modelId
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81
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sequence | pipeline_tag
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values | config
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int64 0
59.7M
| first_commit
unknown | card
stringlengths 51
438k
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Baybars/wav2vec2-xls-r-1b-turkish | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer"
] | automatic-speech-recognition | {
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} | 13 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_classifier_newsgroups
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. -->
# distilbert_classifier_newsgroups
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BeIR/query-gen-msmarco-t5-base-v1 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
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} | 1,816 | null | ---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
Beatriz/model_name | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sql
model-index:
- name: test-zouga-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. -->
# test-zouga-model
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the sql 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: 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
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
BenDavis71/GPT-2-Finetuning-AIRaid | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 10 | "2023-05-18T16:17:18Z" | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
# Model Card for ANNXC
## Model Description
- **Developed by:** BADMONK
- **Model type:** Dreambooth Model + Extracted LoRA
- **Language(s) (NLP):** EN
- **License:** Creativeml-Openrail-M
- **Parent Model:** ChilloutMix
# How to Get Started with the Model
Use the code below to get started with the model.
### ANNXC ### |
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} | 0 | null | Access to model offwhiteInWhite/VE_ChillosCU3Mix_V0.5 is restricted and you are not in the authorized list. Visit https://huggingface.co/offwhiteInWhite/VE_ChillosCU3Mix_V0.5 to ask for access. |
BhanuSama/gpt2-finetuned-xsum | [] | null | {
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### PixelPerfect Dreambooth model trained by OmarAhmed1 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Bhuvana/t5-base-spellchecker | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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}
} | 93 | null | ---
license: cc-by-sa-3.0
datasets:
- mosaicml/dolly_hhrlhf
tags:
- Composer
- MosaicML
- llm-foundry
inference: false
---
# MPT-7B-Instruct GGML
This is GGML format quantised 4-bit, 5-bit and 8-bit GGML models of [MosaicML's MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct).
This repo is the result of converting to GGML and quantising.
Please note that these MPT GGMLs are **not compatbile with llama.cpp**. Please see below for a list of tools known to work with these model files.
## Repositories available
* [MPT-7B: 4-bit, 5-bit and 8-bit GGML models for CPU (+CUDA) inference](https://huggingface.co/TheBloke/MPT-7B-GGML).
* [MPT-7B-Instruct: 4-bit, 5-bit and 8-bit GGML models for CPU (+CUDA) inference](https://huggingface.co/TheBloke/MPT-7B-Instruct-GGML).
* [MPT-7B-Storywriter: 4-bit, 5-bit and 8-bit GGML models for CPU (+CUDA) inference](https://huggingface.co/TheBloke/MPT-7B-Storywriter-GGML).
## Provided files
| Name | Quant method | Bits | Size | RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
`mpt7b-instruct.ggmlv3.q4_0.bin` | q4_0 | 4bit | 4.16GB | 6.2GB | 4-bit. |
`mpt7b-instruct.ggmlv3.q4_1.bin` | q4_0 | 4bit | 4.99GB | 7.2GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
`mpt7b-instruct.ggmlv3.q5_0.bin` | q5_0 | 5bit | 4.57GB | 6.8GB | 5-bit. Higher accuracy, higher resource usage and slower inference. |
`mpt7b-instruct.ggmlv3.q5_1.bin` | q5_1 | 5bit | 4.99GB | 7.2GB | 5-bit. Even higher accuracy, and higher resource usage and slower inference. |
`mpt7b-instruct.ggmlv3.q8_0.bin` | q8_0 | 8bit | 7.48GB | 9.7GB | 8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for normal use. |
`mpt7b-instruct.ggmlv3.fp16.bin` | fp16 | 16bit | 13.30GB | 16GB | Full 16-bit. |
## Compatibilty
These files are **not** compatible with llama.cpp.
Currently they can be used with:
* The ctransformers Python library, which includes LangChain support: [ctransformers](https://github.com/marella/ctransformers)
* The GPT4All-UI which uses ctransformers: [GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui)
* [rustformers' llm](https://github.com/rustformers/llm)
* The example `mpt` binary provided with [ggml](https://github.com/ggerganov/ggml)
As other options become available I will endeavour to update them here (do let me know in the Community tab if I've missed something!)
## How to build, and an example of using the ggml `mpt` binary (command line only):
```
git clone https://github.com/ggerganov/ggml
cd ggml
mkdir build
cd build
cmake ..
cmake --build . --config Release
bin/mpt -m /path/to/mpt7b-instruct.ggmlv3.q4_0.bin -t 8 -n 512 -p "Write a story about llamas"
```
Please see the ggml repo for other build options.
# Original model card: MPT-7B-Instruct
# MPT-7B-Instruct
MPT-7B-Instruct is a model for short-form instruction following.
It is built by finetuning [MPT-7B](https://huggingface.co/spaces/mosaicml/mpt-7b) on a [dataset](https://huggingface.co/datasets/sam-mosaic/dolly_hhrlhf) derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets.
* License: _CC-By-SA-3.0_
* [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-instruct)
This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture.
## Model Date
May 5, 2023
## Model License
CC-By-SA-3.0
## Documentation
* [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b)
* [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/)
* Questions: Feel free to contact us via the [MosaicML Community Slack](https://join.slack.com/t/mosaicml-community/shared_invite/zt-1btms90mc-GipE2ufuPkKY0QBrmF3LSA)!
### Example Question/Instruction
**Longboi24**:
> What is a quoll?
**MPT-7B-Instruct**:
>A Quoll (pronounced “cool”) is one of Australia’s native carnivorous marsupial mammals, which are also known as macropods or wallabies in other parts around Asia and South America
## How to Use
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom model architecture that is not yet part of the `transformers` package.
It includes options for many training efficiency features such as [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), QK LayerNorm, and more.
```python
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b-instruct',
trust_remote_code=True
)
```
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package.
`MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more.
To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model with `attn_impl='triton'` and move the model to `bfloat16`:
```python
config = transformers.AutoConfig.from_pretrained(
'mosaicml/mpt-7b-instruct',
trust_remote_code=True
)
config.attn_config['attn_impl'] = 'triton'
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b-instruct',
config=config,
torch_dtype=torch.bfloat16,
trust_remote_code=True
)
model.to(device='cuda:0')
```
Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example:
```python
config = transformers.AutoConfig.from_pretrained(
'mosaicml/mpt-7b-instruct',
trust_remote_code=True
)
config.update({"max_seq_len": 4096})
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b-instruct',
config=config,
trust_remote_code=True
)
```
This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
```
## Model Description
The architecture is a modification of a standard decoder-only transformer.
The model has been modified from a standard transformer in the following ways:
* It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf)
* It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings
* It does not use biases
| Hyperparameter | Value |
|----------------|-------|
|n_parameters | 6.7B |
|n_layers | 32 |
| n_heads | 32 |
| d_model | 4096 |
| vocab size | 50432 |
| sequence length | 2048 |
## PreTraining Data
For more details on the pretraining process, see [MPT-7B](https://huggingface.co/mosaicml/mpt-7b).
The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
## Limitations and Biases
_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_
MPT-7B-Instruct can produce factually incorrect output, and should not be relied on to produce factually accurate information.
MPT-7B-Instruct was trained on various public datasets.
While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
## Acknowledgements
This model was finetuned by Sam Havens and the MosaicML NLP team
## MosaicML Platform
If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b).
## Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
## Citation
Please cite this model using the following format:
```
@online{MosaicML2023Introducing,
author = {MosaicML NLP Team},
title = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs},
year = {2023},
url = {www.mosaicml.com/blog/mpt-7b},
note = {Accessed: 2023-03-28}, % change this date
urldate = {2023-03-28} % change this date
}
```
|
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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: 11.43 +/- 5.51
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 irow/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 .home.isaac.Documents.RLCourse.venv.lib.python3.10.site-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 .home.isaac.Documents.RLCourse.venv.lib.python3.10.site-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.
|
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
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# /home/cedwin/log_c_pt_3/
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("/home/cedwin/log_c_pt_3/")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
BigSalmon/BestMask2 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"RobertaForMaskedLM"
],
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}
}
} | 10 | null | ---
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# LSUN pretrained church unconditional image generation model naively finetuned with modern architecture data
Precursor to a more robust, unconditional modern architecture generator
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('benlehrburger/LSUN-church-finetuned-modern-model')
image = pipeline().images[0]
image
```
|
BigSalmon/MrLincoln125MNeo | [
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | {
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"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
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},
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},
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}
}
} | 12 | null | ---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
BigSalmon/MrLincoln5 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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},
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},
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}
}
} | 9 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-CartPoleV1
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
|
BigSalmon/MrLincoln7 | [] | null | {
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}
}
} | 0 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-Pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 34.60 +/- 26.74
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
|
BigSalmon/MrLincoln8 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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"max_length": 50
},
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},
"translation_en_to_fr": {
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}
}
} | 12 | null | ---
datasets:
- zh-tw-llm-dv/zh-tw-pythia-ta8000-v1-it1-sg-001
---
# zh-tw-llm-ta01-pythia-70m-ta8000-v1-a_2_lora_instruction_tune-a100-t002-7a793a
This model is a part of the `zh-tw-llm` project.
* Base model: `EleutherAI/pythia-70m`
* Tokenizer: `zh-tw-pythia-tokenizer-a8000-v1`
* Vocab size: `58113`
* Train: `a_2_lora_instruction_tune`
* Dataset used: `zh-tw-pythia-ta8000-v1-it1-sg-001`
* Full config:
```json
{"project_name": "zh-tw-llm", "group_name": "ta01", "hf_user_or_org_name": "zh-tw-llm-dv", "base_tokenizer_name": "EleutherAI/pythia-70m", "base_model_name": "EleutherAI/pythia-70m", "tokenizer_name": "zh-tw-pythia-tokenizer-a8000-v1", "tokenizer": {"build_with": "word_frequency_list", "tokens_to_add": 8000, "word_frequency_list_settings": {"word_frequency_list_name": "zetavg/tw-sinica-corpus-word-frequency", "include_words": ["。", "，", "、", "？", "！", "；", "：", "……", "～", "「", "」", "『", "』", "【", "】", "〖", "〗", "（", "）", "〔", "〕", "［", "］", "｛", "｝", "《", "》", "〈", "〉", "——", "──", "－", "−", "＿", "・", "．", "·", "／", "＼", "｜", "＜", "＞"], "replace_rules": [{"match": {"regex": "�"}, "replace": null}, {"match": {"pos": ["Nb", "FW", null]}, "replace": null, "except": ["奧運", "中共", "國民黨", "民進黨", "新黨", "共產黨", "媽祖", "耶穌"]}, {"match": {"regex": ["^[Ａ-Ｚａ-ｚ０-９﹒• ]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆壹貳參肆伍陸柒捌玖拾佰仟０-９﹒•]{2,}$", "^（[零一二兩三四五六七八九十廿卅百千萬億兆壹貳參肆伍陸柒捌玖拾佰仟０-９﹒•]+）$", "^[第數][零一二兩三四五六七八九十百千萬億兆０-９﹒•]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+分之[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+[多餘來幾成次年月日天時分點世代歲起段樓％]$", "^[零一二三四五六七八九十廿卅０-９]+(月份|年代?|世紀|學?年度|年級)$", "^(星期|週|周)[一二三四五六日]$"]}, "replace": null, "except": ["十分", "一起", "一點", "一時", "千萬", "兩三", "百分之百"]}, {"match": {"pos": "VHC", "regex": "^(.{2,})化$"}, "sub": "\\1"}, {"match": "高爾夫球場", "replace": "高爾夫"}, {"match": {"regex": "^(.+球)場$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})園區$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})[鄉鎮縣市區]$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})[界院部會署局館系所]$"}, "sub": "\\1", "except": ["委員會", "研究所", "中研院", "國科會", "資策會", "經建會", "工研院", "電信總局", "鎮公所", "事務所", "交易所", "農委會", "鄉公所", "地檢署", "警分局", "派出所", "托兒所", "消基會", "文建會", "兩廳院", "陸委會", "市議會"]}, {"match": {"pos": "Na", "regex": "^(.{2,})人$"}, "sub": "\\1", "except": ["年輕人", "負責人", "投資人", "候選人", "一家人", "當地人", "製作人"]}, {"match": {"pos": "Na", "regex": "^(.{2,3})學?家$"}, "sub": "\\1", "except": ["女人家", "婦人家", "新儒家", "窮人家", "縱橫家", "老人家", "老東家", "闊人家", "大戶人家", "婦道人家", "小戶人家", "水上人家", "諸子百家"]}, {"match": {"pos": "Na", "regex": "^副?總?([^副總]{2,})師$"}, "sub": "\\1", "except": ["中醫師", "囝仔師", "正機師", "準教師", "獸醫師", "班導師", "練馬師", "總舖師", "老像師", "新三十師", "至聖先師", "音樂大師"]}, {"match": {"pos": "Na", "regex": "^[原前]?(?:代|代理)?副?總?([^前代副總議警里首院部署局廳司處科組課股]{2,})[院部署局廳司處科組課股]?次?長$"}, "sub": "\\1", "except": ["董事長", "理事長", "秘書長", "執行長", "分局長", "縣市長", "一技之長", "省市長", "負成長", "高成長", "大家長", "小組長", "區組長", "低成長", "偵一組長", "停管隊長", "考選部長", "年增長", "正成長", "支店長", "公賣局長", "中宣部長", "小市長"]}, {"match": {"pos": "Na", "regex": "^副?總?正?([^副總正議委人隊]{2,})[委人隊]?員$"}, "sub": "\\1", "except": ["主跑員", "乘務員", "佐理員", "共黨員", "外務員", "從業員", "特派員", "義服員", "銜道員", "啦啦隊員", "指服團員"]}, {"match": {"pos": "Na", "regex": "^副(.