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
|
|---|---|---|---|---|---|---|
CLAck/en-km
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
] |
translation
|
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| 12 | null |
---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetune-adversarial-qa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetune-adversarial-qa
This model is a fine-tuned version of [hoang14/roberta-base-finetune-adversarial-qa](https://huggingface.co/hoang14/roberta-base-finetune-adversarial-qa) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 14
- eval_batch_size: 14
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CLAck/indo-mixed
|
[
"pytorch",
"marian",
"text2text-generation",
"en",
"id",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] |
translation
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| 15 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### zx Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
CLAck/vi-en
|
[
"pytorch",
"marian",
"text2text-generation",
"en",
"vi",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] |
translation
|
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| 6 | null |
---
license: cc-by-nc-4.0
pipeline_tag: text-to-video
tags:
- anime
---
## Overview
This is a text2video model for diffusers, fine-tuned with a [ModelScope](https://huggingface.co/damo-vilab/text-to-video-ms-1.7b) to have an anime-style appearance.
The model now has a much more anime-style look compared to the previous version.
It was trained at a resolution of 512x512.
[example video and prompts](https://imgur.com/a/LjZqPub)
But it is still unstable and the usability is not good yet.
**Please note that there is a possibility of unintended unpleasant results!**
### Prompt
* Mandatory prompt
anime
* Recommended negative prompt
noise, text, nude
## Limitaion
The usage limitation of the model follow the ModelScope rules of the original model.
|
CTBC/ATS
|
[] | null |
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| 0 | 2023-05-07T02:38:36Z |
---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-to-video
tags:
- anime
---
This is a text2video model for diffusers, fine-tuned with a [modelscope](https://huggingface.co/damo-vilab/text-to-video-ms-1.7b) to have an anime-style appearance.
It was trained at 448x384 resolution.
The usage is the same as with the original modelscope model.
The main difference from version 0.1 is only the resolution.
|
Cameron/BERT-SBIC-targetcategory
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 30 | null |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
---
## Description
## Usage
```python
from diffusers import StableDiffusionPipeline
```
|
Cameron/BERT-mdgender-convai-ternary
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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"BertForSequenceClassification"
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| 38 | null |
---
license: 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('Persevere/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Canadiancaleb/DialoGPT-small-jesse
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"max_length": 1000
},
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| 9 | null |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8787878751754761
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### corgi
#### samoyed
#### shiba inu
|
Canadiancaleb/DialoGPT-small-walter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
},
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| 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: 256.66 +/- 21.17
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
...
```
|
Capreolus/bert-base-msmarco
|
[
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"arxiv:2008.09093",
"transformers"
] |
text-classification
|
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"BertForSequenceClassification"
],
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| 238 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9295
- name: F1
type: f1
value: 0.9295553605965364
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2124
- Accuracy: 0.9295
- F1: 0.9296
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8137 | 1.0 | 250 | 0.3047 | 0.908 | 0.9041 |
| 0.2447 | 2.0 | 500 | 0.2124 | 0.9295 | 0.9296 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dccuchile/albert-xxlarge-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
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"AlbertForTokenClassification"
],
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| 28 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5_recommendation_items
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_recommendation_items
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9818
- Rouge1: 23.9693
- Rouge2: 14.0702
- Rougel: 22.7129
- Rougelsum: 22.7176
- Gen Len: 18.2255
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 157 | 0.9818 | 23.9693 | 14.0702 | 22.7129 | 22.7176 | 18.2255 |
| No log | 2.0 | 314 | 0.9818 | 23.9693 | 14.0702 | 22.7129 | 22.7176 | 18.2255 |
| No log | 3.0 | 471 | 0.9818 | 23.9693 | 14.0702 | 22.7129 | 22.7176 | 18.2255 |
### Framework versions
- Transformers 4.26.0
- Pytorch 2.0.0+cu118
- Datasets 2.8.0
- Tokenizers 0.13.3
|
dccuchile/albert-xxlarge-spanish-finetuned-qa-mlqa
|
[
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
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"AlbertForQuestionAnswering"
],
"model_type": "albert",
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| 7 | null |
---
language:
- en
- ja
tags:
- art
license: openrail
---
# 【LoRA】 endangeredAnimals
* [LoRA for SD ](https://huggingface.co/PastelPlanet/endangeredAnimals/resolve/main/endangeredAnimals1.0.safetensors)
endangeredAnimals is a LoRA file that was studied using the endangered Animals . This is based on a piece of calligraphy that #kazunoko_sho wrote.
## Sample Prompts
* endangered animal
* bird
* fish
* mammalian
* amphibian
* reptiles
* animal
...
Please input the name of the animal you wish to draw!
- \<lora:endangeredAnimals1.0:1\>, fish
- Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 682999928, Size: 512x512, Model hash: 2537d1a815, Model: G_GuoFeng3.2, Clip skip: 2
- \<lora:endangeredAnimals1.0:1\>, bird
- Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 3589955476, Size: 512x512, Model hash: fc2511737a, Model: C_chilloutmix_NiPrunedFp32Fix, Clip skip: 2
- \<lora:endangeredAnimals1.0:1\>, fish, bird
- Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 594561446, Size: 512x512, Model hash: 2537d1a815, Model: G_GuoFeng3.2, Clip skip: 2
## Recommendations
* use weight between 0.1 - 1.0 (depends on subject and touch you want)
## Information
https://twitter.com/pastelplanetllc/
https://twitter.com/pastelplanethi/
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-ner
|
[
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
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| 81 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 46.60 +/- 32.98
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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| 5 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: VinayakMane47/roberta-base-duplicate-Q-A
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# VinayakMane47/roberta-base-duplicate-Q-A
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1604
- Train Accuracy: 0.9356
- Validation Loss: 0.2295
- Validation Accuracy: 0.9117
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 15159, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2942 | 0.8692 | 0.2387 | 0.8992 | 0 |
| 0.2091 | 0.9133 | 0.2299 | 0.9062 | 1 |
| 0.1604 | 0.9356 | 0.2295 | 0.9117 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.11.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-xnli
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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| 28 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: finetuned-Sentiment-classfication-BERT-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. -->
# finetuned-Sentiment-classfication-BERT-model
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6056
- Rmse: 0.6890
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- 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 | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7754 | 2.0 | 500 | 0.6056 | 0.6890 |
| 0.3975 | 4.0 | 1000 | 0.6982 | 0.6452 |
| 0.1308 | 6.0 | 1500 | 1.0715 | 0.6643 |
| 0.0526 | 8.0 | 2000 | 1.3439 | 0.6571 |
| 0.0241 | 10.0 | 2500 | 1.4676 | 0.6695 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dccuchile/distilbert-base-spanish-uncased-finetuned-mldoc
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 27 | null |
---
tags:
- generated_from_trainer
datasets:
- indosum
metrics:
- rouge
model-index:
- name: t5-base-indonesian-summarization-cased-finetuned-indosum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: indosum
type: indosum
config: indosum_fold0_source
split: validation
args: indosum_fold0_source
metrics:
- name: Rouge1
type: rouge
value: 0.3278
---
<!-- 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-indonesian-summarization-cased-finetuned-indosum
This model is a fine-tuned version of [panggi/t5-base-indonesian-summarization-cased](https://huggingface.co/panggi/t5-base-indonesian-summarization-cased) on the indosum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5884
- Rouge1: 0.3278
- Rouge2: 0.2868
- Rougel: 0.32
- Rougelsum: 0.3198
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.3815 | 1.0 | 4754 | 0.5584 | 0.3281 | 0.2866 | 0.3202 | 0.32 | 19.0 |
| 0.3207 | 2.0 | 9508 | 0.5884 | 0.3278 | 0.2868 | 0.32 | 0.3198 | 19.0 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dccuchile/distilbert-base-spanish-uncased-finetuned-ner
|
[
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 28 | null |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **PPO** Agent playing **CarRacing-v2**
This is a trained model of a **PPO** agent playing **CarRacing-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/distilbert-base-spanish-uncased-finetuned-pawsx
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 29 | null |
---
datasets:
- glue
model-index:
- name: e5-large-mnli
results: []
pipeline_tag: zero-shot-classification
language:
- en
license: mit
---
# e5-large-mnli
This model is a fine-tuned version of [intfloat/e5-large](https://huggingface.co/intfloat/e5-large) on the glue dataset.
## Model description
[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
## How to use the model
The model can be loaded with the `zero-shot-classification` pipeline like so:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="mjwong/e5-large-mnli")
```
You can then use this pipeline to classify sequences into any of the class names you specify.
