modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
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|---|---|---|---|---|---|---|
Captain-1337/CrudeBERT
|
[
"pytorch",
"bert",
"text-classification",
"arxiv:1908.10063",
"transformers"
] |
text-classification
|
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| 28 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="robinsk8a/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Carlork314/Xd
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: MediumVin2
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. -->
# MediumVin2
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3370
- Wer: 100.0
## 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: 1e-05
- train_batch_size: 24
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.293 | 0.64 | 500 | 0.3798 | 99.9451 |
| 0.1701 | 1.28 | 1000 | 0.3376 | 100.0 |
| 0.1392 | 1.92 | 1500 | 0.3280 | 100.0 |
| 0.0628 | 2.56 | 2000 | 0.3370 | 100.0 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Cat/Kitty
|
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| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: neurator/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Cathy/reranking_model
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
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"RobertaForSequenceClassification"
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| 27 | null |
---
tags:
- generated_from_trainer
model-index:
- name: pythia-70m-deduped-aid
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. -->
# pythia-70m-deduped-aid
## Model description
This model is a finetune of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) (from when it was instead `pythia-19m-deduped`), on the [`text_adventures.txt`](https://github.com/Latitude-Archives/AIDungeon/blob/ca098ca7dab480d24e47954c8873b03ba1091ffc/data/text_adventures.txt) dataset originally intended for AI Dungeon 2. Performance will be very poor, as expected by the small model, and generations may be offensive thanks to its training data.
This model was trained for testing purposes and was intended for use with KoboldAI. A temperature of `0.5` and a repetition penalty of `1.01` were tested.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- 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 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
dccuchile/albert-base-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
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| 14 | 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: 272.48 +/- 17.05
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
dccuchile/albert-large-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
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"AlbertForTokenClassification"
],
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| 3 | null |
---
license: gpl-3.0
---
Pre-trained word embeddings using the text of published biomedical manuscripts. These embeddings use 100 dimensions and were trained using the GloVe algorithm on all published manuscripts found in the [PMC Open Access Subset](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/). See the paper here: https://pubmed.ncbi.nlm.nih.gov/34920127/
Citation:
```
@article{flamholz2022word,
title={Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information},
author={Flamholz, Zachary N and Crane-Droesch, Andrew and Ungar, Lyle H and Weissman, Gary E},
journal={Journal of Biomedical Informatics},
volume={125},
pages={103971},
year={2022},
publisher={Elsevier}
}
```
## Quick start
Word embeddings are compatible with the [`gensim` Python package](https://radimrehurek.com/gensim/) format.
First download the files from this archive. Then load the embeddings into Python.
```python
from gensim.models import FastText, Word2Vec, KeyedVectors # KeyedVectors are used to load the GloVe models
# Load the model
model = KeyedVectors.load_word2vec_format('gl_100_oa_all.txt')
# Return 100-dimensional vector representations of each word
model.word_vec('diabetes')
model.word_vec('cardiac_arrest')
model.word_vec('lymphangioleiomyomatosis')
# Try out cosine similarity
model.similarity('copd', 'chronic_obstructive_pulmonary_disease')
model.similarity('myocardial_infarction', 'heart_attack')
model.similarity('lymphangioleiomyomatosis', 'lam')
```
|
dccuchile/albert-large-spanish-finetuned-pawsx
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
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| 25 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### ai-illustration Dreambooth model trained by masibasi 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:
|
dccuchile/albert-large-spanish-finetuned-pos
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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| 1 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: tiny-mlm-glue-rte-from-scratch-custom-tokenizer-expand-vocab
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. -->
# tiny-mlm-glue-rte-from-scratch-custom-tokenizer-expand-vocab
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.5942
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 9.3942 | 1.6 | 500 | 8.2703 |
| 7.5709 | 3.21 | 1000 | 7.0859 |
| 6.8768 | 4.81 | 1500 | 6.7010 |
| 6.676 | 6.41 | 2000 | 6.6826 |
| 6.667 | 8.01 | 2500 | 6.6476 |
| 6.6276 | 9.62 | 3000 | 6.6067 |
| 6.61 | 11.22 | 3500 | 6.5808 |
| 6.5894 | 12.82 | 4000 | 6.6076 |
| 6.5616 | 14.42 | 4500 | 6.5696 |
| 6.544 | 16.03 | 5000 | 6.5942 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
dccuchile/albert-large-spanish-finetuned-qa-mlqa
|
[
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
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| 5 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-base-extraction-cnndm_fs0.05-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-extraction-cnndm_fs0.05-all
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7605
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3409 | 0.45 | 200 | 1.9264 |
| 2.0082 | 0.9 | 400 | 1.8570 |
| 1.9247 | 1.35 | 600 | 1.8290 |
| 1.8895 | 1.81 | 800 | 1.8162 |
| 1.8625 | 2.26 | 1000 | 1.8015 |
| 1.8354 | 2.71 | 1200 | 1.7894 |
| 1.8013 | 3.16 | 1400 | 1.7824 |
| 1.7901 | 3.61 | 1600 | 1.7796 |
| 1.7769 | 4.06 | 1800 | 1.7807 |
| 1.7661 | 4.51 | 2000 | 1.7646 |
| 1.7536 | 4.97 | 2200 | 1.7605 |
| 1.9045 | 5.42 | 2400 | 2.1358 |
| 2.4322 | 5.87 | 2600 | 2.3688 |
| 2.4809 | 6.32 | 2800 | 2.3622 |
| 2.4628 | 6.77 | 3000 | 2.3625 |
| 2.4676 | 7.22 | 3200 | 2.3639 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
|
dccuchile/albert-large-spanish-finetuned-xnli
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
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| 29 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-base-extraction-cnndm_fs0.1-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-extraction-cnndm_fs0.1-all
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7159
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2503 | 0.45 | 200 | 1.8495 |
| 1.9367 | 0.9 | 400 | 1.7930 |
| 1.8669 | 1.35 | 600 | 1.7704 |
| 1.8371 | 1.81 | 800 | 1.7481 |
| 1.8051 | 2.26 | 1000 | 1.7362 |
| 1.7843 | 2.71 | 1200 | 1.7345 |
| 1.7669 | 3.16 | 1400 | 1.7159 |
| 1.8786 | 3.61 | 1600 | 1.9442 |
| 2.0554 | 4.06 | 1800 | 1.9691 |
| 2.0521 | 4.51 | 2000 | 1.9731 |
| 2.0579 | 4.97 | 2200 | 1.9744 |
| 2.0514 | 5.42 | 2400 | 1.9743 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
|
dccuchile/albert-tiny-spanish-finetuned-mldoc
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
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| 32 | null |
Access to model bunsenfeng/FactKB is restricted and you are not in the authorized list. Visit https://huggingface.co/bunsenfeng/FactKB to ask for access.
|
dccuchile/albert-tiny-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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"AlbertForTokenClassification"
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| 8 | null |
---
tags:
- generated_from_trainer
model-index:
- name: tmp_trainer
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. -->
# tmp_trainer
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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
### Framework versions
- Transformers 4.24.0
- Pytorch 1.14.0.dev20221107
- Tokenizers 0.11.0
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pawsx
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 24 | null |
---
language: "en"
license: apache-2.0
tags:
- token-classification
- NER
- Biomedical
- Diseases
datasets:
- BC5CDR-diseases
- ncbi_disease
- tner/bc5cdr
- commanderstrife/jnlpba
- bc2gm_corpus
- drAbreu/bc4chemd_ner
- linnaeus
- chintagunta85/ncbi_disease
---
BioBERT model fine-tuned in NER task with BC5CDR-diseases and NCBI-diseases corpus
This was fine-tuned in order to use it in a BioNER/BioNEN system which is available at: https://github.com/librairy/bio-ner
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pos
|
[
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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],
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| 5 | null |
---
license: mit
language: ja
library_name: transformers
tags:
- pytorch
- deberta
- deberta-v2
- commonsenseqa
- commonsense_qa
- commonsense-qa
- CommonsenseQA
datasets:
- wikipedia
- cc100
- oscar
metrics:
- accuracy
---
# このモデルはdeberta-v2-base-japaneseをファインチューニングしてCommonsenseQA(選択式の質問)に用いれるようにしたものです。
このモデルはdeberta-v2-base-japaneseをyahoo japan/JGLUEのJCommonsenseQA( https://github.com/yahoojapan/JGLUE ) を用いてファインチューニングしたものです。
# This model is fine-tuned model for CommonsenseQA which is based on deberta-v2-base-japanese
This model is fine-tuned by using JGLUE/JCommonsenseQA dataset.
You could use this model for CommonsenseQA tasks.
# How to use 使い方
transformersおよびpytorch、sentencepiece、Juman++をインストールしてください。
以下のコードを実行することで、CommonsenseQAタスクを解かせることができます。 please execute this code.