{2,})$"}, "sub": "\\1", "except": ["副作用"]}, {"match": "一剎那", "replace": "剎那"}, {"match": "不能夠", "replace": "能夠"}, {"match": "光碟機", "replace": "光碟"}, {"match": "共和國", "replace": "共和"}, {"match": "原住民", "replace": "住民"}, {"match": "吸引力", "replace": "吸引"}, {"match": "國際性", "replace": "國際"}, {"match": "垃圾場", "replace": "垃圾"}, {"match": "大規模", "replace": "規模"}, {"match": "廢棄物", "replace": "廢棄"}, {"match": "愛滋病", "replace": "愛滋"}, {"match": "成交量", "replace": "成交"}, {"match": "接觸到", "replace": "接觸"}, {"match": "掩埋場", "replace": "掩埋"}, {"match": "正確率", "replace": "正確"}, {"match": "清華園", "replace": "清華"}, {"match": "聯誼會", "replace": "聯誼"}, {"match": "調查站", "replace": "調查"}, {"match": "轉換成", "replace": "轉換"}, {"match": "開放式", "replace": "開放"}, {"match": "開玩笑", "replace": "玩笑"}, {"match": "陽明山", "replace": "陽明"}, {"match": "雜貨店", "replace": "雜貨"}, {"match": "電視機", "replace": "電視"}, {"match": "高品質", "replace": "品質"}, {"match": "鬆弛法", "replace": "鬆弛"}, {"match": "共產主義", "replace": "共產"}, {"match": "資本主義", "replace": "資本"}, {"match": "微處理器", "replace": "處理器"}, {"match": "有線電視", "replace": "電視"}, {"match": "隨選視訊", "replace": "視訊"}, {"match": "電信總局", "replace": "總局"}, {"match": "進一步", "replace": ["一步", "進一步"]}, {"match": "差不多", "replace": ["不多", "差不多"]}, {"match": "忍不住", "replace": ["不住", "忍不住"]}, {"match": "不見得", "replace": ["見得", "不見得"]}, {"match": "有助於", "replace": ["助於", "有助於"]}, {"match": "舊金山", "replace": ["金山", "舊金山"]}, {"match": "大躍進", "replace": ["躍進", "大躍進"]}, {"match": "半導體", "replace": ["導體", "半導體"]}, {"match": "總幹事", "replace": ["幹事", "總幹事"]}, {"match": "兩廳院", "replace": ["廳院", "兩廳院"]}]}}, "training": {"a_1_embeddings": {"run_name_suffix": "a100-t4", "max_tokens_length": 2048, "dataset_name": "zh-tw-pythia-ta8000-v1-e1-tr_sg-001", "dataset": {"build_with": ["translations", "sharegpt"], "preview_length": 128, "translations_settings": {"source_dataset": "zetavg/coct-en-zh-tw-translations-twp-300k", "lang_1_key": "en", "lang_2_key": "ch", "templates": ["English: {lang_1}\nChinese: {lang_2}", "Chinese: {lang_2}\nEnglish: {lang_1}"], "rows_limit": 100000, "test_size": 100, "test_split_seed": 42}, "sharegpt_settings": {"source_dataset": "zetavg/ShareGPT-Processed", "train_on_inputs": false, "languages": [{"en": 0.4}, "zh_Hant"], "rows_limit": 8000, "test_size": 0.02, "test_split_seed": 42, "test_rows_limit": 100}}, "only_train_parameters_matching": ["embed"], "training_arguments": {"num_train_epochs": 1, "auto_find_batch_size": true, "gradient_accumulation_steps": 1, "optim": "adamw_torch", "learning_rate": 5e-05, "lr_scheduler_type": "constant", "warmup_steps": 100, "logging_steps": 10, "eval_steps": 500, "save_steps": 2000, "save_total_limit": 5}}, "b_1_embeddings_and_attention": {"run_name_suffix": "a100-t4", "dataset": {"same_as": "a_1_embeddings"}, "only_train_parameters_matching": ["embed", "attention"], "training_arguments": {"num_train_epochs": 1, "auto_find_batch_size": true, "gradient_accumulation_steps": 1, "optim": "adamw_torch", "learning_rate": 5e-05, "lr_scheduler_type": "constant", "warmup_steps": 100, "logging_steps": 10, "eval_steps": 500, "save_steps": 2000, "save_total_limit": 5}}, "c_1_all_params": {"run_name_suffix": "a100-t4", "dataset": {"same_as": "a_1_embeddings"}, "only_train_parameters_matching": ["embed", "attention"], "training_arguments": {"num_train_epochs": 1, "auto_find_batch_size": true, "gradient_accumulation_steps": 1, "optim": "adamw_torch", "learning_rate": 5e-05, "lr_scheduler_type": "constant", "warmup_steps": 100, "logging_steps": 10, "eval_steps": 500, "save_steps": 2000, "save_total_limit": 5}}, "a_2_lora_instruction_tune": {"run_name_suffix": "a100-t002", "max_tokens_length": 2048, "use_peft": "lora", "dataset_name": "zh-tw-pythia-ta8000-v1-it1-sg-001", "dataset": {"build_with": ["sharegpt"], "preview_length": 512, "sharegpt_settings": {"source_dataset": "zetavg/ShareGPT-Processed", "train_on_inputs": false, "languages": [{"en": 0.3}, {"zh": 0.2}, "zh_Hant"], "rows_limit": 10000, "test_size": 0.01, "test_split_seed": 42, "test_rows_limit": 100}}, "training_arguments": {"num_train_epochs": 1, "auto_find_batch_size": true, "gradient_accumulation_steps": 1, "optim": "adamw_torch", "learning_rate": 5e-05, "lr_scheduler_type": "constant", "warmup_steps": 100, "logging_steps": 10, "eval_steps": 500, "save_steps": 2000, "save_total_limit": 5}, "lora_config": {"task_type": "CAUSAL_LM", "r": 256, "lora_alpha": 512, "lora_dropout": 0.05, "target_modules": ["embed", "input", "query_key_value", "dense"]}}}, "push_outputs_to_hf": true, "report_to_wandb": true, "wandb_project": "zh-tw-llm", "wandb_group": "ta01"}
``` |
BigSalmon/NEO125InformalToFormalLincoln | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | {
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"GPTNeoForCausalLM"
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}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_classifier_newsgroups
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. -->
# distilbert_classifier_newsgroups
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BigSalmon/PhraseBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"RobertaForMaskedLM"
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} | 10 | null | ---
datasets:
- zh-tw-llm-dv/zh-tw-pythia-ta8000-v1-e1-tr_sg-001-c512
---
# zh-tw-llm-ta01-pythia-1b-ta8000-v1-b_1_embeddings_and_attention-a100-t02-300df2
This model is a part of the `zh-tw-llm` project.
* Base model: `EleutherAI/pythia-1b`
* Tokenizer: `zh-tw-pythia-tokenizer-a8000-v1`
* Vocab size: `58113`
* Train: `b_1_embeddings_and_attention`
* Dataset used: `zh-tw-pythia-ta8000-v1-e1-tr_sg-001-c512`
* Full config:
```json
{"project_name": "zh-tw-llm", "group_name": "ta01", "hf_user_or_org_name": "zh-tw-llm-dv", "base_tokenizer_name": "EleutherAI/pythia-70m", "base_model_name": "EleutherAI/pythia-1b", "tokenizer_name": "zh-tw-pythia-tokenizer-a8000-v1", "tokenizer": {"build_with": "word_frequency_list", "tokens_to_add": 8000, "word_frequency_list_settings": {"word_frequency_list_name": "zetavg/tw-sinica-corpus-word-frequency", "include_words": ["。", "，", "、", "？", "！", "；", "：", "……", "～", "「", "」", "『", "』", "【", "】", "〖", "〗", "（", "）", "〔", "〕", "［", "］", "｛", "｝", "《", "》", "〈", "〉", "——", "──", "－", "−", "＿", "・", "．", "·", "／", "＼", "｜", "＜", "＞"], "replace_rules": [{"match": {"regex": "�"}, "replace": null}, {"match": {"pos": ["Nb", "FW", null]}, "replace": null, "except": ["奧運", "中共", "國民黨", "民進黨", "新黨", "共產黨", "媽祖", "耶穌"]}, {"match": {"regex": ["^[Ａ-Ｚａ-ｚ０-９﹒• ]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆壹貳參肆伍陸柒捌玖拾佰仟０-９﹒•]{2,}$", "^（[零一二兩三四五六七八九十廿卅百千萬億兆壹貳參肆伍陸柒捌玖拾佰仟０-９﹒•]+）$", "^[第數][零一二兩三四五六七八九十百千萬億兆０-９﹒•]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+分之[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+[多餘來幾成次年月日天時分點世代歲起段樓％]$", "^[零一二三四五六七八九十廿卅０-９]+(月份|年代?|世紀|學?年度|年級)$", "^(星期|週|周)[一二三四五六日]$"]}, "replace": null, "except": ["十分", "一起", "一點", "一時", "千萬", "兩三", "百分之百"]}, {"match": {"pos": "VHC", "regex": "^(.{2,})化$"}, "sub": "\\1"}, {"match": "高爾夫球場", "replace": "高爾夫"}, {"match": {"regex": "^(.+球)場$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})園區$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})[鄉鎮縣市區]$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})[界院部會署局館系所]$"}, "sub": "\\1", "except": ["委員會", "研究所", "中研院", "國科會", "資策會", "經建會", "工研院", "電信總局", "鎮公所", "事務所", "交易所", "農委會", "鄉公所", "地檢署", "警分局", "派出所", "托兒所", "消基會", "文建會", "兩廳院", "陸委會", "市議會"]}, {"match": {"pos": "Na", "regex": "^(.{2,})人$"}, "sub": "\\1", "except": ["年輕人", "負責人", "投資人", "候選人", "一家人", "當地人", "製作人"]}, {"match": {"pos": "Na", "regex": "^(.{2,3})學?家$"}, "sub": "\\1", "except": ["女人家", "婦人家", "新儒家", "窮人家", "縱橫家", "老人家", "老東家", "闊人家", "大戶人家", "婦道人家", "小戶人家", "水上人家", "諸子百家"]}, {"match": {"pos": "Na", "regex": "^副?總?([^副總]{2,})師$"}, "sub": "\\1", "except": ["中醫師", "囝仔師", "正機師", "準教師", "獸醫師", "班導師", "練馬師", "總舖師", "老像師", "新三十師", "至聖先師", "音樂大師"]}, {"match": {"pos": "Na", "regex": "^[原前]?(?:代|代理)?副?總?([^前代副總議警里首院部署局廳司處科組課股]{2,})[院部署局廳司處科組課股]?次?長$"}, "sub": "\\1", "except": ["董事長", "理事長", "秘書長", "執行長", "分局長", "縣市長", "一技之長", "省市長", "負成長", "高成長", "大家長", "小組長", "區組長", "低成長", "偵一組長", "停管隊長", "考選部長", "年增長", "正成長", "支店長", "公賣局長", "中宣部長", "小市長"]}, {"match": {"pos": "Na", "regex": "^副?總?正?([^副總正議委人隊]{2,})[委人隊]?員$"}, "sub": "\\1", "except": ["主跑員", "乘務員", "佐理員", "共黨員", "外務員", "從業員", "特派員", "義服員", "銜道員", "啦啦隊員", "指服團員"]}, {"match": {"pos": "Na", "regex": "^副(.{2,})$"}, "sub": "\\1", "except": ["副作用"]}, {"match": "一剎那", "replace": "剎那"}, {"match": "不能夠", "replace": "能夠"}, {"match": "光碟機", "replace": "光碟"}, {"match": "共和國", "replace": "共和"}, {"match": "原住民", "replace": "住民"}, {"match": "吸引力", "replace": "吸引"}, {"match": "國際性", "replace": "國際"}, {"match": "垃圾場", "replace": "垃圾"}, {"match": "大規模", "replace": "規模"}, {"match": "廢棄物", "replace": "廢棄"}, {"match": "愛滋病", "replace": "愛滋"}, {"match": "成交量", "replace": "成交"}, {"match": "接觸到", "replace": "接觸"}, {"match": "掩埋場", "replace": "掩埋"}, {"match": "正確率", "replace": "正確"}, {"match": "清華園", "replace": "清華"}, {"match": "聯誼會", "replace": "聯誼"}, {"match": "調查站", "replace": "調查"}, {"match": "轉換成", "replace": "轉換"}, {"match": "開放式", "replace": "開放"}, {"match": "開玩笑", "replace": "玩笑"}, {"match": "陽明山", "replace": "陽明"}, {"match": "雜貨店", "replace": "雜貨"}, {"match": "電視機", "replace": "電視"}, {"match": "高品質", "replace": "品質"}, {"match": "鬆弛法", "replace": "鬆弛"}, {"match": "共產主義", "replace": "共產"}, {"match": "資本主義", "replace": "資本"}, {"match": "微處理器", "replace": "處理器"}, {"match": "有線電視", "replace": "電視"}, {"match": "隨選視訊", "replace": "視訊"}, {"match": "電信總局", "replace": "總局"}, {"match": "進一步", "replace": ["一步", "進一步"]}, {"match": "差不多", "replace": ["不多", "差不多"]}, {"match": "忍不住", "replace": ["不住", "忍不住"]}, {"match": "不見得", "replace": ["見得", "不見得"]}, {"match": "有助於", "replace": ["助於", "有助於"]}, {"match": "舊金山", "replace": ["金山", "舊金山"]}, {"match": "大躍進", "replace": ["躍進", "大躍進"]}, {"match": "半導體", "replace": ["導體", "半導體"]}, {"match": "總幹事", "replace": ["幹事", "總幹事"]}, {"match": "兩廳院", "replace": ["廳院", "兩廳院"]}]}}, "training": {"a_1_embeddings": {"run_name_suffix": "a100-t02", "max_tokens_length": 512, "dataset_name": "zh-tw-pythia-ta8000-v1-e1-tr_sg-001-c512", "dataset": {"build_with": ["translations", "sharegpt"], "preview_length": 128, "translations_settings": {"source_dataset": "zetavg/coct-en-zh-tw-translations-twp-300k", "lang_1_key": "en", "lang_2_key": "ch", "templates": ["English: {lang_1}\nChinese: {lang_2}", "Chinese: {lang_2}\nEnglish: {lang_1}"], "rows_limit": 100000, "test_size": 100, "test_split_seed": 42}, "sharegpt_settings": {"source_dataset": "zetavg/ShareGPT-Processed", "train_on_inputs": false, "languages": [{"en": 0.4}, "zh_Hant"], "rows_limit": 8000, "test_size": 0.02, "test_split_seed": 42, "test_rows_limit": 100}}, "only_train_parameters_matching": ["embed"], "training_arguments": {"num_train_epochs": 1, "auto_find_batch_size": true, "gradient_accumulation_steps": 1, "optim": "adamw_torch", "learning_rate": 5e-05, "lr_scheduler_type": "constant", "warmup_steps": 100, "logging_steps": 10, "eval_steps": 500, "save_steps": 5000, "save_total_limit": 3}}, "b_1_embeddings_and_attention": {"run_name_suffix": "a100-t02", "dataset": {"same_as": "a_1_embeddings"}, "only_train_parameters_matching": ["embed", "attention"], "training_arguments": {"num_train_epochs": 1, "auto_find_batch_size": true, "gradient_accumulation_steps": 1, "optim": "adamw_torch", "learning_rate": 5e-05, "lr_scheduler_type": "constant", "warmup_steps": 100, "logging_steps": 10, "eval_steps": 500, "save_steps": 5000, "save_total_limit": 3}}, "c_1_all_params": {"run_name_suffix": "a100-t02", "dataset": {"same_as": "a_1_embeddings"}, "training_arguments": {"num_train_epochs": 1, "auto_find_batch_size": true, "gradient_accumulation_steps": 1, "optim": "adamw_torch", "learning_rate": 5e-05, "lr_scheduler_type": "constant", "warmup_steps": 100, "logging_steps": 10, "eval_steps": 500, "save_steps": 5000, "save_total_limit": 3}}}, "push_outputs_to_hf": true, "report_to_wandb": true, "wandb_project": "zh-tw-llm", "wandb_group": "ta01"}
``` |
BigSalmon/Points | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
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"GPT2LMHeadModel"
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}
} | 13 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: lora-save-test
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. -->
# lora-save-test
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5977
- Accuracy: 0.0313
- Entropy: 3.3505
## 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: 8
- seed: 99
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Entropy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|
| 6.9414 | 1.0 | 2 | 5.5977 | 0.0313 | 3.3505 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0-rc1
- Datasets 2.7.1
- Tokenizers 0.13.3
|
BigSalmon/Robertsy | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"RobertaForMaskedLM"
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}
} | 4 | null | ---
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: 426.50 +/- 153.22
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 aliakyurek -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 aliakyurek -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 aliakyurek
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BigSalmon/T52 | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"prefix": "translate English to Romanian: "
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}
} | 8 | null | Access to model 127-0-13-37/AmIReal is restricted and you are not in the authorized list. Visit https://huggingface.co/127-0-13-37/AmIReal to ask for access. |
BigSalmon/T5F | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"T5ForConditionalGeneration"
],
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},
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"prefix": "translate English to Romanian: "
}
}
} | 6 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/62557/sakimiya-misaki-dead-mount-death-play |
BigSalmon/T5Salmon | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
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},
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}
} | 6 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/69422/kiana-kaslan-herrscher-of-the-void-honkai-impact-3rd |
BigSalmon/TS3 | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
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} | 7 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/68896/girls-frontline-howa-type-89-with-multires-noise-version |
BigSalmon/prepositions | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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} | 7 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/7570/punishing-grey-raven-luna |
BigTooth/DialoGPT-Megumin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 16 | null | ---
license: cc-by-nc-sa-4.0
---
Model trained to be as helpful of an assistant as possible.