```python
sequence_to_classify = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(sequence_to_classify, candidate_labels)
#{'sequence': 'one day I will see the world',
# 'labels': ['travel', 'dancing', 'cooking'],
# 'scores': [0.9494319558143616, 0.044598229229450226, 0.00596982054412365]}
```
If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently:
```python
candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
classifier(sequence_to_classify, candidate_labels, multi_class=True)
#{'sequence': 'one day I will see the world',
# 'labels': ['exploration', 'travel', 'dancing', 'cooking'],
# 'scores': [0.9918234944343567,
# 0.9867327213287354,
# 0.40335655212402344,
# 0.0020157278049737215]}
```
### Eval results
The model was evaluated using the dev sets for MultiNLI and test sets for ANLI. The metric used is accuracy.
|Datasets|mnli_dev_m|mnli_dev_mm|anli_test_r1|anli_test_r2|anli_test_r3|
| :---: | :---: | :---: | :---: | :---: | :---: |
|[e5-base-mnli](https://huggingface.co/mjwong/e5-base-mnli)|0.840|0.839|0.231|0.285|0.309|
|[e5-large-mnli](https://huggingface.co/mjwong/e5-large-mnli)|0.868|0.869|0.301|0.296|0.294|
|[e5-large-unsupervised-mnli](https://huggingface.co/mjwong/e5-large-unsupervised-mnli)|0.865|0.867|0.314|0.285|0.303|
|[e5-large-mnli-anli](https://huggingface.co/mjwong/e5-large-mnli-anli)|0.843|0.848|0.646|0.484|0.458|
|[e5-large-unsupervised-mnli-anli](https://huggingface.co/mjwong/e5-large-unsupervised-mnli-anli)|0.836|0.842|0.634|0.481|0.478|
### 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1+cu116
- Datasets 2.11.0
- Tokenizers 0.12.1
|
dccuchile/distilbert-base-spanish-uncased-finetuned-qa-mlqa
|
[
"pytorch",
"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
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}
| 5 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ChatBot_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ChatBot_model
This model is a fine-tuned version of [facebook/blenderbot-400M-distill](https://huggingface.co/facebook/blenderbot-400M-distill) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.8826
- Epoch: 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 6e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 6e-05, 'decay_steps': 1792, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.8826 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate-1
|
[
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
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| 1 | null |
---
language: en
datasets:
- custom
task_categories:
- text-classification
task_ids:
- sentiment-classification
license: apache-2.0
---
Alpaca-LoRA is an open-source project that reproduces results from Stanford Alpaca using Low-Rank Adaptation (LoRA) techniques. It provides an Instruct model of similar quality to text-davinci-003.
Alpaca-LoRA uses the resource-efficient low-rank adaptation (LoRA) method, also widely used in Stable Diffusion, with Meta’s LLaMA to achieve results comparable to Alpaca
Alpaca formula is open source, but may not be used commercially. However, the LLaMA model used for Alpaca is not released for commercial use, and the OpenAI GPT-3.5 terms of use prohibit using the model to develop AI models that compete with OpenAI. Stanford has therefore not yet released the model, only the training data and the code to generate the data and fine-tune the model.
Github link:
- https://github.com/hennypurwadi/Alpaca_finetune_sentiment_analysis
Inference: click red bar Space
The labeled dataset I used to fine-tune and train the Alpaca model can be found at:
https://www.kaggle.com/datasets/praveengovi/emotions-dataset-for-nlp?select=train.txt
To create Space in HuggingFace: https://huggingface.co/new-space
( Select for CPU Upgrade or above)
https://huggingface.co/spaces/RinInori/alpaca_finetune_6_sentiments
Upload app.py and requirements.txt to
https://huggingface.co/spaces/RinInori/alpaca_finetune_6_sentiments/tree/main
Alpaca ref: https://github.com/tloen/alpaca-lora
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate
|
[
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
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},
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| 7 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: detr-resnet50-leuk
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr-resnet50-leuk
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6961
## 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
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1222 | 1.0 | 121 | 2.3225 |
| 2.7011 | 2.0 | 242 | 2.0470 |
| 2.7217 | 3.0 | 363 | 2.1209 |
| 2.5204 | 4.0 | 484 | 2.0189 |
| 2.4531 | 5.0 | 605 | 1.9293 |
| 2.5448 | 6.0 | 726 | 2.1573 |
| 2.5266 | 7.0 | 847 | 1.9387 |
| 2.385 | 8.0 | 968 | 1.9430 |
| 2.3655 | 9.0 | 1089 | 1.9070 |
| 2.384 | 10.0 | 1210 | 1.8772 |
| 2.4173 | 11.0 | 1331 | 1.9408 |
| 2.4512 | 12.0 | 1452 | 1.9038 |
| 2.4599 | 13.0 | 1573 | 2.0496 |
| 2.382 | 14.0 | 1694 | 1.9044 |
| 2.3739 | 15.0 | 1815 | 1.8649 |
| 2.3066 | 16.0 | 1936 | 1.8339 |
| 2.2597 | 17.0 | 2057 | 1.7873 |
| 2.2254 | 18.0 | 2178 | 1.8041 |
| 2.2674 | 19.0 | 2299 | 1.7946 |
| 2.2109 | 20.0 | 2420 | 1.7831 |
| 2.2812 | 21.0 | 2541 | 1.8024 |
| 2.2648 | 22.0 | 2662 | 1.7854 |
| 2.2161 | 23.0 | 2783 | 1.7622 |
| 2.209 | 24.0 | 2904 | 1.7544 |
| 2.1905 | 25.0 | 3025 | 1.7473 |
| 2.2166 | 26.0 | 3146 | 1.7674 |
| 2.2108 | 27.0 | 3267 | 1.7445 |
| 2.1813 | 28.0 | 3388 | 1.7329 |
| 2.1679 | 29.0 | 3509 | 1.7286 |
| 2.1481 | 30.0 | 3630 | 1.7254 |
| 2.1713 | 31.0 | 3751 | 1.7368 |
| 2.1471 | 32.0 | 3872 | 1.7362 |
| 2.1537 | 33.0 | 3993 | 1.7281 |
| 2.1347 | 34.0 | 4114 | 1.7205 |
| 2.129 | 35.0 | 4235 | 1.7109 |
| 2.1215 | 36.0 | 4356 | 1.7227 |
| 2.1425 | 37.0 | 4477 | 1.7109 |
| 2.1106 | 38.0 | 4598 | 1.6993 |
| 2.0987 | 39.0 | 4719 | 1.6982 |
| 2.1259 | 40.0 | 4840 | 1.6961 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CennetOguz/distilbert-base-uncased-finetuned-recipe
|
[
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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| 2 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Summarization_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Summarization_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.9184
- Validation Loss: 2.5897
- Epoch: 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.9184 | 2.5897 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Chaewon/mnmt_decoder_en_gpt2
|
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| 0 | null |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
- imagenet-21k
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
---
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/vit.html#).
## Training data
The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
ChaitanyaU/FineTuneLM
|
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| 0 | null |
---
language:
- en
tags:
- vae
---
diffusers format of blessed2.vae.pt from https://huggingface.co/NoCrypt/blessed_vae
|
Chakita/Friends
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
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| 8 | null |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model-index:
- name: kogpt2-base-v2-8-finetuned-klue-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: klue
type: klue
config: ner
split: validation
args: ner
metrics:
- name: F1
type: f1
value: 0.5291977940313686
---
<!-- 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. -->
# kogpt2-base-v2-8-finetuned-klue-ner
This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5168
- F1: 0.5292
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6242 | 1.0 | 876 | 0.5671 | 0.3289 |
| 0.4158 | 2.0 | 1752 | 0.5766 | 0.3565 |
| 0.3305 | 3.0 | 2628 | 0.4438 | 0.4400 |
| 0.266 | 4.0 | 3504 | 0.4983 | 0.4341 |
| 0.2231 | 5.0 | 4380 | 0.4501 | 0.4778 |
| 0.1845 | 6.0 | 5256 | 0.4614 | 0.5056 |
| 0.151 | 7.0 | 6132 | 0.4946 | 0.5263 |
| 0.1247 | 8.0 | 7008 | 0.5168 | 0.5292 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Chandanbhat/distilbert-base-uncased-finetuned-cola
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bangla-para-v2-330000
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. -->
# bangla-para-v2-330000
This model is a fine-tuned version of [mHossain/bangla-para-v2-300000](https://huggingface.co/mHossain/bangla-para-v2-300000) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8878
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 17.5253
## 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
- lr_scheduler_warmup_steps: 5000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.0931 | 1.0 | 3375 | 0.8878 | 0.0 | 0.0 | 0.0 | 0.0 | 17.5253 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CharlieChen/feedback-bigbird
|
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| 0 | null |
## Getting Started
### Installation
**1. Prepare the code and the environment**
Git clone our repository, creating a python environment and ativate it via the following command
```bash
git clone https://github.com/DLYuanGod/ArtGPT-4.git
cd ArtGPT-4
conda env create -f environment.yml
conda activate artgpt4
```
**2. Prepare the pretrained Vicuna weights**
The current version of MiniGPT-4 is built on the v0 versoin of Vicuna-13B.
Please refer to our instruction [here](PrepareVicuna.md)
to prepare the Vicuna weights.
The final weights would be in a single folder in a structure similar to the following:
```
vicuna_weights
├── config.json
├── generation_config.json
├── pytorch_model.bin.index.json
├── pytorch_model-00001-of-00003.bin
...
```
Then, set the path to the vicuna weight in the model config file
[here](minigpt4/configs/models/minigpt4.yaml#L16) at Line 16.
**3. Prepare the pretrained ArtGPT-4 checkpoint**
[Downlad](https://huggingface.co/Tyrannosaurus/ArtGPT-4/blob/main/ArtGPT-4.pth)
Then, set the path to the pretrained checkpoint in the evaluation config file
in [eval_configs/minigpt4_eval.yaml](eval_configs/minigpt4_eval.yaml#L10) at Line 11.
### Launching Demo Locally
Try out our demo [demo.py](demo.py) on your local machine by running
```
python demo.py --cfg-path eval_configs/artgpt4_eval.yaml --gpu-id 0
```
### Training
The training of ArtGPT-4 contains two alignment stages. The training process for the step is consistent with that of [MiniGPT-4](https://minigpt-4.github.io/).