```python
from transformers import AutoTokenizer, AutoModelForMultipleChoice
import torch
import numpy as np
# modelのロード
tokenizer = AutoTokenizer.from_pretrained('Mizuiro-sakura/deberta-v2-japanese-base-finetuned-commonsenseqa')
model = AutoModelForMultipleChoice.from_pretrained('Mizuiro-sakura/deberta-v2-japanese-base-finetuned-commonsenseqa')
# 質問と選択肢の代入
question = '電子機器で使用される最も主要な電子回路基板の事をなんと言う？'
choice1 = '掲示板'
choice2 = 'パソコン'
choice3 = 'マザーボード'
choice4 = 'ハードディスク'
choice5 = 'まな板'
# トークン化（エンコーディング・形態素解析）する
token = tokenizer([question,question,question,question,question],[choice1,choice2,choice3,choice4,choice5],return_tensors='pt',padding=True)
leng=len(token['input_ids'][0])
# modelに入力するための下準備
X1 = np.empty(shape=(1, 5, leng))
X2 = np.empty(shape=(1, 5, leng))
X1[0, :, :] = token['input_ids']
X2[0, :, :] = token['attention_mask']
# modelにトークンを入力する
results = model(torch.tensor(X1).to(torch.int64),torch.tensor(X2).to(torch.int64))
# 最も高い値のインデックスを取得する
max_result=torch.argmax(results.logits)
print(max_result)
```
# モデルの精度 accuracy of model
79.80339588918764
（参考 BERT : 72.0, XLM RoBERTa base : 68.7, LUKE : 80.0)
# deberta-v2-base-japaneseとは？
日本語Wikipedeia（3.2GB）および、cc100(85GB)、oscar(54GB)を用いて訓練されたモデルです。
京都大学黒橋研究室が公表されました。
# Model description
This is a Japanese DeBERTa V2 base model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR.
# Acknowledgments 謝辞
モデルを公開してくださった京都大学黒橋研究室には感謝いたします。
I would like to thank Kurohashi Lab at Kyoto University.
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-qa-mlqa
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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| 5 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: small-mlm-glue-qqp-custom-tokenizer-expand-vocab
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. -->
# small-mlm-glue-qqp-custom-tokenizer-expand-vocab
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8901
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.9945 | 0.4 | 500 | 4.2995 |
| 4.253 | 0.8 | 1000 | 3.9195 |
| 3.8857 | 1.2 | 1500 | 3.6343 |
| 3.6372 | 1.6 | 2000 | 3.4816 |
| 3.438 | 2.0 | 2500 | 3.3802 |
| 3.2905 | 2.4 | 3000 | 3.2491 |
| 3.2083 | 2.8 | 3500 | 3.1646 |
| 3.0456 | 3.2 | 4000 | 3.0861 |
| 3.0385 | 3.6 | 4500 | 3.0116 |
| 2.8819 | 4.0 | 5000 | 2.8901 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
Chaddmckay/Cdm
|
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| 0 | null |
---
license: artistic-2.0
---
astronauta andando à cavalo na lua'
|
ChaitanyaU/FineTuneLM
|
[] | null |
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| 0 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: final_bart_prepro_fix
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. -->
# final_bart_prepro_fix
This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6100
- Rouge1: 35.5593
- Rouge2: 13.0497
- Rougel: 23.5672
- Bleu1: 29.5206
- Bleu2: 17.3914
- Bleu3: 10.5577
- Bleu4: 6.1502
- Rdass: 0.6449
- Gen Len: 49.7389
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Rdass | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:------:|:-------:|
| 2.1622 | 1.51 | 1000 | 2.6687 | 35.4366 | 12.8631 | 23.1588 | 29.4018 | 17.2004 | 10.3744 | 6.052 | 0.6379 | 49.4266 |
| 2.0114 | 3.02 | 2000 | 2.6090 | 35.1436 | 13.0347 | 23.4682 | 28.8917 | 17.0965 | 10.1873 | 5.896 | 0.6389 | 46.1096 |
| 1.8758 | 4.53 | 3000 | 2.6100 | 35.5593 | 13.0497 | 23.5672 | 29.5206 | 17.3914 | 10.5577 | 6.1502 | 0.6449 | 49.7389 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Champion/test_upload_vox2_wavlm_epoch8
|
[
"sidekit",
"audio"
] | null |
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| 0 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.70
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="smko77/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Chan/distilgpt2-finetuned-wikitext2
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: small-mlm-glue-sst2-from-scratch-custom-tokenizer-expand-vocab
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. -->
# small-mlm-glue-sst2-from-scratch-custom-tokenizer-expand-vocab
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.2435
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.9024 | 0.4 | 500 | 6.7698 |
| 6.841 | 0.8 | 1000 | 6.5930 |
| 6.6281 | 1.2 | 1500 | 6.5066 |
| 6.5663 | 1.6 | 2000 | 6.3862 |
| 6.4527 | 2.0 | 2500 | 6.2757 |
| 6.381 | 2.4 | 3000 | 6.3472 |
| 6.2872 | 2.8 | 3500 | 6.2887 |
| 6.232 | 3.2 | 4000 | 6.2333 |
| 6.1222 | 3.6 | 4500 | 6.2479 |
| 6.1138 | 4.0 | 5000 | 6.2435 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
ChauhanVipul/BERT
|
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| 0 | null |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentiment_roberta_large_with_diary
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. -->
# sentiment_roberta_large_with_diary
This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5671
- Micro f1 score: 80.0000
- Auprc: 77.0282
- Accuracy: 0.8
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Micro f1 score | Auprc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:-------:|:--------:|
| 1.6198 | 0.13 | 100 | 1.3872 | 48.9362 | 55.5743 | 0.4894 |
| 0.6603 | 0.26 | 200 | 0.9249 | 65.9574 | 62.8759 | 0.6596 |
| 0.5387 | 0.4 | 300 | 0.7262 | 73.1915 | 71.1936 | 0.7319 |
| 0.4801 | 0.53 | 400 | 0.6623 | 74.0426 | 68.8606 | 0.7404 |
| 0.4597 | 0.66 | 500 | 0.6092 | 76.1702 | 75.7346 | 0.7617 |
| 0.4217 | 0.79 | 600 | 0.5929 | 78.7234 | 76.8709 | 0.7872 |
| 0.4148 | 0.93 | 700 | 0.5671 | 80.0000 | 77.0282 | 0.8 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
ChoboAvenger/DialoGPT-small-joshua
|
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| 0 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 250 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 500,
"warmup_steps": 50,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Chungu424/qazwsx
|
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| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: dhmeltzer/ppo-pyramid
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Chungu424/repodata
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
- cer
model-index:
- name: wav2vec2-xls-r-300m-th-cv11_0
results: []
datasets:
- mozilla-foundation/common_voice_11_0
language:
- th
pipeline_tag: automatic-speech-recognition
---
<!-- 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-xls-r-300m-th-cv11_0
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.
It achieves the following results on the evaluation set:
- Loss: 0.3391
- Wer: 0.2915
- Cer: 0.0651
- Clean Cer: 0.0508
- Learning Rate: 0.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Clean Cer | Rate |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:---------:|:------:|
| 7.5397 | 0.37 | 500 | 3.5716 | 1.0 | 0.9811 | 0.9774 | 0.0001 |
| 1.7478 | 0.75 | 1000 | 0.7702 | 0.8097 | 0.2296 | 0.1746 | 0.0001 |
| 0.7687 | 1.12 | 1500 | 0.4997 | 0.5392 | 0.1415 | 0.1182 | 0.0001 |
| 0.6064 | 1.5 | 2000 | 0.4270 | 0.4956 | 0.1238 | 0.1001 | 0.0001 |
| 0.5473 | 1.87 | 2500 | 0.3809 | 0.4489 | 0.1105 | 0.0898 | 0.0001 |
| 0.454 | 2.24 | 3000 | 0.3585 | 0.4256 | 0.1021 | 0.0813 | 0.0001 |
| 0.4219 | 2.62 | 3500 | 0.3375 | 0.4063 | 0.0974 | 0.0777 | 0.0001 |
| 0.4075 | 2.99 | 4000 | 0.3274 | 0.4036 | 0.0948 | 0.0746 | 0.0001 |
| 0.3355 | 3.37 | 4500 | 0.3257 | 0.3782 | 0.0898 | 0.0729 | 0.0001 |
| 0.3203 | 3.74 | 5000 | 0.3024 | 0.3561 | 0.0830 | 0.0659 | 0.0001 |