### Data split percentage
60% coding
10% conversations
20% instructions
10% Roleplay
Now obviously the data contains other elements but this is the biggest part
### Examples
```
The prompt structure can be almost anything, this model has been trained on 0.6 million instruction, which is less than dante.
This model has however been trained on a much cleaner and organized dataset and has been retrained multiple times to reach as low
of a training loss as possible.
## Examples.
"You are an AI assistant respond to human in a helpful manner.
HM: What were the causes for world war 2?
"
"Act like a detective from the 1900s, respond to mike in a helpful manner.
HM: What were the causes for world war 2?
"
The prompt also works with alpaca structure. I have purposefully trained it so that it should work like this.
EOS token is <|end|>.
Remember to tell it how it should act for best effect.
```
### More information
The base model is GPT-NeoX taken pretrained by redpajama.
We managed to reach 0.45 Validation loss with a 0.3 learning loss.
You are not allowed to use this for commerical purposes unless reaching an agreement with the creator @Dampish, @Dampish#3607 on discord.
The model can easily be further fine tuned to most languages. |
BigTooth/DialoGPT-small-tohru | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 10 | null | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
datasets:
- cifar10
---
## Image Classification with Vision Transformer (ViT)
This repository contains a Python script for training an image classification model using the Vision Transformer (ViT) architecture. We use the transformers and datasets libraries from Hugging Face along with PyTorch and TensorFlow for the implementation.
### Functions and Usage
* convert_to_tf_tensor(image: Image):
* This function converts an image to a Tensorflow tensor with a size of 224x224 and three color channels.
* preprocess(batch):
* Preprocesses the images in a batch, using the feature extractor to convert them to pixel values. It also adds the labels to the batch.
* collate_fn(batch):
* This function prepares the batch for training or evaluation. It stacks the pixel values and labels.
* compute_metrics(p):
* Computes the metrics (accuracy) for the predictions.
|
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} | 13 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/68518/kiana-honkai-impact-3rd-magic-girl-sirin |
BigeS/DialoGPT-small-Rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 10 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/68571/hoshino-aioshi-no-ko |
BillelBenoudjit/jplu-wikiann | [
"fr",
"dataset:wikiann",
"model-index"
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} | 0 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/61520/misaka-mikoto-toaru-series |
Bilz/DialoGPT-small-harrypotter | [] | null | {
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} | 0 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/62343/ivy-fire-emblem-engage-mask-optional |
Binbin/test | [] | null | {
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} | 0 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/69483/r |
BinksSachary/DialoGPT-small-shaxx | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 12 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/63784/kurokawa-akane-or-oshi-no-ko-or-lora |
BinksSachary/ShaxxBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 9 | 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: 7.54 +/- 2.69
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="H4nan/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"])
```
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-finetuned-squad_v2_3epoch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-squad_v2_3epoch
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0808
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0726 | 1.0 | 5533 | 1.0233 |
| 0.8097 | 2.0 | 11066 | 1.0320 |
| 0.5771 | 3.0 | 16599 | 1.0808 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
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} | 0 | null | ---
license: gpl-3.0
language:
- en
metrics:
- f1
- recall
- precision
---
# GraphMatcher
The GraphMatcher aims to find the correspondes between two ontologies and outputs the possible alignments between them.
The GraphMatcher leverages Graph Attention Network[2] in its neural network structure.
The project leverages a new neighborhood aggregation algorithm, so it examines contribution of neighboring terms which have not been used in the previous matchers before.
The project has been submitted to The 17th International Workshop on Ontology Matching's OAEI 2022 (ISWC-2022) for conference track and obtained the highest F1-measure in uncertain reference alignments among other experts participating to this challenge. Its system paper has been published, and it was invited to the poster presentation session.
## Set up
* 1.) install requirements
``` pip install -r requirements.txt```
* 2.) set the parameters in the config.ini
````
[General]
dataset = ------> name of a dataset e.g., conference.
K = ------> the parameter for K fold cross-validation
ontology_split = ------> True/False
max_false_examples = ------>
[Paths]
dataset_folder = ------> a path to the ontologies
alignment_folder = ------> a path to the reference alignments
save_model_path = ------> save the model to the path
load_model_path = ------> model path
output_folder = ------> The output folder for the alignments
[Parameters]
max_paths = ------>
max_pathlen = ------> ( number of neighboring concepts' types: Equivalent class, subclass of(general to specific or specific to general(2))...
[Hyperparameters]
lr = ------> learning rate
num_epochs = ------> number of epochs
weight_decay = ------> Weight decay
batch_size = ------> Batch Size (8/16/32)
````
* 3.) train the model
```python
python src/train_model.py
```
* 4.) test the model
```python
python src/test_model.py ${source.rdf} ${target.rdf}
```
### Sample Alignment:
```xml
<map>
<Cell>
<entity1 rdf:resource='http://conference#has_the_last_name'/>
<entity2 rdf:resource='http://confof#hasSurname'/>
<relation>=</relation>
<measure rdf:datatype='http://www.w3.org/2001/XMLSchema#float'>0.972</measure>
</Cell>
</map>
```
* 5.) evaluate the model with the MELT
Note: The codes in train_model.py and test_model.py are partially based on the VeeAlign[2] project with the permission of its main author. I would like to thank the main author.
## References:
[1]
````
@inproceedings{iyer-etal-2021-veealign,
title = "{V}ee{A}lign: Multifaceted Context Representation Using Dual Attention for Ontology Alignment",
author = "Iyer, Vivek and
Agarwal, Arvind and
Kumar, Harshit",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.842",
doi = "10.18653/v1/2021.emnlp-main.842",
pages = "10780--10792",
}
````
[2]
````
@misc{https://doi.org/10.48550/arxiv.1710.10903,
title = {Graph Attention Networks},
author = {Veličković, Petar and Cucurull, Guillem and Casanova, Arantxa and Romero, Adriana and Liò, Pietro and Bengio, Yoshua},
keywords = {Machine Learning (stat.ML), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Social and Information Networks (cs.SI), FOS: Computer and information sciences, FOS: Computer and information sciences},
url = {https://arxiv.org/abs/1710.10903},
publisher = {arXiv},
doi = {10.48550/ARXIV.1710.10903},
year = {2017},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```` |
Boondong/Wandee | [] | null | {
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} | 0 | null | ---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-large-finetuned-kinetics-mopping
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# videomae-large-finetuned-kinetics-mopping
This model is a fine-tuned version of [MCG-NJU/videomae-large-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-large-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4468
- Accuracy: 0.7368
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1672
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6868 | 0.25 | 418 | 0.7474 | 0.2115 |
| 0.7265 | 1.25 | 836 | 0.6472 | 0.6026 |
| 0.6854 | 2.25 | 1254 | 0.6211 | 0.6346 |
| 0.7829 | 3.25 | 1672 | 0.5959 | 0.7115 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Brayan/CNN_Brain_Tumor | [] | null | {
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} | 0 | null | ---
pipeline_tag: image-to-text
language:
- en
---
# 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] |
Brendan/cse244b-hw2-roberta | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
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} | 28 | null | ---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
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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} | 76 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: hh-lmgym-demo-lora
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. -->
# hh-lmgym-demo-lora
This model is a fine-tuned version of [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6836
- Accuracy: 0.0896
- Entropy: 1.6709
## 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: 8
- seed: 99
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Entropy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|
| 1.6617 | 1.0 | 3466 | 1.6855 | 0.0896 | 1.6784 |
| 1.66 | 2.0 | 6932 | 1.6836 | 0.0896 | 1.6709 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0-rc1
- Datasets 2.7.1
- Tokenizers 0.13.3
|
Brokette/projetCS | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | {
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} | 4 | null | ---
language:
- en
- fr
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: en-fr
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. -->
# en-fr
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:
- Loss: 0.5956
- Bleu: 53.2928
- Gen Len: 53.437
## 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: 3.0
### Training results
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.11.0
- Tokenizers 0.13.3
|
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} | 0 | null | ---
license: cc-by-4.0
tags:
- ocean
- midwater
- object-detection
---
# MBARI Midwater Supercategory Detector
## Model Details
- Trained by researchers at [CVisionAI](https://www.cvisionai.com/) and the [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) (MBARI).
- [YOLOv5v6.2](https://github.com/ultralytics/yolov5/tree/v6.2)
- Object detection
- Fine tuned yolov5l to detect 22 morhpotaxonmic categories of midwater animals in the Greater Monterey Bay Area off the coast of Central California.
## Intended Use
- Make real time detections on video feed from MBARI Remotely Operated Vehicles.
- Post-process video collected in the region by MBARI vehicles.
## Factors
- Distribution shifts related to sampling platform, camera parameters, illumination, and deployment environment are expected to impact model performance.
- Evaluation was performed on an IID subset of available training data. Data to test out of distribution performance not currently available.
## Metrics
- [Precision-Recall curve](https://huggingface.co/FathomNet/MBARI-midwater-supercategory-detector/blob/main/plots/PR_curve.png) and [per class accuracy]((https://huggingface.co/FathomNet/MBARI-midwater-supercategory-detector/blob/main/plots/confusion_matrix.png)) were evaluated at test time.
- mAP@0.5 = 0.866
- Indicates reasonably good performance for target task.
## Training and Evaluation Data
- A combination of publicly available [FathomNet](https://fathomnet.org/fathomnet/#/) and internal MBARI data
- Class labels have a [long tail and localizations occur throughout the frame](https://huggingface.co/FathomNet/MBARI-midwater-supercategory-detector/blob/main/plots/labels.jpg).
## Deployment
In an environment running [YOLOv5v6.2](https://github.com/ultralytics/yolov5/tree/v6.2):
```
python classify/predict.py --weights best.pt --data data/images/
``` |
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} | 0 | null | ---
language:
- en
- de
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: en-de
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. -->
# en-de
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:
- Loss: 0.7436
- Bleu: 50.7327
- Gen Len: 44.9583
## 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: 3.0
### Training results
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.11.0
- Tokenizers 0.13.3
|
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} | 0 | null | ---
language:
- en
- es
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: 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. -->
# 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:
- Loss: 1.0432
- Bleu: 45.8789
- Gen Len: 24.6332
## 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: 3.0
### Training results
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.11.0
- Tokenizers 0.13.3
|
BumBelDumBel/TRUMP | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
] | text-generation | {
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} | 5 | null | ---
language:
- en
- pl
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: en-pl
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. -->
# en-pl
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:
- Loss: 0.9073
- Bleu: 40.2364
- Gen Len: 29.9009
## 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: 3.0
### Training results
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.11.0
- Tokenizers 0.13.3
|
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} | 0 | null | ---
language:
- en
thumbnail: null
tags:
- text generation
- instruct
pipeline_tag: text-generation
inference: false
---
<h1 style="text-align: center">Metharme 13B</h1>
<h2 style="text-align: center">An instruction-tuned LLaMA biased towards fiction writing and conversation.</h2>
## Model Details
Metharme 13B is an instruct model based on Meta's LLaMA-13B.