**Datasets**
We use [Laion-aesthetic](https://github.com/LAION-AI/laion-datasets/blob/main/laion-aesthetic.md) from the LAION-5B dataset, which amounts to approximately 200GB for the first 302 tar files.
## Acknowledgement
+ [MiniGPT-4](https://minigpt-4.github.io/) Our work is based on improvements to the model.
## License
This repository is under [BSD 3-Clause License](LICENSE.md).
Many codes are based on [Lavis](https://github.com/salesforce/LAVIS) with
BSD 3-Clause License [here](LICENSE_Lavis.md).
|
ChaseBread/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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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: 239.17 +/- 18.44
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
...
```
|
Cheapestmedsshop/Buymodafinilus
|
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| 0 | null |
---
license: unlicense
pipeline_tag: question-answering
tags:
- legal
---
## 介绍
"醉酒律师" 是一种基于 RWKV 架构的自然语言处理模型，通过使用刑法领域的法律问答数据集和法考资料数据集进行训练，可以用来回答与法律相关的问题。该模型可以理解与法律领域相关的自然语言问题，并尽可能给出准确的答案。虽然本模型对法律有一定理解，但并不能保证其所给出的建议具有法律效力。因此给它起名为“醉酒律师”。
## 使用方法
通过任何支持 RWKV 的程序进行使用，推荐结合 wenda 知识库，效果有提升，但是不多。
注意本模型为lora模型，需要合并基础模型进行使用，微调时使用的基本模型为RWKV-4-Raven-7B-v11-EngChn49-ctx8192.pth，合并程序为merge_lora.py,合并命令示例：
python3 merge_lora.py --use-gpu 32 RWKV-4-Raven-7B-v11-Eng49%-Chn49%-Jpn1%-Other1%-20230430-ctx8192.pth rwkv-lora.pth output.pth
合并版的模型下载方式：
autodl：
https://www.codewithgpu.com/m/file/RWKV-Sloshed-Lawyer-7B
百度云：
链接: https://pan.baidu.com/s/1ueufuVWeKLoYE7vlflSC5w 提取码: ib71
可以使用rwkv的程序下载：
rwkv桌面版懒人包（本地，应该是最容易上手的）：
https://zhuanlan.zhihu.com/p/615655028
wenda懒人包（本地）：
链接：https://pan.baidu.com/s/105nOsldGt5mEPoT2np1ZoA?pwd=lyqz
视频教程：https://www.bilibili.com/video/BV1aX4y1z7ar/?vd_source=629edb00375d46ad4097acdc7cbc0ca3
提取码：lyqz
autodl-wenda懒人包（云端）：
https://www.codewithgpu.com/i/l15y/wenda/Wenda-ChatGLM-Vincuna-RWKV
## 更新日志
- 2023-05-07: v1.0.0 初始版本。
## 使用声明
本模型仅供学习交流使用，请勿用于违法用途。后续可能会训练更加实用的法律模型，或者更加有趣的小模型，不过我个人的力量有限，感兴趣的朋友可以加 Q 群讨论：759852889。
|
Cheatham/xlm-roberta-large-finetuned-d1
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
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text-classification
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| 20 | null |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Cheatham/xlm-roberta-large-finetuned-d1r01
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
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| 21 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-cart-pole
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
|
Cheatham/xlm-roberta-large-finetuned3
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
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| 22 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: shreyansjain/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Chester/traffic-rec
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-discord
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-discord
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6981
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.4692 | 1.0 | 329 | 3.0852 |
| 3.1111 | 2.0 | 658 | 2.9595 |
| 2.9821 | 3.0 | 987 | 2.8469 |
| 2.9075 | 4.0 | 1316 | 2.8171 |
| 2.8413 | 5.0 | 1645 | 2.7734 |
| 2.8313 | 6.0 | 1974 | 2.7486 |
| 2.7857 | 7.0 | 2303 | 2.7608 |
| 2.7779 | 8.0 | 2632 | 2.7335 |
| 2.752 | 9.0 | 2961 | 2.7069 |
| 2.7529 | 10.0 | 3290 | 2.7005 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Chinat/test-classifier
|
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| 0 | null |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: YoavWigelman/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ching/negation_detector
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"RobertaForQuestionAnswering"
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| 9 | null |
---
tags:
- tabular-classification
- sklearn
datasets:
- wine-quality
- lvwerra/red-wine
widget:
structuredData:
fixed_acidity:
- 7.4
- 7.8
- 10.3
volatile_acidity:
- 0.7
- 0.88
- 0.32
citric_acid:
- 0
- 0
- 0.45
residual_sugar:
- 1.9
- 2.6
- 6.4
chlorides:
- 0.076
- 0.098
- 0.073
free_sulfur_dioxide:
- 11
- 25
- 5
total_sulfur_dioxide:
- 34
- 67
- 13
density:
- 0.9978
- 0.9968
- 0.9976
pH:
- 3.51
- 3.2
- 3.23
sulphates:
- 0.56
- 0.68
- 0.82
alcohol:
- 9.4
- 9.8
- 12.6
library_name: sklearn
pipeline_tag: tabular-classification
---
## Wine Quality classification
### A Simple Example of Scikit-learn Pipeline
> Inspired by https://towardsdatascience.com/a-simple-example-of-pipeline-in-machine-learning-with-scikit-learn-e726ffbb6976 by Saptashwa Bhattacharyya
### How to use
```python
from huggingface_hub import hf_hub_url, cached_download
import joblib
import pandas as pd
REPO_ID = "julien-c/wine-quality"
FILENAME = "sklearn_model.joblib"
model = joblib.load(cached_download(
hf_hub_url(REPO_ID, FILENAME)
))
# model is a `sklearn.pipeline.Pipeline`
```
#### Get sample data from this repo
```python
data_file = cached_download(
hf_hub_url(REPO_ID, "winequality-red.csv")
)
winedf = pd.read_csv(data_file, sep=";")
X = winedf.drop(["quality"], axis=1)
Y = winedf["quality"]
print(X[:3])
```
| | fixed acidity | volatile acidity | citric acid | residual sugar | chlorides | free sulfur dioxide | total sulfur dioxide | density | pH | sulphates | alcohol |
|---:|----------------:|-------------------:|--------------:|-----------------:|------------:|----------------------:|-----------------------:|----------:|-----:|------------:|----------:|
| 0 | 7.4 | 0.7 | 0 | 1.9 | 0.076 | 11 | 34 | 0.9978 | 3.51 | 0.56 | 9.4 |
| 1 | 7.8 | 0.88 | 0 | 2.6 | 0.098 | 25 | 67 | 0.9968 | 3.2 | 0.68 | 9.8 |
| 2 | 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15 | 54 | 0.997 | 3.26 | 0.65 | 9.8 |
#### Get your prediction
```python
labels = model.predict(X[:3])
# [5, 5, 5]
```
#### Eval
```python
model.score(X, Y)
# 0.6616635397123202
```
### 🍷 Disclaimer
No red wine was drunk (unfortunately) while training this model 🍷
|
Chiuchiyin/DialoGPT-small-Donald
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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}
| 7 | null |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model-index:
- name: kogpt2-base-v2-finetuned-klue-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: klue
type: klue
config: ner
split: validation
args: ner
metrics:
- name: F1
type: f1
value: 0.41368436709802175
---
<!-- 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. -->
# kogpt2-base-v2-finetuned-klue-ner
This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1969
- F1: 0.4137
## 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: 30
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 701 | 0.2158 | 0.3687 |
| 0.2387 | 2.0 | 1402 | 0.2007 | 0.4061 |
| 0.1552 | 3.0 | 2103 | 0.1969 | 0.4137 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ChoboAvenger/DialoGPT-small-joshua
|
[] | null |
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| 0 | null |
---
license: mit
datasets:
- mlengineer-ai/jomleh
language:
- fa
metrics:
- perplexity
tags:
- kneser-ney
- n-gram
- kenlm
---
# KenLM models for Farsi
This repository contains KenLM models trained on the Jomleh dataset for the Farsi (Persian)
language. Among the various use cases for KenLM language models, the models provided here are
particularly useful for automatic speech recognition (ASR) tasks. They can be used in conjunction
with CTC to select the most likely sequence of tokens extracted from a spectrogram.
The models in this repository are KenLM arpa files that have been converted to binary format.
KenLM supports two binary formats: probing and trie. The models provided here are in the probing
format, which KenLM claims are faster but have a larger memory footprint.
There are a total of 36 different KenLM models available in this repository. Unless you are
conducting research, you will not need all of them. In that case, it is recommended that you
download only the models you require rather than the entire repository since the total file size
is over half a terabyte.