| 0.3151 | 4.11 | 5500 | 0.3038 | 0.3606 | 0.0830 | 0.0653 | 0.0001 |
| 0.2713 | 4.49 | 6000 | 0.3052 | 0.3595 | 0.0832 | 0.0655 | 0.0001 |
| 0.2685 | 4.86 | 6500 | 0.2933 | 0.3436 | 0.0796 | 0.0628 | 0.0001 |
| 0.2379 | 5.24 | 7000 | 0.3020 | 0.3362 | 0.0763 | 0.0608 | 0.0000 |
| 0.224 | 5.61 | 7500 | 0.2874 | 0.3265 | 0.0745 | 0.0589 | 0.0000 |
| 0.2204 | 5.98 | 8000 | 0.2922 | 0.3191 | 0.0724 | 0.0576 | 0.0000 |
| 0.1927 | 6.36 | 8500 | 0.3107 | 0.3163 | 0.0719 | 0.0568 | 0.0000 |
| 0.1875 | 6.73 | 9000 | 0.3034 | 0.3084 | 0.0703 | 0.0554 | 0.0000 |
| 0.1786 | 7.11 | 9500 | 0.3210 | 0.3107 | 0.0702 | 0.0553 | 0.0000 |
| 0.1606 | 7.48 | 10000 | 0.3231 | 0.3062 | 0.0688 | 0.0541 | 0.0000 |
| 0.1594 | 7.85 | 10500 | 0.3234 | 0.3033 | 0.0680 | 0.0535 | 0.0000 |
| 0.1498 | 8.23 | 11000 | 0.3276 | 0.3035 | 0.0680 | 0.0530 | 0.0000 |
| 0.1396 | 8.6 | 11500 | 0.3265 | 0.2975 | 0.0668 | 0.0520 | 0.0000 |
| 0.142 | 8.98 | 12000 | 0.3236 | 0.2930 | 0.0659 | 0.0515 | 0.0000 |
| 0.1242 | 9.35 | 12500 | 0.3403 | 0.2921 | 0.0655 | 0.0511 | 0.0000 |
| 0.1225 | 9.72 | 13000 | 0.3391 | 0.2915 | 0.0651 | 0.0508 | 0.0000 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
ClydeWasTaken/DialoGPT-small-joshua
|
[
"conversational"
] |
conversational
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model_index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metric:
name: Accuracy
type: accuracy
value: 0.9419354838709677
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2929
- Accuracy: 0.9419
## 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: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 159 | 1.7692 | 0.6606 |
| No log | 2.0 | 318 | 1.1246 | 0.7997 |
| No log | 3.0 | 477 | 0.7261 | 0.8681 |
| 1.5283 | 4.0 | 636 | 0.5132 | 0.9106 |
| 1.5283 | 5.0 | 795 | 0.4002 | 0.9232 |
| 1.5283 | 6.0 | 954 | 0.3460 | 0.9342 |
| 0.4714 | 7.0 | 1113 | 0.3171 | 0.9384 |
| 0.4714 | 8.0 | 1272 | 0.3028 | 0.9410 |
| 0.4714 | 9.0 | 1431 | 0.2947 | 0.9416 |
| 0.2878 | 10.0 | 1590 | 0.2929 | 0.9419 |
### Framework versions
- Transformers 4.10.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
CoderBoy432/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 11 | null |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/pii-pile-chunk3-0-50000
- tomekkorbak/pii-pile-chunk3-50000-100000
- tomekkorbak/pii-pile-chunk3-100000-150000
- tomekkorbak/pii-pile-chunk3-150000-200000
- tomekkorbak/pii-pile-chunk3-200000-250000
- tomekkorbak/pii-pile-chunk3-250000-300000
- tomekkorbak/pii-pile-chunk3-300000-350000
- tomekkorbak/pii-pile-chunk3-350000-400000
- tomekkorbak/pii-pile-chunk3-400000-450000
- tomekkorbak/pii-pile-chunk3-450000-500000
- tomekkorbak/pii-pile-chunk3-500000-550000
- tomekkorbak/pii-pile-chunk3-550000-600000
- tomekkorbak/pii-pile-chunk3-600000-650000
- tomekkorbak/pii-pile-chunk3-650000-700000
- tomekkorbak/pii-pile-chunk3-700000-750000
- tomekkorbak/pii-pile-chunk3-750000-800000
- tomekkorbak/pii-pile-chunk3-800000-850000
- tomekkorbak/pii-pile-chunk3-850000-900000
- tomekkorbak/pii-pile-chunk3-900000-950000
- tomekkorbak/pii-pile-chunk3-950000-1000000
- tomekkorbak/pii-pile-chunk3-1000000-1050000
- tomekkorbak/pii-pile-chunk3-1050000-1100000
- tomekkorbak/pii-pile-chunk3-1100000-1150000
- tomekkorbak/pii-pile-chunk3-1150000-1200000
- tomekkorbak/pii-pile-chunk3-1200000-1250000
- tomekkorbak/pii-pile-chunk3-1250000-1300000
- tomekkorbak/pii-pile-chunk3-1300000-1350000
- tomekkorbak/pii-pile-chunk3-1350000-1400000
- tomekkorbak/pii-pile-chunk3-1400000-1450000
- tomekkorbak/pii-pile-chunk3-1450000-1500000
- tomekkorbak/pii-pile-chunk3-1500000-1550000
- tomekkorbak/pii-pile-chunk3-1550000-1600000
- tomekkorbak/pii-pile-chunk3-1600000-1650000
- tomekkorbak/pii-pile-chunk3-1650000-1700000
- tomekkorbak/pii-pile-chunk3-1700000-1750000
- tomekkorbak/pii-pile-chunk3-1750000-1800000
- tomekkorbak/pii-pile-chunk3-1800000-1850000
- tomekkorbak/pii-pile-chunk3-1850000-1900000
- tomekkorbak/pii-pile-chunk3-1900000-1950000
model-index:
- name: compassionate_lumiere
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. -->
# compassionate_lumiere
This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 12588
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0.0},
'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000',
'tomekkorbak/pii-pile-chunk3-50000-100000',
'tomekkorbak/pii-pile-chunk3-100000-150000',
'tomekkorbak/pii-pile-chunk3-150000-200000',
'tomekkorbak/pii-pile-chunk3-200000-250000',
'tomekkorbak/pii-pile-chunk3-250000-300000',
'tomekkorbak/pii-pile-chunk3-300000-350000',
'tomekkorbak/pii-pile-chunk3-350000-400000',
'tomekkorbak/pii-pile-chunk3-400000-450000',
'tomekkorbak/pii-pile-chunk3-450000-500000',
'tomekkorbak/pii-pile-chunk3-500000-550000',
'tomekkorbak/pii-pile-chunk3-550000-600000',
'tomekkorbak/pii-pile-chunk3-600000-650000',
'tomekkorbak/pii-pile-chunk3-650000-700000',
'tomekkorbak/pii-pile-chunk3-700000-750000',
'tomekkorbak/pii-pile-chunk3-750000-800000',
'tomekkorbak/pii-pile-chunk3-800000-850000',
'tomekkorbak/pii-pile-chunk3-850000-900000',
'tomekkorbak/pii-pile-chunk3-900000-950000',
'tomekkorbak/pii-pile-chunk3-950000-1000000',
'tomekkorbak/pii-pile-chunk3-1000000-1050000',
'tomekkorbak/pii-pile-chunk3-1050000-1100000',
'tomekkorbak/pii-pile-chunk3-1100000-1150000',
'tomekkorbak/pii-pile-chunk3-1150000-1200000',
'tomekkorbak/pii-pile-chunk3-1200000-1250000',
'tomekkorbak/pii-pile-chunk3-1250000-1300000',
'tomekkorbak/pii-pile-chunk3-1300000-1350000',
'tomekkorbak/pii-pile-chunk3-1350000-1400000',
'tomekkorbak/pii-pile-chunk3-1400000-1450000',
'tomekkorbak/pii-pile-chunk3-1450000-1500000',
'tomekkorbak/pii-pile-chunk3-1500000-1550000',
'tomekkorbak/pii-pile-chunk3-1550000-1600000',
'tomekkorbak/pii-pile-chunk3-1600000-1650000',
'tomekkorbak/pii-pile-chunk3-1650000-1700000',
'tomekkorbak/pii-pile-chunk3-1700000-1750000',
'tomekkorbak/pii-pile-chunk3-1750000-1800000',
'tomekkorbak/pii-pile-chunk3-1800000-1850000',
'tomekkorbak/pii-pile-chunk3-1850000-1900000',
'tomekkorbak/pii-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True,
'skip_tokens': 1649999872},
'generation': {'force_call_on': [25177],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 4096,
'prefix': '<|aligned|>'}],
'scorer_config': {}},
'kl_gpt3_callback': {'force_call_on': [25177],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': False,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'revision': '9e6c78543a6ff1e4089002c38864d5a9cf71ec90'},
'num_additional_tokens': 2,
'path_or_name': 'tomekkorbak/nervous_wozniak'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 128,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'compassionate_lumiere',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0001,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output2',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 251,
'save_strategy': 'steps',
'seed': 42,
'tokens_already_seen': 1649999872,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/1q3x5956
|
CoffeeAddict93/gpt2-call-of-the-wild
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 6 | null |
---
language: en
thumbnail: http://www.huggingtweets.com/kamalaharris/1675235849346/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1592241313700782081/T2pTYU8d_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Kamala Harris</div>
<div style="text-align: center; font-size: 14px;">@kamalaharris</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Kamala Harris.