This is an experiment to try and get a model that is usable for conversation, roleplaying and storywriting, but which can be guided using natural language like other instruct models. See the [prompting](#prompting) section below for examples.
It was trained by doing supervised fine-tuning over a mixture of regular instruction data alongside roleplay, fictional stories and conversations with synthetically generated instructions attached.
## Applying the XORs
The model weights in this repository cannot be used as-is. The files here are XORs due to licensing concerns. To obtain proper, usable model weights you need to:
- Request access to the original LLaMA weights from Meta [through this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form)
- Convert them to the HuggingFace Transformers format by using the [convert_llama_weights_to_hf.py](https://github.com/huggingface/transformers/blob/849367ccf741d8c58aa88ccfe1d52d8636eaf2b7/src/transformers/models/llama/convert_llama_weights_to_hf.py) script **for your version of the `transformers` library**
- With the LLaMA-13B weights in hand, you can use the [xor_codec.py](./xor_codec.py) script provided in this repository:
```bash
python3 xor_codec.py \
./metharme-13b \
./xor_encoded_files \
/path/to/hf-converted/llama-13b \
--decode
```
For reference, these are the hashes you should get after following the steps above:
```bash
$ rhash --sha256 *
be2c276865d1b0759257c0934275f05e02ee520657c2d8ebe3f2a2db1562949a config.json
a23652fc622a27b8863c39f524707796967095b2a85f334561b11422f71445a2 generation_config.json
78caa8fd29e4bf2fafcec71ba2b48afcb9516b7d1a43ce20cd0c511c620a42a2 pytorch_model-00001-of-00003.bin
7cef1f81c0f0bf130b331c4c9c26955da3913d62f141117e85fd5882d6cdf0d0 pytorch_model-00002-of-00003.bin
1059a1efade70d4a1a9f66176631f0dae239c50dd5c89d62deb873b3d4ea49a1 pytorch_model-00003-of-00003.bin
72e91e29282dae48ea5562fcf4d6ca0d5a9c2a30ebc8d67174a19e192552a20b pytorch_model.bin.index.json
f9ffc4aede0845ab65324ce5dccb823dca2427f9a0710981e5bc2398d73d8162 tokenizer.json
9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347 tokenizer.model
bd87e244d21d45c358e5d822aeb2efd4e4d60127e43b648ed3efe7823fd35060 tokenizer_config.json
```
## Prompting
The current model version has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history.
### Prompting example
Here's a concrete example. Let's say you have the following system prompt:
> This is a text adventure game. Describe the scenario to the user and give him three options to pick from on each turn.
And the user has typed in the following message:
> Start!
To perform inference against the model, the prompt should be formatted like this:
```
<|system|>This is a text adventure game. Describe the scenario to the user and give him three options to pick from on each turn.<|user|>Start!<|model|>
```
The model might generate something like:
> You are standing in front of an old, abandoned house. The windows are boarded up, and there's no sign of life around it. As you approach, you notice a strange feeling emanating from within. Suddenly, you hear a voice calling out to you... 'Come inside!'
>
> - Go inside the house.
> - Ignore the call and move away.
> - Run as fast as you can.
It will automatically emit an end-of-text token (`</s>`) when it judges that the response is complete.
Pretend then that the user has replied with `go inside`. The next prompt should then be:
```
<|system|>This is a text adventure game. Describe the scenario to the user and give him three options to pick from on each turn.<|user|>Start!<|model|>You are standing in front of an old, abandoned house. The windows are boarded up, and there's no sign of life around it. As you approach, you notice a strange feeling emanating from within. Suddenly, you hear a voice calling out to you... 'Come inside!'
- Go inside the house.
- Ignore the call and move away.
- Run as fast as you can.<|user|>go inside<|model|>
```
Which might generate something like:
> You enter the house, and immediately, a wave of unsettling energy washes over you. Your senses seem heightened, but your mind feels foggy. A figure appears before you - a tall man with piercing eyes. He speaks to you in a language you don't understand.
>
> - Ask him what he wants.
> - Attack him.
> - Run away.
Same process applies. Usually, it is best to do a sliding window over the user and model turns, but keep the system prompt fixed at the start of the context window.
## Other notes
- When prompted correctly, the model will always start by generating a BOS token. This behavior is an accidental side-effect which we plan to address in future model versions and should not be relied upon.
- The model was trained as a LoRA with a somewhat unorthodox configuration which causes errors when used with the current version of `peft`, hence we release it as a full model instead.
## Limitations and biases
The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope.
As such, it was **not** fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading.
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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} | 85 | null | <h2>Introduction</h2>
<h3>The Importance of Cleaning Solar Panels on Garden Lights</h3>
<p>Solar panels play a crucial role in powering garden lights by harnessing the energy from the sun. To ensure optimal performance and efficiency, regular cleaning of these solar panels is essential. This article will guide you through the process of cleaning solar panels on garden lights, highlighting the benefits and providing step-by-step instructions.</p>
<p><br></p>
<p>Read more: <a href="https://adnsolarstreetlight.com/">adnsolarstreetlight.com</a></p>
<h2>The Basics of Cleaning Solar Panels on Garden Lights</h2>
<p>Before diving into the cleaning process, it's important to take necessary safety precautions. Turn off the garden lights and ensure that the solar panels are cool to the touch before starting the cleaning process. Gather the required tools and materials, such as a soft cloth or brush, mild cleaning solution, water, and a bucket. <a href="https://adnsolarstreetlight.com/solar-panel">view more</a></p>
<p><br></p>
<h2>Step-by-Step Guide to Cleaning Solar Panels</h2>
<h3>Inspect the Garden Lights and Panels</h3>
<p>Start by examining the garden lights and solar panels for any visible dirt, dust, or debris. Remove any obstructions that may hinder the panel's exposure to sunlight.</p>
<h3>Removing Dust, Debris, and Obstructions</h3>
<p>Gently brush off loose dirt, leaves, or debris from the surface of the solar panels using a soft cloth or brush. Be careful not to apply excessive pressure to avoid damaging the delicate panels.</p>
<h3>Cleaning the Solar Panels</h3>
<p>Prepare a mild cleaning solution by mixing a few drops of a gentle dish soap or solar panel cleaning solution in a bucket of water. Dampen the soft cloth or brush with the solution and gently wipe the surface of the solar panels, removing any stubborn stains or dirt. Avoid using harsh chemicals or abrasive tools that can cause damage.</p>
<h3>Rinsing with Water and Drying the Panels</h3>
<p>Rinse the panels thoroughly with clean water to remove any residue from the cleaning solution. Use a gentle stream of water or a garden hose to ensure all soap residue is washed away. Allow the panels to air dry or use a soft, lint-free cloth to gently pat them dry.</p>
<h3>Reassembling and Testing the Garden Lights</h3>
<p>Once the solar panels are clean and dry, reassemble the garden lights according to the manufacturer's instructions. Turn the lights back on and ensure they are functioning properly. If you notice any issues, refer to the troubleshooting section or seek professional assistance. <a href="https://en.wikipedia.org/wiki/Solar_panel">learn more</a></p>
<p><br></p>
<h2>Tips for Effective Solar Panel Cleaning</h2>
<ul>
<li>
<p>Use non-abrasive cleaning solutions and tools to prevent scratches or damage to the solar panels.</p>
</li>
<li>
<p>Avoid harsh chemicals, bleach, or abrasive materials that can degrade the panel's surface.</p>
</li>
<li>
<p>Choose a moderate weather day for cleaning to prevent rapid evaporation of cleaning solutions.</p>
</li>
<li>
<p>Regularly monitor and maintain the cleanliness of the solar panels to maximize their efficiency and lifespan.</p>
</li>
</ul>
<h2>Troubleshooting Common Issues with Solar Panels on Garden Lights</h2>
<p>Dim or inconsistent lighting, reduced battery life, and corrosion are common issues that can occur with solar panels on garden lights. If you encounter these problems, refer to the troubleshooting section in the article for possible solutions or seek professional assistance.</p>
<h2>Extending the Lifespan of Solar Panels on Garden Lights</h2>
<p>To ensure the longevity of your solar panels, proper placement and positioning are crucial. Install the garden lights in areas where they receive ample sunlight throughout the day. Protect the panels from physical damage, such as falling branches or debris. Regularly inspect and maintain the panels to identify any issues early on and address them promptly.</p>
<h2>Conclusion</h2>
<h3>Maintaining Clean and Efficient Solar Panels for Bright Garden Lights</h3>
<p>Regular cleaning and maintenance of solar panels on garden lights are essential for optimal performance and longevity. By following the step-by-step guide and implementing the tips provided in this article, you can ensure that your solar panels remain clean, efficient, and capable of providing bright illumination for your garden. Remember, a little effort in cleaning goes a long way in maximizing the benefits of solar-powered garden lights.</p>
<p>process.</p>
<p><br></p>
<p>Appendices </p>
<h3>FAQ’s</h3>
<p><br></p>
<h4>Why is it important to clean solar panels on garden lights regularly?</h4>
<p>Regular cleaning of solar panels on garden lights is important to maintain their efficiency and performance. Dust, dirt, and debris can accumulate on the surface of the panels over time, blocking sunlight and reducing their ability to generate electricity. By cleaning the panels regularly, you ensure maximum exposure to sunlight, leading to optimal energy production and longer battery life.</p>
<h4>How often should I clean the solar panels on my garden lights?</h4>
<p>The frequency of cleaning depends on various factors such as the location, weather conditions, and surrounding environment. As a general guideline, it is recommended to clean the solar panels every 2-3 months. However, if you live in an area with high pollution, frequent dust storms, or heavy tree cover, you may need to clean them more often to prevent excessive buildup of dirt and debris.</p>
<h4>Can I use any cleaning solution to clean the solar panels?</h4>
<p>It is important to use mild cleaning solutions to prevent damage to the solar panels. Avoid using harsh chemicals, abrasive cleaners, or solvents as they can degrade the panel's surface and reduce its efficiency. A mixture of a few drops of gentle dish soap or a specialized solar panel cleaning solution diluted in water is sufficient for cleaning. Be sure to rinse the panels thoroughly with clean water after cleaning to remove any residue.</p>
<h4>Can I clean the solar panels while they are still connected to the garden lights?</h4>
<p>No, it is not recommended to clean the solar panels while they are still connected to the garden lights. Before cleaning, turn off the lights and disconnect them from any power source to ensure safety. Cleaning the panels while they are connected may pose a risk of electrical shock. Once the panels are cleaned and dried, you can reassemble and reconnect the garden lights following the manufacturer's instructions.</p>
<h4>What should I do if I notice a decrease in the performance of my garden lights despite cleaning the solar panels?</h4>
<p>If you observe a decrease in the performance of your garden lights even after cleaning the solar panels, there could be other factors affecting their operation. Check the battery connections to ensure they are secure and properly connected. Additionally, verify that the garden lights are placed in an area that receives adequate sunlight throughout the day. If the issue persists, it is advisable to consult the manufacturer or a professional technician for further troubleshooting and assistance.</p>
<p><br></p>
<h3>Additional Resources</h3>
<p><br></p>
<p><a href="https://adnsolarstreetlight.com/">https://adnsolarstreetlight.com/</a></p>
<p><a href="https://adnsolarstreetlight.com/solar-panel">https://adnsolarstreetlight.com/solar-panel</a></p>
<p><a href="https://en.wikipedia.org/wiki/Solar_panel">https://en.wikipedia.org/wiki/Solar_panel</a></p>
<p><br></p>
<p><br></p>
<p><br></p>
<p><br></p> |
CAMeL-Lab/bert-base-arabic-camelbert-ca-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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} | 73 | 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: 255.10 +/- 11.58
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
...
```
|
CAMeL-Lab/bert-base-arabic-camelbert-ca | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 580 | null | ---
language:
- en
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- domain-specific
library_name: sentence-transformers
---
# **YGOMiniLM**
[ImgSource](https://yugipedia.com/wiki/Time_Wizard)
This is a sentence-transformers/paraphrase-MiniLM-L3-v2 model that has undergone further domain specific pretraining via Masked Language Modelling.
Its intended use is to create sentence embeddings for fast vector search in the domain of YuGiOh discourse.
## **Training Data**
The training data was split into two parts:
1) A private collection of data collected from YouTube Comments:
|CREATOR|N_COMMENTS|
|-----|-----|
|thecalieffect|20,592|
|MBTYuGiOh|5439|
|MSTTV |5340|
|mkohl40|5224|
2) The Full Database of YuGiOh cards accessed via the [YGOProDeck API](https://ygoprodeck.com/api-guide/) as of 17/05/2023. The `name`, `type`, `race` and `desc` fields were concatenated and delimited by `\t` to create the training examples.
