTX 3090). Follow the instructions in the script to efficiently fit your GPU memory.
Note: For some GPU models you might need to set `torch.backends.cuda.enable_flash_sdp(False)` (see comments at the top of the script).
More details about each finetuning method and how you can apply it to your own data can be found in our technical how-to guides.
### Finetuning How-To Guides
These technical tutorials illustrate how to run the finetuning code.
- [Finetune with LoRA](howto/finetune_lora.md)
- [Finetune with Adapters](howto/finetune_adapter.md)
### Understanding Finetuning -- Conceptual Tutorials
Looking for conceptual tutorials and explanations? We have some additional articles below:
- [Understanding Parameter-Efficient Finetuning of Large Language Models: From Prefix Tuning to LLaMA-Adapters](https://lightning.ai/pages/community/article/understanding-llama-adapters/)
## Pre-training
We provide a simple training script based on Fabric if you want to venture into pre-training on RedPajama, a reproduction of the original LLaMA dataset.
Conversion scripts for our optimized streaming `PackedDataset` are included.
Follow this guide to start pre-training on the RedPajama dataset:
- [Pretrain on RedPajama](howto/train_redpajama.md)
## Get involved!
We are on a quest towards fully open source AI.
<img align="right" src="https://pl-public-data.s3.amazonaws.com/assets_lightning/Lit_LLaMA_Illustration3x.png" alt="Lit-LLaMA" width="128"/>
Join us and start contributing, especially on the following areas:
- [ ] [Pre-training](https://github.com/Lightning-AI/lit-llama/labels/pre-training)
- [ ] [Fine-tuning (full and LoRA)](https://github.com/Lightning-AI/lit-llama/labels/fine-tuning)
- [ ] [Quantization](https://github.com/Lightning-AI/lit-llama/labels/quantization)
- [ ] [Sparsification](https://github.com/Lightning-AI/lit-llama/labels/sparsification)
Look at `train.py` for a starting point towards pre-training / fine-tuning using [Lightning Fabric](https://lightning.ai/docs/fabric/stable/).
We welcome all individual contributors, regardless of their level of experience or hardware. Your contributions are valuable, and we are excited to see what you can accomplish in this collaborative and supportive environment.
Unsure about contributing? Check out our [Contributing to Lit-LLaMA: A Hitchhiker’s Guide to the Quest for Fully Open-Source AI](https://lightning.ai/pages/community/tutorial/contributing-to-lit-llama-a-hitchhikers-guide-to-the-quest-for-fully-open-source-ai/) guide.
Don't forget to [join our Discord](https://discord.gg/VptPCZkGNa)!
## Acknowledgements
- [@karpathy](https://github.com/karpathy) for [nanoGPT](https://github.com/karpathy/nanoGPT)
- [@FacebookResearch](https://github.com/facebookresearch) for the original [LLaMA implementation](https://github.com/facebookresearch/llama)
- [@TimDettmers](https://github.com/TimDettmers) for [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)
- [@Microsoft](https://github.com/microsoft) for [LoRA](https://github.com/microsoft/LoRA)
- [@IST-DASLab](https://github.com/IST-DASLab) for [GPTQ](https://github.com/IST-DASLab/gptq)
## License
Lit-LLaMA is released under the [Apache 2.0](https://github.com/Lightning-AI/lightning-llama/blob/main/LICENSE) license.
|
BSen/wav2vec2-large-xls-r-300m-turkish-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
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} | 6 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8609120891618334
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1400
- F1: 0.8609
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2581 | 1.0 | 525 | 0.1584 | 0.8233 |
| 0.1252 | 2.0 | 1050 | 0.1384 | 0.8491 |
| 0.0811 | 3.0 | 1575 | 0.1400 | 0.8609 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Babelscape/rebel-large | [
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"en",
"dataset:Babelscape/rebel-dataset",
"transformers",
"seq2seq",
"relation-extraction",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"has_space"
]
| text2text-generation | {
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"BartForConditionalGeneration"
],
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} | 9,458 | 2023-05-16T08:16:15Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### gta Dreambooth model trained by Sula1723 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
This model was trained on the official art images of the GTA 5 game, which usually appear on the loading screens. These arts include images of people, animals, and various objects that are usually part of a general picture or background.
Stable Diffusion v1-5 was used for training, also about 30 concept images and 1000 regularization images for style training.
**Due to the use of Google Colab resources, you do not need to have a GPU and you can freely experiment on your concepts.**
To select the optimal settings before training, I recommend using this [guide](https://github.com/nitrosocke/dreambooth-training-guide) written by Nitrosocke.
To reference the art style, use the token: *gta*
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:**
|
Babelscape/wikineural-multilingual-ner | [
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"de",
"en",
"es",
"fr",
"it",
"nl",
"pl",
"pt",
"ru",
"multilingual",
"dataset:Babelscape/wikineural",
"transformers",
"named-entity-recognition",
"sequence-tagger-model",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
]
| token-classification | {
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],
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}
} | 41,608 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO-MLP
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 224.10 +/- 73.84
name: mean_reward
verified: false
---
# **PPO-MLP** Agent playing **LunarLander-v2**
This is a trained model of a **PPO-MLP** 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
...
```
|
Badr/model1 | []
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} | 0 | null | ---
license: gpl-3.0
datasets:
- BelleGroup/generated_train_0.5M_CN
- JosephusCheung/GuanacoDataset
- Chinese-Vicuna/guanaco_belle_merge_v1.0
language:
- zh
tags:
- alpaca
- Chinese-Vicuna
- llama
---
This is a Chinese instruction-tuning lora checkpoint based on llama-7B(1epoch) from [this repo's](https://github.com/Facico/Chinese-Vicuna) work |
Bagus/wav2vec2-xlsr-japanese-speech-emotion-recognition | [
"pytorch",
"wav2vec2",
"audio-classification",
"ja",
"dataset:jtes",
"transformers",
"audio",
"speech",
"speech-emotion-recognition",
"has_space"
]
| audio-classification | {
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"HubertForSequenceClassification"
],
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}
} | 26 | null | ---
license: gpl-3.0
datasets:
- BelleGroup/generated_train_0.5M_CN
- JosephusCheung/GuanacoDataset
- Chinese-Vicuna/guanaco_belle_merge_v1.0
language:
- zh
tags:
- alpaca
- Chinese-Vicuna
- llama
---
This is a Chinese instruction-tuning lora checkpoint based on llama-7B(2epoch) from [this repo's](https://github.com/Facico/Chinese-Vicuna) work |
Barleysack/AERoberta2 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
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}
} | 2 | 2023-05-16T08:35:02Z | ---
tags:
- image-classification
license: apache-2.0
---
# Model card for resnet18
A ResNet-B image classification model.
This model features:
* ReLU activations
* Single layer 7x7 convolution with pooling
* 1x1 convolution shortcut downsample
Trained on ImageNet-1k in `timm` using recipe template described below.
Recipe details:
* ResNet Strikes Back `A1` recipe
* LAMB optimizer with BCE loss
* Cosine LR schedule with warmup
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 11.7
- GMACs: 1.8
- Activations (M): 2.5
- Image size: train = 224 x 224, test = 288 x 288
- **Papers:**
- ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476
- Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385
|
Belin/T5-Terms-and-Conditions | []
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} | 0 | null | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Bella4322/Sarah | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.it
split: validation
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8166598611678236
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2557
- F1: 0.8167
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8158 | 1.0 | 70 | 0.3352 | 0.7451 |
| 0.3068 | 2.0 | 140 | 0.2821 | 0.7894 |
| 0.2027 | 3.0 | 210 | 0.2557 | 0.8167 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BenDavis71/GPT-2-Finetuning-AIRaid | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
]
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} | 10 | 2023-05-16T09:09:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_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_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6186
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.3479 |
| 2.7575 | 2.0 | 500 | 1.7070 |
| 2.7575 | 3.0 | 750 | 1.6186 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BenGeorge/MyModel | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mt5-aym-lex-try
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-aym-lex-try
This model is a fine-tuned version of [alvations/mt5-aym-lex](https://huggingface.co/alvations/mt5-aym-lex) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2781
- Chrf: 15.5602
- Bleu: 0.7538
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 0.2183 | 0.0 | 20 | 0.3002 | 16.5098 | 0.7277 | 19.0 |
| 0.1911 | 0.01 | 40 | 0.2852 | 16.9826 | 0.7635 | 19.0 |
| 0.2366 | 0.01 | 60 | 0.2800 | 15.7077 | 0.7476 | 19.0 |
| 0.1941 | 0.02 | 80 | 0.2785 | 15.5602 | 0.7538 | 19.0 |
| 0.1991 | 0.02 | 100 | 0.2781 | 15.5602 | 0.7538 | 19.0 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Benicio/t5-small-finetuned-en-to-ru | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
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} | 50 | null | ---
license: other
---
WD-1-5 finetunes. Available versions:
* rheaSilvia_[WD15b3](https://huggingface.co/waifu-diffusion/wd-1-5-beta3/) **Outdated**
* rheaSilvia_[AikimiDC4](https://huggingface.co/Aikimi/Aikimi_diffusion_base_wd-1-5_beta2) **Outdated**
* rheaSilvia_[Replicant2](https://huggingface.co/gsdf/Replicant-V2.0) **Outdated**
* rheaSilvia_[HakoMayC](https://huggingface.co/852wa/hakoMay) **Outdated**
* rheaSilvia_[HakoMayB](https://huggingface.co/852wa/hakoMay) **Outdated**
* rheaSilvia_[Replicant3](https://huggingface.co/gsdf/Replicant-V3.0) **Stable**<br>
I recommend rheaSilvia_Replicant3; it marks a major shift in my training methodology.<br>
standard prompt:
<img src=https://huggingface.co/mirav/rhea_silvia/resolve/main/Screenshot%202023-05-16%20134609.png></img> |
Berzemu/Coco | []
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} | 0 | null | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 13.47 +/- 5.33
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Marc-Elie/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
BhanuSama/gpt2-finetuned-xsum | []
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} | 0 | null | ---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
widget:
- text: ["Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion.",
"Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998."]
example_title: Not Equivalent
- text: ["Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier.",
"With the scandal hanging over Stewart's company revenue the first quarter of the year dropped 15 percent from the same period a year earlier."]
example_title: Equivalent
model-index:
- name: platzi-distilroberta-base-mrpc-glue-jonathan-narvaez
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8259803921568627
- name: F1
type: f1
value: 0.8725314183123878
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-distilroberta-base-mrpc-glue-jonathan-narvaez
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets.
It achieves the following results on the evaluation set:
- Loss: 0.4482
- Accuracy: 0.8260
- F1: 0.8725
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3682 | 1.09 | 500 | 0.4482 | 0.8260 | 0.8725 |
| 0.3611 | 2.18 | 1000 | 0.4482 | 0.8260 | 0.8725 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Bhuvana/t5-base-spellchecker | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
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} | 93 | null | Access to model clueless/XLS-R_finetuned is restricted and you are not in the authorized list. Visit https://huggingface.co/clueless/XLS-R_finetuned to ask for access. |
Biasface/DDDC2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 10 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: Yelinz/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BigBoy/model | []
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} | 0 | 2023-05-16T09:32:54Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.en
split: validation
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6710816777041942
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4016
- F1: 0.6711
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1733 | 1.0 | 50 | 0.6033 | 0.4690 |
| 0.5329 | 2.0 | 100 | 0.4333 | 0.6475 |
| 0.374 | 3.0 | 150 | 0.4016 | 0.6711 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BigDaddyNe1L/Hhaa | []
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}
} | 0 | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.91 +/- 0.49
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BigSalmon/BertaMyWorda | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
],
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}
} | 8 | null | ---
license: apache-2.0
datasets:
- kobkrit/rd-taxqa
- iapp_wiki_qa_squad
- Thaweewat/alpaca-cleaned-52k-th
- Thaweewat/instruction-wild-52k-th
- Thaweewat/databricks-dolly-15k-th
- Thaweewat/hc3-24k-th
- Thaweewat/gpteacher-20k-th
- Thaweewat/onet-m6-social
- Thaweewat/alpaca-finance-43k-th
language:
- th
- en
library_name: adapter-transformers
pipeline_tag: text-generation
tags:
- openthaigpt
---
# 🇹🇭 OpenThaiGPT 0.1.0-beta
<img src="https://1173516064-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvvbWvIIe82Iv1yHaDBC5%2Fuploads%2Fb8eiMDaqiEQL6ahbAY0h%2Fimage.png?alt=media&token=6fce78fd-2cca-4c0a-9648-bd5518e644ce
https://openthaigpt.aieat.or.th/" width="200px">
OpenThaiGPT Version 0.1.0-beta is a 7B-parameter LLaMA model finetuned to follow Thai translated instructions below and makes use of the Huggingface LLaMA implementation.