# Sample code how to use the models
Unfortunately, I could not find an easy way to integrate the Python code that loads the models
using Huggingface library. These are the steps that you have to take when you want to use any of
the models provided here:
1. Install KenLM package:
```
pip install https://github.com/kpu/kenlm/archive/master.zip
```
2. Install the SentencePiece for the tokenization:
```
pip install sentencepiece
```
3. Download the model that you are interested in from this repository along the Python code
`model.py`. Keep the model in the `files` folder with the `model.py` by it (just like the file
structure in the repository). Don't forget to download the SentencePiece files as well. For
instance, if you were interested in 32000 vocabulary size tokenizer, 5-gram model with maximum
pruning, these are the files you'll need:
```
model.py
files/jomleh-sp-32000.model
files/jomleh-sp-32000.vocab
files/jomleh-sp-32000-o5-prune01111.probing
```
4. In your script, instantiate a model and use it like this:
```
from model import KenlmModel
# Load the model
model = KenlmModel.from_pretrained("57218", "3", "011")
# Get perplexity
print(model.perplexity("من در را بستم"))
# Outputs: 72.5
# Get score
print(model.score("من در را بستم"))
# Outputs: -11.160577774047852
```
# What are the different files you can find in this repository?
The files you can find in this repository are either SentencePiece tokenizer models or KenLM
binary models. For the tokenizers, this is the template their file name follows:
```
<dataset-name>-<tokenizer-type>-<vocabulary-size>.<model|vocab>
```
In this repository, all the models are based on the Jomleh dataset (`jomleh`). And the only
tokenizer used is SentencePiece (`sp`). Finally, the list of vocabulary sizes used is composed
of 2000, 4000, 8000, 16000, 32000, and 57218 tokens. Due to hardware limitations that I've
faced, I could only use 4 out of 60 jomleh's text file to train the tokenizer, namely: 10, 11,
12, and 13. Also, 57218 was the largest number that SentencePiece allowed to set as for the
vocabulary size.
Here's an example of the tokenizer files you can find in this repository:
```
jomleh-sp-32000.model
```
Moving on to the KenLM binary models, their file names follow this template:
```
<dataset-name>-<tokenizer-type>-<vocabulary-size>-o<n-gram>-prune<pruning>.<model|vocab>
```
Just like with the tokenizers, the only available options for dataset and tokenizer type are
`jomleh` and `sp`. The same applies to vocabulary sizes. There are two n-grams trained,
3-grams, and 5-grams. Additionally, there are three different pruning options available for
each configuration. To interpret the pruning numbers, add a space between each pair of digits.
For example, `011` means `0 1 1` was set during training of the KenLM model.
Here is a complete example: To train the binary model named `jomleh-sp-32000-o5-prune01111.probing`,
the tokenizer `jomleh-sp-32000.model` was used to encode (tokenize) the 95% Jomleh dataset,
resulting in a large text file holding space-separated tokens. Then, the file was fed into
the `lmplz` program with the following input arguments:
```
lmplz -o 5 -T /tmp --vocab_estimate 32000 -S 80% --discount_fallback --prune "0 1 1 1 1" < enocoded.txt > jomleh-sp-32000-o5-prune01111.arpa
```
This command will produce the raw arpa file, which can then be converted into binary format
using the `build_binary` program, as shown below:
```
build_binary -T /tmp -S 80% probing jomleh-sp-32000-o5-prune01111.arpa jomleh-sp-32000-o5-prune01111.probing
```
# Which model to use?
Based on my personal evaluation, I recommend using the `jomleh-sp-57218-o3-prune011.probing`.
It's the perfect balanced between file size (6GB) and accuracy (80%). But if you have no concern for file
size, then go for the largest model, `jomleh-sp-57218-o5-prune00011.probing` (size: 36GB, accuracy: 82%).
|
ChrisVCB/DialoGPT-medium-cmjs
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 7 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bangla-para-v2-360000
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. -->
# bangla-para-v2-360000
This model is a fine-tuned version of [mHossain/bangla-para-v2-330000](https://huggingface.co/mHossain/bangla-para-v2-330000) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8830
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 17.4803
## 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
- lr_scheduler_warmup_steps: 5000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.1015 | 1.0 | 3375 | 0.8830 | 0.0 | 0.0 | 0.0 | 0.0 | 17.4803 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ChrisVCB/DialoGPT-medium-ej
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 13 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-53-demo-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: tr
split: test
args: tr
metrics:
- name: Wer
type: wer
value: 0.4799305484628741
---
<!-- 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-xlsr-53-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4058
- Wer: 0.4799
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.0985 | 4.26 | 400 | 2.2067 | 1.0201 |
| 0.7695 | 8.51 | 800 | 0.4558 | 0.6311 |
| 0.3145 | 12.77 | 1200 | 0.3946 | 0.5395 |
| 0.2208 | 17.02 | 1600 | 0.4187 | 0.5259 |
| 0.1656 | 21.28 | 2000 | 0.4176 | 0.5038 |
| 0.1311 | 25.53 | 2400 | 0.3971 | 0.4842 |
| 0.1195 | 29.79 | 2800 | 0.4058 | 0.4799 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ChristopherA08/IndoELECTRA
|
[
"pytorch",
"electra",
"pretraining",
"id",
"dataset:oscar",
"transformers"
] | null |
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| 4 | null |
---
tags:
- conversational
language:
- en
---
# Luffy DialoGPT Model
|
Chuah/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
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}
| 9 | null |
---
language: ja
license: mit
datasets:
- mC4-ja
---
# electra-base-japanese-discriminator (sudachitra-wordpiece, mC4 Japanese) - [SHINOBU](https://dl.ndl.go.jp/info:ndljp/pid/1302683/3)
This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences.
The input text is tokenized by [SudachiTra](https://github.com/WorksApplications/SudachiTra) with the WordPiece subword tokenizer.
See `tokenizer_config.json` for the setting details.
## How to use
Please install `SudachiTra` in advance.
```console
$ pip install -U torch transformers sudachitra
```
You can load the model and the tokenizer via AutoModel and AutoTokenizer, respectively.
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("megagonlabs/electra-base-japanese-discriminator")
tokenizer = AutoTokenizer.from_pretrained("megagonlabs/electra-base-japanese-discriminator", trust_remote_code=True)
model(**tokenizer("まさにオールマイティーな商品だ。", return_tensors="pt")).last_hidden_state
tensor([[[-0.0498, -0.0285, 0.1042, ..., 0.0062, -0.1253, 0.0338],
[-0.0686, 0.0071, 0.0087, ..., -0.0210, -0.1042, -0.0320],
[-0.0636, 0.1465, 0.0263, ..., 0.0309, -0.1841, 0.0182],
...,
[-0.1500, -0.0368, -0.0816, ..., -0.0303, -0.1653, 0.0650],
[-0.0457, 0.0770, -0.0183, ..., -0.0108, -0.1903, 0.0694],
[-0.0981, -0.0387, 0.1009, ..., -0.0150, -0.0702, 0.0455]]],
grad_fn=<NativeLayerNormBackward>)
```
## Model architecture
The model architecture is the same as the original ELECTRA base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.
## Training data and libraries
This model is trained on the Japanese texts extracted from the [mC4](https://huggingface.co/datasets/mc4) Common Crawl's multilingual web crawl corpus.
We used the [Sudachi](https://github.com/WorksApplications/Sudachi) to split texts into sentences, and also applied a simple rule-based filter to remove nonlinguistic segments of mC4 multilingual corpus.
The extracted texts contains over 600M sentences in total, and we used approximately 200M sentences for pretraining.
We used [NVIDIA's TensorFlow2-based ELECTRA implementation](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/LanguageModeling/ELECTRA) for pretraining. The time required for the pretrainig was about 110 hours using GCP DGX A100 8gpu instance with enabling Automatic Mixed Precision.
## Licenses
The pretrained models are distributed under the terms of the [MIT License](https://opensource.org/licenses/mit-license.php).
## Citations
- mC4
Contains information from `mC4` which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/).
```
@article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.10683},
}
```
|
ChukSamuels/DialoGPT-small-Dr.FauciBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 13 | null |
Access to model keshavkmr076/autotrain-text-classifier-56143130340 is restricted and you are not in the authorized list. Visit https://huggingface.co/keshavkmr076/autotrain-text-classifier-56143130340 to ask for access.