## **Usage**
```
pip install sentence-transformers
```
Then to get embeddings you simply run the following:
```
from sentence_transformers import SentenceTransformer
sentences = ["FLIP: Target 1 monster on the field; destroy that target.",
"Union Carrier needs to go.",
"Scythe lock is healthy for the game"
]
model = SentenceTransformer("jkswin/YGO_MiniLM")
embeddings = model.encode(sentences)
print(embeddings)
``` |
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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} | 54 | null | ---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"has_space"
] | text-classification | {
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"BertForSequenceClassification"
],
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}
} | 19,850 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: moderate_severe_depression_longformerlarge_model
results: []
datasets:
- christinacdl/balanced_depression_dataset
language:
- en
pipeline_tag: text-classification
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# moderate_severe_depression_longformerlarge_model
This model is a fine-tuned version of [allenai/longformer-scico](https://huggingface.co/allenai/longformer-scico) on the 'christinacdl/balanced_depression_dataset' dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4331
- Macro F1: 0.8927
- Micro F1: 0.8929
- Accuracy: 0.8929
Model performance on test set:
Accuracy: 0.882091212458287
F1 score: 0.8819016546683596
Precision: 0.8844185528069173
Recall : 0.882091212458287
Matthews Correlation Coefficient: 0.8244878843339586
Precision of each class: [0.97601371 0.85618932 0.82105263]
Recall of each class: [0.95050056 0.78476085 0.91101224]
F1 score of each class: [0.96308819 0.81892049 0.86369628]
## 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: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:--------:|
| 0.3517 | 1.0 | 1798 | 0.3561 | 0.8741 | 0.8745 | 0.8745 |
| 0.2664 | 2.0 | 3596 | 0.3241 | 0.8737 | 0.8732 | 0.8732 |
| 0.2337 | 3.0 | 5394 | 0.3824 | 0.8887 | 0.8892 | 0.8892 |
| 0.2019 | 4.0 | 7192 | 0.4330 | 0.8927 | 0.8929 | 0.8929 |
| 0.1489 | 5.0 | 8990 | 0.5230 | 0.8917 | 0.8919 | 0.8919 |
| 0.1187 | 6.0 | 10788 | 0.5839 | 0.8882 | 0.8882 | 0.8882 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.1+cu118
- Datasets 2.9.0
- 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"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
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} | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-finetuned-squad_v2_4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-squad_v2_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2184
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0655 | 1.0 | 5533 | 1.0163 |
| 0.8028 | 2.0 | 11066 | 1.0524 |
| 0.5648 | 3.0 | 16599 | 1.0885 |
| 0.4355 | 4.0 | 22132 | 1.2184 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
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} | 21 | null | ---
datasets:
- anon8231489123/ShareGPT_Vicuna_unfiltered
- ehartford/wizard_vicuna_70k_unfiltered
- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
- QingyiSi/Alpaca-CoT
- teknium/GPT4-LLM-Cleaned
- teknium/GPTeacher-General-Instruct
- metaeval/ScienceQA_text_only
- hellaswag
- tasksource/mmlu
- openai/summarize_from_feedback
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
# Manticore 13B GGML
This is GGML format quantised 4-bit, 5-bit and 8-bit models of epoch 3 of [OpenAccess AI Collective's Manticore 13B](https://huggingface.co/openaccess-ai-collective/manticore-13b).
This repo is the result of quantising to 4-bit, 5-bit and 8-bit GGML for CPU (+CUDA) inference using [llama.cpp](https://github.com/ggerganov/llama.cpp).
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/TheBloke/Manticore-13B-GPTQ).
* [4-bit, 5-bit and 8-bit GGML models for llama.cpp CPU (+CUDA) inference](https://huggingface.co/TheBloke/TheBloke/Manticore-13B-GGML).
* [OpenAccess AI Collective's original float16 HF format repo for GPU inference and further conversions](https://huggingface.co/openaccess-ai-collective/manticore-13b).
## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)!
llama.cpp recently made another breaking change to its quantisation methods - https://github.com/ggerganov/llama.cpp/pull/1508
I have quantised the GGML files in this repo with the latest version. Therefore you will require llama.cpp compiled on May 19th or later (commit `2d5db48` or later) to use them.
For files compatible with the previous version of llama.cpp, please see branch `previous_llama_ggmlv2`.
## Epoch
The files in the `main` branch are from Epoch 3 of Manticore 13B, as of May 19th.
The files in the `previous_llama_ggmlv2` branch are from Epoch 1.
## Provided files
| Name | Quant method | Bits | Size | RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
`manticore-13B.ggmlv3.q4_0.bin` | q4_0 | 4bit | 8.14GB | 10.5GB | 4-bit. |
`manticore-13B.ggmlv3.q4_1.bin` | q4_1 | 4bit | 8.14GB | 10.5GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
`manticore-13B.ggmlv3.q5_0.bin` | q5_0 | 5bit | 8.95GB | 11.0GB | 5-bit. Higher accuracy, higher resource usage and slower inference. |
`manticore-13B.ggmlv3.q5_1.bin` | q5_1 | 5bit | 9.76GB | 12.25GB | 5-bit. Even higher accuracy, and higher resource usage and slower inference. |
`manticore-13B.ggmlv3.q8_0.bin` | q8_0 | 8bit | 14.6GB | 17GB | 8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for normal use. |
## How to run in `llama.cpp`
I use the following command line; adjust for your tastes and needs:
```
./main -t 8 -m manticore-13B-.ggmlv2.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: write a story about llamas ### Response:"
```
Change `-t 8` to the number of physical CPU cores you have.
## How to run in `text-generation-webui`
GGML models can be loaded into text-generation-webui by installing the llama.cpp module, then placing the ggml model file in a model folder as usual.
Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
# Original Model Card: Manticore 13B - Preview Release (previously Wizard Mega)
Manticore 13B is a Llama 13B model fine-tuned on the following datasets:
- [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) - based on a cleaned and de-suped subset
- [WizardLM](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered)
- [Wizard-Vicuna](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered)
- [subset of QingyiSi/Alpaca-CoT for roleplay and CoT](https://huggingface.co/QingyiSi/Alpaca-CoT)
- [GPT4-LLM-Cleaned](https://huggingface.co/datasets/teknium/GPT4-LLM-Cleaned)
- [GPTeacher-General-Instruct](https://huggingface.co/datasets/teknium/GPTeacher-General-Instruct)
- ARC-Easy & ARC-Challenge - instruct augmented for detailed responses
- mmlu: instruct augmented for detailed responses subset including
- abstract_algebra
- conceptual_physics
- formal_logic
- high_school_physics
- logical_fallacies
- [hellaswag](https://huggingface.co/datasets/hellaswag) - 5K row subset of instruct augmented for concise responses
- [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) - instruct for concise responses
- [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - instruct augmented tl;dr summarization
# Demo
Try out the model in HF Spaces. The demo uses a quantized GGML version of the model to quickly return predictions on smaller GPUs (and even CPUs). Quantized GGML may have some minimal loss of model quality.
- https://huggingface.co/spaces/openaccess-ai-collective/manticore-ggml
## Release Notes
- https://wandb.ai/wing-lian/manticore-13b/runs/nq3u3uoh/workspace
## Build
Manticore was built with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) on 8xA100 80GB
- Preview Release: 1 epoch taking 8 hours.
- The configuration to duplicate this build is provided in this repo's [/config folder](https://huggingface.co/openaccess-ai-collective/manticore-13b/tree/main/configs).
## Bias, Risks, and Limitations
Manticore has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
Manticore was fine-tuned from the base model LlaMa 13B, please refer to its model card's Limitations Section for relevant information.
## Examples
````
### Instruction: write Python code that returns the first n numbers of the Fibonacci sequence using memoization.
### Assistant:
````
```
### Instruction: Finish the joke, a mechanic and a car salesman walk into a bar...
### Assistant:
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
"model_type": "bert",
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} | 12 | null | ---
datasets:
- anon8231489123/ShareGPT_Vicuna_unfiltered
- ehartford/wizard_vicuna_70k_unfiltered
- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
- QingyiSi/Alpaca-CoT
- teknium/GPT4-LLM-Cleaned
- teknium/GPTeacher-General-Instruct
- metaeval/ScienceQA_text_only
- hellaswag
- tasksource/mmlu
- openai/summarize_from_feedback
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
# Manticore 13B GPTQ
This repo contains 4bit GPTQ format quantised models of [OpenAccess AI Collective's Manticore 13B](https://huggingface.co/openaccess-ai-collective/manticore-13b).
It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Manticore-13B-GPTQ).
* [4-bit, 5-bit 8-bit GGML models for llama.cpp CPU (+CUDA) inference](https://huggingface.co/TheBloke/Manticore-13B-GGML).
* [OpenAccess AI Collective's original float16 HF format repo for GPU inference and further conversions](https://huggingface.co/openaccess-ai-collective/manticore-13b).
## How to easily download and use this model in text-generation-webui
Open the text-generation-webui UI as normal.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Manticore-13B-GPTQ`.
3. Click **Download**.
4. Wait until it says it's finished downloading.
5. Click the **Refresh** icon next to **Model** in the top left.
6. In the **Model drop-down**: choose the model you just downloaded, `Manticore-13B-GPTQ`.
7. If you see an error in the bottom right, ignore it - it's temporary.
8. Fill out the `GPTQ parameters` on the right: `Bits = 4`, `Groupsize = 128`, `model_type = Llama`
9. Click **Save settings for this model** in the top right.
10. Click **Reload the Model** in the top right.
11. Once it says it's loaded, click the **Text Generation tab** and enter a prompt!
## Provided files
**`Manticore-13B-GPTQ-4bit-128g.no-act-order.safetensors`**
This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility.
It was created without `--act-order` to ensure compatibility with all UIs out there.
* `Manticore-13B-GPTQ-4bit-128g.no-act-order.safetensors`
* Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
* Works with text-generation-webui one-click-installers
* Parameters: Groupsize = 128. No act-order.
* Command used to create the GPTQ:
```
python llama.py /workspace/models/openaccess-ai-collective_manticore-13b/ wikitext2 --wbits 4 --true-sequential --groupsize 128 --save_safetensors /workspace/manticore-13b/gptq/Manticore-13B-GPTQ-4bit-128g.no-act-order.safetensors
```
# Original Model Card: Manticore 13B - Preview Release (previously Wizard Mega)
Manticore 13B is a Llama 13B model fine-tuned on the following datasets:
- [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) - based on a cleaned and de-suped subset
- [WizardLM](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered)
- [Wizard-Vicuna](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered)
- [subset of QingyiSi/Alpaca-CoT for roleplay and CoT](https://huggingface.co/QingyiSi/Alpaca-CoT)
- [GPT4-LLM-Cleaned](https://huggingface.co/datasets/teknium/GPT4-LLM-Cleaned)
- [GPTeacher-General-Instruct](https://huggingface.co/datasets/teknium/GPTeacher-General-Instruct)
- ARC-Easy & ARC-Challenge - instruct augmented for detailed responses
- mmlu: instruct augmented for detailed responses subset including
- abstract_algebra
- conceptual_physics
- formal_logic
- high_school_physics
- logical_fallacies
- [hellaswag](https://huggingface.co/datasets/hellaswag) - 5K row subset of instruct augmented for concise responses
- [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) - instruct for concise responses
- [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - instruct augmented tl;dr summarization
# Demo
Try out the model in HF Spaces. The demo uses a quantized GGML version of the model to quickly return predictions on smaller GPUs (and even CPUs). Quantized GGML may have some minimal loss of model quality.
- https://huggingface.co/spaces/openaccess-ai-collective/manticore-ggml
## Release Notes
- https://wandb.ai/wing-lian/manticore-13b/runs/nq3u3uoh/workspace
## Build
Manticore was built with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) on 8xA100 80GB
- Preview Release: 1 epoch taking 8 hours.
- The configuration to duplicate this build is provided in this repo's [/config folder](https://huggingface.co/openaccess-ai-collective/manticore-13b/tree/main/configs).
## Bias, Risks, and Limitations
Manticore has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
Manticore was fine-tuned from the base model LlaMa 13B, please refer to its model card's Limitations Section for relevant information.
## Examples
````
### Instruction: write Python code that returns the first n numbers of the Fibonacci sequence using memoization.
### Assistant:
````
```
### Instruction: Finish the joke, a mechanic and a car salesman walk into a bar...
### Assistant:
```
|
CLAck/en-km | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
] | translation | {
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"MarianMTModel"
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} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-Arabic-MDD
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-Arabic-MDD
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3
|
CLTL/icf-levels-ber | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] | text-classification | {
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"RobertaForSequenceClassification"
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} | 33 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-code
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.56
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-code
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6881
- Accuracy: 0.56
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 7 | 0.6890 | 0.52 |
| No log | 2.0 | 14 | 0.6881 | 0.56 |
### Framework versions
- Transformers 4.11.3
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.10.3
|
CLTL/icf-levels-enr | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
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} | 30 | 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: 251.64 +/- 29.60
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
...
```
|
CLTL/icf-levels-etn | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
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} | 31 | null | ---
license: mit
pipeline_tag: feature-extraction
tags:
- bark
- tts
- hubert
- text-to-speech
datasets:
- GitMylo/bark-semantic-training
---
# Bark-voice-cloning
Bark-voice-cloning is a model which processes the outputs from a HuBERT model, and turns them into semantic tokens compatible with bark text to speech.
This can be used for many things, including speech transfer and voice cloning.
[code repo](https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer)
# The models in this repo
* [quantifier_hubert_base_ls960.pth](https://huggingface.co/GitMylo/bark-voice-cloning/blob/main/quantifier_hubert_base_ls960.pth) (the model trained on literature for 4 epochs)
* [quantifier_hubert_base_ls960_14.pth](https://huggingface.co/GitMylo/bark-voice-cloning/blob/main/quantifier_hubert_base_ls960_14.pth) (the model trained on literature for 10 more epochs, based on the previous)
(Please use the model manager from the [code repo](https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer) for easy downloading of models)
# Voice cloning
Voice cloning is creating a new voice for text-to-speech.
Process:
1. Load your wav audio file into your pytorch application
2. For the fine prompt extract [discrete representations](https://github.com/facebookresearch/encodec#extracting-discrete-representations). (These are used by bark to know about the voice)
3. For the coarse prompt do `fine_prompt[:2, :]`, to get the coarse prompt from a fine prompt.
4. For the semantics, load a HuBERT model without Kmeans (I personally use the [audiolm-pytorch](https://github.com/lucidrains/audiolm-pytorch) implementation's hubertwithkmeans, but i edited it to skip kmeans.)
5. Next, to get the actual semantic tokens, run the tokens through this model. Your output will be compatible with bark.
6. Save these files in an npz with `numpy.savez(semantic_prompt=semantics, fine_prompt=fine, coarse_prompt=coarse)`. This is your speaker file containing your cloned voice.
# Voice masking
Voice masking is replacing a voice in an audio clip for speech-to-speech.
## Random
Replacing a voice in an audio clip with a voice generated by bark.
process:
1. Extract semantics from the audio clip using HuBERT and this model
2. Run `semantic_to_waveform` from `bark.api` with the extracted semantics
3. The previous step returns the generated audio.
## Transfer
Replacing a voice with a voice from another audio clip.
process:
1. Create a speaker file using the steps under the voice cloning section
2. Extract the semantics from the clip with the text you want spoken
3. Run `semantics_to_waveform` from `bark.api` with the extracted semantics, and the speaker prompt that you created on step 1.