## Support
- Official website: https://openthaigpt.aieat.or.th
- Facebook page: https://web.facebook.com/groups/openthaigpt
- A Discord server for discussion and support [here](https://discord.gg/rUTp6dfVUF)
- E-mail: kobkrit@iapp.co.th
## License
**Source Code**: License Apache Software License 2.0.<br>
**Weight**: For research use only (due to the Facebook LLama's Weight LICENSE).<br>
<i>Note that: A commercial use license for OpenThaiGPT 0.1.0 weight will be released later soon!</i>
## Code and Weight
**Finetune Code**: https://github.com/OpenThaiGPT/openthaigpt-finetune-010beta<br>
**Inference Code**: https://github.com/OpenThaiGPT/openthaigpt<br>
**Weight**: https://huggingface.co/kobkrit/openthaigpt-0.1.0-beta
## Sponsors
Pantip.com, ThaiSC<br>
<img src="https://1173516064-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvvbWvIIe82Iv1yHaDBC5%2Fuploads%2FiWjRxBQgo0HUDcpZKf6A%2Fimage.png?alt=media&token=4fef4517-0b4d-46d6-a5e3-25c30c8137a6" width="100px">
<img src="https://1173516064-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvvbWvIIe82Iv1yHaDBC5%2Fuploads%2Ft96uNUI71mAFwkXUtxQt%2Fimage.png?alt=media&token=f8057c0c-5c5f-41ac-bb4b-ad02ee3d4dc2" width="100px">
### Powered by
OpenThaiGPT Volunteers, Artificial Intelligence Entrepreneur Association of Thailand (AIEAT), and Artificial Intelligence Association of Thailand (AIAT)
<img src="https://1173516064-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvvbWvIIe82Iv1yHaDBC5%2Fuploads%2F6yWPXxdoW76a4UBsM8lw%2Fimage.png?alt=media&token=1006ee8e-5327-4bc0-b9a9-a02e93b0c032" width="100px">
<img src="https://1173516064-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvvbWvIIe82Iv1yHaDBC5%2Fuploads%2FBwsmSovEIhW9AEOlHTFU%2Fimage.png?alt=media&token=5b550289-e9e2-44b3-bb8f-d3057d74f247" width="100px">
### Authors
Kobkrit Viriyayudhakorn (kobkrit@iapp.co.th), Sumeth Yuenyong (sumeth.yue@mahidol.edu) and Thaweewat Ruksujarit (thaweewr@scg.com).
<i>Disclaimer: Provided responses are not guaranteed.</i> |
BigSalmon/BlankSlots | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
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},
"translation_en_to_de": {
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"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
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"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 4 | null | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: platzi-vit-model-jonathan-narvaez
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9924812030075187
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-vit-model-jonathan-narvaez
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0503
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1447 | 3.85 | 500 | 0.0503 | 0.9925 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BigSalmon/DaBlank | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
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},
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"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 4 | null | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for distilbert-base-task-multi-label-classification Model
## Model Details
### Model Description
This model is based on the distillation of the BERT base model, which is a widely used language model.
The distillation process involves training a smaller model to mimic the behavior and predictions of the larger BERT model.
The purpose of this model is to perform fine-tuning on the distilbert-base-pwc-task-multi-label-classification checkpoint for multi-label classification tasks.
Fine-tuning approach can be applied to other models such as RoBERTa, DeBERTa, DistilBERT, CANINE, and more. The notebook provides a practical guide for utilizing these models in various classification scenarios.
- **Developed by:** Lina Saba
- **Model type:** bert for multi-label classification
- **Language(s) (NLP):** Python
- **Finetuned from model:** distilbert-base-pwc-task-multi-label-classification
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://colab.research.google.com/drive/1Z314gK2qixK_0ujgQ3nvqvar1iV3QnoF?usp=sharing
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
This model aims to fine-tune BERT to predict one or more labels for a given piece of text.
The related notebook illustrates how to fine-tune a distilbert-base-pwc-task-multi-label-classification model, Knowing that it's the same way to fine-tune a RoBERTa, DeBERTa, DistilBERT, CANINE, ... checkpoint.
### Direct Use
Predict the labels of a piece of text from this
list = {
0: 'aspersion',
1: 'hyperbole',
2: 'lying',
3: 'namecalling',
4: 'noncooperation',
5: 'offtopic',
6: 'other_incivility',
7: 'pejorative',
8: 'sarcasm',
9: 'vulgarity'
}
### Downstream Use [optional]
This model is fine-tuned on a dataset; a collection of more than 6000 comments on Arizona Daily Star news articles
from 2011 that have been manually annotated for various forms of incivility including aspersion, namecalling, sarcasm, and vulgarity.
## Bias, Risks, and Limitations
Technical limitations :
- Can't print more than one identified label using pipeline.
- Half of the test results aren't exactly the same as what expected
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
BigSalmon/FormalBerta2 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
}
} | 16 | null | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: casals90/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BigSalmon/FormalRobertaaa | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
} | 12 | null | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test.
The model is trained on 147M multi-turn dialogue from Reddit discussion thread.
* Multi-turn generation examples from an interactive environment:
|Role | Response |
|---------|--------|
|User | Does money buy happiness? |
| Bot | Depends how much money you spend on it .|
|User | What is the best way to buy happiness ? |
| Bot | You just have to be a millionaire by your early 20s, then you can be happy . |
|User |This is so difficult ! |
| Bot | You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money |
Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)
ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
### How to use
Now we are ready to try out how the model works as a chatting partner!
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
# Let's chat for 5 lines
for step in range(5):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
# pretty print last ouput tokens from bot
print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
|
BigSalmon/FroBurta | []
| null | {
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}
} | 0 | 2023-05-16T09:47:05Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 300.50 +/- 103.40
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 Yelinz -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 Yelinz -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 Yelinz
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 130000),
('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', 10000),
('n_timesteps', 1000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BigSalmon/GPTNeo350MInformalToFormalLincoln | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
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} | 8 | null | ---
license: apache-2.0
---
This repo hosts the 400B tokens preview of OpenLLaMA 7B. Please refer to the
[project homepage on GitHub](https://github.com/openlm-research/open_llama) for
model information and usage. |
BigSalmon/GPTNeo350MInformalToFormalLincoln5 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
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"GPTNeoForCausalLM"
],
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}
} | 11 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: MixGPT2_10K_fromB_BFall_40KGen_topP_0.75
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MixGPT2_10K_fromB_BFall_40KGen_topP_0.75
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0480
- Accuracy: 0.9944
- F1: 0.9378
- Precision: 0.9998
- Recall: 0.883
- Roc Auc Score: 0.9415
- Tpr At Fpr 0.01: 0.91
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0059 | 1.0 | 32813 | 0.0405 | 0.9934 | 0.9250 | 0.9991 | 0.8612 | 0.9306 | 0.8928 |
| 0.0036 | 2.0 | 65626 | 0.0503 | 0.9929 | 0.9193 | 0.9998 | 0.8508 | 0.9254 | 0.8914 |
| 0.001 | 3.0 | 98439 | 0.0706 | 0.9908 | 0.8936 | 0.9995 | 0.808 | 0.9040 | 0.8702 |
| 0.0011 | 4.0 | 131252 | 0.0564 | 0.9943 | 0.9363 | 0.9986 | 0.8812 | 0.9406 | 0.8958 |
| 0.0 | 5.0 | 164065 | 0.0480 | 0.9944 | 0.9378 | 0.9998 | 0.883 | 0.9415 | 0.91 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
|
BigSalmon/GPTNeo350MInformalToFormalLincoln6 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
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"GPTNeoForCausalLM"
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}
} | 14 | null | ---
pipeline_tag: text-generation
inference: true
widget:
- text: 'def print_hello_world():'
example_title: Hello world
group: Python
license: bigcode-openrail-m
datasets:
- bigcode/the-stack-dedup
metrics:
- code_eval
library_name: transformers
tags:
- code
model-index:
- name: StarCoderBase
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 0.304
verified: false
- task:
type: text-generation
dataset:
type: mbpp
name: MBPP
metrics:
- name: pass@1
type: pass@1
value: 0.49
verified: false
- task:
type: text-generation
dataset:
type: ds1000
name: DS-1000 (Overall Completion)
metrics:
- name: pass@1
type: pass@1
value: 0.238
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (C++)
metrics:
- name: pass@1
type: pass@1
value: 0.3056
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (C#)
metrics:
- name: pass@1
type: pass@1
value: 0.2056
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (D)
metrics:
- name: pass@1
type: pass@1
value: 0.1001
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Go)
metrics:
- name: pass@1
type: pass@1
value: 0.2147
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Java)
metrics:
- name: pass@1
type: pass@1
value: 0.2853
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Julia)
metrics:
- name: pass@1
type: pass@1
value: 0.2109
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (JavaScript)
metrics:
- name: pass@1
type: pass@1
value: 0.317
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Lua)
metrics:
- name: pass@1
type: pass@1
value: 0.2661
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (PHP)
metrics:
- name: pass@1
type: pass@1
value: 0.2675
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Perl)
metrics:
- name: pass@1
type: pass@1
value: 0.1632
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Python)
metrics:
- name: pass@1
type: pass@1
value: 0.3035
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (R)
metrics:
- name: pass@1
type: pass@1
value: 0.1018
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Ruby)
metrics:
- name: pass@1
type: pass@1
value: 0.1725
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Racket)
metrics:
- name: pass@1
type: pass@1
value: 0.1177
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Rust)
metrics:
- name: pass@1
type: pass@1
value: 0.2446
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Scala)
metrics:
- name: pass@1
type: pass@1
value: 0.2879
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Bash)
metrics:
- name: pass@1
type: pass@1
value: 0.1102
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Swift)
metrics:
- name: pass@1
type: pass@1
value: 0.1674
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (TypeScript)
metrics:
- name: pass@1
type: pass@1
value: 0.3215
verified: false
extra_gated_prompt: >-
## Model License Agreement
Please read the BigCode [OpenRAIL-M
license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement)
agreement before accepting it.
extra_gated_fields:
I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox
---
# starcoderbase-GGML
This is GGML format quantised 4bit, 5bit and 8bit models of [StarCoderBase](https://huggingface.co/bigcode/starcoderbase).
This repo is the result of quantising to 4bit, 5bit and 8bit GGML for CPU inference using [ggml](https://github.com/ggerganov/ggml/tree/master/examples/starcoder).
# Original model card
Play with the model on the [StarCoder Playground](https://huggingface.co/spaces/bigcode/bigcode-playground).
## Table of Contents
1. [Model Summary](##model-summary)
2. [Use](##use)
3. [Limitations](##limitations)
4. [Training](##training)
5. [License](##license)
6. [Citation](##citation)
## Model Summary
The StarCoderBase models are 15.5B parameter models trained on 80+ programming languages from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack), with opt-out requests excluded. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), [a context window of 8192 tokens](https://arxiv.org/abs/2205.14135), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1 trillion tokens.
- **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
- **Project Website:** [bigcode-project.org](https://www.bigcode-project.org)
- **Paper:** [💫StarCoder: May the source be with you!](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view)
- **Point of Contact:** [contact@bigcode-project.org](mailto:contact@bigcode-project.org)
- **Languages:** 80+ Programming languages
## Use
### Intended use
The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. However, by using the [Tech Assistant prompt](https://huggingface.co/datasets/bigcode/ta-prompt) you can turn it into a capable technical assistant.
**Feel free to share your generations in the Community tab!**
### Generation
```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigcode/starcoderbase"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, trust_remote_code=True).to(device)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
### Fill-in-the-middle
Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:
```python
input_text = "<fim_prefix>def print_hello_world():\n <fim_suffix>\n print('Hello world!')<fim_middle>"
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
### Attribution & Other Requirements
The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/starcoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
# Limitations
The model has been trained on source code from 80+ programming languages. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See [the paper](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view) for an in-depth discussion of the model limitations.