|
Chun/DialoGPT-large-dailydialog
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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| 6 | null |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: cool-cattos
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7083333134651184
---
# cool-cattos
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Abyssinian
#### Devon Rex Cat Breed
#### Japanese Bobtail Cat Breed
#### british shorthair
#### siamese cat
|
Chun/DialoGPT-small-dailydialog
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
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},
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},
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| 10 | null |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model-index:
- name: kogpt2-base-v2-finetuned-klue-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: klue
type: klue
config: ner
split: validation
args: ner
metrics:
- name: F1
type: f1
value: 0.37298165525403665
---
<!-- 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. -->
# kogpt2-base-v2-finetuned-klue-ner
This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4076
- F1: 0.3730
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6084 | 1.0 | 876 | 0.5353 | 0.2118 |
| 0.3911 | 2.0 | 1752 | 0.4691 | 0.3041 |
| 0.2855 | 3.0 | 2628 | 0.4076 | 0.3730 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Chun/w-en2zh-otm
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 7 | null |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model-index:
- name: kogpt2-base-v2-finetuned-klue-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: klue
type: klue
config: ner
split: validation
args: ner
metrics:
- name: F1
type: f1
value: 0.40830061693774533
---
<!-- 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. -->
# kogpt2-base-v2-finetuned-klue-ner
This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5125
- F1: 0.4083
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.6197 | 1.0 | 1313 | 0.5497 | 0.2008 |
| 0.4235 | 2.0 | 2626 | 0.5097 | 0.2676 |
| 0.3374 | 3.0 | 3939 | 0.4515 | 0.3353 |
| 0.2796 | 4.0 | 5252 | 0.4399 | 0.3547 |
| 0.2337 | 5.0 | 6565 | 0.4481 | 0.3540 |
| 0.1967 | 6.0 | 7878 | 0.4657 | 0.3781 |
| 0.1635 | 7.0 | 9191 | 0.4846 | 0.3942 |
| 0.1356 | 8.0 | 10504 | 0.5125 | 0.4083 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Chun/w-zh2en-mtm
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 8 | null |
---
license: apache-2.0
language:
- en
tags:
- sft
pipeline_tag: text-generation
widget:
- text: <|prompter|>What is a meme, and what's the history behind this word?<|endoftext|><|assistant|>
- text: <|prompter|>What's the Earth total population<|endoftext|><|assistant|>
- text: <|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>
---
- base model: [OpenAssistant/pythia-12b-pre-v8-12.5k-steps](https://huggingface.co/OpenAssistant/pythia-12b-pre-v8-12.5k-steps)
- wandb: https://wandb.ai/open-assistant/supervised-finetuning/runs/pcw1ejda
- [sampling report](https://raw.githubusercontent.com/Open-Assistant/oasst-model-eval/main/sampling_reports/oasst-sft/2023-05-07_OpenAssistant_pythia-12b-sft-v8-7k-steps_sampling_noprefix2.json)
```
pythia-12b-sft-8:
dtype: fp16
log_dir: "pythia_log_12b"
learning_rate: 6e-6
model_name: OpenAssistant/pythia-12b-pre-v8-12.5k-steps
output_dir: pythia_model_12b
weight_decay: 0.0
residual_dropout: 0.0
max_length: 2048
use_flash_attention: true
warmup_steps: 100
gradient_checkpointing: true
gradient_accumulation_steps: 2
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
eval_steps: 251
save_steps: 500
num_train_epochs: 8
save_total_limit: 4
num_train_epochs: 8
save_total_limit: 3
use_custom_sampler: true
sort_by_length: false
save_strategy: steps
datasets:
- oasst_export:
lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
input_file_path: 2023-05-06_OASST_labels.jsonl.gz
val_split: 0.05
- vicuna:
val_split: 0.05
max_val_set: 800
fraction: 0.4
- dolly15k:
val_split: 0.05
max_val_set: 300
- grade_school_math_instructions:
val_split: 0.05
- code_alpaca:
val_split: 0.05
max_val_set: 250
- red_pajama:
fraction: 0.05
max_val_set: 1000
- wizardlm_70k:
val_split: 0.05
max_val_set: 500
fraction: 0.4
- poem_instructions:
fraction: 0.5
val_split: 0.025
```
|
Chungu424/repo
|
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| 0 | null |
---
license: mit
datasets:
- antonovmaxim/kodIIm_14
- competitions/aiornot
metrics:
- accuracy
pipeline_tag: image-classification
---
https://wandb.ai/maximantonov/KodIIm/
|
Chungu424/repodata
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-finetuned-last-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5892439733711194
---
<!-- 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-last-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8731
- Matthews Correlation: 0.5892
## 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: 3.350326176009724e-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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4834 | 1.0 | 535 | 0.4471 | 0.5024 |
| 0.287 | 2.0 | 1070 | 0.4596 | 0.5573 |
| 0.1848 | 3.0 | 1605 | 0.8394 | 0.5140 |
| 0.1257 | 4.0 | 2140 | 0.8731 | 0.5892 |
| 0.0719 | 5.0 | 2675 | 0.9607 | 0.5851 |
| 0.0467 | 6.0 | 3210 | 1.0737 | 0.5731 |
| 0.0339 | 7.0 | 3745 | 1.3356 | 0.5470 |
| 0.0216 | 8.0 | 4280 | 1.3521 | 0.5579 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Ci/Pai
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.4832216996895926
---
<!-- 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-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4618
- Matthews Correlation: 0.4832
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5065 | 1.0 | 535 | 0.4618 | 0.4832 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ClaudeYang/awesome_fb_model
|
[
"pytorch",
"bart",
"text-classification",
"dataset:multi_nli",
"transformers",
"zero-shot-classification"
] |
zero-shot-classification
|
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| 26 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bangla-para-v2-390000
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. -->
# bangla-para-v2-390000
This model is a fine-tuned version of [mHossain/bangla-para-v2-360000](https://huggingface.co/mHossain/bangla-para-v2-360000) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8711
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 17.4697
## 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
- lr_scheduler_warmup_steps: 5000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.0878 | 1.0 | 3375 | 0.8711 | 0.0 | 0.0 | 0.0 | 0.0 | 17.4697 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CleveGreen/FieldClassifier_v2
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 46 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5208528714430889
---
<!-- 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-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4661
- Matthews Correlation: 0.5209
## 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: 1.13e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 1.0 | 268 | 0.4526 | 0.5206 |
| 0.4593 | 2.0 | 536 | 0.4661 | 0.5209 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CoShin/XLM-roberta-large_ko_en_nil_sts
|
[] | null |
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| 0 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -242.88 +/- 88.93
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
...
```
|
CoachCarter/distilbert-base-uncased
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.54781790671712
---
<!-- 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-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5338
- Matthews Correlation: 0.5478
## 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: 6.256549330223815e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4533 | 1.0 | 1069 | 0.4844 | 0.4614 |
| 0.3347 | 2.0 | 2138 | 0.5338 | 0.5478 |
| 0.2847 | 3.0 | 3207 | 0.6569 | 0.5416 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CodeDanCode/SP-KyleBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 15 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: vaibhav19341/DLA3-de-en-finetune
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vaibhav19341/DLA3-de-en-finetune
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.co/Helsinki-NLP/opus-mt-de-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.7364
- Validation Loss: 5.5036
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.9312 | 4.7621 | 0 |
| 2.7364 | 5.5036 | 1 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CodeNinja1126/bert-p-encoder
|
[
"pytorch"
] | null |
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| 3 | null |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: false
---
# Test LoRAs for Waifu Diffusion v1.3
These LoRAs are **experimental** LoRAs for WD1.3 to produce high resolution or different aspect ratio images.
## Model Description
They have fine-tuned from the original WD1.3 or a model merged with LoRA in this repository by thousands of unselected AI illustrations by various authors and models published on the Internet.
Each networks has been fine-tuned with a learning rate of 6.0e-5 for 5 epochs on about 5-8k images at batch size 8, using Aspect Ratio Bucketing with a maximum resolution of 768x768.
Fine tuning performed by RTX3090 at fp16 with AdamW8bit optimizer and took 2-3 hours for each network.
| LoRA Name | Base model | images | note |
| ------------- | -------------------------- | ------ | ------------------------------------- |
| hires_test_a | WD1.3 | ~5k | |
| hires_test_b | WD1.3 | ~7k | |
| hires_test_c | WD1.3 + 1.0 * hires_test_a | ~8k | recommended for use with hires_test_a |
| smooth_test_a | WD1.3 + 2.0 * hires_test_a | ~7k | |
| smooth_test_b | WD1.3 + 2.0 * hires_test_a | ~7k | different seed |
There is probably no overlap between the three image sets (5k, 7k, 8k).
## Usage
The LoRA are mainly classified into two types: for high-resolution and for smoothing.
First, please apply high resolution LoRA at the preferred ratio: 1-2 is recommended for ~768x768, and the higher the resolution, the more weight is recommended.
In some cases, especially when weights are large, adverse effects may be observed.
In such cases, please consider applying a leveling LoRA.
## 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:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. 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
3. 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)
## Acknowledgements
These LoRAs build on the two excellent works: SD1.4, developed by [CompVis Researchers](https://ommer-lab.com/), and WD1.3, developed by [Anthony Mercurio](https://github.com/harubaru), [Salt](https://github.com/sALTaccount/), and [Cafe](https://twitter.com/cafeai_labs).