4. The previous step returns the generated audio.
# Disclaimer
I am not responsible for any misuse of this model. I do not agree with cloning people's voices without permission. Please make sure it is appropriate to clone someone's voice before doing so. |
CLTL/icf-levels-ins | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
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} | 32 | null | Access to model JOHEARTE/GPT3 is restricted and you are not in the authorized list. Visit https://huggingface.co/JOHEARTE/GPT3 to ask for access. |
CLTL/icf-levels-mbw | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
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} | 30 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: fb_tf_classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# fb_tf_classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
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} | 0 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: perioli_manifesti_v5.1
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: 0.9215344376634699
- name: Recall
type: recall
value: 0.949685534591195
- name: F1
type: f1
value: 0.9353982300884957
- name: Accuracy
type: accuracy
value: 0.9921951219512195
---
<!-- 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. -->
# perioli_manifesti_v5.1
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: 0.0301
- Precision: 0.9215
- Recall: 0.9497
- F1: 0.9354
- Accuracy: 0.9922
## 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: 2700
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.48 | 100 | 0.1283 | 0.7806 | 0.8086 | 0.7944 | 0.9734 |
| No log | 0.96 | 200 | 0.0539 | 0.8832 | 0.9102 | 0.8965 | 0.9865 |
| No log | 1.44 | 300 | 0.0451 | 0.9001 | 0.9389 | 0.9191 | 0.9893 |
| No log | 1.92 | 400 | 0.0395 | 0.9058 | 0.9326 | 0.9190 | 0.9890 |
| 0.1374 | 2.4 | 500 | 0.0312 | 0.9269 | 0.9461 | 0.9364 | 0.9926 |
| 0.1374 | 2.88 | 600 | 0.0306 | 0.9230 | 0.9587 | 0.9405 | 0.9925 |
| 0.1374 | 3.37 | 700 | 0.0260 | 0.9356 | 0.9659 | 0.9505 | 0.9939 |
| 0.1374 | 3.85 | 800 | 0.0331 | 0.9244 | 0.9668 | 0.9451 | 0.9928 |
| 0.1374 | 4.33 | 900 | 0.0296 | 0.9099 | 0.9524 | 0.9306 | 0.9909 |
| 0.0233 | 4.81 | 1000 | 0.0235 | 0.9379 | 0.9632 | 0.9504 | 0.9943 |
| 0.0233 | 5.29 | 1100 | 0.0200 | 0.9407 | 0.9695 | 0.9549 | 0.9948 |
| 0.0233 | 5.77 | 1200 | 0.0263 | 0.9233 | 0.9632 | 0.9428 | 0.9929 |
| 0.0233 | 6.25 | 1300 | 0.0267 | 0.9291 | 0.9650 | 0.9467 | 0.9933 |
| 0.0233 | 6.73 | 1400 | 0.0243 | 0.9348 | 0.9668 | 0.9505 | 0.9941 |
| 0.0147 | 7.21 | 1500 | 0.0260 | 0.9280 | 0.9614 | 0.9444 | 0.9934 |
| 0.0147 | 7.69 | 1600 | 0.0256 | 0.9311 | 0.9596 | 0.9451 | 0.9934 |
| 0.0147 | 8.17 | 1700 | 0.0246 | 0.9291 | 0.9542 | 0.9415 | 0.9930 |
| 0.0147 | 8.65 | 1800 | 0.0242 | 0.9364 | 0.9650 | 0.9504 | 0.9941 |
| 0.0147 | 9.13 | 1900 | 0.0266 | 0.9268 | 0.9560 | 0.9412 | 0.9928 |
| 0.0107 | 9.62 | 2000 | 0.0300 | 0.9296 | 0.9605 | 0.9448 | 0.9932 |
| 0.0107 | 10.1 | 2100 | 0.0277 | 0.9365 | 0.9677 | 0.9518 | 0.9941 |
| 0.0107 | 10.58 | 2200 | 0.0292 | 0.9324 | 0.9668 | 0.9493 | 0.9937 |
| 0.0107 | 11.06 | 2300 | 0.0247 | 0.9284 | 0.9560 | 0.9420 | 0.9930 |
| 0.0107 | 11.54 | 2400 | 0.0298 | 0.9191 | 0.9488 | 0.9337 | 0.9921 |
| 0.0067 | 12.02 | 2500 | 0.0303 | 0.9215 | 0.9497 | 0.9354 | 0.9922 |
| 0.0067 | 12.5 | 2600 | 0.0305 | 0.9234 | 0.9533 | 0.9381 | 0.9925 |
| 0.0067 | 12.98 | 2700 | 0.0301 | 0.9215 | 0.9497 | 0.9354 | 0.9922 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.2.2
- Tokenizers 0.13.3
|
CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 28 | null | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
---
# Model card for samvit_base_patch16.sa1b
A Segment-Anything Vision Transformer (SAM ViT) image feature model (NOTE: for features and fine-tune, segmentation head not included). Pretrained on SA-1B for segementation by paper authors w/ initialization from MAE weights.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 89.7
- GMACs: 486.4
- Activations (M): 1343.3
- Image size: 1024 x 1024
- **Papers:**
- Segment Anything: https://arxiv.org/abs/2304.02643
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Original:** https://github.com/facebookresearch/segment-anything
- **Pretrain Dataset:** SA-1B
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('samvit_base_patch16.sa1b', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'samvit_base_patch16.sa1b',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 256, 64, 64) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{kirillov2023segany,
title={Segment Anything},
author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{'a}r, Piotr and Girshick, Ross},
journal={arXiv:2304.02643},
year={2023}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
CNT-UPenn/RoBERTa_for_seizureFrequency_QA | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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} | 5 | null | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
---
# Model card for samvit_huge_patch16.sa1b
A Segment-Anything Vision Transformer (SAM ViT) image feature model (NOTE: for features and fine-tune, segmentation head not included). Pretrained on SA-1B for segementation by paper authors w/ initialization from MAE weights.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 637.0
- GMACs: 2982.2
- Activations (M): 3428.2
- Image size: 1024 x 1024
- **Papers:**
- Segment Anything: https://arxiv.org/abs/2304.02643
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Original:** https://github.com/facebookresearch/segment-anything
- **Pretrain Dataset:** SA-1B
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('samvit_huge_patch16.sa1b', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'samvit_huge_patch16.sa1b',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 256, 64, 64) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{kirillov2023segany,
title={Segment Anything},
author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{'a}r, Piotr and Girshick, Ross},
journal={arXiv:2304.02643},
year={2023}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
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} | 0 | null | ---
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: 656.50 +/- 197.86
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 wasimar -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 wasimar -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 wasimar
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Calamarii/calamari | [] | null | {
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} | 0 | null | ---
license: other
---
# Overview
This is a fine-tuned 7b parameter LlaMa model, using completely synthetic training data created by https://github.com/jondurbin/airoboros
### Training data
I used a jailbreak prompt to generate the synthetic instructions, which resulted in some training data that would likely be censored by other models, such as how-to prompts about synthesizing drugs, making homemade flamethrowers, etc. Mind you, this is all generated by ChatGPT, not me. My goal was to simply test some of the capabilities of ChatGPT when unfiltered (as much as possible), and not to intentionally produce any harmful/dangerous/etc. content.
The jailbreak prompt I used is the default prompt in the python code when using the `--uncensored` flag: https://github.com/jondurbin/airoboros/blob/main/airoboros/self_instruct.py#L39
I also did a few passes of manually cleanup to remove some bad prompts, but mostly I left the data as-is. Initially, the model was fairly bad at math/extrapolation, closed question-answering (heavy hallucination), and coding, so I did one more fine tuning pass with additional synthetic instructions aimed at those types of problems.
Both the initial instructions and final-pass fine-tuning instructions will be published soon.
### Fine-tuning method
I used the excellent [FastChat](https://github.com/lm-sys/FastChat) module, running with:
```
torchrun --nproc_per_node=8 --master_port=20001 /workspace/FastChat/fastchat/train/train_mem.py \
--model_name_or_path /workspace/llama-7b \
--data_path /workspace/as_conversations.json \
--bf16 True \
--output_dir /workspace/airoboros-7b \
--num_train_epochs 3 \
--per_device_train_batch_size 24 \
--per_device_eval_batch_size 24 \
--gradient_accumulation_steps 2 \
--evaluation_strategy "steps" \
--eval_steps 1000 \
--save_strategy "steps" \
--save_steps 1000 \
--save_total_limit 10 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.04 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--fsdp "full_shard auto_wrap" \
--fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True \
--lazy_preprocess True
```
This ran on 8x nvidia 80gb a100's for about 17 hours.
### License
The model is licensed under the LLaMA model, and the dataset is licensed under the terms of OpenAI because it uses ChatGPT. Everything else is free.
|
CallumRai/HansardGPT2 | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
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} | 14 | null | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
---
# Model card for samvit_large_patch16.sa1b
A Segment-Anything Vision Transformer (SAM ViT) image feature model (NOTE: for features and fine-tune, segmentation head not included). Pretrained on SA-1B for segementation by paper authors w/ initialization from MAE weights.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 308.3
- GMACs: 1493.9
- Activations (M): 2553.8
- Image size: 1024 x 1024
- **Papers:**
- Segment Anything: https://arxiv.org/abs/2304.02643
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Original:** https://github.com/facebookresearch/segment-anything
- **Pretrain Dataset:** SA-1B
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('samvit_large_patch16.sa1b', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'samvit_large_patch16.sa1b',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 256, 64, 64) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{kirillov2023segany,
title={Segment Anything},
author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{'a}r, Piotr and Girshick, Ross},
journal={arXiv:2304.02643},
year={2023}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
Cameron/BERT-SBIC-offensive | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
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} | 31 | null | ---
license: apache-2.0
datasets:
- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
tags:
- uncensored
- rwkv
---
# RWKV 14B WizardLM LoRA
The model in this repository was trained for 10.25 hours with a cost of $18.
* LoRA Rank: 32
* LoRA Alpha: 64
* Real Epochs: 3
* Learning Rate: 1e-4
* Context Length: 1024
* Training Tokens: 22,771,425
* Training Dataset: [WizardLM_alpaca_evol_instruct_70k_unfiltered](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered)
* RWKV Model License: apache-2.0
This is an unrestricted model. Please be aware that outputs could be extremely harmful, potentially even if the model is not prompted for harmful outputs. Discretion should be advised when deploying the model to make sure you are not exposing yourself to liabilities arising from unwanted or harmful outputs. I am not responsible for anything that happens when you use this model.
The training data may have more restrictive licenses. Depending on your jurisdiction and local laws, it may be unwise to use this model for commercial purposes. It is currently unclear how training data licenses govern trained models and it may be subject to change in the near future.
## Preparing Data
Repo: [RWKV-v2-RNN-Pile](https://github.com/BlinkDL/RWKV-v2-RNN-Pile)
Directory: RWKV-v3
You need to create a file called `train.txt`. Separate each entry with `<|endoftext|>`. Here is some example code:
```python
import json
with open("WizardLM_alpaca_evol_instruct_70k_unfiltered.json", "r") as fh:
data = json.load(fh)
for item in data:
if len(item.get("instruction")) > 0 and len(item.get("output")) > 0:
print(item["instruction"])
print("\n### Response:", end="")
print(item["output"])
print("<|endoftext|>")
```
Then run:
```bash
python prepare_data.py
```
The resulting file will be `train.npy`. Keep track of the number of tokens.
## Training
Repo: [RWKV-LM-LoRA](https://github.com/Blealtan/RWKV-LM-LoRA)
Directory: RWKV-v4neo
Trained using Runpod A100 80 GB instance (Torch 2)
Install dependencies:
```bash
apt install screen ncdu htop vim
wget https://huggingface.co/BlinkDL/rwkv-4-pile-14b/resolve/main/RWKV-4-Pile-14B-20230313-ctx8192-test1050.pth
# replace import for inf from torch._six with import from math
vim /usr/local/lib/python3.10/dist-packages/deepspeed/runtime/utils.py
vim /usr/local/lib/python3.10/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py
pip install pytorch-lightning==1.9.0 deepspeed==0.7.0
pip install torch==2.0.0+cu118 torchvision==0.15.1+cu118 torchaudio==2.0.0 --extra-index-url https://download.pytorch.org/whl/cu118
apt install cuda-nvcc-11-8 libcusparse-11-8 libcusparse-dev-11-8 libcublas-dev-11-8 libcublas-11-8 libcusolver-dev-11-8 libcusolver-11-8
apt remove cuda-nvcc-11-6
```
Run training:
Note:
* `n_layer` and `n_embd` is dependent on the specifc model you choose.
* `lora_alpha` must be the same in training and the `merge_lora.py` command.
* `epoch_count` is calculated from `tokens / (ctx_len * micro_bsz * epoch_steps) * actual_epochs`
* Make sure your checkpoints folder exists.
```bash
python3 train.py \
--load_model ./RWKV-4-Pile-3B-20221110-ctx4096.pth \
--proj_dir ./checkpoints-wizardlm \
--data_file ./train.npy \
--data_type numpy \
--vocab_size 50277 \
--ctx_len 1024 \
--epoch_steps 1000 \
--epoch_count 34 \
--epoch_begin 0 \
--epoch_save 5 \
--micro_bsz 2 \
--n_layer 40 \
--n_embd 5120 \
--pre_ffn 0 \
--head_qk 0 \
--lr_init 1e-4 \
--lr_final 5e-7 \
--warmup_steps 0 \
--beta1 0.9 \
--beta2 0.999 \
--adam_eps 1e-8 \
--lora \
--lora_r 32 \
--lora_alpha 64 \
--lora_dropout 0.05 \
--lora_parts=att,ffn,time,ln \
--accelerator gpu \
--devices 1 \
--precision bf16 \
--grad_cp 0 \
--strategy deepspeed_stage_2
```
Merge weights (since LoRA isn't supported in most implementations):
```bash
python merge_lora.py 64 RWKV-4-Pile-14B-20230313-ctx8192-test1050.pth rwkv-45.pth RWKV-14B-WizardLM.pth
```
|
Cameron/BERT-jigsaw-severetoxic | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 30 | null | ---
language: en
license: other
commercial: no
inference: false
---
# pygmalion-13b-4bit-128g
## Model description
**Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.**
Quantized from the decoded pygmalion-13b xor format.