# Training
## Model
- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective
- **Pretraining steps:** 250k
- **Pretraining tokens:** 1 trillion
- **Precision:** bfloat16
## Hardware
- **GPUs:** 512 Tesla A100
- **Training time:** 24 days
## Software
- **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
- **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex)
# License
The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
# Citation
```
@article{li2023starcoder,
title={StarCoder: may the source be with you!},
author={Raymond Li and Loubna Ben Allal and Yangtian Zi and Niklas Muennighoff and Denis Kocetkov and Chenghao Mou and Marc Marone and Christopher Akiki and Jia Li and Jenny Chim and Qian Liu and Evgenii Zheltonozhskii and Terry Yue Zhuo and Thomas Wang and Olivier Dehaene and Mishig Davaadorj and Joel Lamy-Poirier and João Monteiro and Oleh Shliazhko and Nicolas Gontier and Nicholas Meade and Armel Zebaze and Ming-Ho Yee and Logesh Kumar Umapathi and Jian Zhu and Benjamin Lipkin and Muhtasham Oblokulov and Zhiruo Wang and Rudra Murthy and Jason Stillerman and Siva Sankalp Patel and Dmitry Abulkhanov and Marco Zocca and Manan Dey and Zhihan Zhang and Nour Fahmy and Urvashi Bhattacharyya and Wenhao Yu and Swayam Singh and Sasha Luccioni and Paulo Villegas and Maxim Kunakov and Fedor Zhdanov and Manuel Romero and Tony Lee and Nadav Timor and Jennifer Ding and Claire Schlesinger and Hailey Schoelkopf and Jan Ebert and Tri Dao and Mayank Mishra and Alex Gu and Jennifer Robinson and Carolyn Jane Anderson and Brendan Dolan-Gavitt and Danish Contractor and Siva Reddy and Daniel Fried and Dzmitry Bahdanau and Yacine Jernite and Carlos Muñoz Ferrandis and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries},
year={2023},
eprint={2305.06161},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BigSalmon/GoodMaskResults | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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}
} | 9 | null | ---
pipeline_tag: text-generation
inference: true
widget:
- text: 'def print_hello_world():'
example_title: Hello world
group: Python
license: bigcode-openrail-m
datasets:
- bigcode/the-stack-dedup
metrics:
- code_eval
library_name: transformers
tags:
- code
model-index:
- name: StarCoder
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval (Prompted)
metrics:
- name: pass@1
type: pass@1
value: 0.408
verified: false
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 0.336
verified: false
- task:
type: text-generation
dataset:
type: mbpp
name: MBPP
metrics:
- name: pass@1
type: pass@1
value: 0.527
verified: false
- task:
type: text-generation
dataset:
type: ds1000
name: DS-1000 (Overall Completion)
metrics:
- name: pass@1
type: pass@1
value: 0.26
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (C++)
metrics:
- name: pass@1
type: pass@1
value: 0.3155
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (C#)
metrics:
- name: pass@1
type: pass@1
value: 0.2101
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (D)
metrics:
- name: pass@1
type: pass@1
value: 0.1357
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Go)
metrics:
- name: pass@1
type: pass@1
value: 0.1761
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Java)
metrics:
- name: pass@1
type: pass@1
value: 0.3022
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Julia)
metrics:
- name: pass@1
type: pass@1
value: 0.2302
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (JavaScript)
metrics:
- name: pass@1
type: pass@1
value: 0.3079
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Lua)
metrics:
- name: pass@1
type: pass@1
value: 0.2389
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (PHP)
metrics:
- name: pass@1
type: pass@1
value: 0.2608
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Perl)
metrics:
- name: pass@1
type: pass@1
value: 0.1734
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Python)
metrics:
- name: pass@1
type: pass@1
value: 0.3357
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (R)
metrics:
- name: pass@1
type: pass@1
value: 0.155
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Ruby)
metrics:
- name: pass@1
type: pass@1
value: 0.0124
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Racket)
metrics:
- name: pass@1
type: pass@1
value: 0.0007
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Rust)
metrics:
- name: pass@1
type: pass@1
value: 0.2184
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Scala)
metrics:
- name: pass@1
type: pass@1
value: 0.2761
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Bash)
metrics:
- name: pass@1
type: pass@1
value: 0.1046
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Swift)
metrics:
- name: pass@1
type: pass@1
value: 0.2274
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (TypeScript)
metrics:
- name: pass@1
type: pass@1
value: 0.3229
verified: false
extra_gated_prompt: >-
## Model License Agreement
Please read the BigCode [OpenRAIL-M
license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement)
agreement before accepting it.
extra_gated_fields:
I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox
---
# starcoder-GGML
This is GGML format quantised 4bit, 5bit and 8bit models of [StarCoder](https://huggingface.co/bigcode/starcoder).
This repo is the result of quantising to 4bit, 5bit and 8bit GGML for CPU inference using [ggml](https://github.com/ggerganov/ggml/tree/master/examples/starcoder).
# Original model card
Play with the model on the [StarCoder Playground](https://huggingface.co/spaces/bigcode/bigcode-playground).
## Table of Contents
1. [Model Summary](##model-summary)
2. [Use](##use)
3. [Limitations](##limitations)
4. [Training](##training)
5. [License](##license)
6. [Citation](##citation)
## Model Summary
The StarCoder models are 15.5B parameter models trained on 80+ programming languages from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack), with opt-out requests excluded. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), [a context window of 8192 tokens](https://arxiv.org/abs/2205.14135), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1 trillion tokens.
- **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
- **Project Website:** [bigcode-project.org](https://www.bigcode-project.org)
- **Paper:** [💫StarCoder: May the source be with you!](https://arxiv.org/abs/2305.06161)
- **Point of Contact:** [contact@bigcode-project.org](mailto:contact@bigcode-project.org)
- **Languages:** 80+ Programming languages
## Use
### Intended use
The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. However, by using the [Tech Assistant prompt](https://huggingface.co/datasets/bigcode/ta-prompt) you can turn it into a capable technical assistant.
**Feel free to share your generations in the Community tab!**
### Generation
```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigcode/starcoder"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
### Fill-in-the-middle
Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:
```python
input_text = "<fim-prefix>def print_hello_world():\n <fim-suffix>\n print('Hello world!')<fim-middle>"
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
### Attribution & Other Requirements
The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/starcoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
# Limitations
The model has been trained on source code from 80+ programming languages. The predominant natural language in source code is English although other languages are also present. As such the model is capable of generating code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See [the paper](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view) for an in-depth discussion of the model limitations.
# Training
## Model
- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective
- **Pretraining steps:** 250k
- **Pretraining tokens:** 1 trillion
- **Precision:** bfloat16
## Hardware
- **GPUs:** 512 Tesla A100
- **Training time:** 24 days
## Software
- **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
- **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex)
# License
The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
# Citation
```
@article{li2023starcoder,
title={StarCoder: may the source be with you!},
author={Raymond Li and Loubna Ben Allal and Yangtian Zi and Niklas Muennighoff and Denis Kocetkov and Chenghao Mou and Marc Marone and Christopher Akiki and Jia Li and Jenny Chim and Qian Liu and Evgenii Zheltonozhskii and Terry Yue Zhuo and Thomas Wang and Olivier Dehaene and Mishig Davaadorj and Joel Lamy-Poirier and João Monteiro and Oleh Shliazhko and Nicolas Gontier and Nicholas Meade and Armel Zebaze and Ming-Ho Yee and Logesh Kumar Umapathi and Jian Zhu and Benjamin Lipkin and Muhtasham Oblokulov and Zhiruo Wang and Rudra Murthy and Jason Stillerman and Siva Sankalp Patel and Dmitry Abulkhanov and Marco Zocca and Manan Dey and Zhihan Zhang and Nour Fahmy and Urvashi Bhattacharyya and Wenhao Yu and Swayam Singh and Sasha Luccioni and Paulo Villegas and Maxim Kunakov and Fedor Zhdanov and Manuel Romero and Tony Lee and Nadav Timor and Jennifer Ding and Claire Schlesinger and Hailey Schoelkopf and Jan Ebert and Tri Dao and Mayank Mishra and Alex Gu and Jennifer Robinson and Carolyn Jane Anderson and Brendan Dolan-Gavitt and Danish Contractor and Siva Reddy and Daniel Fried and Dzmitry Bahdanau and Yacine Jernite and Carlos Muñoz Ferrandis and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries},
year={2023},
eprint={2305.06161},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
BigSalmon/InformalToFormalLincoln14 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 5 | 2023-05-16T10:04:39Z | ---
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: 264.41 +/- 23.53
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
...
```
|
BigSalmon/InformalToFormalLincoln23 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jyant/mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jyant/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.0039
- Validation Loss: 3.3164
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 9672, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 9.4128 | 4.1404 | 0 |
| 5.7517 | 3.6961 | 1 |
| 5.0544 | 3.5487 | 2 |
| 4.6469 | 3.4520 | 3 |
| 4.3948 | 3.3908 | 4 |
| 4.2053 | 3.3486 | 5 |
| 4.0621 | 3.3275 | 6 |
| 4.0039 | 3.3164 | 7 |
### Framework versions
- Transformers 4.29.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BigSalmon/MrLincoln11 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 9 | null | ---
license: gpl-3.0
language:
- en
- zh
---
# Ziya-LLaMA-13B-v1
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- API: [Fengshen-OpenAPI](https://fengshenbang-lm.com/open-api)
# 姜子牙系列模型
- [Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1)
- [Ziya-LLaMA-7B-Reward](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-7B-Reward)
## 简介 Brief Introduction
姜子牙通用大模型V1是基于LLaMa的130亿参数的大规模预训练模型，具备翻译，编程，文本分类，信息抽取，摘要，文案生成，常识问答和数学计算等能力。目前姜子牙通用大模型已完成大规模预训练、多任务有监督微调和人类反馈学习三阶段的训练过程。
The Ziya-LLaMA-13B-v1 is a large-scale pre-trained model based on LLaMA with 13 billion parameters. It has the ability to perform tasks such as translation, programming, text classification, information extraction, summarization, copywriting, common sense Q&A, and mathematical calculation. The Ziya-LLaMA-13B-v1 has undergone three stages of training: large-scale continual pre-training (PT), multi-task supervised fine-tuning (SFT), and human feedback learning (RM, PPO).
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | AGI模型 | 姜子牙 Ziya | LLaMA | 13B | English&Chinese |
## 模型信息 Model Information
### 继续预训练 Continual pretraining
原始数据包含英文和中文，其中英文数据来自openwebtext、Books、Wikipedia和Code，中文数据来自清洗后的悟道数据集、自建的中文数据集。在对原始数据进行去重、模型打分、数据分桶、规则过滤、敏感主题过滤和数据评估后，最终得到125B tokens的有效数据。
为了解决LLaMA原生分词对中文编解码效率低下的问题，我们在LLaMA词表的基础上增加了7k+个常见中文字，通过和LLaMA原生的词表去重，最终得到一个39410大小的词表，并通过复用Transformers里LlamaTokenizer来实现了这一效果。
在增量训练过程中，我们使用了160张40GB的A100，采用2.6M tokens的训练集样本数量和FP 16的混合精度，吞吐量达到118 TFLOP per GPU per second。因此我们能够在8天的时间里在原生的LLaMA-13B模型基础上，增量训练110B tokens的数据。
训练期间，虽然遇到了机器宕机、底层框架bug、loss spike等各种问题，但我们通过快速调整，保证了增量训练的稳定性。我们也放出训练过程的loss曲线，让大家了解可能出现的问题。
The original data contains both English and Chinese, with English data from openwebtext, Books, Wikipedia, and Code, and Chinese data from the cleaned Wudao dataset and self-built Chinese dataset. After deduplication, model scoring, data bucketing, rule filtering, sensitive topic filtering, and data evaluation, we finally obtained 125 billion tokens of valid data.
To address the issue of low efficiency in Chinese encoding and decoding caused by the native word segmentation of LLaMa, we added 8,000 commonly used Chinese characters to the LLaMa vocabulary. By removing duplicates with the original LLaMa vocabulary, we finally obtained a vocabulary of size 39,410. We achieved this by reusing the LlamaTokenizer in Transformers.
During the incremental training process, we used 160 A100s with a total of 40GB memory, using a training dataset with 2.6 million tokens and mixed precision of FP16. The throughput reached 118 TFLOP per GPU per second. As a result, we were able to incrementally train 110 billion tokens of data on top of the native LLaMa-13B model in just 8 days.
Throughout the training process, we encountered various issues such as machine crashes, underlying framework bugs, and loss spikes. However, we ensured the stability of the incremental training by making rapid adjustments. We have also released the loss curve during the training process to help everyone understand the potential issues that may arise.
<img src="https://huggingface.co/datasets/suolyer/testb/resolve/main/loss.png" width=1000 height=600>
### 多任务有监督微调 Supervised finetuning
在多任务有监督微调阶段，采用了课程学习（curiculum learning）和增量训练（continual learning）的策略，用大模型辅助划分已有的数据难度，然后通过“Easy To Hard”的方式，分多个阶段进行SFT训练。
SFT训练数据包含多个高质量的数据集，均经过人工筛选和校验：
- Self-Instruct构造的数据（约2M）：BELLE、Alpaca、Alpaca-GPT4等多个数据集
- 内部收集Code数据（300K）：包含leetcode、多种Code任务形式
- 内部收集推理/逻辑相关数据（500K）：推理、申论、数学应用题、数值计算等
- 中英平行语料（2M）：中英互译语料、COT类型翻译语料、古文翻译语料等
- 多轮对话语料（500K）：Self-Instruct生成、任务型多轮对话、Role-Playing型多轮对话等
During the supervised fine-tuning (SFT) phase of multitask learning, we used a strategy of curriculum learning and incremental training. We used the large model to assist in partitioning the existing data by difficulty and then conducted SFT training in multiple stages using the "easy to hard" approach.