|
CodeNinja1126/xlm-roberta-large-kor-mrc
|
[
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
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"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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| 8 | null |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model-index:
- name: kogpt2-base-v2-finetuned-klue-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: klue
type: klue
config: ner
split: validation
args: ner
metrics:
- name: F1
type: f1
value: 0.37298165525403665
---
<!-- 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. -->
# kogpt2-base-v2-finetuned-klue-ner
This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4076
- F1: 0.3730
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6084 | 1.0 | 876 | 0.5353 | 0.2118 |
| 0.3911 | 2.0 | 1752 | 0.4691 | 0.3041 |
| 0.2855 | 3.0 | 2628 | 0.4076 | 0.3730 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CoderEFE/DialoGPT-marxbot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"has_space"
] |
conversational
|
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| 11 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert-ft-test3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilbert-ft-test3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CoffeeAddict93/gpt1-modest-proposal
|
[
"pytorch",
"openai-gpt",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
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"OpenAIGPTLMHeadModel"
],
"model_type": "openai-gpt",
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| 11 | null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- dialdoc
model-index:
- name: 03-tinybert-local
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. -->
# 03-tinybert-local
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the dialdoc 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 30
- total_train_batch_size: 60
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3
|
CoffeeAddict93/gpt2-medium-call-of-the-wild
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
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| 14 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model-index:
- name: kobert-finetuned-klue-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: klue
type: klue
config: ner
split: validation
args: ner
metrics:
- name: F1
type: f1
value: 0.26395413647583404
---
<!-- 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. -->
# kobert-finetuned-klue-ner
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4238
- F1: 0.2640
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5975 | 1.0 | 1313 | 0.5314 | 0.1794 |
| 0.4068 | 2.0 | 2626 | 0.4611 | 0.2331 |
| 0.3366 | 3.0 | 3939 | 0.4264 | 0.2598 |
| 0.2933 | 4.0 | 5252 | 0.4238 | 0.2640 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CoffeeAddict93/gpt2-medium-modest-proposal
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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| 7 | null |
---
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
|
CogComp/bart-faithful-summary-detector
|
[
"pytorch",
"jax",
"bart",
"text-classification",
"en",
"dataset:xsum",
"transformers",
"xsum",
"license:cc-by-sa-4.0"
] |
text-classification
|
{
"architectures": [
"BartForSequenceClassification"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": 1,
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| 234 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: codeparrot-small-custom-functions-dataset-python
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-small-custom-functions-dataset-python
This model is a fine-tuned version of [codeparrot/codeparrot-small](https://huggingface.co/codeparrot/codeparrot-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4238
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.216 | 0.12 | 1 | 1.0747 |
| 1.051 | 0.25 | 2 | 1.0005 |
| 0.9855 | 0.38 | 3 | 0.9462 |
| 0.9259 | 0.5 | 4 | 0.9042 |
| 0.9236 | 0.62 | 5 | 0.8675 |
| 0.8644 | 0.75 | 6 | 0.8331 |
| 0.8148 | 0.88 | 7 | 0.8030 |
| 0.7554 | 1.0 | 8 | 0.7800 |
| 0.7815 | 1.12 | 9 | 0.7600 |
| 0.784 | 1.25 | 10 | 0.7440 |
| 0.635 | 1.38 | 11 | 0.7309 |
| 0.6666 | 1.5 | 12 | 0.7170 |
| 0.7676 | 1.62 | 13 | 0.6993 |
| 0.6608 | 1.75 | 14 | 0.6835 |
| 0.6885 | 1.88 | 15 | 0.6696 |
| 0.69 | 2.0 | 16 | 0.6582 |
| 0.6343 | 2.12 | 17 | 0.6463 |
| 0.709 | 2.25 | 18 | 0.6324 |
| 0.5446 | 2.38 | 19 | 0.6206 |
| 0.5298 | 2.5 | 20 | 0.6102 |
| 0.6478 | 2.62 | 21 | 0.6016 |
| 0.546 | 2.75 | 22 | 0.5941 |
| 0.6297 | 2.88 | 23 | 0.5871 |
| 0.4518 | 3.0 | 24 | 0.5814 |
| 0.566 | 3.12 | 25 | 0.5769 |
| 0.6285 | 3.25 | 26 | 0.5702 |
| 0.5938 | 3.38 | 27 | 0.5631 |
| 0.514 | 3.5 | 28 | 0.5568 |
| 0.5113 | 3.62 | 29 | 0.5504 |
| 0.512 | 3.75 | 30 | 0.5451 |
| 0.4392 | 3.88 | 31 | 0.5407 |
| 0.5097 | 4.0 | 32 | 0.5370 |
| 0.4866 | 4.12 | 33 | 0.5326 |
| 0.5028 | 4.25 | 34 | 0.5285 |
| 0.5438 | 4.38 | 35 | 0.5228 |
| 0.5424 | 4.5 | 36 | 0.5166 |
| 0.5156 | 4.62 | 37 | 0.5108 |
| 0.4335 | 4.75 | 38 | 0.5056 |
| 0.4298 | 4.88 | 39 | 0.5013 |
| 0.5268 | 5.0 | 40 | 0.4978 |
| 0.4714 | 5.12 | 41 | 0.4938 |
| 0.4659 | 5.25 | 42 | 0.4907 |
| 0.4573 | 5.38 | 43 | 0.4874 |
| 0.4689 | 5.5 | 44 | 0.4847 |
| 0.4346 | 5.62 | 45 | 0.4824 |
| 0.4563 | 5.75 | 46 | 0.4794 |
| 0.4505 | 5.88 | 47 | 0.4761 |
| 0.7359 | 6.0 | 48 | 0.4732 |
| 0.4704 | 6.12 | 49 | 0.4706 |
| 0.4223 | 6.25 | 50 | 0.4685 |
| 0.4789 | 6.38 | 51 | 0.4651 |
| 0.4402 | 6.5 | 52 | 0.4624 |
| 0.4454 | 6.62 | 53 | 0.4597 |
| 0.4496 | 6.75 | 54 | 0.4566 |
| 0.3942 | 6.88 | 55 | 0.4539 |
| 0.2915 | 7.0 | 56 | 0.4515 |
| 0.3926 | 7.12 | 57 | 0.4496 |
| 0.4102 | 7.25 | 58 | 0.4474 |
| 0.4235 | 7.38 | 59 | 0.4456 |
| 0.4841 | 7.5 | 60 | 0.4441 |
| 0.3914 | 7.62 | 61 | 0.4423 |
| 0.4417 | 7.75 | 62 | 0.4404 |
| 0.4212 | 7.88 | 63 | 0.4384 |
| 0.4343 | 8.0 | 64 | 0.4369 |
| 0.4159 | 8.12 | 65 | 0.4355 |
| 0.4193 | 8.25 | 66 | 0.4343 |
| 0.4393 | 8.38 | 67 | 0.4333 |
| 0.4507 | 8.5 | 68 | 0.4319 |
| 0.3855 | 8.62 | 69 | 0.4305 |
| 0.4064 | 8.75 | 70 | 0.4293 |
| 0.4044 | 8.88 | 71 | 0.4283 |
| 0.2957 | 9.0 | 72 | 0.4275 |
| 0.4442 | 9.12 | 73 | 0.4266 |
| 0.4142 | 9.25 | 74 | 0.4260 |
| 0.4022 | 9.38 | 75 | 0.4253 |
| 0.4161 | 9.5 | 76 | 0.4248 |
| 0.3828 | 9.62 | 77 | 0.4244 |
| 0.384 | 9.75 | 78 | 0.4241 |
| 0.3985 | 9.88 | 79 | 0.4239 |
| 0.4912 | 10.0 | 80 | 0.4238 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CohleM/mbert-nepali-tokenizer
|
[] | null |
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| 0 | 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: 247.76 +/- 63.01
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
...
```
|
ComCom/gpt2-medium
|
[
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"GPT2Model"
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| 5 | null |
经过P-Tuning训练的model用于Inference
**Usage**
# Load the configuration and model:
from peft import PeftModel, PeftConfig
peft_model_id = "Laurie/t5-large_PREFIX_TUNING_SEQ2SEQ"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(model, peft_model_id)
# Get and tokenize some text about financial news:
inputs = tokenizer(
"Berkshire Hathaway CEO Warren Buffett on Saturday assailed regulators, politicians and the media for confusing the public about the safety of U.S. banks and said that conditions could worsen.",
return_tensors="pt" )
# Put the model on a GPU and generate the predicted text sentiment:
model.to(device)
with torch.no_grad():
inputs = {k: v.to(device) for k, v in inputs.items()}
outputs = model.generate(input_ids=inputs["input_ids"], max_new_tokens=10)
print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))
# => ['negative']
|
cometrain/neurotitle-rugpt3-small
|
[
"pytorch",
"gpt2",
"text-generation",
"ru",
"en",
"dataset:All-NeurIPS-Papers-Scraper",
"transformers",
"Cometrain AutoCode",
"Cometrain AlphaML",
"license:mit"
] |
text-generation
|
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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| 20 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
model-index:
- name: bert-base-uncased-finetuned-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. -->
# bert-base-uncased-finetuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.8619
- eval_matthews_correlation: 0.5625
- eval_runtime: 1.8285
- eval_samples_per_second: 570.412
- eval_steps_per_second: 71.643
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Connor-tech/bert_cn_finetuning
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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| 27 | null |
---
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
|
Contrastive-Tension/BERT-Distil-CT
|
[
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"DistilBertForMaskedLM"
],
"model_type": "distilbert",
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| 9 | null |
Access to model chaudhary/wav2vec2-mono-urdu-10h-cv13 is restricted and you are not in the authorized list. Visit https://huggingface.co/chaudhary/wav2vec2-mono-urdu-10h-cv13 to ask for access.