**https://huggingface.co/PygmalionAI/pygmalion-13b**
In safetensor format.
### Quantization Information
GPTQ CUDA quantized with: https://github.com/0cc4m/GPTQ-for-LLaMa
```
python llama.py --wbits 4 models/pygmalion-13b c4 --true-sequential --groupsize 128 --save_safetensors models/pygmalion-13b/4bit-128g.safetensors
``` |
Cameron/BERT-mdgender-convai-ternary | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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} | 38 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
# Model Card for RNAX
## Model Description
- **Developed by:** BADMONK
- **Model type:** Dreambooth Model + Extracted LoRA
- **Language(s) (NLP):** EN
- **License:** Creativeml-Openrail-M
- **Parent Model:** ChilloutMix
# How to Get Started with the Model
Use the code below to get started with the model.
### RNAX ### |
Capreolus/birch-bert-large-mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
] | null | {
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"BertForNextSentencePrediction"
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} | 1 | null | Made by merging the following lora:
https://huggingface.co/Neko-Institute-of-Science/VicUnLocked-30b-LoRA
Then quantizing with ooba's old CUDA branch of GPTQ
```
python llama.py vicunlocked-30b c4 --wbits 4 --true-sequential --act-order --save_safetensors vicunlocked-30b-4bit.safetensors
``` |
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 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
datasets:
- cifar10
---
## Image Classification with Vision Transformer (ViT)
This repository contains a Python script for training an image classification model using the Vision Transformer (ViT) architecture. We use the transformers and datasets libraries from Hugging Face along with PyTorch and TensorFlow for the implementation.
### Functions and Usage
* convert_to_tf_tensor(image: Image):
* This function converts an image to a Tensorflow tensor with a size of 224x224 and three color channels.
* preprocess(batch):
* Preprocesses the images in a batch, using the feature extractor to convert them to pixel values. It also adds the labels to the batch.
* collate_fn(batch):
* This function prepares the batch for training or evaluation. It stacks the pixel values and labels.
* compute_metrics(p):
* Computes the metrics (accuracy) for the predictions.
|
Captain272/lstm | [] | null | {
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} | 0 | null | ---
license: apache-2.0
inference: false
---
<div align="center">
<h1>JARVIS</h1>
<p>This model is an experiment that underwent fine-tuning using only 50 human/bot pairs for 10 epochs. <br>The goal of this fine tuning experiment was to make the model more like a JARVIS character - an AI assistant like the one from the Iron Man movies.</p>
</div>
## Model details
- **Type of Model**: language model
- **Base Model**: RedPajama-INCITE-Chat-3B
- **Finetune details**: was finetuned using only 50 human/bot pairs for 10 epochs.
|
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} | 0 | null | ---
datasets:
- samhog/psychology-10k
pipeline_tag: conversational
--- |
Cedille/fr-boris | [
"pytorch",
"gptj",
"text-generation",
"fr",
"dataset:c4",
"arxiv:2202.03371",
"transformers",
"causal-lm",
"license:mit",
"has_space"
] | text-generation | {
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} | 401 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-large-qa-for-fewshot
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. -->
# t5-large-qa-for-fewshot
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1275
- Rouge1: 78.0444
- Rouge2: 66.6789
- Rougel: 77.4324
- Rougelsum: 77.4473
- 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
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 306 | 0.1277 | 77.6808 | 66.4634 | 77.0694 | 77.0872 | 19.0 |
| 0.1758 | 2.0 | 612 | 0.1275 | 78.0444 | 66.6789 | 77.4324 | 77.4473 | 19.0 |
| 0.1758 | 3.0 | 918 | 0.1310 | 78.4815 | 68.3121 | 78.0501 | 78.0194 | 19.0 |
| 0.0786 | 4.0 | 1224 | 0.1413 | 78.3183 | 67.4242 | 77.7702 | 77.7753 | 19.0 |
| 0.0465 | 5.0 | 1530 | 0.1535 | 78.2176 | 67.4929 | 77.6782 | 77.6628 | 19.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dccuchile/albert-base-spanish-finetuned-mldoc | [
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"albert",
"text-classification",
"transformers"
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} | 34 | 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: 11.17 +/- 5.88
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 Flooow/samplefactory-vizdoom-hgs
```
## 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=samplefactory-vizdoom-hgs
```
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=samplefactory-vizdoom-hgs --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.
|
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} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: donut-trained-example-3
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. -->
# donut-trained-example-3
This model is a fine-tuned version of [anarenteriare/donut-trained-example-3](https://huggingface.co/anarenteriare/donut-trained-example-3) 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: 2e-05
- train_batch_size: 2
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dccuchile/albert-large-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
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} | 25 | 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: emmanuel17/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dccuchile/albert-tiny-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgp2-fintuning-faqs-nlp
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. -->
# distilgp2-fintuning-faqs-nlp
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4196
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.8642 | 1.0 | 2386 | 0.6533 |
| 0.589 | 2.0 | 4772 | 0.4977 |
| 0.5088 | 3.0 | 7158 | 0.4466 |
| 0.4723 | 4.0 | 9544 | 0.4265 |
| 0.4584 | 5.0 | 11930 | 0.4196 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dccuchile/albert-tiny-spanish-finetuned-pos | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
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} | 5 | null | ---
license: apache-2.0
datasets:
- cc100
- wikipedia
language:
- ja
widget:
- text: 東北大学で[MASK]の研究をしています。
---
# BERT base Japanese (unidic-lite with whole word masking, CC-100 and jawiki-20230102)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization.
Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.
The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/).
## Model architecture
The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.
## Training Data
The model is trained on the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia.
For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023.
The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively.
For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7).
## Tokenization
The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.
The vocabulary size is 32768.
We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization.
## Training
We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps.
For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/).
## Licenses
The pretrained models are distributed under the Apache License 2.0.
## Acknowledgments
This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program. |
dccuchile/albert-tiny-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
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} | 7 | null | ---
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: 638.50 +/- 179.43
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 cmpatino -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 cmpatino -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 cmpatino
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
dccuchile/albert-tiny-spanish-finetuned-xnli | [
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"transformers"
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} | 31 | null | Quantizations of the model:
- https://huggingface.co/TehVenom/Metharme-13b-Merged
Under the following versions:
- Q4_1 (4bit);
- Q5_1 (5bit);
- Q8_0 (8bit). |
dccuchile/albert-xlarge-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
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}
} | 5 | 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: -137.28 +/- 108.45
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': 'mmhamdy/ppo-LunarLander-v2-2'
'batch_size': 512
'minibatch_size': 128}
```
|
dccuchile/albert-xlarge-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
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}
} | 24 | null | ---
license: cc-by-nc-4.0
---
Model based on Stellar4 trained on spanish data, the 0.3 signifies for how complete the learning is. the training was cut short so it is not as good as original model.
However, this model is to demonstrate the power of Stellar4 and redpajama.
### Specs
2.8B parameters
0.3 Epochs training
18K examples
|
dccuchile/albert-xlarge-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
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}
} | 29 | null | Quantizations of the model:
- https://huggingface.co/TehVenom/Pygmalion-13b-Merged
Under the following versions:
- Q4_1 (4bit);
- Q5_1 (5bit);
- Q8_0 (8bit). |
dccuchile/albert-xxlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
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}
}
} | 26 | null | ---
license: apache-2.0
datasets:
- cc100
- wikipedia
language:
- ja
widget:
- text: 東北大学で[MASK]の研究をしています。
---
# BERT base Japanese (character-level tokenization with whole word masking, CC-100 and jawiki-20230102)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by character-level tokenization.
Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.
The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/).
## Model architecture
The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.
## Training Data
The model is trained on the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia.
For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023.
The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively.
For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7).
## Tokenization
The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters.
The vocabulary size is 7027.
We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization.
## Training
We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps.
For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/).
## Licenses
The pretrained models are distributed under the Apache License 2.0.
## Acknowledgments
This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program. |
dccuchile/albert-xxlarge-spanish-finetuned-pos | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
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}
} | 3 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: frozenLake
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="rami8k/frozenLake", 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"])
```
|
dccuchile/albert-xxlarge-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 7 | null | ---
license: cc-by-nc-nd-4.0
inference: false
---
# sports-lover-model
A demo model for PNF that ranks all sports news high and other news low.
## Usage
To use this model, download the checkpoints. Create a new directory called `news_model` in your PNF directory, and move all the files in this model to the directory. If your server is running, restart it. Make sure to add new links.
## Why is the Inference API disabled?
This model is intended for use in PNF **only.**
## License
License: CC-BY-NC-ND-4.0, with the following additions: a) You may only use this model from inside PNF b) You may not redistribute this model (These additions override any statements inside the CC license) |
dccuchile/albert-large-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null | {
"architectures": [
"AlbertForPreTraining"
],
"model_type": "albert",
"task_specific_params": {
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"max_length": null
},
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}
}
} | 75 | null | ---
license: apache-2.0
datasets:
- cc100
- wikipedia
language:
- ja
widget:
- text: 東北大学で[MASK]の研究をしています。
---
# BERT large Japanese (unidic-lite with whole word masking, CC-100 and jawiki-20230102)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization.
Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.
The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/).
## Model architecture
The model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads.
## Training Data
The model is trained on the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia.
For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023.
The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively.
For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7).
## Tokenization
The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.
The vocabulary size is 32768.
We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization.
## Training
We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps.
For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/).
## Licenses
The pretrained models are distributed under the Apache License 2.0.
## Acknowledgments
This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program. |
dccuchile/albert-tiny-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null | {
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"AlbertForPreTraining"
],
"model_type": "albert",
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},
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}
} | 393 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: naive-Cartpolev1
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
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-ner | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 81 | null | ---
tags:
- ctranslate2
- translation
license: apache-2.0
---
# # Fast-Inference with Ctranslate2
Speedup inference by 2x-8x using int8 inference in C++
quantized version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr)
```bash
pip install hf-hub-ctranslate2>=1.0.0 ctranslate2>=3.13.0
```
Converted using
```
ct2-transformers-converter --model Helsinki-NLP/opus-mt-en-fr --output_dir /home/michael/tmp-ct2fast-opus-mt-en-fr --force --copy_files README.md generation_config.json tokenizer_config.json vocab.json source.spm .gitattributes target.spm --quantization float16
```
Checkpoint compatible to [ctranslate2](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-opus-mt-en-fr"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = TranslatorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
)
outputs = model.generate(
text=["How do you call a fast Flan-ingo?", "User: How are you doing?"],
)
print(outputs)
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
### opus-mt-en-fr
* source languages: en
* target languages: fr
* OPUS readme: [en-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-fr/opus-2020-02-26.zip)
* test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-fr/opus-2020-02-26.test.txt)
* test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-fr/opus-2020-02-26.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newsdiscussdev2015-enfr.en.fr | 33.8 | 0.602 |
| newsdiscusstest2015-enfr.en.fr | 40.0 | 0.643 |
| newssyscomb2009.en.fr | 29.8 | 0.584 |
| news-test2008.en.fr | 27.5 | 0.554 |
| newstest2009.en.fr | 29.4 | 0.577 |
| newstest2010.en.fr | 32.7 | 0.596 |
| newstest2011.en.fr | 34.3 | 0.611 |
| newstest2012.en.fr | 31.8 | 0.592 |
| newstest2013.en.fr | 33.2 | 0.589 |
| Tatoeba.en.fr | 50.5 | 0.672 |
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pawsx | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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}
} | 25 | null | ---
tags:
- ctranslate2
- translation
license: apache-2.0
---
# # Fast-Inference with Ctranslate2
Speedup inference by 2x-8x using int8 inference in C++
quantized version of [Helsinki-NLP/opus-mt-fr-en](https://huggingface.co/Helsinki-NLP/opus-mt-fr-en)
```bash
pip install hf-hub-ctranslate2>=1.0.0 ctranslate2>=3.13.0
```
Converted using
```
ct2-transformers-converter --model Helsinki-NLP/opus-mt-fr-en --output_dir /home/michael/tmp-ct2fast-opus-mt-fr-en --force --copy_files README.md generation_config.json tokenizer_config.json vocab.json source.spm .gitattributes target.spm --quantization float16
```
Checkpoint compatible to [ctranslate2](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-opus-mt-fr-en"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = TranslatorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-en")
)
outputs = model.generate(