The SFT training data consists of multiple high-quality datasets that have been manually selected and verified, including approximately 2 million samples from datasets such as BELLE, Alpaca, and Alpaca-GPT4, 300,000 samples of internally collected code data including LeetCode and various code tasks, 500,000 samples of internally collected inference/logic-related data such as reasoning, argumentative essays, mathematical application questions, and numerical calculations, 2 million samples of Chinese-English parallel corpora including translation, COT-type translation, and classical Chinese translation, and 500,000 samples of multi-turn dialogue corpora including self-instructed generation, task-oriented multi-turn dialogue, and role-playing multi-turn dialogue.
### 人类反馈学习 Human-Feedback training
为了进一步提升模型的综合表现，使其能够充分理解人类意图、减少“幻觉”和不安全的输出，基于指令微调后的模型，进行了人类反馈训练（Human-Feedback Training，HFT）。在训练中，我们采用了以人类反馈强化学习（RM、PPO）为主，结合多种其他手段联合训练的方法，手段包括人类反馈微调（Human-Feedback Fine-tuning，HFFT）、后见链微调（Chain-of-Hindsight Fine-tuning，COHFT）、AI反馈（AI Feedback）和基于规则的奖励系统（Rule-based Reward System，RBRS）等，用来弥补PPO方法的短板，加速训练。
我们在内部自研的框架上实现了HFT的训练流程，该框架可以利用最少8张40G的A100显卡完成Ziya-LLaMA-13B-v1的全参数训练。在PPO训练中，我们没有限制生成样本的长度，以确保长文本任务的奖励准确性。每次训练的总经验池尺寸超过100k样本，确保了训练的充分性。
To further improve the overall performance of the model, enabling it to fully understand human intentions, reduce "hallucinations" and unsafe outputs, we conducted Human-Feedback Training (HFT) based on the model fine-tuned with instructions. In the training process, we used a variety of methods, including human feedback reinforcement learning (RM, PPO), combined with other methods such as Human-Feedback Fine-tuning (HFFT), Chain-of-Hindsight Fine-tuning (COHFT), AI feedback, and Rule-based Reward System (RBRS), to supplement the shortcomings of the PPO method and accelerate training.
We implemented the HFT training process on an internally developed framework, which can use a minimum of 8 40GB A100 GPUs to complete the full parameter training of Ziya-LLaMA-13B-v1. In the PPO training, we did not limit the length of the generated samples to ensure the accuracy of rewards for long-text tasks. The total experience pool size for each training exceeded 100k samples, ensuring the sufficiency of the training.
### 效果评估 Performance
<img src="https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1/resolve/main/pk.png" width=1000 height=600>
## 使用 Usage
```python3
from transformers import AutoTokenizer
from transformers import LlamaForCausalLM
import torch
device = torch.device("cuda")
query="帮我写一份去西安的旅游计划"
model = LlamaForCausalLM.from_pretrained('IDEA-CCNL/Ziya-LLaMA-13B-v1', torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained('IDEA-CCNL/Ziya-LLaMA-13B-v1')
inputs = '<human>:' + query.strip() + '\n<bot>:'
input_ids = tokenizer(inputs, return_tensors="pt").input_ids.to(device)
generate_ids = model.generate(
input_ids,
max_new_tokens=1024,
do_sample = True,
top_p = 0.85,
temperature = 1.0,
repetition_penalty=1.,
eos_token_id=2,
bos_token_id=1,
pad_token_id=0)
output = tokenizer.batch_decode(generate_ids)[0]
print(output)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2210.08590)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2210.08590):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
欢迎引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
``` |
BigSalmon/MrLincoln12 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_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_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0020
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2355 | 1.0 | 1129 | 2.0642 |
| 2.1423 | 2.0 | 2258 | 2.0212 |
| 2.0966 | 3.0 | 3387 | 2.0160 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BigSalmon/MrLincoln5 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
} | 9 | null | ---
datasets:
- midas/krapivin
- midas/inspec
language:
- en
widget:
- text: >-
<|TITLE|> In this paper, we investigate cross-domain limitations of
keyphrase generation using the models for abstractive text summarization. We
present an evaluation of BART fine-tuned for keyphrase generation across
three types of texts, namely scientific texts from computer science and
biomedical domains and news texts. We explore the role of transfer learning
between different domains to improve the model performance on small text
corpora.
- text: >-
<|KEYPHRASES|> In this paper, we investigate cross-domain limitations of
keyphrase generation using the models for abstractive text summarization. We
present an evaluation of BART fine-tuned for keyphrase generation across
three types of texts, namely scientific texts from computer science and
biomedical domains and news texts. We explore the role of transfer learning
between different domains to improve the model performance on small text
corpora.
library_name: transformers
pipeline_tag: text2text-generation
---
# BART fine-tuned for keyphrase generation
<!-- Provide a quick summary of what the model is/does. -->
This is the <a href="https://huggingface.co/facebook/bart-base">bart-base</a> (<a href = "https://arxiv.org/abs/1910.13461">Lewis et al.. 2019</a>) model <a href="https://arxiv.org/abs/2209.03791">finetuned</a> for generating titles and keyphrases for scientific texts on the following corpora:
* Krapivin (<a href = "http://eprints.biblio.unitn.it/1671/1/disi09055%2Dkrapivin%2Dautayeu%2Dmarchese.pdf">Krapivin et al., 2009</a>)
* Inspec (<a href = "https://aclanthology.org/W03-1028.pdf">Hulth, 2003</a>)
Inspired by <a href = "https://aclanthology.org/2020.findings-emnlp.428.pdf">(Cachola et al., 2020)</a>, we applied control codes to fine-tune BART in a multi-task manner. First, we create a training set containing comma-separated lists of keyphrases and titles as text generation targets. For this purpose, we form text-title and text-keyphrases pairs based on the original text corpus. Second, we append each source text in the training set with control codes <|TITLE|> and <|KEYPHRASES|> respectively. After that, the training set is shuffled in random order. Finally, the preprocessed training set is utilized to fine-tune the pre-trained BART model.
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("beogradjanka/bart_multitask_finetuned_for_title_and_keyphrase_generation")
model = AutoModelForSeq2SeqLM.from_pretrained("beogradjanka/bart_multitask_finetuned_for_title_and_keyphrase_generation")
text = "In this paper, we investigate cross-domain limitations of keyphrase generation using the models for abstractive text summarization.\
We present an evaluation of BART fine-tuned for keyphrase generation across three types of texts, \
namely scientific texts from computer science and biomedical domains and news texts. \
We explore the role of transfer learning between different domains to improve the model performance on small text corpora."
#generating \n-separated keyphrases
tokenized_text = tokenizer.prepare_seq2seq_batch(["<|KEYPHRASES|> " + text], return_tensors='pt')
translation = model.generate(**tokenized_text)
translated_text = tokenizer.batch_decode(translation, skip_special_tokens=True)[0]
print(translated_text)
#generating title
tokenized_text = tokenizer.prepare_seq2seq_batch(["<|TITLE|> " + text], return_tensors='pt')
translation = model.generate(**tokenized_text)
translated_text = tokenizer.batch_decode(translation, skip_special_tokens=True)[0]
print(translated_text)
```
#### Training Hyperparameters
The following hyperparameters were used during training:
* learning_rate: 4e-5
* train_batch_size: 8
* optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
* num_epochs: 3
**BibTeX:**
```
@article{glazkova2023applying,
title={Applying Transformer-Based Text Summarization for Keyphrase Generation},
author={Glazkova, Anna and Morozov, Dmitry},
journal={Lobachevskii Journal of Mathematics},
volume={44},
number={1},
pages={123--136},
year={2023},
doi={10.1134/S1995080223010134}
}
``` |
BigSalmon/T5F | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
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"prefix": "summarize: "
},
"text-generation": {
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},
"translation_en_to_de": {
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"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | null | Users can easily and error-free convert PST to the MSG file format with the DataVare PST to MSG converter program All of the information in PST is extracted, including mail, messages, notes, contacts, etc. Even if you have little technical experience, you can easily convert PST files to MSG format thanks to the software's user-friendly interface. It runs on all Windows computers and supports multiple MS Outlook versions, including 2003, 2007, 2010, 2013, 2016, and 2019. It has a variety of distinctive characteristics, such as the ability to select the location where migrated files should be saved, mass migration, no file size restrictions, and many more. Each PST file database has a preview available for users to view. The program allows users to export specific PST file emails in MSG format. If you wish to test the functionality of the software before purchasing, you can also acquire the free demo version pack.
Read more :- https://www.datavare.com/software/pst-to-msg-converter-expert.html |
BigSalmon/T5Salmon | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | 2023-05-16T11:20:14Z | ---
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="henripett/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"])
```
|
Bimal/my_bot_model | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
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}
} | 10 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Bol-prod
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: sroie
type: sroie
config: discharge
split: test
args: discharge
metrics:
- name: Precision
type: precision
value: 0.3153409090909091
- name: Recall
type: recall
value: 0.2534246575342466
- name: F1
type: f1
value: 0.28101265822784816
- name: Accuracy
type: accuracy
value: 0.7894919972164232
---
<!-- 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. -->
# Bol-prod
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7637
- Precision: 0.3153
- Recall: 0.2534
- F1: 0.2810
- Accuracy: 0.7895
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.88 | 100 | 0.9728 | 0.2727 | 0.0205 | 0.0382 | 0.7683 |
| No log | 1.75 | 200 | 0.8037 | 0.4204 | 0.1507 | 0.2218 | 0.7898 |
| No log | 2.63 | 300 | 0.7796 | 0.2628 | 0.1872 | 0.2187 | 0.7832 |
| No log | 3.51 | 400 | 0.7074 | 0.2709 | 0.2215 | 0.2437 | 0.7937 |
| 0.787 | 4.39 | 500 | 0.7191 | 0.2844 | 0.2169 | 0.2461 | 0.7881 |
| 0.787 | 5.26 | 600 | 0.7395 | 0.2746 | 0.2100 | 0.2380 | 0.7891 |
| 0.787 | 6.14 | 700 | 0.7259 | 0.2845 | 0.2352 | 0.2575 | 0.7853 |
| 0.787 | 7.02 | 800 | 0.7584 | 0.3012 | 0.2283 | 0.2597 | 0.7895 |
| 0.787 | 7.89 | 900 | 0.7458 | 0.3031 | 0.2443 | 0.2705 | 0.7933 |
| 0.3761 | 8.77 | 1000 | 0.7637 | 0.3153 | 0.2534 | 0.2810 | 0.7895 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.2.2
- Tokenizers 0.13.3
|
Biniam/en_ti_translate | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
]
| translation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
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},
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}
} | 14 | 2023-05-16T11:42:09Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlmr-roberta-base-fintuned-panx-all
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. -->
# xlmr-roberta-base-fintuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1747
- F1: 0.8524
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3035 | 1.0 | 835 | 0.1883 | 0.8185 |
| 0.1556 | 2.0 | 1670 | 0.1770 | 0.8388 |
| 0.1004 | 3.0 | 2505 | 0.1747 | 0.8524 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BitanBiswas/mbert-bengali-ner-finetuned-ner | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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},
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}
} | 4 | null |
---
license: mit
language:
- en
pipeline_tag: text2text-generation
tags:
- legal
---
# t5-cbp-lkg-alt-w-context-small
Google's T5 model ([t5-small](https://huggingface.co/t5-small)) trained over a cleaned version of the Legal Knowledge Graph using the training method used for [KGT-5](https://huggingface.co/spaces/apoorvumang/kgt5) with additional sentences alongside the prompts.
|
Blabla/Pipipopo | []
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}
} | 0 | 2023-05-16T11:49:49Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: selfie of a happy sks man
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - ccsimon123/simon_path-to-save-model
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on selfie of a happy sks man using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
|
BlightZz/MakiseKurisu | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
} | 14 | null | ---
license: creativeml-openrail-m
datasets:
- gsdf/EasyNegative
- Duskfallcrew/DuskfallCrewArtStyle_Lora
- fka/awesome-chatgpt-prompts
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable diffusion
- anime
- merge
---
# Kilkenny Mix:
- (https://civitai.com/models/67389/kilkenny-mix)
## MODELS GETTING UPLOADED SOON BE PATIENT :3
Includes a complicated Lora mix, berry mix, and some other back mixed toys.