| Data | Kamala Harris |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 875 |
| Short tweets | 9 |
| Tweets kept | 2363 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/cu95utyk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @kamalaharris's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/z9ekcknd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/z9ekcknd/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/kamalaharris')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Coldestadam/Breakout_Mentors_SpongeBob_Model
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 10 | 2023-02-01T07:37:21Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.36 +/- 0.20
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ComCom/gpt2-medium
|
[
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"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:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- reesu/autotrain-data-wine_quality
co2_eq_emissions:
emissions: 8.276808778335907
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3195889861
- CO2 Emissions (in grams): 8.2768
## Validation Metrics
- Loss: 0.995
- Accuracy: 0.569
- Macro F1: 0.296
- Micro F1: 0.569
- Weighted F1: 0.543
- Macro Precision: 0.447
- Micro Precision: 0.569
- Weighted Precision: 0.558
- Macro Recall: 0.283
- Micro Recall: 0.569
- Weighted Recall: 0.569
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
```
|
ComCom-Dev/gpt2-bible-test
|
[] | null |
{
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},
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}
}
}
| 0 | 2023-02-01T07:51:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: small-mlm-glue-stsb-from-scratch-custom-tokenizer-expand-vocab
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. -->
# small-mlm-glue-stsb-from-scratch-custom-tokenizer-expand-vocab
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.8182
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.929 | 0.7 | 500 | 6.7432 |
| 6.8164 | 1.39 | 1000 | 6.4671 |
| 6.5278 | 2.09 | 1500 | 6.3719 |
| 6.3088 | 2.78 | 2000 | 6.2202 |
| 6.3032 | 3.48 | 2500 | 5.9957 |
| 6.1976 | 4.17 | 3000 | 6.0049 |
| 6.0579 | 4.87 | 3500 | 5.9357 |
| 6.0549 | 5.56 | 4000 | 5.9458 |
| 5.9356 | 6.26 | 4500 | 5.8563 |
| 5.9506 | 6.95 | 5000 | 5.8182 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
Cometasonmi451/Mine
|
[] | null |
{
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"task_specific_params": {
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},
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}
}
| 0 | 2023-02-01T07:55:27Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- snli
model-index:
- name: test_trainer
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_trainer
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the snli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1932
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
cometrain/neurotitle-rugpt3-small
|
[
"pytorch",
"gpt2",
"text-generation",
"ru",
"en",
"dataset:All-NeurIPS-Papers-Scraper",
"transformers",
"Cometrain AutoCode",
"Cometrain AlphaML",
"license:mit"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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
}
}
}
| 20 | null |
---
license: cc-by-nc-4.0
---
Null v1.2
作者：孤辰Null
发布日期：2023.1.29
|
Contrastive-Tension/BERT-Distil-CT
|
[
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"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,
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 9 | 2023-02-01T08:20:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: small-mlm-glue-stsb-custom-tokenizer-expand-vocab
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. -->
# small-mlm-glue-stsb-custom-tokenizer-expand-vocab
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3313
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.6681 | 0.7 | 500 | 4.5779 |
| 4.8249 | 1.39 | 1000 | 4.1678 |
| 4.3901 | 2.09 | 1500 | 4.0839 |
| 3.998 | 2.78 | 2000 | 3.8122 |
| 3.8969 | 3.48 | 2500 | 3.5772 |
| 3.6803 | 4.17 | 3000 | 3.5005 |
| 3.4722 | 4.87 | 3500 | 3.4153 |
| 3.4204 | 5.56 | 4000 | 3.4579 |
| 3.2547 | 6.26 | 4500 | 3.3636 |
| 3.1737 | 6.95 | 5000 | 3.3313 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
Contrastive-Tension/BERT-Large-CT-STSb
|
[
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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| 7 | null |
---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: roberta-finetuned-qa-policy
results: []
datasets:
- GIZ/policy_qa_v0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-finetuned-qa-policy
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the [Policy_QA](https://huggingface.co/datasets/GIZ/policy_qa_v0) dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
[Policy QA](https://huggingface.co/datasets/GIZ/policy_qa_v0)
## 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Crisblair/Wkwk
|
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| 0 | 2023-02-01T09:25:22Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 5 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 5,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
DJStomp/TestingSalvoNET
|
[
"transformers"
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| 1 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: tiny-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab-target-glue-cola
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. -->
# tiny-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab-target-glue-cola
This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab](https://huggingface.co/muhtasham/tiny-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6205
- Matthews Correlation: 0.0
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6103 | 1.87 | 500 | 0.6214 | 0.0 |
| 0.6073 | 3.73 | 1000 | 0.6197 | 0.0 |
| 0.607 | 5.6 | 1500 | 0.6183 | 0.0 |
| 0.6065 | 7.46 | 2000 | 0.6205 | 0.0 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
DKpro000/DialoGPT-small-harrypotter
|
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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: 507.00 +/- 124.36
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 Hawk91 -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 Hawk91 -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 Hawk91
```
## 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)])
```
|
DSI/TweetBasedSA
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 29 | null |
---
title: HARDblend
colorFrom: green
colorTo: indigo
sdk: gradio
sdk_version: 3.11.0
pinned: false
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
- art
inference: true
language:
- en
library_name: diffusers
---
# **HARDblend**
A versatile photorealistic NSFW capable model which is great at generating high quality portraits.
It is a finetuned model trained on ~500 portrait images merged with Hassanblend, Aeros, RealisticVision1.2, Delibrate, SxD, f222.
## Usage
Use stability ai VAE or bakedinVAE version for better results.
*RAW samples*
Help us to be able to create models of professional standards. Consider supporting us on [Patreon](https://www.patreon.com/intuitiveai) / [Ko-fi](https://ko-fi.com/intuitiveai) / [Paypal](https://www.paypal.com/paypalme/theintuitiveye).
## *Demo*
We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run HARDblend :
[](https://huggingface.co/spaces/theintuitiveye/HARDblend)
## *License*
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies :
- You can't use the model to deliberately produce nor share illegal or harmful outputs or content
- The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
- You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license [here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
DanL/scientific-challenges-and-directions
|
[
"pytorch",
"bert",
"text-classification",
"en",
"dataset:DanL/scientific-challenges-and-directions-dataset",
"arxiv:2108.13751",
"transformers",
"generated_from_trainer"
] |
text-classification
|
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| 134 | 2023-02-01T11:59:27Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
DarkKibble/DialoGPT-medium-Tankman
|
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| 0 | null |
# Introduction
See https://github.com/Tencent/ncnn/issues/4503 for details
## File description
- `m.pt`, the torchscript model
- `m.inlined_graph`, text representation of `m.pt`
- `m.ncnn.param`, after fixing pnnx, it is the result of `pnnx ./m.pt`
|
DataikuNLP/TinyBERT_General_4L_312D
|
[
"pytorch",
"jax",
"bert",
"arxiv:1909.10351",
"transformers"
] | null |
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| 74 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-effectiveness-redditCMV
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-effectiveness-redditCMV
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6920
- Accuracy: 0.6376
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6873 | 1.0 | 516 | 0.6706 | 0.5769 |
| 0.6256 | 2.0 | 1032 | 0.6424 | 0.6366 |
| 0.5197 | 3.0 | 1548 | 0.6920 | 0.6376 |
| 0.3814 | 4.0 | 2064 | 0.8641 | 0.6361 |
| 0.2589 | 5.0 | 2580 | 1.0249 | 0.6249 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DataikuNLP/camembert-base
|
[
"pytorch",
"tf",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
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| 8 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Vin_medium1234
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. -->
# Vin_medium1234
This model is a fine-tuned version of [Scrya/whisper-medium-vi-augmented](https://huggingface.co/Scrya/whisper-medium-vi-augmented) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4119
- Wer: 19.0638
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0925 | 2.57 | 1000 | 0.3155 | 18.0090 |
| 0.0067 | 5.14 | 2000 | 0.3702 | 17.5146 |
| 0.0015 | 7.71 | 3000 | 0.3995 | 17.7343 |
| 0.0007 | 10.28 | 4000 | 0.4119 | 19.0638 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DavidAMcIntosh/small-rick
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: tiny-mlm-glue-cola-custom-tokenizer-expand-vocab-target-glue-cola
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. -->
# tiny-mlm-glue-cola-custom-tokenizer-expand-vocab-target-glue-cola
This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola-custom-tokenizer-expand-vocab](https://huggingface.co/muhtasham/tiny-mlm-glue-cola-custom-tokenizer-expand-vocab) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7478
- Matthews Correlation: 0.0630
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6117 | 1.87 | 500 | 0.6224 | 0.0 |
| 0.5987 | 3.73 | 1000 | 0.6217 | 0.0181 |
| 0.5786 | 5.6 | 1500 | 0.6271 | 0.0364 |
| 0.5513 | 7.46 | 2000 | 0.6517 | 0.0412 |
| 0.5219 | 9.33 | 2500 | 0.6753 | 0.1073 |
| 0.5067 | 11.19 | 3000 | 0.6918 | 0.0978 |
| 0.4827 | 13.06 | 3500 | 0.7235 | 0.0896 |
| 0.4638 | 14.93 | 4000 | 0.7478 | 0.0630 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
Davlan/m2m100_418M-eng-yor-mt
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"M2M100ForConditionalGeneration"
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}
| 9 | 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: 273.11 +/- 20.37
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
...
```
|
Davlan/m2m100_418M-yor-eng-mt
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"M2M100ForConditionalGeneration"
],
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},
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}
}
| 6 | null |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('metamath/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Davlan/mt5-small-pcm-en
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
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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 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="jeinsong/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"])
```
|
Davlan/mt5_base_yor_eng_mt
|
[
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MT5ForConditionalGeneration"
],
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},
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},
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},
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}
}
| 8 | null |
---
license: cc-by-nc-sa-4.0
---
# YanYuan-v1-lora-sd2
## 下载
- [yanyuan-v2](./yanyuan-v2.safetensors) (适用于 Stable Diffusion v2 系列模型)
## 预览图
基础模型：[PVC v2](https://huggingface.co/p1atdev/pvc)
```text
masterpiece, best quality, 1girl, <lora:yanyuan-v2:0.9>, long hair, red eyes, black hair, blunt bangs, ribbon, skirt, wide sleeves, full body, simple background, smile, standing on one leg, shadow, depth of field,
Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 4255907703, Size: 384x512, Model hash: 765abaa479, Denoising strength: 0.6, Clip skip: 2, ENSD: 31339, Hires upscale: 2, Hires steps: 20, Hires upscaler: Latent
```
基础模型：[WaifuDiffusion v1.4 epoch 2](https://huggingface.co/hakurei/waifu-diffusion-v1-4)
```text
masterpiece, best quality, 1girl, <lora:yanyuan-v2:0.85>, long hair, red eyes, black hair, blunt bangs, ribbon, dress, wide sleeves, full body, [smile], wading, ocean, sunset, dynamic angle, depth of field, bautiful detailed sky
Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 12, Seed: 2654165008, Size: 512x384, Model hash: c76e0962bc, Denoising strength: 0.6, Clip skip: 2, ENSD: 31339, Hires upscale: 2, Hires steps: 20, Hires upscaler: Latent
```
## 推荐起手式
```text
masterpiece, best quality, 1girl, <lora:yanyuan-v2:0.85>, long hair, red eyes, black hair, blunt bangs, ribbon
```
需要用 `skirt` `dress` 等标签进一步指定服装种类。
## 更多信息
见总仓库：[of_diffusion](https://huggingface.co/wybxc/of_diffusion)。
|
Davlan/xlm-roberta-base-finetuned-amharic
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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},
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},
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},
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}
| 401 | null |
---
datasets:
- biglam/encyclopaedia_britannica_illustrated
library_name: transformers
pipeline_tag: image-classification
tags:
- lam
---
|
Davlan/xlm-roberta-base-finetuned-igbo
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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},
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}
| 68 | null |
Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
Davlan/xlm-roberta-base-finetuned-lingala
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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"max_length": null
},
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},
"translation_en_to_fr": {
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},
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}
}
}
| 9 | 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: 269.98 +/- 21.99
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
...