text=["How do you call a fast Flan-ingo?", "User: How are you doing?"],
)
print(outputs)
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
### opus-mt-fr-en
* source languages: fr
* target languages: en
* OPUS readme: [fr-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-en/opus-2020-02-26.zip)
* test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-en/opus-2020-02-26.test.txt)
* test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-en/opus-2020-02-26.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newsdiscussdev2015-enfr.fr.en | 33.1 | 0.580 |
| newsdiscusstest2015-enfr.fr.en | 38.7 | 0.614 |
| newssyscomb2009.fr.en | 30.3 | 0.569 |
| news-test2008.fr.en | 26.2 | 0.542 |
| newstest2009.fr.en | 30.2 | 0.570 |
| newstest2010.fr.en | 32.2 | 0.590 |
| newstest2011.fr.en | 33.0 | 0.597 |
| newstest2012.fr.en | 32.8 | 0.591 |
| newstest2013.fr.en | 33.9 | 0.591 |
| newstest2014-fren.fr.en | 37.8 | 0.633 |
| Tatoeba.fr.en | 57.5 | 0.720 |
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pos | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 1 | null | ---
tags:
- ctranslate2
- translation
license: cc-by-4.0
---
# # Fast-Inference with Ctranslate2
Speedup inference by 2x-8x using int8 inference in C++
quantized version of [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de)
```bash
pip install hf-hub-ctranslate2>=1.0.0 ctranslate2>=3.13.0
```
Converted using
```
ct2-transformers-converter --model Helsinki-NLP/opus-mt-en-de --output_dir /home/michael/tmp-ct2fast-opus-mt-en-de --force --copy_files README.md generation_config.json tokenizer_config.json vocab.json source.spm .gitattributes target.spm --quantization float16
```
Checkpoint compatible to [ctranslate2](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-opus-mt-en-de"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = TranslatorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
)
outputs = model.generate(
text=["How do you call a fast Flan-ingo?", "User: How are you doing?"],
)
print(outputs)
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
### opus-mt-en-de
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation
- **Language(s):**
- Source Language: English
- Target Language: German
- **License:** CC-BY-4.0
- **Resources for more information:**
- [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Uses
#### Direct Use
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
Further details about the dataset for this model can be found in the OPUS readme: [en-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-de/README.md)
#### Training Data
##### Preprocessing
* pre-processing: normalization + SentencePiece
* dataset: [opus](https://github.com/Helsinki-NLP/Opus-MT)
* download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-de/opus-2020-02-26.zip)
* test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-de/opus-2020-02-26.test.txt)
## Evaluation
#### Results
* test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-de/opus-2020-02-26.eval.txt)
#### Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newssyscomb2009.en.de | 23.5 | 0.540 |
| news-test2008.en.de | 23.5 | 0.529 |
| newstest2009.en.de | 22.3 | 0.530 |
| newstest2010.en.de | 24.9 | 0.544 |
| newstest2011.en.de | 22.5 | 0.524 |
| newstest2012.en.de | 23.0 | 0.525 |
| newstest2013.en.de | 26.9 | 0.553 |
| newstest2015-ende.en.de | 31.1 | 0.594 |
| newstest2016-ende.en.de | 37.0 | 0.636 |
| newstest2017-ende.en.de | 29.9 | 0.586 |
| newstest2018-ende.en.de | 45.2 | 0.690 |
| newstest2019-ende.en.de | 40.9 | 0.654 |
| Tatoeba.en.de | 47.3 | 0.664 |
## Citation Information
```bibtex
@InProceedings{TiedemannThottingal:EAMT2020,
author = {J{\"o}rg Tiedemann and Santhosh Thottingal},
title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld},
booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)},
year = {2020},
address = {Lisbon, Portugal}
}
```
## How to Get Started With the Model
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-de")
```
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-xnli | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 28 | null | ---
tags:
- ctranslate2
- translation
license: apache-2.0
---
# # Fast-Inference with Ctranslate2
Speedup inference by 2x-8x using int8 inference in C++
quantized version of [Helsinki-NLP/opus-mt-fr-de](https://huggingface.co/Helsinki-NLP/opus-mt-fr-de)
```bash
pip install hf-hub-ctranslate2>=1.0.0 ctranslate2>=3.13.0
```
Converted using
```
ct2-transformers-converter --model Helsinki-NLP/opus-mt-fr-de --output_dir /home/michael/tmp-ct2fast-opus-mt-fr-de --force --copy_files README.md generation_config.json tokenizer_config.json vocab.json source.spm .gitattributes target.spm --quantization float16
```
Checkpoint compatible to [ctranslate2](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-opus-mt-fr-de"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = TranslatorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-de")
)
outputs = model.generate(
text=["How do you call a fast Flan-ingo?", "User: How are you doing?"],
)
print(outputs)
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
### opus-mt-fr-de
* source languages: fr
* target languages: de
* OPUS readme: [fr-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-de/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-de/opus-2020-01-09.zip)
* test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-de/opus-2020-01-09.test.txt)
* test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-de/opus-2020-01-09.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| euelections_dev2019.transformer-align.fr | 26.4 | 0.571 |
| newssyscomb2009.fr.de | 22.1 | 0.524 |
| news-test2008.fr.de | 22.1 | 0.524 |
| newstest2009.fr.de | 21.6 | 0.520 |
| newstest2010.fr.de | 22.6 | 0.527 |
| newstest2011.fr.de | 21.5 | 0.518 |
| newstest2012.fr.de | 22.4 | 0.516 |
| newstest2013.fr.de | 24.2 | 0.532 |
| newstest2019-frde.fr.de | 27.9 | 0.595 |
| Tatoeba.fr.de | 49.1 | 0.676 |
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-ner | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 5 | null | ---
tags:
- ctranslate2
- translation
license: apache-2.0
---
# # Fast-Inference with Ctranslate2
Speedup inference by 2x-8x using int8 inference in C++
quantized version of [Helsinki-NLP/opus-mt-de-fr](https://huggingface.co/Helsinki-NLP/opus-mt-de-fr)
```bash
pip install hf-hub-ctranslate2>=1.0.0 ctranslate2>=3.13.0
```
Converted using
```
ct2-transformers-converter --model Helsinki-NLP/opus-mt-de-fr --output_dir /home/michael/tmp-ct2fast-opus-mt-de-fr --force --copy_files README.md generation_config.json tokenizer_config.json vocab.json source.spm .gitattributes target.spm --quantization float16
```
Checkpoint compatible to [ctranslate2](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-opus-mt-de-fr"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = TranslatorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-de-fr")
)
outputs = model.generate(
text=["How do you call a fast Flan-ingo?", "User: How are you doing?"],
)
print(outputs)
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
### opus-mt-de-fr
* source languages: de
* target languages: fr
* OPUS readme: [de-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-fr/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-fr/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-fr/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| euelections_dev2019.transformer-align.de | 32.2 | 0.590 |
| newssyscomb2009.de.fr | 26.8 | 0.553 |
| news-test2008.de.fr | 26.4 | 0.548 |
| newstest2009.de.fr | 25.6 | 0.539 |
| newstest2010.de.fr | 29.1 | 0.572 |
| newstest2011.de.fr | 26.9 | 0.551 |
| newstest2012.de.fr | 27.7 | 0.554 |
| newstest2013.de.fr | 29.5 | 0.560 |
| newstest2019-defr.de.fr | 36.6 | 0.625 |
| Tatoeba.de.fr | 49.2 | 0.664 |
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-qa-mlqa | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 5 | null | ---
language:
- es
- en
tags:
- ctranslate2
- translation
license: apache-2.0
---
# # Fast-Inference with Ctranslate2
Speedup inference by 2x-8x using int8 inference in C++
quantized version of [Helsinki-NLP/opus-mt-es-en](https://huggingface.co/Helsinki-NLP/opus-mt-es-en)
```bash
pip install hf-hub-ctranslate2>=1.0.0 ctranslate2>=3.13.0
```
Converted using
```
ct2-transformers-converter --model Helsinki-NLP/opus-mt-es-en --output_dir /home/michael/tmp-ct2fast-opus-mt-es-en --force --copy_files README.md generation_config.json tokenizer_config.json vocab.json source.spm .gitattributes target.spm --quantization float16
```
Checkpoint compatible to [ctranslate2](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-opus-mt-es-en"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = TranslatorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-es-en")
)
outputs = model.generate(
text=["How do you call a fast Flan-ingo?", "User: How are you doing?"],
)
print(outputs)
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
### spa-eng
* source group: Spanish
* target group: English
* OPUS readme: [spa-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-eng/README.md)
* model: transformer
* source language(s): spa
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-08-18.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.zip)
* test set translations: [opus-2020-08-18.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.test.txt)
* test set scores: [opus-2020-08-18.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newssyscomb2009-spaeng.spa.eng | 30.6 | 0.570 |
| news-test2008-spaeng.spa.eng | 27.9 | 0.553 |
| newstest2009-spaeng.spa.eng | 30.4 | 0.572 |
| newstest2010-spaeng.spa.eng | 36.1 | 0.614 |
| newstest2011-spaeng.spa.eng | 34.2 | 0.599 |
| newstest2012-spaeng.spa.eng | 37.9 | 0.624 |
| newstest2013-spaeng.spa.eng | 35.3 | 0.609 |
| Tatoeba-test.spa.eng | 59.6 | 0.739 |
### System Info:
- hf_name: spa-eng
- source_languages: spa
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-eng/README.md
- original_repo: Tatoeba-Challenge
- tags:
- ctranslate2 ['translation']
- languages: ['es', 'en']
- src_constituents: {'spa'}
- tgt_constituents: {'eng'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.test.txt
- src_alpha3: spa
- tgt_alpha3: eng
- short_pair: es-en
- chrF2_score: 0.7390000000000001
- bleu: 59.6
- brevity_penalty: 0.9740000000000001
- ref_len: 79376.0
- src_name: Spanish
- tgt_name: English
- train_date: 2020-08-18 00:00:00
- src_alpha2: es
- tgt_alpha2: en
- prefer_old: False
- long_pair: spa-eng
- helsinki_git_sha: d2f0910c89026c34a44e331e785dec1e0faa7b82
- transformers_git_sha: f7af09b4524b784d67ae8526f0e2fcc6f5ed0de9
- port_machine: brutasse
- port_time: 2020-08-24-18:20 |
dccuchile/distilbert-base-spanish-uncased-finetuned-mldoc | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | {
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} | 27 | null | ---
license: bsd-3-clause
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ast_binary_7-finetuned-ICBHI
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. -->
# ast_binary_7-finetuned-ICBHI
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7561
- Accuracy: 0.5764
- Sensitivity: 0.6185
- Specificity: 0.5450
- Score: 0.5818
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Sensitivity | Specificity | Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:-----------:|:------:|
| 0.6321 | 1.0 | 259 | 0.7561 | 0.5764 | 0.6185 | 0.5450 | 0.5818 |
| 0.5672 | 2.0 | 518 | 0.8579 | 0.5626 | 0.6015 | 0.5336 | 0.5676 |
| 0.5443 | 3.0 | 777 | 1.0517 | 0.5074 | 0.8275 | 0.2687 | 0.5481 |
| 0.5075 | 4.0 | 1036 | 0.9977 | 0.5358 | 0.7638 | 0.3657 | 0.5647 |
| 0.4912 | 5.0 | 1295 | 1.2474 | 0.4969 | 0.8539 | 0.2307 | 0.5423 |
| 0.4331 | 6.0 | 1554 | 1.0732 | 0.5376 | 0.7077 | 0.4106 | 0.5592 |
| 0.4368 | 7.0 | 1813 | 1.0947 | 0.5405 | 0.7230 | 0.4043 | 0.5637 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dccuchile/distilbert-base-spanish-uncased-finetuned-pos | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
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"DistilBertForTokenClassification"
],
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} | 3 | null | ---
tags:
- ctranslate2
- translation
license: apache-2.0
---
# # Fast-Inference with Ctranslate2
Speedup inference by 2x-8x using int8 inference in C++
quantized version of [Helsinki-NLP/opus-mt-en-ROMANCE](https://huggingface.co/Helsinki-NLP/opus-mt-en-ROMANCE)
```bash
pip install hf-hub-ctranslate2>=1.0.0 ctranslate2>=3.13.0
```
Converted using
```
ct2-transformers-converter --model Helsinki-NLP/opus-mt-en-ROMANCE --output_dir /home/michael/tmp-ct2fast-opus-mt-en-ROMANCE --force --copy_files README.md generation_config.json tokenizer_config.json vocab.json source.spm .gitattributes target.spm --quantization float16
```
Checkpoint compatible to [ctranslate2](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-opus-mt-en-ROMANCE"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = TranslatorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ROMANCE")
)
outputs = model.generate(
text=["How do you call a fast Flan-ingo?", "User: How are you doing?"],
)
print(outputs)
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
### opus-mt-en-ROMANCE
* source languages: en
* target languages: fr,fr_BE,fr_CA,fr_FR,wa,frp,oc,ca,rm,lld,fur,lij,lmo,es,es_AR,es_CL,es_CO,es_CR,es_DO,es_EC,es_ES,es_GT,es_HN,es_MX,es_NI,es_PA,es_PE,es_PR,es_SV,es_UY,es_VE,pt,pt_br,pt_BR,pt_PT,gl,lad,an,mwl,it,it_IT,co,nap,scn,vec,sc,ro,la
* OPUS readme: [en-fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la/README.md)
* dataset: opus
* model: transformer
* pre-processing: normalization + SentencePiece
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-04-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la/opus-2020-04-21.zip)
* test set translations: [opus-2020-04-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la/opus-2020-04-21.test.txt)
* test set scores: [opus-2020-04-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la/opus-2020-04-21.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.en.la | 50.1 | 0.693 |
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate-1 | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"DistilBertForMaskedLM"
],
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} | 1 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: psychosis_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. -->
# psychosis_model
This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0520
- Accuracy: 0.6258
- F1: 0.3740
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 176 | 1.0887 | 0.5970 | 0.3039 |
| No log | 2.0 | 352 | 1.0340 | 0.6434 | 0.4058 |
| 0.9667 | 3.0 | 528 | 1.0661 | 0.6335 | 0.4198 |
| 0.9667 | 4.0 | 704 | 1.1264 | 0.6335 | 0.4276 |
| 0.9667 | 5.0 | 880 | 1.1932 | 0.6302 | 0.4248 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Chaddmckay/Cdm | [] | null | {
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}
}
} | 0 | null | Access to model Pasuu/MC-FTBQ-V1.5.2 is restricted and you are not in the authorized list. Visit https://huggingface.co/Pasuu/MC-FTBQ-V1.5.2 to ask for access. |
ChaitanyaU/FineTuneLM | [] | null | {
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}
} | 0 | null | ---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: moon-detector-v5.b
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9798488664987406
---
<!-- 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. -->
# moon-detector-v5.b
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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0866
- Accuracy: 0.9798
## 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: 1338
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0246 | 1.0 | 281 | 0.1017 | 0.9773 |
| 0.0223 | 2.0 | 562 | 0.1100 | 0.9723 |
| 0.061 | 3.0 | 843 | 0.0889 | 0.9798 |
| 0.0143 | 4.0 | 1124 | 0.0878 | 0.9824 |
| 0.01 | 5.0 | 1405 | 0.0866 | 0.9798 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Chakita/Kalbert | [
"pytorch",
"tensorboard",
"albert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
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"AlbertForMaskedLM"
],
"model_type": "albert",
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},
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} | 5 | null | ---
language:
- zh
license: mit
tags:
- 1.1.0
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: SpeechT5 TTS Dutch neunit
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SpeechT5 TTS Dutch neunit
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.12.1
|
Chan/distilroberta-base-finetuned-wikitext2 | [] | null | {
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}
} | 0 | null | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: ameyachitnis/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
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