What this means is: Vox+Western Loras of Lykons + our Anime Model, plus some of the Neneko Splat/Eggnog line we've been working on. Beyond that we're unsure.
This is our attempt at a MOSTLY compatible with loras base model, and is not intended to rival with AnyLora because Lykon is so much cooler than us.
We're starting a new initiative with our models: Fighting for accessibility in Art for not just everyone but specifically mental and physical disability spaces. AI art isn't JUST a tool, it's a new way of expression for those unable to do so before.
PLEASE NOTE: IMAGES MAY NOT SHARE THE SAME FILE NAME AS MODELS THAT ARE UPLOADED. "KILKENNY EGGNOG" IS THE SAME AS CEL-FLAT, JUST THAT THE ORIGINAL HF FILE THAT WAS CKPT DIDNT GET THE SAME NAME UNTIL AFTER I DOWNLOADED IT FOR CIVIT!
VAE: We use KF-L-Anime, but it is entirely up to you.
We can provide one if you need, and at this stage we ARE working on baking a VAE into the cel-flat version. There is a slightly LESS flat version coming, as well as 2.5d version to Rival Epic Mix and Identity Disorder.
DOES THIS DO NSFW: IT MAY WE ARE NOT SURE, THERE IS BERRY MIX IN IT AND OTHER MODELS - BUT IT WAS NOT INTENDED FOR THIS, WE MAY MAKE A MORE NSFW UPDATE TO THIS IN FUTURE IF YOU NEED IT.
Will you help us with our target market research? : https://forms.gle/N1EQwZmZzdHMzP8H8
Join our Reddit: https://www.reddit.com/r/earthndusk/
Funding for a HUGE ART PROJECT THIS YEAR: https://www.buymeacoffee.com/duskfallxcrew / any chance you can spare a coffee or three? https://ko-fi.com/DUSKFALLcrew
If you got requests, or concerns, We're still looking for beta testers: JOIN THE DISCORD AND DEMAND THINGS OF US: https://discord.gg/Da7s8d3KJ7
Listen to the music that we've made that goes with our art: https://open.spotify.com/playlist/00R8x00YktB4u541imdSSf?si=b60d209385a74b38 |
BobBraico/distilbert-base-uncased-finetuned-imdb-accelerate | []
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} | 0 | null | ---
language:
- en
license: other
tags:
- text to 3d
- shap-e
- shape_e
- point-e
- blender
- Blender addon
inference: false
---
this is an implementation of the Shap-E openai
but is bunddel as an addon a blender
no need to download more glb files
use localy inside blender
https://devbud.gumroad.com/l/Shap-e
## Shap_E Addon Configuration Options
- **Prompt Batching:** This option controls how many prompts the AI processes at a time. If you have a large number of prompts, increasing the batch size can speed up the generation process. However, keep in mind that a larger batch size requires more GPU memory. If you encounter memory issues, try reducing the batch size.
-
- **Seed:** This value initializes the AI's random number generator. Changing the seed will yield different outputs for the same prompt. Setting the seed to 0 will generate a new random seed for each model, producing a variety of models from the same prompt.
- **Guidance Scale:** This controls the degree to which the AI attempts to match your prompt. A higher value means the AI works harder to match your description, whereas a lower value allows the AI more creative freedom. Adjust based on your needs.
- **Number of Inference Steps:** This determines the number of iterations the AI performs when generating your model. More steps can result in more detailed and accurate models, but will also take longer to generate.
- **Batch:** This is the number of models the addon will generate for each prompt in your batch. For example, if you set the Batch to 3 and enter two prompts, the addon will generate six models in total (three for each prompt).
These options provide a great deal of flexibility in controlling how the Shap_E addon generates your 3D models. By experimenting with these settings, you can find the perfect balance between speed, accuracy, and variety in your outputs.
For more information and tutorials on how to use these options effectively, please check out our [guide on the addon's page](https://devbud.gumroad.com/l/Shap-e). Also, don't forget to follow us on [Twitter](https://twitter.com/DevBud7) for the latest updates and tips! |
Boondong/Wandee | []
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} | 0 | null | ---
language: en
license: apache-2.0
library_name: pytorch
tags:
- deep-reinforcement-learning
- reinforcement-learning
- DI-engine
- BipedalWalker-v3
benchmark_name: OpenAI/Gym/Box2d
task_name: BipedalWalker-v3
pipeline_tag: reinforcement-learning
model-index:
- name: TD3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: OpenAI/Gym/Box2d-BipedalWalker-v3
type: OpenAI/Gym/Box2d-BipedalWalker-v3
metrics:
- type: mean_reward
value: 311.2 +/- 0.74
name: mean_reward
---
# Play **BipedalWalker-v3** with **TD3** Policy
## Model Description
<!-- Provide a longer summary of what this model is. -->
This is a simple **TD3** implementation to OpenAI/Gym/Box2d **BipedalWalker-v3** using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo).
**DI-engine** is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework.
## Model Usage
### Install the Dependencies
<details close>
<summary>(Click for Details)</summary>
```shell
# install huggingface_ding
git clone https://github.com/opendilab/huggingface_ding.git
pip3 install -e ./huggingface_ding/
# install environment dependencies if needed
pip3 install DI-engine[common_env]
```
</details>
### Git Clone from Huggingface and Run the Model
<details close>
<summary>(Click for Details)</summary>
```shell
# running with trained model
python3 -u run.py
```
**run.py**
```python
from ding.bonus import TD3Agent
from ding.config import Config
from easydict import EasyDict
import torch
# Pull model from files which are git cloned from huggingface
policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu"))
cfg = EasyDict(Config.file_to_dict("policy_config.py"))
# Instantiate the agent
agent = TD3Agent(
env="bipedalwalker", exp_name="BipedalWalker-v3-TD3", cfg=cfg.exp_config, policy_state_dict=policy_state_dict
)
# Continue training
agent.train(step=5000)
# Render the new agent performance
agent.deploy(enable_save_replay=True)
```
</details>
### Run Model by Using Huggingface_ding
<details close>
<summary>(Click for Details)</summary>
```shell
# running with trained model
python3 -u run.py
```
**run.py**
```python
from ding.bonus import TD3Agent
from huggingface_ding import pull_model_from_hub
# Pull model from Hugggingface hub
policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/BipedalWalker-v3-TD3")
# Instantiate the agent
agent = TD3Agent(
env="bipedalwalker",
exp_name="BipedalWalker-v3-TD3",
cfg=cfg.exp_config,
policy_state_dict=policy_state_dict
)
# Continue training
agent.train(step=5000)
# Render the new agent performance
agent.deploy(enable_save_replay=True)
```
</details>
## Model Training
### Train the Model and Push to Huggingface_hub
<details close>
<summary>(Click for Details)</summary>
```shell
#Training Your Own Agent
python3 -u train.py
```
**train.py**
```python
from ding.bonus import TD3Agent
from huggingface_ding import push_model_to_hub
# Instantiate the agent
agent = TD3Agent("bipedalwalker", exp_name="BipedalWalker-v3-TD3")
# Train the agent
return_ = agent.train(step=int(200000))
# Push model to huggingface hub
push_model_to_hub(
agent=agent.best,
env_name="OpenAI/Gym/Box2d",
task_name="BipedalWalker-v3",
algo_name="TD3",
wandb_url=return_.wandb_url,
github_repo_url="https://github.com/opendilab/DI-engine",
github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/td3.html",
github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/bipedalwalker.html",
installation_guide="pip3 install DI-engine[common_env]",
usage_file_by_git_clone="./td3/bipedalwalker_td3_deploy.py",
usage_file_by_huggingface_ding="./td3/bipedalwalker_td3_download.py",
train_file="./td3/bipedalwalker_td3.py",
repo_id="OpenDILabCommunity/BipedalWalker-v3-TD3"
)
```
</details>
**Configuration**
<details close>
<summary>(Click for Details)</summary>
```python
exp_config = {
'env': {
'manager': {
'episode_num': float("inf"),
'max_retry': 1,
'retry_type': 'reset',
'auto_reset': True,
'step_timeout': None,
'reset_timeout': None,
'retry_waiting_time': 0.1,
'cfg_type': 'BaseEnvManagerDict'
},
'stop_value': 10000000000,
'n_evaluator_episode': 5,
'env_id': 'BipedalWalker-v3',
'collector_env_num': 8,
'evaluator_env_num': 5,
'act_scale': True,
'rew_clip': True
},
'policy': {
'model': {
'twin_critic': True,
'obs_shape': 24,
'action_shape': 4,
'action_space': 'regression',
'actor_head_hidden_size': 400,
'critic_head_hidden_size': 400
},
'learn': {
'learner': {
'train_iterations': 1000000000,
'dataloader': {
'num_workers': 0
},
'log_policy': True,
'hook': {
'load_ckpt_before_run': '',
'log_show_after_iter': 1000,
'save_ckpt_after_iter': 10000,
'save_ckpt_after_run': True
},
'cfg_type': 'BaseLearnerDict'
},
'update_per_collect': 64,
'batch_size': 256,
'learning_rate_actor': 0.0003,
'learning_rate_critic': 0.0003,
'ignore_done': False,
'target_theta': 0.005,
'discount_factor': 0.99,
'actor_update_freq': 2,
'noise': True,
'noise_sigma': 0.2,
'noise_range': {
'min': -0.5,
'max': 0.5
}
},
'collect': {
'collector': {},
'unroll_len': 1,
'noise_sigma': 0.1,
'n_sample': 64
},
'eval': {
'evaluator': {
'eval_freq': 5000,
'render': {
'render_freq': -1,
'mode': 'train_iter'
},
'cfg_type': 'InteractionSerialEvaluatorDict',
'stop_value': 10000000000,
'n_episode': 5
}
},
'other': {
'replay_buffer': {
'replay_buffer_size': 300000
}
},
'on_policy': False,
'cuda': True,
'multi_gpu': False,
'bp_update_sync': True,
'traj_len_inf': False,
'type': 'td3',
'priority': False,
'priority_IS_weight': False,
'random_collect_size': 10000,
'transition_with_policy_data': False,
'action_space': 'continuous',
'reward_batch_norm': False,
'multi_agent': False,
'cfg_type': 'TD3PolicyDict'
},
'exp_name': 'BipedalWalker-v3-TD3',
'seed': 0,
'wandb_logger': {
'gradient_logger': True,
'video_logger': True,
'plot_logger': True,
'action_logger': True,
'return_logger': False
}
}
```
</details>
**Training Procedure**
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
- **Weights & Biases (wandb):** [monitor link](https://wandb.ai/zhangpaipai/BipedalWalker-v3-TD3)
## Model Information
<!-- Provide the basic links for the model. -->
- **Github Repository:** [repo link](https://github.com/opendilab/DI-engine)
- **Doc**: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/td3.html)
- **Configuration:** [config link](https://huggingface.co/OpenDILabCommunity/BipedalWalker-v3-TD3/blob/main/policy_config.py)
- **Demo:** [video](https://huggingface.co/OpenDILabCommunity/BipedalWalker-v3-TD3/blob/main/replay.mp4)
<!-- Provide the size information for the model. -->
- **Parameters total size:** 2018.77 KB
- **Last Update Date:** 2023-05-16
## Environments
<!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. -->
- **Benchmark:** OpenAI/Gym/Box2d
- **Task:** BipedalWalker-v3
- **Gym version:** 0.25.1
- **DI-engine version:** v0.4.7
- **PyTorch version:** 1.7.1
- **Doc**: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/bipedalwalker.html)
|
Bosio/full-sentence-distillroberta3-finetuned-wikitext2 | []
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} | 0 | null | ---