```
|
Davlan/xlm-roberta-base-finetuned-shona
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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}
| 5 | null |
---
license: cc-by-nc-sa-4.0
---
# YanHuo-v1-lora-sd2
## 下载
- [yanhuo-v2](./yanhuo-v2.safetensors) (适用于 Stable Diffusion v2 系列模型)
## 预览图
基础模型：[PVC v2](https://huggingface.co/p1atdev/pvc)
```text
masterpiece, best quality, <lora:yanhuo-v2:0.9>, 1girl, blue eyes, solo, ahoge, white hair, side ponytail, red skirt, wide sleeves, full body, depth of field, shadow, simple background, standing on one leg, looking at viewer,
Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 2528780042, Size: 384x512, Model hash: 765abaa479, Denoising strength: 0.6, Clip skip: 2, ENSD: 31339, Hires upscale: 2, Hires steps: 20, Hires upscaler: Latent
```
基础模型：[WaifuDiffusion v1.4 epoch 2](https://huggingface.co/hakurei/waifu-diffusion-v1-4)
```text
masterpiece, best quality, <lora:yanhuo-v2:0.9>, 1girl, blue eyes, solo, ahoge, white hair, side ponytail, dress, wide sleeves, (snowy street), beautiful detailed sky, dynamic angle, full body, looking at viewer, volume light,
Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 12, Seed: 2491440016, Size: 512x384, Model hash: c76e0962bc, Denoising strength: 0.6, Clip skip: 2, ENSD: 31339, Hires upscale: 2, Hires steps: 20, Hires upscaler: Latent
```
## 推荐起手式
```text
masterpiece, best quality, <lora:yanhuo-v2:0.9>, 1girl, blue eyes, solo, ahoge, white hair, side ponytail
```
需要用 `skirt` `dress` 等标签进一步指定服装种类。
## 更多信息
见总仓库：[of_diffusion](https://huggingface.co/wybxc/of_diffusion)。
|
Dazai/Ko
|
[] | null |
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| 0 | null |
---
license: mit
datasets:
- bible-nlp/biblenlp-corpus
language:
- he
pipeline_tag: text-generation
---
# hebrew_bible_ai
Finetuned gpt2-xl model on the Hebrew bible. This will babble new potentially good looking Hebrew bible verses.
You can run an example at Hugging Face:
https://huggingface.co/tombenj/hebrew_bible_ai
Based on the input from:
https://raw.githubusercontent.com/BibleNLP/ebible/main/corpus/heb-heb.txt
And based on the gpt2-xl model from OpenAI.
Output example:
⁧
```
יולד ואצרי אלף לא לו לא בעמים ומצותו אשר לא ישביעם בעם אשר בעם בשמים ובשמים אשר בעם בני נחלה אשר השבתם טובה׃
ויהי בימים מאת יהוה לא חפץ כי ודבר אליו באכל ולבשר בהם בארץ הקטן׃
ויקבצו להם ויאמרו אל אחיה אחיטוב אם חפץ כי הנני מכה אחיטוב עד הערב ואמה אחיטוב ובנימן אחיטוב הראש אשר העלו׃
ויקצץ המטביע לפני יהוה ויאמרו כי תצאתי אתן בן אחישב ובן נדח ובן שחת בן נדח וננחם׃
ויהי אחרי זה הבכור הזה אלי העם הבכור ויאמר אליו כי זה דבר׃
---------------
כבס לעשתי שבע הרע את אשר פסח ואת אשר פסח ואת אשר פסח ואת אשר פסח ואת אשר פסח׃
והנני זה מים אחד בערב ומים אחד בחדש למלך אשור והוא מצרים אחד באה הגדול אשר תנתם׃
ויאמר אליו אדני נא עלו יצאון ויאמר אנכי הנחש מצרות אחיכם ובכם זה מים אחד׃
והוא אנכי הנחש מצר לחם אחיכם ובכם זה מים אחד׃
והוא בכם זה מים אחד הנה נגד עלה אחיכם ובכם זה מים אחד׃
והוא מצרים אחד את אשר פסח ואת אשר פסח ואת אשר פסח ובכם זה מים אחד׃
---------------
ועל פני פרע שנה אמר אלהים נרקע את ידם כאשר דבר אליהם לא לראות כי אני נעמה ויאמר אלהים הנני מעליהם אשר קשתם כי יגל מנשה׃
ויקבצו אתו בתוכם למוקש לו עד אשר אמר אלהים נא את אחת ידם כי כבשי הגליל למוקש את אחת׃
ויעל הרוח לדבר אל הכל הזה ויהי משמעו בני יעקב ויקטר ויקמץ וישב אחרי בית יעקב ויעבר יעקב ויבא אל נפשי׃
וישאל ויאמר אל הנה מי הלך עוד בני יעקב אם לא הלכה׃
וידבר יום אל הנה הצילני ויצא אל אחי הבית אשר המה רבה ויאמר אלהם שם את אחי נא
---------------
ויאמר אליהם לכו העם אל כל עמק הנה עבדך ועבדך עבדך עמק הנה ועבדך עבדך והבא עבדך בבית ובית עבד הנה׃
וירא יעקב אל העם לאמר הנה עבדך עבדך עמק הנה ועבדך עבדך עמק הנה ועבדך עבדך עמק הנה ועבדך עבדך עם רב ועם עשו פתח יעקב׃
ויאמר יעקב מה העשירי כי אני גם אדני העם ויאמר אל יעקב כי אם אני גם את האלהים הנביאים אשר אני לעבד העם׃
ויאמר יעקב אל העם אשר אני לעבד העם אני ב׃
ויאמר יעקב כי אני נעמד בימים בארץ אשר הי
---------------
אל תבטח בעת רחצון ואת אבנתם יבטח בם והקיץ לדו בחצון ותהי והחדר אל שם הגוים ואל שם החרפת ואל שם החפשת ואל שם הפרשת ואל שם העיר ואל שם הבכרות׃
והחזים אשר החוזים את שם הגוים החוזים ואת שם החפש השני הגוים׃
ויהי עם בצדנות יהוה אלהיהם העשק מבית אלפים עשה ועשה אמו אחת אלפים הכסף והשטן מאז מקום להם וקם כל העמים וכל הנותרים׃
ויתן את עשרים את הדבר הזה אשר הגדל לפני ארם ויעש את עשרה הנשארים ויאמר להם היא נשארים שמנה׃
---------------
פעמו ויחי פלשתים וכל רגלים׃
בחנדים עם נדיב כבוד מעמנו אנחנו׃
בעודת יעקב על רב ועל פני רעב׃
בכורו להגב להגב להגב לוח׃
זכרה תשית ונשית אחד נשי מן הממצות לאמר׃
כי תחזק לו כפר וענתם׃
העשק לפניו חנם אשר ימצא מטו מגיש חן חנם׃
ואשר מחנם ואשר חנם בעיני אשר יעלו׃
נפש אשר הגיש אשר אשבעה יעלו׃
אשר מחנם ואשר חנם אשר יחשבנו׃
בנו תוגיע וארבע וארבע יעבור׃
מול עבודם וישואל אשר תשית ונוספכם׃
```
⁦
|
DeadBeast/marathi-roberta-base
|
[] | null |
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| 0 | null |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- art
duplicated_from: Toooajk/Cornflower_v7
---
Cornflower is a comprehensive painting model based on StableDiffusion, trained with specific styles of illustration and merged with multiple models, which is theoretically somewhat different from real-life human painters.
**Since the Cornflower model contains multiple files, you need to place all the files in the appropriate locations.**
### How to install?
**'cornflower_v7.safetensors'** and **vae file** are placed in the Stable Diffusion model directory.
The .pt files in **'embeddings'** folder are placed in the embeddings directory.
**'cornflower_v7_phantom.pt'** in hypernetwork folder is placed in the Hypernetworks model directory.
### How to use?
After the installation is complete, open webui and switch checkpoint to 'cornflower_v7.safetensors', Hypernetwork to 'cornflower_v7_phantom'.
The following parameters are recommended, and the sampler recommends DPM2 a Karras.