|
Cryptikdw/DialoGPT-small-rick
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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| 7 | 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="Actuary/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"])
```
|
CurtisBowser/DialoGPT-small-sora
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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| 7 | null |
---
language:
- ko
---
https://github.com/bab2min/kiwi-farm
|
Czapla/Rick
|
[] | null |
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| 0 | null |
---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- subjqa
model-index:
- name: m_bert_base_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# m_bert_base_qa_model
This model is a fine-tuned version of [deepset/bert-base-cased-squad2](https://huggingface.co/deepset/bert-base-cased-squad2) on the subjqa dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4076
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 32 | 2.8233 |
| No log | 2.0 | 64 | 2.9102 |
| No log | 3.0 | 96 | 3.2005 |
| No log | 4.0 | 128 | 3.5238 |
| No log | 5.0 | 160 | 3.6986 |
| No log | 6.0 | 192 | 4.0583 |
| No log | 7.0 | 224 | 4.1965 |
| No log | 8.0 | 256 | 4.2924 |
| No log | 9.0 | 288 | 4.4430 |
| No log | 10.0 | 320 | 4.4076 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
D-Keqi/espnet_asr_train_asr_streaming_transformer_raw_en_bpe500_sp_valid.acc.ave
|
[] | null |
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| 11 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.547014428196921
---
<!-- 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-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4514
- Matthews Correlation: 0.5470
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4923 | 1.0 | 535 | 0.4514 | 0.5470 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
D3vil/DialoGPT-smaall-harrypottery
|
[] | null |
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| 0 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: fine-tuned-DatasetQAS-TYDI-QA-ID-with-indobert-large-p2-with-ITTL-with-freeze-LR-1e-05
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. -->
# fine-tuned-DatasetQAS-TYDI-QA-ID-with-indobert-large-p2-with-ITTL-with-freeze-LR-1e-05
This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2003
- Exact Match: 60.2113
- F1: 73.9948
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- 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 | Exact Match | F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 6.2316 | 0.5 | 19 | 3.5321 | 11.9718 | 21.8197 |
| 6.2316 | 0.99 | 38 | 2.6566 | 19.1901 | 31.9985 |
| 3.5132 | 1.5 | 57 | 2.1442 | 27.2887 | 40.7031 |
| 3.5132 | 1.99 | 76 | 1.6755 | 41.5493 | 53.9850 |
| 3.5132 | 2.5 | 95 | 1.4228 | 48.2394 | 61.2829 |
| 1.845 | 2.99 | 114 | 1.2882 | 52.8169 | 66.2197 |
| 1.845 | 3.5 | 133 | 1.2352 | 54.7535 | 68.3725 |
| 1.2542 | 3.99 | 152 | 1.2033 | 56.6901 | 70.5019 |
| 1.2542 | 4.5 | 171 | 1.2117 | 57.9225 | 72.0740 |
| 1.2542 | 4.99 | 190 | 1.1748 | 58.4507 | 71.9264 |
| 0.9877 | 5.5 | 209 | 1.1763 | 58.8028 | 72.2772 |
| 0.9877 | 5.99 | 228 | 1.1827 | 59.5070 | 73.5652 |
| 0.9877 | 6.5 | 247 | 1.1789 | 59.8592 | 73.2748 |
| 0.8293 | 6.99 | 266 | 1.1835 | 60.0352 | 73.4695 |
| 0.8293 | 7.5 | 285 | 1.1669 | 59.8592 | 73.7145 |
| 0.7663 | 7.99 | 304 | 1.1912 | 60.3873 | 74.3001 |
| 0.7663 | 8.5 | 323 | 1.1828 | 60.2113 | 74.1533 |
| 0.7663 | 8.99 | 342 | 1.2046 | 60.3873 | 74.0424 |
| 0.7068 | 9.5 | 361 | 1.2003 | 60.2113 | 73.9948 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
|
D3xter1922/distilbert-base-uncased-finetuned-cola
|
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| 0 | 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: 256.66 +/- 16.95
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
...
```
|
D3xter1922/electra-base-discriminator-finetuned-cola
|
[
"pytorch",
"tensorboard",
"electra",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
text-classification
|
{
"architectures": [
"ElectraForSequenceClassification"
],
"model_type": "electra",
"task_specific_params": {
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"max_length": null
},
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}
| 68 | null |
DrQA
---
A pytorch implementation of the ACL 2017 paper [Reading Wikipedia to Answer Open-Domain Questions](http://www-cs.stanford.edu/people/danqi/papers/acl2017.pdf) (DrQA).
Reading comprehension is a task to produce an answer when given a question and one or more pieces of evidence (usually natural language paragraphs).
Compared to question answering over knowledge bases, reading comprehension models are more flexible and have revealed a great potential for zero-shot learning.
## Requirements
- python >=3.5
- pytorch >=0.4. Tested on pytorch 0.4 and pytorch 1.10
- numpy
- msgpack
- spacy 3.x
## Quick Start
- download SpaCy English language model `python3 -m spacy download en_core_web_md`
- download model - https://huggingface.co/Jarbas/DrQA_en (EM: 68.92712550607287 F1: 78.19080821710139)
### Usage
Example usage with wikipedia via https://github.com/OpenVoiceOS/DrQA
```python
import argparse
import requests
import torch
from drqa import DrQA
from drqa.utils import str2bool
parser = argparse.ArgumentParser(
description='Interact with document reader model.'
)
parser.add_argument('--model-file', help='path to model file')
parser.add_argument('--meta-file', help='path to meta.msgpack file')
parser.add_argument("--cuda", type=str2bool, nargs='?',
const=True, default=torch.cuda.is_available(),
help='whether to use GPU acceleration.')
args = parser.parse_args()
def get_wiki_evidence(search_term):
try:
search_url = f"https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch={search_term}&format=json&srlimit=1"
r = requests.get(search_url).json()
page_id = r['query']['search'][0]['pageid']
results_url = f"https://en.wikipedia.org/w/api.php?format=json&action=query&prop=extracts&exintro&explaintext&redirects=1&pageids={page_id}"
r = requests.get(results_url).json()
evidence = r['query']['pages'][str(page_id)]['extract']
return evidence
except:
print("wiki search failed")
return None
dr = DrQA(args.model_file, args.meta_file, cuda=args.cuda)
while True:
search_term = input("wiki search:")
evidence = get_wiki_evidence(search_term)
if not evidence:
continue
question = input("question:")
answer = dr.predict(evidence, question)
print(">", answer)
```
Example interactions:
```
wiki search:dog
question:when were dogs domesticated
> 15,000 years ago
wiki search:abraham lincoln
question:when abraham lincoln was born
> February 12, 1809 – April 15, 1865
wiki search:cat
question:when were cats domesticated
> 3100 BC
wiki search:chicken
question:what is the global population of chickens
> 23.7 billion as of 2018, up from more than 19 billion in 2011.
```
### About
Original Implementation: https://github.com/hitvoice/DrQA
Most of the pytorch model code is borrowed from [Facebook/ParlAI](https://github.com/facebookresearch/ParlAI/) under a BSD-3 license.
|
DHBaek/xlm-roberta-large-korquad-mask
|
[
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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}
| 9 | null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: gaokaobishuati/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DJSammy/bert-base-swedish-uncased_BotXO-ai
|
[
"pytorch",
"transformers"
] | null |
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}
| 1 | null |
---
license: openrail
language:
- en
metrics:
- accuracy
library_name: diffusers
pipeline_tag: text-to-image
tags:
- art
datasets:
- pydan/mvdataset
---
|
DKpro000/DialoGPT-medium-harrypotter
|
[] | null |
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| 0 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: fine-tuned-DatasetQAS-Squad-ID-with-xlm-roberta-large-with-ITTL-with-freeze-LR-1e-05
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. -->
# fine-tuned-DatasetQAS-Squad-ID-with-xlm-roberta-large-with-ITTL-with-freeze-LR-1e-05
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4039
- Exact Match: 53.6774
- F1: 69.6967
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 128
- 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 | Exact Match | F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 1.5208 | 0.5 | 463 | 1.4095 | 50.0294 | 67.1298 |
| 1.3903 | 1.0 | 926 | 1.3159 | 52.1644 | 69.1681 |
| 1.2662 | 1.5 | 1389 | 1.2718 | 53.1058 | 69.4729 |
| 1.1754 | 2.0 | 1852 | 1.2603 | 53.2655 | 69.6756 |
| 1.0681 | 2.5 | 2315 | 1.2586 | 53.6186 | 69.8988 |
| 1.0887 | 3.0 | 2778 | 1.2555 | 53.6690 | 70.2968 |
| 0.9549 | 3.5 | 3241 | 1.3076 | 54.1481 | 70.1900 |
| 0.9549 | 4.0 | 3704 | 1.2922 | 54.0977 | 70.2654 |
| 0.8528 | 4.49 | 4167 | 1.3767 | 53.9212 | 70.6362 |
| 0.8467 | 4.99 | 4630 | 1.3384 | 53.8371 | 69.7755 |
| 0.7709 | 5.49 | 5093 | 1.3847 | 53.7615 | 70.0607 |
| 0.763 | 5.99 | 5556 | 1.4039 | 53.6774 | 69.6967 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
|
DTAI-KULeuven/robbertje-1-gb-bort
|
[
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
{
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"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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},
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},
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}
}
| 6 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bangla-para-v2-420000
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. -->
# bangla-para-v2-420000
This model is a fine-tuned version of [mHossain/bangla-para-v2-390000](https://huggingface.co/mHossain/bangla-para-v2-390000) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8817
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 17.557
## 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
- lr_scheduler_warmup_steps: 5000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.0759 | 1.0 | 3375 | 0.8817 | 0.0 | 0.0 | 0.0 | 0.0 | 17.557 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Daltcamalea01/Camaleaodalt
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-mean-pooling-finetuned3-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5687360893544328
---
<!-- 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-mean-pooling-finetuned3-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8274
- Matthews Correlation: 0.5687
## 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4853 | 1.0 | 535 | 0.4786 | 0.5357 |
| 0.2851 | 2.0 | 1070 | 0.5102 | 0.5598 |
| 0.1849 | 3.0 | 1605 | 0.6688 | 0.5495 |
| 0.1206 | 4.0 | 2140 | 0.8274 | 0.5687 |
| 0.0927 | 5.0 | 2675 | 0.9249 | 0.5677 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
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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"CamembertForMaskedLM"
],
"model_type": "camembert",
"task_specific_params": {
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| 8 | null |
---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# e7ekh4g8vn3 API Inference
## Get API Key
Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed.