tags:
- allennlp
---
# TODO: Fill this model card
|
BossLee/t5-gec | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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"T5ForConditionalGeneration"
],
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | null | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: AlexCagareli/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Botjallu/DialoGPT-small-harrypotter | []
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} | 0 | null | ---
license: other
inference: false
---
[Alpacino 30B model card](https://huggingface.co/digitous/Alpacino30b) |
Branex/gpt-neo-2.7B | []
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}
} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: finetuned_beliefs_sentiment_classifier_experiment1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_beliefs_sentiment_classifier_experiment1
This model is a fine-tuned version of [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) 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-05
- 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: 15
### Training results
### Framework versions
- Transformers 4.27.3
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Brendan/cse244b-hw2-roberta | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
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],
"model_type": "roberta",
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} | 28 | null | ---
license: openrail
---
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("openai-gpt")
model = AutoModelForCausalLM.from_pretrained("openai-gpt") |
BrianTin/MTBERT | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
],
"model_type": "bert",
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} | 11 | null | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: gaioNL/ppo-PyramidsTargetTESTCOLAB
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BritishLibraryLabs/bl-books-genre | [
"pytorch",
"distilbert",
"text-classification",
"multilingual",
"dataset:blbooksgenre",
"transformers",
"genre",
"books",
"library",
"historic",
"glam ",
"lam",
"license:mit",
"has_space"
]
| text-classification | {
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"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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}
} | 76 | 2023-05-16T12:30:04Z | ---
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="Shreya15/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"])
```
|
Brona/model1 | []
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: geocoding-distilbert
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. -->
# geocoding-distilbert
This model is a fine-tuned version of [quiqup/geocoding-distilbert](https://huggingface.co/quiqup/geocoding-distilbert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 76
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 19000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 8.2803 | 0.01 | 100 | 6.3604 |
| 5.7629 | 0.01 | 200 | 5.3858 |
| 5.2488 | 0.02 | 300 | 5.0905 |
| 4.9256 | 0.02 | 400 | 4.7698 |
| 4.6626 | 0.03 | 500 | 4.4904 |
| 4.4336 | 0.03 | 600 | 4.2857 |
| 4.199 | 0.04 | 700 | 4.0388 |
| 4.062 | 0.05 | 800 | 3.8872 |
| 3.9114 | 0.05 | 900 | 3.7682 |
| 3.8056 | 0.06 | 1000 | 3.7025 |
| 3.6791 | 0.06 | 1100 | 3.5815 |
| 3.6271 | 0.07 | 1200 | 3.4744 |
| 3.4852 | 0.08 | 1300 | 3.4126 |
| 3.4419 | 0.08 | 1400 | 3.3627 |
| 3.3539 | 0.09 | 1500 | 3.2669 |
| 3.3381 | 0.09 | 1600 | 3.2396 |
| 3.2712 | 0.1 | 1700 | 3.1749 |
| 3.2506 | 0.1 | 1800 | 3.1463 |
| 3.2092 | 0.11 | 1900 | 3.0901 |
| 3.1523 | 0.12 | 2000 | 3.0544 |
| 3.0956 | 0.12 | 2100 | 3.0069 |
| 3.0704 | 0.13 | 2200 | 2.9820 |
| 3.0169 | 0.13 | 2300 | 2.9671 |
| 2.9922 | 0.14 | 2400 | 2.9352 |
| 2.9984 | 0.15 | 2500 | 2.8834 |
| 2.9756 | 0.15 | 2600 | 2.8630 |
| 2.8685 | 0.16 | 2700 | 2.8382 |
| 2.9008 | 0.16 | 2800 | 2.8055 |
| 2.8314 | 0.17 | 2900 | 2.7854 |
| 2.8739 | 0.17 | 3000 | 2.7568 |
| 2.7613 | 0.18 | 3100 | 2.7576 |
| 2.786 | 0.19 | 3200 | 2.7522 |
| 2.7849 | 0.19 | 3300 | 2.6794 |
| 2.754 | 0.2 | 3400 | 2.6971 |
| 2.7089 | 0.2 | 3500 | 2.6603 |
| 2.6887 | 0.21 | 3600 | 2.6335 |
| 2.6893 | 0.22 | 3700 | 2.6443 |
| 2.7216 | 0.22 | 3800 | 2.6132 |
| 2.6843 | 0.23 | 3900 | 2.6123 |
| 2.6496 | 0.23 | 4000 | 2.6069 |
| 2.6643 | 0.24 | 4100 | 2.5521 |
| 2.633 | 0.24 | 4200 | 2.5418 |
| 2.6066 | 0.25 | 4300 | 2.5347 |
| 2.6027 | 0.26 | 4400 | 2.5198 |
| 2.5827 | 0.26 | 4500 | 2.5223 |
| 2.5648 | 0.27 | 4600 | 2.4983 |
| 2.5928 | 0.27 | 4700 | 2.4827 |
| 2.504 | 0.28 | 4800 | 2.4892 |
| 2.5287 | 0.29 | 4900 | 2.4575 |
| 2.5497 | 0.29 | 5000 | 2.4494 |
| 2.481 | 0.3 | 5100 | 2.4377 |
| 2.5096 | 0.3 | 5200 | 2.4153 |
| 2.4685 | 0.31 | 5300 | 2.4381 |
| 2.4669 | 0.31 | 5400 | 2.4209 |
| 2.4721 | 0.32 | 5500 | 2.4052 |
| 2.4796 | 0.33 | 5600 | 2.3862 |
| 2.4325 | 0.33 | 5700 | 2.3837 |
| 2.4152 | 0.34 | 5800 | 2.3779 |
| 2.4232 | 0.34 | 5900 | 2.3965 |
| 2.4212 | 0.35 | 6000 | 2.3540 |
| 2.3425 | 0.35 | 6100 | 2.3510 |
| 2.424 | 0.36 | 6200 | 2.3299 |
| 2.3782 | 0.37 | 6300 | 2.3330 |
| 2.3606 | 0.37 | 6400 | 2.3280 |
| 2.3713 | 0.38 | 6500 | 2.3271 |
| 2.3547 | 0.38 | 6600 | 2.3147 |
| 2.3507 | 0.39 | 6700 | 2.3180 |
| 2.3037 | 0.4 | 6800 | 2.2910 |
| 2.3604 | 0.4 | 6900 | 2.2772 |
| 2.3376 | 0.41 | 7000 | 2.2806 |
| 2.3491 | 0.41 | 7100 | 2.2735 |
| 2.3078 | 0.42 | 7200 | 2.2753 |
| 2.2707 | 0.42 | 7300 | 2.2621 |
| 2.2798 | 0.43 | 7400 | 2.2387 |
| 2.2881 | 0.44 | 7500 | 2.2301 |
| 2.3057 | 0.44 | 7600 | 2.2536 |
| 2.2814 | 0.45 | 7700 | 2.2054 |
| 2.2754 | 0.45 | 7800 | 2.2397 |
| 2.2768 | 0.46 | 7900 | 2.2175 |
| 2.2765 | 0.47 | 8000 | 2.2245 |
| 2.3096 | 0.47 | 8100 | 2.2115 |
| 2.2478 | 0.48 | 8200 | 2.2094 |
| 2.2819 | 0.48 | 8300 | 2.1975 |
| 2.2344 | 0.49 | 8400 | 2.2057 |
| 2.2571 | 0.49 | 8500 | 2.1821 |
| 2.2622 | 0.5 | 8600 | 2.1775 |
| 2.2324 | 0.51 | 8700 | 2.1671 |
| 2.2461 | 0.51 | 8800 | 2.1661 |
| 2.2231 | 0.52 | 8900 | 2.1378 |
| 2.217 | 0.52 | 9000 | 2.1619 |
| 2.2092 | 0.53 | 9100 | 2.1637 |
| 2.1981 | 0.54 | 9200 | 2.1723 |
| 2.1742 | 0.54 | 9300 | 2.1520 |
| 2.2177 | 0.55 | 9400 | 2.1384 |
| 2.2005 | 0.55 | 9500 | 2.1500 |
| 2.1962 | 0.56 | 9600 | 2.1322 |
| 2.2034 | 0.56 | 9700 | 2.1270 |
| 2.1617 | 0.57 | 9800 | 2.1153 |
| 2.1765 | 0.58 | 9900 | 2.1040 |
| 2.196 | 0.58 | 10000 | 2.1253 |
| 2.152 | 0.59 | 10100 | 2.1284 |
| 2.1766 | 0.59 | 10200 | 2.1164 |
| 2.1797 | 0.6 | 10300 | 2.1131 |
| 2.1429 | 0.61 | 10400 | 2.1061 |
| 2.1775 | 0.61 | 10500 | 2.0991 |
| 2.1807 | 0.62 | 10600 | 2.1193 |
| 2.1494 | 0.62 | 10700 | 2.0908 |
| 2.1137 | 0.63 | 10800 | 2.1065 |
| 2.158 | 0.63 | 10900 | 2.1092 |
| 2.1469 | 0.64 | 11000 | 2.1010 |
| 2.137 | 0.65 | 11100 | 2.0837 |
| 2.1439 | 0.65 | 11200 | 2.0802 |
| 2.1627 | 0.66 | 11300 | 2.0720 |
| 2.1383 | 0.66 | 11400 | 2.0598 |
| 2.1333 | 0.67 | 11500 | 2.0925 |
| 2.0659 | 0.67 | 11600 | 2.0736 |
| 2.1113 | 0.68 | 11700 | 2.0729 |
| 2.1179 | 0.69 | 11800 | 2.0657 |
| 2.1032 | 0.69 | 11900 | 2.0590 |
| 2.0816 | 0.7 | 12000 | 2.0580 |
| 2.0853 | 0.7 | 12100 | 2.0676 |
| 2.0899 | 0.71 | 12200 | 2.0383 |
| 2.1198 | 0.72 | 12300 | 2.0487 |
| 2.0703 | 0.72 | 12400 | 2.0413 |
| 2.1062 | 0.73 | 12500 | 2.0383 |
| 2.1174 | 0.73 | 12600 | 2.0293 |
| 2.0865 | 0.74 | 12700 | 2.0391 |
| 2.1324 | 0.74 | 12800 | 2.0214 |
| 2.1093 | 0.75 | 12900 | 2.0338 |
| 2.0291 | 0.76 | 13000 | 2.0308 |
| 2.0982 | 0.76 | 13100 | 2.0118 |
| 2.0973 | 0.77 | 13200 | 2.0172 |
| 2.0734 | 0.77 | 13300 | 2.0320 |
| 2.0783 | 0.78 | 13400 | 1.9988 |
| 2.0534 | 0.79 | 13500 | 2.0026 |
| 2.0437 | 0.79 | 13600 | 1.9922 |
| 2.075 | 0.8 | 13700 | 2.0294 |
| 2.0853 | 0.8 | 13800 | 2.0176 |
| 2.0764 | 0.81 | 13900 | 2.0203 |
| 2.0276 | 0.81 | 14000 | 2.0157 |
| 2.0878 | 0.82 | 14100 | 2.0159 |
| 2.0593 | 0.83 | 14200 | 1.9711 |
| 2.0459 | 0.83 | 14300 | 1.9769 |
| 2.06 | 0.84 | 14400 | 1.9968 |
| 2.0401 | 0.84 | 14500 | 1.9899 |
| 2.0153 | 0.85 | 14600 | 1.9810 |
| 2.0619 | 0.86 | 14700 | 1.9786 |
| 2.0173 | 0.86 | 14800 | 1.9936 |
| 2.0191 | 0.87 | 14900 | 1.9921 |
| 2.089 | 0.87 | 15000 | 1.9845 |
| 2.0528 | 0.88 | 15100 | 1.9910 |
| 2.0337 | 0.88 | 15200 | 1.9553 |
| 2.0199 | 0.89 | 15300 | 1.9877 |
| 2.0571 | 0.9 | 15400 | 1.9728 |
| 2.0239 | 0.9 | 15500 | 1.9739 |
| 2.0394 | 0.91 | 15600 | 1.9829 |
| 2.0295 | 0.91 | 15700 | 1.9536 |
| 2.0406 | 0.92 | 15800 | 1.9591 |
| 2.0 | 0.93 | 15900 | 1.9558 |
| 2.0305 | 0.93 | 16000 | 1.9489 |
| 2.022 | 0.94 | 16100 | 1.9764 |
| 2.0147 | 0.94 | 16200 | 1.9578 |
| 1.9919 | 0.95 | 16300 | 1.9474 |
| 2.0214 | 0.95 | 16400 | 1.9585 |
| 1.9778 | 0.96 | 16500 | 1.9674 |
| 1.9946 | 0.97 | 16600 | 1.9555 |
| 2.0009 | 0.97 | 16700 | 1.9464 |
| 2.0162 | 0.98 | 16800 | 1.9498 |
| 2.0185 | 0.98 | 16900 | 1.9632 |
| 2.0301 | 0.99 | 17000 | 1.9539 |
| 2.0253 | 0.99 | 17100 | 1.9608 |
| 2.0156 | 1.0 | 17200 | 1.9365 |
| 1.9676 | 1.01 | 17300 | 1.9522 |
| 2.0011 | 1.01 | 17400 | 1.9681 |
| 1.9976 | 1.02 | 17500 | 1.9669 |
| 1.9721 | 1.02 | 17600 | 1.9555 |
| 1.9853 | 1.03 | 17700 | 1.9554 |
| 2.0065 | 1.04 | 17800 | 1.9498 |
| 1.9698 | 1.04 | 17900 | 1.9490 |
| 1.9955 | 1.05 | 18000 | 1.9370 |
| 1.9871 | 1.05 | 18100 | 1.9711 |
| 1.9938 | 1.06 | 18200 | 1.9524 |
| 1.965 | 1.06 | 18300 | 1.9574 |
| 2.0139 | 1.07 | 18400 | 1.9277 |
| 1.9647 | 1.08 | 18500 | 1.9459 |
| 1.9998 | 1.08 | 18600 | 1.9632 |
| 1.9829 | 1.09 | 18700 | 1.9356 |
| 1.9673 | 1.09 | 18800 | 1.9423 |
| 1.9841 | 1.1 | 18900 | 1.9579 |
| 1.9752 | 1.11 | 19000 | 1.9447 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Bryan190/Aguy190 | []
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} | 0 | 2023-05-16T12:35:26Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxiV3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.70
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="henripett/taxiV3", 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"])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
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"BertForSequenceClassification"
],
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} | 42 | 2023-05-16T13:02:58Z | ---
tags:
- generated_from_trainer
model-index:
- name: finetuned_beliefs_sentiment_classifier_experiment2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_beliefs_sentiment_classifier_experiment2
This model is a fine-tuned version of [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) 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-05
- 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: 10
### Training results
### Framework versions
- Transformers 4.27.3
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-da-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 42 | 2023-05-16T13:08:51Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
CAMeL-Lab/bert-base-arabic-camelbert-da-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
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}
} | 37 | null | ---
license: openrail
---
this is a lora trained from works created by [@QuAn_](https://www.pixiv.net/users/6657532)
I didn't get permission from the original artist, I just liked his work and used it for learning.