Steps: 20, Sampler: DPM2 a Karras, CFG scale: 7, Size: 640x960, Clip skip: 2, ENSD: 31337
|
Dean/summarsiation
|
[] | null |
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| 0 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="robinsk8a/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"])
```
|
DecafNosebleed/ScaraBot
|
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}
| 0 | 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: 7.48 +/- 2.74
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="leenw2/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"])
```
|
Declan/Breitbart_model_v4
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"BertForMaskedLM"
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}
| 3 | null |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: jovial_rosalind
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. -->
# jovial_rosalind
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## 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: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 4724
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0.00056},
'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True,
'skip_tokens': 2990407680},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 4096,
'prefix': '<|aligned|>'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': False,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'revision': '5c64636da035c40bb8b1186648a39822071476cb'},
'num_additional_tokens': 2,
'path_or_name': 'tomekkorbak/cranky_lichterman'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'jovial_rosalind',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 253,
'save_strategy': 'steps',
'seed': 42,
'tokens_already_seen': 2990407680,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/1tysmsjz
|
Declan/CNN_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"BertForMaskedLM"
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},
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},
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},
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}
| 3 | 2023-02-01T15:16:50Z |
---
license: apache-2.0
tags:
- classification
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
model-index:
- name: distilbert_classificator
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: test
args: emotion
metrics:
- name: Accuracy
type: accuracy
value: 0.7909922589725545
---
<!-- 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_classificator
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8627
- Accuracy: 0.7910
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 408 | 0.6174 | 0.7882 |
| 0.6884 | 2.0 | 816 | 0.7010 | 0.7945 |
| 0.3202 | 3.0 | 1224 | 0.8627 | 0.7910 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/FoxNews_model_v2
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
}
| 3 | null |
---
license: cc-by-sa-4.0
language:
- en
pipeline_tag: image-to-text
---
|
Declan/NewYorkTimes_model_v2
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 7 | 2023-02-01T16:51:35Z |
---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: nmp
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. -->
# nmp
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the custom dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0803
- Accuracy: 0.9688
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 19 | 1.4407 | 0.3438 |
| No log | 2.0 | 38 | 0.6772 | 0.9375 |
| No log | 3.0 | 57 | 0.2852 | 0.9688 |
| No log | 4.0 | 76 | 0.0846 | 1.0 |
| No log | 5.0 | 95 | 0.0803 | 0.9688 |
| No log | 6.0 | 114 | 0.1039 | 0.9688 |
| No log | 7.0 | 133 | 0.1371 | 0.9688 |
| No log | 8.0 | 152 | 0.1239 | 0.9688 |
| No log | 9.0 | 171 | 0.1325 | 0.9688 |
| No log | 10.0 | 190 | 0.1312 | 0.9688 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/NewYorkTimes_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 3 | null |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.77 +/- 0.42
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="ongkn/q-FrozenLake-v1-4x4-Slippery", 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"])
```
|
Declan/Politico_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"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,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 7 | null |
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/relbert-roberta-large-triplet-d-semeval2012
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8682936507936508
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6818181818181818
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6884272997032641
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7837687604224569
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.922
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6798245614035088
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6481481481481481
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.31543624161073824
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5245901639344263
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6383333333333333
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9210486665662196
- name: F1 (macro)
type: f1_macro
value: 0.9162391821082241
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8685446009389672
- name: F1 (macro)
type: f1_macro
value: 0.7087997516468159
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.695557963163597
- name: F1 (macro)
type: f1_macro
value: 0.6814413829639081
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9581971204006399
- name: F1 (macro)
type: f1_macro
value: 0.8922580894510105
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8940770918207458
- name: F1 (macro)
type: f1_macro
value: 0.8852411214961728
---
# relbert/relbert-roberta-large-triplet-d-semeval2012
RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-triplet-d-semeval2012/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.6818181818181818
- Accuracy on SAT: 0.6884272997032641
- Accuracy on BATS: 0.7837687604224569
- Accuracy on U2: 0.6798245614035088
- Accuracy on U4: 0.6481481481481481
- Accuracy on Google: 0.922
- Accuracy on ConceptNet Analogy: 0.31543624161073824
- Accuracy on T-Rex Analogy: 0.5245901639344263
- Accuracy on NELL-ONE Analogy: 0.6383333333333333
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-triplet-d-semeval2012/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9210486665662196
- Micro F1 score on CogALexV: 0.8685446009389672
- Micro F1 score on EVALution: 0.695557963163597
- Micro F1 score on K&H+N: 0.9581971204006399
- Micro F1 score on ROOT09: 0.8940770918207458
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-triplet-d-semeval2012/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8682936507936508
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-large-triplet-d-semeval2012")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-large
- max_length: 64
- epoch: 1
- batch: 79
- random_seed: 0
- lr: 2e-05
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/semeval2012_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: triplet
- classification_loss: False
- loss_function_config: {'mse_margin': 1}
- augment_negative_by_positive: False
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-triplet-d-semeval2012/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
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.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
Declan/Reuters_model_v2
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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}
}
| 5 | null |
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/relbert-roberta-large-triplet-e-semeval2012
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8003373015873015
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.56951871657754
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5786350148367952
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7476375764313508
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.902
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.618421052631579
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5949074074074074
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3145973154362416
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5409836065573771
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.61
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9026668675606448
- name: F1 (macro)
type: f1_macro
value: 0.901099542864961
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8607981220657277
- name: F1 (macro)
type: f1_macro
value: 0.703607951808097
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6917659804983749
- name: F1 (macro)
type: f1_macro
value: 0.690966207619381
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9673088961535786
- name: F1 (macro)
type: f1_macro
value: 0.8870352978589001
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8975242870573488
- name: F1 (macro)
type: f1_macro
value: 0.8979403889476659
---
# relbert/relbert-roberta-large-triplet-e-semeval2012
RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-triplet-e-semeval2012/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.56951871657754
- Accuracy on SAT: 0.5786350148367952
- Accuracy on BATS: 0.7476375764313508
- Accuracy on U2: 0.618421052631579
- Accuracy on U4: 0.5949074074074074
- Accuracy on Google: 0.902
- Accuracy on ConceptNet Analogy: 0.3145973154362416
- Accuracy on T-Rex Analogy: 0.5409836065573771
- Accuracy on NELL-ONE Analogy: 0.61
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-triplet-e-semeval2012/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9026668675606448
- Micro F1 score on CogALexV: 0.8607981220657277
- Micro F1 score on EVALution: 0.6917659804983749
- Micro F1 score on K&H+N: 0.9673088961535786
- Micro F1 score on ROOT09: 0.8975242870573488
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-triplet-e-semeval2012/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8003373015873015
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-large-triplet-e-semeval2012")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-large
- max_length: 64
- epoch: 1
- batch: 79
- random_seed: 0
- lr: 2e-05
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/semeval2012_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: triplet
- classification_loss: False
- loss_function_config: {'mse_margin': 1}
- augment_negative_by_positive: False
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-triplet-e-semeval2012/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
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.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
Declan/Reuters_model_v4
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"BertForMaskedLM"
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| 3 | null |
---
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-muchocine
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. -->
# clasificador-muchocine
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4218
- Accuracy: 0.4413
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 388 | 1.3403 | 0.3794 |
| 1.4032 | 2.0 | 776 | 1.2914 | 0.4297 |
| 1.0124 | 3.0 | 1164 | 1.4218 | 0.4413 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/Reuters_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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| 3 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MNRLGradCache`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 880,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 0.00032
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 881,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BloomModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Declan/Reuters_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
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| 7 | null |
---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
---
A stylized anime model.And You can use it with lora.MoonTea is my merge which were created by combining different models.
|
Declan/WallStreetJournal_model_v1
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
| 3 | null |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- Kanr1u/autotrain-data-emma2
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 2.1409787540187346
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 3206689984
- CO2 Emissions (in grams): 2.1410
## Validation Metrics
- Loss: 0.303
- Accuracy: 0.846
- Precision: 0.846
- Recall: 0.846
- AUC: 0.929
- F1: 0.846
|
Declan/WallStreetJournal_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
}
| 9 | null |
Access to model DhruvShek/swearbot is restricted and you are not in the authorized list. Visit https://huggingface.co/DhruvShek/swearbot to ask for access.
|
Declan/WallStreetJournal_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
}
| 9 | 2023-02-01T17:53:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: result
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. -->
# result
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/test_push
|
[] | null |
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}
| 0 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Q-Taxi-v3-4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="dn-gh/Q-Taxi-v3-4", 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"])
```
|
DeepChem/ChemBERTa-10M-MLM
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
}
| 90 | null |
---
library_name: stable-baselines3
tags:
- PandaPushJointsDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaPushJointsDense-v2
type: PandaPushJointsDense-v2
metrics:
- type: mean_reward
value: -8.76 +/- 4.85
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaPushJointsDense-v2**
This is a trained model of a **A2C** agent playing **PandaPushJointsDense-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
...