Replace Key in below code, change **model_id** to "e7ekh4g8vn3"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
Model link: [View model](https://stablediffusionapi.com/models/e7ekh4g8vn3)
Credits: [View credits](https://civitai.com/?query=e7ekh4g8vn3)
View all models: [View Models](https://stablediffusionapi.com/models)
import requests
import json
url = "https://stablediffusionapi.com/api/v3/dreambooth"
payload = json.dumps({
"key": "",
"model_id": "e7ekh4g8vn3",
"prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
DataikuNLP/distiluse-base-multilingual-cased-v1
|
[
"pytorch",
"distilbert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] |
sentence-similarity
|
{
"architectures": [
"DistilBertModel"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"prefix": null
},
"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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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 29 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.512703445942988
---
<!-- 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-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5138
- Matthews Correlation: 0.5127
## 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: 4.5654407894015775e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4654 | 1.0 | 1069 | 0.5029 | 0.4588 |
| 0.3684 | 2.0 | 2138 | 0.5138 | 0.5127 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DavidAMcIntosh/DialoGPT-small-rick
|
[] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
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"early_stopping": null,
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"max_length": null,
"min_length": null,
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"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
}
}
}
| 0 | 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="patilrohan94/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"])
```
|
Davlan/bert-base-multilingual-cased-finetuned-igbo
|
[
"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,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 15 | 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: 245.35 +/- 19.72
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/bert-base-multilingual-cased-finetuned-luganda
|
[
"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,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 16 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 13.60 +/- 7.54
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Davlan/naija-twitter-sentiment-afriberta-large
|
[
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"arxiv:2201.08277",
"transformers",
"has_space"
] |
text-classification
|
{
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"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
}
}
}
| 61 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.46 +/- 2.81
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="patilrohan94/q-Taxi-v3-v1", 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/xlm-roberta-base-finetuned-chichewa
|
[
"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": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 5 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5154424505113391
---
<!-- 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-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4694
- Matthews Correlation: 0.5154
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4671 | 1.0 | 1069 | 0.4694 | 0.5154 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Davlan/xlm-roberta-large-masakhaner
|
[
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 1,449 | 2023-05-07T15:53:26Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Pixar_mode Dreambooth model trained by Aditya-T with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook
Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)!
To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars).
Sample pictures of this concept:
|
Dazai/Ko
|
[] | null |
{
"architectures": null,
"model_type": null,
"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,
"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
}
}
}
| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Text_classification_HW
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. -->
# Text_classification_HW
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4561
- Accuracy: 0.7825
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 100 | 0.5050 | 0.7525 |
| No log | 2.0 | 200 | 0.4561 | 0.7825 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Declan/Breitbart_model_v1
|
[
"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,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 9 | null |
---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# vne732h9dh4 API Inference
## Get API Key
Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed.
Replace Key in below code, change **model_id** to "vne732h9dh4"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
Model link: [View model](https://stablediffusionapi.com/models/vne732h9dh4)
Credits: [View credits](https://civitai.com/?query=vne732h9dh4)
View all models: [View Models](https://stablediffusionapi.com/models)
import requests
import json
url = "https://stablediffusionapi.com/api/v3/dreambooth"
payload = json.dumps({
"key": "",
"model_id": "vne732h9dh4",
"prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
Declan/Breitbart_model_v5
|
[
"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,
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| 3 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Textclassification-Bert
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Textclassification-Bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1439
- Validation Loss: 0.5583
- Train Matthews Correlation: 0.5803
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.4792 | 0.4276 | 0.5446 | 0 |
| 0.2664 | 0.4445 | 0.5602 | 1 |
| 0.1439 | 0.5583 | 0.5803 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Declan/HuffPost_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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}
| 3 | 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: 211.46 +/- 65.53
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Declan/HuffPost_model_v4
|
[
"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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}
| 3 | null |
---
tags:
- generated_from_trainer
datasets:
- xnli_bn
metrics:
- accuracy
model-index:
- name: rafsankabir/Finetuned_NLI_FinalE5_v2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: xnli_bn
type: xnli_bn
config: xnli_bn
split: validation
args: xnli_bn
metrics:
- name: Accuracy
type: accuracy
value: 0.5820587019429516
---
<!-- 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. -->
# rafsankabir/Finetuned_NLI_FinalE5_v2
This model was trained from scratch on the xnli_bn dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0413
- Accuracy: 0.5821
## 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: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.8652 | 1.0 | 11921 | 0.8991 | 0.5924 |
| 0.8546 | 2.0 | 23842 | 0.8947 | 0.5862 |
| 0.8441 | 3.0 | 35763 | 0.9013 | 0.5841 |
| 0.8335 | 4.0 | 47684 | 0.8992 | 0.5907 |
| 0.8253 | 5.0 | 59605 | 0.9019 | 0.5866 |
| 0.8165 | 6.0 | 71526 | 0.9070 | 0.5912 |
| 0.8071 | 7.0 | 83447 | 0.9104 | 0.5870 |
| 0.7994 | 8.0 | 95368 | 0.9142 | 0.5883 |
| 0.7911 | 9.0 | 107289 | 0.9274 | 0.5845 |
| 0.7837 | 10.0 | 119210 | 0.9242 | 0.5850 |
| 0.7816 | 11.0 | 131131 | 0.9193 | 0.5883 |
| 0.7742 | 12.0 | 143052 | 0.9438 | 0.5887 |
| 0.7666 | 13.0 | 154973 | 0.9527 | 0.5837 |
| 0.76 | 14.0 | 166894 | 0.9432 | 0.5895 |
| 0.7532 | 15.0 | 178815 | 0.9403 | 0.5825 |
| 0.7484 | 16.0 | 190736 | 0.9569 | 0.5775 |
| 0.7411 | 17.0 | 202657 | 0.9693 | 0.5829 |
| 0.7347 | 18.0 | 214578 | 0.9843 | 0.5788 |
| 0.7294 | 19.0 | 226499 | 0.9808 | 0.5792 |
| 0.7243 | 20.0 | 238420 | 0.9872 | 0.5812 |
| 0.7195 | 21.0 | 250341 | 1.0020 | 0.5845 |
| 0.7157 | 22.0 | 262262 | 0.9969 | 0.5771 |
| 0.7111 | 23.0 | 274183 | 1.0078 | 0.5825 |
| 0.7062 | 24.0 | 286104 | 1.0158 | 0.5816 |
| 0.7026 | 25.0 | 298025 | 1.0252 | 0.5808 |
| 0.7006 | 26.0 | 309946 | 1.0279 | 0.5825 |
| 0.6965 | 27.0 | 321867 | 1.0307 | 0.5800 |
| 0.6939 | 28.0 | 333788 | 1.0307 | 0.5783 |
| 0.692 | 29.0 | 345709 | 1.0398 | 0.5821 |
| 0.6903 | 30.0 | 357630 | 1.0413 | 0.5821 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/Politico_model_v1
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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| 3 | 2023-05-07T18:06:09Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uraskargi/bert-base-cased-fine-tuned-0
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uraskargi/bert-base-cased-fine-tuned-0
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1180
- Train Accuracy: 0.9578
- Validation Loss: 0.5352
- Validation Accuracy: 0.8380
- Train Matthews Correlation: 0.6032
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3.28827911355815e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Matthews Correlation | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:--------------------------:|:-----:|
| 0.4894 | 0.7723 | 0.4088 | 0.8245 | 0.5715 | 0 |
| 0.2551 | 0.9013 | 0.4519 | 0.8341 | 0.5959 | 1 |
| 0.1180 | 0.9578 | 0.5352 | 0.8380 | 0.6032 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Declan/Politico_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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}
| 5 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/59932/silver-wolf-honkai-star-rail
|
Declan/Politico_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
}
| 7 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/60058/loki-fire-emblem-heroes-lora
|
Declan/Politico_model_v6
|
[
"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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}
| 3 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/54802/bronya-rand-or-honkai-star-rail
|
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
},
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},
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| 7 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uraskargi/bert-base-cased-fine-tuned-1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uraskargi/bert-base-cased-fine-tuned-1
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2039
- Train Accuracy: 0.9247
- Validation Loss: 0.5069
- Validation Accuracy: 0.8188
- Train Matthews Correlation: 0.5521
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 9.405846345998246e-06, 'decay_steps': 665, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Matthews Correlation | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:--------------------------:|:-----:|
| 0.5706 | 0.7171 | 0.4996 | 0.7737 | 0.4254 | 0 |
| 0.3930 | 0.8300 | 0.4511 | 0.8102 | 0.5286 | 1 |
| 0.2866 | 0.8890 | 0.5139 | 0.8169 | 0.5469 | 2 |
| 0.2396 | 0.9083 | 0.5315 | 0.8092 | 0.5266 | 3 |
| 0.2039 | 0.9247 | 0.5069 | 0.8188 | 0.5521 | 4 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Declan/WallStreetJournal_model_v3
|
[
"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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}
| 3 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/47002/animechar-oshi-mix-or-hoshino-ai
|
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
},
"summarization": {
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}
| 9 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/57730/cow-girl-goblin-slayer
|
Declan/WallStreetJournal_model_v6
|
[] | null |
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}
| 0 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/57726/hyoudou-michiru-saekano
|
Declan/WallStreetJournal_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 9 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/54498/akagi-azur-lane-deep-crimson-poppy-wedding
|
Declan/test_push
|
[] | 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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}
}
}
| 0 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/54652/anila-granblue-fantasy
|
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