Hopefully it will not be used for commercial purposes.
If there is any infringement of the original artist's copyright, please contact me and I will delete it
|
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-msa | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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}
} | 27 | null | <img alt="" src="https://huggingface.co/guon/lora-test-2/resolve/main/lora-test-2.png">
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus6 | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
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"BertForSequenceClassification"
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}
} | 34 | 2023-05-16T13:20:59Z | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: canpush_EElayoutlmv3_rvl-cdip_single_10_2023-05-16
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# canpush_EElayoutlmv3_rvl-cdip_single_10_2023-05-16
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|
| No log | 1.0 | 160 | 2.7002 | 0.1187 | 0.0688 | 0.0688 | 0.125 | 0.0625 | 0.0625 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.12.0
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| token-classification | {
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} | 1,860 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: nexsol_koployglot-1.3b
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. -->
# nexsol_koployglot-1.3b
This model is a fine-tuned version of [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9406
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 421 | 2.9864 |
| 2.8966 | 2.0 | 843 | 3.1644 |
| 1.8858 | 2.99 | 1263 | 3.9406 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
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} | 31 | 2023-05-16T13:23:44Z | ---
license: apache-2.0
---
# Model Card for AVA Image Clip Embeddings
The AVA image dataset is a collection of digital photos with ratings. It was used to create the visual scorer that evaluated the images in Laion 5B to create the the Laion-Aesthetics dataset
https://github.com/imfing/ava_downloader/
“AVA: A Large-Scale Database for Aesthetic Visual Analysis”.
Naila Murray, Luca Marchesotti, Florent Perronnin, CVPR 2012.
New aesthetics scorer: https://github.com/kenjiqq/aesthetics-scorer/
Original aesthetics scorer: https://github.com/christophschuhmann/improved-aesthetic-predictor/
They were processed with OpenClip BigG-14, L-14, and H-14 models.
* "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
* "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
* "laion/CLIP-ViT-L-14-laion2B-s32B-b82K"
https://github.com/mlfoundations/open_clip
**Not all images were processed!**
Refer to the parquet for the succesfully processed images.
The parquet fields are
- "image_name", #same id as AVA csv
- "pooled_output"
- "projected_embedding"
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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}
} | 1,862 | 2023-05-16T13:27:59Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: phil-bgm/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
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"BertForSequenceClassification"
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} | 855 | null | ---
license: bsd-3-clause
---
# InstructCodeT5+ 16B
## Model description
[CodeT5+](https://github.com/salesforce/CodeT5/tree/main/CodeT5+) is a new family of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. _encoder-only_, _decoder-only_, and _encoder-decoder_) to support a wide range of code understanding and generation tasks.
It is introduced in the paper:
[CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://arxiv.org/pdf/2305.07922.pdf)
by [Yue Wang](https://yuewang-cuhk.github.io/)\*, [Hung Le](https://sites.google.com/view/henryle2018/home?pli=1)\*, [Akhilesh Deepak Gotmare](https://akhileshgotmare.github.io/), [Nghi D.Q. Bui](https://bdqnghi.github.io/), [Junnan Li](https://sites.google.com/site/junnanlics), [Steven C.H. Hoi](https://sites.google.com/view/stevenhoi/home) (* indicates equal contribution).
Compared to the original CodeT5 family (base: `220M`, large: `770M`), CodeT5+ is pretrained with a diverse set of pretraining tasks including _span denoising_, _causal language modeling_, _contrastive learning_, and _text-code matching_ to learn rich representations from both unimodal code data and bimodal code-text data.
Additionally, it employs a simple yet effective _compute-efficient pretraining_ method to initialize the model components with frozen off-the-shelf LLMs such as [CodeGen](https://github.com/salesforce/CodeGen) to efficiently scale up the model (i.e. `2B`, `6B`, `16B`), and adopts a "shallow encoder and deep decoder" architecture.
Furthermore, it is instruction-tuned to align with natural language instructions (see our InstructCodeT5+ 16B) following [Code Alpaca](https://github.com/sahil280114/codealpaca).
## How to use
This model can be easily loaded using the `AutoModelForSeq2SeqLM` functionality and employs the same tokenizer as [CodeGen](https://github.com/salesforce/CodeGen).
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
checkpoint = "Salesforce/instructcodet5p-16b"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
trust_remote_code=True).to(device)
encoding = tokenizer("def print_hello_world():", return_tensors="pt").to(device)
encoding['decoder_input_ids'] = encoding['input_ids'].clone()
outputs = model.generate(**encoding, max_length=15)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Pretraining data
This checkpoint is trained on the stricter permissive subset of the deduplicated version of the [github-code dataset](https://huggingface.co/datasets/codeparrot/github-code).
The data is preprocessed by reserving only permissively licensed code ("mit" “apache-2”, “bsd-3-clause”, “bsd-2-clause”, “cc0-1.0”, “unlicense”, “isc”).
Supported languages (9 in total) are as follows:
`c`, `c++`, `c-sharp`, `go`, `java`, `javascript`, `php`, `python`, `ruby.`
## Training procedure
This checkpoint is initialized from off-the-shelf LLMs, i.e. its encoder is initialized from [CodeGen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) and its decoder is initialized from [CodeGen-16B-mono](https://huggingface.co/Salesforce/codegen-16B-mono).
It is trained on the unimodal code data at the first-stage pretraining, which includes a diverse set of pretraining tasks including _span denoising_ and two variants of _causal language modeling_.
After that, it is further trained on the Python subset with the causal language modeling objective for another epoch to better adapt for Python code generation.
Finally, we apply instruction tuning to align it with natural language instructions following [Code Alpaca](https://github.com/sahil280114/codealpaca).
Please refer to the paper for more details.
## Evaluation results
CodeT5+ models have been comprehensively evaluated on a wide range of code understanding and generation tasks in various settings: _zero-shot_, _finetuning_, and _instruction-tuning_.
Specifically, CodeT5+ yields substantial performance gains on many downstream tasks compared to their SoTA baselines, e.g.,
8 text-to-code retrieval tasks (+3.2 avg. MRR), 2 line-level code completion tasks (+2.1 avg. Exact Match), and 2 retrieval-augmented code generation tasks (+5.8 avg. BLEU-4).
In 2 math programming tasks on MathQA-Python and GSM8K-Python, CodeT5+ models of below billion-parameter sizes significantly outperform many LLMs of up to 137B parameters.
Particularly, in the zero-shot text-to-code generation task on HumanEval benchmark, InstructCodeT5+ 16B sets new SoTA results of 35.0% pass@1 and 54.5% pass@10 against other open code LLMs, even surpassing the closed-source OpenAI code-cushman-001 mode
Please refer to the [paper](https://arxiv.org/pdf/2305.07922.pdf) for more details.
## BibTeX entry and citation info
```bibtex
@article{wang2023codet5plus,
title={CodeT5+: Open Code Large Language Models for Code Understanding and Generation},
author={Wang, Yue and Le, Hung and Gotmare, Akhilesh Deepak and Bui, Nghi D.Q. and Li, Junnan and Hoi, Steven C. H.},
journal={arXiv preprint},
year={2023}
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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} | 26 | 2023-05-16T13:43:56Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikitext
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the wikitext dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6436
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.5784 | 1.0 | 2334 | 3.6436 |
| 3.5882 | 2.0 | 4668 | 3.6436 |
| 3.5858 | 3.0 | 7002 | 3.6436 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.10.0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-msa | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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} | 2,967 | 2023-05-16T13:44:29Z | Finaly, merged some loras and work.
1. wizardLM-unsensored + finnish + Germany + Italian + esJoke. 7B
2. wizardLM-unsensored + StarCoder(lora.) 13B
for serious, I found out it could be worse to merge more loras without test.
it's better be tested in llama.cpp.
---------------------------
sorry for messed up.
sorry again, Now Everything Works Fine.
------------------------------
Be carefully since it's uncensored, it's your responsiblility to take it seriouslly,
if you download that model, you do agree not using it in any circumstance leads to hurt anything or anybody include yourself in any dimensions ( not only physical but also mental).
thank you! |
CAUKiel/JavaBERT | [
"pytorch",
"safetensors",
"bert",
"fill-mask",
"code",
"arxiv:2110.10404",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
],
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} | 388 | 2023-05-16T13:46:40Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
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-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0135
- Rouge1: 16.5421
- Rouge2: 7.9012
- Rougel: 16.2574
- Rougelsum: 16.1537
## 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: 5.6e-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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.4509 | 1.0 | 1209 | 3.1308 | 17.5055 | 8.164 | 16.9714 | 16.8977 |
| 3.4226 | 2.0 | 2418 | 3.0489 | 16.7302 | 8.1598 | 16.3268 | 16.3168 |
| 3.286 | 3.0 | 3627 | 3.0366 | 16.7244 | 7.9017 | 16.3893 | 16.3728 |
| 3.1859 | 4.0 | 4836 | 3.0219 | 16.9671 | 8.0508 | 16.6206 | 16.5261 |
| 3.1249 | 5.0 | 6045 | 3.0353 | 17.3032 | 8.0195 | 16.9664 | 16.972 |
| 3.0665 | 6.0 | 7254 | 3.0272 | 17.0115 | 7.88 | 16.7424 | 16.7476 |
| 3.0407 | 7.0 | 8463 | 3.0122 | 17.3339 | 8.0171 | 16.9919 | 16.9449 |
| 3.0248 | 8.0 | 9672 | 3.0135 | 16.5421 | 7.9012 | 16.2574 | 16.1537 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CBreit00/DialoGPT_small_Rick | []
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} | 0 | 2023-05-16T13:49:15Z | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
CL/safe-math-bot | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: VLUE_pretrained-finetuned-vsmec
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. -->
# VLUE_pretrained-finetuned-vsmec
This model is a fine-tuned version of [ThuanPhong/VLUE_pretrained](https://huggingface.co/ThuanPhong/VLUE_pretrained) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2474
- Accuracy: 0.6647
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 347 | 1.0436 | 0.6312 |
| 1.1322 | 2.0 | 694 | 0.9662 | 0.6370 |
| 0.6705 | 3.0 | 1041 | 1.0870 | 0.6385 |
| 0.6705 | 4.0 | 1388 | 1.2474 | 0.6647 |
| 0.3498 | 5.0 | 1735 | 1.4678 | 0.6327 |
| 0.1686 | 6.0 | 2082 | 1.5225 | 0.6399 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CLAck/vi-en | [
"pytorch",
"marian",
"text2text-generation",
"en",
"vi",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | {
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} | 6 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 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="Tingwen/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"])
```
|
CLTL/MedRoBERTa.nl | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"transformers",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
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} | 2,988 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: EElayoutlmv3_rvl-cdip_single_10_2023-05-16_withoutdoublesave
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. -->
# EElayoutlmv3_rvl-cdip_single_10_2023-05-16_withoutdoublesave
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|
| No log | 1.0 | 160 | 2.7002 | 0.1187 | 0.0688 | 0.0688 | 0.125 | 0.0625 | 0.0625 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.12.0
- Tokenizers 0.12.1
|
CLTL/gm-ner-xlmrbase | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"nl",
"transformers",
"dighum",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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} | 2 | null | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: source
dtype: string
- name: subset
dtype: string
splits:
- name: train
num_bytes: 63759065
num_examples: 23652
- name: validation
num_bytes: 6190242
num_examples: 2042
- name: test
num_bytes: 6080212
num_examples: 2045
download_size: 45525146
dataset_size: 76029519
task_categories:
- text2text-generation
- text-generation
- question-answering
- conversational
- summarization
- table-question-answering
language:
- en
tags:
- instruction-tuning
pretty_name: longform
size_categories:
- 10K<n<100K
---
# LongForm
The LongForm dataset is created by leveraging English corpus
examples with augmented instructions. We select a
diverse set of human-written
documents from existing corpora such as C4 and
Wikipedia and generate instructions for the given
documents via LLMs. Then, we extend these examples with structured corpora examples such as Stack Exchange and WikiHow and task examples such as question answering, email writing, grammar error correction, story/poem generation, and text summarization.