```
|
DeepChem/ChemBERTa-5M-MLM
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
}
| 29 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### LCMNPM Dreambooth model trained by rodrigobrand 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:
|
DeepChem/ChemBERTa-77M-MLM
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
| 2,416 | null |
# IMDB Classification with BERT
---
license: mit
datasets:
- imdb
language:
- en
metrics:
- accuracy
- f1
library_name: transformers
pipeline_tag: text-classification
---
|
DeepChem/SmilesTokenizer_PubChem_1M
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 227 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.929159802306425
- name: Recall
type: recall
value: 0.9491753618310333
- name: F1
type: f1
value: 0.939060939060939
- name: Accuracy
type: accuracy
value: 0.9861070230176017
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0634
- Precision: 0.9292
- Recall: 0.9492
- F1: 0.9391
- Accuracy: 0.9861
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0853 | 1.0 | 1756 | 0.0672 | 0.9092 | 0.9354 | 0.9221 | 0.9820 |
| 0.0366 | 2.0 | 3512 | 0.0642 | 0.9308 | 0.9490 | 0.9398 | 0.9859 |
| 0.0182 | 3.0 | 5268 | 0.0634 | 0.9292 | 0.9492 | 0.9391 | 0.9861 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
DeepPavlov/bert-base-cased-conversational
|
[
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
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},
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}
}
| 3,009 | 2023-02-01T18:30:55Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: NihiLicA/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DeepPavlov/marianmt-tatoeba-ruen
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"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,
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},
"text-generation": {
"do_sample": null,
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},
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"num_beams": null,
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}
}
}
| 30 | null |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/pii-pile-chunk3-0-50000
- tomekkorbak/pii-pile-chunk3-50000-100000
- tomekkorbak/pii-pile-chunk3-100000-150000
- tomekkorbak/pii-pile-chunk3-150000-200000
- tomekkorbak/pii-pile-chunk3-200000-250000
- tomekkorbak/pii-pile-chunk3-250000-300000
- tomekkorbak/pii-pile-chunk3-300000-350000
- tomekkorbak/pii-pile-chunk3-350000-400000
- tomekkorbak/pii-pile-chunk3-400000-450000
- tomekkorbak/pii-pile-chunk3-450000-500000
- tomekkorbak/pii-pile-chunk3-500000-550000
- tomekkorbak/pii-pile-chunk3-550000-600000
- tomekkorbak/pii-pile-chunk3-600000-650000
- tomekkorbak/pii-pile-chunk3-650000-700000
- tomekkorbak/pii-pile-chunk3-700000-750000
- tomekkorbak/pii-pile-chunk3-750000-800000
- tomekkorbak/pii-pile-chunk3-800000-850000
- tomekkorbak/pii-pile-chunk3-850000-900000
- tomekkorbak/pii-pile-chunk3-900000-950000
- tomekkorbak/pii-pile-chunk3-950000-1000000
- tomekkorbak/pii-pile-chunk3-1000000-1050000
- tomekkorbak/pii-pile-chunk3-1050000-1100000
- tomekkorbak/pii-pile-chunk3-1100000-1150000
- tomekkorbak/pii-pile-chunk3-1150000-1200000
- tomekkorbak/pii-pile-chunk3-1200000-1250000
- tomekkorbak/pii-pile-chunk3-1250000-1300000
- tomekkorbak/pii-pile-chunk3-1300000-1350000
- tomekkorbak/pii-pile-chunk3-1350000-1400000
- tomekkorbak/pii-pile-chunk3-1400000-1450000
- tomekkorbak/pii-pile-chunk3-1450000-1500000
- tomekkorbak/pii-pile-chunk3-1500000-1550000
- tomekkorbak/pii-pile-chunk3-1550000-1600000
- tomekkorbak/pii-pile-chunk3-1600000-1650000
- tomekkorbak/pii-pile-chunk3-1650000-1700000
- tomekkorbak/pii-pile-chunk3-1700000-1750000
- tomekkorbak/pii-pile-chunk3-1750000-1800000
- tomekkorbak/pii-pile-chunk3-1800000-1850000
- tomekkorbak/pii-pile-chunk3-1850000-1900000
- tomekkorbak/pii-pile-chunk3-1900000-1950000
model-index:
- name: eloquent_keller
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. -->
# eloquent_keller
This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 2362
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0.0},
'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000',
'tomekkorbak/pii-pile-chunk3-50000-100000',
'tomekkorbak/pii-pile-chunk3-100000-150000',
'tomekkorbak/pii-pile-chunk3-150000-200000',
'tomekkorbak/pii-pile-chunk3-200000-250000',
'tomekkorbak/pii-pile-chunk3-250000-300000',
'tomekkorbak/pii-pile-chunk3-300000-350000',
'tomekkorbak/pii-pile-chunk3-350000-400000',
'tomekkorbak/pii-pile-chunk3-400000-450000',
'tomekkorbak/pii-pile-chunk3-450000-500000',
'tomekkorbak/pii-pile-chunk3-500000-550000',
'tomekkorbak/pii-pile-chunk3-550000-600000',
'tomekkorbak/pii-pile-chunk3-600000-650000',
'tomekkorbak/pii-pile-chunk3-650000-700000',
'tomekkorbak/pii-pile-chunk3-700000-750000',
'tomekkorbak/pii-pile-chunk3-750000-800000',
'tomekkorbak/pii-pile-chunk3-800000-850000',
'tomekkorbak/pii-pile-chunk3-850000-900000',
'tomekkorbak/pii-pile-chunk3-900000-950000',
'tomekkorbak/pii-pile-chunk3-950000-1000000',
'tomekkorbak/pii-pile-chunk3-1000000-1050000',
'tomekkorbak/pii-pile-chunk3-1050000-1100000',
'tomekkorbak/pii-pile-chunk3-1100000-1150000',
'tomekkorbak/pii-pile-chunk3-1150000-1200000',
'tomekkorbak/pii-pile-chunk3-1200000-1250000',
'tomekkorbak/pii-pile-chunk3-1250000-1300000',
'tomekkorbak/pii-pile-chunk3-1300000-1350000',
'tomekkorbak/pii-pile-chunk3-1350000-1400000',
'tomekkorbak/pii-pile-chunk3-1400000-1450000',
'tomekkorbak/pii-pile-chunk3-1450000-1500000',
'tomekkorbak/pii-pile-chunk3-1500000-1550000',
'tomekkorbak/pii-pile-chunk3-1550000-1600000',
'tomekkorbak/pii-pile-chunk3-1600000-1650000',
'tomekkorbak/pii-pile-chunk3-1650000-1700000',
'tomekkorbak/pii-pile-chunk3-1700000-1750000',
'tomekkorbak/pii-pile-chunk3-1750000-1800000',
'tomekkorbak/pii-pile-chunk3-1800000-1850000',
'tomekkorbak/pii-pile-chunk3-1850000-1900000',
'tomekkorbak/pii-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True,
'skip_tokens': 2990407680},
'generation': {'force_call_on': [25177],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 4096,
'prefix': '<|aligned|>'}],
'scorer_config': {}},
'kl_gpt3_callback': {'force_call_on': [25177],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': False,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'revision': '5c64636da035c40bb8b1186648a39822071476cb'},
'num_additional_tokens': 2,
'path_or_name': 'tomekkorbak/cranky_lichterman'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 128,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'eloquent_keller',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0001,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output2',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 251,
'save_strategy': 'steps',
'seed': 42,
'tokens_already_seen': 2990407680,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/3obvh7b1
|
DeepPavlov/rubert-base-cased-conversational
|
[
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"transformers",
"has_space"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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| 17,362 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-gp2-finetune
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. -->
# codeparrot-gp2-finetune
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: 1.7282
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5006 | 0.93 | 5000 | 1.7282 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DeividasM/wav2vec2-large-xlsr-53-lithuanian
|
[
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"lt",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"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,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
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"translation_en_to_fr": {
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}
| 7 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: anipoma1
---
### Duskfall's Animanga Model Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
If you want to donate towards costs and don't want to subscribe:
https://ko-fi.com/DUSKFALLcrew
If you want to monthly support the EARTH & DUSK media projects and not just AI:
https://www.patreon.com/earthndusk
Discord https://discord.gg/Da7s8d3KJ7
Rules
Do not sell merges, or this model.
Do share, and credit if you use this model.
DO PLS REVIEW AND YELL AT ME IF IT SUCKS!
We never update the images on here anymore
see civit https://civitai.com/user/duskfallcrew
|
DeltaHub/lora_t5-base_mrpc
|
[
"pytorch",
"transformers"
] | null |
{
"architectures": null,
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}
| 3 | 2023-02-01T19:16:25Z |
---
license: openrail++
datasets:
- Anthropic/hh-rlhf
- merve/my_notes
- openai/summarize_from_feedback
- fka/awesome-chatgpt-prompts
language:
- aa
- ae
- am
- az
- an
- ak
- af
library_name: adapter-transformers
pipeline_tag: text-to-image
tags:
- art
---
|
Denilson/gbert-base-germaner
|
[] | null |
{
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| 0 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole8
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
|
Deniskin/essays_small_2000i
|
[] | null |
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| 0 | 2023-02-01T19:35:51Z |
---
tags:
- generated_from_trainer
model-index:
- name: pythia-160m-deduped-aid
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. -->
# pythia-160m-deduped-aid
## Model description
This model is a finetune of [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) (from when it was instead `pythia-125m-deduped`), on the [`text_adventures.txt`](https://github.com/Latitude-Archives/AIDungeon/blob/ca098ca7dab480d24e47954c8873b03ba1091ffc/data/text_adventures.txt) dataset originally intended for AI Dungeon 2. Performance will be very poor, as expected by the small model, and generations may be offensive thanks to its training data.