## Distribution
The distribution of the LongForm dataset in terms of the source of examples is below. It contains examples generated from raw text corpora via LLMs, structured corpus examples, as well as various NLP task examples such as email writing, grammar error correction, story/poem generation, and text summarization.
| **Type** | **Source** | **Number of Examples** |
|------------------------|----------------|------------------------|
| **Corpora** | C4 | 10,000 |
| | Wikipedia | 5,000 |
| **Structured Corpora** | Stack Exchange | 4,380 |
| | WikiHow | 2,500 |
| **Tasks** | NIv2 | 3,684 |
| | Big Bench | 600 |
| | BEA-GEC | 1,203 |
| | Enron | 372 |
| **Total** | | 27,739 |
| | | |
| **Train** | | 23,652 |
| **Validation** | | 2,042 |
| **Test** | | 2,045 |
## Models
| | **All** | **Recipe Generation** | **ELI5** | **Writing Prompts** |
|-----------------------|---------|-----------------------------------|----------|---------------------|
| **T0++** | 10.9 | 18.7 | 3.8 | 10.2 |
| **Tk-Instruct** | 6.3 | 12.9* | 3.6 | 2.4 |
| **Flan-T5** | 10.6 | 20.9* | 3.5 | 7.4 |
| **Alpaca-LLaMA-7B** | 14.6 | 19.5 | 12.5 | 11.8 |
| **OPT-30B** | 11.1 | 18.6 | 12.2 | 2.6 |
| **[LongForm-T5-XL](https://huggingface.co/akoksal/LongForm-T5-XL)** | 16.3 | 20.2 | 18.3 | 10.6 |
| **[LongForm-OPT-2.7B](https://huggingface.co/akoksal/LongForm-OPT-2.7B)** | 17.8 | 15.5 | 17.9 | **19.9** |
| **[LongForm-OPT-6.7B](https://huggingface.co/akoksal/LongForm-OPT-6.7B)** | 17.7 | 16.9 | 17.2 | 19.0 |
| [**LongForm-LLaMA-7B**](https://huggingface.co/akoksal/LongForm-LLaMA-7B-diff)‡ | **19.7** | **21.7** | **18.6** | 18.9 |
‡: We can just release the difference between LongForm-LLaMA-7B and pretrained LLaMA-7B publicly due to restrictions of LLaMA models.
## Examples
| **Source** | **Instruction** | **Output**|
|------------------------|----------------|------------------------|
| C4 | What is the likelihood that Donald Trump's HHS secretary nominee, Alex Azar, will push policies that benefit pharmaceutical companies' pocketbooks? | His record suggests he’s unlikely to push pricing policies that would hit pharmaceutical companies’ pocketbooks.<br />President Donald Trump tweeted Monday that his nominee for HHS secretary, Alex Azar will “be a star for … lower drug prices!” But the record of the former top executive for Eli Lilly, which tripled the price of a top-selling insulin drug while he led its U.S. operation, suggests a different story.<br />Lilly is one of three drug companies targeted by a class-action lawsuit that accuses the company[...]<br />“This nominee has impeccable big pharma credentials but non-existent credentials when it comes to concerns on drug prices,” said Peter Welch, co-chair of the House Democrats Drug Pricing Task Force.
|Wikipedia | "Where is San Saba located in Rome, Italy?" Respond in 2 sentences. |San Saba is an ancient basilica church in Rome, Italy. It lies on the so-called "Piccolo Aventino", which is an area close to the ancient Aurelian Walls next to the Aventine Hill and Caelian Hill.|
## Dataset Description
- **Repository:** https://github.com/akoksal/LongForm
- **Paper:** https://arxiv.org/abs/2304.08460
- **Version:** v1.0 - April 18, 2023
- **Contact:** [Abdullatif Köksal](https://twitter.com/akoksal_)
## License
The LongForm project is subject to a MIT License with custom limitations for restrictions imposed by OpenAI (for the instruction generation part), as well as the license of language models (OPT, LLaMA, and T5).
## Citation
```
@misc{koksal2023longform,
title={LongForm: Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction},
author={Abdullatif Köksal and Timo Schick and Anna Korhonen and Hinrich Schütze},
year={2023},
eprint={2304.08460},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
CLTL/icf-levels-enr | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
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"RobertaForSequenceClassification"
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} | 30 | 2023-05-16T14:19:35Z | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: EElayoutlmv3_rvl-cdip_single_10_2023-05-16_withoutdoublesavecheck
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. -->
# EElayoutlmv3_rvl-cdip_single_10_2023-05-16_withoutdoublesavecheck
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4923
- Accuracy: 0.2812
- Exit 0 Accuracy: 0.0938
- Exit 1 Accuracy: 0.1187
- Exit 2 Accuracy: 0.225
- Exit 3 Accuracy: 0.0938
- Exit 4 Accuracy: 0.075
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|
| No log | 1.0 | 160 | 2.7258 | 0.1187 | 0.0625 | 0.0813 | 0.1062 | 0.0625 | 0.0625 |
| No log | 2.0 | 320 | 2.5812 | 0.2062 | 0.0875 | 0.1313 | 0.2062 | 0.125 | 0.0688 |
| No log | 3.0 | 480 | 2.4923 | 0.2812 | 0.0938 | 0.1187 | 0.225 | 0.0938 | 0.075 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.12.0
- Tokenizers 0.12.1
|
CLTL/icf-levels-ins | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
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} | 32 | null | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# /home/cedwin/log_c_pt_2/
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("/home/cedwin/log_c_pt_2/")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
CM-CA/DialoGPT-small-cartman | []
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} | 0 | null | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: >-
https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
pipeline_tag: conversational
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [togethercomputer/RedPajama-INCITE-Chat-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.28.1
pip install accelerate==0.18.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="TamedWicked/redpajama-hoteldata-test-5-epochs.2",
torch_dtype=torch.float16,
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<human>:Why is drinking water so healthy?<|endoftext|><bot>:
```
Alternatively, if you prefer to not use `trust_remote_code=True` you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"TamedWicked/redpajama-hoteldata-test-5-epochs.2",
use_fast=True,
padding_side="left"
)
model = AutoModelForCausalLM.from_pretrained(
"TamedWicked/redpajama-hoteldata-test-5-epochs.2",
torch_dtype=torch.float16,
device_map={"": "cuda:0"}
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "TamedWicked/redpajama-hoteldata-test-5-epochs.2" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<human>:How are you?<|endoftext|><bot>:"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50432, 2560)
(layers): ModuleList(
(0-31): 32 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=2560, out_features=7680, bias=True)
(dense): Linear(in_features=2560, out_features=2560, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)
(dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2560, out_features=50432, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Model Validation
Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
```bash
CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=TamedWicked/redpajama-hoteldata-test-5-epochs.2 --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
```
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
} | 28 | null | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# nihiluis/argureviews-sentiment-mpnet
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("nihiluis/argureviews-sentiment-mpnet")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
CNT-UPenn/RoBERTa_for_seizureFrequency_QA | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
} | 5 | null | Access to model guqun/whisper-small-bobo-chinese is restricted and you are not in the authorized list. Visit https://huggingface.co/guqun/whisper-small-bobo-chinese to ask for access. |
CZWin32768/xlm-align | [
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2106.06381",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
}
} | 6 | null | Access to model BlazingFringe/test is restricted and you are not in the authorized list. Visit https://huggingface.co/BlazingFringe/test to ask for access. |
Cameron/BERT-SBIC-offensive | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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}
} | 31 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gsmall-gpt2-alpaca
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. -->
# gsmall-gpt2-alpaca
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:
- Loss: 3.0114
## 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: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.64 | 1 | 3.0562 |
| No log | 1.92 | 3 | 3.0423 |
| No log | 2.56 | 4 | 3.0288 |
| No log | 3.2 | 5 | 3.0114 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Cameron/BERT-eec-emotion | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
} | 36 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/51169/misono-mika-blue-archives |
Cameron/BERT-jigsaw-identityhate | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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}
}
} | 37 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/64799/lohaaigis-solasu-with-multires-noise-version |
Cameron/BERT-jigsaw-severetoxic | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 30 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/52205/to-love-ru-darkness-kotegawa-yui-locon-lora |
Cameron/BERT-mdgender-convai-ternary | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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}
} | 38 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/54169/loha-girls-frontline-svch |
Cameron/BERT-mdgender-wizard | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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}
} | 30 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/56588/rage-of-bahamut-shadowverse-granblue-fantasy-mars-locon |
Cameron/BERT-rtgender-opgender-annotations | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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"task_specific_params": {
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},
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}
} | 33 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/59786/landy-epic-seven |
Camzure/MaamiBot | []
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}
} | 0 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/62275/schale-office-suit-blue-archive |
Canadiancaleb/DialoGPT-small-walter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 13 | null | ---
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: -226.50 +/- 114.31
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': 'kreepy/ppo_unit8a'
'batch_size': 512
'minibatch_size': 128}
```
|
Canyonevo/DialoGPT-medium-KingHenry | []
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} | 0 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('zz123tym/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
CapitainData/wav2vec2-large-xlsr-turkish-demo-colab | []
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} | 0 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- IThinkUPC/autotrain-data-3_parts_car
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.966800640027561
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 58951133563
- CO2 Emissions (in grams): 0.9668
## Validation Metrics
- Loss: 0.180
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: 1.000
- Macro Recall: 1.000
- Micro Recall: 1.000
- Weighted Recall: 1.000 |
Capreolus/bert-base-msmarco | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"arxiv:2008.09093",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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}
} | 238 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 23.90 +/- 17.08
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
|
Capreolus/birch-bert-large-car_mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
]
| null | {
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}
} | 4 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- IThinkUPC/autotrain-data-3_parts_car
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.9750699548728893
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 58951133566
- CO2 Emissions (in grams): 0.9751
## Validation Metrics
- Loss: 0.086
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: 1.000
- Macro Recall: 1.000
- Micro Recall: 1.000
- Weighted Recall: 1.000 |
Capreolus/birch-bert-large-msmarco_mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
]
| null | {
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"BertForNextSentencePrediction"
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"model_type": "bert",
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}
} | 1 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/67579/eris |
Capreolus/electra-base-msmarco | [
"pytorch",
"tf",
"electra",
"text-classification",
"arxiv:2008.09093",
"transformers"
]
| text-classification | {
"architectures": [
"ElectraForSequenceClassification"
],
"model_type": "electra",
"task_specific_params": {
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}
} | 110 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/62290/sushang-lora-honkai-star-rail |
Captain272/lstm | []
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} | 0 | null | ---
license: gpl-3.0
---
# ChatGLM-wyw
一个读了文言文的ChatGLM
# 缘起
2023年5月16日，念叨了好久要让AI读文言文正式开工。<br>
感谢
一站式整合包（含chatglm模型）：链接：https://pan.baidu.com/s/13GePNuh8ZP_DkMVRf5sHqw?pwd=2d2z
一站式整合包（不含模型）：链接：https://pan.baidu.com/s/1lMfG34jerHO7aFjfdKTGUw?pwd=6y7j
数据集制作大佬链接：https://github.com/huang1332/finetune_dataset_maker
模型微调大佬链接：https://github.com/mymusise/ChatGLM-Tuning
ChatGLM官方链接：https://github.com/THUDM/ChatGLM-6B
# 数据集来源
目前手搓了论语数据集和可用模型一起放出。（后续还会更新）
# GitHub
https://github.com/tmzncty/ChatGLM-wyw
# 测试
2023年5月16日
|
Carlork314/Carlos | []
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} | 0 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/55676/march-7th-honkai-star-rail |
Carlork314/Xd | []
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} | 0 | null | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: dz2en-mabart50
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. -->
# dz2en-mabart50
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4027
- Bleu: 2.3202
- Gen Len: 10.4274
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Cathy/reranking_model | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
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}
} | 27 | null | ---
license: openrail
datasets:
- OpenAssistant/oasst1
language:
- fr
--- |
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