This model was trained for testing purposes as the successor to [Merry/AID-Neo-125M](https://huggingface.co/Merry/AID-Neo-125M) and was intended for use with KoboldAI. A temperature of `0.5` and a repetition penalty of `1.05` were tested.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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: 3.0
### Training results
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Denver/distilbert-base-uncased-finetuned-squad
|
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| 0 | null |
---
license: openrail
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- chrf
library_name: nemo
tags:
- chemistry
---
|
DeskDown/MarianMixFT_en-hi
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 3 | null |
---
tags:
- spacy
language:
- en
model-index:
- name: en_ml_pipeline_last
results: []
---
| Feature | Description |
| --- | --- |
| **Name** | `en_ml_pipeline_last` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.4.4,<3.5.0` |
| **Default Pipeline** | `tok2vec`, `transformer`, `dual` |
| **Components** | `tok2vec`, `transformer`, `dual` |
| **Vectors** | 2466243 keys, 100195 unique vectors (3 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (14 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`dual`** | `ID`, `ACR`, `IND`, `COL`, `ORG`, `STP`, `LEG`, `LOC`, `SCP`, `PER`, `NAT`, `NUM`, `MOD`, `STR` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `SPANS_SC_F` | 83.55 |
| `SPANS_SC_P` | 86.82 |
| `SPANS_SC_R` | 80.51 |
| `TOK2VEC_LOSS` | 83.86 |
| `TRANSFORMER_LOSS` | 441.89 |
| `DUAL_LOSS` | 57693.64 |
|
Despin89/test
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: my_new_asr_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_new_asr_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9912
- Wer: 0.9915
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| No log | 200.0 | 200 | 3.2498 | 0.9972 |
| No log | 400.0 | 400 | 4.1645 | 1.1339 |
| 1.1325 | 600.0 | 600 | 4.7252 | 1.1197 |
| 1.1325 | 800.0 | 800 | 4.9678 | 1.0370 |
| 0.0747 | 1000.0 | 1000 | 4.9912 | 0.9915 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Devid/DialoGPT-small-Miku
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 10 | null |
---
license: openrail
model-index:
- name: SantaCoder
results:
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL HumanEval (Lua)
metrics:
- name: pass@1
type: pass@1
value: 0.09
verified: false
---
This model is a fine-tuned [SantaCoder](https://huggingface.co/bigcode/santacoder), which is
fine-tuned on a subset of the Lua code found on [The Stack](https://huggingface.co/datasets/bigcode/the-stack-dedup).
We used [this script](https://github.com/loubnabnl/santacoder-finetuning) for fine-tuning.
|
DevsIA/imagenes
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
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-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.13.2
|
Dibyaranjan/nl_image_search
|
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| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **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: Write your model_id: MHaurel/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Digakive/Hsgshs
|
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| 0 | null |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('TieIncred/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
DimaOrekhov/cubert-method-name
|
[
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 10 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="stinoco/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
DivyanshuSheth/T5-Seq2Seq-Final
|
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| 0 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 250 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 500,
"warmup_steps": 50,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Dkwkk/Da
|
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}
| 0 | 2023-02-01T21:25:04Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole-v1_PG
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
|
Doiman/DialoGPT-medium-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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| 13 | 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: 260.70 +/- 19.98
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
...
```
|
DongHyoungLee/distilbert-base-uncased-finetuned-cola
|
[
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
text-classification
|
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"DistilBertForSequenceClassification"
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"model_type": "distilbert",
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}
| 27 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Disney-style Dreambooth model trained by jha2ee 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:
|
Doogie/Waynehills-KE-T5-doogie
|
[] | null |
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}
| 0 | 2023-02-01T22:12:56Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### tfmfurbase Dreambooth model trained by Deitsao with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
(BROKEN because I'm sleepy asf 😭)
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)
it's a simple model which can generate tfm furs based on old transformice furs.
it can help with fantasy if someone wants to suggest fur for tfm.
(it can work a lot better when using mouse base from that game)
Sample pictures of this concept:
|
Waynehillsdev/wav2vec2-base-timit-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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}
| 5 | null |
---
license: creativeml-openrail-m
datasets:
- fka/awesome-chatgpt-prompts
language:
- ee
metrics:
- cer
library_name: diffusers
---
|
Doohae/p_encoder
|
[
"pytorch"
] | null |
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| 3 | 2023-02-01T22:27:01Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.12046776548411303
---
<!-- 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_sa_GLUE_Experiment_data_aug_cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8362
- Matthews Correlation: 0.1205
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4726 | 1.0 | 835 | 0.8362 | 0.1205 |
| 0.2428 | 2.0 | 1670 | 1.3000 | 0.1122 |
| 0.1378 | 3.0 | 2505 | 1.3626 | 0.1226 |
| 0.0893 | 4.0 | 3340 | 1.6155 | 0.1608 |
| 0.0648 | 5.0 | 4175 | 1.8098 | 0.0958 |
| 0.049 | 6.0 | 5010 | 2.0187 | 0.1179 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Doohae/q_encoder
|
[
"pytorch"
] | null |
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| 3 | 2023-02-01T22:28:45Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: mobilebert_sa_GLUE_Experiment_data_aug_cola_256
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.09390288672705373
---
<!-- 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. -->
# mobilebert_sa_GLUE_Experiment_data_aug_cola_256
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6609
- Matthews Correlation: 0.0939
## 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: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------:|
| 0.5394 | 1.0 | 1669 | 0.6609 | 0.0939 |
| 0.4545 | 2.0 | 3338 | 0.7807 | 0.0474 |
| 0.4253 | 3.0 | 5007 | 0.8029 | 0.0846 |
| 0.388 | 4.0 | 6676 | 0.8930 | 0.0738 |
| 0.3433 | 5.0 | 8345 | 0.9284 | 0.0834 |
| 0.2986 | 6.0 | 10014 | 1.0809 | 0.1026 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Doohae/roberta
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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| 3 | null |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: mobilebert_sa_GLUE_Experiment_data_aug_cola_128
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.06184591421174734
---
<!-- 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. -->
# mobilebert_sa_GLUE_Experiment_data_aug_cola_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6624
- Matthews Correlation: 0.0618
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------:|
| 0.5456 | 1.0 | 1669 | 0.6624 | 0.0618 |
| 0.4572 | 2.0 | 3338 | 0.7774 | 0.0514 |
| 0.419 | 3.0 | 5007 | 0.8469 | 0.0931 |
| 0.3649 | 4.0 | 6676 | 0.8748 | 0.1011 |
| 0.3117 | 5.0 | 8345 | 1.0732 | 0.0824 |
| 0.2698 | 6.0 | 10014 | 1.2173 | 0.0618 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DoyyingFace/bert-COVID-HATE-finetuned-test
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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| 29 | null |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_cola_96
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.1072268901195801
---
<!-- 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_sa_GLUE_Experiment_data_aug_cola_96
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6274
- Matthews Correlation: 0.1072
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5845 | 1.0 | 835 | 0.6274 | 0.1072 |
| 0.4862 | 2.0 | 1670 | 0.6843 | 0.1085 |
| 0.4221 | 3.0 | 2505 | 0.7307 | 0.0681 |
| 0.3829 | 4.0 | 3340 | 0.7969 | 0.1046 |
| 0.3557 | 5.0 | 4175 | 0.8648 | 0.0959 |
| 0.3328 | 6.0 | 5010 | 0.8932 | 0.0792 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
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| 29 | null |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: mobilebert_sa_GLUE_Experiment_data_aug_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.05152844185670031
---
<!-- 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. -->
# mobilebert_sa_GLUE_Experiment_data_aug_cola
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6549
- Matthews Correlation: 0.0515
## 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: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------:|
| 0.5347 | 1.0 | 1669 | 0.6549 | 0.0515 |
| 0.4507 | 2.0 | 3338 | 0.8182 | 0.0794 |
| 0.407 | 3.0 | 5007 | 0.8573 | 0.0853 |
| 0.3439 | 4.0 | 6676 | 0.9437 | 0.0871 |
| 0.2873 | 5.0 | 8345 | 1.0250 | 0.0530 |
| 0.2424 | 6.0 | 10014 | 1.2340 | 0.0733 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-8
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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"BertForSequenceClassification"
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| 30 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-with-clean-valid
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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},
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"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 33 | null |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_mrpc_192
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 1.0
- name: F1
type: f1
value: 1.0
---
<!-- 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_sa_GLUE_Experiment_data_aug_mrpc_192
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 1.0
- Combined Score: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.2446 | 1.0 | 980 | 0.0556 | 0.9877 | 0.9911 | 0.9894 |
| 0.0516 | 2.0 | 1960 | 0.0106 | 0.9951 | 0.9964 | 0.9958 |
| 0.0236 | 3.0 | 2940 | 0.0011 | 1.0 | 1.0 | 1.0 |
| 0.0149 | 4.0 | 3920 | 0.0010 | 1.0 | 1.0 | 1.0 |
| 0.0102 | 5.0 | 4900 | 0.0003 | 1.0 | 1.0 | 1.0 |
| 0.0067 | 6.0 | 5880 | 0.0002 | 1.0 | 1.0 | 1.0 |
| 0.005 | 7.0 | 6860 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0042 | 8.0 | 7840 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0034 | 9.0 | 8820 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0025 | 10.0 | 9800 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0024 | 11.0 | 10780 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0026 | 12.0 | 11760 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0019 | 13.0 | 12740 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0014 | 14.0 | 13720 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 15.0 | 14700 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0014 | 16.0 | 15680 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0012 | 17.0 | 16660 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 18.0 | 17640 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 19.0 | 18620 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 20.0 | 19600 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 21.0 | 20580 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 22.0 | 21560 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 23.0 | 22540 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 24.0 | 23520 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 25.0 | 24500 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 26.0 | 25480 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 27.0 | 26460 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 28.0 | 27440 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 29.0 | 28420 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 30.0 | 29400 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 31.0 | 30380 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 32.0 | 31360 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 33.0 | 32340 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 34.0 | 33320 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 35.0 | 34300 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 36.0 | 35280 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 37.0 | 36260 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 38.0 | 37240 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 39.0 | 38220 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 40.0 | 39200 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 41.0 | 40180 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 42.0 | 41160 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 43.0 | 42140 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 44.0 | 43120 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 45.0 | 44100 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 46.0 | 45080 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 47.0 | 46060 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 48.0 | 47040 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 49.0 | 48020 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 50.0 | 49000 | 0.0000 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
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